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Sample records for gene profiles predict

  1. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

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    Wang, Jing; Ma, Zihao; Carr, Steven A.; Mertins, Philipp; Zhang, Hui; Zhang, Zhen; Chan, Daniel W.; Ellis, Matthew J. C.; Townsend, R. Reid; Smith, Richard D.; McDermott, Jason E.; Chen, Xian; Paulovich, Amanda G.; Boja, Emily S.; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Rodland, Karin D.; Liebler, Daniel C.; Zhang, Bing

    2016-11-11

    Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies

  2. Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

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    Liying Yang

    2016-01-01

    Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

  3. Cell-specific prediction and application of drug-induced gene expression profiles.

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    Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

    2018-01-01

    Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

  4. Prediction of metastasis from low-malignant breast cancer by gene expression profiling

    DEFF Research Database (Denmark)

    Thomassen, Mads; Tan, Qihua; Eiriksdottir, Freyja

    2007-01-01

    examined in these studies is the low-risk patients for whom outcome is very difficult to predict with currently used methods. These patients do not receive adjuvant treatment according to the guidelines of the Danish Breast Cancer Cooperative Group (DBCG). In this study, 26 tumors from low-risk patients...... with different characteristics and risk, expression-based classification specifically developed in low-risk patients have higher predictive power in this group.......Promising results for prediction of outcome in breast cancer have been obtained by genome wide gene expression profiling. Some studies have suggested that an extensive overtreatment of breast cancer patients might be reduced by risk assessment with gene expression profiling. A patient group hardly...

  5. A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors

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    2017-02-01

    affecting the function of Fanconi Anemia (FA) genes ( FANCA /B/C/D2/E/F/G/I/J/L/M, PALB2) or DNA damage response genes involved in HR 5 (ATM, ATR...Award Number: W81XWH-10-1-0585 TITLE: A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors...To) 15 July 2010 – 2 Nov.2016 4. TITLE AND SUBTITLE A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP

  6. Prediction of lymphatic metastasis based on gene expression profile analysis after brachytherapy for early-stage oral tongue carcinoma

    International Nuclear Information System (INIS)

    Watanabe, Hiroshi; Mogushi, Kaoru; Miura, Masahiko; Yoshimura, Ryo-ichi; Kurabayashi, Tohru; Shibuya, Hitoshi; Tanaka, Hiroshi; Noda, Shuhei; Iwakawa, Mayumi; Imai, Takashi

    2008-01-01

    Background and purpose: The management of lymphatic metastasis of early-stage oral tongue carcinoma patients is crucial for its prognosis. The purpose of this study was to evaluate the predictive ability of lymphatic metastasis after brachytherapy (BRT) for early-stage tongue carcinoma based on gene expression profiling. Patients and methods: Pre-therapeutic biopsies from 39 patients with T1 or T2 tongue cancer were analyzed for gene expression signatures using Codelink Uniset Human 20K Bioarray. All patients were treated with low dose-rate BRT for their primary lesions and underwent strict follow-up under a wait-and-see policy for cervical lymphatic metastasis. Candidate genes were selected for predicting lymph-node status in the reference group by the permutation test. Predictive accuracy was further evaluated by the prediction strength (PS) scoring system using an independent validation group. Results: We selected a set of 19 genes whose expression differed significantly between classes with or without lymphatic metastasis in the reference group. The lymph-node status in the validation group was predicted by the PS scoring system with an accuracy of 76%. Conclusions: Gene expression profiling using 19 genes in primary tumor tissues may allow prediction of lymphatic metastasis after BRT for early-stage oral tongue carcinoma

  7. Response-predictive gene expression profiling of glioma progenitor cells in vitro.

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    Sylvia Moeckel

    Full Text Available High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist.In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response in vitro. This approach allowed us (a to enrich specimens for brain tumor initiating cells and (b to confront cells with a therapeutic agent before expression profiling.As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed to predict therapy-induced impairment of proliferation in vitro.For the tyrosine kinase inhibitor Sunitinib used in this dataset, the approach revealed additional predictive information in comparison to the evaluation of classical signaling analysis.

  8. HPV and high-risk gene expression profiles predict response to chemoradiotherapy in head and neck cancer, independent of clinical factors

    International Nuclear Information System (INIS)

    Jong, Monique C. de; Pramana, Jimmy; Knegjens, Joost L.; Balm, Alfons J.M.; Brekel, Michiel W.M. van den; Hauptmann, Michael; Begg, Adrian C.; Rasch, Coen R.N.

    2010-01-01

    Purpose: The purpose of this study was to combine gene expression profiles and clinical factors to provide a better prediction model of local control after chemoradiotherapy for advanced head and neck cancer. Material and methods: Gene expression data were available for a series of 92 advanced stage head and neck cancer patients treated with primary chemoradiotherapy. The effect of the Chung high-risk and Slebos HPV expression profiles on local control was analyzed in a model with age at diagnosis, gender, tumor site, tumor volume, T-stage and N-stage and HPV profile status. Results: Among 75 patients included in the study, the only factors significantly predicting local control were tumor site (oral cavity vs. Pharynx, hazard ratio 4.2 [95% CI 1.4-12.5]), Chung gene expression status (high vs. Low risk profile, hazard ratio 4.4 [95% CI 1.5-13.3]) and HPV profile (negative vs. Positive profile, hazard ratio 6.2 [95% CI 1.7-22.5]). Conclusions: Chung high-risk expression profile and a negative HPV expression profile were significantly associated with increased risk of local recurrence after chemoradiotherapy in advanced pharynx and oral cavity tumors, independent of clinical factors.

  9. Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor.

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    Vasselli, James R; Shih, Joanna H; Iyengar, Shuba R; Maranchie, Jodi; Riss, Joseph; Worrell, Robert; Torres-Cabala, Carlos; Tabios, Ray; Mariotti, Andra; Stearman, Robert; Merino, Maria; Walther, McClellan M; Simon, Richard; Klausner, Richard D; Linehan, W Marston

    2003-06-10

    To identify potential molecular determinants of tumor biology and possible clinical outcomes, global gene-expression patterns were analyzed in the primary tumors of patients with metastatic renal cell cancer by using cDNA microarrays. We used grossly dissected tumor masses that included tumor, blood vessels, connective tissue, and infiltrating immune cells to obtain a gene-expression "profile" from each primary tumor. Two patterns of gene expression were found within this uniformly staged patient population, which correlated with a significant difference in overall survival between the two patient groups. Subsets of genes most significantly associated with survival were defined, and vascular cell adhesion molecule-1 (VCAM-1) was the gene most predictive for survival. Therefore, despite the complex biological nature of metastatic cancer, basic clinical behavior as defined by survival may be determined by the gene-expression patterns expressed within the compilation of primary gross tumor cells. We conclude that survival in patients with metastatic renal cell cancer can be correlated with the expression of various genes based solely on the expression profile in the primary kidney tumor.

  10. Predicting Recurrence and Progression of Noninvasive Papillary Bladder Cancer at Initial Presentation Based on Quantitative Gene Expression Profiles

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    Birkhahn, M.; Mitra, A.P.; Williams, Johan

    2010-01-01

    % specificity. Since this is a small retrospective study using medium-throughput profiling, larger confirmatory studies are needed. Conclusions: Gene expression profiling across relevant cancer pathways appears to be a promising approach for Ta bladder tumor outcome prediction at initial diagnosis......Background: Currently, tumor grade is the best predictor of outcome at first presentation of noninvasive papillary (Ta) bladder cancer. However, reliable predictors of Ta tumor recurrence and progression for individual patients, which could optimize treatment and follow-up schedules based...... on specific tumor biology, are yet to be identified. Objective: To identify genes predictive for recurrence and progression in Ta bladder cancer at first presentation using a quantitative, pathway-specific approach. Design, setting, and participants: Retrospective study of patients with Ta G2/3 bladder tumors...

  11. EvoCor: a platform for predicting functionally related genes using phylogenetic and expression profiles.

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    Dittmar, W James; McIver, Lauren; Michalak, Pawel; Garner, Harold R; Valdez, Gregorio

    2014-07-01

    The wealth of publicly available gene expression and genomic data provides unique opportunities for computational inference to discover groups of genes that function to control specific cellular processes. Such genes are likely to have co-evolved and be expressed in the same tissues and cells. Unfortunately, the expertise and computational resources required to compare tens of genomes and gene expression data sets make this type of analysis difficult for the average end-user. Here, we describe the implementation of a web server that predicts genes involved in affecting specific cellular processes together with a gene of interest. We termed the server 'EvoCor', to denote that it detects functional relationships among genes through evolutionary analysis and gene expression correlation. This web server integrates profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determine putative functional linkages between pairs of genes. This server is easy to use and freely available at http://pilot-hmm.vbi.vt.edu/. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  12. Development and validation of a gene profile predicting benefit of postmastectomy radiotherapy in patients with high-risk breast cancer: a study of gene expression in the DBCG82bc cohort.

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    Tramm, Trine; Mohammed, Hayat; Myhre, Simen; Kyndi, Marianne; Alsner, Jan; Børresen-Dale, Anne-Lise; Sørlie, Therese; Frigessi, Arnoldo; Overgaard, Jens

    2014-10-15

    To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into "high LRR risk" and "low LRR risk" groups. PMRT significantly reduced risk of LRR in "high LRR risk" patients, whereas "low LRR risk" patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. ©2014 American Association for Cancer Research.

  13. An algorithm to discover gene signatures with predictive potential

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    Hallett Robin M

    2010-09-01

    Full Text Available Abstract Background The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. Results Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. Conclusions We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

  14. Prediction of graft-versus-host disease in humans by donor gene-expression profiling.

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    Chantal Baron

    2007-01-01

    Full Text Available BACKGROUND: Graft-versus-host disease (GVHD results from recognition of host antigens by donor T cells following allogeneic hematopoietic cell transplantation (AHCT. Notably, histoincompatibility between donor and recipient is necessary but not sufficient to elicit GVHD. Therefore, we tested the hypothesis that some donors may be "stronger alloresponders" than others, and consequently more likely to elicit GVHD. METHODS AND FINDINGS: To this end, we measured the gene-expression profiles of CD4(+ and CD8(+ T cells from 50 AHCT donors with microarrays. We report that pre-AHCT gene-expression profiling segregates donors whose recipient suffered from GVHD or not. Using quantitative PCR, established statistical tests, and analysis of multiple independent training-test datasets, we found that for chronic GVHD the "dangerous donor" trait (occurrence of GVHD in the recipient is under polygenic control and is shaped by the activity of genes that regulate transforming growth factor-beta signaling and cell proliferation. CONCLUSIONS: These findings strongly suggest that the donor gene-expression profile has a dominant influence on the occurrence of GVHD in the recipient. The ability to discriminate strong and weak alloresponders using gene-expression profiling could pave the way to personalized transplantation medicine.

  15. Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

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    Karacali Bilge

    2007-10-01

    Full Text Available Abstract Background Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a all genes on the microarray platform and b a list of known disease-related genes (a priori selection. We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms. Results Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform. Conclusion Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine

  16. Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles.

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    Zhu, Jie; Qin, Yufang; Liu, Taigang; Wang, Jun; Zheng, Xiaoqi

    2013-01-01

    Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance. In this paper, a new diffusion-based method was proposed to prioritize candidate disease genes. Diffusion profile of a disease was defined as the stationary distribution of candidate genes given a random walk with restart where similarities between phenotypes are incorporated. Then, candidate disease genes are prioritized by comparing their diffusion profiles with that of the disease. Finally, the effectiveness of our method was demonstrated through the leave-one-out cross-validation against control genes from artificial linkage intervals and randomly chosen genes. Comparative study showed that our method achieves improved performance compared to some classical diffusion-based methods. To further illustrate our method, we used our algorithm to predict new causing genes of 16 multifactorial diseases including Prostate cancer and Alzheimer's disease, and the top predictions were in good consistent with literature reports. Our study indicates that integration of multiple information sources, especially the phenotype similarity profile data, and introduction of global similarity measure between disease and gene diffusion profiles are helpful for prioritizing candidate disease genes. Programs and data are available upon request.

  17. Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

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    Bartsch, Georg; Mitra, Anirban P; Mitra, Sheetal A; Almal, Arpit A; Steven, Kenneth E; Skinner, Donald G; Fry, David W; Lenehan, Peter F; Worzel, William P; Cote, Richard J

    2016-02-01

    Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor. Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina®). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77% sensitivity and 85% specificity to predict recurrence in the training set, and 69% and 62%, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80% sensitivity and 90% specificity in the training set, and 71% and 67%, respectively, in the test set. Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  18. Gene Expression Profiling to Predict Clinical Outcome of Breast Cancer: reproducing, analyzing and extending the Nature publication by vhVeer et al

    NARCIS (Netherlands)

    Li R.; Visser, H.M.

    2010-01-01

    Chemotherapy and hormonal therapy as adjuvant systemic therapies to inhibit breast cancer recurrence are not necessary for each patient. In Veer's paper "Gene expression profiling predicts clinical outcome of breast cancer" (Nature 2002, PMID: 11823860), they introduced a method based on DNA

  19. A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

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    Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

    2015-01-01

    Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

  20. In Silico Analysis of Microarray-Based Gene Expression Profiles Predicts Tumor Cell Response to Withanolides

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    Thomas Efferth

    2012-05-01

    Full Text Available Withania somnifera (L. Dunal (Indian ginseng, winter cherry, Solanaceae is widely used in traditional medicine. Roots are either chewed or used to prepare beverages (aqueous decocts. The major secondary metabolites of Withania somnifera are the withanolides, which are C-28-steroidal lactone triterpenoids. Withania somnifera extracts exert chemopreventive and anticancer activities in vitro and in vivo. The aims of the present in silico study were, firstly, to investigate whether tumor cells develop cross-resistance between standard anticancer drugs and withanolides and, secondly, to elucidate the molecular determinants of sensitivity and resistance of tumor cells towards withanolides. Using IC50 concentrations of eight different withanolides (withaferin A, withaferin A diacetate, 3-azerininylwithaferin A, withafastuosin D diacetate, 4-B-hydroxy-withanolide E, isowithanololide E, withafastuosin E, and withaperuvin and 19 established anticancer drugs, we analyzed the cross-resistance profile of 60 tumor cell lines. The cell lines revealed cross-resistance between the eight withanolides. Consistent cross-resistance between withanolides and nitrosoureas (carmustin, lomustin, and semimustin was also observed. Then, we performed transcriptomic microarray-based COMPARE and hierarchical cluster analyses of mRNA expression to identify mRNA expression profiles predicting sensitivity or resistance towards withanolides. Genes from diverse functional groups were significantly associated with response of tumor cells to withaferin A diacetate, e.g. genes functioning in DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others.

  1. Embryo quality predictive models based on cumulus cells gene expression

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    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  2. Profiled support vector machines for antisense oligonucleotide efficacy prediction

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    Martín-Guerrero José D

    2004-09-01

    Full Text Available Abstract Background This paper presents the use of Support Vector Machines (SVMs for prediction and analysis of antisense oligonucleotide (AO efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1 feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE, and (2 AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. Results In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278 and predicted high (>75% inhibition of gene expression and low efficacy (http://aosvm.cgb.ki.se/. Conclusions The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.

  3. A seven-gene CpG-island methylation panel predicts breast cancer progression

    International Nuclear Information System (INIS)

    Li, Yan; Melnikov, Anatoliy A.; Levenson, Victor; Guerra, Emanuela; Simeone, Pasquale; Alberti, Saverio; Deng, Youping

    2015-01-01

    DNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors. CpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes. Seven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63–79 %, 53–84 % specificities, positive predictive values of 59–83 % and negative predictive values of 63–80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression. Our findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings. The online version of this article (doi:10.1186/s12885-015-1412-9) contains supplementary

  4. Altered global gene expression profiles in human gastrointestinal epithelial Caco2 cells exposed to nanosilver

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    Saura C. Sahu

    Full Text Available Extensive consumer exposure to food- and cosmetics-related consumer products containing nanosilver is of public safety concern. Therefore, there is a need for suitable in vitro models and sensitive predictive rapid screening methods to assess their toxicity. Toxicogenomic profile showing subtle changes in gene expressions following nanosilver exposure is a sensitive toxicological endpoint for this purpose. We evaluated the Caco2 cells and global gene expression profiles as tools for predictive rapid toxicity screening of nanosilver. We evaluated and compared the gene expression profiles of Caco-2 cells exposed to 20 nm and 50 nm nanosilver at a concentration 2.5 μg/ml. The global gene expression analysis of Caco2 cells exposed to 20 nm nanosilver showed that a total of 93 genes were altered at 4 h exposure, out of which 90 genes were up-regulated and 3 genes were down-regulated. The 24 h exposure of 20 nm silver altered 15 genes in Caco2 cells, out of which 14 were up-regulated and one was down-regulated. The most pronounced changes in gene expression were detected at 4 h. The greater size (50 nm nanosilver at 4 h exposure altered more genes by more different pathways than the smaller (20 nm one. Metallothioneins and heat shock proteins were highly up-regulated as a result of exposure to both the nanosilvers. The cellular pathways affected by the nanosilver exposure is likely to lead to increased toxicity. The results of our study presented here suggest that the toxicogenomic characterization of Caco2 cells is a valuable in vitro tool for assessing toxicity of nanomaterials such as nanosilver. Keywords: Nanosilver, Silver nanoparticles, Nanoparticles, Toxicogenomics, DNA microarray, Global gene expression profiles, Caco2 cells

  5. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Science.gov (United States)

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  6. Combining gene signatures improves prediction of breast cancer survival.

    Directory of Open Access Journals (Sweden)

    Xi Zhao

    Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

  7. A deep auto-encoder model for gene expression prediction.

    Science.gov (United States)

    Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

    2017-11-17

    Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

  8. Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.

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    David M Mutch

    Full Text Available BACKGROUND: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss could always be differentiated from non-responders (<4 kgs weight loss. We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier improved prediction accuracy to 80.9%+/-2.2%. CONCLUSION: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

  9. Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles.

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    Sarah K Meadows

    2010-07-01

    Full Text Available In the event of a terrorist-mediated attack in the United States using radiological or improvised nuclear weapons, it is expected that hundreds of thousands of people could be exposed to life-threatening levels of ionizing radiation. We have recently shown that genome-wide expression analysis of the peripheral blood (PB can generate gene expression profiles that can predict radiation exposure and distinguish the dose level of exposure following total body irradiation (TBI. However, in the event a radiation-mass casualty scenario, many victims will have heterogeneous exposure due to partial shielding and it is unknown whether PB gene expression profiles would be useful in predicting the status of partially irradiated individuals. Here, we identified gene expression profiles in the PB that were characteristic of anterior hemibody-, posterior hemibody- and single limb-irradiation at 0.5 Gy, 2 Gy and 10 Gy in C57Bl6 mice. These PB signatures predicted the radiation status of partially irradiated mice with a high level of accuracy (range 79-100% compared to non-irradiated mice. Interestingly, PB signatures of partial body irradiation were poorly predictive of radiation status by site of injury (range 16-43%, suggesting that the PB molecular response to partial body irradiation was anatomic site specific. Importantly, PB gene signatures generated from TBI-treated mice failed completely to predict the radiation status of partially irradiated animals or non-irradiated controls. These data demonstrate that partial body irradiation, even to a single limb, generates a characteristic PB signature of radiation injury and thus may necessitate the use of multiple signatures, both partial body and total body, to accurately assess the status of an individual exposed to radiation.

  10. The usability of a 15-gene hypoxia classifier as a universal hypoxia profile in various cancer cell types

    DEFF Research Database (Denmark)

    Sørensen, Brita Singers; Knudsen, Anders Bisgård; Wittrup, Catja Foged

    2015-01-01

    genes, with BNIP3 not being upregulated at hypoxic conditions in 3 out of 6 colon cancer cell lines, and ALDOA in OE21 and FAM162A and SLC2A1 in SW116 only showing limited hypoxia induction. Furthermore, in the esophagus cell lines, the normoxic and hypoxic expression levels of LOX and BNIP3 were below...... the tissue type dependency of hypoxia induced genes included in a 15-gene hypoxic profile in carcinoma cell lines from prostate, colon, and esophagus cancer, and demonstrated that in vitro, with minor fluctuations, the genes in the hypoxic profile are hypoxia inducible, and the hypoxia profile may......BACKGROUND AND PURPOSE: A 15-gene hypoxia profile has previously demonstrated to have both prognostic and predictive impact for hypoxic modification in squamous cell carcinoma of the head and neck. This gene expression profile may also have a prognostic value in other histological cancer types...

  11. Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

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    Richard Danger

    2018-01-01

    Full Text Available Bronchiolitis obliterans syndrome (BOS, the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group, and 26 samples at or after BOS diagnosis (diagnosis group. An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group. We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1, T-cell leukemia/lymphoma protein 1A (TCL1A, and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p < 0.01 and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS.

  12. Effectiveness of gene expression profiling for response prediction of rectal cancer to preoperative radiotherapy

    International Nuclear Information System (INIS)

    Ojima, Eiki; Inoue, Yasuhiro; Miki, Chikao; Kusunoki, Masato; Mori, Masaki

    2007-01-01

    Our aim was to determine whether the expression levels of specific genes could predict clinical radiosensitivity in human colorectal cancer. Radioresistant colorectal cancer cell lines were established by repeated X-ray exposure (total, 100 Gy), and the gene expressions of the parent and radioresistant cell lines were compared in a microarray analysis. To verify the microarray data, we carried out a reverse transcriptase-polymerase chain reaction analysis of identified genes in clinical samples from 30 irradiated rectal cancer patients. A comparison of the intensity data for the parent and three radioresistant cell lines revealed 17 upregulated and 142 downregulated genes in all radioresistant cell lines. Next, we focused on two upregulated genes, PTMA (prothymosin α) and EIF5a2 (eukaryotic translation initiation factor 5A), in the radioresistant cell lines. In clinical samples, the expression of PTMA was significantly higher in the minor effect group than in the major effect group (P=0.004), but there were no significant differences in EIF5a2 expression between the two groups. We identified radiation-related genes in colorectal cancer and demonstrated that PTMA may play an important role in radiosensitivity. Our findings suggest that PTMA may be a novel marker for predicting the effectiveness of radiotherapy in clinical cases. (author)

  13. Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

    International Nuclear Information System (INIS)

    Korkola, James E; Waldman, Frederic M; Blaveri, Ekaterina; DeVries, Sandy; Moore, Dan H II; Hwang, E Shelley; Chen, Yunn-Yi; Estep, Anne LH; Chew, Karen L; Jensen, Ronald H

    2007-01-01

    Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients

  14. Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods.

    Science.gov (United States)

    Wang, Liming; Zhu, L; Luan, R; Wang, L; Fu, J; Wang, X; Sui, L

    2016-10-10

    Dilated cardiomyopathy (DCM) is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs) were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs) and microRNAs (miRNAs) of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT) were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family). Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1), potential TFs, as well as potential miRNAs, might be involved in DCM.

  15. Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods

    Directory of Open Access Journals (Sweden)

    Liming Wang

    Full Text Available Dilated cardiomyopathy (DCM is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs and microRNAs (miRNAs of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family. Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1, potential TFs, as well as potential miRNAs, might be involved in DCM.

  16. mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

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    Hala Alshamlan

    2015-01-01

    Full Text Available An artificial bee colony (ABC is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR, and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO. The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

  17. mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

    Science.gov (United States)

    Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

    2015-01-01

    An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

  18. Concerted down-regulation of immune-system related genes predicts metastasis in colorectal carcinoma

    International Nuclear Information System (INIS)

    Fehlker, Marion; Huska, Matthew R; Jöns, Thomas; Andrade-Navarro, Miguel A; Kemmner, Wolfgang

    2014-01-01

    This study aimed at the identification of prognostic gene expression markers in early primary colorectal carcinomas without metastasis at the time point of surgery by analyzing genome-wide gene expression profiles using oligonucleotide microarrays. Cryo-conserved tumor specimens from 45 patients with early colorectal cancers were examined, with the majority of them being UICC stage II or earlier and with a follow-up time of 41–115 months. Gene expression profiling was performed using Whole Human Genome 4x44K Oligonucleotide Microarrays. Validation of microarray data was performed on five of the genes in a smaller cohort. Using a novel algorithm based on the recursive application of support vector machines (SVMs), we selected a signature of 44 probes that discriminated between patients developing later metastasis and patients with a good prognosis. Interestingly, almost half of the genes was related to the patients’ immune response and showed reduced expression in the metastatic cases. Whereas up to now gene signatures containing genes with various biological functions have been described for prediction of metastasis in CRC, in this study metastasis could be well predicted by a set of gene expression markers consisting exclusively of genes related to the MHC class II complex involved in immune response. Thus, our data emphasize that the proper function of a comprehensive network of immune response genes is of vital importance for the survival of colorectal cancer patients

  19. Histone modification profiles are predictive for tissue/cell-type specific expression of both protein-coding and microRNA genes

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    Zhang Michael Q

    2011-05-01

    Full Text Available Abstract Background Gene expression is regulated at both the DNA sequence level and through modification of chromatin. However, the effect of chromatin on tissue/cell-type specific gene regulation (TCSR is largely unknown. In this paper, we present a method to elucidate the relationship between histone modification/variation (HMV and TCSR. Results A classifier for differentiating CD4+ T cell-specific genes from housekeeping genes using HMV data was built. We found HMV in both promoter and gene body regions to be predictive of genes which are targets of TCSR. For example, the histone modification types H3K4me3 and H3K27ac were identified as the most predictive for CpG-related promoters, whereas H3K4me3 and H3K79me3 were the most predictive for nonCpG-related promoters. However, genes targeted by TCSR can be predicted using other type of HMVs as well. Such redundancy implies that multiple type of underlying regulatory elements, such as enhancers or intragenic alternative promoters, which can regulate gene expression in a tissue/cell-type specific fashion, may be marked by the HMVs. Finally, we show that the predictive power of HMV for TCSR is not limited to protein-coding genes in CD4+ T cells, as we successfully predicted TCSR targeted genes in muscle cells, as well as microRNA genes with expression specific to CD4+ T cells, by the same classifier which was trained on HMV data of protein-coding genes in CD4+ T cells. Conclusion We have begun to understand the HMV patterns that guide gene expression in both tissue/cell-type specific and ubiquitous manner.

  20. Statistical modelling of transcript profiles of differentially regulated genes

    Directory of Open Access Journals (Sweden)

    Sergeant Martin J

    2008-07-01

    Full Text Available Abstract Background The vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression. Results Quantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Split-line" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t = A + (B + CtRt + ε. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data

  1. Gene expression profiling reveals multiple toxicity endpoints induced by hepatotoxicants

    Energy Technology Data Exchange (ETDEWEB)

    Huang Qihong; Jin Xidong; Gaillard, Elias T.; Knight, Brian L.; Pack, Franklin D.; Stoltz, James H.; Jayadev, Supriya; Blanchard, Kerry T

    2004-05-18

    Microarray technology continues to gain increased acceptance in the drug development process, particularly at the stage of toxicology and safety assessment. In the current study, microarrays were used to investigate gene expression changes associated with hepatotoxicity, the most commonly reported clinical liability with pharmaceutical agents. Acetaminophen, methotrexate, methapyrilene, furan and phenytoin were used as benchmark compounds capable of inducing specific but different types of hepatotoxicity. The goal of the work was to define gene expression profiles capable of distinguishing the different subtypes of hepatotoxicity. Sprague-Dawley rats were orally dosed with acetaminophen (single dose, 4500 mg/kg for 6, 24 and 72 h), methotrexate (1 mg/kg per day for 1, 7 and 14 days), methapyrilene (100 mg/kg per day for 3 and 7 days), furan (40 mg/kg per day for 1, 3, 7 and 14 days) or phenytoin (300 mg/kg per day for 14 days). Hepatic gene expression was assessed using toxicology-specific gene arrays containing 684 target genes or expressed sequence tags (ESTs). Principal component analysis (PCA) of gene expression data was able to provide a clear distinction of each compound, suggesting that gene expression data can be used to discern different hepatotoxic agents and toxicity endpoints. Gene expression data were applied to the multiplicity-adjusted permutation test and significantly changed genes were categorized and correlated to hepatotoxic endpoints. Repression of enzymes involved in lipid oxidation (acyl-CoA dehydrogenase, medium chain, enoyl CoA hydratase, very long-chain acyl-CoA synthetase) were associated with microvesicular lipidosis. Likewise, subsets of genes associated with hepatotocellular necrosis, inflammation, hepatitis, bile duct hyperplasia and fibrosis have been identified. The current study illustrates that expression profiling can be used to: (1) distinguish different hepatotoxic endpoints; (2) predict the development of toxic endpoints; and

  2. Predictive gene lists for breast cancer prognosis: A topographic visualisation study

    Directory of Open Access Journals (Sweden)

    Lowe David

    2008-04-01

    Full Text Available Abstract Background The controversy surrounding the non-uniqueness of predictive gene lists (PGL of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE and the Locally Linear Embedding(LLE techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion The random correlation effect to an arbitrary outcome induced by small subset selection from very high

  3. Automatic assignment of prokaryotic genes to functional categories using literature profiling.

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    Raul Torrieri

    Full Text Available In the last years, there was an exponential increase in the number of publicly available genomes. Once finished, most genome projects lack financial support to review annotations. A few of these gene annotations are based on a combination of bioinformatics evidence, however, in most cases, annotations are based solely on sequence similarity to a previously known gene, which was most probably annotated in the same way. As a result, a large number of predicted genes remain unassigned to any functional category despite the fact that there is enough evidence in the literature to predict their function. We developed a classifier trained with term-frequency vectors automatically disclosed from text corpora of an ensemble of genes representative of each functional category of the J. Craig Venter Institute Comprehensive Microbial Resource (JCVI-CMR ontology. The classifier achieved up to 84% precision with 68% recall (for confidence≥0.4, F-measure 0.76 (recall and precision equally weighted in an independent set of 2,220 genes, from 13 bacterial species, previously classified by JCVI-CMR into unambiguous categories of its ontology. Finally, the classifier assigned (confidence≥0.7 to functional categories a total of 5,235 out of the ∼24 thousand genes previously in categories "Unknown function" or "Unclassified" for which there is literature in MEDLINE. Two biologists reviewed the literature of 100 of these genes, randomly picket, and assigned them to the same functional categories predicted by the automatic classifier. Our results confirmed the hypothesis that it is possible to confidently assign genes of a real world repository to functional categories, based exclusively on the automatic profiling of its associated literature. The LitProf--Gene Classifier web server is accessible at: www.cebio.org/litprofGC.

  4. A prognostic profile of hypoxia-induced genes for localised high-grade soft tissue sarcoma

    DEFF Research Database (Denmark)

    Aggerholm-Pedersen, Ninna; Sørensen, Brita Singers; Overgaard, Jens

    2016-01-01

    sarcoma (STS). METHODS: The hypoxia-induced gene quantification was performed by real-time quantitative PCR (RT-qPCR) of formalin-fixed, paraffin-embedded tissue samples. The gene expression cut-points were determined in a test cohort of 55 STS patients and used to allocate each patient into a more......BACKGROUND: For decades, tumour hypoxia has been pursued as a cancer treatment target. However, prognostic and predictive biomarkers are essential for the use of this target in the clinic. This study investigates the prognostic value of a hypoxia-induced gene profile in localised soft tissue...

  5. Vertebrate gene predictions and the problem of large genes

    DEFF Research Database (Denmark)

    Wang, Jun; Li, ShengTing; Zhang, Yong

    2003-01-01

    To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistent...

  6. Predictive profiling and its legal limits : Effectiveness gone forever

    NARCIS (Netherlands)

    Lammerant, Hans; de Hert, Paul; van der Sloot, B.; Broeders, D.; Schrijvers, E.

    2016-01-01

    We examine predictive group profiling in the Big Data context as an instrument of governmental control and regulation. We first define profiling by drawing some useful distinctions (section 6.1). We then discuss examples of predictive group profiling from policing (such as parole prediction methods

  7. Expression profiling of hypothetical genes in Desulfovibrio vulgaris leads to improved functional annotation

    Energy Technology Data Exchange (ETDEWEB)

    Elias, Dwayne A.; Mukhopadhyay, Aindrila; Joachimiak, Marcin P.; Drury, Elliott C.; Redding, Alyssa M.; Yen, Huei-Che B.; Fields, Matthew W.; Hazen, Terry C.; Arkin, Adam P.; Keasling, Jay D.; Wall, Judy D.

    2008-10-27

    Hypothetical and conserved hypothetical genes account for>30percent of sequenced bacterial genomes. For the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough, 347 of the 3634 genes were annotated as conserved hypothetical (9.5percent) along with 887 hypothetical genes (24.4percent). Given the large fraction of the genome, it is plausible that some of these genes serve critical cellular roles. The study goals were to determine which genes were expressed and provide a more functionally based annotation. To accomplish this, expression profiles of 1234 hypothetical and conserved genes were used from transcriptomic datasets of 11 environmental stresses, complemented with shotgun LC-MS/MS and AMT tag proteomic data. Genes were divided into putatively polycistronic operons and those predicted to be monocistronic, then classified by basal expression levels and grouped according to changes in expression for one or multiple stresses. 1212 of these genes were transcribed with 786 producing detectable proteins. There was no evidence for expression of 17 predicted genes. Except for the latter, monocistronic gene annotation was expanded using the above criteria along with matching Clusters of Orthologous Groups. Polycistronic genes were annotated in the same manner with inferences from their proximity to more confidently annotated genes. Two targeted deletion mutants were used as test cases to determine the relevance of the inferred functional annotations.

  8. Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

    International Nuclear Information System (INIS)

    Yu, Jack X; Sieuwerts, Anieta M; Zhang, Yi; Martens, John WM; Smid, Marcel; Klijn, Jan GM; Wang, Yixin; Foekens, John A

    2007-01-01

    Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients. The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways. We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different

  9. Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

    Science.gov (United States)

    Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

    2018-02-23

    Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

  10. Evaluation of gene expression profile of keratinocytes in response to JP-8 jet fuel

    International Nuclear Information System (INIS)

    Espinoza, Luis A.; Li Peijun; Lee, Richard Y.; Wang Yue; Boulares, A. Hamid; Clarke, Robert; Smulson, Mark E.

    2004-01-01

    The skin is the principal barrier against any environmental insult. Therefore, there is a high risk for a large number of military and civilian personnel exposed to jet fuel JP-8 to suffer percutaneous absorption of this fuel. This paper reports the use of cDNA microarray to identify the gene expression profile in normal human epidermal keratinocytes exposed to JP-8 for 24-h and 7-day periods. The effects of JP-8 exposure on keratinocytes at these two different periods induced a set of genes with altered expression in response to this type of insult. Microarray data were visualized using a novel algorithm based on simple statistical analyses to reduce data dimensionality and identify subsets of discriminant genes. Predictive neural networks were built using a multiplayer perceptron to carry out a proper classification task in microarray data in the untreated versus JP-8-treated samples. The pattern of expressions in response to JP-8 provides evidences that detoxificant-related and cell growth regulator genes with the most variability in the level of expression may be useful genetic markers in adverse health effects of personnel exposed to JP-8. The approaches in our analysis provide a simple, safe, novel, and effective method that is reliable in identifying and analyzing gene expression in samples treated with JP-8 or over potential toxic agents. Gene expression data from these studies can be used to build accurate predictive models that separate different molecular profiles. The data establish the use and effectiveness of these approaches for future prospective studies

  11. Gene Expression Differences in Peripheral Blood of Parkinson's Disease Patients with Distinct Progression Profiles.

    Directory of Open Access Journals (Sweden)

    Raquel Pinho

    Full Text Available The prognosis of neurodegenerative disorders is clinically challenging due to the inexistence of established biomarkers for predicting disease progression. Here, we performed an exploratory cross-sectional, case-control study aimed at determining whether gene expression differences in peripheral blood may be used as a signature of Parkinson's disease (PD progression, thereby shedding light into potential molecular mechanisms underlying disease development. We compared transcriptional profiles in the blood from 34 PD patients who developed postural instability within ten years with those of 33 patients who did not develop postural instability within this time frame. Our study identified >200 differentially expressed genes between the two groups. The expression of several of the genes identified was previously found deregulated in animal models of PD and in PD patients. Relevant genes were selected for validation by real-time PCR in a subset of patients. The genes validated were linked to nucleic acid metabolism, mitochondria, immune response and intracellular-transport. Interestingly, we also found deregulation of these genes in a dopaminergic cell model of PD, a simple paradigm that can now be used to further dissect the role of these molecular players on dopaminergic cell loss. Altogether, our study provides preliminary evidence that expression changes in specific groups of genes and pathways, detected in peripheral blood samples, may be correlated with differential PD progression. Our exploratory study suggests that peripheral gene expression profiling may prove valuable for assisting in prediction of PD prognosis, and identifies novel culprits possibly involved in dopaminergic cell death. Given the exploratory nature of our study, further investigations using independent, well-characterized cohorts will be essential in order to validate our candidates as predictors of PD prognosis and to definitively confirm the value of gene expression

  12. Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis.

    Directory of Open Access Journals (Sweden)

    Robert W Chapman

    Full Text Available Inherited gene transcripts deposited in oocytes direct early embryonic development in all vertebrates, but transcript profiles indicative of embryo developmental competence have not previously been identified. We employed artificial intelligence to model profiles of maternal ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in the striped bass (Morone saxatilis, a farmed species with serious egg quality problems. In models developed using artificial neural networks (ANNs and supervised machine learning, collective changes in the expression of a limited suite of genes (233 representing 90% of the eventual variance in embryo survival. Egg quality related to minor changes in gene expression (<0.2-fold, with most individual transcripts making a small contribution (<1% to the overall prediction of egg quality. These findings indicate that the predictive power of the transcriptome as regards egg quality resides not in levels of individual genes, but rather in the collective, coordinated expression of a suite of transcripts constituting a transcriptomic "fingerprint". Correlation analyses of the corresponding candidate genes indicated that dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence. The affected gene networks are centrally involved in regulation of early development in all vertebrates, including humans. By assessing collective levels of the relevant ovarian transcripts via ANNs we were able, for the first time in any vertebrate, to accurately predict the subsequent embryo developmental potential of eggs from individual females. Our results show that the transcriptomic fingerprint evidencing developmental dysfunction is highly predictive of, and therefore likely to regulate, egg quality, a biologically complex trait crucial to reproductive fitness.

  13. Identifying potential maternal genes of Bombyx mori using digital gene expression profiling

    Science.gov (United States)

    Xu, Pingzhen

    2018-01-01

    Maternal genes present in mature oocytes play a crucial role in the early development of silkworm. Although maternal genes have been widely studied in many other species, there has been limited research in Bombyx mori. High-throughput next generation sequencing provides a practical method for gene discovery on a genome-wide level. Herein, a transcriptome study was used to identify maternal-related genes from silkworm eggs. Unfertilized eggs from five different stages of early development were used to detect the changing situation of gene expression. The expressed genes showed different patterns over time. Seventy-six maternal genes were annotated according to homology analysis with Drosophila melanogaster. More than half of the differentially expressed maternal genes fell into four expression patterns, while the expression patterns showed a downward trend over time. The functional annotation of these material genes was mainly related to transcription factor activity, growth factor activity, nucleic acid binding, RNA binding, ATP binding, and ion binding. Additionally, twenty-two gene clusters including maternal genes were identified from 18 scaffolds. Altogether, we plotted a profile for the maternal genes of Bombyx mori using a digital gene expression profiling method. This will provide the basis for maternal-specific signature research and improve the understanding of the early development of silkworm. PMID:29462160

  14. Meta-analysis of Cancer Gene Profiling Data.

    Science.gov (United States)

    Roy, Janine; Winter, Christof; Schroeder, Michael

    2016-01-01

    The simultaneous measurement of thousands of genes gives the opportunity to personalize and improve cancer therapy. In addition, the integration of meta-data such as protein-protein interaction (PPI) information into the analyses helps in the identification and prioritization of genes from these screens. Here, we describe a computational approach that identifies genes prognostic for outcome by combining gene profiling data from any source with a network of known relationships between genes.

  15. Gene expression profiling of fast- and slow- growing gonadotroph non-functioning pituitary adenomas

    DEFF Research Database (Denmark)

    Falch, Camilla Maria; Sundaram, Arvind Y M; Øystese, Kristin Astrid

    2018-01-01

    Objective Reliable biomarkers associated with aggressiveness of non-functioning gonadotroph adenomas (GAs) are lacking. As the growth of tumor remnants is highly variable, molecular markers for growth potential prediction are necessary. We hypothesized that fast- and slow - growing GAs present......, GPM6A and six EMT-related genes (SPAG9, SKIL, MTDH, HOOK1, CNOT6L and PRKACB). MTDH, but not EMCN, demonstrated involvement in cell migration and association with EMT-markers. Conclusions Fast- and slow- growing GAs present different gene expression profiles and genes related to EMT have higher...... expression in fast-growing tumors. In addition to MTDH, identified as an important contributor to aggressiveness, the other genes might represent markers for tumor growth potential and possible targets for drug therapy. ....

  16. Association between gene expression profile of the primary tumor and chemotherapy response of metastatic breast cancer

    NARCIS (Netherlands)

    Savci-Heijink, Cemile Dilara; Halfwerk, Hans; Koster, Jan; van de Vijver, Marc Joan

    2017-01-01

    Background: To better predict the likelihood of response to chemotherapy, we have conducted a study comparing the gene expression patterns of primary tumours with their corresponding response to systemic chemotherapy in the metastatic setting. Methods: mRNA expression profiles of breast carcinomas

  17. Exploring gene expression signatures for predicting disease free survival after resection of colorectal cancer liver metastases.

    Directory of Open Access Journals (Sweden)

    Nikol Snoeren

    Full Text Available BACKGROUND AND OBJECTIVES: This study was designed to identify and validate gene signatures that can predict disease free survival (DFS in patients undergoing a radical resection for their colorectal liver metastases (CRLM. METHODS: Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France and the University Medical Center Utrecht (The Netherlands. Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort. RESULT: No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort. CONCLUSIONS: No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.

  18. Expression profiling identifies genes involved in emphysema severity

    Directory of Open Access Journals (Sweden)

    Bowman Rayleen V

    2009-09-01

    Full Text Available Abstract Chronic obstructive pulmonary disease (COPD is a major public health problem. The aim of this study was to identify genes involved in emphysema severity in COPD patients. Gene expression profiling was performed on total RNA extracted from non-tumor lung tissue from 30 smokers with emphysema. Class comparison analysis based on gas transfer measurement was performed to identify differentially expressed genes. Genes were then selected for technical validation by quantitative reverse transcriptase-PCR (qRT-PCR if also represented on microarray platforms used in previously published emphysema studies. Genes technically validated advanced to tests of biological replication by qRT-PCR using an independent test set of 62 lung samples. Class comparison identified 98 differentially expressed genes (p p Gene expression profiling of lung from emphysema patients identified seven candidate genes associated with emphysema severity including COL6A3, SERPINF1, ZNHIT6, NEDD4, CDKN2A, NRN1 and GSTM3.

  19. Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease.

    Science.gov (United States)

    Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

    2014-01-01

    We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. http://rged.wall-eva.net. © The Author(s) 2014. Published by Oxford University Press.

  20. Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease

    Science.gov (United States)

    Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

    2014-01-01

    We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Availability and implementation: Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. Database URL: http://rged.wall-eva.net PMID:25252782

  1. Combining gene prediction methods to improve metagenomic gene annotation

    Directory of Open Access Journals (Sweden)

    Rosen Gail L

    2011-01-01

    Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

  2. Gene expression profile data for mouse facial development

    Directory of Open Access Journals (Sweden)

    Sonia M. Leach

    2017-08-01

    Full Text Available This article contains data related to the research articles "Spatial and Temporal Analysis of Gene Expression during Growth and Fusion of the Mouse Facial Prominences" (Feng et al., 2009 [1] and “Systems Biology of facial development: contributions of ectoderm and mesenchyme” (Hooper et al., 2017 In press [2]. Embryonic mammalian craniofacial development is a complex process involving the growth, morphogenesis, and fusion of distinct facial prominences into a functional whole. Aberrant gene regulation during this process can lead to severe craniofacial birth defects, including orofacial clefting. As a means to understand the genes involved in facial development, we had previously dissected the embryonic mouse face into distinct prominences: the mandibular, maxillary or nasal between E10.5 and E12.5. The prominences were then processed intact, or separated into ectoderm and mesenchyme layers, prior analysis of RNA expression using microarrays (Feng et al., 2009, Hooper et al., 2017 in press [1,2]. Here, individual gene expression profiles have been built from these datasets that illustrate the timing of gene expression in whole prominences or in the separated tissue layers. The data profiles are presented as an indexed and clickable list of the genes each linked to a graphical image of that gene׳s expression profile in the ectoderm, mesenchyme, or intact prominence. These data files will enable investigators to obtain a rapid assessment of the relative expression level of any gene on the array with respect to time, tissue, prominence, and expression trajectory.

  3. PROSPECT improves cis-acting regulatory element prediction by integrating expression profile data with consensus pattern searches

    Science.gov (United States)

    Fujibuchi, Wataru; Anderson, John S. J.; Landsman, David

    2001-01-01

    Consensus pattern and matrix-based searches designed to predict cis-acting transcriptional regulatory sequences have historically been subject to large numbers of false positives. We sought to decrease false positives by incorporating expression profile data into a consensus pattern-based search method. We have systematically analyzed the expression phenotypes of over 6000 yeast genes, across 121 expression profile experiments, and correlated them with the distribution of 14 known regulatory elements over sequences upstream of the genes. Our method is based on a metric we term probabilistic element assessment (PEA), which is a ranking of potential sites based on sequence similarity in the upstream regions of genes with similar expression phenotypes. For eight of the 14 known elements that we examined, our method had a much higher selectivity than a naïve consensus pattern search. Based on our analysis, we have developed a web-based tool called PROSPECT, which allows consensus pattern-based searching of gene clusters obtained from microarray data. PMID:11574681

  4. Equivalent Gene Expression Profiles between Glatopa™ and Copaxone®.

    Directory of Open Access Journals (Sweden)

    Josephine S D'Alessandro

    Full Text Available Glatopa™ is a generic glatiramer acetate recently approved for the treatment of patients with relapsing forms of multiple sclerosis. Gene expression profiling was performed as a means to evaluate equivalence of Glatopa and Copaxone®. Microarray analysis containing 39,429 unique probes across the entire genome was performed in murine glatiramer acetate--responsive Th2-polarized T cells, a test system highly relevant to the biology of glatiramer acetate. A closely related but nonequivalent glatiramoid molecule was used as a control to establish assay sensitivity. Multiple probe-level (Student's t-test and sample-level (principal component analysis, multidimensional scaling, and hierarchical clustering statistical analyses were utilized to look for differences in gene expression induced by the test articles. The analyses were conducted across all genes measured, as well as across a subset of genes that were shown to be modulated by Copaxone. The following observations were made across multiple statistical analyses: the expression of numerous genes was significantly changed by treatment with Copaxone when compared against media-only control; gene expression profiles induced by Copaxone and Glatopa were not significantly different; and gene expression profiles induced by Copaxone and the nonequivalent glatiramoid were significantly different, underscoring the sensitivity of the test system and the multiple analysis methods. Comparative analysis was also performed on sets of transcripts relevant to T-cell biology and antigen presentation, among others that are known to be modulated by glatiramer acetate. No statistically significant differences were observed between Copaxone and Glatopa in the expression levels (magnitude and direction of these glatiramer acetate-regulated genes. In conclusion, multiple methods consistently supported equivalent gene expression profiles between Copaxone and Glatopa.

  5. Profiling cancer

    DEFF Research Database (Denmark)

    Ciro, Marco; Bracken, Adrian P; Helin, Kristian

    2003-01-01

    In the past couple of years, several very exciting studies have demonstrated the enormous power of gene-expression profiling for cancer classification and prediction of patient survival. In addition to promising a more accurate classification of cancer and therefore better treatment of patients......, gene-expression profiling can result in the identification of novel potential targets for cancer therapy and a better understanding of the molecular mechanisms leading to cancer....

  6. Identification of gene expression profiling associated with erlotinib-related skin toxicity in pancreatic adenocarcinoma patients

    Energy Technology Data Exchange (ETDEWEB)

    Caba, Octavio, E-mail: ocaba@ujaen.es [Department of Health Sciences, University of Jaen, Jaen (Spain); Irigoyen, Antonio, E-mail: antonioirigoyen@yahoo.com [Department of Medical Oncology, Virgen de la Salud Hospital, Toledo (Spain); Jimenez-Luna, Cristina, E-mail: crisjilu@ugr.es [Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, Granada (Spain); Benavides, Manuel, E-mail: manuel.benavides.sspa@juntadeandalucia.es [Department of Medical Oncology, Virgen de la Victoria Hospital, Malaga (Spain); Ortuño, Francisco M., E-mail: fortuno@ugr.es [Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies, University of Granada, Granada (Spain); Gallego, Javier, E-mail: j.gallegoplazas@gmail.com [Department of Medical Oncology, General Universitario de Elche Hospital, Alicante (Spain); Rojas, Ignacio, E-mail: irojas@ugr.es [Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies, University of Granada, Granada (Spain); Guillen-Ponce, Carmen, E-mail: carmen.guillen@salud.madrid.org [Department of Medical Oncology, Ramón y Cajal University Hospital, Madrid (Spain); Torres, Carolina, E-mail: ctorres@uic.edu [Department of Medicine, Division of Gastroenterology and Hepatology, University of Illinois at Chicago, Chicago, IL (United States); Aranda, Enrique, E-mail: enrique.aranda@imibic.org [Maimonides Institute of Biomedical Research (IMIBIC), Reina Sofía Hospital, University of Córdoba, Córdoba (Spain); Prados, Jose, E-mail: jcprados@ugr.es [Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, Granada (Spain)

    2016-11-15

    Erlotinib is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that showed activity against pancreatic ductal adenocarcinoma (PDAC). The drug's most frequently reported side effect as a result of EGFR inhibition is skin rash (SR), a symptom which has been associated with a better therapeutic response to the drug. Gene expression profiling can be used as a tool to predict which patients will develop this important cutaneous manifestation. The aim of the present study was to identify which genes may influence the appearance of SR in PDAC patients. The study included 34 PDAC patients treated with erlotinib: 21 patients developed any grade of SR, while 13 patients did not (controls). Before administering any chemotherapy regimen and the development of SR, we collected RNA from peripheral blood samples of all patients and studied the differential gene expression pattern using the Illumina microarray platform HumanHT-12 v4 Expression BeadChip. Seven genes (FAM46C, IFITM3, GMPR, DENND6B, SELENBP1, NOL10, and SIAH2), involved in different pathways including regulatory, migratory, and signalling processes, were downregulated in PDAC patients with SR. Our results suggest the existence of a gene expression profiling significantly correlated with erlotinib-induced SR in PDAC that could be used as prognostic indicator in this patients. - Highlights: • Skin rash (SR) is the most characteristic side effect of erlotinib in PDAC patients. • Erlotinib-induced SR has been associated with a better clinical outcome. • Gene expression profiling was used to determine who will develop this manifestation. • 7 genes involved in different pathways were downregulated in PDAC patients with SR. • Our profile correlated with erlotinib-induced SR in PDAC could be used for prognosis.

  7. MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

    Directory of Open Access Journals (Sweden)

    Yang Yi-Fan

    2007-03-01

    Full Text Available Abstract Background Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. Results This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs and Translation Initiation Sites (TISs. The former is based on a linguistic "Entropy Density Profile" (EDP model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. Conclusion Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.

  8. Appendix 1:Upregulated genes in gene expression profile (P<0.05 ...

    Indian Academy of Sciences (India)

    lazi

    Appendix 1: Upregulated genes in gene expression profile«P2). Probe_s. Gene_Symbol pvalues foldchange. Probe_S. et_ID. Gene_Symbol pvalues foldchange. et_ID. 1370355. 1393751. Scd1. 1.35E-04. 25.77. Loc1009122508.06E-03. 2.55. -at at. 1398250. 1370870. Acot1. 2.43E-02. 12.18. Me1.

  9. Gene expression signatures that predict radiation exposure in mice and humans.

    Directory of Open Access Journals (Sweden)

    Holly K Dressman

    2007-04-01

    Full Text Available The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation.We have made use of gene expression analysis of peripheral blood (PB mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans.We suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.

  10. Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer

    International Nuclear Information System (INIS)

    Karlsson, Elin; Delle, Ulla; Danielsson, Anna; Olsson, Björn; Abel, Frida; Karlsson, Per; Helou, Khalil

    2008-01-01

    It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved. The research on these tumours was approved by the Medical Faculty Research

  11. MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

    Directory of Open Access Journals (Sweden)

    Kohlbacher Oliver

    2009-09-01

    Full Text Available Abstract Background Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy. Results We extended our previous MultiLoc predictor by incorporating phylogenetic profiles and Gene Ontology terms. Two different datasets were used for training the system, resulting in two versions of this high-accuracy prediction method. One version is specialized for globular proteins and predicts up to five localizations, whereas a second version covers all eleven main eukaryotic subcellular localizations. In a benchmark study with five localizations, MultiLoc2 performs considerably better than other methods for animal and plant proteins and comparably for fungal proteins. Furthermore, MultiLoc2 performs clearly better when using a second dataset that extends the benchmark study to all eleven main eukaryotic subcellular localizations. Conclusion MultiLoc2 is an extensive high-performance subcellular protein localization prediction system. By incorporating phylogenetic profiles and Gene Ontology terms MultiLoc2 yields higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. MultiLoc2 is available as user-friendly and free web-service, available at: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc2.

  12. Advanced colorectal adenoma related gene expression signature may predict prognostic for colorectal cancer patients with adenoma-carcinoma sequence.

    Science.gov (United States)

    Li, Bing; Shi, Xiao-Yu; Liao, Dai-Xiang; Cao, Bang-Rong; Luo, Cheng-Hua; Cheng, Shu-Jun

    2015-01-01

    There are still no absolute parameters predicting progression of adenoma into cancer. The present study aimed to characterize functional differences on the multistep carcinogenetic process from the adenoma-carcinoma sequence. All samples were collected and mRNA expression profiling was performed by using Agilent Microarray high-throughput gene-chip technology. Then, the characteristics of mRNA expression profiles of adenoma-carcinoma sequence were described with bioinformatics software, and we analyzed the relationship between gene expression profiles of adenoma-adenocarcinoma sequence and clinical prognosis of colorectal cancer. The mRNA expressions of adenoma-carcinoma sequence were significantly different between high-grade intraepithelial neoplasia group and adenocarcinoma group. The biological process of gene ontology function enrichment analysis on differentially expressed genes between high-grade intraepithelial neoplasia group and adenocarcinoma group showed that genes enriched in the extracellular structure organization, skeletal system development, biological adhesion and itself regulated growth regulation, with the P value after FDR correction of less than 0.05. In addition, IPR-related protein mainly focused on the insulin-like growth factor binding proteins. The variable trends of gene expression profiles for adenoma-carcinoma sequence were mainly concentrated in high-grade intraepithelial neoplasia and adenocarcinoma. The differentially expressed genes are significantly correlated between high-grade intraepithelial neoplasia group and adenocarcinoma group. Bioinformatics analysis is an effective way to study the gene expression profiles in the adenoma-carcinoma sequence, and may provide an effective tool to involve colorectal cancer research strategy into colorectal adenoma or advanced adenoma.

  13. Whole genome transcript profiling of drug induced steatosis in rats reveals a gene signature predictive of outcome.

    Directory of Open Access Journals (Sweden)

    Nishika Sahini

    Full Text Available Drug induced steatosis (DIS is characterised by excess triglyceride accumulation in the form of lipid droplets (LD in liver cells. To explore mechanisms underlying DIS we interrogated the publically available microarray data from the Japanese Toxicogenomics Project (TGP to study comprehensively whole genome gene expression changes in the liver of treated rats. For this purpose a total of 17 and 12 drugs which are diverse in molecular structure and mode of action were considered based on their ability to cause either steatosis or phospholipidosis, respectively, while 7 drugs served as negative controls. In our efforts we focused on 200 genes which are considered to be mechanistically relevant in the process of lipid droplet biogenesis in hepatocytes as recently published (Sahini and Borlak, 2014. Based on mechanistic considerations we identified 19 genes which displayed dose dependent responses while 10 genes showed time dependency. Importantly, the present study defined 9 genes (ANGPTL4, FABP7, FADS1, FGF21, GOT1, LDLR, GK, STAT3, and PKLR as signature genes to predict DIS. Moreover, cross tabulation revealed 9 genes to be regulated ≥10 times amongst the various conditions and included genes linked to glucose metabolism, lipid transport and lipogenesis as well as signalling events. Additionally, a comparison between drugs causing phospholipidosis and/or steatosis revealed 26 genes to be regulated in common including 4 signature genes to predict DIS (PKLR, GK, FABP7 and FADS1. Furthermore, a comparison between in vivo single dose (3, 6, 9 and 24 h and findings from rat hepatocyte studies (2 h, 8 h, 24 h identified 10 genes which are regulated in common and contained 2 DIS signature genes (FABP7, FGF21. Altogether, our studies provide comprehensive information on mechanistically linked gene expression changes of a range of drugs causing steatosis and phospholipidosis and encourage the screening of DIS signature genes at the preclinical stage.

  14. Identification of Candidate B-Lymphoma Genes by Cross-Species Gene Expression Profiling

    Science.gov (United States)

    Tompkins, Van S.; Han, Seong-Su; Olivier, Alicia; Syrbu, Sergei; Bair, Thomas; Button, Anna; Jacobus, Laura; Wang, Zebin; Lifton, Samuel; Raychaudhuri, Pradip; Morse, Herbert C.; Weiner, George; Link, Brian; Smith, Brian J.; Janz, Siegfried

    2013-01-01

    Comparative genome-wide expression profiling of malignant tumor counterparts across the human-mouse species barrier has a successful track record as a gene discovery tool in liver, breast, lung, prostate and other cancers, but has been largely neglected in studies on neoplasms of mature B-lymphocytes such as diffuse large B cell lymphoma (DLBCL) and Burkitt lymphoma (BL). We used global gene expression profiles of DLBCL-like tumors that arose spontaneously in Myc-transgenic C57BL/6 mice as a phylogenetically conserved filter for analyzing the human DLBCL transcriptome. The human and mouse lymphomas were found to have 60 concordantly deregulated genes in common, including 8 genes that Cox hazard regression analysis associated with overall survival in a published landmark dataset of DLBCL. Genetic network analysis of the 60 genes followed by biological validation studies indicate FOXM1 as a candidate DLBCL and BL gene, supporting a number of studies contending that FOXM1 is a therapeutic target in mature B cell tumors. Our findings demonstrate the value of the “mouse filter” for genomic studies of human B-lineage neoplasms for which a vast knowledge base already exists. PMID:24130802

  15. ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

    Science.gov (United States)

    Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Mendiola, Marta; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B.; Machado, Isidro; Ramos, David; Gironella, Meritxell; Espinosa-Salinas, Isabel; Ramos, Ricardo; Martín-Hernández, Roberto; Risueño, Alberto; De Las Rivas, Javier; Reglero, Guillermo; Yaya, Ricardo; Fernández-Martos, Carlos; Aparicio, Jorge; Maurel, Joan; Feliu, Jaime; de Molina, Ana Ramírez

    2015-01-01

    Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group. PMID:25749516

  16. Freedom of expression: cell-type-specific gene profiling.

    Science.gov (United States)

    Otsuki, Leo; Cheetham, Seth W; Brand, Andrea H

    2014-01-01

    Cell fate and behavior are results of differential gene regulation, making techniques to profile gene expression in specific cell types highly desirable. Many methods now enable investigation at the DNA, RNA and protein level. This review introduces the most recent and popular techniques, and discusses key issues influencing the choice between these such as ease, cost and applicability of information gained. Interdisciplinary collaborations will no doubt contribute further advances, including not just in single cell type but single-cell expression profiling. © 2014 Wiley Periodicals, Inc.

  17. Profiling Gene Expression in Germinating Brassica Roots.

    Science.gov (United States)

    Park, Myoung Ryoul; Wang, Yi-Hong; Hasenstein, Karl H

    2014-01-01

    Based on previously developed solid-phase gene extraction (SPGE) we examined the mRNA profile in primary roots of Brassica rapa seedlings for highly expressed genes like ACT7 (actin7), TUB (tubulin1), UBQ (ubiquitin), and low expressed GLK (glucokinase) during the first day post-germination. The assessment was based on the mRNA load of the SPGE probe of about 2.1 ng. The number of copies of the investigated genes changed spatially along the length of primary roots. The expression level of all genes differed significantly at each sample position. Among the examined genes ACT7 expression was most even along the root. UBQ was highest at the tip and root-shoot junction (RS). TUB and GLK showed a basipetal gradient. The temporal expression of UBQ was highest in the MZ 9 h after primary root emergence and higher than at any other sample position. Expressions of GLK in EZ and RS increased gradually over time. SPGE extraction is the result of oligo-dT and oligo-dA hybridization and the results illustrate that SPGE can be used for gene expression profiling at high spatial and temporal resolution. SPGE needles can be used within two weeks when stored at 4 °C. Our data indicate that gene expression studies that are based on the entire root miss important differences in gene expression that SPGE is able to resolve for example growth adjustments during gravitropism.

  18. Gene Structures, Evolution and Transcriptional Profiling of the WRKY Gene Family in Castor Bean (Ricinus communis L.).

    Science.gov (United States)

    Zou, Zhi; Yang, Lifu; Wang, Danhua; Huang, Qixing; Mo, Yeyong; Xie, Guishui

    2016-01-01

    WRKY proteins comprise one of the largest transcription factor families in plants and form key regulators of many plant processes. This study presents the characterization of 58 WRKY genes from the castor bean (Ricinus communis L., Euphorbiaceae) genome. Compared with the automatic genome annotation, one more WRKY-encoding locus was identified and 20 out of the 57 predicted gene models were manually corrected. All RcWRKY genes were shown to contain at least one intron in their coding sequences. According to the structural features of the present WRKY domains, the identified RcWRKY genes were assigned to three previously defined groups (I-III). Although castor bean underwent no recent whole-genome duplication event like physic nut (Jatropha curcas L., Euphorbiaceae), comparative genomics analysis indicated that one gene loss, one intron loss and one recent proximal duplication occurred in the RcWRKY gene family. The expression of all 58 RcWRKY genes was supported by ESTs and/or RNA sequencing reads derived from roots, leaves, flowers, seeds and endosperms. Further global expression profiles with RNA sequencing data revealed diverse expression patterns among various tissues. Results obtained from this study not only provide valuable information for future functional analysis and utilization of the castor bean WRKY genes, but also provide a useful reference to investigate the gene family expansion and evolution in Euphorbiaceus plants.

  19. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

    Science.gov (United States)

    Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

    2004-03-05

    Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

  20. Gene Expression Profiling of Peripheral Blood From Kidney Transplant Recipients for the Early Detection of Digestive System Cancer.

    Science.gov (United States)

    Kusaka, M; Okamoto, M; Takenaka, M; Sasaki, H; Fukami, N; Kataoka, K; Ito, T; Kenmochi, T; Hoshinaga, K; Shiroki, R

    2017-06-01

    Kidney transplant recipients are at increased risk of developing cancer in comparison with the general population. To effectively manage post-transplantation malignancies, it is essential to proactively monitor patients. A long-term intensive screening program was associated with a reduced incidence of cancer after transplantation. This study evaluated the usefulness of the gene expression profiling of peripheral blood samples obtained from kidney transplant patients and adopted a screening test for detecting cancer of the digestive system (gastric, colon, pancreas, and biliary tract). Nineteen patients were included in this study and a total of 53 gene expression screening tests were performed. The gene expression profiles of blood-delivered total RNA and whole genome human gene expression profiles were obtained. We investigated the expression levels of 2665 genes associated with digestive cancers and counted the number of genes in which expression was altered. A hierarchical clustering analysis was also performed. The final prediction of the cancer possibility was determined according to an algorithm. The number of genes in which expression was altered was significantly increased in the kidney transplant recipients in comparison with the general population (1091 ± 63 vs 823 ± 94; P = .0024). The number of genes with altered expression decreased after the induction of mechanistic target of rapamycin (mTOR) inhibitor (1484 ± 227 vs 883 ± 154; P = .0439). No cases of possible digestive cancer were detected in this study period. The gene expression profiling of peripheral blood samples may be a useful and noninvasive diagnostic tool that allows for the early detection of cancer of the digestive system. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

    Science.gov (United States)

    Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

    2018-05-10

    Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

  2. Gene expression profile associated with superimposed non-alcoholic fatty liver disease and hepatic fibrosis in patients with chronic hepatitis C.

    Science.gov (United States)

    Younossi, Zobair M; Afendy, Arian; Stepanova, Maria; Hossain, Noreen; Younossi, Issah; Ankrah, Kathy; Gramlich, Terry; Baranova, Ancha

    2009-10-01

    Hepatic steatosis occurs in 40-70% of patients chronically infected with hepatitis C virus [chronic hepatitis C (CH-C)]. Hepatic steatosis in CH-C is associated with progressive liver disease and a low response rate to antiviral therapy. Gene expression profiles were examined in CH-C patients with and without hepatic steatosis, non-alcoholic steatohepatitis (NASH) and fibrosis. This study included 65 CH-C patients who were not receiving antiviral treatment. Total RNA was extracted from peripheral blood mononuclear cells, quantified and used for one-step reverse transcriptase-polymerase chain reaction to profile 153 mRNAs that were normalized with six 'housekeeping' genes and a reference RNA. Multiple regression and stepwise selection assessed differences in gene expression and the models' performances were evaluated. Models predicting the grade of hepatic steatosis in patients with CH-C genotype 3 involved two genes: SOCS1 and IFITM1, which progressively changed their expression level with the increasing grade of steatosis. On the other hand, models predicting hepatic steatosis in non-genotype 3 patients highlighted MIP-1 cytokine encoding genes: CCL3 and CCL4 as well as IFNAR and PRKRIR. Expression levels of PRKRIR and SMAD3 differentiated patients with and without superimposed NASH only in the non-genotype 3 cohort (area under the receiver operating characteristic curve=0.822, P-value 0.006]. Gene expression signatures related to hepatic fibrosis were not genotype specific. Gene expression might predict moderate to severe hepatic steatosis, NASH and fibrosis in patients with CH-C, providing potential insights into the pathogenesis of hepatic steatosis and fibrosis in these patients.

  3. Variation-preserving normalization unveils blind spots in gene expression profiling

    Science.gov (United States)

    Roca, Carlos P.; Gomes, Susana I. L.; Amorim, Mónica J. B.; Scott-Fordsmand, Janeck J.

    2017-01-01

    RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the normalization of gene expression levels, which is currently performed following the implicit assumption that most genes are not differentially expressed. Here, we present a mathematical approach to normalization that makes no assumption of this sort. We have found that variation in gene expression is much larger than currently believed, and that it can be measured with available assays. Our results also explain, at least partially, the reproducibility problems encountered in transcriptomics studies. We expect that this improvement in detection will help efforts to realize the full potential of gene expression profiling, especially in analyses of cellular processes involving complex modulations of gene expression. PMID:28276435

  4. Towards precise classification of cancers based on robust gene functional expression profiles

    Directory of Open Access Journals (Sweden)

    Zhu Jing

    2005-03-01

    Full Text Available Abstract Background Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. Results Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. Conclusion This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge

  5. Liver Gene Expression Profiles of Rats Treated with Clofibric Acid

    Science.gov (United States)

    Michel, Cécile; Desdouets, Chantal; Sacre-Salem, Béatrice; Gautier, Jean-Charles; Roberts, Ruth; Boitier, Eric

    2003-01-01

    Clofibric acid (CLO) is a peroxisome proliferator (PP) that acts through the peroxisome proliferator activated receptor α, leading to hepatocarcinogenesis in rodents. CLO-induced hepatocarcinogenesis is a multi-step process, first transforming normal liver cells into foci. The combination of laser capture microdissection (LCM) and genomics has the potential to provide expression profiles from such small cell clusters, giving an opportunity to understand the process of cancer development in response to PPs. To our knowledge, this is the first evaluation of the impact of the successive steps of LCM procedure on gene expression profiling by comparing profiles from LCM samples to those obtained with non-microdissected liver samples collected after a 1 month CLO treatment in the rat. We showed that hematoxylin and eosin (H&E) staining and laser microdissection itself do not impact on RNA quality. However, the overall process of the LCM procedure affects the RNA quality, resulting in a bias in the gene profiles. Nonetheless, this bias did not prevent accurate determination of a CLO-specific molecular signature. Thus, gene-profiling analysis of microdissected foci, identified by H&E staining may provide insight into the mechanisms underlying non-genotoxic hepatocarcinogenesis in the rat by allowing identification of specific genes that are regulated by CLO in early pre-neoplastic foci. PMID:14633594

  6. Radioactive cDNA microarrys for gene expression profiles in antidepressant therapy

    International Nuclear Information System (INIS)

    Lee, M. S.; Han, B. J.; Cha, J. H.; Ryu, Y. M.; Shin, E. K.; Park, J. H.; Park, Y. H.; Kim, M. K.

    2002-01-01

    Using radioactive cDNA microarray, we investigated a pattern of gene regulation under treatment of antidepressant on patients of depressive disoder. Basic microarray technology was performed as previously described in our research. The bioinformatic selection of human cDNAs, which is specifically designed for psychiatry, neurology, and signal transduction, were arrayed on nylon membranes. Using with 33P-labeled probes, this method provided highly sensitive gene expression profiles of our interest including brain receptors, drug metabolism, and cellular signalings. Gene expression profiles were also classified into several categories in accordance with the gene-regulation of antidepressant. The gene profiles of our interest were significantly up- (16 genes, >2.0 of Z-ratio) or down- (24 genes, <-2.0 of Z ratio) regulated when compared the good responsed group with the bad-responsed one. Consequently, we demonstrated that radioactive human cDNA microarray is highly likely to be an efficient technology for evaluating the gene regulation of antidepressants, such as selective serotonin-reuptake inhibitors (SSRIs), by using high-throughput biotechnology

  7. Expression profiles of genes involved in tanshinone biosynthesis of ...

    Indian Academy of Sciences (India)

    Expression profiles of genes involved in tanshinone biosynthesis of two. Salvia miltiorrhiza genotypes with different tanshinone contents. Zhenqiao Song, Jianhua Wang and Xingfeng Li. J. Genet. 95, 433–439. Table 1. S. miltiorrhiza genes and primer pairs used for qRT-PCR. Gene. GenBank accession. Primer name.

  8. Single-cell gene-expression profiling and its potential diagnostic applications

    Czech Academy of Sciences Publication Activity Database

    Stahlberg, A.; Kubista, Mikael; Aman, P.

    2011-01-01

    Roč. 11, č. 7 (2011), s. 735-740 ISSN 1473-7159 R&D Projects: GA ČR(CZ) GAP303/10/1338; GA ČR(CZ) GA301/09/1752 Institutional research plan: CEZ:AV0Z50520701 Keywords : gene-expression profiling * RT-qPCR * single-cell gene-expression profiling Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 4.859, year: 2011

  9. Gene expression profiles in skeletal muscle after gene electrotransfer

    DEFF Research Database (Denmark)

    Hojman, Pernille; Zibert, John R; Gissel, Hanne

    2007-01-01

    BACKGROUND: Gene transfer by electroporation (DNA electrotransfer) to muscle results in high level long term transgenic expression, showing great promise for treatment of e.g. protein deficiency syndromes. However little is known about the effects of DNA electrotransfer on muscle fibres. We have...... caused down-regulation of structural proteins e.g. sarcospan and catalytic enzymes. Injection of DNA induced down-regulation of intracellular transport proteins e.g. sentrin. The effects on muscle fibres were transient as the expression profiles 3 weeks after treatment were closely related......) followed by a long low voltage pulse (LV, 100 V/cm, 400 ms); a pulse combination optimised for efficient and safe gene transfer. Muscles were transfected with green fluorescent protein (GFP) and excised at 4 hours, 48 hours or 3 weeks after treatment. RESULTS: Differentially expressed genes were...

  10. Diurnal and circadian expression profiles of glycerolipid biosynthetic genes in Arabidopsis.

    Science.gov (United States)

    Nakamura, Yuki; Andrés, Fernando; Kanehara, Kazue; Liu, Yu-chi; Coupland, George; Dörmann, Peter

    2014-01-01

    Glycerolipid composition in plant membranes oscillates in response to diurnal change. However, its functional significance remained unclear. A recent discovery that Arabidopsis florigen FT binds diurnally oscillating phosphatidylcholine molecules to promote flowering suggests that diurnal oscillation of glycerolipid composition is an important input in flowering time control. Taking advantage of public microarray data, we globally analyzed the expression pattern of glycerolipid biosynthetic genes in Arabidopsis under long-day, short-day, and continuous light conditions. The results revealed that 12 genes associated with glycerolipid metabolism showed significant oscillatory profiles. Interestingly, expression of most of these genes followed circadian profiles, suggesting that glycerolipid biosynthesis is partially under clock regulation. The oscillating expression profile of one representative gene, PECT1, was analyzed in detail. Expression of PECT1 showed a circadian pattern highly correlated with that of the clock-regulated gene GIGANTEA. Thus, our study suggests that a considerable number of glycerolipid biosynthetic genes are under circadian control.

  11. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    International Nuclear Information System (INIS)

    Hao, Ke; Zhong, Hua; Greenawalt, Danielle; Ferguson, Mark D; Ng, Irene O; Sham, Pak C; Poon, Ronnie T; Molony, Cliona; Schadt, Eric E; Dai, Hongyue; Luk, John M; Lamb, John; Zhang, Chunsheng; Xie, Tao; Wang, Kai; Zhang, Bin; Chudin, Eugene; Lee, Nikki P; Mao, Mao

    2011-01-01

    The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome

  12. Development of Castration Resistant Prostate Cancer can be Predicted by a DNA Hypermethylation Profile.

    Science.gov (United States)

    Angulo, Javier C; Andrés, Guillermo; Ashour, Nadia; Sánchez-Chapado, Manuel; López, Jose I; Ropero, Santiago

    2016-03-01

    Detection of DNA hypermethylation has emerged as a novel molecular biomarker for prostate cancer diagnosis and evaluation of prognosis. We sought to define whether a hypermethylation profile of patients with prostate cancer on androgen deprivation would predict castrate resistant prostate cancer. Genome-wide methylation analysis was performed using a methylation cancer panel in 10 normal prostates and 45 tumor samples from patients placed on androgen deprivation who were followed until castrate resistant disease developed. Castrate resistant disease was defined according to EAU (European Association of Urology) guideline criteria. Two pathologists reviewed the Gleason score, Ki-67 index and neuroendocrine differentiation. Hierarchical clustering analysis was performed and relationships with outcome were investigated by Cox regression and log rank analysis. We found 61 genes that were significantly hypermethylated in greater than 20% of tumors analyzed. Three clusters of patients were characterized by a DNA methylation profile, including 1 at risk for earlier castrate resistant disease (log rank p = 0.019) and specific mortality (log rank p = 0.002). Hypermethylation of ETV1 (HR 3.75) and ZNF215 (HR 2.89) predicted disease progression despite androgen deprivation. Hypermethylation of IRAK3 (HR 13.72), ZNF215 (HR 4.81) and SEPT9 (HR 7.64) were independent markers of prognosis. Prostate specific antigen greater than 25 ng/ml, Gleason pattern 5, Ki-67 index greater than 12% and metastasis at diagnosis also predicted a negative response to androgen deprivation. Study limitations included the retrospective design and limited number of cases. Epigenetic silencing of the mentioned genes could be novel molecular markers for the prognosis of advanced prostate cancer. It might predict castrate resistance during hormone deprivation and, thus, disease specific mortality. Gene hypermethylation is associated with disease progression in patients who receive hormone therapy. It

  13. Quantitative Methylation Profiles for Multiple Tumor Suppressor Gene Promoters in Salivary Gland Tumors

    Science.gov (United States)

    Durr, Megan L.; Mydlarz, Wojciech K.; Shao, Chunbo; Zahurak, Marianna L.; Chuang, Alice Y.; Hoque, Mohammad O.; Westra, William H.; Liegeois, Nanette J.; Califano, Joseph A.; Sidransky, David; Ha, Patrick K.

    2010-01-01

    Background Methylation profiling of tumor suppressor gene (TSGs) promoters is quickly becoming a powerful diagnostic tool for the early detection, prognosis, and even prediction of clinical response to treatment. Few studies address this in salivary gland tumors (SGTs); hence the promoter methylation profile of various TSGs was quantitatively assessed in primary SGT tissue to determine if tumor-specific alterations could be detected. Methodology DNA isolated from 78 tumor and 17 normal parotid gland specimens was assayed for promoter methylation status of 19 TSGs by fluorescence-based, quantitative methylation-specific PCR (qMSP). The data were utilized in a binary fashion as well as quantitatively (using a methylation quotient) allowing for better profiling and interpretation of results. Principal Findings The average number of methylation events across the studied genes was highest in salivary duct carcinoma (SDC), with a methylation value of 9.6, compared to the normal 4.5 (ptrend for increasing methylation in APC, Mint 1, PGP9.5, RAR-β, and Timp3. Conclusions/Significance Screening promoter methylation profiles in SGTs showed considerable heterogeneity. The methylation status of certain markers was surprisingly high in even normal salivary tissue, confirming the need for such controls. Several TSGs were found to be associated with malignant SGTs, especially SDC. Further study is needed to evaluate the potential use of these associations in the detection, prognosis, and therapeutic outcome of these rare tumors. PMID:20520817

  14. Gene structure, phylogeny and expression profile of the sucrose ...

    Indian Academy of Sciences (India)

    Gene structure, phylogeny and expression profile of the sucrose synthase gene family in .... 24, 701–713. Bate N. and Twell D. 1998 Functional architecture of a late pollen .... Manzara T. and Gruissem W. 1988 Organization and expression.

  15. Radiation-induced gene expression in human subcutaneous fibroblasts is predictive of radiation-induced fibrosis

    DEFF Research Database (Denmark)

    Rødningen, Olaug Kristin; Børresen-Dale, Anne-Lise; Alsner, Jan

    2008-01-01

    BACKGROUND AND PURPOSE: Breast cancer patients show a large variation in normal tissue reactions after ionizing radiation (IR) therapy. One of the most common long-term adverse effects of ionizing radiotherapy is radiation-induced fibrosis (RIF), and several attempts have been made over the last...... years to develop predictive assays for RIF. Our aim was to identify basal and radiation-induced transcriptional profiles in fibroblasts from breast cancer patients that might be related to the individual risk of RIF in these patients. MATERIALS AND METHODS: Fibroblast cell lines from 31 individuals......-treated fibroblasts. Transcriptional differences in basal and radiation-induced gene expression profiles were investigated using 15K cDNA microarrays, and results analyzed by both SAM and PAM. RESULTS: Sixty differentially expressed genes were identified by applying SAM on 10 patients with the highest risk of RIF...

  16. Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

    Science.gov (United States)

    Mutch, David M.; Temanni, M. Ramzi; Henegar, Corneliu; Combes, Florence; Pelloux, Véronique; Holst, Claus; Sørensen, Thorkild I. A.; Astrup, Arne; Martinez, J. Alfredo; Saris, Wim H. M.; Viguerie, Nathalie; Langin, Dominique; Zucker, Jean-Daniel; Clément, Karine

    2007-01-01

    Background The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. Methodology/Principal Findings The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8–12 kgs weight loss) could always be differentiated from non-responders (diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition. PMID:18094752

  17. Advanced colorectal adenoma related gene expression signature may predict prognostic for colorectal cancer patients with adenoma-carcinoma sequence

    OpenAIRE

    Li, Bing; Shi, Xiao-Yu; Liao, Dai-Xiang; Cao, Bang-Rong; Luo, Cheng-Hua; Cheng, Shu-Jun

    2015-01-01

    Background: There are still no absolute parameters predicting progression of adenoma into cancer. The present study aimed to characterize functional differences on the multistep carcinogenetic process from the adenoma-carcinoma sequence. Methods: All samples were collected and mRNA expression profiling was performed by using Agilent Microarray high-throughput gene-chip technology. Then, the characteristics of mRNA expression profiles of adenoma-carcinoma sequence were described with bioinform...

  18. Digital sorting of complex tissues for cell type-specific gene expression profiles.

    Science.gov (United States)

    Zhong, Yi; Wan, Ying-Wooi; Pang, Kaifang; Chow, Lionel M L; Liu, Zhandong

    2013-03-07

    Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles. Furthermore, these algorithms tend to report biased estimations. Here, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies. The results suggest that DSA is a specific and sensitivity algorithm in gene expression profile deconvolution and will be useful in studying individual cell types of complex tissues.

  19. Gene expression profiles in stages II and III colon cancers

    DEFF Research Database (Denmark)

    Thorsteinsson, Morten; Kirkeby, Lene T; Hansen, Raino

    2012-01-01

    PURPOSE: A 128-gene signature has been proposed to predict outcome in patients with stages II and III colorectal cancers. In the present study, we aimed to reproduce and validate the 128-gene signature in external and independent material. METHODS: Gene expression data from the original material...... were retrieved from the Gene Expression Omnibus (GEO) (n¿=¿111) in addition to a Danish data set (n¿=¿37). All patients had stages II and III colon cancers. A Prediction Analysis of Microarray classifier, based on the 128-gene signature and the original training set of stage I (n¿=¿65) and stage IV (n...... correctly predicted as stage IV-like, and the remaining patients were predicted as stage I-like and unclassifiable, respectively. Stage II patients could not be stratified. CONCLUSIONS: The 128-gene signature showed reproducibility in stage III colon cancer, but could not predict recurrence in stage II...

  20. Predictability of Genetic Interactions from Functional Gene Modules

    Directory of Open Access Journals (Sweden)

    Jonathan H. Young

    2017-02-01

    Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

  1. Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer.

    Science.gov (United States)

    Gianni, Luca; Zambetti, Milvia; Clark, Kim; Baker, Joffre; Cronin, Maureen; Wu, Jenny; Mariani, Gabriella; Rodriguez, Jaime; Carcangiu, Marialuisa; Watson, Drew; Valagussa, Pinuccia; Rouzier, Roman; Symmans, W Fraser; Ross, Jeffrey S; Hortobagyi, Gabriel N; Pusztai, Lajos; Shak, Steven

    2005-10-10

    We sought to identify gene expression markers that predict the likelihood of chemotherapy response. We also tested whether chemotherapy response is correlated with the 21-gene Recurrence Score assay that quantifies recurrence risk. Patients with locally advanced breast cancer received neoadjuvant paclitaxel and doxorubicin. RNA was extracted from the pretreatment formalin-fixed paraffin-embedded core biopsies. The expression of 384 genes was quantified using reverse transcriptase polymerase chain reaction and correlated with pathologic complete response (pCR). The performance of genes predicting for pCR was tested in patients from an independent neoadjuvant study where gene expression was obtained using DNA microarrays. Of 89 assessable patients (mean age, 49.9 years; mean tumor size, 6.4 cm), 11 (12%) had a pCR. Eighty-six genes correlated with pCR (unadjusted P < .05); pCR was more likely with higher expression of proliferation-related genes and immune-related genes, and with lower expression of estrogen receptor (ER) -related genes. In 82 independent patients treated with neoadjuvant paclitaxel and doxorubicin, DNA microarray data were available for 79 of the 86 genes. In univariate analysis, 24 genes correlated with pCR with P < .05 (false discovery, four genes) and 32 genes showed correlation with P < .1 (false discovery, eight genes). The Recurrence Score was positively associated with the likelihood of pCR (P = .005), suggesting that the patients who are at greatest recurrence risk are more likely to have chemotherapy benefit. Quantitative expression of ER-related genes, proliferation genes, and immune-related genes are strong predictors of pCR in women with locally advanced breast cancer receiving neoadjuvant anthracyclines and paclitaxel.

  2. Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

    Directory of Open Access Journals (Sweden)

    Christof Winter

    Full Text Available Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

  3. Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates.

    Science.gov (United States)

    Yabu, Julie M; Siebert, Janet C; Maecker, Holden T

    2016-01-01

    Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize

  4. Gene expression patterns in formalin-fixed, paraffin-embedded core biopsies predict docetaxel chemosensitivity in breast cancer patients.

    Science.gov (United States)

    Chang, Jenny C; Makris, Andreas; Gutierrez, M Carolina; Hilsenbeck, Susan G; Hackett, James R; Jeong, Jennie; Liu, Mei-Lan; Baker, Joffre; Clark-Langone, Kim; Baehner, Frederick L; Sexton, Krsytal; Mohsin, Syed; Gray, Tara; Alvarez, Laura; Chamness, Gary C; Osborne, C Kent; Shak, Steven

    2008-03-01

    Previously, we had identified gene expression patterns that predicted response to neoadjuvant docetaxel. Other studies have validated that a high Recurrence Score (RS) by the 21-gene RT-PCR assay is predictive of worse prognosis but better response to chemotherapy. We investigated whether tumor expression of these 21 genes and other candidate genes can predict response to docetaxel. Core biopsies from 97 patients were obtained before treatment with neoadjuvant docetaxel (4 cycles, 100 mg/m2 q3 weeks). Three 10-microm FFPE sections were submitted for quantitative RT-PCR assays of 192 genes that were selected from our previous work and the literature. Of the 97 patients, 81 (84%) had sufficient invasive cancer, 80 (82%) had sufficient RNA for QRTPCR assay, and 72 (74%) had clinical response data. Mean age was 48.5 years, and the median tumor size was 6 cm. Clinical complete responses (CR) were observed in 12 (17%), partial responses in 41 (57%), stable disease in 17 (24%), and progressive disease in 2 patients (3%). A significant relationship (P<0.05) between gene expression and CR was observed for 14 genes, including CYBA. CR was associated with lower expression of the ER gene group and higher expression of the proliferation gene group from the 21 gene assay. Of note, CR was more likely with a high RS (P=0.008). We have established molecular profiles of sensitivity to docetaxel. RT-PCR technology provides a potential platform for a predictive test of docetaxel chemosensitivity using small amounts of routinely processed material.

  5. Early gene expression profiles of patients with chronic hepatitis C treated with pegylated interferon-alfa and ribavirin.

    Science.gov (United States)

    Younossi, Zobair M; Baranova, Ancha; Afendy, Arian; Collantes, Rochelle; Stepanova, Maria; Manyam, Ganiraju; Bakshi, Anita; Sigua, Christopher L; Chan, Joanne P; Iverson, Ayuko A; Santini, Christopher D; Chang, Sheng-Yung P

    2009-03-01

    Responsiveness to hepatitis C virus (HCV) therapy depends on viral and host factors. Our aim was to assess sustained virologic response (SVR)-associated early gene expression in patients with HCV receiving pegylated interferon-alpha2a (PEG-IFN-alpha2a) or PEG-IFN-alpha2b and ribavirin with the duration based on genotypes. Blood samples were collected into PAXgene tubes prior to treatment as well as 1, 7, 28, and 56 days after treatment. From the peripheral blood cells, total RNA was extracted, quantified, and used for one-step reverse transcription polymerase chain reaction to profile 154 messenger RNAs. Expression levels of messenger RNAs were normalized with six "housekeeping" genes and a reference RNA. Multiple regression and stepwise selection were performed to assess differences in gene expression at different time points, and predictive performance was evaluated for each model. A total of 68 patients were enrolled in the study and treated with combination therapy. The results of gene expression showed that SVR could be predicted by the gene expression of signal transducer and activator of transcription-6 (STAT-6) and suppressor of cytokine signaling-1 in the pretreatment samples. After 24 hours, SVR was predicted by the expression of interferon-dependent genes, and this dependence continued to be prominent throughout the treatment. Early gene expression during anti-HCV therapy may elucidate important molecular pathways that may be influencing the probability of achieving virologic response.

  6. Microarray analysis of the gene expression profile in triethylene ...

    African Journals Online (AJOL)

    Microarray analysis of the gene expression profile in triethylene glycol dimethacrylate-treated human dental pulp cells. ... Conclusions: Our results suggest that TEGDMA can change the many functions of hDPCs through large changes in gene expression levels and complex interactions with different signaling pathways.

  7. Prediction of Phenotypic Antimicrobial Resistance Profiles From Whole Genome Sequences of Non-typhoidal Salmonella enterica.

    Science.gov (United States)

    Neuert, Saskia; Nair, Satheesh; Day, Martin R; Doumith, Michel; Ashton, Philip M; Mellor, Kate C; Jenkins, Claire; Hopkins, Katie L; Woodford, Neil; de Pinna, Elizabeth; Godbole, Gauri; Dallman, Timothy J

    2018-01-01

    Surveillance of antimicrobial resistance (AMR) in non-typhoidal Salmonella enterica (NTS), is essential for monitoring transmission of resistance from the food chain to humans, and for establishing effective treatment protocols. We evaluated the prediction of phenotypic resistance in NTS from genotypic profiles derived from whole genome sequencing (WGS). Genes and chromosomal mutations responsible for phenotypic resistance were sought in WGS data from 3,491 NTS isolates received by Public Health England's Gastrointestinal Bacteria Reference Unit between April 2014 and March 2015. Inferred genotypic AMR profiles were compared with phenotypic susceptibilities determined for fifteen antimicrobials using EUCAST guidelines. Discrepancies between phenotypic and genotypic profiles for one or more antimicrobials were detected for 76 isolates (2.18%) although only 88/52,365 (0.17%) isolate/antimicrobial combinations were discordant. Of the discrepant results, the largest number were associated with streptomycin (67.05%, n = 59). Pan-susceptibility was observed in 2,190 isolates (62.73%). Overall, resistance to tetracyclines was most common (26.27% of isolates, n = 917) followed by sulphonamides (23.72%, n = 828) and ampicillin (21.43%, n = 748). Multidrug resistance (MDR), i.e., resistance to three or more antimicrobial classes, was detected in 848 isolates (24.29%) with resistance to ampicillin, streptomycin, sulphonamides and tetracyclines being the most common MDR profile ( n = 231; 27.24%). For isolates with this profile, all but one were S . Typhimurium and 94.81% ( n = 219) had the resistance determinants bla TEM-1, strA-strB, sul2 and tet (A). Extended-spectrum β-lactamase genes were identified in 41 isolates (1.17%) and multiple mutations in chromosomal genes associated with ciprofloxacin resistance in 82 isolates (2.35%). This study showed that WGS is suitable as a rapid means of determining AMR patterns of NTS for public health surveillance.

  8. Constitutive gene expression profile segregates toxicity in locally advanced breast cancer patients treated with high-dose hyperfractionated radical radiotherapy

    International Nuclear Information System (INIS)

    Henríquez Hernández, Luis Alberto; Lara, Pedro Carlos; Pinar, Beatriz; Bordón, Elisa; Gallego, Carlos Rodríguez; Bilbao, Cristina; Pérez, Leandro Fernández; Morales, Amílcar Flores

    2009-01-01

    Breast cancer patients show a wide variation in normal tissue reactions after radiotherapy. The individual sensitivity to x-rays limits the efficiency of the therapy. Prediction of individual sensitivity to radiotherapy could help to select the radiation protocol and to improve treatment results. The aim of this study was to assess the relationship between gene expression profiles of ex vivo un-irradiated and irradiated lymphocytes and the development of toxicity due to high-dose hyperfractionated radiotherapy in patients with locally advanced breast cancer. Raw data from microarray experiments were uploaded to the Gene Expression Omnibus Database http://www.ncbi.nlm.nih.gov/geo/ (GEO accession GSE15341). We obtained a small group of 81 genes significantly regulated by radiotherapy, lumped in 50 relevant pathways. Using ANOVA and t-test statistical tools we found 20 and 26 constitutive genes (0 Gy) that segregate patients with and without acute and late toxicity, respectively. Non-supervised hierarchical clustering was used for the visualization of results. Six and 9 pathways were significantly regulated respectively. Concerning to irradiated lymphocytes (2 Gy), we founded 29 genes that separate patients with acute toxicity and without it. Those genes were gathered in 4 significant pathways. We could not identify a set of genes that segregates patients with and without late toxicity. In conclusion, we have found an association between the constitutive gene expression profile of peripheral blood lymphocytes and the development of acute and late toxicity in consecutive, unselected patients. These observations suggest the possibility of predicting normal tissue response to irradiation in high-dose non-conventional radiation therapy regimens. Prospective studies with higher number of patients are needed to validate these preliminary results

  9. Identification and expression profiling analysis of TCP family genes involved in growth and development in maize.

    Science.gov (United States)

    Chai, Wenbo; Jiang, Pengfei; Huang, Guoyu; Jiang, Haiyang; Li, Xiaoyu

    2017-10-01

    The TCP family is a group of plant-specific transcription factors. TCP genes encode proteins harboring bHLH structure, which is implicated in DNA binding and protein-protein interactions and known as the TCP domain. TCP genes play important roles in plant development and have been evolutionarily and functionally elaborated in various plants, however, no overall phylogenetic analysis or expression profiling of TCP genes in Zea mays has been reported. In the present study, a systematic analysis of molecular evolution and functional prediction of TCP family genes in maize ( Z . mays L.) has been conducted. We performed a genome-wide survey of TCP genes in maize, revealing the gene structure, chromosomal location and phylogenetic relationship of family members. Microsynteny between grass species and tissue-specific expression profiles were also investigated. In total, 29 TCP genes were identified in the maize genome, unevenly distributed on the 10 maize chromosomes. Additionally, ZmTCP genes were categorized into nine classes based on phylogeny and purifying selection may largely be responsible for maintaining the functions of maize TCP genes. What's more, microsynteny analysis suggested that TCP genes have been conserved during evolution. Finally, expression analysis revealed that most TCP genes are expressed in the stem and ear, which suggests that ZmTCP genes influence stem and ear growth. This result is consistent with the previous finding that maize TCP genes represses the growth of axillary organs and enables the formation of female inflorescences. Altogether, this study presents a thorough overview of TCP family in maize and provides a new perspective on the evolution of this gene family. The results also indicate that TCP family genes may be involved in development stage in plant growing conditions. Additionally, our results will be useful for further functional analysis of the TCP gene family in maize.

  10. A comparative analysis of soft computing techniques for gene prediction.

    Science.gov (United States)

    Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

    2013-07-01

    The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process

    International Nuclear Information System (INIS)

    Chandran, Uma R; Ma, Changqing; Dhir, Rajiv; Bisceglia, Michelle; Lyons-Weiler, Maureen; Liang, Wenjing; Michalopoulos, George; Becich, Michael; Monzon, Federico A

    2007-01-01

    Prostate cancer is characterized by heterogeneity in the clinical course that often does not correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets. Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors. The metastatic samples are highly heterogenous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodelling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1). We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer

  12. Entropy-based gene ranking without selection bias for the predictive classification of microarray data

    Directory of Open Access Journals (Sweden)

    Serafini Maria

    2003-11-01

    Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

  13.  DNA microarray-based gene expression profiling in diagnosis, assessing prognosis and predicting response to therapy in colorectal cancer

    Directory of Open Access Journals (Sweden)

    Przemysław Kwiatkowski

    2012-06-01

    Full Text Available  Colorectal cancer is the most common cancer of the gastrointestinal tract. It is considered as a biological model of a certain type of cancerogenesis process in which progression from an early to late stage adenoma and cancer is accompanied by distinct genetic alterations.Clinical and pathological parameters commonly used in clinical practice are often insufficient to determine groups of patients suitable for personalized treatment. Moreover, reliable molecular markers with high prognostic value have not yet been determined. Molecular studies using DNA-based microarrays have identified numerous genes involved in cell proliferation and differentiation during the process of cancerogenesis. Assessment of the genetic profile of colorectal cancer using the microarray technique might be a useful tool in determining the groups of patients with different clinical outcomes who would benefit from additional personalized treatment.The main objective of this study was to present the current state of knowledge on the practical application of gene profiling techniques using microarrays for determining diagnosis, prognosis and response to treatment in colorectal cancer.

  14. Gene expression profile of blood cells for the prediction of delayed cerebral ischemia after intracranial aneurysm rupture: a pilot study in humans.

    Science.gov (United States)

    Baumann, Antoine; Devaux, Yvan; Audibert, Gérard; Zhang, Lu; Bracard, Serge; Colnat-Coulbois, Sophie; Klein, Olivier; Zannad, Faiez; Charpentier, Claire; Longrois, Dan; Mertes, Paul-Michel

    2013-01-01

    Delayed cerebral ischemia (DCI) is a potentially devastating complication after intracranial aneurysm rupture and its mechanisms remain poorly elucidated. Early identification of the patients prone to developing DCI after rupture may represent a major breakthrough in its prevention and treatment. The single gene approach of DCI has demonstrated interest in humans. We hypothesized that whole genome expression profile of blood cells may be useful for better comprehension and prediction of aneurysmal DCI. Over a 35-month period, 218 patients with aneurysm rupture were included in this study. DCI was defined as the occurrence of a new delayed neurological deficit occurring within 2 weeks after aneurysm rupture with evidence of ischemia either on perfusion-diffusion MRI, CT angiography or CT perfusion imaging, or with cerebral angiography. DCI patients were matched against controls based on 4 out of 5 criteria (age, sex, Fisher grade, aneurysm location and smoking status). Genome-wide expression analysis of blood cells obtained at admission was performed by microarrays. Transcriptomic analysis was performed using long oligonucleotide microarrays representing 25,000 genes. Quantitative PCR: 1 µg of total RNA extracted was reverse-transcribed, and the resulting cDNA was diluted 10-fold before performing quantitative PCR. Microarray data were first analyzed by 'Significance Analysis of Microarrays' software which includes the Benjamini correction for multiple testing. In a second step, microarray data fold change was compared using a two-tailed, paired t test. Analysis of receiver-operating characteristic (ROC) curves and the area under the ROC curves were used for prediction analysis. Logistic regression models were used to investigate the additive value of multiple biomarkers. A total of 16 patients demonstrated DCI. Significance Analysis of Microarrays software failed to retrieve significant genes, most probably because of the heterogeneity of the patients included in

  15. Discovering time-lagged rules from microarray data using gene profile classifiers

    Directory of Open Access Journals (Sweden)

    Ponzoni Ignacio

    2011-04-01

    Full Text Available Abstract Background Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes. Results This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2, which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations. Conclusions A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.

  16. Gene Expression Commons: an open platform for absolute gene expression profiling.

    Directory of Open Access Journals (Sweden)

    Jun Seita

    Full Text Available Gene expression profiling using microarrays has been limited to comparisons of gene expression between small numbers of samples within individual experiments. However, the unknown and variable sensitivities of each probeset have rendered the absolute expression of any given gene nearly impossible to estimate. We have overcome this limitation by using a very large number (>10,000 of varied microarray data as a common reference, so that statistical attributes of each probeset, such as the dynamic range and threshold between low and high expression, can be reliably discovered through meta-analysis. This strategy is implemented in a web-based platform named "Gene Expression Commons" (https://gexc.stanford.edu/ which contains data of 39 distinct highly purified mouse hematopoietic stem/progenitor/differentiated cell populations covering almost the entire hematopoietic system. Since the Gene Expression Commons is designed as an open platform, investigators can explore the expression level of any gene, search by expression patterns of interest, submit their own microarray data, and design their own working models representing biological relationship among samples.

  17. Exploring the Optimal Strategy to Predict Essential Genes in Microbes

    Directory of Open Access Journals (Sweden)

    Yao Lu

    2011-12-01

    Full Text Available Accurately predicting essential genes is important in many aspects of biology, medicine and bioengineering. In previous research, we have developed a machine learning based integrative algorithm to predict essential genes in bacterial species. This algorithm lends itself to two approaches for predicting essential genes: learning the traits from known essential genes in the target organism, or transferring essential gene annotations from a closely related model organism. However, for an understudied microbe, each approach has its potential limitations. The first is constricted by the often small number of known essential genes. The second is limited by the availability of model organisms and by evolutionary distance. In this study, we aim to determine the optimal strategy for predicting essential genes by examining four microbes with well-characterized essential genes. Our results suggest that, unless the known essential genes are few, learning from the known essential genes in the target organism usually outperforms transferring essential gene annotations from a related model organism. In fact, the required number of known essential genes is surprisingly small to make accurate predictions. In prokaryotes, when the number of known essential genes is greater than 2% of total genes, this approach already comes close to its optimal performance. In eukaryotes, achieving the same best performance requires over 4% of total genes, reflecting the increased complexity of eukaryotic organisms. Combining the two approaches resulted in an increased performance when the known essential genes are few. Our investigation thus provides key information on accurately predicting essential genes and will greatly facilitate annotations of microbial genomes.

  18. Stress amplifies sex differences in primate prefrontal profiles of gene expression.

    Science.gov (United States)

    Lee, Alex G; Hagenauer, Megan; Absher, Devin; Morrison, Kathleen E; Bale, Tracy L; Myers, Richard M; Watson, Stanley J; Akil, Huda; Schatzberg, Alan F; Lyons, David M

    2017-11-02

    Stress is a recognized risk factor for mood and anxiety disorders that occur more often in women than men. Prefrontal brain regions mediate stress coping, cognitive control, and emotion. Here, we investigate sex differences and stress effects on prefrontal cortical profiles of gene expression in squirrel monkey adults. Dorsolateral, ventrolateral, and ventromedial prefrontal cortical regions from 18 females and 12 males were collected after stress or no-stress treatment conditions. Gene expression profiles were acquired using HumanHT-12v4.0 Expression BeadChip arrays adapted for squirrel monkeys. Extensive variation between prefrontal cortical regions was discerned in the expression of numerous autosomal and sex chromosome genes. Robust sex differences were also identified across prefrontal cortical regions in the expression of mostly autosomal genes. Genes with increased expression in females compared to males were overrepresented in mitogen-activated protein kinase and neurotrophin signaling pathways. Many fewer genes with increased expression in males compared to females were discerned, and no molecular pathways were identified. Effect sizes for sex differences were greater in stress compared to no-stress conditions for ventromedial and ventrolateral prefrontal cortical regions but not dorsolateral prefrontal cortex. Stress amplifies sex differences in gene expression profiles for prefrontal cortical regions involved in stress coping and emotion regulation. Results suggest molecular targets for new treatments of stress disorders in human mental health.

  19. Gene expression profiles of glucose toxicity-exposed islet microvascular endothelial cells.

    Science.gov (United States)

    Liu, Mingming; Lu, Wenbao; Hou, Qunxing; Wang, Bing; Sheng, Youming; Wu, Qingbin; Li, Bingwei; Liu, Xueting; Zhang, Xiaoyan; Li, Ailing; Zhang, Honggang; Xiu, Ruijuan

    2018-03-25

    Islet microcirculation is mainly composed by IMECs. The aim of the study was to investigate the differences in gene expression profiles of IMECs upon glucose toxicity exposure and insulin treatment. IMECs were treated with 5.6 mmol L -1 glucose, 35 mmol L -1 glucose, and 35 mmol L -1 glucose plus 10 -8  mol L -1 insulin, respectively. Gene expression profiles were determined by microarray and verified by qPCR. GO terms and KEGG analysis were performed to assess the potential roles of differentially expressed genes. The interaction and expression tendency of differentially expressed genes were analyzed by Path-Net algorithm. Compared with glucose toxicity-exposed IMECs, 1574 mRNAs in control group and 2870 mRNAs in insulin-treated IMECs were identified with differential expression, respectively. GO and KEGG pathway analysis revealed that these genes conferred roles in regulation of apoptosis, proliferation, migration, adhesion, and metabolic process etc. Additionally, MAPK signaling pathway and apoptosis were the dominant nodes in Path-Net. IMECs survival and function pathways were significantly changed, and the expression tendency of genes from euglycemia and glucose toxicity exposure to insulin treatment was revealed and enriched in 7 patterns. Our study provides a microcirculatory framework for gene expression profiles of glucose toxicity-exposed IMECs. © 2018 John Wiley & Sons Ltd.

  20. Glycosyltransferase Gene Expression Profiles Classify Cancer Types and Propose Prognostic Subtypes

    Science.gov (United States)

    Ashkani, Jahanshah; Naidoo, Kevin J.

    2016-05-01

    Aberrant glycosylation in tumours stem from altered glycosyltransferase (GT) gene expression but can the expression profiles of these signature genes be used to classify cancer types and lead to cancer subtype discovery? The differential structural changes to cellular glycan structures are predominantly regulated by the expression patterns of GT genes and are a hallmark of neoplastic cell metamorphoses. We found that the expression of 210 GT genes taken from 1893 cancer patient samples in The Cancer Genome Atlas (TCGA) microarray data are able to classify six cancers; breast, ovarian, glioblastoma, kidney, colon and lung. The GT gene expression profiles are used to develop cancer classifiers and propose subtypes. The subclassification of breast cancer solid tumour samples illustrates the discovery of subgroups from GT genes that match well against basal-like and HER2-enriched subtypes and correlates to clinical, mutation and survival data. This cancer type glycosyltransferase gene signature finding provides foundational evidence for the centrality of glycosylation in cancer.

  1. A method to identify differential expression profiles of time-course gene data with Fourier transformation.

    Science.gov (United States)

    Kim, Jaehee; Ogden, Robert Todd; Kim, Haseong

    2013-10-18

    Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization.The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be

  2. Computational prediction of miRNA genes from small RNA sequencing data

    Directory of Open Access Journals (Sweden)

    Wenjing eKang

    2015-01-01

    Full Text Available Next-generation sequencing now for the first time allows researchers to gauge the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. miRNAs are 22 nucleotide small RNAs (sRNAs that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq, which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field.

  3. Gene-specific function prediction for non-synonymous mutations in monogenic diabetes genes.

    Directory of Open Access Journals (Sweden)

    Quan Li

    Full Text Available The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.

  4. Gene expression profiling of canine osteosarcoma reveals genes associated with short and long survival times

    Directory of Open Access Journals (Sweden)

    Rao Nagesha AS

    2009-09-01

    Full Text Available Abstract Background Gene expression profiling of spontaneous tumors in the dog offers a unique translational opportunity to identify prognostic biomarkers and signaling pathways that are common to both canine and human. Osteosarcoma (OS accounts for approximately 80% of all malignant bone tumors in the dog. Canine OS are highly comparable with their human counterpart with respect to histology, high metastatic rate and poor long-term survival. This study investigates the prognostic gene profile among thirty-two primary canine OS using canine specific cDNA microarrays representing 20,313 genes to identify genes and cellular signaling pathways associated with survival. This, the first report of its kind in dogs with OS, also demonstrates the advantages of cross-species comparison with human OS. Results The 32 tumors were classified into two prognostic groups based on survival time (ST. They were defined as short survivors (dogs with poor prognosis: surviving fewer than 6 months and long survivors (dogs with better prognosis: surviving 6 months or longer. Fifty-one transcripts were found to be differentially expressed, with common upregulation of these genes in the short survivors. The overexpressed genes in short survivors are associated with possible roles in proliferation, drug resistance or metastasis. Several deregulated pathways identified in the present study, including Wnt signaling, Integrin signaling and Chemokine/cytokine signaling are comparable to the pathway analysis conducted on human OS gene profiles, emphasizing the value of the dog as an excellent model for humans. Conclusion A molecular-based method for discrimination of outcome for short and long survivors is useful for future prognostic stratification at initial diagnosis, where genes and pathways associated with cell cycle/proliferation, drug resistance and metastasis could be potential targets for diagnosis and therapy. The similarities between human and canine OS makes the

  5. Effector genomics accelerates discovery and functional profiling of potato disease resistance and phytophthora infestans avirulence genes.

    Directory of Open Access Journals (Sweden)

    Vivianne G A A Vleeshouwers

    Full Text Available Potato is the world's fourth largest food crop yet it continues to endure late blight, a devastating disease caused by the Irish famine pathogen Phytophthora infestans. Breeding broad-spectrum disease resistance (R genes into potato (Solanum tuberosum is the best strategy for genetically managing late blight but current approaches are slow and inefficient. We used a repertoire of effector genes predicted computationally from the P. infestans genome to accelerate the identification, functional characterization, and cloning of potentially broad-spectrum R genes. An initial set of 54 effectors containing a signal peptide and a RXLR motif was profiled for activation of innate immunity (avirulence or Avr activity on wild Solanum species and tentative Avr candidates were identified. The RXLR effector family IpiO induced hypersensitive responses (HR in S. stoloniferum, S. papita and the more distantly related S. bulbocastanum, the source of the R gene Rpi-blb1. Genetic studies with S. stoloniferum showed cosegregation of resistance to P. infestans and response to IpiO. Transient co-expression of IpiO with Rpi-blb1 in a heterologous Nicotiana benthamiana system identified IpiO as Avr-blb1. A candidate gene approach led to the rapid cloning of S. stoloniferum Rpi-sto1 and S. papita Rpi-pta1, which are functionally equivalent to Rpi-blb1. Our findings indicate that effector genomics enables discovery and functional profiling of late blight R genes and Avr genes at an unprecedented rate and promises to accelerate the engineering of late blight resistant potato varieties.

  6. Arabidopsis mRNA polyadenylation machinery: comprehensive analysis of protein-protein interactions and gene expression profiling

    Directory of Open Access Journals (Sweden)

    Mo Min

    2008-05-01

    extensive protein network was revealed for plant polyadenylation machinery, in which all predicted proteins were found to be connecting to the complex. The gene expression profiles are indicative that specialized sub-complexes may be formed to carry out targeted processing of mRNA in different developmental stages and tissue types. These results offer a roadmap for further functional characterizations of the protein factors, and for building models when testing the genetic contributions of these genes in plant growth and development.

  7. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells

    Directory of Open Access Journals (Sweden)

    Xu Huilei

    2010-12-01

    Full Text Available Abstract Background Mouse embryonic stem cells (mESCs are derived from the inner cell mass of a developing blastocyst and can be cultured indefinitely in-vitro. Their distinct features are their ability to self-renew and to differentiate to all adult cell types. Genes that maintain mESCs self-renewal and pluripotency identity are of interest to stem cell biologists. Although significant steps have been made toward the identification and characterization of such genes, the list is still incomplete and controversial. For example, the overlap among candidate self-renewal and pluripotency genes across different RNAi screens is surprisingly small. Meanwhile, machine learning approaches have been used to analyze multi-dimensional experimental data and integrate results from many studies, yet they have not been applied to specifically tackle the task of predicting and classifying self-renewal and pluripotency gene membership. Results For this study we developed a classifier, a supervised machine learning framework for predicting self-renewal and pluripotency mESCs stemness membership genes (MSMG using support vector machines (SVM. The data used to train the classifier was derived from mESCs-related studies using mRNA microarrays, measuring gene expression in various stages of early differentiation, as well as ChIP-seq studies applied to mESCs profiling genome-wide binding of key transcription factors, such as Nanog, Oct4, and Sox2, to the regulatory regions of other genes. Comparison to other classification methods using the leave-one-out cross-validation method was employed to evaluate the accuracy and generality of the classification. Finally, two sets of candidate genes from genome-wide RNA interference screens are used to test the generality and potential application of the classifier. Conclusions Our results reveal that an SVM approach can be useful for prioritizing genes for functional validation experiments and complement the analyses of high

  8. Presymptomatic Diagnosis of Celiac Disease in Predisposed Children: The Role of Gene Expression Profile.

    Science.gov (United States)

    Galatola, Martina; Cielo, Donatella; Panico, Camilla; Stellato, Pio; Malamisura, Basilio; Carbone, Lorenzo; Gianfrani, Carmen; Troncone, Riccardo; Greco, Luigi; Auricchio, Renata

    2017-09-01

    The prevalence of celiac disease (CD) has increased significantly in recent years, and risk prediction and early diagnosis have become imperative especially in at-risk families. In a previous study, we identified individuals with CD based on the expression profile of a set of candidate genes in peripheral blood monocytes. Here we evaluated the expression of a panel of CD candidate genes in peripheral blood mononuclear cells from at-risk infants long time before any symptom or production of antibodies. We analyzed the gene expression of a set of 9 candidate genes, associated with CD, in 22 human leukocyte antigen predisposed children from at-risk families for CD, studied from birth to 6 years of age. Nine of them developed CD (patients) and 13 did not (controls). We analyzed gene expression at 3 different time points (age matched in the 2 groups): 4-19 months before diagnosis, at the time of CD diagnosis, and after at least 1 year of a gluten-free diet. At similar age points, controls were also evaluated. Three genes (KIAA, TAGAP [T-cell Activation GTPase Activating Protein], and SH2B3 [SH2B Adaptor Protein 3]) were overexpressed in patients, compared with controls, at least 9 months before CD diagnosis. At a stepwise discriminant analysis, 4 genes (RGS1 [Regulator of G-protein signaling 1], TAGAP, TNFSF14 [Tumor Necrosis Factor (Ligand) Superfamily member 14], and SH2B3) differentiate patients from controls before serum antibodies production and clinical symptoms. Multivariate equation correctly classified CD from non-CD children in 95.5% of patients. The expression of a small set of candidate genes in peripheral blood mononuclear cells can predict CD at least 9 months before the appearance of any clinical and serological signs of the disease.

  9. Relationship between the prognostic and predictive value of the intrinsic subtypes and a validated gene profile predictive of loco-regional control and benefit from post-mastectomy radiotherapy in patients with high-risk breast cancer

    DEFF Research Database (Denmark)

    Tramm, Trine; Kyndi, Marianne; Myhre, Simen

    2014-01-01

    , and has shown prognostic impact in terms of loco-regional failure and predictive impact for PMRT. Reports have also shown predictive value in terms of benefit of PMRT from intrinsic subtypes and derived approximations. The aim of this study was to examine: 1) the agreement between various methods...... for determining the intrinsic subtypes; and 2) the relationship between the prognostic and predictive impact of the DBCG-RT profile and the intrinsic subtypes. MATERIAL AND METHODS: Intrinsic subtypes and the DBCG-RT profile was determined from microarray analysis based on fresh frozen tissue from 191 patients...... and predictive information obtained from the DBCG-RT profile cannot be substituted by any approximation of the tumors intrinsic subtype. The predictive value of the intrinsic subtypes in terms of PMRT was influenced by the method used for assignment to the intrinsic subtypes....

  10. Analysis of gene expression profile microarray data in complex regional pain syndrome.

    Science.gov (United States)

    Tan, Wulin; Song, Yiyan; Mo, Chengqiang; Jiang, Shuangjian; Wang, Zhongxing

    2017-09-01

    The aim of the present study was to predict key genes and proteins associated with complex regional pain syndrome (CRPS) using bioinformatics analysis. The gene expression profiling microarray data, GSE47603, which included peripheral blood samples from 4 patients with CRPS and 5 healthy controls, was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in CRPS patients compared with healthy controls were identified using the GEO2R online tool. Functional enrichment analysis was then performed using The Database for Annotation Visualization and Integrated Discovery online tool. Protein‑protein interaction (PPI) network analysis was subsequently performed using Search Tool for the Retrieval of Interaction Genes database and analyzed with Cytoscape software. A total of 257 DEGs were identified, including 243 upregulated genes and 14 downregulated ones. Genes in the human leukocyte antigen (HLA) family were most significantly differentially expressed. Enrichment analysis demonstrated that signaling pathways, including immune response, cell motion, adhesion and angiogenesis were associated with CRPS. PPI network analysis revealed that key genes, including early region 1A binding protein p300 (EP300), CREB‑binding protein (CREBBP), signal transducer and activator of transcription (STAT)3, STAT5A and integrin α M were associated with CRPS. The results suggest that the immune response may therefore serve an important role in CRPS development. In addition, genes in the HLA family, such as HLA‑DQB1 and HLA‑DRB1, may present potential biomarkers for the diagnosis of CRPS. Furthermore, EP300, its paralog CREBBP, and the STAT family genes, STAT3 and STAT5 may be important in the development of CRPS.

  11. Flies selected for longevity retain a young gene expression profile

    DEFF Research Database (Denmark)

    Sarup, Pernille Merete; Sørensen, Peter; Loeschcke, Volker

    2011-01-01

      We investigated correlated responses in the transcriptomes of longevity-selected lines of Drosophila melanogaster to identify pathways that affect life span in metazoan systems. We evaluated the gene expression profile in young, middle-aged, and old male flies, finding that 530 genes were...

  12. CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

    Science.gov (United States)

    Kong, Hualei; Tong, Pan; Zhao, Xiaodong; Sun, Jielin; Li, Hua

    2018-03-01

    In the past decade, molecular classification of cancer has gained high popularity owing to its high predictive power on clinical outcomes as compared with traditional methods commonly used in clinical practice. In particular, using gene expression profiles, recent studies have successfully identified a number of gene sets for the delineation of cancer subtypes that are associated with distinct prognosis. However, identification of such gene sets remains a laborious task due to the lack of tools with flexibility, integration and ease of use. To reduce the burden, we have developed an R package, CAsubtype, to efficiently identify gene sets predictive of cancer subtypes and clinical outcomes. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes. In conclusion, CAsubtype is a flexible and well-integrated tool in the R environment to identify gene sets for cancer subtype identification and clinical outcome prediction. Its simple R commands and comprehensive data sets enable efficient examination of the clinical value of any given gene set, thus facilitating hypothesis generating and testing in biological and

  13. High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity.

    Science.gov (United States)

    De Wolf, Hans; Cougnaud, Laure; Van Hoorde, Kirsten; De Bondt, An; Wegner, Joerg K; Ceulemans, Hugo; Göhlmann, Hinrich

    2018-04-01

    By adding biological information, beyond the chemical properties and desired effect of a compound, uncharted compound areas and connections can be explored. In this study, we add transcriptional information for 31K compounds of Janssen's primary screening deck, using the HT L1000 platform and assess (a) the transcriptional connection score for generating compound similarities, (b) machine learning algorithms for generating target activity predictions, and (c) the scaffold hopping potential of the resulting hits. We demonstrate that the transcriptional connection score is best computed from the significant genes only and should be interpreted within its confidence interval for which we provide the stats. These guidelines help to reduce noise, increase reproducibility, and enable the separation of specific and promiscuous compounds. The added value of machine learning is demonstrated for the NR3C1 and HSP90 targets. Support Vector Machine models yielded balanced accuracy values ≥80% when the expression values from DDIT4 & SERPINE1 and TMEM97 & SPR were used to predict the NR3C1 and HSP90 activity, respectively. Combining both models resulted in 22 new and confirmed HSP90-independent NR3C1 inhibitors, providing two scaffolds (i.e., pyrimidine and pyrazolo-pyrimidine), which could potentially be of interest in the treatment of depression (i.e., inhibiting the glucocorticoid receptor (i.e., NR3C1), while leaving its chaperone, HSP90, unaffected). As such, the initial hit rate increased by a factor 300, as less, but more specific chemistry could be screened, based on the upfront computed activity predictions.

  14. Characteristic gene expression profiles in the progression from liver cirrhosis to carcinoma induced by diethylnitrosamine in a rat model

    Directory of Open Access Journals (Sweden)

    Zhu Jin

    2009-07-01

    progression of liver cancer in rats. The data obtained from the gene expression profiles will allow us to acquire insights into the molecular mechanisms of hepatocarcinogenesis and identify specific genes (or gene products that can be used for early molecular diagnosis, risk analysis, prognosis prediction, and development of new therapies.

  15. APRIL is a novel clinical chemo-resistance biomarker in colorectal adenocarcinoma identified by gene expression profiling

    International Nuclear Information System (INIS)

    Petty, Russell D; Wang, Weiguang; Gilbert, Fiona; Semple, Scot; Collie-Duguid, Elaina SR; Samuel, Leslie M; Murray, Graeme I; MacDonald, Graham; O'Kelly, Terrence; Loudon, Malcolm; Binnie, Norman; Aly, Emad; McKinlay, Aileen

    2009-01-01

    5-Fluorouracil(5FU) and oral analogues, such as capecitabine, remain one of the most useful agents for the treatment of colorectal adenocarcinoma. Low toxicity and convenience of administration facilitate use, however clinical resistance is a major limitation. Investigation has failed to fully explain the molecular mechanisms of resistance and no clinically useful predictive biomarkers for 5FU resistance have been identified. We investigated the molecular mechanisms of clinical 5FU resistance in colorectal adenocarcinoma patients in a prospective biomarker discovery project utilising gene expression profiling. The aim was to identify novel 5FU resistance mechanisms and qualify these as candidate biomarkers and therapeutic targets. Putative treatment specific gene expression changes were identified in a transcriptomics study of rectal adenocarcinomas, biopsied and profiled before and after pre-operative short-course radiotherapy or 5FU based chemo-radiotherapy, using microarrays. Tumour from untreated controls at diagnosis and resection identified treatment-independent gene expression changes. Candidate 5FU chemo-resistant genes were identified by comparison of gene expression data sets from these clinical specimens with gene expression signatures from our previous studies of colorectal cancer cell lines, where parental and daughter lines resistant to 5FU were compared. A colorectal adenocarcinoma tissue microarray (n = 234, resected tumours) was used as an independent set to qualify candidates thus identified. APRIL/TNFSF13 mRNA was significantly upregulated following 5FU based concurrent chemo-radiotherapy and in 5FU resistant colorectal adenocarcinoma cell lines but not in radiotherapy alone treated colorectal adenocarcinomas. Consistent withAPRIL's known function as an autocrine or paracrine secreted molecule, stromal but not tumour cell protein expression by immunohistochemistry was correlated with poor prognosis (p = 0.019) in the independent set

  16. Gaussian interaction profile kernels for predicting drug-target interaction.

    Science.gov (United States)

    van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena

    2011-11-01

    The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.

  17. Comparison of lists of genes based on functional profiles

    Directory of Open Access Journals (Sweden)

    Salicrú Miquel

    2011-10-01

    Full Text Available Abstract Background How to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO. Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example. Results A new global testing procedure and a method incorporating it are presented. Instead of testing separately for each GO class, a single global test for all classes under consideration is performed. The test is based on the distance between the functional profiles, defined as the joint frequencies of annotation in a given set of GO classes. These classes may be chosen at one or more GO levels. The new global test is more powerful and accurate with respect to type I errors than the usual class-by-class approach. When applied to some real datasets, the results suggest that the method may also provide useful information that complements the tests performed using a class-by-class approach if gene counts are sparse in some classes. An R library, goProfiles, implements these methods and is available from Bioconductor, http://bioconductor.org/packages/release/bioc/html/goProfiles.html. Conclusions The method provides an inferential basis for deciding whether two lists are functionally different. For global comparisons it is preferable to the global chi-square test of homogeneity. Furthermore, it may provide additional information if used in conjunction with class-by-class methods.

  18. Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    KAUST Repository

    Hou, Siqing

    2018-05-21

    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.

  19. Subchronic effects of valproic acid on gene expression profiles for lipid metabolism in mouse liver

    International Nuclear Information System (INIS)

    Lee, Min-Ho; Kim, Mingoo; Lee, Byung-Hoon; Kim, Ju-Han; Kang, Kyung-Sun; Kim, Hyung-Lae; Yoon, Byung-Il; Chung, Heekyoung; Kong, Gu; Lee, Mi-Ock

    2008-01-01

    Valproic acid (VPA) is used clinically to treat epilepsy, however it induces hepatotoxicity such as microvesicular steatosis. Acute hepatotoxicity of VPA has been well documented by biochemical studies and microarray analysis, but little is known about the chronic effects of VPA in the liver. In the present investigation, we profiled gene expression patterns in the mouse liver after subchronic treatment with VPA. VPA was administered orally at a dose of 100 mg/kg/day or 500 mg/kg/day to ICR mice, and the livers were obtained after 1, 2, or 4 weeks. The activities of serum liver enzymes did not change, whereas triglyceride concentration increased significantly. Microarray analysis revealed that 1325 genes of a set of 32,996 individual genes were VPA responsive when examined by two-way ANOVA (P 1.5). Consistent with our previous results obtained using an acute VPA exposure model (Lee et al., Toxicol Appl Pharmacol. 220:45-59, 2007), the most significantly over-represented biological terms for these genes included lipid, fatty acid, and steroid metabolism. Biological pathway analysis suggests that the genes responsible for increased biosynthesis of cholesterol and triglyceride, and for decreased fatty acid β-oxidation contribute to the abnormalities in lipid metabolism induced by subchronic VPA treatment. A comparison of the VPA-responsive genes in the acute and subchronic models extracted 15 commonly altered genes, such as Cyp4a14 and Adpn, which may have predictive power to distinguish the mode of action of hepatotoxicants. Our data provide a better understanding of the molecular mechanisms of VPA-induced hepatotoxicity and useful information to predict steatogenic hepatotoxicity

  20. Comparison of predicted and measured pulsed-column profiles and inventories

    International Nuclear Information System (INIS)

    Ostenak, C.A.; Cermak, A.F.

    1983-01-01

    Nuclear materials accounting and process control in fuels reprocessing plants can be improved by near-real-time estimation of the in-process inventory in solvent-extraction contactors. Experimental studies were conducted on pilot- and plant-scale pulsed columns by Allied-General Nuclear Service (AGNS), and the extensive uranium concentration-profile and inventory data were analyzed by Los Alamos and AGNS to develop and evaluate different predictive inventory techniques. Preliminary comparisons of predicted and measured pulsed-column profiles and inventories show promise for using these predictive techniques to improve nuclear materials accounting and process control in fuels reprocessing plants

  1. Gene structure, phylogeny and expression profile of the sucrose synthase gene family in cacao (Theobroma cacao L.).

    Science.gov (United States)

    Li, Fupeng; Hao, Chaoyun; Yan, Lin; Wu, Baoduo; Qin, Xiaowei; Lai, Jianxiong; Song, Yinghui

    2015-09-01

    In higher plants, sucrose synthase (Sus, EC 2.4.1.13) is widely considered as a key enzyme involved in sucrose metabolism. Although, several paralogous genes encoding different isozymes of Sus have been identified and characterized in multiple plant genomes, to date detailed information about the Sus genes is lacking for cacao. This study reports the identification of six novel Sus genes from economically important cacao tree. Analyses of the gene structure and phylogeny of the Sus genes demonstrated evolutionary conservation in the Sus family across cacao and other plant species. The expression of cacao Sus genes was investigated via real-time PCR in various tissues, different developmental phases of leaf, flower bud and pod. The Sus genes exhibited distinct but partially redundant expression profiles in cacao, with TcSus1, TcSus5 and TcSus6, being the predominant genes in the bark with phloem, TcSus2 predominantly expressing in the seed during the stereotype stage. TcSus3 and TcSus4 were significantly detected more in the pod husk and seed coat along the pod development, and showed development dependent expression profiles in the cacao pod. These results provide new insights into the evolution, and basic information that will assist in elucidating the functions of cacao Sus gene family.

  2. Gene Structures, Evolution, Classification and Expression Profiles of the Aquaporin Gene Family in Castor Bean (Ricinus communis L..

    Directory of Open Access Journals (Sweden)

    Zhi Zou

    Full Text Available Aquaporins (AQPs are a class of integral membrane proteins that facilitate the passive transport of water and other small solutes across biological membranes. Castor bean (Ricinus communis L., Euphobiaceae, an important non-edible oilseed crop, is widely cultivated for industrial, medicinal and cosmetic purposes. Its recently available genome provides an opportunity to analyze specific gene families. In this study, a total of 37 full-length AQP genes were identified from the castor bean genome, which were assigned to five subfamilies, including 10 plasma membrane intrinsic proteins (PIPs, 9 tonoplast intrinsic proteins (TIPs, 8 NOD26-like intrinsic proteins (NIPs, 6 X intrinsic proteins (XIPs and 4 small basic intrinsic proteins (SIPs on the basis of sequence similarities. Functional prediction based on the analysis of the aromatic/arginine (ar/R selectivity filter, Froger's positions and specificity-determining positions (SDPs showed a remarkable difference in substrate specificity among subfamilies. Homology analysis supported the expression of all 37 RcAQP genes in at least one of examined tissues, e.g., root, leaf, flower, seed and endosperm. Furthermore, global expression profiles with deep transcriptome sequencing data revealed diverse expression patterns among various tissues. The current study presents the first genome-wide analysis of the AQP gene family in castor bean. Results obtained from this study provide valuable information for future functional analysis and utilization.

  3. Comparison of Cluster Lensing Profiles with Lambda CDM Predictions

    Energy Technology Data Exchange (ETDEWEB)

    Broadhurst, Tom; /Tel Aviv U.; Umetsu, Keiichi; /Taipei, Inst. Astron. Astrophys.; Medezinski, Elinor; /Tel Aviv U.; Oguri, Masamune; /KIPAC, Menlo Park; Rephaeli, Yoel; /Tel Aviv U. /San Diego, CASS

    2008-05-21

    We derive lens distortion and magnification profiles of four well known clusters observed with Subaru. Each cluster is very well fitted by the general form predicted for Cold Dark Matter (CDM) dominated halos, with good consistency found between the independent distortion and magnification measurements. The inferred level of mass concentration is surprisingly high, 8 < c{sub vir} < 15 ( = 10.39 {+-} 0.91), compared to the relatively shallow profiles predicted by the {Lambda}CDM model, c{sub vir} = 5.06 {+-} 1.10 (for = 1.25 x 10{sup 15} M{sub {circle_dot}}/h). This represents a 4{sigma} discrepancy, and includes the relatively modest effects of projection bias and profile evolution derived from N-body simulations, which oppose each other with little residual effect. In the context of CDM based cosmologies, this discrepancy implies some modification of the widely assumed spectrum of initial density perturbations, so clusters collapse earlier (z {ge} 1) than predicted (z < 0.5) when the Universe was correspondingly denser.

  4. Expression profile of genes coding for carotenoid biosynthetic ...

    Indian Academy of Sciences (India)

    Expression profile of genes coding for carotenoid biosynthetic pathway during ripening and their association with accumulation of lycopene in tomato fruits. Shuchi Smita, Ravi Rajwanshi, Sangram Keshari Lenka, Amit Katiyar, Viswanathan Chinnusamy and. Kailash Chander Bansal. J. Genet. 92, 363–368. Table 1.

  5. A chronological expression profile of gene activity during embryonic mouse brain development.

    Science.gov (United States)

    Goggolidou, P; Soneji, S; Powles-Glover, N; Williams, D; Sethi, S; Baban, D; Simon, M M; Ragoussis, I; Norris, D P

    2013-12-01

    The brain is a functionally complex organ, the patterning and development of which are key to adult health. To help elucidate the genetic networks underlying mammalian brain patterning, we conducted detailed transcriptional profiling during embryonic development of the mouse brain. A total of 2,400 genes were identified as showing differential expression between three developmental stages. Analysis of the data identified nine gene clusters to demonstrate analogous expression profiles. A significant group of novel genes of as yet undiscovered biological function were detected as being potentially relevant to brain development and function, in addition to genes that have previously identified roles in the brain. Furthermore, analysis for genes that display asymmetric expression between the left and right brain hemispheres during development revealed 35 genes as putatively asymmetric from a combined data set. Our data constitute a valuable new resource for neuroscience and neurodevelopment, exposing possible functional associations between genes, including novel loci, and encouraging their further investigation in human neurological and behavioural disorders.

  6. Radioactive cDNA microarray (II): Gene expression profiling of antidepressant treatment by human cDNA microarray

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Ji Hye; Kang, Rhee Hun; Ham, Byung Joo; Lee, Min Su; Shin, Kyung Ho; Choe, Jae Gol; Kim, Meyoung Kon [College of Medicine, Univ. of Korea, Seoul (Korea, Republic of)

    2003-07-01

    Major depressive disorder is a prevalent psychiatric disorder in primary care, associated with impaired patient functioning and well-being. Fluoxetine is a selective serotonin-reuptake inhibitors (SSRIs) and is a commonly prescribed antidepressant compound. Its action is primarily attributed to selective inhibition of the reuptake of serotonin (5-hydroxytryptamine) in the central nervous system. Objectives ; the aims of this study were two-fold: (1) to determine the usefulness for investigation of the transcription profiles in depression patients, and (2) to assess the differences in gene expression profiles between positive response group and negative response groups by fluoxetine treatment. This study included 53 patients with major depression (26 in positive response group with antidepressant treatment, 27 in negative response group with antidepressant treatment), and 53 healthy controls. To examine the difference of gene expression profile in depression patients, radioactive complementary DNA microarrays were used to evaluate changes in the expression of 1,152 genes in total. Using 33p-labeled probes, this method provided highly sensitive gene expression profiles including brain receptors, drug metabolism, and cellular signaling. Gene transcription profiles were classified into several categories in accordance with the antidepressant gene-regulation. The gene profiles were significantly up-(22 genes) and down-(16 genes) regulated in the positive response group when compared to the control group. Also, in the negative response group, 35 genes were up-regulated and 8 genes were down-regulated when compared to the control group. Consequently, we demonstrated that radioactive human cDNA microarray is highly likely to be an efficient technology for evaluating the gene regulation of antidepressants, such as selective serotonin-reuptake inhibitors (SSRIs), by using high-throughput biotechnology.

  7. Radioactive cDNA microarray (II): Gene expression profiling of antidepressant treatment by human cDNA microarray

    International Nuclear Information System (INIS)

    Lee, Ji Hye; Kang, Rhee Hun; Ham, Byung Joo; Lee, Min Su; Shin, Kyung Ho; Choe, Jae Gol; Kim, Meyoung Kon

    2003-01-01

    Major depressive disorder is a prevalent psychiatric disorder in primary care, associated with impaired patient functioning and well-being. Fluoxetine is a selective serotonin-reuptake inhibitors (SSRIs) and is a commonly prescribed antidepressant compound. Its action is primarily attributed to selective inhibition of the reuptake of serotonin (5-hydroxytryptamine) in the central nervous system. Objectives ; the aims of this study were two-fold: (1) to determine the usefulness for investigation of the transcription profiles in depression patients, and (2) to assess the differences in gene expression profiles between positive response group and negative response groups by fluoxetine treatment. This study included 53 patients with major depression (26 in positive response group with antidepressant treatment, 27 in negative response group with antidepressant treatment), and 53 healthy controls. To examine the difference of gene expression profile in depression patients, radioactive complementary DNA microarrays were used to evaluate changes in the expression of 1,152 genes in total. Using 33p-labeled probes, this method provided highly sensitive gene expression profiles including brain receptors, drug metabolism, and cellular signaling. Gene transcription profiles were classified into several categories in accordance with the antidepressant gene-regulation. The gene profiles were significantly up-(22 genes) and down-(16 genes) regulated in the positive response group when compared to the control group. Also, in the negative response group, 35 genes were up-regulated and 8 genes were down-regulated when compared to the control group. Consequently, we demonstrated that radioactive human cDNA microarray is highly likely to be an efficient technology for evaluating the gene regulation of antidepressants, such as selective serotonin-reuptake inhibitors (SSRIs), by using high-throughput biotechnology

  8. Genome-wide analysis of immune system genes by EST profiling

    Science.gov (United States)

    Giallourakis, Cosmas; Benita, Yair; Molinie, Benoit; Cao, Zhifang; Despo, Orion; Pratt, Henry E.; Zukerberg, Lawrence R.; Daly, Mark J.; Rioux, John D.; Xavier, Ramnik J.

    2013-01-01

    Profiling studies of mRNA and miRNA, particularly microarray-based studies, have been extensively used to create compendia of genes that are preferentially expressed in the immune system. In some instances, functional studies have been subsequently pursued. Recent efforts such as ENCODE have demonstrated the benefit of coupling RNA-Seq analysis with information from expressed sequence tags (ESTs) for transcriptomic analysis. However, the full characterization and identification of transcripts that function as modulators of human immune responses remains incomplete. In this study, we demonstrate that an integrated analysis of human ESTs provides a robust platform to identify the immune transcriptome. Beyond recovering a reference set of immune-enriched genes and providing large-scale cross-validation of previous microarray studies, we discovered hundreds of novel genes preferentially expressed in the immune system, including non-coding RNAs. As a result, we have established the Immunogene database, representing an integrated EST “road map” of gene expression in human immune cells, which can be used to further investigate the function of coding and non-coding genes in the immune system. Using this approach, we have uncovered a unique metabolic gene signature of human macrophages and identified PRDM15 as a novel overexpressed gene in human lymphomas. Thus we demonstrate the utility of EST profiling as a basis for further deconstruction of physiologic and pathologic immune processes. PMID:23616578

  9. Global analysis of transcriptome responses and gene expression profiles to cold stress of Jatropha curcas L.

    Science.gov (United States)

    Wang, Haibo; Zou, Zhurong; Wang, Shasha; Gong, Ming

    2013-01-01

    Jatropha curcas L., also called the Physic nut, is an oil-rich shrub with multiple uses, including biodiesel production, and is currently exploited as a renewable energy resource in many countries. Nevertheless, because of its origin from the tropical MidAmerican zone, J. curcas confers an inherent but undesirable characteristic (low cold resistance) that may seriously restrict its large-scale popularization. This adaptive flaw can be genetically improved by elucidating the mechanisms underlying plant tolerance to cold temperatures. The newly developed Illumina Hiseq™ 2000 RNA-seq and Digital Gene Expression (DGE) are deep high-throughput approaches for gene expression analysis at the transcriptome level, using which we carefully investigated the gene expression profiles in response to cold stress to gain insight into the molecular mechanisms of cold response in J. curcas. In total, 45,251 unigenes were obtained by assembly of clean data generated by RNA-seq analysis of the J. curcas transcriptome. A total of 33,363 and 912 complete or partial coding sequences (CDSs) were determined by protein database alignments and ESTScan prediction, respectively. Among these unigenes, more than 41.52% were involved in approximately 128 known metabolic or signaling pathways, and 4,185 were possibly associated with cold resistance. DGE analysis was used to assess the changes in gene expression when exposed to cold condition (12°C) for 12, 24, and 48 h. The results showed that 3,178 genes were significantly upregulated and 1,244 were downregulated under cold stress. These genes were then functionally annotated based on the transcriptome data from RNA-seq analysis. This study provides a global view of transcriptome response and gene expression profiling of J. curcas in response to cold stress. The results can help improve our current understanding of the mechanisms underlying plant cold resistance and favor the screening of crucial genes for genetically enhancing cold resistance

  10. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.

    Science.gov (United States)

    Schaffter, Thomas; Marbach, Daniel; Floreano, Dario

    2011-08-15

    Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual and supporting data. Supplementary data are available at Bioinformatics online. dario.floreano@epfl.ch.

  11. DNA Array-Based Gene Profiling

    Science.gov (United States)

    Mocellin, Simone; Provenzano, Maurizio; Rossi, Carlo Riccardo; Pilati, Pierluigi; Nitti, Donato; Lise, Mario

    2005-01-01

    Cancer is a heterogeneous disease in most respects, including its cellularity, different genetic alterations, and diverse clinical behaviors. Traditional molecular analyses are reductionist, assessing only 1 or a few genes at a time, thus working with a biologic model too specific and limited to confront a process whose clinical outcome is likely to be governed by the combined influence of many genes. The potential of functional genomics is enormous, because for each experiment, thousands of relevant observations can be made simultaneously. Accordingly, DNA array, like other high-throughput technologies, might catalyze and ultimately accelerate the development of knowledge in tumor cell biology. Although in its infancy, the implementation of DNA array technology in cancer research has already provided investigators with novel data and intriguing new hypotheses on the molecular cascade leading to carcinogenesis, tumor aggressiveness, and sensitivity to antiblastic agents. Given the revolutionary implications that the use of this technology might have in the clinical management of patients with cancer, principles of DNA array-based tumor gene profiling need to be clearly understood for the data to be correctly interpreted and appreciated. In the present work, we discuss the technical features characterizing this powerful laboratory tool and review the applications so far described in the field of oncology. PMID:15621987

  12. The prediction of BRDFs from surface profile measurements

    International Nuclear Information System (INIS)

    Church, E.L.; Takacs, P.Z.; Leonard, T.A.

    1989-01-01

    This paper discusses methods of predicting the BRDF of smooth surfaces from profile measurements of their surface finish. The conversion of optical profile data to the BRDF at the same wavelength is essentially independent of scattering models, while the conversion of mechanical measurements, and wavelength scaling in general, are model dependent. Procedures are illustrated for several surfaces, including two from the recent HeNe BRDF round robin, and results are compared with measured data. Reasonable agreement is found except for surfaces which involve significant scattering from isolated surface defects which are poorly sampled in the profile data

  13. Molecular serotyping, virulence gene profiling and pathogenicity of Streptococcus agalactiae isolated from tilapia farms in Thailand by multiplex PCR.

    Science.gov (United States)

    Kannika, K; Pisuttharachai, D; Srisapoome, P; Wongtavatchai, J; Kondo, H; Hirono, I; Unajak, S; Areechon, N

    2017-06-01

    This study aimed to biotype Streptococcus agalactiae isolated from tilapia farms in Thailand based on molecular biotyping methods and to determine the correlation between the serotype and virulence of bacteria. In addition to a biotyping (serotyping) technique based on multiplex PCR of cps genes, in this study, we developed multiplex PCR typing of Group B streptococcus (GBS) virulence genes to examine three clusters of virulence genes and their correlation with the pathogenicity of S. agalactiae. The epidemiology of S. agalactiae in Thailand was analysed to provide bacterial genetic information towards a future rational vaccine strategy for tilapia culture systems. Streptococcus agalactiae were isolated from diseased tilapia from different areas of Thailand. A total of 124 S. agalactiae isolates were identified by phenotypic analysis and confirmed by 16S rRNA PCR. Bacterial genotyping was conducted based on (i) molecular serotyping of the capsular polysaccharide (cps) gene cluster and (ii) virulence gene profiling using multiplex PCR analysis of 14 virulence genes (lmb, scpB, pavA, cspA, spb1, cyl, bca, rib, fbsA, fbsB, cfb, hylB, bac and pbp1A/ponA). Only serotypes Ia and III were found in this study; serotype Ia lacks the lmb, scpB and spb1 genes, whereas serotype III lacks only the bac gene. Virulence tests in juvenile Nile tilapia demonstrated a correlation between the pathogenicity of the bacteria and their virulence gene profile, with serotype III showing higher virulence than serotype Ia. Epidemiological analysis showed an almost equal distribution in all regions of Thailand, except serotype III was found predominantly in the southern areas. Only two serotypes of S. agalactiae were isolated from diseased tilapia in Thailand. Serotype Ia showed fewer virulence genes and lower virulence than serotype III. Both serotypes showed a similar distribution throughout Thailand. We identified two major serotypes of S. agalactiae isolates associated with the outbreak in

  14. Neighboring Genes Show Correlated Evolution in Gene Expression

    Science.gov (United States)

    Ghanbarian, Avazeh T.; Hurst, Laurence D.

    2015-01-01

    When considering the evolution of a gene’s expression profile, we commonly assume that this is unaffected by its genomic neighborhood. This is, however, in contrast to what we know about the lack of autonomy between neighboring genes in gene expression profiles in extant taxa. Indeed, in all eukaryotic genomes genes of similar expression-profile tend to cluster, reflecting chromatin level dynamics. Does it follow that if a gene increases expression in a particular lineage then the genomic neighbors will also increase in their expression or is gene expression evolution autonomous? To address this here we consider evolution of human gene expression since the human-chimp common ancestor, allowing for both variation in estimation of current expression level and error in Bayesian estimation of the ancestral state. We find that in all tissues and both sexes, the change in gene expression of a focal gene on average predicts the change in gene expression of neighbors. The effect is highly pronounced in the immediate vicinity (genes increasing their expression in humans tend to avoid nuclear lamina domains and be enriched for the gene activator 5-hydroxymethylcytosine, we conclude that, most probably owing to chromatin level control of gene expression, a change in gene expression of one gene likely affects the expression evolution of neighbors, what we term expression piggybacking, an analog of hitchhiking. PMID:25743543

  15. Reranking candidate gene models with cross-species comparison for improved gene prediction

    Directory of Open Access Journals (Sweden)

    Pereira Fernando CN

    2008-10-01

    Full Text Available Abstract Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc. Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.

  16. Dopamine Gene Profiling to Predict Impulse Control and Effects of Dopamine Agonist Ropinirole.

    Science.gov (United States)

    MacDonald, Hayley J; Stinear, Cathy M; Ren, April; Coxon, James P; Kao, Justin; Macdonald, Lorraine; Snow, Barry; Cramer, Steven C; Byblow, Winston D

    2016-07-01

    Dopamine agonists can impair inhibitory control and cause impulse control disorders for those with Parkinson disease (PD), although mechanistically this is not well understood. In this study, we hypothesized that the extent of such drug effects on impulse control is related to specific dopamine gene polymorphisms. This double-blind, placebo-controlled study aimed to examine the effect of single doses of 0.5 and 1.0 mg of the dopamine agonist ropinirole on impulse control in healthy adults of typical age for PD onset. Impulse control was measured by stop signal RT on a response inhibition task and by an index of impulsive decision-making on the Balloon Analogue Risk Task. A dopamine genetic risk score quantified basal dopamine neurotransmission from the influence of five genes: catechol-O-methyltransferase, dopamine transporter, and those encoding receptors D1, D2, and D3. With placebo, impulse control was better for the high versus low genetic risk score groups. Ropinirole modulated impulse control in a manner dependent on genetic risk score. For the lower score group, both doses improved response inhibition (decreased stop signal RT) whereas the lower dose reduced impulsiveness in decision-making. Conversely, the higher score group showed a trend for worsened response inhibition on the lower dose whereas both doses increased impulsiveness in decision-making. The implications of the present findings are that genotyping can be used to predict impulse control and whether it will improve or worsen with the administration of dopamine agonists.

  17. Distinct gene expression profiles in ovarian cancer linked to Lynch syndrome

    DEFF Research Database (Denmark)

    Jönsson, Jenny-Maria; Bartuma, Katarina; Dominguez-Valentin, Mev

    2014-01-01

    Ovarian cancer linked to Lynch syndrome represents a rare subset that typically presents at young age as early-stage tumors with an overrepresentation of endometrioid and clear cell histologies. We investigated the molecular profiles of Lynch syndrome-associated and sporadic ovarian cancer...... with the aim to identify key discriminators and central tumorigenic mechanisms in hereditary ovarian cancer. Global gene expression profiling using whole-genome c-DNA-mediated Annealing, Selection, extension, and Ligation was applied to 48 histopathologically matched Lynch syndrome-associated and sporadic...... ovarian cancers. Lynch syndrome-associated and sporadic ovarian cancers differed by 349 significantly deregulated genes, including PTPRH, BIRC3, SHH and TNFRSF6B. The genes involved were predominantly linked to cell growth, proliferation, and cell-to-cell signaling and interaction. When stratified...

  18. Gene expression profiling in respond to TBT exposure in small abalone Haliotis diversicolor.

    Science.gov (United States)

    Jia, Xiwei; Zou, Zhihua; Wang, Guodong; Wang, Shuhong; Wang, Yilei; Zhang, Ziping

    2011-10-01

    In this study, we investigated the gene expression profiling of small abalone, Haliotis diversicolor by tributyltin (TBT) exposure using a cDNA microarray containing 2473 unique transcripts. Totally, 107 up-regulated genes and 41 down-regulated genes were found. For further investigation of candidate genes from microarray data and EST analysis, quantitative real-time PCR was performed at 6 h, 24 h, 48 h, 96 h and 192 h TBT exposure. 26 genes were found to be significantly differentially expressed in different time course, 3 of them were unknown. Some gene homologues like cellulose, endo-beta-1,4-glucanase, ferritin subunit 1 and thiolester containing protein II CG7052-PB might be the good biomarker candidate for TBT monitor. The identification of stress response genes and their expression profiles will permit detailed investigation of the defense responses of small abalone genes. Published by Elsevier Ltd.

  19. Blood Gene Expression Profiling of Breast Cancer Survivors Experiencing Fibrosis

    International Nuclear Information System (INIS)

    Landmark-Hoyvik, Hege; Dumeaux, Vanessa; Reinertsen, Kristin V.; Edvardsen, Hege; Fossa, Sophie D.; Borresen-Dale, Anne-Lise

    2011-01-01

    Purpose: To extend knowledge on the mechanisms and pathways involved in maintenance of radiation-induced fibrosis (RIF) by performing gene expression profiling of whole blood from breast cancer (BC) survivors with and without fibrosis 3-7 years after end of radiotherapy treatment. Methods and Materials: Gene expression profiles from blood were obtained for 254 BC survivors derived from a cohort of survivors, treated with adjuvant radiotherapy for breast cancer 3-7 years earlier. Analyses of transcriptional differences in blood gene expression between BC survivors with fibrosis (n = 31) and BC survivors without fibrosis (n = 223) were performed using R version 2.8.0 and tools from the Bioconductor project. Gene sets extracted through a literature search on fibrosis and breast cancer were subsequently used in gene set enrichment analysis. Results: Substantial differences in blood gene expression between BC survivors with and without fibrosis were observed, and 87 differentially expressed genes were identified through linear analysis. Transforming growth factor-β1 signaling was identified as the most significant gene set, showing a down-regulation of most of the core genes, together with up-regulation of a transcriptional activator of the inhibitor of fibrinolysis, Plasminogen activator inhibitor 1 in the BC survivors with fibrosis. Conclusion: Transforming growth factor-β1 signaling was found down-regulated during the maintenance phase of fibrosis as opposed to the up-regulation reported during the early, initiating phase of fibrosis. Hence, once the fibrotic tissue has developed, the maintenance phase might rather involve a deregulation of fibrinolysis and altered degradation of extracellular matrix components.

  20. A genome-wide gene function prediction resource for Drosophila melanogaster.

    Directory of Open Access Journals (Sweden)

    Han Yan

    2010-08-01

    Full Text Available Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

  1. Risk stratification in myelodysplastic syndromes: is there a role for gene expression profiling?

    Science.gov (United States)

    Zeidan, Amer M; Prebet, Thomas; Saad Aldin, Ehab; Gore, Steven David

    2014-04-01

    Evaluation of: Pellagatti A, Benner A, Mills KI et al. Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes. J. Clin. Oncol. 31(28), 3557-3564 (2013). Patients with myelodysplastic syndromes (MDS) exhibit wide heterogeneity in clinical outcomes making accurate risk-stratification an integral part of the risk-adaptive management paradigm. Current prognostic schemes for MDS rely on clinicopathological parameters. Despite the increasing knowledge of the genetic landscape of MDS and the prognostic impact of many newly discovered molecular aberrations, none to date has been incorporated formally into the major risk models. Efforts are ongoing to use data generated from genome-wide high-throughput techniques to improve the 'individualized' outcome prediction for patients. We here discuss an important paper in which gene expression profiling (GEP) technology was applied to marrow CD34(+) cells from 125 MDS patients to generate and validate a standardized GEP-based prognostic signature.

  2. Gene expression profiling of two distinct neuronal populations in the rodent spinal cord.

    Directory of Open Access Journals (Sweden)

    Jesper Ryge

    Full Text Available BACKGROUND: In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. METHODOLOGY/PRINCIPAL FINDINGS: We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50-250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. CONCLUSIONS/SIGNIFICANCE: We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional

  3. Transcriptome profiling in conifers and the PiceaGenExpress database show patterns of diversification within gene families and interspecific conservation in vascular gene expression

    Directory of Open Access Journals (Sweden)

    Raherison Elie

    2012-08-01

    Full Text Available Abstract Background Conifers have very large genomes (13 to 30 Gigabases that are mostly uncharacterized although extensive cDNA resources have recently become available. This report presents a global overview of transcriptome variation in a conifer tree and documents conservation and diversity of gene expression patterns among major vegetative tissues. Results An oligonucleotide microarray was developed from Picea glauca and P. sitchensis cDNA datasets. It represents 23,853 unique genes and was shown to be suitable for transcriptome profiling in several species. A comparison of secondary xylem and phelloderm tissues showed that preferential expression in these vascular tissues was highly conserved among Picea spp. RNA-Sequencing strongly confirmed tissue preferential expression and provided a robust validation of the microarray design. A small database of transcription profiles called PiceaGenExpress was developed from over 150 hybridizations spanning eight major tissue types. In total, transcripts were detected for 92% of the genes on the microarray, in at least one tissue. Non-annotated genes were predominantly expressed at low levels in fewer tissues than genes of known or predicted function. Diversity of expression within gene families may be rapidly assessed from PiceaGenExpress. In conifer trees, dehydrins and late embryogenesis abundant (LEA osmotic regulation proteins occur in large gene families compared to angiosperms. Strong contrasts and low diversity was observed in the dehydrin family, while diverse patterns suggested a greater degree of diversification among LEAs. Conclusion Together, the oligonucleotide microarray and the PiceaGenExpress database represent the first resource of this kind for gymnosperm plants. The spruce transcriptome analysis reported here is expected to accelerate genetic studies in the large and important group comprised of conifer trees.

  4. Gene expression profiling in Ishikawa cells: A fingerprint for estrogen active compounds

    International Nuclear Information System (INIS)

    Boehme, Kathleen; Simon, Stephanie; Mueller, Stefan O.

    2009-01-01

    Several anthropogenous and naturally occurring substances, referred to as estrogen active compounds (EACs), are able to interfere with hormone and in particular estrogen receptor signaling. EACs can either cause adverse health effects in humans and wildlife populations or have beneficial effects on estrogen-dependent diseases. The aim of this study was to examine global gene expression profiles in estrogen receptor (ER)-proficient Ishikawa plus and ER-deficient Ishikawa minus endometrial cancer cells treated with selected well-known EACs (Diethylstilbestrol, Genistein, Zearalenone, Resveratrol, Bisphenol A and o,p'-DDT). We also investigated the effect of the pure antiestrogen ICI 182,780 (ICI) on the expression patterns caused by these compounds. Transcript levels were quantified 24 h after compound treatment using Illumina BeadChip Arrays. We identified 87 genes with similar expression changes in response to all EAC treatments in Ishikawa plus. ICI lowered the magnitude or reversed the expression of these genes, indicating ER dependent regulation. Apart from estrogenic gene regulation, Bisphenol A, o,p'-DDT, Zearalenone, Genistein and Resveratrol displayed similarities to ICI in their expression patterns, suggesting mixed estrogenic/antiestrogenic properties. In particular, the predominant antiestrogenic expression response of Resveratrol could be clearly distinguished from the other test compounds, indicating a distinct mechanism of action. Divergent gene expression patterns of the phytoestrogens, as well as weaker estrogenic gene expression regulation determined for the anthropogenous chemicals Bisphenol A and o,p'-DDT, warrants a careful assessment of potential detrimental and/or beneficial effects of EACs. The characteristic expression fingerprints and the identified subset of putative marker genes can be used for screening chemicals with an unknown mode of action and for predicting their potential to exert endocrine disrupting effects

  5. Identification of genes showing differential expression profile ...

    Indian Academy of Sciences (India)

    3Department of Natural Sciences, International Christian University, Mitaka, Tokyo 181-8585, Japan ... the changes of expression predicted from gene function suggested association ... ate School of Science and Technology, Niigata University.

  6. A multiplex branched DNA assay for parallel quantitative gene expression profiling.

    Science.gov (United States)

    Flagella, Michael; Bui, Son; Zheng, Zhi; Nguyen, Cung Tuong; Zhang, Aiguo; Pastor, Larry; Ma, Yunqing; Yang, Wen; Crawford, Kimberly L; McMaster, Gary K; Witney, Frank; Luo, Yuling

    2006-05-01

    We describe a novel method to quantitatively measure messenger RNA (mRNA) expression of multiple genes directly from crude cell lysates and tissue homogenates without the need for RNA purification or target amplification. The multiplex branched DNA (bDNA) assay adapts the bDNA technology to the Luminex fluorescent bead-based platform through the use of cooperative hybridization, which ensures an exceptionally high degree of assay specificity. Using in vitro transcribed RNA as reference standards, we demonstrated that the assay is highly specific, with cross-reactivity less than 0.2%. We also determined that the assay detection sensitivity is 25,000 RNA transcripts with intra- and interplate coefficients of variance of less than 10% and less than 15%, respectively. Using three 10-gene panels designed to measure proinflammatory and apoptosis responses, we demonstrated sensitive and specific multiplex gene expression profiling directly from cell lysates. The gene expression change data demonstrate a high correlation coefficient (R(2)=0.94) compared with measurements obtained using the single-plex bDNA assay. Thus, the multiplex bDNA assay provides a powerful means to quantify the gene expression profile of a defined set of target genes in large sample populations.

  7. Aging: a portrait from gene expression profile in blood cells.

    Science.gov (United States)

    Calabria, Elisa; Mazza, Emilia Maria Cristina; Dyar, Kenneth Allen; Pogliaghi, Silvia; Bruseghini, Paolo; Morandi, Carlo; Salvagno, Gian Luca; Gelati, Matteo; Guidi, Gian Cesare; Bicciato, Silvio; Schiaffino, Stefano; Schena, Federico; Capelli, Carlo

    2016-08-01

    The availability of reliable biomarkers of aging is important not only to monitor the effect of interventions and predict the timing of pathologies associated with aging but also to understand the mechanisms and devise appropriate countermeasures. Blood cells provide an easily available tissue and gene expression profiles from whole blood samples appear to mirror disease states and some aspects of the aging process itself. We report here a microarray analysis of whole blood samples from two cohorts of healthy adult and elderly subjects, aged 43±3 and 68±4 years, respectively, to monitor gene expression changes in the initial phase of the senescence process. A number of significant changes were found in the elderly compared to the adult group, including decreased levels of transcripts coding for components of the mitochondrial respiratory chain, which correlate with a parallel decline in the maximum rate of oxygen consumption (VO2max), as monitored in the same subjects. In addition, blood cells show age-related changes in the expression of several markers of immunosenescence, inflammation and oxidative stress. These findings support the notion that the immune system has a major role in tissue homeostasis and repair, which appears to be impaired since early stages of the aging process.

  8. Inductive matrix completion for predicting gene-disease associations.

    Science.gov (United States)

    Natarajan, Nagarajan; Dhillon, Inderjit S

    2014-06-15

    Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has bigdata.ices.utexas.edu/project/gene-disease. © The Author 2014. Published by Oxford University Press.

  9. Signaling pathway-focused gene expression profiling in pressure overloaded hearts

    Directory of Open Access Journals (Sweden)

    Marco Musumeci

    2011-01-01

    Full Text Available The β-blocker propranolol displays antihypertrophic and antifibrotic properties in the heart subjected to pressure overload. Yet the underlying mechanisms responsible for these important effects remain to be completely understood. The purpose of this study was to determine signaling pathway-focused gene expression profile associated with the antihypertrophic action of propranolol in pressure overloaded hearts. To address this question, a focused real-time PCR array was used to screen left ventricular RNA expression of 84 gene transcripts representative of 18 different signaling pathways in C57BL/6 mice subjected to transverse aortic constriction (TAC or sham surgery. On the surgery day, mice received either propranolol (80 mg/kg/day or vehicle for 14 days. TAC caused a 49% increase in the left ventricular weight-to-body weight (LVW/BW ratio without changing gene expression. Propranolol blunted LVW/BW ratio increase by approximately 50% while causing about a 3-fold increase in the expression of two genes, namely Brca1 and Cdkn2a, belonging to the TGF-beta and estrogen pathways, respectively. In conclusion, after 2 weeks of pressure overload, TAC hearts show a gene expression profile superimposable to that of sham hearts. Conversely, propranolol treatment is associated with an increased expression of genes which negatively regulate cell cycle progression. It remains to be established whether a mechanistic link between gene expression changes and the antihypertrophic action of propranolol occurs.

  10. Microarray profiling of mononuclear peripheral blood cells identifies novel candidate genes related to chemoradiation response in rectal cancer.

    Directory of Open Access Journals (Sweden)

    Pablo Palma

    Full Text Available Preoperative chemoradiation significantly improves oncological outcome in locally advanced rectal cancer. However there is no effective method of predicting tumor response to chemoradiation in these patients. Peripheral blood mononuclear cells have emerged recently as pathology markers of cancer and other diseases, making possible their use as therapy predictors. Furthermore, the importance of the immune response in radiosensivity of solid organs led us to hypothesized that microarray gene expression profiling of peripheral blood mononuclear cells could identify patients with response to chemoradiation in rectal cancer. Thirty five 35 patients with locally advanced rectal cancer were recruited initially to perform the study. Peripheral blood samples were obtained before neaodjuvant treatment. RNA was extracted and purified to obtain cDNA and cRNA for hybridization of microarrays included in Human WG CodeLink bioarrays. Quantitative real time PCR was used to validate microarray experiment data. Results were correlated with pathological response, according to Mandard´s criteria and final UICC Stage (patients with tumor regression grade 1-2 and downstaging being defined as responders and patients with grade 3-5 and no downstaging as non-responders. Twenty seven out of 35 patients were finally included in the study. We performed a multiple t-test using Significance Analysis of Microarrays, to find those genes differing significantly in expression, between responders (n = 11 and non-responders (n = 16 to CRT. The differently expressed genes were: BC 035656.1, CIR, PRDM2, CAPG, FALZ, HLA-DPB2, NUPL2, and ZFP36. The measurement of FALZ (p = 0.029 gene expression level determined by qRT-PCR, showed statistically significant differences between the two groups. Gene expression profiling reveals novel genes in peripheral blood samples of mononuclear cells that could predict responders and non-responders to chemoradiation in patients with

  11. ABC gene expression profiles have clinical importance and possibly form a new hallmark of cancer.

    Science.gov (United States)

    Dvorak, Pavel; Pesta, Martin; Soucek, Pavel

    2017-05-01

    Adenosine triphosphate-binding cassette proteins constitute a large family of active transporters through extracellular and intracellular membranes. Increased drug efflux based on adenosine triphosphate-binding cassette protein activity is related to the development of cancer cell chemoresistance. Several articles have focused on adenosine triphosphate-binding cassette gene expression profiles (signatures), based on the expression of all 49 human adenosine triphosphate-binding cassette genes, in individual tumor types and reported connections to established clinicopathological features. The aim of this study was to test our theory about the existence of adenosine triphosphate-binding cassette gene expression profiles common to multiple types of tumors, which may modify tumor progression and provide clinically relevant information. Such general adenosine triphosphate-binding cassette profiles could constitute a new attribute of carcinogenesis. Our combined cohort consisted of tissues from 151 cancer patients-breast, colorectal, and pancreatic carcinomas. Standard protocols for RNA isolation and quantitative real-time polymerase chain reaction were followed. Gene expression data from individual tumor types as well as a merged tumor dataset were analyzed by bioinformatics tools. Several general adenosine triphosphate-binding cassette profiles, with differences in gene functions, were established and shown to have significant relations to clinicopathological features such as tumor size, histological grade, or clinical stage. Genes ABCC7, A3, A8, A12, and C8 prevailed among the most upregulated or downregulated ones. In conclusion, the results supported our theory about general adenosine triphosphate-binding cassette gene expression profiles and their importance for cancer on clinical as well as research levels. The presence of ABCC7 (official symbol CFTR) among the genes with key roles in the profiles supports the emerging evidence about its crucial role in various

  12. Developmental gene expression profiles of the human pathogen Schistosoma japonicum

    Directory of Open Access Journals (Sweden)

    McManus Donald P

    2009-03-01

    Full Text Available Abstract Background The schistosome blood flukes are complex trematodes and cause a chronic parasitic disease of significant public health importance worldwide, schistosomiasis. Their life cycle is characterised by distinct parasitic and free-living phases involving mammalian and snail hosts and freshwater. Microarray analysis was used to profile developmental gene expression in the Asian species, Schistosoma japonicum. Total RNAs were isolated from the three distinct environmental phases of the lifecycle – aquatic/snail (eggs, miracidia, sporocysts, cercariae, juvenile (lung schistosomula and paired but pre-egg laying adults and adult (paired, mature males and egg-producing females, both examined separately. Advanced analyses including ANOVA, principal component analysis, and hierarchal clustering provided a global synopsis of gene expression relationships among the different developmental stages of the schistosome parasite. Results Gene expression profiles were linked to the major environmental settings through which the developmental stages of the fluke have to adapt during the course of its life cycle. Gene ontologies of the differentially expressed genes revealed a wide range of functions and processes. In addition, stage-specific, differentially expressed genes were identified that were involved in numerous biological pathways and functions including calcium signalling, sphingolipid metabolism and parasite defence. Conclusion The findings provide a comprehensive database of gene expression in an important human pathogen, including transcriptional changes in genes involved in evasion of the host immune response, nutrient acquisition, energy production, calcium signalling, sphingolipid metabolism, egg production and tegumental function during development. This resource should help facilitate the identification and prioritization of new anti-schistosome drug and vaccine targets for the control of schistosomiasis.

  13. Gene prediction validation and functional analysis of redundant pathways

    DEFF Research Database (Denmark)

    Sønderkær, Mads

    2011-01-01

    have employed a large mRNA-seq data set to improve and validate ab initio predicted gene models. This direct experimental evidence also provides reliable determinations of UTR regions and polyadenylation sites, which are not easily predicted in plants. Furthermore, once an annotated genome sequence...... is available, gene expression by mRNA-Seq enables acquisition of a more complete overview of gene isoform usage in complex enzymatic pathways enabling the identification of key genes. Metabolism in potatoes This information is useful e.g. for crop improvement based on manipulation of agronomically important...

  14. Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.

    Science.gov (United States)

    Gao, Haiyan; Yang, Mei; Zhang, Xiaolan

    2018-04-01

    The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.

  15. Validity of a manual soft tissue profile prediction method following mandibular setback osteotomy.

    Science.gov (United States)

    Kolokitha, Olga-Elpis

    2007-10-01

    The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication.

  16. Metastatic canine mammary carcinomas can be identified by a gene expression profile that partly overlaps with human breast cancer profiles

    International Nuclear Information System (INIS)

    Klopfleisch, Robert; Lenze, Dido; Hummel, Michael; Gruber, Achim D

    2010-01-01

    Similar to human breast cancer mammary tumors of the female dog are commonly associated with a fatal outcome due to the development of distant metastases. However, the molecular defects leading to metastasis are largely unknown and the value of canine mammary carcinoma as a model for human breast cancer is unclear. In this study, we analyzed the gene expression signatures associated with mammary tumor metastasis and asked for parallels with the human equivalent. Messenger RNA expression profiles of twenty-seven lymph node metastasis positive or negative canine mammary carcinomas were established by microarray analysis. Differentially expressed genes were functionally characterized and associated with molecular pathways. The findings were also correlated with published data on human breast cancer. Metastatic canine mammary carcinomas had 1,011 significantly differentially expressed genes when compared to non-metastatic carcinomas. Metastatic carcinomas had a significant up-regulation of genes associated with cell cycle regulation, matrix modulation, protein folding and proteasomal degradation whereas cell differentiation genes, growth factor pathway genes and regulators of actin organization were significantly down-regulated. Interestingly, 265 of the 1,011 differentially expressed canine genes are also related to human breast cancer and, vice versa, parts of a human prognostic gene signature were identified in the expression profiles of the metastatic canine tumors. Metastatic canine mammary carcinomas can be discriminated from non-metastatic carcinomas by their gene expression profiles. More than one third of the differentially expressed genes are also described of relevance for human breast cancer. Many of the differentially expressed genes are linked to functions and pathways which appear to be relevant for the induction and maintenance of metastatic progression and may represent new therapeutic targets. Furthermore, dogs are in some aspects suitable as a

  17. Early diffusion of gene expression profiling in breast cancer patients associated with areas of high income inequality.

    Science.gov (United States)

    Ponce, Ninez A; Ko, Michelle; Liang, Su-Ying; Armstrong, Joanne; Toscano, Michele; Chanfreau-Coffinier, Catherine; Haas, Jennifer S

    2015-04-01

    With the Affordable Care Act reducing coverage disparities, social factors could prominently determine where and for whom innovations first diffuse in health care markets. Gene expression profiling is a potentially cost-effective innovation that guides chemotherapy decisions in early-stage breast cancer, but adoption has been uneven across the United States. Using a sample of commercially insured women, we evaluated whether income inequality in metropolitan areas was associated with receipt of gene expression profiling during its initial diffusion in 2006-07. In areas with high income inequality, gene expression profiling receipt was higher than elsewhere, but it was associated with a 10.6-percentage-point gap between high- and low-income women. In areas with low rates of income inequality, gene expression profiling receipt was lower, with no significant differences by income. Even among insured women, income inequality may indirectly shape diffusion of gene expression profiling, with benefits accruing to the highest-income patients in the most unequal places. Policies reducing gene expression profiling disparities should address low-inequality areas and, in unequal places, practice settings serving low-income patients. Project HOPE—The People-to-People Health Foundation, Inc.

  18. Microarray gene expression profiling and analysis in renal cell carcinoma

    Directory of Open Access Journals (Sweden)

    Sadhukhan Provash

    2004-06-01

    Full Text Available Abstract Background Renal cell carcinoma (RCC is the most common cancer in adult kidney. The accuracy of current diagnosis and prognosis of the disease and the effectiveness of the treatment for the disease are limited by the poor understanding of the disease at the molecular level. To better understand the genetics and biology of RCC, we profiled the expression of 7,129 genes in both clear cell RCC tissue and cell lines using oligonucleotide arrays. Methods Total RNAs isolated from renal cell tumors, adjacent normal tissue and metastatic RCC cell lines were hybridized to affymatrix HuFL oligonucleotide arrays. Genes were categorized into different functional groups based on the description of the Gene Ontology Consortium and analyzed based on the gene expression levels. Gene expression profiles of the tissue and cell line samples were visualized and classified by singular value decomposition. Reverse transcription polymerase chain reaction was performed to confirm the expression alterations of selected genes in RCC. Results Selected genes were annotated based on biological processes and clustered into functional groups. The expression levels of genes in each group were also analyzed. Seventy-four commonly differentially expressed genes with more than five-fold changes in RCC tissues were identified. The expression alterations of selected genes from these seventy-four genes were further verified using reverse transcription polymerase chain reaction (RT-PCR. Detailed comparison of gene expression patterns in RCC tissue and RCC cell lines shows significant differences between the two types of samples, but many important expression patterns were preserved. Conclusions This is one of the initial studies that examine the functional ontology of a large number of genes in RCC. Extensive annotation, clustering and analysis of a large number of genes based on the gene functional ontology revealed many interesting gene expression patterns in RCC. Most

  19. Identification and profiling of microRNAs and their target genes from developing caprine skeletal Muscle.

    Directory of Open Access Journals (Sweden)

    Yanhong Wang

    Full Text Available Goat is an important agricultural animal for meat production. Functional studies have demonstrated that microRNAs (miRNAs regulate gene expression at the post-transcriptional level and play an important role in various biological processes. Although studies on miRNAs expression profiles have been performed in various animals, relatively limited information about goat muscle miRNAs has been reported. To investigate the miRNAs involved in regulating different periods of skeletal muscle development, we herein performed a comprehensive research for expression profiles of caprine miRNAs during two developmental stages of skeletal muscles: fetal stage and six month-old stage. As a result, 15,627,457 and 15,593,721 clean reads were obtained from the fetal goat library (FC and the six month old goat library (SMC, respectively. 464 known miRNAs and 83 novel miRNA candidates were identified. Furthermore, by comparing the miRNA profile, 336 differentially expressed miRNAs were identified and then the potential targets of the differentially expressed miRNAs were predicted. To understand the regulatory network of miRNAs during muscle development, the mRNA expression profiles for the two development stages were characterized and 7322 differentially expressed genes (DEGs were identified. Then the potential targets of miRNAs were compared to the DEGs, the intersection of the two gene sets were screened out and called differentially expressed targets (DE-targets, which were involved in 231 pathways. Ten of the 231 pathways that have smallest P-value were shown as network figures. Based on the analysis of pathways and networks, we found that miR-424-5p and miR-29a might have important regulatory effect on muscle development, which needed to be further studied. This study provided the first global view of the miRNAs in caprine muscle tissues. Our results help elucidation of complex regulatory networks between miRNAs and mRNAs and for the study of muscle

  20. Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy.

    Science.gov (United States)

    Honeyborne, Isobella; McHugh, Timothy D; Kuittinen, Iitu; Cichonska, Anna; Evangelopoulos, Dimitrios; Ronacher, Katharina; van Helden, Paul D; Gillespie, Stephen H; Fernandez-Reyes, Delmiro; Walzl, Gerhard; Rousu, Juho; Butcher, Philip D; Waddell, Simon J

    2016-04-07

    New treatment options are needed to maintain and improve therapy for tuberculosis, which caused the death of 1.5 million people in 2013 despite potential for an 86 % treatment success rate. A greater understanding of Mycobacterium tuberculosis (M.tb) bacilli that persist through drug therapy will aid drug development programs. Predictive biomarkers for treatment efficacy are also a research priority. Genome-wide transcriptional profiling was used to map the mRNA signatures of M.tb from the sputa of 15 patients before and 3, 7 and 14 days after the start of standard regimen drug treatment. The mRNA profiles of bacilli through the first 2 weeks of therapy reflected drug activity at 3 days with transcriptional signatures at days 7 and 14 consistent with reduced M.tb metabolic activity similar to the profile of pre-chemotherapy bacilli. These results suggest that a pre-existing drug-tolerant M.tb population dominates sputum before and after early drug treatment, and that the mRNA signature at day 3 marks the killing of a drug-sensitive sub-population of bacilli. Modelling patient indices of disease severity with bacterial gene expression patterns demonstrated that both microbiological and clinical parameters were reflected in the divergent M.tb responses and provided evidence that factors such as bacterial load and disease pathology influence the host-pathogen interplay and the phenotypic state of bacilli. Transcriptional signatures were also defined that predicted measures of early treatment success (rate of decline in bacterial load over 3 days, TB test positivity at 2 months, and bacterial load at 2 months). This study defines the transcriptional signature of M.tb bacilli that have been expectorated in sputum after two weeks of drug therapy, characterizing the phenotypic state of bacilli that persist through treatment. We demonstrate that variability in clinical manifestations of disease are detectable in bacterial sputa signatures, and that the changing M.tb m

  1. Establishment of a 12-gene expression signature to predict colon cancer prognosis

    Directory of Open Access Journals (Sweden)

    Dalong Sun

    2018-06-01

    Full Text Available A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA. The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets: GSE39582 includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics; GSE17538 is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients. The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS: Kaplan Meier (KM Log Rank p = 0.0034; overall survival (OS: KM Log Rank p = 0.0336 in GSE17538. For patients with proficient mismatch repair system (pMMR in GSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rank p = 0.005; Relapse free survival (RFS: KM Log Rank p = 0.022. Interestingly, advanced stage patients were significantly enriched in high 12-gene score group (Fisher’s exact test p = 0.0003. After stage stratification, the signature could still distinguish poor prognosis patients in GSE17538 from good prognosis within stage II (Log Rank p = 0.01 and stage II & III (Log Rank p = 0.017 in the outcome of DFS. Within stage III or II/III pMMR patients treated with Adjuvant Chemotherapies (ACT and patients with higher 12-gene score showed poorer prognosis (III, OS: KM Log Rank p = 0.046; III & II, OS: KM Log Rank p = 0.041. Among stage II/III pMMR patients

  2. Gene expression profiles of lung adenocarcinoma linked to histopathological grading and survival but not to EGF-R status: a microarray study

    Directory of Open Access Journals (Sweden)

    Passlick Bernward

    2010-03-01

    Full Text Available Abstract Background Several different gene expression signatures have been proposed to predict response to therapy and clinical outcome in lung adenocarcinoma. Herein, we investigate if elements of published gene sets can be reproduced in a small dataset, and how gene expression profiles based on limited sample size relate to clinical parameters including histopathological grade and EGFR protein expression. Methods Affymetrix Human Genome U133A platform was used to obtain gene expression profiles of 28 pathologically and clinically annotated adenocarcinomas of the lung. EGFR status was determined by fluorescent in situ hybridization and immunohistochemistry. Results Using unsupervised clustering algorithms, the predominant gene expression signatures correlated with the histopathological grade but not with EGFR protein expression as detected by immunohistochemistry. In a supervised analysis, the signature of high grade tumors but not of EGFR overexpressing cases showed significant enrichment of gene sets reflecting MAPK activation and other potential signaling cascades downstream of EGFR. Out of four different previously published gene sets that had been linked to prognosis, three showed enrichment in the gene expression signature associated with favorable prognosis. Conclusions In this dataset, histopathological tumor grades but not EGFR status were associated with dominant gene expression signatures and gene set enrichment reflecting oncogenic pathway activation, suggesting that high immunohistochemistry EGFR scores may not necessarily be linked to downstream effects that cause major changes in gene expression patterns. Published gene sets showed association with patient survival; however, the small sample size of this study limited the options for a comprehensive validation of previously reported prognostic gene expression signatures.

  3. Gene expression profiles in adenosine-treated human mast cells ...

    African Journals Online (AJOL)

    Gene expression profiles in adenosine-treated human mast cells. ... SW Kang, JE Jeong, CH Kim, SH Choi, SH Chae, SA Jun, HJ Cha, JH Kim, YM Lee, YS ... beta 4, ring finger protein, high-mobility group, calmodulin 2, RAN binding protein, ...

  4. Gene expression profiles in BCL11B-siRNA treated malignant T cells

    Directory of Open Access Journals (Sweden)

    Grabarczyk Piotr

    2011-05-01

    Full Text Available Abstract Background Downregulation of the B-cell chronic lymphocytic leukemia (CLL/lymphoma11B (BCL11B gene by small interfering RNA (siRNA leads to growth inhibition and apoptosis of the human T-cell acute lymphoblastic leukemia (T-ALL cell line Molt-4. To further characterize the molecular mechanism, a global gene expression profile of BCL11B-siRNA -treated Molt-4 cells was established. The expression profiles of several genes were further validated in the BCL11B-siRNA -treated Molt-4 cells and primary T-ALL cells. Results 142 genes were found to be upregulated and 109 genes downregulated in the BCL11B-siRNA -treated Molt-4 cells by microarray analysis. Among apoptosis-related genes, three pro-apoptotic genes, TNFSF10, BIK, BNIP3, were upregulated and one anti-apoptotic gene, BCL2L1 was downregulated. Moreover, the expression of SPP1 and CREBBP genes involved in the transforming growth factor (TGF-β pathway was down 16-fold. Expression levels of TNFSF10, BCL2L1, SPP1, and CREBBP were also examined by real-time PCR. A similar expression pattern of TNFSF10, BCL2L1, and SPP1 was identified. However, CREBBP was not downregulated in the BLC11B-siRNA -treated Molt-4 cells. Conclusion BCL11B-siRNA treatment altered expression profiles of TNFSF10, BCL2L1, and SPP1 in both Molt-4 T cell line and primary T-ALL cells.

  5. Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy

    Directory of Open Access Journals (Sweden)

    Chi-Cheng Huang

    2013-01-01

    Full Text Available Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n=535. The agreement between PAM50 centroid-based single sample prediction (SSP and PLS-regression was excellent (weighted Kappa: 0.988 within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression. If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed. Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes.

  6. Virulence gene profiles of avian pathogenic Escherichia coli isolated from chickens with colibacillosis in Bulawayo, Zimbabwe

    Directory of Open Access Journals (Sweden)

    Joshua Mbanga

    2015-04-01

    Full Text Available Colibacillosis, a disease caused by avian pathogenic Escherichia coli (APEC, is one of the main causes of economic losses in the poultry industry worldwide. This study was carried out in order to determine the APEC-associated virulence genes contained by E. coli isolates causing colibacillosis in chickens. A total of 45 E. coli isolates were obtained from the diagnostics and research branch of the Central Veterinary Laboratories, Bulawayo, Zimbabwe. These isolates were obtained from chickens with confirmed cases of colibacillosis after postmortem examination. The presence of the iutA, hlyF, ompT, frz, sitD, fimH, kpsM, sitA, sopB, uvrY, pstB and vat genes were investigated by multiplex polymerase chain reaction (PCR assay. Of the 45 isolates, 93% were positive for the presence of at least one virulence gene. The three most prevalent virulence genes were iutA (80%, fimH (33.3% and hlyF (24.4%. The kpsM, pstB and ompT genes had the lowest prevalence, having been detected in only 2.2% of the isolates. All 12 virulence genes studied were detected in the 45 APEC isolates. Virulence gene profiles were constructed for each APEC isolate from the multiplex data. The APEC isolates were profiled as 62.2% fitting profile A, 31.1% profile B and 6.7% profile C. None of the isolates had more than seven virulence genes. Virulence profiles of Zimbabwean APEC isolates are different from those previously reported. Zimbabwean APEC isolates appear to be less pathogenic and may rely on environmental factors and stress in hosts to establish infection.

  7. Gene expression profiles associated with anaemia and ITPA genotypes in patients with chronic hepatitis C (CH-C).

    Science.gov (United States)

    Birerdinc, A; Estep, M; Afendy, A; Stepanova, M; Younossi, I; Baranova, A; Younossi, Z M

    2012-06-01

    Anaemia is a common side effect of ribavirin (RBV) which is used for the treatment of hepatitis C. Inosine triphosphatase gene polymorphism (C to A) protects against RBV-induced anaemia. The aim of our study was to genotype patients for inosine triphosphatase gene polymorphism rs1127354 SNP (CC or CA) and associate treatment-induced anaemia with gene expression profile and genotypes. We used 67 hepatitis C patients with available gene expression, clinical, laboratory data and whole-blood samples. Whole blood was used to determine inosine triphosphatase gene polymorphism rs1127354 genotypes (CC or CA). The cohort with inosine triphosphatase gene polymorphism CA genotype revealed a distinct pattern of protection against anaemia and a lower drop in haemoglobin. A variation in the propensity of CC carriers to develop anaemia prompted us to look for additional predictors of anaemia during pegylated interferon (PEG-IFN) and RBV. Pretreatment blood samples of patients receiving a full course of PEG-IFN and RBV were used to assess expression of 153 genes previously implicated in host response to viral infections. The gene expression data were analysed according to presence of anaemia and inosine triphosphatase gene polymorphism genotypes. Thirty-six genes were associated with treatment-related anaemia, six of which are involved in the response to hypoxia pathway (HIF1A, AIF1, RHOC, PTEN, LCK and PDGFB). There was a substantial overlap between sustained virological response (SVR)-predicting and anaemia-related genes; however, of the nine JAK-STAT pathway-related genes associated with SVR, none were implicated in anaemia. These observations exclude the direct involvement of antiviral response in the development of anaemia associated with PEG-IFN and RBV treatment, whereas another, distinct component within the SVR-associated gene expression response may predict anaemia. We have identified baseline gene expression signatures associated with RBV-induced anaemia and identified

  8. A comparison on radar range profiles between in-flight measurements and RCS-predictions

    NARCIS (Netherlands)

    Heiden, R. van der; Ewijk, L.J. van; Groen, F.C.A.

    1998-01-01

    The validation of Radar Cross Section (RCS) prediction techniques against real measurements is crucial to acquire confidence in predictions when measurements are nut available. In this paper we present the results of a comparison on one-dimensional signatures, i.e. radar range profiles. The profiles

  9. Gene expression profile change and growth inhibition in Drosophila larvae treated with azadirachtin.

    Science.gov (United States)

    Lai, Duo; Jin, Xiaoyong; Wang, Hao; Yuan, Mei; Xu, Hanhong

    2014-09-20

    Azadirachtin is a botanical insecticide that affects various biological processes. The effects of azadirachtin on the digital gene expression profile and growth inhibition in Drosophila larvae have not been investigated. In this study, we applied high-throughput sequencing technology to detect the differentially expressed genes of Drosophila larvae regulated by azadirachtin. A total of 15,322 genes were detected, and 28 genes were found to be significantly regulated by azadirachtin. Biological process and pathway analysis showed that azadirachtin affected starch and sucrose metabolism, defense response, signal transduction, instar larval or pupal development, and chemosensory behavior processes. The genes regulated by azadirachtin were mainly enriched in starch and sucrose metabolism. This study provided a general digital gene expression profile of dysregulated genes in response to azadirachtin and showed that azadirachtin provoked potent growth inhibitory effects in Drosophila larvae by regulating the genes of cuticular protein, amylase, and odorant-binding protein. Finally, we propose a potential mechanism underlying the dysregulation of the insulin/insulin-like growth factor signaling pathway by azadirachtin. Copyright © 2014 Elsevier B.V. All rights reserved.

  10. Mining a database of single amplified genomes from Red Sea brine pool extremophiles—improving reliability of gene function prediction using a profile and pattern matching algorithm (PPMA)

    Science.gov (United States)

    Grötzinger, Stefan W.; Alam, Intikhab; Ba Alawi, Wail; Bajic, Vladimir B.; Stingl, Ulrich; Eppinger, Jörg

    2014-01-01

    Reliable functional annotation of genomic data is the key-step in the discovery of novel enzymes. Intrinsic sequencing data quality problems of single amplified genomes (SAGs) and poor homology of novel extremophile's genomes pose significant challenges for the attribution of functions to the coding sequences identified. The anoxic deep-sea brine pools of the Red Sea are a promising source of novel enzymes with unique evolutionary adaptation. Sequencing data from Red Sea brine pool cultures and SAGs are annotated and stored in the Integrated Data Warehouse of Microbial Genomes (INDIGO) data warehouse. Low sequence homology of annotated genes (no similarity for 35% of these genes) may translate into false positives when searching for specific functions. The Profile and Pattern Matching (PPM) strategy described here was developed to eliminate false positive annotations of enzyme function before progressing to labor-intensive hyper-saline gene expression and characterization. It utilizes InterPro-derived Gene Ontology (GO)-terms (which represent enzyme function profiles) and annotated relevant PROSITE IDs (which are linked to an amino acid consensus pattern). The PPM algorithm was tested on 15 protein families, which were selected based on scientific and commercial potential. An initial list of 2577 enzyme commission (E.C.) numbers was translated into 171 GO-terms and 49 consensus patterns. A subset of INDIGO-sequences consisting of 58 SAGs from six different taxons of bacteria and archaea were selected from six different brine pool environments. Those SAGs code for 74,516 genes, which were independently scanned for the GO-terms (profile filter) and PROSITE IDs (pattern filter). Following stringent reliability filtering, the non-redundant hits (106 profile hits and 147 pattern hits) are classified as reliable, if at least two relevant descriptors (GO-terms and/or consensus patterns) are present. Scripts for annotation, as well as for the PPM algorithm, are available

  11. Mining a database of single amplified genomes from Red Sea brine pool extremophiles-improving reliability of gene function prediction using a profile and pattern matching algorithm (PPMA).

    KAUST Repository

    Grötzinger, Stefan W.

    2014-04-07

    Reliable functional annotation of genomic data is the key-step in the discovery of novel enzymes. Intrinsic sequencing data quality problems of single amplified genomes (SAGs) and poor homology of novel extremophile\\'s genomes pose significant challenges for the attribution of functions to the coding sequences identified. The anoxic deep-sea brine pools of the Red Sea are a promising source of novel enzymes with unique evolutionary adaptation. Sequencing data from Red Sea brine pool cultures and SAGs are annotated and stored in the Integrated Data Warehouse of Microbial Genomes (INDIGO) data warehouse. Low sequence homology of annotated genes (no similarity for 35% of these genes) may translate into false positives when searching for specific functions. The Profile and Pattern Matching (PPM) strategy described here was developed to eliminate false positive annotations of enzyme function before progressing to labor-intensive hyper-saline gene expression and characterization. It utilizes InterPro-derived Gene Ontology (GO)-terms (which represent enzyme function profiles) and annotated relevant PROSITE IDs (which are linked to an amino acid consensus pattern). The PPM algorithm was tested on 15 protein families, which were selected based on scientific and commercial potential. An initial list of 2577 enzyme commission (E.C.) numbers was translated into 171 GO-terms and 49 consensus patterns. A subset of INDIGO-sequences consisting of 58 SAGs from six different taxons of bacteria and archaea were selected from six different brine pool environments. Those SAGs code for 74,516 genes, which were independently scanned for the GO-terms (profile filter) and PROSITE IDs (pattern filter). Following stringent reliability filtering, the non-redundant hits (106 profile hits and 147 pattern hits) are classified as reliable, if at least two relevant descriptors (GO-terms and/or consensus patterns) are present. Scripts for annotation, as well as for the PPM algorithm, are available

  12. Transcription profiles of mitochondrial genes correlate with mitochondrial DNA haplotypes in a natural population of Silene vulgaris

    Directory of Open Access Journals (Sweden)

    Olson Matthew S

    2010-01-01

    Full Text Available Abstract Background Although rapid changes in copy number and gene order are common within plant mitochondrial genomes, associated patterns of gene transcription are underinvestigated. Previous studies have shown that the gynodioecious plant species Silene vulgaris exhibits high mitochondrial diversity and occasional paternal inheritance of mitochondrial markers. Here we address whether variation in DNA molecular markers is correlated with variation in transcription of mitochondrial genes in S. vulgaris collected from natural populations. Results We analyzed RFLP variation in two mitochondrial genes, cox1 and atp1, in offspring of ten plants from a natural population of S. vulgaris in Central Europe. We also investigated transcription profiles of the atp1 and cox1 genes. Most DNA haplotypes and transcription profiles were maternally inherited; for these, transcription profiles were associated with specific mitochondrial DNA haplotypes. One individual exhibited a pattern consistent with paternal inheritance of mitochondrial DNA; this individual exhibited a transcription profile suggestive of paternal but inconsistent with maternal inheritance. We found no associations between gender and transcript profiles. Conclusions Specific transcription profiles of mitochondrial genes were associated with specific mitochondrial DNA haplotypes in a natural population of a gynodioecious species S. vulgaris. Our findings suggest the potential for a causal association between rearrangements in the plant mt genome and transcription product variation.

  13. Gene expression profiles of mouse spermatogenesis during recovery from irradiation

    DEFF Research Database (Denmark)

    Shah, Fozia J; Tanaka, Masami; Nielsen, John E

    2009-01-01

    BACKGROUND: Irradiation or chemotherapy that suspend normal spermatogenesis is commonly used to treat various cancers. Fortunately, spermatogenesis in many cases can be restored after such treatments but knowledge is limited about the re-initiation process. Earlier studies have described the cell......BACKGROUND: Irradiation or chemotherapy that suspend normal spermatogenesis is commonly used to treat various cancers. Fortunately, spermatogenesis in many cases can be restored after such treatments but knowledge is limited about the re-initiation process. Earlier studies have described...... the cellular changes that happen during recovery from irradiation by means of histology. We have earlier generated gene expression profiles during induction of spermatogenesis in mouse postnatal developing testes and found a correlation between profiles and the expressing cell types. The aim of the present...... work was to utilize the link between expression profile and cell types to follow the cellular changes that occur during post-irradiation recovery of spermatogenesis in order to describe recovery by means of gene expression. METHODS: Adult mouse testes were subjected to irradiation with 1 Gy...

  14. Genomic profiling in Down syndrome acute lymphoblastic leukemia identifies histone gene deletions associated with altered methylation profiles

    Science.gov (United States)

    Loudin, Michael G.; Wang, Jinhua; Leung, Hon-Chiu Eastwood; Gurusiddappa, Sivashankarappa; Meyer, Julia; Condos, Gregory; Morrison, Debra; Tsimelzon, Anna; Devidas, Meenakshi; Heerema, Nyla A.; Carroll, Andrew J.; Plon, Sharon E.; Hunger, Stephen P.; Basso, Giuseppe; Pession, Andrea; Bhojwani, Deepa; Carroll, William L.; Rabin, Karen R.

    2014-01-01

    Patients with Down syndrome (DS) and acute lymphoblastic leukemia (ALL) have distinct clinical and biological features. Whereas most DS-ALL cases lack the sentinel cytogenetic lesions that guide risk assignment in childhood ALL, JAK2 mutations and CRLF2 overexpression are highly enriched. To further characterize the unique biology of DS-ALL, we performed genome-wide profiling of 58 DS-ALL and 68 non-Down syndrome (NDS) ALL cases by DNA copy number, loss of heterozygosity, gene expression, and methylation analyses. We report a novel deletion within the 6p22 histone gene cluster as significantly more frequent in DS-ALL, occurring in 11 DS (22%) and only two NDS cases (3.1%) (Fisher’s exact p = 0.002). Homozygous deletions yielded significantly lower histone expression levels, and were associated with higher methylation levels, distinct spatial localization of methylated promoters, and enrichment of highly methylated genes for specific pathways and transcription factor binding motifs. Gene expression profiling demonstrated heterogeneity of DS-ALL cases overall, with supervised analysis defining a 45-transcript signature associated with CRLF2 overexpression. Further characterization of pathways associated with histone deletions may identify opportunities for novel targeted interventions. PMID:21647151

  15. Global discriminative learning for higher-accuracy computational gene prediction.

    Directory of Open Access Journals (Sweden)

    Axel Bernal

    2007-03-01

    Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

  16. Microenvironment alters epigenetic and gene expression profiles in Swarm rat chondrosarcoma tumors

    International Nuclear Information System (INIS)

    Hamm, Christopher A; Wang, Deli; Malchenko, Sergey; Fatima Bonaldo, Maria de; Casavant, Thomas L; Hendrix, Mary JC; Soares, Marcelo B; Stevens, Jeff W; Xie, Hehuang; Vanin, Elio F; Morcuende, Jose A; Abdulkawy, Hakeem; Seftor, Elisabeth A; Sredni, Simone T; Bischof, Jared M

    2010-01-01

    Chondrosarcomas are malignant cartilage tumors that do not respond to traditional chemotherapy or radiation. The 5-year survival rate of histologic grade III chondrosarcoma is less than 30%. An animal model of chondrosarcoma has been established - namely, the Swarm Rat Chondrosarcoma (SRC) - and shown to resemble the human disease. Previous studies with this model revealed that tumor microenvironment could significantly influence chondrosarcoma malignancy. To examine the effect of the microenvironment, SRC tumors were initiated at different transplantation sites. Pyrosequencing assays were utilized to assess the DNA methylation of the tumors, and SAGE libraries were constructed and sequenced to determine the gene expression profiles of the tumors. Based on the gene expression analysis, subsequent functional assays were designed to determine the relevancy of the specific genes in the development and progression of the SRC. The site of transplantation had a significant impact on the epigenetic and gene expression profiles of SRC tumors. Our analyses revealed that SRC tumors were hypomethylated compared to control tissue, and that tumors at each transplantation site had a unique expression profile. Subsequent functional analysis of differentially expressed genes, albeit preliminary, provided some insight into the role that thymosin-β4, c-fos, and CTGF may play in chondrosarcoma development and progression. This report describes the first global molecular characterization of the SRC model, and it demonstrates that the tumor microenvironment can induce epigenetic alterations and changes in gene expression in the SRC tumors. We documented changes in gene expression that accompany changes in tumor phenotype, and these gene expression changes provide insight into the pathways that may play a role in the development and progression of chondrosarcoma. Furthermore, specific functional analysis indicates that thymosin-β4 may have a role in chondrosarcoma metastasis

  17. Microenvironment alters epigenetic and gene expression profiles in Swarm rat chondrosarcoma tumors

    Directory of Open Access Journals (Sweden)

    Hamm Christopher A

    2010-09-01

    Full Text Available Abstract Background Chondrosarcomas are malignant cartilage tumors that do not respond to traditional chemotherapy or radiation. The 5-year survival rate of histologic grade III chondrosarcoma is less than 30%. An animal model of chondrosarcoma has been established - namely, the Swarm Rat Chondrosarcoma (SRC - and shown to resemble the human disease. Previous studies with this model revealed that tumor microenvironment could significantly influence chondrosarcoma malignancy. Methods To examine the effect of the microenvironment, SRC tumors were initiated at different transplantation sites. Pyrosequencing assays were utilized to assess the DNA methylation of the tumors, and SAGE libraries were constructed and sequenced to determine the gene expression profiles of the tumors. Based on the gene expression analysis, subsequent functional assays were designed to determine the relevancy of the specific genes in the development and progression of the SRC. Results The site of transplantation had a significant impact on the epigenetic and gene expression profiles of SRC tumors. Our analyses revealed that SRC tumors were hypomethylated compared to control tissue, and that tumors at each transplantation site had a unique expression profile. Subsequent functional analysis of differentially expressed genes, albeit preliminary, provided some insight into the role that thymosin-β4, c-fos, and CTGF may play in chondrosarcoma development and progression. Conclusion This report describes the first global molecular characterization of the SRC model, and it demonstrates that the tumor microenvironment can induce epigenetic alterations and changes in gene expression in the SRC tumors. We documented changes in gene expression that accompany changes in tumor phenotype, and these gene expression changes provide insight into the pathways that may play a role in the development and progression of chondrosarcoma. Furthermore, specific functional analysis indicates that

  18. Global analysis of transcriptome responses and gene expression profiles to cold stress of Jatropha curcas L.

    Directory of Open Access Journals (Sweden)

    Haibo Wang

    Full Text Available BACKGROUND: Jatropha curcas L., also called the Physic nut, is an oil-rich shrub with multiple uses, including biodiesel production, and is currently exploited as a renewable energy resource in many countries. Nevertheless, because of its origin from the tropical MidAmerican zone, J. curcas confers an inherent but undesirable characteristic (low cold resistance that may seriously restrict its large-scale popularization. This adaptive flaw can be genetically improved by elucidating the mechanisms underlying plant tolerance to cold temperatures. The newly developed Illumina Hiseq™ 2000 RNA-seq and Digital Gene Expression (DGE are deep high-throughput approaches for gene expression analysis at the transcriptome level, using which we carefully investigated the gene expression profiles in response to cold stress to gain insight into the molecular mechanisms of cold response in J. curcas. RESULTS: In total, 45,251 unigenes were obtained by assembly of clean data generated by RNA-seq analysis of the J. curcas transcriptome. A total of 33,363 and 912 complete or partial coding sequences (CDSs were determined by protein database alignments and ESTScan prediction, respectively. Among these unigenes, more than 41.52% were involved in approximately 128 known metabolic or signaling pathways, and 4,185 were possibly associated with cold resistance. DGE analysis was used to assess the changes in gene expression when exposed to cold condition (12°C for 12, 24, and 48 h. The results showed that 3,178 genes were significantly upregulated and 1,244 were downregulated under cold stress. These genes were then functionally annotated based on the transcriptome data from RNA-seq analysis. CONCLUSIONS: This study provides a global view of transcriptome response and gene expression profiling of J. curcas in response to cold stress. The results can help improve our current understanding of the mechanisms underlying plant cold resistance and favor the screening of

  19. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning.

    Science.gov (United States)

    He, Zhili; Zhang, Ping; Wu, Linwei; Rocha, Andrea M; Tu, Qichao; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D; Wu, Liyou; Yang, Yunfeng; Elias, Dwayne A; Watson, David B; Adams, Michael W W; Fields, Matthew W; Alm, Eric J; Hazen, Terry C; Adams, Paul D; Arkin, Adam P; Zhou, Jizhong

    2018-02-20

    Contamination from anthropogenic activities has significantly impacted Earth's biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly ( P contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. IMPORTANCE Disentangling the relationships between biodiversity and ecosystem functioning is an important but poorly understood topic in ecology. Predicting ecosystem functioning on the basis of biodiversity is even more difficult, particularly with microbial biomarkers. As an exploratory effort, this study used key microbial functional genes as biomarkers to provide predictive understanding of environmental contamination and ecosystem functioning. The results indicated that the overall functional gene richness/diversity decreased as uranium increased in groundwater, while specific key microbial guilds increased significantly as

  20. Gene-expression profiling after exposure to C-ion beams

    International Nuclear Information System (INIS)

    Saegusa, Kumiko; Furuno, Aki; Ishikawa, Kenichi; Ishikawa, Atsuko; Ohtsuka, Yoshimi; Kawai, Seiko; Imai, Takashi; Nojima, Kumie

    2005-01-01

    It is recognized that carbon-ion beam kills cancer cells more efficiently than X-ray. In this study we have compared cellular gene expression response after carbon-ion beam exposure with that after X-ray exposure. Gene expression profiles of cultured neonatal human dermal fibroblasts (NHDF) at 0, 1, 3, 6, 12, 18, and 24 hr after exposure to 0.1, 2 and 5 Gy of X-ray or carbon-ion beam were obtained using 22K oligonucleotide microarray. N-way ANOVA analysis of whole gene expression data sets selected 960 genes for carbon-ion beam and 977 genes for X-ray, respectively. Interestingly, majority of these genes (91% for carbon-ion beam and 88% for X-ray, respectively) were down regulated. The selected genes were further classified by their dose-dependence or time-dependence of gene expression change (fold change>1.5). It was revealed that genes involved in cell proliferation had tendency to show time-dependent up regulation by carbon-ion beam. Another N-way ANOVA analysis was performed to select 510 genes, and further selection was made to find 70 genes that showed radiation species-dependent gene expression change (fold change>1.25). These genes were then categorized by the K-Mean clustering method into 4 clusters. Each cluster showed tendency to contain genes involved in cell cycle regulation, cell death, responses to stress and metabolisms, respectively. (author)

  1. Radiation-associated breast tumors display a distinct gene expression profile

    DEFF Research Database (Denmark)

    Broeks, Annegien; Braaf, Linde M; Wessels, Lodewyk F A

    2010-01-01

    PURPOSE: Women who received irradiation for Hodgkin's lymphoma have a strong increased risk for developing breast cancer. Approximately 90% of the breast cancers in these patients can be attributed to their radiation treatment, rendering such series extremely useful to determine whether a common...... radiation-associated cause underlies the carcinogenic process. METHODS AND MATERIALS: In this study we used gene expression profiling technology to assess gene expression changes in radiation-associated breast tumors compared with a set of control breast tumors of women unexposed to radiation, diagnosed...... at the same age. RNA was obtained from fresh frozen tissue samples from 22 patients who developed breast cancer after Hodgkin's lymphoma (BfHL) and from 20 control breast tumors. RESULTS: Unsupervised hierarchical clustering of the profile data resulted in a clustering of the radiation-associated tumors...

  2. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain.

    Science.gov (United States)

    Krienen, Fenna M; Yeo, B T Thomas; Ge, Tian; Buckner, Randy L; Sherwood, Chet C

    2016-01-26

    The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.

  3. Microarray-based cancer prediction using soft computing approach.

    Science.gov (United States)

    Wang, Xiaosheng; Gotoh, Osamu

    2009-05-26

    One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

  4. Ageing Drosophila selected for longevity retain a young gene expression profile

    DEFF Research Database (Denmark)

    Sarup, Pernille Merete

    and longevity selected lines. Among the latter genes we found a clear overrepresentation of genes involved in immune functions supporting the hypothesis of the life shortening effect of an overactive immune system (inflammaging). Eighty-four genes were differentially expressed at the same physiological age...... between control and longevity selected lines, and the overlap between the same chronological and physiological age gene lists counted 40 candidate genes for increased longevity. Among these were genes with functions in starvation resistance, a regulator of immune responses and several genes which have......  We have investigated how the gene-expression profile of longevity selected lines of Drosophila melanogaster differed from control lines in young, middle-aged and old male flies. 530 genes were differentially expressed between selected and control flies at the same chronological age. We used...

  5. Common and rare variants in the exons and regulatory regions of osteoporosis-related genes improve osteoporotic fracture risk prediction.

    Science.gov (United States)

    Lee, Seung Hun; Kang, Moo Il; Ahn, Seong Hee; Lim, Kyeong-Hye; Lee, Gun Eui; Shin, Eun-Soon; Lee, Jong-Eun; Kim, Beom-Jun; Cho, Eun-Hee; Kim, Sang-Wook; Kim, Tae-Ho; Kim, Hyun-Ju; Yoon, Kun-Ho; Lee, Won Chul; Kim, Ghi Su; Koh, Jung-Min; Kim, Shin-Yoon

    2014-11-01

    Osteoporotic fracture risk is highly heritable, but genome-wide association studies have explained only a small proportion of the heritability to date. Genetic data may improve prediction of fracture risk in osteopenic subjects and assist early intervention and management. To detect common and rare variants in coding and regulatory regions related to osteoporosis-related traits, and to investigate whether genetic profiling improves the prediction of fracture risk. This cross-sectional study was conducted in three clinical units in Korea. Postmenopausal women with extreme phenotypes (n = 982) were used for the discovery set, and 3895 participants were used for the replication set. We performed targeted resequencing of 198 genes. Genetic risk scores from common variants (GRS-C) and from common and rare variants (GRS-T) were calculated. Nineteen common variants in 17 genes (of the discovered 34 functional variants in 26 genes) and 31 rare variants in five genes (of the discovered 87 functional variants in 15 genes) were associated with one or more osteoporosis-related traits. Accuracy of fracture risk classification was improved in the osteopenic patients by adding GRS-C to fracture risk assessment models (6.8%; P risk in an osteopenic individual.

  6. Ensemble attribute profile clustering: discovering and characterizing groups of genes with similar patterns of biological features

    Directory of Open Access Journals (Sweden)

    Bissell MJ

    2006-03-01

    Full Text Available Abstract Background Ensemble attribute profile clustering is a novel, text-based strategy for analyzing a user-defined list of genes and/or proteins. The strategy exploits annotation data present in gene-centered corpora and utilizes ideas from statistical information retrieval to discover and characterize properties shared by subsets of the list. The practical utility of this method is demonstrated by employing it in a retrospective study of two non-overlapping sets of genes defined by a published investigation as markers for normal human breast luminal epithelial cells and myoepithelial cells. Results Each genetic locus was characterized using a finite set of biological properties and represented as a vector of features indicating attributes associated with the locus (a gene attribute profile. In this study, the vector space models for a pre-defined list of genes were constructed from the Gene Ontology (GO terms and the Conserved Domain Database (CDD protein domain terms assigned to the loci by the gene-centered corpus LocusLink. This data set of GO- and CDD-based gene attribute profiles, vectors of binary random variables, was used to estimate multiple finite mixture models and each ensuing model utilized to partition the profiles into clusters. The resultant partitionings were combined using a unanimous voting scheme to produce consensus clusters, sets of profiles that co-occured consistently in the same cluster. Attributes that were important in defining the genes assigned to a consensus cluster were identified. The clusters and their attributes were inspected to ascertain the GO and CDD terms most associated with subsets of genes and in conjunction with external knowledge such as chromosomal location, used to gain functional insights into human breast biology. The 52 luminal epithelial cell markers and 89 myoepithelial cell markers are disjoint sets of genes. Ensemble attribute profile clustering-based analysis indicated that both lists

  7. Can survival prediction be improved by merging gene expression data sets?

    Directory of Open Access Journals (Sweden)

    Haleh Yasrebi

    Full Text Available BACKGROUND: High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS: Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS: Merging did not deteriorate performance on average despite (a The diversity of microarray platforms used. (b The heterogeneity of patients cohorts. (c The heterogeneity of breast cancer disease. (d Substantial variation of time to death or relapse. (e The reduced number of genes in the merged data

  8. Fetal mesenchymal stromal cells differentiating towards chondrocytes acquire a gene expression profile resembling human growth plate cartilage.

    Directory of Open Access Journals (Sweden)

    Sandy A van Gool

    Full Text Available We used human fetal bone marrow-derived mesenchymal stromal cells (hfMSCs differentiating towards chondrocytes as an alternative model for the human growth plate (GP. Our aims were to study gene expression patterns associated with chondrogenic differentiation to assess whether chondrocytes derived from hfMSCs are a suitable model for studying the development and maturation of the GP. hfMSCs efficiently formed hyaline cartilage in a pellet culture in the presence of TGFβ3 and BMP6. Microarray and principal component analysis were applied to study gene expression profiles during chondrogenic differentiation. A set of 232 genes was found to correlate with in vitro cartilage formation. Several identified genes are known to be involved in cartilage formation and validate the robustness of the differentiating hfMSC model. KEGG pathway analysis using the 232 genes revealed 9 significant signaling pathways correlated with cartilage formation. To determine the progression of growth plate cartilage formation, we compared the gene expression profile of differentiating hfMSCs with previously established expression profiles of epiphyseal GP cartilage. As differentiation towards chondrocytes proceeds, hfMSCs gradually obtain a gene expression profile resembling epiphyseal GP cartilage. We visualized the differences in gene expression profiles as protein interaction clusters and identified many protein clusters that are activated during the early chondrogenic differentiation of hfMSCs showing the potential of this system to study GP development.

  9. Probability-based collaborative filtering model for predicting gene-disease associations.

    Science.gov (United States)

    Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

    2017-12-28

    Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

  10. Global gene expression profiling in PAI-1 knockout murine heart and kidney: molecular basis of cardiac-selective fibrosis.

    Directory of Open Access Journals (Sweden)

    Asish K Ghosh

    Full Text Available Fibrosis is defined as an abnormal matrix remodeling due to excessive synthesis and accumulation of extracellular matrix proteins in tissues during wound healing or in response to chemical, mechanical and immunological stresses. At present, there is no effective therapy for organ fibrosis. Previous studies demonstrated that aged plasminogen activator inhibitor-1 (PAI-1 knockout mice develop spontaneously cardiac-selective fibrosis without affecting any other organs. We hypothesized that differential expressions of profibrotic and antifibrotic genes in PAI-1 knockout hearts and unaffected organs lead to cardiac selective fibrosis. In order to address this prediction, we have used a genome-wide gene expression profiling of transcripts derived from aged PAI-1 knockout hearts and kidneys. The variations of global gene expression profiling were compared within four groups: wildtype heart vs. knockout heart; wildtype kidney vs. knockout kidney; knockout heart vs. knockout kidney and wildtype heart vs. wildtype kidney. Analysis of illumina-based microarray data revealed that several genes involved in different biological processes such as immune system processing, response to stress, cytokine signaling, cell proliferation, adhesion, migration, matrix organization and transcriptional regulation were affected in hearts and kidneys by the absence of PAI-1, a potent inhibitor of urokinase and tissue-type plasminogen activator. Importantly, the expressions of a number of genes, involved in profibrotic pathways including Ankrd1, Pi16, Egr1, Scx, Timp1, Timp2, Klf6, Loxl1 and Klotho, were deregulated in PAI-1 knockout hearts compared to wildtype hearts and PAI-1 knockout kidneys. While the levels of Ankrd1, Pi16 and Timp1 proteins were elevated during EndMT, the level of Timp4 protein was decreased. To our knowledge, this is the first comprehensive report on the influence of PAI-1 on global gene expression profiling in the heart and kidney and its implication

  11. Exposure of Lactating Dairy Cows to Acute Pre-Ovulatory Heat Stress Affects Granulosa Cell-Specific Gene Expression Profiles in Dominant Follicles

    Science.gov (United States)

    Vanselow, Jens; Vernunft, Andreas; Koczan, Dirk; Spitschak, Marion; Kuhla, Björn

    2016-01-01

    High environmental temperatures induce detrimental effects on various reproductive processes in cattle. According to the predicted global warming the number of days with unfavorable ambient temperatures will further increase. The objective of this study was to investigate effects of acute heat stress during the late pre-ovulatory phase on morphological, physiological and molecular parameters of dominant follicles in cycling cows during lactation. Eight German Holstein cows in established lactation were exposed to heat stress (28°C) or thermoneutral conditions (15°C) with pair-feeding for four days. After hormonal heat induction growth of the respective dominant follicles was monitored by ultrasonography for two days, then an ovulatory GnRH dose was given and follicular steroid hormones and granulosa cell-specific gene expression profiles were determined 23 hrs thereafter. The data showed that the pre-ovulatory growth of dominant follicles and the estradiol, but not the progesterone concentrations tended to be slightly affected. mRNA microarray and hierarchical cluster analysis revealed distinct expression profiles in granulosa cells derived from heat stressed compared to pair-fed animals. Among the 255 affected genes heatstress-, stress- or apoptosis associated genes were not present. But instead, we found up-regulation of genes essentially involved in G-protein coupled signaling pathways, extracellular matrix composition, and several members of the solute carrier family as well as up-regulation of FST encoding follistatin. In summary, the data of the present study show that acute pre-ovulatory heat stress can specifically alter gene expression profiles in granulosa cells, however without inducing stress related genes and pathways and suggestively can impair follicular growth due to affecting the activin-inhibin-follistatin system. PMID:27532452

  12. Gene expression profiles of fin regeneration in loach (Paramisgurnus dabryanu).

    Science.gov (United States)

    Li, Li; He, Jingya; Wang, Linlin; Chen, Weihua; Chang, Zhongjie

    2017-11-01

    Teleost fins can regenerate accurate position-matched structure and function after amputation. However, we still lack systematic transcriptional profiling and methodologies to understand the molecular basis of fin regeneration. After histological analysis, we established a suppression subtraction hybridization library containing 418 distinct sequences expressed differentially during the process of blastema formation and differentiation in caudal fin regeneration. Genome ontology and comparative analysis of differential distribution of our data and the reference zebrafish genome showed notable subcategories, including multi-organism processes, response to stimuli, extracellular matrix, antioxidant activity, and cell junction function. KEGG pathway analysis allowed the effective identification of relevant genes in those pathways involved in tissue morphogenesis and regeneration, including tight junction, cell adhesion molecules, mTOR and Jak-STAT signaling pathway. From relevant function subcategories and signaling pathways, 78 clones were examined for further Southern-blot hybridization. Then, 17 genes were chosen and characterized using semi-quantitative PCR. Then 4 candidate genes were identified, including F11r, Mmp9, Agr2 and one without a match to any database. After real-time quantitative PCR, the results showed obvious expression changes in different periods of caudal fin regeneration. We can assume that the 4 candidates, likely valuable genes associated with fin regeneration, deserve additional attention. Thus, our study demonstrated how to investigate the transcript profiles with an emphasis on bioinformatics intervention and how to identify potential genes related to fin regeneration processes. The results also provide a foundation or knowledge for further research into genes and molecular mechanisms of fin regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Altered gene-expression profile in rat plasma and promoted body ...

    African Journals Online (AJOL)

    Altered gene-expression profile in rat plasma and promoted body and brain development ... The study was aimed to explore how the prenatal EE impacts affect the ... positively promote the body and nervous system development of offspring, ...

  14. Expression profiles of sugarcane under drought conditions: Variation in gene regulation

    Directory of Open Access Journals (Sweden)

    Júlio César Farias de Andrade

    2015-01-01

    Full Text Available AbstractDrought is a major factor in decreased sugarcane productivity because of the resulting morphophysiological effects that it causes. Gene expression studies that have examined the influence of water stress in sugarcane have yielded divergent results, indicating the absence of a fixed pattern of changes in gene expression. In this work, we investigated the expression profiles of 12 genes in the leaves of a drought-tolerant genotype (RB72910 of sugarcane and compared the results with those of other studies. The genotype was subjected to 80–100% water availability (control condition and 0–20% water availability (simulated drought. To analyze the physiological status, the SPAD index, Fv/Fm ratio, net photosynthesis (A, stomatal conductance (gs and stomatal transpiration (E were measured. Total RNA was extracted from leaves and the expression of SAMDC, ZmPIP2-1 protein, ZmTIP4-2 protein, WIP protein, LTP protein, histone H3, DNAj, ferredoxin I, β-tubulin, photosystem I, gene 1 and gene 2 was analyzed by quantitative real-time PCR (RT-PCR. Important differences in the expression profiles of these genes were observed when compared with other genotypes, suggesting that complex defense mechanisms are activated in response to water stress. However, there was no recognizable pattern for the changes in expression of the different proteins associated with tolerance to drought stress.

  15. The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

    Science.gov (United States)

    Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

    2014-06-01

    With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

  16. Identifying arsenic trioxide (ATO) functions in leukemia cells by using time series gene expression profiles.

    Science.gov (United States)

    Yang, Hong; Lin, Shan; Cui, Jingru

    2014-02-10

    Arsenic trioxide (ATO) is presently the most active single agent in the treatment of acute promyelocytic leukemia (APL). In order to explore the molecular mechanism of ATO in leukemia cells with time series, we adopted bioinformatics strategy to analyze expression changing patterns and changes in transcription regulation modules of time series genes filtered from Gene Expression Omnibus database (GSE24946). We totally screened out 1847 time series genes for subsequent analysis. The KEGG (Kyoto encyclopedia of genes and genomes) pathways enrichment analysis of these genes showed that oxidative phosphorylation and ribosome were the top 2 significantly enriched pathways. STEM software was employed to compare changing patterns of gene expression with assigned 50 expression patterns. We screened out 7 significantly enriched patterns and 4 tendency charts of time series genes. The result of Gene Ontology showed that functions of times series genes mainly distributed in profiles 41, 40, 39 and 38. Seven genes with positive regulation of cell adhesion function were enriched in profile 40, and presented the same first increased model then decreased model as profile 40. The transcription module analysis showed that they mainly involved in oxidative phosphorylation pathway and ribosome pathway. Overall, our data summarized the gene expression changes in ATO treated K562-r cell lines with time and suggested that time series genes mainly regulated cell adhesive. Furthermore, our result may provide theoretical basis of molecular biology in treating acute promyelocytic leukemia. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

    Science.gov (United States)

    Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

    2014-01-01

    Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

  18. Mining a database of single amplified genomes from Red Sea brine pool extremophiles – Improving reliability of gene function prediction using a profile and pattern matching algorithm (PPMA

    Directory of Open Access Journals (Sweden)

    Stefan Wolfgang Grötzinger

    2014-04-01

    Full Text Available Reliable functional annotation of genomic data is the key-step in the discovery of novel enzymes. Intrinsic sequencing data quality problems of single amplified genomes (SAGs and poor homology of novel extremophile’s genomes pose significant challenges for the attribution of functions to the coding sequences identified. The anoxic deep-sea brine pools of the Red Sea are a promising source of novel enzymes with unique evolutionary adaptation. Sequencing data from Red Sea brine pool cultures and SAGs are annotated and stored in the INDIGO data warehouse. Low sequence homology of annotated genes (no similarity for 35% of these genes may translate into false positives when searching for specific functions. The Profile & Pattern Matching (PPM strategy described here was developed to eliminate false positive annotations of enzyme function before progressing to labor-intensive hyper-saline gene expression and characterization. It utilizes InterPro-derived Gene Ontology (GO-terms (which represent enzyme function profiles and annotated relevant PROSITE IDs (which are linked to an amino acid consensus pattern. The PPM algorithm was tested on 15 protein families, which were selected based on scientific and commercial potential. An initial list of 2,577 E.C. numbers was translated into 171 GO-terms and 49 consensus patterns. A subset of INDIGO-sequences consisting of 58 SAGs from six different taxons of bacteria and archaea were selected from 6 different brine pool environments. Those SAGs code for 74,516 genes, which were independently scanned for the GO-terms (profile filter and PROSITE IDs (pattern filter. Following stringent reliability filtering, the non-redundant hits (106 profile hits and 147 pattern hits are classified as reliable, if at least two relevant descriptors (GO-terms and/or consensus patterns are present. Scripts for annotation, as well as for the PPM algorithm, are available through the INDIGO website.

  19. Minimal gene selection for classification and diagnosis prediction based on gene expression profile

    Directory of Open Access Journals (Sweden)

    Alireza Mehridehnavi

    2013-01-01

    Conclusion: We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.

  20. Predicting Recurrence and Progression of Noninvasive Papillary Bladder Cancer at Initial Presentation Based on Quantitative Gene Expression Profiles

    DEFF Research Database (Denmark)

    Birkhahn, M.; Mitra, A.P.; Williams, Johan

    2010-01-01

    Background: Currently, tumor grade is the best predictor of outcome at first presentation of noninvasive papillary (Ta) bladder cancer. However, reliable predictors of Ta tumor recurrence and progression for individual patients, which could optimize treatment and follow-up schedules based...... on specific tumor biology, are yet to be identified. Objective: To identify genes predictive for recurrence and progression in Ta bladder cancer at first presentation using a quantitative, pathway-specific approach. Design, setting, and participants: Retrospective study of patients with Ta G2/3 bladder tumors...... at initial presentation with three distinct clinical outcomes: absence of recurrence (n = 16), recurrence without progression (n = 16), and progression to carcinoma in situ or invasive disease (n = 16). Measurements: Expressions of 24 genes that feature in relevant pathways that are deregulated in bladder...

  1. Prediction of highly expressed genes in microbes based on chromatin accessibility

    Directory of Open Access Journals (Sweden)

    Ussery David W

    2007-02-01

    Full Text Available Abstract Background It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes. Results We find that absolute gene expression levels are correlated with the position preference in many microbial genomes. It is postulated that in these regions, the DNA may be more accessible to the transcriptional machinery. Moreover, ribosomal proteins and ribosomal RNA are encoded by DNA having significantly lower position preference values than other genes in fast-replicating microbes. Conclusion This insight into DNA structure-dependent gene expression in microbes may be exploited for predicting the expression of non-translated genes such as non-coding RNAs that may not be predicted by any of the conventional codon usage bias approaches.

  2. Semi-supervised prediction of gene regulatory networks using ...

    Indian Academy of Sciences (India)

    2015-09-28

    Sep 28, 2015 ... Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging ... two types of methods differ primarily based on whether ..... negligible, allowing us to draw the qualitative conclusions .... research will be conducted to develop additional biologically.

  3. Identification and Expression Profiling of Chemosensory Genes in Dendrolimus punctatus Walker

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    Su-fang Zhang

    2017-07-01

    Full Text Available Dendrolimus punctatus Walker is a serious pest affecting conifers in southern China. As extensive pesticide spraying is currently required to control D. punctatus, new control strategies are urgently needed. Chemosensory genes represent potential molecular targets for development of alternative pest control strategies, and the expression characteristics of these genes provide an indication of their function. To date, little information is available regarding chemosensory genes in D. punctatus or their expression profiles at different development stages and in various tissues. Here, we assembled and analyzed the transcriptomes of D. punctatus collected at different developmental stages and in a range of organs, using next-generation sequencing. A total of 171 putative chemosensory genes were identified, encoding 53 odorant binding proteins, 26 chemosensory proteins, 60 odorant receptors (OR, 12 gustatory receptors (GR, 18 ionotropic receptors (IR, and 2 sensory neuron membrane proteins (SNMPs. Expression analysis indicated that the antennae possess the largest number of highly expressed olfactory genes and that olfactory gene expression patterns in the eggs, larvae, and head were similar to one another, with each having moderate numbers of highly expressed olfactory genes. Fat body, ovary, midgut, and testis tissues also had similar olfactory gene expression patterns, including few highly expressed olfactory genes. Of particular note, we identified only two pheromone binding proteins and no pheromone receptors in D. punctatus, similar to our previous findings in Dendrolimus houi and Dendrolimus kikuchii, suggesting that insects of the Dendrolimus genus have different pheromone recognition characteristics to other Lepidopteran insects. Overall, this extensive expression profile analysis provides a clear map of D. punctatus chemosensory genes, and will facilitate functional studies and the development of new pest control methods in the future.

  4. Hormone-replacement therapy influences gene expression profiles and is associated with breast-cancer prognosis: a cohort study

    Directory of Open Access Journals (Sweden)

    Skoog Lambert

    2006-06-01

    Full Text Available Abstract Background Postmenopausal hormone-replacement therapy (HRT increases breast-cancer risk. The influence of HRT on the biology of the primary tumor, however, is not well understood. Methods We obtained breast-cancer gene expression profiles using Affymetrix human genome U133A arrays. We examined the relationship between HRT-regulated gene profiles, tumor characteristics, and recurrence-free survival in 72 postmenopausal women. Results HRT use in patients with estrogen receptor (ER protein positive tumors (n = 72 was associated with an altered regulation of 276 genes. Expression profiles based on these genes clustered ER-positive tumors into two molecular subclasses, one of which was associated with HRT use and had significantly better recurrence free survival despite lower ER levels. A comparison with external data suggested that gene regulation in tumors associated with HRT was negatively correlated with gene regulation induced by short-term estrogen exposure, but positively correlated with the effect of tamoxifen. Conclusion Our findings suggest that post-menopausal HRT use is associated with a distinct gene expression profile related to better recurrence-free survival and lower ER protein levels. Tentatively, HRT-associated gene expression in tumors resembles the effect of tamoxifen exposure on MCF-7 cells.

  5. Integrating circadian activity and gene expression profiles to predict chronotoxicity of Drosophila suzukii response to insecticides.

    Science.gov (United States)

    Hamby, Kelly A; Kwok, Rosanna S; Zalom, Frank G; Chiu, Joanna C

    2013-01-01

    Native to Southeast Asia, Drosophila suzukii (Matsumura) is a recent invader that infests intact ripe and ripening fruit, leading to significant crop losses in the U.S., Canada, and Europe. Since current D. suzukii management strategies rely heavily on insecticide usage and insecticide detoxification gene expression is under circadian regulation in the closely related Drosophila melanogaster, we set out to determine if integrative analysis of daily activity patterns and detoxification gene expression can predict chronotoxicity of D. suzukii to insecticides. Locomotor assays were performed under conditions that approximate a typical summer or winter day in Watsonville, California, where D. suzukii was first detected in North America. As expected, daily activity patterns of D. suzukii appeared quite different between 'summer' and 'winter' conditions due to differences in photoperiod and temperature. In the 'summer', D. suzukii assumed a more bimodal activity pattern, with maximum activity occurring at dawn and dusk. In the 'winter', activity was unimodal and restricted to the warmest part of the circadian cycle. Expression analysis of six detoxification genes and acute contact bioassays were performed at multiple circadian times, but only in conditions approximating Watsonville summer, the cropping season, when most insecticide applications occur. Five of the genes tested exhibited rhythmic expression, with the majority showing peak expression at dawn (ZT0, 6am). We observed significant differences in the chronotoxicity of D. suzukii towards malathion, with highest susceptibility at ZT0 (6am), corresponding to peak expression of cytochrome P450s that may be involved in bioactivation of malathion. High activity levels were not found to correlate with high insecticide susceptibility as initially hypothesized. Chronobiology and chronotoxicity of D. suzukii provide valuable insights for monitoring and control efforts, because insect activity as well as insecticide timing

  6. Predicting Low Energy Dopant Implant Profiles in Semiconductors using Molecular Dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Beardmore, K.M.; Gronbech-Jensen, N.

    1999-05-02

    The authors present a highly efficient molecular dynamics scheme for calculating dopant density profiles in group-IV alloy, and III-V zinc blende structure materials. Their scheme incorporates several necessary methods for reducing computational overhead, plus a rare event algorithm to give statistical accuracy over several orders of magnitude change in the dopant concentration. The code uses a molecular dynamics (MD) model to describe ion-target interactions. Atomic interactions are described by a combination of 'many-body' and pair specific screened Coulomb potentials. Accumulative damage is accounted for using a Kinchin-Pease type model, inelastic energy loss is represented by a Firsov expression, and electronic stopping is described by a modified Brandt-Kitagawa model which contains a single adjustable ion-target dependent parameter. Thus, the program is easily extensible beyond a given validation range, and is therefore truly predictive over a wide range of implant energies and angles. The scheme is especially suited for calculating profiles due to low energy and to situations where a predictive capability is required with the minimum of experimental validation. They give examples of using the code to calculate concentration profiles and 2D 'point response' profiles of dopants in crystalline silicon and gallium-arsenide. Here they can predict the experimental profile over five orders of magnitude for <100> and <110> channeling and for non-channeling implants at energies up to hundreds of keV.

  7. Identification of specific gene expression profiles in fibroblasts derived from middle ear cholesteatoma.

    Science.gov (United States)

    Yoshikawa, Mamoru; Kojima, Hiromi; Wada, Kota; Tsukidate, Toshiharu; Okada, Naoko; Saito, Hirohisa; Moriyama, Hiroshi

    2006-07-01

    To investigate the role of fibroblasts in the pathogenesis of cholesteatoma. Tissue specimens were obtained from our patients. Middle ear cholesteatoma-derived fibroblasts (MECFs) and postauricular skin-derived fibroblasts (SFs) as controls were then cultured for a few weeks. These fibroblasts were stimulated with interleukin (IL) 1alpha and/or IL-1beta before gene expression assays. We used the human genome U133A probe array (GeneChip) and real-time polymerase chain reaction to examine and compare the gene expression profiles of the MECFs and SFs. Six patients who had undergone tympanoplasty. The IL-1alpha-regulated genes were classified into 4 distinct clusters on the basis of profiles differentially regulated by SF and MECF using a hierarchical clustering analysis. The messenger RNA expressions of LARC (liver and activation-regulated chemokine), GMCSF (granulocyte-macrophage colony-stimulating factor), epiregulin, ICAM1 (intercellular adhesion molecule 1), and TGFA (transforming growth factor alpha) were more strongly up-regulated by IL-1alpha and/or IL-1beta in MECF than in SF, suggesting that these fibroblasts derived from different tissues retained their typical gene expression profiles. Fibroblasts may play a role in hyperkeratosis of middle ear cholesteatoma by releasing molecules involved in inflammation and epidermal growth. These fibroblasts may retain tissue-specific characteristics presumably controlled by epigenetic mechanisms.

  8. Gene expression profiles reveal key pathways and genes associated with neuropathic pain in patients with spinal cord injury.

    Science.gov (United States)

    He, Xijing; Fan, Liying; Wu, Zhongheng; He, Jiaxuan; Cheng, Bin

    2017-04-01

    Previous gene expression profiling studies of neuropathic pain (NP) following spinal cord injury (SCI) have predominantly been performed in animal models. The present study aimed to investigate gene alterations in patients with spinal cord injury and to further examine the mechanisms underlying NP following SCI. The GSE69901 gene expression profile was downloaded from the public Gene Expression Omnibus database. Samples of peripheral blood mononuclear cells (PBMCs) derived from 12 patients with intractable NP and 13 control patients without pain were analyzed to identify the differentially expressed genes (DEGs), followed by functional enrichment analysis and protein‑protein interaction (PPI) network construction. In addition, a transcriptional regulation network was constructed and functional gene clustering was performed. A total of 70 upregulated and 61 downregulated DEGs were identified in the PBMC samples from patients with NP. The upregulated and downregulated genes were significantly involved in different Gene Ontology terms and pathways, including focal adhesion, T cell receptor signaling pathway and mitochondrial function. Glycogen synthase kinase 3 β (GSK3B) was identified as a hub protein in the PPI network. In addition, ornithine decarboxylase 1 (ODC1) and ornithine aminotransferase (OAT) were regulated by additional transcription factors in the regulation network. GSK3B, OAT and ODC1 were significantly enriched in two functional gene clusters, the function of mitochondrial membrane and DNA binding. Focal adhesion and the T cell receptor signaling pathway may be significantly linked with NP, and GSK3B, OAT and ODC1 may be potential targets for the treatment of NP.

  9. Predicting response to primary chemotherapy: gene expression profiling of paraffin-embedded core biopsy tissue.

    Science.gov (United States)

    Mina, Lida; Soule, Sharon E; Badve, Sunil; Baehner, Fredrick L; Baker, Joffre; Cronin, Maureen; Watson, Drew; Liu, Mei-Lan; Sledge, George W; Shak, Steve; Miller, Kathy D

    2007-06-01

    Primary chemotherapy provides an ideal opportunity to correlate gene expression with response to treatment. We used paraffin-embedded core biopsies from a completed phase II trial to identify genes that correlate with response to primary chemotherapy. Patients with newly diagnosed stage II or III breast cancer were treated with sequential doxorubicin 75 mg/M2 q2 wks x 3 and docetaxel 40 mg/M2 weekly x 6; treatment order was randomly assigned. Pretreatment core biopsy samples were interrogated for genes that might correlate with pathologic complete response (pCR). In addition to the individual genes, the correlation of the Oncotype DX Recurrence Score with pCR was examined. Of 70 patients enrolled in the parent trial, core biopsies samples with sufficient RNA for gene analyses were available from 45 patients; 9 (20%) had inflammatory breast cancer (IBC). Six (14%) patients achieved a pCR. Twenty-two of the 274 candidate genes assessed correlated with pCR (p < 0.05). Genes correlating with pCR could be grouped into three large clusters: angiogenesis-related genes, proliferation related genes, and invasion-related genes. Expression of estrogen receptor (ER)-related genes and Recurrence Score did not correlate with pCR. In an exploratory analysis we compared gene expression in IBC to non-inflammatory breast cancer; twenty-four (9%) of the genes were differentially expressed (p < 0.05), 5 were upregulated and 19 were downregulated in IBC. Gene expression analysis on core biopsy samples is feasible and identifies candidate genes that correlate with pCR to primary chemotherapy. Gene expression in IBC differs significantly from noninflammatory breast cancer.

  10. Subacute effects of hexabromocyclododecane (HBCD) on hepatic gene expression profiles in rats

    International Nuclear Information System (INIS)

    Canton, Rocio F.; Peijnenburg, Ad A.C.M.; Hoogenboom, Ron L.A.P.; Piersma, Aldert H.; Ven, Leo T.M. van der; Berg, Martin van den; Heneweer, Marjoke

    2008-01-01

    Hexabromoyclododecane (HBCD), used as flame retardant (FR) mainly in textile industry and in polystyrene foam manufacture, has been identified as a contaminant at levels comparable to other brominated FRs (BFRs). HBCD levels in biota are increasing slowly and seem to reflect the local market demand. The toxicological database of HBCD is too limited to perform at present a solid risk assessment, combining data from exposure and effect studies. In order to fill in some gaps, a 28-day HBCD repeated dose study (OECD407) was done in Wistar rats. In the present work liver tissues from these animals were used for gene expression profile analysis. Results show clear gender specificity with females having a higher number of regulated genes and therefore being more sensitive to HBCD than males. Several specific pathways were found to be affected by HBCD exposure, like PPAR-mediated regulation of lipid metabolism, triacylglycerol metabolism, cholesterol biosynthesis, and phase I and II pathways. These results were corroborated with quantitative RT-PCR analysis. Cholesterol biosynthesis and lipid metabolism were especially down-regulated in females. Genes involved in phase I and II metabolism were up-regulated predominantly in males, which could explain the observed lower HBCD hepatic disposition in male rats in this 28-day study. These sex-specific differences in gene expression profiles could also underlie sex-specific differences in toxicity (e.g. decreased thyroid hormone or increased serum cholesterol levels). To our knowledge, this is the fist study that describes the changes in rat hepatic gene profiles caused by this commonly used flame retardant

  11. Global gene expression profile progression in Gaucher disease mouse models

    Directory of Open Access Journals (Sweden)

    Zhang Wujuan

    2011-01-01

    Full Text Available Abstract Background Gaucher disease is caused by defective glucocerebrosidase activity and the consequent accumulation of glucosylceramide. The pathogenic pathways resulting from lipid laden macrophages (Gaucher cells in visceral organs and their abnormal functions are obscure. Results To elucidate this pathogenic pathway, developmental global gene expression analyses were conducted in distinct Gba1 point-mutated mice (V394L/V394L and D409 V/null. About 0.9 to 3% of genes had altered expression patterns (≥ ± 1.8 fold change, representing several categories, but particularly macrophage activation and immune response genes. Time course analyses (12 to 28 wk of INFγ-regulated pro-inflammatory (13 and IL-4-regulated anti-inflammatory (11 cytokine/mediator networks showed tissue differential profiles in the lung and liver of the Gba1 mutant mice, implying that the lipid-storage macrophages were not functionally inert. The time course alterations of the INFγ and IL-4 pathways were similar, but varied in degree in these tissues and with the Gba1 mutation. Conclusions Biochemical and pathological analyses demonstrated direct relationships between the degree of tissue glucosylceramides and the gene expression profile alterations. These analyses implicate IFNγ-regulated pro-inflammatory and IL-4-regulated anti-inflammatory networks in differential disease progression with implications for understanding the Gaucher disease course and pathophysiology.

  12. Automatic selection of reference taxa for protein-protein interaction prediction with phylogenetic profiling

    DEFF Research Database (Denmark)

    Simonsen, Martin; Maetschke, S.R.; Ragan, M.A.

    2012-01-01

    Motivation: Phylogenetic profiling methods can achieve good accuracy in predicting protein–protein interactions, especially in prokaryotes. Recent studies have shown that the choice of reference taxa (RT) is critical for accurate prediction, but with more than 2500 fully sequenced taxa publicly......: We present three novel methods for automating the selection of RT, using machine learning based on known protein–protein interaction networks. One of these methods in particular, Tree-Based Search, yields greatly improved prediction accuracies. We further show that different methods for constituting...... phylogenetic profiles often require very different RT sets to support high prediction accuracy....

  13. Identification of rat genes by TWINSCAN gene prediction, RT-PCR, and direct sequencing

    DEFF Research Database (Denmark)

    Wu, Jia Qian; Shteynberg, David; Arumugam, Manimozhiyan

    2004-01-01

    an alternative approach: reverse transcription-polymerase chain reaction (RT-PCR) and direct sequencing based on dual-genome de novo predictions from TWINSCAN. We tested 444 TWINSCAN-predicted rat genes that showed significant homology to known human genes implicated in disease but that were partially...... in the single-intron experiment. Spliced sequences were amplified in 46 cases (34%). We conclude that this procedure for elucidating gene structures with native cDNA sequences is cost-effective and will become even more so as it is further optimized.......The publication of a draft sequence of a third mammalian genome--that of the rat--suggests a need to rethink genome annotation. New mammalian sequences will not receive the kind of labor-intensive annotation efforts that are currently being devoted to human. In this paper, we demonstrate...

  14. Geometrical theory to predict eccentric photorefraction intensity profiles in the human eye

    Science.gov (United States)

    Roorda, Austin; Campbell, Melanie C. W.; Bobier, W. R.

    1995-08-01

    In eccentric photorefraction, light returning from the retina of the eye is photographed by a camera focused on the eye's pupil. We use a geometrical model of eccentric photorefraction to generate intensity profiles across the pupil image. The intensity profiles for three different monochromatic aberration functions induced in a single eye are predicted and show good agreement with the measured eccentric photorefraction intensity profiles. A directional reflection from the retina is incorporated into the calculation. Intensity profiles for symmetric and asymmetric aberrations are generated and measured. The latter profile shows a dependency on the source position and the meridian. The magnitude of the effect of thresholding on measured pattern extents is predicted. Monochromatic aberrations in human eyes will cause deviations in the eccentric photorefraction measurements from traditional crescents caused by defocus and may cause misdiagnoses of ametropia or anisometropia. Our results suggest that measuring refraction along the vertical meridian is preferred for screening studies with the eccentric photorefractor.

  15. Epigenetic Alteration by DNA Methylation of ESR1, MYOD1 and hTERT Gene Promoters is Useful for Prediction of Response in Patients of Locally Advanced Invasive Cervical Carcinoma Treated by Chemoradiation.

    Science.gov (United States)

    Sood, S; Patel, F D; Ghosh, S; Arora, A; Dhaliwal, L K; Srinivasan, R

    2015-12-01

    Locally advanced invasive cervical cancer [International Federation of Gynecology and Obstetrics (FIGO) IIB/III] is treated by chemoradiation. The response to treatment is variable within a given FIGO stage. Therefore, the aim of the present study was to evaluate the gene promoter methylation profile and corresponding transcript expression of a panel of six genes to identify genes which could predict the response of patients treated by chemoradiation. In total, 100 patients with invasive cervical cancer in FIGO stage IIB/III who underwent chemoradiation treatment were evaluated. Ten patients developed systemic metastases during therapy and were excluded. On the basis of patient follow-up, 69 patients were chemoradiation-sensitive, whereas 21 were chemoradiation-resistant. Gene promoter methylation and gene expression was determined by TaqMan assay and quantitative real-time PCR, respectively, in tissue samples. The methylation frequency of ESR1, BRCA1, RASSF1A, MLH1, MYOD1 and hTERT genes ranged from 40 to 70%. Univariate and hierarchical cluster analysis revealed that gene promoter methylation of MYOD1, ESR1 and hTERT could predict for chemoradiation response. A pattern of unmethylated MYOD1, unmethylated ESR1 and methylated hTERT promoter as well as lower ESR1 transcript levels predicted for chemoradiation resistance. Methylation profiling of a panel of three genes that includes MYOD1, ESR1 and hTERT may be useful to predict the response of invasive cervical carcinoma patients treated with standard chemoradiation therapy. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  16. [Differential gene expression profile in ischemic myocardium of Wistar rats with acute myocardial infarction: the study on gene construction, identification and function].

    Science.gov (United States)

    Guo, Chun Yu; Yin, Hui Jun; Jiang, Yue Rong; Xue, Mei; Zhang, Lu; Shi, Da Zhuo

    2008-06-18

    To construct the differential genes expressed profile in the ischemic myocardium tissue reduced from acute myocardial infarction(AMI), and determine the biological functions of target genes. AMI model was generated by ligation of the left anterior descending coronary artery in Wistar rats. Total RNA was extracted from the normal and the ischemic heart tissues under the ligation point 7 days after the operation. Differential gene expression profiles of the two samples were constructed using Long Serial Analysis of Gene Expression(LongSAGE). Real time fluorescence quantitative PCR was used to verify gene expression profile and to identify the expression of 2 functional genes. The activities of enzymes from functional genes were determined by histochemistry. A total of 15,966 tags were screened from the normal and the ischemic LongSAGE maps. The similarities of the sequences were compared using the BLAST algebra in NCBI and 7,665 novel tags were found. In the ischemic tissue 142 genes were significantly changed compared with those in the normal tissue (Ppathways of oxidation and phosphorylation, ATP synthesis and glycolysis. The partial genes identified by LongSAGE were confirmed using real time fluorescence quantitative PCR. Two genes related to energy metabolism, COX5a and ATP5e, were screened and quantified. Expression of two functional genes down-regulated at their mRNA levels and the activities of correlative functional enzymes decreased compared with those in the normal tissue. AMI causes a series of changes in gene expression, in which the abnormal expression of genes related to energy metabolism could be one of the molecular mechanisms of AMI. The intervention of the expressions of COX5a and ATP5e may be a new target for AMI therapy.

  17. Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes.

    Directory of Open Access Journals (Sweden)

    Josefin Skogsberg

    2008-03-01

    Full Text Available Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr(-/-Apo(100/100Mttp(flox/flox Mx1-Cre. Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies.

  18. Network analysis of genomic alteration profiles reveals co-altered functional modules and driver genes for glioblastoma.

    Science.gov (United States)

    Gu, Yunyan; Wang, Hongwei; Qin, Yao; Zhang, Yujing; Zhao, Wenyuan; Qi, Lishuang; Zhang, Yuannv; Wang, Chenguang; Guo, Zheng

    2013-03-01

    The heterogeneity of genetic alterations in human cancer genomes presents a major challenge to advancing our understanding of cancer mechanisms and identifying cancer driver genes. To tackle this heterogeneity problem, many approaches have been proposed to investigate genetic alterations and predict driver genes at the individual pathway level. However, most of these approaches ignore the correlation of alteration events between pathways and miss many genes with rare alterations collectively contributing to carcinogenesis. Here, we devise a network-based approach to capture the cooperative functional modules hidden in genome-wide somatic mutation and copy number alteration profiles of glioblastoma (GBM) from The Cancer Genome Atlas (TCGA), where a module is a set of altered genes with dense interactions in the protein interaction network. We identify 7 pairs of significantly co-altered modules that involve the main pathways known to be altered in GBM (TP53, RB and RTK signaling pathways) and highlight the striking co-occurring alterations among these GBM pathways. By taking into account the non-random correlation of gene alterations, the property of co-alteration could distinguish oncogenic modules that contain driver genes involved in the progression of GBM. The collaboration among cancer pathways suggests that the redundant models and aggravating models could shed new light on the potential mechanisms during carcinogenesis and provide new indications for the design of cancer therapeutic strategies.

  19. Lipase genes in Mucor circinelloides: identification, sub-cellular location, phylogenetic analysis and expression profiling during growth and lipid accumulation.

    Science.gov (United States)

    Zan, Xinyi; Tang, Xin; Chu, Linfang; Zhao, Lina; Chen, Haiqin; Chen, Yong Q; Chen, Wei; Song, Yuanda

    2016-10-01

    Lipases or triacylglycerol hydrolases are widely spread in nature and are particularly common in the microbial world. The filamentous fungus Mucor circinelloides is a potential lipase producer, as it grows well in triacylglycerol-contained culture media. So far only one lipase from M. circinelloides has been characterized, while the majority of lipases remain unknown in this fungus. In the present study, 47 potential lipase genes in M. circinelloides WJ11 and 30 potential lipase genes in M. circinelloides CBS 277.49 were identified by extensive bioinformatics analysis. An overview of these lipases is presented, including several characteristics, sub-cellular location, phylogenetic analysis and expression profiling of the lipase genes during growth and lipid accumulation. All of these proteins contained the consensus sequence for a classical lipase (GXSXG motif) and were divided into four types including α/β-hydrolase_1, α/β-hydrolase_3, class_3 and GDSL lipase (GDSL) based on gene annotations. Phylogenetic analyses revealed that class_3 family and α/β-hydrolase_3 family were the conserved lipase family in M. circinelloides. Additionally, some lipases also contained a typical acyltransferase motif of H-(X) 4-D, and these lipases may play a dual role in lipid metabolism, catalyzing both lipid hydrolysis and transacylation reactions. The differential expression of all lipase genes were confirmed by quantitative real-time PCR, and the expression profiling were analyzed to predict the possible biological roles of these lipase genes in lipid metabolism in M. circinelloides. We preliminarily hypothesized that lipases may be involved in triacylglycerol degradation, phospholipid synthesis and beta-oxidation. Moreover, the results of sub-cellular localization, the presence of signal peptide and transcriptional analyses of lipase genes indicated that four lipase in WJ11 most likely belong to extracellular lipases with a signal peptide. These findings provide a platform

  20. Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

    Science.gov (United States)

    Lenka, Sangram K; Lohia, Bikash; Kumar, Abhay; Chinnusamy, Viswanathan; Bansal, Kailash C

    2009-02-01

    Abscisic acid (ABA), the popular plant stress hormone, plays a key role in regulation of sub-set of stress responsive genes. These genes respond to ABA through specific transcription factors which bind to cis-regulatory elements present in their promoters. We discovered the ABA Responsive Element (ABRE) core (ACGT) containing CGMCACGTGB motif as over-represented motif among the promoters of ABA responsive co-expressed genes in rice. Targeted gene prediction strategy using this motif led to the identification of 402 protein coding genes potentially regulated by ABA-dependent molecular genetic network. RT-PCR analysis of arbitrarily chosen 45 genes from the predicted 402 genes confirmed 80% accuracy of our prediction. Plant Gene Ontology (GO) analysis of ABA responsive genes showed enrichment of signal transduction and stress related genes among diverse functional categories.

  1. Gene expression profile altered by orthodontic tooth movement during healing of surgical alveolar defect.

    Science.gov (United States)

    Choi, Eun-Kyung; Lee, Jae-Hyung; Baek, Seung-Hak; Kim, Su-Jung

    2017-06-01

    We explored the gene expression profile altered by orthodontic tooth movement (OTM) during the healing of surgical alveolar defects in beagles. An OTM-related healing model was established where a maxillary second premolar was protracted into the critical-sized defect for 6 weeks (group DT6). As controls, natural healing models without OTM were set at 2 weeks (group D2) and at 6 weeks (group D6) after surgery. Total RNAs were extracted from dissected tissue blocks containing the regenerated defects and additionally from sound alveolar bone as a baseline (group C). mRNA profiling was performed using microarray analysis. Functional annotations of gene clusters based on differentially expressed genes among groups indicated that the gene expression profile of group DT6 had a stronger similarity to that of group D2 than to group D6. The genes participating in high woven-bone fraction in group DT6 could be identified as TNFSF11, MMP13, SPP1, and DMP1, which were verified by quantitative real-time polymerase chain reactions. We investigated at the gene level that OTM can affect the healing state of surgical defects serving as favorable matrices for OTM with defect regeneration. It would be a basis on selecting putative genes to be therapeutically applied for tissue-friendly accelerated orthodontics in the future. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

  2. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. The role of gene-gene interaction in the prediction of criminal behavior.

    Science.gov (United States)

    Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

    2014-04-01

    A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Predicting fatty acid profiles in blood based on food intake and the FADS1 rs174546 SNP.

    Science.gov (United States)

    Hallmann, Jacqueline; Kolossa, Silvia; Gedrich, Kurt; Celis-Morales, Carlos; Forster, Hannah; O'Donovan, Clare B; Woolhead, Clara; Macready, Anna L; Fallaize, Rosalind; Marsaux, Cyril F M; Lambrinou, Christina-Paulina; Mavrogianni, Christina; Moschonis, George; Navas-Carretero, Santiago; San-Cristobal, Rodrigo; Godlewska, Magdalena; Surwiłło, Agnieszka; Mathers, John C; Gibney, Eileen R; Brennan, Lorraine; Walsh, Marianne C; Lovegrove, Julie A; Saris, Wim H M; Manios, Yannis; Martinez, Jose Alfredo; Traczyk, Iwona; Gibney, Michael J; Daniel, Hannelore

    2015-12-01

    A high intake of n-3 PUFA provides health benefits via changes in the n-6/n-3 ratio in blood. In addition to such dietary PUFAs, variants in the fatty acid desaturase 1 (FADS1) gene are also associated with altered PUFA profiles. We used mathematical modeling to predict levels of PUFA in whole blood, based on multiple hypothesis testing and bootstrapped LASSO selected food items, anthropometric and lifestyle factors, and the rs174546 genotypes in FADS1 from 1607 participants (Food4Me Study). The models were developed using data from the first reported time point (training set) and their predictive power was evaluated using data from the last reported time point (test set). Among other food items, fish, pizza, chicken, and cereals were identified as being associated with the PUFA profiles. Using these food items and the rs174546 genotypes as predictors, models explained 26-43% of the variability in PUFA concentrations in the training set and 22-33% in the test set. Selecting food items using multiple hypothesis testing is a valuable contribution to determine predictors, as our models' predictive power is higher compared to analogue studies. As unique feature, we additionally confirmed our models' power based on a test set. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. A lifetime prediction method for LEDs considering mission profiles

    DEFF Research Database (Denmark)

    Qu, Xiaohui; Wang, Huai; Zhan, Xiaoqing

    2016-01-01

    and to benchmark the cost-competitiveness of different lighting technologies. The existing lifetime data released by LED manufacturers or standard organizations are usually applicable only for specific temperature and current levels. Significant lifetime discrepancies may be observed in field operations due...... to the varying operational and environmental conditions during the entire service time (i.e., mission profiles). To overcome the challenge, this paper proposes an advanced lifetime prediction method, which takes into account the field operation mission profiles and the statistical properties of the life data...

  6. In silico gene expression profiling in Cannabis sativa.

    Science.gov (United States)

    Massimino, Luca

    2017-01-01

    The cannabis plant and its active ingredients (i.e., cannabinoids and terpenoids) have been socially stigmatized for half a century. Luckily, with more than 430,000 published scientific papers and about 600 ongoing and completed clinical trials, nowadays cannabis is employed for the treatment of many different medical conditions. Nevertheless, even if a large amount of high-throughput functional genomic data exists, most researchers feature a strong background in molecular biology but lack advanced bioinformatics skills. In this work, publicly available gene expression datasets have been analyzed giving rise to a total of 40,224 gene expression profiles taken from cannabis plant tissue at different developmental stages. The resource presented here will provide researchers with a starting point for future investigations with Cannabis sativa .

  7. Transcriptional profiling of protein expression related genes of Pichia pastoris under simulated microgravity.

    Directory of Open Access Journals (Sweden)

    Feng Qi

    Full Text Available The physiological responses and transcription profiling of Pichia pastoris GS115 to simulated microgravity (SMG were substantially changed compared with normal gravity (NG control. We previously reported that the recombinant P. pastoris grew faster under SMG than NG during methanol induction phase and the efficiencies of recombinant enzyme production and secretion were enhanced under SMG, which was considered as the consequence of changed transcriptional levels of some key genes. In this work, transcriptiome profiling of P. pastoris cultured under SMG and NG conditions at exponential and stationary phases were determined using next-generation sequencing (NGS technologies. Four categories of 141 genes function as methanol utilization, protein chaperone, RNA polymerase and protein transportation or secretion classified according to Gene Ontology (GO were chosen to be analyzed on the basis of NGS results. And 80 significantly changed genes were weighted and estimated by Cluster 3.0. It was found that most genes of methanol metabolism (85% of 20 genes and protein transportation or secretion (82.2% of 45 genes were significantly up-regulated under SMG. Furthermore the quantity and fold change of up-regulated genes in exponential phase of each category were higher than those of stationary phase. The results indicate that the up-regulated genes of methanol metabolism and protein transportation or secretion mainly contribute to enhanced production and secretion of the recombinant protein under SMG.

  8. Classification of breast cancer patients using somatic mutation profiles and machine learning approaches.

    Science.gov (United States)

    Vural, Suleyman; Wang, Xiaosheng; Guda, Chittibabu

    2016-08-26

    The high degree of heterogeneity observed in breast cancers makes it very difficult to classify the cancer patients into distinct clinical subgroups and consequently limits the ability to devise effective therapeutic strategies. Several classification strategies based on ER/PR/HER2 expression or the expression profiles of a panel of genes have helped, but such methods often produce misleading results due to their dynamic nature. In contrast, somatic DNA mutations are relatively stable and lead to initiation and progression of many sporadic cancers. Hence in this study, we explore the use of gene mutation profiles to classify, characterize and predict the subgroups of breast cancers. We analyzed the whole exome sequencing data from 358 ethnically similar breast cancer patients in The Cancer Genome Atlas (TCGA) project. Somatic and non-synonymous single nucleotide variants identified from each patient were assigned a quantitative score (C-score) that represents the extent of negative impact on the gene function. Using these scores with non-negative matrix factorization method, we clustered the patients into three subgroups. By comparing the clinical stage of patients, we identified an early-stage-enriched and a late-stage-enriched subgroup. Comparison of the mutation scores of early and late-stage-enriched subgroups identified 358 genes that carry significantly higher mutations rates in the late stage subgroup. Functional characterization of these genes revealed important functional gene families that carry a heavy mutational load in the late state rich subgroup of patients. Finally, using the identified subgroups, we also developed a supervised classification model to predict the stage of the patients. This study demonstrates that gene mutation profiles can be effectively used with unsupervised machine-learning methods to identify clinically distinguishable breast cancer subgroups. The classification model developed in this method could provide a reasonable

  9. Automated Protocol for Large-Scale Modeling of Gene Expression Data.

    Science.gov (United States)

    Hall, Michelle Lynn; Calkins, David; Sherman, Woody

    2016-11-28

    With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In particular, we present predictive models with an average accuracy of greater than 70% across a highly diverse ∼1000 gene expression profile. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

  10. No specific gene expression signature in human granulosa and cumulus cells for prediction of oocyte fertilisation and embryo implantation.

    Directory of Open Access Journals (Sweden)

    Tanja Burnik Papler

    Full Text Available In human IVF procedures objective and reliable biomarkers of oocyte and embryo quality are needed in order to increase the use of single embryo transfer (SET and thus prevent multiple pregnancies. During folliculogenesis there is an intense bi-directional communication between oocyte and follicular cells. For this reason gene expression profile of follicular cells could be an important indicator and biomarker of oocyte and embryo quality. The objective of this study was to identify gene expression signature(s in human granulosa (GC and cumulus (CC cells predictive of successful embryo implantation and oocyte fertilization. Forty-one patients were included in the study and individual GC and CC samples were collected; oocytes were cultivated separately, allowing a correlation with IVF outcome and elective SET was performed. Gene expression analysis was performed using microarrays, followed by a quantitative real-time PCR validation. After statistical analysis of microarray data, there were no significantly differentially expressed genes (FDR<0,05 between non-fertilized and fertilized oocytes and non-implanted and implanted embryos in either of the cell type. Furthermore, the results of quantitative real-time PCR were in consent with microarray data as there were no significant differences in gene expression of genes selected for validation. In conclusion, we did not find biomarkers for prediction of oocyte fertilization and embryo implantation in IVF procedures in the present study.

  11. Microarray analysis of gene expression profiles in ripening pineapple fruits.

    Science.gov (United States)

    Koia, Jonni H; Moyle, Richard L; Botella, Jose R

    2012-12-18

    Pineapple (Ananas comosus) is a tropical fruit crop of significant commercial importance. Although the physiological changes that occur during pineapple fruit development have been well characterized, little is known about the molecular events that occur during the fruit ripening process. Understanding the molecular basis of pineapple fruit ripening will aid the development of new varieties via molecular breeding or genetic modification. In this study we developed a 9277 element pineapple microarray and used it to profile gene expression changes that occur during pineapple fruit ripening. Microarray analyses identified 271 unique cDNAs differentially expressed at least 1.5-fold between the mature green and mature yellow stages of pineapple fruit ripening. Among these 271 sequences, 184 share significant homology with genes encoding proteins of known function, 53 share homology with genes encoding proteins of unknown function and 34 share no significant homology with any database accession. Of the 237 pineapple sequences with homologs, 160 were up-regulated and 77 were down-regulated during pineapple fruit ripening. DAVID Functional Annotation Cluster (FAC) analysis of all 237 sequences with homologs revealed confident enrichment scores for redox activity, organic acid metabolism, metalloenzyme activity, glycolysis, vitamin C biosynthesis, antioxidant activity and cysteine peptidase activity, indicating the functional significance and importance of these processes and pathways during pineapple fruit development. Quantitative real-time PCR analysis validated the microarray expression results for nine out of ten genes tested. This is the first report of a microarray based gene expression study undertaken in pineapple. Our bioinformatic analyses of the transcript profiles have identified a number of genes, processes and pathways with putative involvement in the pineapple fruit ripening process. This study extends our knowledge of the molecular basis of pineapple fruit

  12. Alteration in gene expression profile and oncogenicity of esophageal squamous cell carcinoma by RIZ1 upregulation.

    Science.gov (United States)

    Dong, Shang-Wen; Li, Dong; Xu, Cong; Sun, Pei; Wang, Yuan-Guo; Zhang, Peng

    2013-10-07

    To investigate the effect of retinoblastoma protein-interacting zinc finger gene 1 (RIZ1) upregulation in gene expression profile and oncogenicity of human esophageal squamous cell carcinoma (ESCC) cell line TE13. TE13 cells were transfected with pcDNA3.1(+)/RIZ1 and pcDNA3.1(+). Changes in gene expression profile were screened and the microarray results were confirmed by reverse transcription-polymerase chain reaction (RT-PCR). Nude mice were inoculated with TE13 cells to establish ESCC xenografts. After two weeks, the inoculated mice were randomly divided into three groups. Tumors were injected with normal saline, transfection reagent pcDNA3.1(+) and transfection reagent pcDNA3.1(+)/RIZ1, respectively. Tumor development was quantified, and changes in gene expression of RIZ1 transfected tumors were detected by RT-PCR and Western blotting. DNA microarray data showed that RIZ1 transfection induced widespread changes in gene expression profile of cell line TE13, with 960 genes upregulated and 1163 downregulated. Treatment of tumor xenografts with RIZ1 recombinant plasmid significantly inhibited tumor growth, decreased tumor size, and increased expression of RIZ1 mRNA compared to control groups. The changes in gene expression profile were also observed in vivo after RIZ1 transfection. Most of the differentially expressed genes were associated with cell development, supervision of viral replication, lymphocyte costimulatory and immune system development in esophageal cells. RIZ1 gene may be involved in multiple cancer pathways, such as cytokine receptor interaction and transforming growth factor beta signaling. The development and progression of esophageal cancer are related to the inactivation of RIZ1. Virus infection may also be an important factor.

  13. Gene profile analysis of osteoblast genes differentially regulated by histone deacetylase inhibitors

    Directory of Open Access Journals (Sweden)

    Lamblin Anne-Francoise

    2007-10-01

    Full Text Available Abstract Background Osteoblast differentiation requires the coordinated stepwise expression of multiple genes. Histone deacetylase inhibitors (HDIs accelerate the osteoblast differentiation process by blocking the activity of histone deacetylases (HDACs, which alter gene expression by modifying chromatin structure. We previously demonstrated that HDIs and HDAC3 shRNAs accelerate matrix mineralization and the expression of osteoblast maturation genes (e.g. alkaline phosphatase, osteocalcin. Identifying other genes that are differentially regulated by HDIs might identify new pathways that contribute to osteoblast differentiation. Results To identify other osteoblast genes that are altered early by HDIs, we incubated MC3T3-E1 preosteoblasts with HDIs (trichostatin A, MS-275, or valproic acid for 18 hours in osteogenic conditions. The promotion of osteoblast differentiation by HDIs in this experiment was confirmed by osteogenic assays. Gene expression profiles relative to vehicle-treated cells were assessed by microarray analysis with Affymetrix GeneChip 430 2.0 arrays. The regulation of several genes by HDIs in MC3T3-E1 cells and primary osteoblasts was verified by quantitative real-time PCR. Nine genes were differentially regulated by at least two-fold after exposure to each of the three HDIs and six were verified by PCR in osteoblasts. Four of the verified genes (solute carrier family 9 isoform 3 regulator 1 (Slc9a3r1, sorbitol dehydrogenase 1, a kinase anchor protein, and glutathione S-transferase alpha 4 were induced. Two genes (proteasome subunit, beta type 10 and adaptor-related protein complex AP-4 sigma 1 were suppressed. We also identified eight growth factors and growth factor receptor genes that are significantly altered by each of the HDIs, including Frizzled related proteins 1 and 4, which modulate the Wnt signaling pathway. Conclusion This study identifies osteoblast genes that are regulated early by HDIs and indicates pathways that

  14. HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

    Science.gov (United States)

    Zaman, Rianon; Chowdhury, Shahana Yasmin; Rashid, Mahmood A; Sharma, Alok; Dehzangi, Abdollah; Shatabda, Swakkhar

    2017-01-01

    DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

  15. HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

    Directory of Open Access Journals (Sweden)

    Rianon Zaman

    2017-01-01

    Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

  16. Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

    Science.gov (United States)

    de Groot, Reinoud; Lüthi, Joel; Lindsay, Helen; Holtackers, René; Pelkmans, Lucas

    2018-01-23

    High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

  17. Improving functional modules discovery by enriching interaction networks with gene profiles

    KAUST Repository

    Salem, Saeed; Alroobi, Rami; Banitaan, Shadi; Seridi, Loqmane; Aljarah, Ibrahim; Brewer, James

    2013-01-01

    networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional

  18. Gene expression profiling in mouse lung following polymeric hexamethylene diisocyanate exposure

    International Nuclear Information System (INIS)

    Lee, C.-T.; Ylostalo, Joni; Friedman, Mitchell; Hoyle, Gary W.

    2005-01-01

    Isocyanates are a common cause of occupational lung disease. Hexamethylene diisocyanate (HDI), a component of polyurethane spray paints, can induce respiratory symptoms, inflammation, lung function impairment, and isocyanate asthma. The predominant form of HDI in polyurethane paints is a nonvolatile polyisocyanate known as HDI biuret trimer (HDI-BT). Exposure of mice to aerosolized HDI-BT results in pathological effects, including pulmonary edema, lung inflammation, cellular proliferation, and fibrotic lesions, which occur with distinct time courses following exposure. To identify genes that mediate lung pathology in the distinct temporal phases after exposure, gene expression profiles in HDI-BT-exposed C57BL/6J mouse lungs were analyzed. RNase protection assay (RPA) of genes involved in apoptosis, cell survival, and inflammation revealed increased expression of IκBα, Fas, Bcl-X L , TNFα, KC, MIP-2, IL-6, and GM-CSF following HDI-BT exposure. Microarray analysis of approximately 10 000 genes was performed on lung RNA collected from mice 6, 18, and 90 h after HDI-BT exposure and from unexposed mice. Classes of genes whose expression was increased 6 h after exposure included those involved in stress responses (particularly oxidative stress and thiol redox balance), growth arrest, apoptosis, signal transduction, and inflammation. Types of genes whose expression was increased at 18 h included proteinases, anti-proteinases, cytoskeletal molecules, and inflammatory mediators. Transcripts increased at 90 h included extracellular matrix components, transcription factors, inflammatory mediators, and cell cycle regulators. This characterization of the gene expression profile in lungs exposed to HDI-BT will provide a basis for investigating injury and repair pathways that are operative during isocyanate-induced lung disease

  19. Gene expression profile identifies potential biomarkers for human intervertebral disc degeneration.

    Science.gov (United States)

    Guo, Wei; Zhang, Bin; Li, Yan; Duan, Hui-Quan; Sun, Chao; Xu, Yun-Qiang; Feng, Shi-Qing

    2017-12-01

    The present study aimed to reveal the potential genes associated with the pathogenesis of intervertebral disc degeneration (IDD) by analyzing microarray data using bioinformatics. Gene expression profiles of two regions of the intervertebral disc were compared between patients with IDD and controls. GSE70362 containing two groups of gene expression profiles, 16 nucleus pulposus (NP) samples from patients with IDD and 8 from controls, and 16 annulus fibrosus (AF) samples from patients with IDD and 8 from controls, was downloaded from the Gene Expression Omnibus database. A total of 93 and 114 differentially expressed genes (DEGs) were identified in NP and AF samples, respectively, using a limma software package for the R programming environment. Gene Ontology (GO) function enrichment analysis was performed to identify the associated biological functions of DEGs in IDD, which indicated that the DEGs may be involved in various processes, including cell adhesion, biological adhesion and extracellular matrix organization. Pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that the identified DEGs were potentially involved in focal adhesion and the p53 signaling pathway. Further analysis revealed that there were 35 common DEGs observed between the two regions (NP and AF), which may be further regulated by 6 clusters of microRNAs (miRNAs) retrieved with WebGestalt. The genes in the DEG‑miRNA regulatory network were annotated using GO function and KEGG pathway enrichment analysis, among which extracellular matrix organization was the most significant disrupted biological process and focal adhesion was the most significant dysregulated pathway. In addition, the result of protein‑protein interaction network modules demonstrated the involvement of inflammatory cytokine interferon signaling in IDD. These findings may not only advance the understanding of the pathogenesis of IDD, but also identify novel potential

  20. Gene Expression Profiling of Xeroderma Pigmentosum

    Directory of Open Access Journals (Sweden)

    Bowden Nikola A

    2006-05-01

    Full Text Available Abstract Xeroderma pigmentosum (XP is a rare recessive disorder that is characterized by extreme sensitivity to UV light. UV light exposure results in the formation of DNA damage such as cyclobutane dimers and (6-4 photoproducts. Nucleotide excision repair (NER orchestrates the removal of cyclobutane dimers and (6-4 photoproducts as well as some forms of bulky chemical DNA adducts. The disease XP is comprised of 7 complementation groups (XP-A to XP-G, which represent functional deficiencies in seven different genes, all of which are believed to be involved in NER. The main clinical feature of XP is various forms of skin cancers; however, neurological degeneration is present in XPA, XPB, XPD and XPG complementation groups. The relationship between NER and other types of DNA repair processes is now becoming evident but the exact relationships between the different complementation groups remains to be precisely determined. Using gene expression analysis we have identified similarities and differences after UV light exposure between the complementation groups XP-A, XP-C, XP-D, XP-E, XP-F, XP-G and an unaffected control. The results reveal that there is a graded change in gene expression patterns between the mildest, most similar to the control response (XP-E and the severest form (XP-A of the disease, with the exception of XP-D. Distinct differences between the complementation groups with neurological symptoms (XP-A, XP-D and XP-G and without (XP-C, XP-E and XP-F were also identified. Therefore, this analysis has revealed distinct gene expression profiles for the XP complementation groups and the first step towards understanding the neurological symptoms of XP.

  1. Gene profiling and circulating tumor cells as biomarker to prognostic of patients with locoregional breast cancer.

    Science.gov (United States)

    Kuniyoshi, Renata K; Gehrke, Flávia de Sousa; Alves, Beatriz C A; Vilas-Bôas, Viviane; Coló, Anna E; Sousa, Naiara; Nunes, João; Fonseca, Fernando L A; Del Giglio, Auro

    2015-09-01

    The gene profile of primary tumors, as well as the identification of circulating tumor cells (CTCs), can provide important prognostic and predictive information. In this study, our objective was to perform tumor gene profiling (TGP) in combination with CTC characterization in women with nonmetastatic breast cancer. Biological samples (from peripheral blood and tumors) from 167 patients diagnosed with stage I, II, and III mammary carcinoma, who were also referred for adjuvant/neoadjuvant chemotherapy, were assessed for the following parameters: (a) the presence of CTCs identified by the expression of CK-19 and c-erbB-2 in the peripheral blood mononuclear cell (PBMC) fraction by quantitative reverse transcription PCR (RT-PCR) and (b) the TGP, which was determined by analyzing the expression of 21 genes in paraffin-embedded tissue samples by quantitative multiplex RT-PCR with the Plexor® system. We observed a statistically significant correlation between the progression-free interval (PFI) and the clinical stage (p = 0.000701), the TGP score (p = 0.006538), and the presence of hormone receptors in the tumor (p = 0.0432). We observed no correlation between the PFI and the presence or absence of CK-19 or HER2 expression in the PBMC fraction prior to the start of treatment or in the two following readouts. Multivariate analysis revealed that only the TGP score significantly correlated with the PFI (p = 0.029247). The TGP is an important prognostic variable for patients with locoregional breast cancer. The presence of CTCs adds no prognostic value to the information already provided by the TGP.

  2. Differential gene expression profile in pig adipose tissue treated with/without clenbuterol

    Directory of Open Access Journals (Sweden)

    Deng Xue M

    2007-11-01

    Full Text Available Abstract Background Clenbuterol, a beta-agonist, can dramatically reduce pig adipose accumulation at high dosages. However, it has been banned in pig production because people who eat pig products treated with clenbuterol can be poisoned by the clenbuterol residues. To understand the molecular mechanism for this fat reduction, cDNA microarray, real-time PCR, two-dimensional electrophoresis and mass spectra were used to study the differential gene expression profiles of pig adipose tissues treated with/without clenbuterol. The objective of this research is to identify novel genes and physiological pathways that potentially facilitate clenbuterol induced reduction of adipose accumulation. Results Clenbuterol was found to improve the lean meat percentage about 10 percent (P Conclusion Pig fat accumulation was reduced dramatically with clenbuterol treatment. Histological sections and global evaluation of gene expression after administration of clenbuterol in pigs identified profound changes in adipose cells. With clenbuterol stimulation, adipose cell volumes decreased and their gene expression profile changed, which indicate some metabolism processes have been also altered. Although the biological functions of the differentially expressed genes are not completely known, higher expressions of these molecules in adipose tissue might contribute to the reduction of fat accumulation. Among these genes, five lipid metabolism related genes were of special interest for further study, including apoD and apoR. The apoR expression was increased at both the RNA and protein levels. The apoR may be one of the critical molecules through which clenbuterol reduces fat accumulation.

  3. Transcript Profiling Distinguishes Complete Treatment Responders With Locally Advanced Cervical Cancer

    Directory of Open Access Journals (Sweden)

    Jorge Fernandez-Retana

    2015-04-01

    Full Text Available Cervical cancer (CC mortality is a major public health concern since it is the second cause of cancer-related deaths among women. Patients diagnosed with locally advanced CC (LACC have an important rate of recurrence and treatment failure. Conventional treatment for LACC is based on chemotherapy and radiotherapy; however, up to 40% of patients will not respond to conventional treatment; hence, we searched for a prognostic gene signature able to discriminate patients who do not respond to the conventional treatment employed to treat LACC. Tumor biopsies were profiled with genome-wide high-density expression microarrays. Class prediction was performed in tumor tissues and the resultant gene signature was validated by quantitative reverse transcription–polymerase chain reaction. A 27-predictive gene profile was identified through its association with pathologic response. The 27-gene profile was validated in an independent set of patients and was able to distinguish between patients diagnosed as no response versus complete response. Gene expression analysis revealed two distinct groups of tumors diagnosed as LACC. Our findings could provide a strategy to select patients who would benefit from neoadjuvant radiochemotherapy-based treatment.

  4. Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis

    Directory of Open Access Journals (Sweden)

    Li He

    2014-01-01

    Full Text Available For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.

  5. Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

    Science.gov (United States)

    Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

    2017-10-03

    Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

  6. A new algorithm predicts pressure and temperature profiles of gas/gas-condensate transmission pipelines

    Energy Technology Data Exchange (ETDEWEB)

    Mokhatab, Saied [OIEC - Oil Industries' Engineering and Construction Group, Tehran (Iran, Islamic Republic of); Vatani, Ali [University of Tehran (Iran, Islamic Republic of)

    2003-07-01

    The main objective of the present study has been the development of a relatively simple analytical algorithm for predicting flow temperature and pressure profiles along the two-phase, gas/gas-condensate transmission pipelines. Results demonstrate the ability of the method to predict reasonably accurate pressure gradient and temperature gradient profiles under operating conditions. (author)

  7. Gene expression profiling in woman with women with breast cancer in a Saudi population

    International Nuclear Information System (INIS)

    Amer, Saud M. Bin; Maqbool, Z.; Nirmal, Maimoona S.; Hussain, Syed S.; Jeprel, Hatim A.; Qattan, Amal T.; Tulbah, Asma M.; Malik, Osama A.; Al-Tweigeri, Taher A.

    2008-01-01

    Objective was to generate consensus gene expression profiles of invasive breast tumors from a small cohort of Saudi females and to explore the possibility that they may be broadly conserved between Caucasian and Middle Eastern populations. This study was performed at King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia, from January 2005 to January 2007. Gene expression profiles were generated from 38 invasive breast tumors and 8 tumor adjacent tissues (TATs) using BD Atlas cDNA expression arrays containing 1176 genes. Results were confirmed by reverse transcriptase polymerase chain reaction and analyzed by 2-dimensional unsupervised hierarchical clustering. The analysis identified 48 differentially expressed genes in tumors from which 25 are already reported by various western studies. Forty-three of these genes were also differentially expressed in TATs. The same data set has been able to distinguish between tumors and the TAT's, interestingly by using only 4 of the differentially expressed genes. Moreover, we were able to group the patients according to prognosis to an extent by hierarchical clustering. Our results indicate that expression profiles between Saudi females with breast cancer and the Caucasian population are conserved to some extent, and can be used to classify patients according to prognostic groups. We also suggest 3 differentially expressed genes (IGHG3, CDK3 and RPS9) in tumors may have a novel role in breast cancer. In addition, the role of TATs is much more essential in breast cancer and needs to be explored thoroughly. (author)

  8. Expression Profiling of Genes Related to Endothelial Cells Biology in Patients with Type 2 Diabetes and Patients with Prediabetes

    Directory of Open Access Journals (Sweden)

    Sara Moradipoor

    2016-01-01

    Full Text Available Endothelial dysfunction appears to be an early sign indicating vascular damage and predicts the progression of atherosclerosis and cardiovascular disorders. Extensive clinical and experimental evidence suggests that endothelial dysfunction occurs in Type 2 Diabetes Mellitus (T2DM and prediabetes patients. This study was carried out with an aim to appraise the expression levels in the peripheral blood of 84 genes related to endothelial cells biology in patients with diagnosed T2DM or prediabetes, trying to identify new genes whose expression might be changed under these pathological conditions. The study covered a total of 45 participants. The participants were divided into three groups: group 1, patients with T2DM; group 2, patients with prediabetes; group 3, control group. The gene expression analysis was performed using the Endothelial Cell Biology RT2 Profiler PCR Array. In the case of T2DM, 59 genes were found to be upregulated, and four genes were observed to be downregulated. In prediabetes patients, increased expression was observed for 49 genes, with two downregulated genes observed. Our results indicate that diabetic and prediabetic conditions change the expression levels of genes related to endothelial cells biology and, consequently, may increase the risk for occurrence of endothelial dysfunction.

  9. Gene expression profiling reveals underlying molecular mechanisms of the early stages of tamoxifen-induced rat hepatocarcinogenesis

    International Nuclear Information System (INIS)

    Pogribny, Igor P.; Bagnyukova, Tetyana V.; Tryndyak, Volodymyr P.; Muskhelishvili, Levan; Rodriguez-Juarez, Rocio; Kovalchuk, Olga; Han Tao; Fuscoe, James C.; Ross, Sharon A.; Beland, Frederick A.

    2007-01-01

    Tamoxifen is a widely used anti-estrogenic drug for chemotherapy and, more recently, for the chemoprevention of breast cancer. Despite the indisputable benefits of tamoxifen in preventing the occurrence and re-occurrence of breast cancer, the use of tamoxifen has been shown to induce non-alcoholic steatohepatitis, which is a life-threatening fatty liver disease with a risk of progression to cirrhosis and hepatocellular carcinoma. In recent years, the high-throughput microarray technology for large-scale analysis of gene expression has become a powerful tool for increasing the understanding of the molecular mechanisms of carcinogenesis and for identifying new biomarkers with diagnostic and predictive values. In the present study, we used the high-throughput microarray technology to determine the gene expression profiles in the liver during early stages of tamoxifen-induced rat hepatocarcinogenesis. Female Fisher 344 rats were fed a 420 ppm tamoxifen containing diet for 12 or 24 weeks, and gene expression profiles were determined in liver of control and tamoxifen-exposed rats. The results indicate that early stages of tamoxifen-induced liver carcinogenesis are characterized by alterations in several major cellular pathways, specifically those involved in the tamoxifen metabolism, lipid metabolism, cell cycle signaling, and apoptosis/cell proliferation control. One of the most prominent changes during early stages of tamoxifen-induced hepatocarcinogenesis is dysregulation of signaling pathways in cell cycle progression from the G 1 to S phase, evidenced by the progressive and sustained increase in expression of the Pdgfc, Calb3, Ets1, and Ccnd1 genes accompanied by the elevated level of the PI3K, p-PI3K, Akt1/2, Akt3, and cyclin B, D1, and D3 proteins. The early appearance of these alterations suggests their importance in the mechanism of neoplastic cell transformation induced by tamoxifen

  10. Gene expression profile of AIDS-related Kaposi's sarcoma

    International Nuclear Information System (INIS)

    Cornelissen, Marion; Kuyl, Antoinette C van der; Burg, Remco van den; Zorgdrager, Fokla; Noesel, Carel JM van; Goudsmit, Jaap

    2003-01-01

    Kaposi's Sarcoma (KS) is a proliferation of aberrant vascular structures lined by spindle cells, and is caused by a gammaherpes virus (HHV8/KSHV). Its course is aggravated by co-infection with HIV-1, where the timing of infection with HIV-1 and HHV8 is important for the clinical outcome. In order to better understand the pathogenesis of KS, we have analysed tissue from two AIDS-KS lesions, and from normal skin by serial analysis of gene expression (SAGE). Semi-quantitative RT-PCR was then used to validate the results. The expression profile of AIDS-related KS (AIDS-KS) reflects an active process in the skin. Transcripts of HHV8 were found to be very low, and HIV-1 mRNA was not detected by SAGE, although it could be found using RT-PCR. Comparing the expression profile of AIDS-KS tissue with publicly available SAGE libraries suggested that AIDS-KS mRNA levels are most similar to those in an artificially mixed library of endothelial cells and leukocytes, in line with the description of KS lesions as containing spindle cells with endothelial characteristics, and an inflammatory infiltrate. At least 64 transcripts were found to be significantly elevated, and 28 were statistically downregulated in AIDS-KS compared to normal skin. Five of the upregulated mRNAs, including Tie 1 and sialoadhesin/CD169, were confirmed by semi-quantitative PCR to be elevated in additional AIDS-KS biopsies. Antibodies to sialoadhesin/CD169, a known marker of activated macrophages, were shown to specifically label tumour macrophages. The expression profile of AIDS-KS showed 64 genes to be significantly upregulated, and 28 genes downregulated, compared with normal skin. One of the genes with increased expression was sialoadhesin (CD169). Antibodies to sialoadhesin/CD169 specifically labelled tumour-associated macrophages, suggesting that macrophages present in AIDS-KS lesions belong to a subset of human CD169+ macrophages

  11. Gene expression profiles of primary colorectal carcinomas, liver metastases, and carcinomatoses

    Directory of Open Access Journals (Sweden)

    Myklebost Ola

    2007-01-01

    Full Text Available Abstract Background Despite the fact that metastases are the leading cause of colorectal cancer deaths, little is known about the underlying molecular changes in these advanced disease stages. Few have studied the overall gene expression levels in metastases from colorectal carcinomas, and so far, none has investigated the peritoneal carcinomatoses by use of DNA microarrays. Therefore, the aim of the present study is to investigate and compare the gene expression patterns of primary carcinomas (n = 18, liver metastases (n = 4, and carcinomatoses (n = 4, relative to normal samples from the large bowel. Results Transcriptome profiles of colorectal cancer metastases independent of tumor site, as well as separate profiles associated with primary carcinomas, liver metastases, or peritoneal carcinomatoses, were assessed by use of Bayesian statistics. Gains of chromosome arm 5p are common in peritoneal carcinomatoses and several candidate genes (including PTGER4, SKP2, and ZNF622 mapping to this region were overexpressed in the tumors. Expression signatures stratified on TP53 mutation status were identified across all tumors regardless of stage. Furthermore, the gene expression levels for the in vivo tumors were compared with an in vitro model consisting of cell lines representing all three tumor stages established from one patient. Conclusion By statistical analysis of gene expression data from primary colorectal carcinomas, liver metastases, and carcinomatoses, we are able to identify genetic patterns associated with the different stages of tumorigenesis.

  12. Predicting cellular growth from gene expression signatures.

    Directory of Open Access Journals (Sweden)

    Edoardo M Airoldi

    2009-01-01

    Full Text Available Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.

  13. Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

    International Nuclear Information System (INIS)

    Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

    2000-01-01

    A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society

  14. Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

    Energy Technology Data Exchange (ETDEWEB)

    Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

    2000-09-18

    A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society.

  15. A network approach to predict pathogenic genes for Fusarium graminearum.

    Science.gov (United States)

    Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

    2010-10-04

    Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which

  16. A network approach to predict pathogenic genes for Fusarium graminearum.

    Directory of Open Access Journals (Sweden)

    Xiaoping Liu

    Full Text Available Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB, which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other

  17. Distinct differences in global gene expression profiles in non-implanted blastocysts and blastocysts resulting in live birth

    DEFF Research Database (Denmark)

    Kirkegaard, Kirstine Kjær; Fredsted, Palle Villesen; Jensen, Jacob Malte

    2015-01-01

    Results from animal models points towards the existence of a gene expression profile that is distinguishably different in viable embryos compared with non-viable embryos. Knowledge of human embryo transcripts is however limited, in particular with regard to how gene expression is related...... to clinical outcome. The purpose of the present study was therefore to determine the global gene expression profiles of human blastocysts. Next Generation Sequencing was used to identify genes that were differentially expressed in non-implanted embryos and embryos resulting in live birth. Three trophectoderm...

  18. Sex-specific patterns and deregulation of endocrine pathways in the gene expression profiles of Bangladeshi adults exposed to arsenic contaminated drinking water

    Energy Technology Data Exchange (ETDEWEB)

    Muñoz, Alexandra; Chervona, Yana [New York University School of Medicine, Nelson Institute of Environmental Medicine, Tuxedo, NY (United States); Hall, Megan [Department of Epidemiology, Mailman School of Public Health, Columbia University, New York (United States); Kluz, Thomas [New York University School of Medicine, Nelson Institute of Environmental Medicine, Tuxedo, NY (United States); Gamble, Mary V., E-mail: mvg7@columbia.edu [Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York (United States); Costa, Max, E-mail: Max.Costa@nyumc.org [New York University School of Medicine, Nelson Institute of Environmental Medicine, Tuxedo, NY (United States)

    2015-05-01

    Arsenic contamination of drinking water occurs globally and is associated with numerous diseases including skin, lung and bladder cancers, and cardiovascular disease. Recent research indicates that arsenic may be an endocrine disruptor. This study was conducted to evaluate the nature of gene expression changes among males and females exposed to arsenic contaminated water in Bangladesh at high and low doses. Twenty-nine (55% male) Bangladeshi adults with water arsenic exposure ranging from 50 to 1000 μg/L were selected from the Folic Acid Creatinine Trial. RNA was extracted from peripheral blood mononuclear cells for gene expression profiling using Affymetrix 1.0 ST arrays. Differentially expressed genes were assessed between high and low exposure groups for males and females separately and findings were validated using quantitative real-time PCR. There were 534 and 645 differentially expressed genes (p < 0.05) in the peripheral blood mononuclear cells of males and females, respectively, when high and low water arsenic exposure groups were compared. Only 43 genes overlapped between the two sexes, with 29 changing in opposite directions. Despite the difference in gene sets both males and females exhibited common biological changes including deregulation of 17β-hydroxysteroid dehydrogenase enzymes, deregulation of genes downstream of Sp1 (specificity protein 1) transcription factor, and prediction of estrogen receptor alpha as a key hub in cardiovascular networks. Arsenic-exposed adults exhibit sex-specific gene expression profiles that implicate involvement of the endocrine system. Due to arsenic's possible role as an endocrine disruptor, exposure thresholds for arsenic may require different parameters for males and females. - Highlights: • Males and females exhibit unique gene expression changes in response to arsenic. • Only 23 genes are common among the differentially expressed genes for the sexes. • Male and female gene lists exhibit common

  19. Sex-specific patterns and deregulation of endocrine pathways in the gene expression profiles of Bangladeshi adults exposed to arsenic contaminated drinking water

    International Nuclear Information System (INIS)

    Muñoz, Alexandra; Chervona, Yana; Hall, Megan; Kluz, Thomas; Gamble, Mary V.; Costa, Max

    2015-01-01

    Arsenic contamination of drinking water occurs globally and is associated with numerous diseases including skin, lung and bladder cancers, and cardiovascular disease. Recent research indicates that arsenic may be an endocrine disruptor. This study was conducted to evaluate the nature of gene expression changes among males and females exposed to arsenic contaminated water in Bangladesh at high and low doses. Twenty-nine (55% male) Bangladeshi adults with water arsenic exposure ranging from 50 to 1000 μg/L were selected from the Folic Acid Creatinine Trial. RNA was extracted from peripheral blood mononuclear cells for gene expression profiling using Affymetrix 1.0 ST arrays. Differentially expressed genes were assessed between high and low exposure groups for males and females separately and findings were validated using quantitative real-time PCR. There were 534 and 645 differentially expressed genes (p < 0.05) in the peripheral blood mononuclear cells of males and females, respectively, when high and low water arsenic exposure groups were compared. Only 43 genes overlapped between the two sexes, with 29 changing in opposite directions. Despite the difference in gene sets both males and females exhibited common biological changes including deregulation of 17β-hydroxysteroid dehydrogenase enzymes, deregulation of genes downstream of Sp1 (specificity protein 1) transcription factor, and prediction of estrogen receptor alpha as a key hub in cardiovascular networks. Arsenic-exposed adults exhibit sex-specific gene expression profiles that implicate involvement of the endocrine system. Due to arsenic's possible role as an endocrine disruptor, exposure thresholds for arsenic may require different parameters for males and females. - Highlights: • Males and females exhibit unique gene expression changes in response to arsenic. • Only 23 genes are common among the differentially expressed genes for the sexes. • Male and female gene lists exhibit common

  20. A Lifetime Prediction Method for LEDs Considering Real Mission Profiles

    DEFF Research Database (Denmark)

    Qu, Xiaohui; Wang, Huai; Zhan, Xiaoqing

    2017-01-01

    operations due to the varying operational and environmental conditions during the entire service time (i.e., mission profiles). To overcome the challenge, this paper proposes an advanced lifetime prediction method, which takes into account the field operation mission profiles and also the statistical......The Light-Emitting Diode (LED) has become a very promising alternative lighting source with the advantages of longer lifetime and higher efficiency than traditional ones. The lifetime prediction of LEDs is important to guide the LED system designers to fulfill the design specifications...... properties of the life data available from accelerated degradation testing. The electrical and thermal characteristics of LEDs are measured by a T3Ster system, used for the electro-thermal modeling. It also identifies key variables (e.g., heat sink parameters) that can be designed to achieve a specified...

  1. Gene expression profiling in circulating cells (ctcs) of breast carcinoma patients

    Czech Academy of Sciences Publication Activity Database

    Kološtová, K.; Pinterová, D.; Tesařová, P.; Mikulová, V.; Kubecová, M.; Brychta, M.; Rusňáková, Vendula; Kasimir-Bauer, S.; Kubista, Mikael

    2010-01-01

    Roč. 21, suppl. 4 (2010), s. 49-59 ISSN 0923-7534 Institutional research plan: CEZ:AV0Z50520701 Keywords : Circulating tumor cells * Breast cancer * Gene expression profiling Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 6.452, year: 2010

  2. NESmapper: accurate prediction of leucine-rich nuclear export signals using activity-based profiles.

    Directory of Open Access Journals (Sweden)

    Shunichi Kosugi

    2014-09-01

    Full Text Available The nuclear export of proteins is regulated largely through the exportin/CRM1 pathway, which involves the specific recognition of leucine-rich nuclear export signals (NESs in the cargo proteins, and modulates nuclear-cytoplasmic protein shuttling by antagonizing the nuclear import activity mediated by importins and the nuclear import signal (NLS. Although the prediction of NESs can help to define proteins that undergo regulated nuclear export, current methods of predicting NESs, including computational tools and consensus-sequence-based searches, have limited accuracy, especially in terms of their specificity. We found that each residue within an NES largely contributes independently and additively to the entire nuclear export activity. We created activity-based profiles of all classes of NESs with a comprehensive mutational analysis in mammalian cells. The profiles highlight a number of specific activity-affecting residues not only at the conserved hydrophobic positions but also in the linker and flanking regions. We then developed a computational tool, NESmapper, to predict NESs by using profiles that had been further optimized by training and combining the amino acid properties of the NES-flanking regions. This tool successfully reduced the considerable number of false positives, and the overall prediction accuracy was higher than that of other methods, including NESsential and Wregex. This profile-based prediction strategy is a reliable way to identify functional protein motifs. NESmapper is available at http://sourceforge.net/projects/nesmapper.

  3. Understanding and Predicting Profile Structure and Parametric Scaling of Intrinsic Rotation

    Science.gov (United States)

    Wang, Weixing

    2016-10-01

    It is shown for the first time that turbulence-driven residual Reynolds stress can account for both the shape and magnitude of the observed intrinsic toroidal rotation profile. Nonlinear, global gyrokinetic simulations using GTS of DIII-D ECH plasmas indicate a substantial ITG fluctuation-induced non-diffusive momentum flux generated around a mid-radius-peaked intrinsic toroidal rotation profile. The non-diffusive momentum flux is dominated by the residual stress with a negligible contribution from the momentum pinch. The residual stress profile shows a robust anti-gradient, dipole structure in a set of ECH discharges with varying ECH power. Such interesting features of non-diffusive momentum fluxes, in connection with edge momentum sources and sinks, are found to be critical to drive the non-monotonic core rotation profiles in the experiments. Both turbulence intensity gradient and zonal flow ExB shear are identified as major contributors to the generation of the k∥-asymmetry needed for the residual stress generation. By balancing the residual stress and the momentum diffusion, a self-organized, steady-state rotation profile is calculated. The predicted core rotation profiles agree well with the experimentally measured main-ion toroidal rotation. The validated model is further used to investigate the characteristic dependence of global rotation profile structure in the multi-dimensional parametric space covering turbulence type, q-profile structure and collisionality with the goal of developing physics understanding needed for rotation profile control and optimization. Interesting results obtained include intrinsic rotation reversal induced by ITG-TEM transition in flat-q profile regime and by change in q-profile from weak to normal shear.. Fluctuation-generated poloidal Reynolds stress is also shown to significantly modify the neoclassical poloidal rotation in a way consistent with experimental observations. Finally, the first-principles-based model is applied

  4. A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

    Science.gov (United States)

    Glusman, Gustavo; Qin, Shizhen; El-Gewely, M. Raafat; Siegel, Andrew F; Roach, Jared C; Hood, Leroy; Smit, Arian F. A

    2006-01-01

    The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.” PMID:16543943

  5. Comparisons of Crosswind Velocity Profile Estimates Used in Fast-Time Wake Vortex Prediction Models

    Science.gov (United States)

    Pruis, Mathew J.; Delisi, Donald P.; Ahmad, Nashat N.

    2011-01-01

    Five methods for estimating crosswind profiles used in fast-time wake vortex prediction models are compared in this study. Previous investigations have shown that temporal and spatial variations in the crosswind vertical profile have a large impact on the transport and time evolution of the trailing vortex pair. The most important crosswind parameters are the magnitude of the crosswind and the gradient in the crosswind shear. It is known that pulsed and continuous wave lidar measurements can provide good estimates of the wind profile in the vicinity of airports. In this study comparisons are made between estimates of the crosswind profiles from a priori information on the trajectory of the vortex pair as well as crosswind profiles derived from different sensors and a regional numerical weather prediction model.

  6. Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium

    Directory of Open Access Journals (Sweden)

    Dhir Rajiv

    2004-08-01

    Full Text Available Abstract Background Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations. Results Using gene expression profiling we have demonstrated a similar whole transcriptome gene expression patterns in prostate cancer cells from human and rat prostate cancer cell lines both at baseline expression levels and after treatment with physiologic concentrations of the proposed chemopreventive agent Selenium. Using both the human PC3 and rat PAII prostate cancer cell lines have gone on to identify a subset of one hundred and fifty-four genes that demonstrate a similar level of differential expression to Selenium treatment in both species. Further analysis and data mining for two genes, the Insulin like Growth Factor Binding protein 3, and Retinoic X Receptor alpha, demonstrates an association with prostate cancer, functional pathway links, and protein-protein interactions that make these genes prime candidates for explaining the mechanism of Selenium's chemopreventive effect in prostate cancer. These genes are subsequently validated by western blots showing Selenium based induction and using

  7. Gene expression profile of endoscopically active and inactive ulcerative colitis: preliminary data.

    Science.gov (United States)

    Ţieranu, Cristian George; Dobre, Maria; Mănuc, Teodora Ecaterina; Milanesi, Elena; Pleşea, Iancu Emil; Popa, Caterina; Mănuc, Mircea; Ţieranu, Ioana; Preda, Carmen Monica; Diculescu, Mihai Mircea; Ionescu, Elena Mirela; Becheanu, Gabriel

    2017-01-01

    Multiple cytokines and chemokines related to immune response, apoptosis and inflammation have been identified as molecules implicated in ulcerative colitis (UC) pathogenesis. The aim of this study was to identify the differences at gene expression level of a panel of candidate genes in mucosa from patients with active UC (UCA), patients in remission (UCR), and normal controls. Eleven individuals were enrolled in the study: eight UC patients (four with active lesions, four with mucosal healing) and three controls without inflammatory bowel disease (IBD) seen on endoscopy. All the individuals underwent mucosal biopsy during colonoscopy. Gene expression profile was evaluated by polymerase chain reaction (PCR) array, investigating 84 genes implicated in apoptosis, inflammation, immune response, cellular adhesion, tissue remodeling and mucous secretion. Seventeen and three genes out of 84 were found significantly differentially expressed in UCA and UCR compared to controls, respectively. In particular, REG1A and CHI3L1 genes reported an up-regulation in UCA with a fold difference above 200. In UCR patients, the levels of CASP1, LYZ and ISG15 were different compared to controls. However, since a significant up-regulation of both CASP1 and LYZ was observed also in the UCA group, only ISG15 levels remained associated to the remission state. ISG15, that plays a key role in the innate immune response, seemed to be specifically associated to the UC remission state. These preliminary data represent a starting point for defining the gene profile of UC in different stages in Romanian population. Identification of genes implicated in UC pathogenesis could be useful to select new therapeutic targets.

  8. Gene Expression Profiling of Early Stage Non-Small Cell Lung Cancer

    NARCIS (Netherlands)

    J. Hou (Jun)

    2010-01-01

    textabstractNSCLC is a highly heterogeneous malignancy with a poor prognosis. Treatment for NSCLC is currently based on a combination of pathological staging and histological classification. Recently, gene expression-based NSCLC profiling is proven a superior approach to stratify cancer cases with

  9. Expression profile of immune response genes in patients with Severe Acute Respiratory Syndrome

    Directory of Open Access Journals (Sweden)

    Tai Dessmon

    2005-01-01

    Full Text Available Abstract Background Severe acute respiratory syndrome (SARS emerged in later February 2003, as a new epidemic form of life-threatening infection caused by a novel coronavirus. However, the immune-pathogenesis of SARS is poorly understood. To understand the host response to this pathogen, we investigated the gene expression profiles of peripheral blood mononuclear cells (PBMCs derived from SARS patients, and compared with healthy controls. Results The number of differentially expressed genes was found to be 186 under stringent filtering criteria of microarray data analysis. Several genes were highly up-regulated in patients with SARS, such as, the genes coding for Lactoferrin, S100A9 and Lipocalin 2. The real-time PCR method verified the results of the gene array analysis and showed that those genes that were up-regulated as determined by microarray analysis were also found to be comparatively up-regulated by real-time PCR analysis. Conclusions This differential gene expression profiling of PBMCs from patients with SARS strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response, rather than a specific immune response against a viral infection, as we observed a complete lack of cytokine genes usually triggered during a viral infection. Our study shows for the first time how the immune system responds to the SARS infection, and opens new possibilities for designing new diagnostics and treatments for this new life-threatening disease.

  10. RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes.

    Science.gov (United States)

    Chen, Junwen; Hou, Kai; Qin, Peng; Liu, Hongchang; Yi, Bin; Yang, Wenting; Wu, Wei

    2014-07-07

    Stevia (Stevia rebaudiana) is an important medicinal plant that yields diterpenoid steviol glycosides (SGs). SGs are currently used in the preparation of medicines, food products and neutraceuticals because of its sweetening property (zero calories and about 300 times sweeter than sugar). Recently, some progress has been made in understanding the biosynthesis of SGs in Stevia, but little is known about the molecular mechanisms underlying this process. Additionally, the genomics of Stevia, a non-model species, remains uncharacterized. The recent advent of RNA-Seq, a next generation sequencing technology, provides an opportunity to expand the identification of Stevia genes through in-depth transcript profiling. We present a comprehensive landscape of the transcriptome profiles of three genotypes of Stevia with divergent SG compositions characterized using RNA-seq. 191,590,282 high-quality reads were generated and then assembled into 171,837 transcripts with an average sequence length of 969 base pairs. A total of 80,160 unigenes were annotated, and 14,211 of the unique sequences were assigned to specific metabolic pathways by the Kyoto Encyclopedia of Genes and Genomes. Gene sequences of all enzymes known to be involved in SG synthesis were examined. A total of 143 UDP-glucosyltransferase (UGT) unigenes were identified, some of which might be involved in SG biosynthesis. The expression patterns of eight of these genes were further confirmed by RT-QPCR. RNA-seq analysis identified candidate genes encoding enzymes responsible for the biosynthesis of SGs in Stevia, a non-model plant without a reference genome. The transcriptome data from this study yielded new insights into the process of SG accumulation in Stevia. Our results demonstrate that RNA-Seq can be successfully used for gene identification and transcript profiling in a non-model species.

  11. Improving functional modules discovery by enriching interaction networks with gene profiles

    KAUST Repository

    Salem, Saeed

    2013-05-01

    Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.

  12. GeneChip expression profiling reveals the alterations of energy metabolism related genes in osteocytes under large gradient high magnetic fields.

    Science.gov (United States)

    Wang, Yang; Chen, Zhi-Hao; Yin, Chun; Ma, Jian-Hua; Li, Di-Jie; Zhao, Fan; Sun, Yu-Long; Hu, Li-Fang; Shang, Peng; Qian, Ai-Rong

    2015-01-01

    The diamagnetic levitation as a novel ground-based model for simulating a reduced gravity environment has recently been applied in life science research. In this study a specially designed superconducting magnet with a large gradient high magnetic field (LG-HMF), which can provide three apparent gravity levels (μ-g, 1-g, and 2-g), was used to simulate a space-like gravity environment. Osteocyte, as the most important mechanosensor in bone, takes a pivotal position in mediating the mechano-induced bone remodeling. In this study, the effects of LG-HMF on gene expression profiling of osteocyte-like cell line MLO-Y4 were investigated by Affymetrix DNA microarray. LG-HMF affected osteocyte gene expression profiling. Differentially expressed genes (DEGs) and data mining were further analyzed by using bioinfomatic tools, such as DAVID, iReport. 12 energy metabolism related genes (PFKL, AK4, ALDOC, COX7A1, STC1, ADM, CA9, CA12, P4HA1, APLN, GPR35 and GPR84) were further confirmed by real-time PCR. An integrated gene interaction network of 12 DEGs was constructed. Bio-data mining showed that genes involved in glucose metabolic process and apoptosis changed notablly. Our results demostrated that LG-HMF affected the expression of energy metabolism related genes in osteocyte. The identification of sensitive genes to special environments may provide some potential targets for preventing and treating bone loss or osteoporosis.

  13. GeneChip expression profiling reveals the alterations of energy metabolism related genes in osteocytes under large gradient high magnetic fields.

    Directory of Open Access Journals (Sweden)

    Yang Wang

    Full Text Available The diamagnetic levitation as a novel ground-based model for simulating a reduced gravity environment has recently been applied in life science research. In this study a specially designed superconducting magnet with a large gradient high magnetic field (LG-HMF, which can provide three apparent gravity levels (μ-g, 1-g, and 2-g, was used to simulate a space-like gravity environment. Osteocyte, as the most important mechanosensor in bone, takes a pivotal position in mediating the mechano-induced bone remodeling. In this study, the effects of LG-HMF on gene expression profiling of osteocyte-like cell line MLO-Y4 were investigated by Affymetrix DNA microarray. LG-HMF affected osteocyte gene expression profiling. Differentially expressed genes (DEGs and data mining were further analyzed by using bioinfomatic tools, such as DAVID, iReport. 12 energy metabolism related genes (PFKL, AK4, ALDOC, COX7A1, STC1, ADM, CA9, CA12, P4HA1, APLN, GPR35 and GPR84 were further confirmed by real-time PCR. An integrated gene interaction network of 12 DEGs was constructed. Bio-data mining showed that genes involved in glucose metabolic process and apoptosis changed notablly. Our results demostrated that LG-HMF affected the expression of energy metabolism related genes in osteocyte. The identification of sensitive genes to special environments may provide some potential targets for preventing and treating bone loss or osteoporosis.

  14. Prediction of Metastasis and Recurrence in Colorectal Cancer Based on Gene Expression Analysis: Ready for the Clinic?

    Energy Technology Data Exchange (ETDEWEB)

    Shibayama, Masaki [Sysmex Corporation, Central Research Laboratories, Kobe 651-2271 (Japan); Maak, Matthias; Nitsche, Ulrich [Chirurgische Klinik, Klinikum Rechts der Isar der TUM, München 81657 (Germany); Gotoh, Kengo [Sysmex Corporation, Central Research Laboratories, Kobe 651-2271 (Japan); Rosenberg, Robert; Janssen, Klaus-Peter, E-mail: klaus-peter.janssen@lrz.tum.de [Chirurgische Klinik, Klinikum Rechts der Isar der TUM, München 81657 (Germany)

    2011-07-07

    Cancers of the colon and rectum, which rank among the most frequent human tumors, are currently treated by surgical resection in locally restricted tumor stages. However, disease recurrence and formation of local and distant metastasis frequently occur even in cases with successful curative resection of the primary tumor (R0). Recent technological advances in molecular diagnostic analysis have led to a wealth of knowledge about the changes in gene transcription in all stages of colorectal tumors. Differential gene expression, or transcriptome analysis, has been proposed by many groups to predict disease recurrence, clinical outcome, and also response to therapy, in addition to the well-established clinico-pathological factors. However, the clinical usability of gene expression profiling as a reliable and robust prognostic tool that allows evidence-based clinical decisions is currently under debate. In this review, we will discuss the most recent data on the prognostic significance and potential clinical application of genome wide expression analysis in colorectal cancer.

  15. Prediction of Metastasis and Recurrence in Colorectal Cancer Based on Gene Expression Analysis: Ready for the Clinic?

    International Nuclear Information System (INIS)

    Shibayama, Masaki; Maak, Matthias; Nitsche, Ulrich; Gotoh, Kengo; Rosenberg, Robert; Janssen, Klaus-Peter

    2011-01-01

    Cancers of the colon and rectum, which rank among the most frequent human tumors, are currently treated by surgical resection in locally restricted tumor stages. However, disease recurrence and formation of local and distant metastasis frequently occur even in cases with successful curative resection of the primary tumor (R0). Recent technological advances in molecular diagnostic analysis have led to a wealth of knowledge about the changes in gene transcription in all stages of colorectal tumors. Differential gene expression, or transcriptome analysis, has been proposed by many groups to predict disease recurrence, clinical outcome, and also response to therapy, in addition to the well-established clinico-pathological factors. However, the clinical usability of gene expression profiling as a reliable and robust prognostic tool that allows evidence-based clinical decisions is currently under debate. In this review, we will discuss the most recent data on the prognostic significance and potential clinical application of genome wide expression analysis in colorectal cancer

  16. Real-Time Gene Expression Profiling of Live Shewanella Oneidensis Cells

    Energy Technology Data Exchange (ETDEWEB)

    Xiaoliang Sunney Xie

    2009-03-30

    steady-state distribution of protein concentration in live cells, considering that protein production occurs in random bursts with an exponentially distributed number of molecules. This model allows for the extraction of kinetic parameters of gene expression from steady-state distributions of protein concentration in a cell population, which are available from single cell data obtained by fluorescence microscopy. [Phys. Rev. Lett. 97, 168302 (2006)]. A major objective in the Genome to Life (GtL) program is to monitor and understand the gene expression profile of a complete bacterial genome. We developed genetic and imaging methods for sensitive protein expression profiling in individual S. oneidensis cell. We have made good progress in constructing YFP-library with several hundred chromosomal fusion proteins and studied protein expression profiling in living Shewanella oneidensis cells. Fluorescence microscopy revealed the average abundance of specific proteins, as well as their noise in gene expression level across a population. We also explored ways to adapt our fluorescence measurement for other growth conditions, such as anaerobic growth.

  17. cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila.

    Science.gov (United States)

    Yang, Tzu-Hsien; Wang, Chung-Ching; Hung, Po-Cheng; Wu, Wei-Sheng

    2014-01-01

    Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms

  18. Profiling helper T cell subset gene expression in deer mice

    Directory of Open Access Journals (Sweden)

    Hjelle Brian

    2006-08-01

    Full Text Available Abstract Background Deer mice (Peromyscus maniculatus are the most common mammals in North America and are reservoirs for several zoonotic agents, including Sin Nombre virus (SNV, the principal etiologic agent of hantavirus cardiopulmonary syndrome (HCPS in North America. Unlike human HCPS patients, SNV-infected deer mice show no overt pathological symptoms, despite the presence of virus in the lungs. A neutralizing IgG antibody response occurs, but the virus establishes a persistent infection. Limitations of detailed analysis of deer mouse immune responses to SNV are the lack of reagents and methods for evaluating such responses. Results We developed real-time PCR-based detection assays for several immune-related transcription factor and cytokine genes from deer mice that permit the profiling of CD4+ helper T cells, including markers of Th1 cells (T-bet, STAT4, IFNγ, TNF, LT, Th2 cells (GATA-3, STAT6, IL-4, IL-5 and regulatory T cells (Fox-p3, IL-10, TGFβ1. These assays compare the expression of in vitro antigen-stimulated and unstimulated T cells from individual deer mice. Conclusion We developed molecular methods for profiling immune gene expression in deer mice, including a multiplexed real-time PCR assay for assessing expression of several cytokine and transcription factor genes. These assays should be useful for characterizing the immune responses of experimentally- and naturally-infected deer mice.

  19. Differential genome-wide gene expression profiling of bovine largest and second-largest follicles: identification of genes associated with growth of dominant follicles

    Directory of Open Access Journals (Sweden)

    Takahashi Toru

    2010-02-01

    Full Text Available Abstract Background Bovine follicular development is regulated by numerous molecular mechanisms and biological pathways. In this study, we tried to identify differentially expressed genes between largest (F1 and second-largest follicles (F2, and classify them by global gene expression profiling using a combination of microarray and quantitative real-time PCR (QPCR analysis. The follicular status of F1 and F2 were further evaluated in terms of healthy and atretic conditions by investigating mRNA localization of identified genes. Methods Global gene expression profiles of F1 (10.7 +/- 0.7 mm and F2 (7.8 +/- 0.2 mm were analyzed by hierarchical cluster analysis and expression profiles of 16 representative genes were confirmed by QPCR analysis. In addition, localization of six identified transcripts was investigated in healthy and atretic follicles using in situ hybridization. The healthy or atretic condition of examined follicles was classified by progesterone and estradiol concentrations in follicular fluid. Results Hierarchical cluster analysis of microarray data classified the follicles into two clusters. Cluster A was composed of only F2 and was characterized by high expression of 31 genes including IGFBP5, whereas cluster B contained only F1 and predominantly expressed 45 genes including CYP19 and FSHR. QPCR analysis confirmed AMH, CYP19, FSHR, GPX3, PlGF, PLA2G1B, SCD and TRB2 were greater in F1 than F2, while CCL2, GADD45A, IGFBP5, PLAUR, SELP, SPP1, TIMP1 and TSP2 were greater in F2 than in F1. In situ hybridization showed that AMH and CYP19 were detected in granulosa cells (GC of healthy as well as atretic follicles. PlGF was localized in GC and in the theca layer (TL of healthy follicles. IGFBP5 was detected in both GC and TL of atretic follicles. GADD45A and TSP2 were localized in both GC and TL of atretic follicles, whereas healthy follicles expressed them only in GC. Conclusion We demonstrated that global gene expression profiling of F

  20. Gene expression profiles as prognostic markers in women with ovarian cancer

    DEFF Research Database (Denmark)

    Jochumsen, Kirsten M; Tan, Qihua; Høgdall, Estrid V

    2009-01-01

    toward investigations for more individualized therapies and the use of gene expression profiles in the clinical practice. RNA from tumor tissue from 43 Danish patients with serous epithelial ovarian carcinoma (11 International Federation of Gynecology and Obstetrics [FIGO] stage I/II, 32 FIGO stage III...

  1. Cultivation characteristics and gene expression profiles of Aspergillus oryzae by membrane-surface liquid culture, shaking-flask culture, and agar-plate culture.

    Science.gov (United States)

    Imanaka, Hiroyuki; Tanaka, Soukichi; Feng, Bin; Imamura, Koreyoshi; Nakanishi, Kazuhiro

    2010-03-01

    We cultivated a filamentous fungus, Aspergillus oryzae IAM 2706 by three different cultivation methods, i.e., shaking-flask culture (SFC), agar-plate culture (APC), and membrane-surface liquid culture (MSLC), to elucidate the differences of its behaviors by different cultivation methods under the same media, by measuring the growth, secretion of proteases and alpha-amylase, secreted protein level, and gene transcriptional profile by the DNA microarray analysis. The protease activities detected by MSLC and APC were much higher than that by SFC, using both modified Czapek-Dox (mCD) and dextrin-peptone-yeast extract (DPY) media. The alpha-amylase activity was detected in MSLC and APC in a much larger extent than that in SFC when DPY medium was used. On the basis of SDS-PAGE analyses and N-terminal amino acid sequences, 6 proteins were identified in the supernatants of the culture broths using DPY medium, among which oryzin (alkaline protease) and alpha-amylase were detected at a much higher extent for APC and MSLC than those for SFC while only oryzin was detected in mCD medium, in accordance with the activity measurements. A microarray analysis for the fungi cultivated by SFC, APC, and MSLC using mCD medium was carried out to elucidate the differences in the gene transcriptional profile by the cultivation methods. The gene transcriptional profile obtained for the MSLC sample showed a similar tendency to the APC sample while it was quite different from that for the SFC sample. Most of the genes specifically transcribed in the MSLC sample versus those in the SFC sample with a 10-fold up-regulation or higher were unknown or predicted proteins. However, transcription of oryzin gene was only slightly up-regulated in the MSLC sample and that of alpha-amylase gene, slightly down-regulated. Copyright 2009 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

  2. Complete gene expression profiling of Saccharopolyspora erythraea using GeneChip DNA microarrays

    Directory of Open Access Journals (Sweden)

    Bordoni Roberta

    2007-11-01

    Full Text Available Abstract Background The Saccharopolyspora erythraea genome sequence, recently published, presents considerable divergence from those of streptomycetes in gene organization and function, confirming the remarkable potential of S. erythraea for producing many other secondary metabolites in addition to erythromycin. In order to investigate, at whole transcriptome level, how S. erythraea genes are modulated, a DNA microarray was specifically designed and constructed on the S. erythraea strain NRRL 2338 genome sequence, and the expression profiles of 6494 ORFs were monitored during growth in complex liquid medium. Results The transcriptional analysis identified a set of 404 genes, whose transcriptional signals vary during growth and characterize three distinct phases: a rapid growth until 32 h (Phase A; a growth slowdown until 52 h (Phase B; and another rapid growth phase from 56 h to 72 h (Phase C before the cells enter the stationary phase. A non-parametric statistical method, that identifies chromosomal regions with transcriptional imbalances, determined regional organization of transcription along the chromosome, highlighting differences between core and non-core regions, and strand specific patterns of expression. Microarray data were used to characterize the temporal behaviour of major functional classes and of all the gene clusters for secondary metabolism. The results confirmed that the ery cluster is up-regulated during Phase A and identified six additional clusters (for terpenes and non-ribosomal peptides that are clearly regulated in later phases. Conclusion The use of a S. erythraea DNA microarray improved specificity and sensitivity of gene expression analysis, allowing a global and at the same time detailed picture of how S. erythraea genes are modulated. This work underlines the importance of using DNA microarrays, coupled with an exhaustive statistical and bioinformatic analysis of the results, to understand the transcriptional

  3. Gene expression profiling of liver cancer stem cells by RNA-sequencing.

    Directory of Open Access Journals (Sweden)

    David W Y Ho

    Full Text Available BACKGROUND: Accumulating evidence supports that tumor growth and cancer relapse are driven by cancer stem cells. Our previous work has demonstrated the existence of CD90(+ liver cancer stem cells (CSCs in hepatocellular carcinoma (HCC. Nevertheless, the characteristics of these cells are still poorly understood. In this study, we employed a more sensitive RNA-sequencing (RNA-Seq to compare the gene expression profiling of CD90(+ cells sorted from tumor (CD90(+CSCs with parallel non-tumorous liver tissues (CD90(+NTSCs and elucidate the roles of putative target genes in hepatocarcinogenesis. METHODOLOGY/PRINCIPAL FINDINGS: CD90(+ cells were sorted respectively from tumor and adjacent non-tumorous human liver tissues using fluorescence-activated cell sorting. The amplified RNAs of CD90(+ cells from 3 HCC patients were subjected to RNA-Seq analysis. A differential gene expression profile was established between CD90(+CSCs and CD90(+NTSCs, and validated by quantitative real-time PCR (qRT-PCR on the same set of amplified RNAs, and further confirmed in an independent cohort of 12 HCC patients. Five hundred genes were differentially expressed (119 up-regulated and 381 down-regulated genes between CD90(+CSCs and CD90(+NTSCs. Gene ontology analysis indicated that the over-expressed genes in CD90(+CSCs were associated with inflammation, drug resistance and lipid metabolism. Among the differentially expressed genes, glypican-3 (GPC3, a member of glypican family, was markedly elevated in CD90(+CSCs compared to CD90(+NTSCs. Immunohistochemistry demonstrated that GPC3 was highly expressed in forty-two human liver tumor tissues but absent in adjacent non-tumorous liver tissues. Flow cytometry indicated that GPC3 was highly expressed in liver CD90(+CSCs and mature cancer cells in liver cancer cell lines and human liver tumor tissues. Furthermore, GPC3 expression was positively correlated with the number of CD90(+CSCs in liver tumor tissues. CONCLUSIONS

  4. Gene Expression Profiling of Liver Cancer Stem Cells by RNA-Sequencing

    Science.gov (United States)

    Lam, Chi Tat; Ng, Michael N. P.; Yu, Wan Ching; Lau, Joyce; Wan, Timothy; Wang, Xiaoqi; Yan, Zhixiang; Liu, Hang; Fan, Sheung Tat

    2012-01-01

    Background Accumulating evidence supports that tumor growth and cancer relapse are driven by cancer stem cells. Our previous work has demonstrated the existence of CD90+ liver cancer stem cells (CSCs) in hepatocellular carcinoma (HCC). Nevertheless, the characteristics of these cells are still poorly understood. In this study, we employed a more sensitive RNA-sequencing (RNA-Seq) to compare the gene expression profiling of CD90+ cells sorted from tumor (CD90+CSCs) with parallel non-tumorous liver tissues (CD90+NTSCs) and elucidate the roles of putative target genes in hepatocarcinogenesis. Methodology/Principal Findings CD90+ cells were sorted respectively from tumor and adjacent non-tumorous human liver tissues using fluorescence-activated cell sorting. The amplified RNAs of CD90+ cells from 3 HCC patients were subjected to RNA-Seq analysis. A differential gene expression profile was established between CD90+CSCs and CD90+NTSCs, and validated by quantitative real-time PCR (qRT-PCR) on the same set of amplified RNAs, and further confirmed in an independent cohort of 12 HCC patients. Five hundred genes were differentially expressed (119 up-regulated and 381 down-regulated genes) between CD90+CSCs and CD90+NTSCs. Gene ontology analysis indicated that the over-expressed genes in CD90+CSCs were associated with inflammation, drug resistance and lipid metabolism. Among the differentially expressed genes, glypican-3 (GPC3), a member of glypican family, was markedly elevated in CD90+CSCs compared to CD90+NTSCs. Immunohistochemistry demonstrated that GPC3 was highly expressed in forty-two human liver tumor tissues but absent in adjacent non-tumorous liver tissues. Flow cytometry indicated that GPC3 was highly expressed in liver CD90+CSCs and mature cancer cells in liver cancer cell lines and human liver tumor tissues. Furthermore, GPC3 expression was positively correlated with the number of CD90+CSCs in liver tumor tissues. Conclusions/Significance The identified genes

  5. Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

    Science.gov (United States)

    Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

    2017-09-01

    Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  6. Differential expression gene profiling in human lymphocyte after 6 h irradiated

    International Nuclear Information System (INIS)

    Li Jianguo; Qin Xiujun; Zhang Wei; Xu Chaoqi; Li Weibin; Dang Xuhong; Zuo Yahui

    2011-01-01

    Objective: To provide the evidence of health damage for the staff irradiated from the gene level. Methods: The study analyzed the differential transcriptional profile of normal human lymphocyte and human lymphocyte irradiated with 0.1 Gy, 0.2 Gy, 0.5 Gy, 1.0 Gy by whole genome chip after 6 h irradiated. Results: The results showed that there were 1177 differentially expressed genes with 0.1 Gy after 6 h irradiation, and there were 1922 differentially expressed genes with 0.2 Gy after 6 h irradiation, and there were 492 differentially expressed genes with 0.5 Gy after 6 h irradiation, 2615 differentially expressed genes with 1.0 Gy after 6 h irradiation, 114 differentially expressed genes in 4 dose points after 6 h irradiation. RT-PCR results indicated that the relative quantity's result of EGR1, HLA-DMB and TAIAP1 was consistent with gene chip data. Conclusion: The study found many significant different genes in human lymphocyte with different doses after 6 h irradiation, which will provide a basis for the further radiation-different-genes and the mechanism of radiation damage. (authors)

  7. PanCoreGen - Profiling, detecting, annotating protein-coding genes in microbial genomes.

    Science.gov (United States)

    Paul, Sandip; Bhardwaj, Archana; Bag, Sumit K; Sokurenko, Evgeni V; Chattopadhyay, Sujay

    2015-12-01

    A large amount of genomic data, especially from multiple isolates of a single species, has opened new vistas for microbial genomics analysis. Analyzing the pan-genome (i.e. the sum of genetic repertoire) of microbial species is crucial in understanding the dynamics of molecular evolution, where virulence evolution is of major interest. Here we present PanCoreGen - a standalone application for pan- and core-genomic profiling of microbial protein-coding genes. PanCoreGen overcomes key limitations of the existing pan-genomic analysis tools, and develops an integrated annotation-structure for a species-specific pan-genomic profile. It provides important new features for annotating draft genomes/contigs and detecting unidentified genes in annotated genomes. It also generates user-defined group-specific datasets within the pan-genome. Interestingly, analyzing an example-set of Salmonella genomes, we detect potential footprints of adaptive convergence of horizontally transferred genes in two human-restricted pathogenic serovars - Typhi and Paratyphi A. Overall, PanCoreGen represents a state-of-the-art tool for microbial phylogenomics and pathogenomics study. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Supervised classification of combined copy number and gene expression data

    Directory of Open Access Journals (Sweden)

    Riccadonna S.

    2007-12-01

    Full Text Available In this paper we apply a predictive profiling method to genome copy number aberrations (CNA in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+ and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.

  9. Transcriptional Profiling and Identification of Heat-Responsive Genes in Perennial Ryegrass by RNA-Sequencing

    Directory of Open Access Journals (Sweden)

    Kehua Wang

    2017-06-01

    Full Text Available Perennial ryegrass (Lolium perenne is one of the most widely used forage and turf grasses in the world due to its desirable agronomic qualities. However, as a cool-season perennial grass species, high temperature is a major factor limiting its performance in warmer and transition regions. In this study, a de novo transcriptome was generated using a cDNA library constructed from perennial ryegrass leaves subjected to short-term heat stress treatment. Then the expression profiling and identification of perennial ryegrass heat response genes by digital gene expression analyses was performed. The goal of this work was to produce expression profiles of high temperature stress responsive genes in perennial ryegrass leaves and further identify the potentially important candidate genes with altered levels of transcript, such as those genes involved in transcriptional regulation, antioxidant responses, plant hormones and signal transduction, and cellular metabolism. The de novo assembly of perennial ryegrass transcriptome in this study obtained more total and annotated unigenes compared to previously published ones. Many DEGs identified were genes that are known to respond to heat stress in plants, including HSFs, HSPs, and antioxidant related genes. In the meanwhile, we also identified four gene candidates mainly involved in C4 carbon fixation, and one TOR gene. Their exact roles in plant heat stress response need to dissect further. This study would be important by providing the gene resources for improving heat stress tolerance in both perennial ryegrass and other cool-season perennial grass plants.

  10. Oxidative stress gene expression profile in inbred mouse after ischemia/reperfusion small bowel injury.

    Science.gov (United States)

    Bertoletto, Paulo Roberto; Ikejiri, Adauto Tsutomu; Somaio Neto, Frederico; Chaves, José Carlos; Teruya, Roberto; Bertoletto, Eduardo Rodrigues; Taha, Murched Omar; Fagundes, Djalma José

    2012-11-01

    To determine the profile of gene expressions associated with oxidative stress and thereby contribute to establish parameters about the role of enzyme clusters related to the ischemia/reperfusion intestinal injury. Twelve male inbred mice (C57BL/6) were randomly assigned: Control Group (CG) submitted to anesthesia, laparotomy and observed by 120 min; Ischemia/reperfusion Group (IRG) submitted to anesthesia, laparotomy, 60 min of small bowel ischemia and 60 min of reperfusion. A pool of six samples was submitted to the qPCR-RT protocol (six clusters) for mouse oxidative stress and antioxidant defense pathways. On the 84 genes investigated, 64 (76.2%) had statistic significant expression and 20 (23.8%) showed no statistical difference to the control group. From these 64 significantly expressed genes, 60 (93.7%) were up-regulated and 04 (6.3%) were down-regulated. From the group with no statistical significantly expression, 12 genes were up-regulated and 8 genes were down-regulated. Surprisingly, 37 (44.04%) showed a higher than threefold up-regulation and then arbitrarily the values was considered as a very significant. Thus, 37 genes (44.04%) were expressed very significantly up-regulated. The remained 47 (55.9%) genes were up-regulated less than three folds (35 genes - 41.6%) or down-regulated less than three folds (12 genes - 14.3%). The intestinal ischemia and reperfusion promote a global hyper-expression profile of six different clusters genes related to antioxidant defense and oxidative stress.

  11. Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data.

    Science.gov (United States)

    Kim, Marlene Thai; Huang, Ruili; Sedykh, Alexander; Wang, Wenyi; Xia, Menghang; Zhu, Hao

    2016-05-01

    Hepatotoxicity accounts for a substantial number of drugs being withdrawn from the market. Using traditional animal models to detect hepatotoxicity is expensive and time-consuming. Alternative in vitro methods, in particular cell-based high-throughput screening (HTS) studies, have provided the research community with a large amount of data from toxicity assays. Among the various assays used to screen potential toxicants is the antioxidant response element beta lactamase reporter gene assay (ARE-bla), which identifies chemicals that have the potential to induce oxidative stress and was used to test > 10,000 compounds from the Tox21 program. The ARE-bla computational model and HTS data from a big data source (PubChem) were used to profile environmental and pharmaceutical compounds with hepatotoxicity data. Quantitative structure-activity relationship (QSAR) models were developed based on ARE-bla data. The models predicted the potential oxidative stress response for known liver toxicants when no ARE-bla data were available. Liver toxicants were used as probe compounds to search PubChem Bioassay and generate a response profile, which contained thousands of bioassays (> 10 million data points). By ranking the in vitro-in vivo correlations (IVIVCs), the most relevant bioassay(s) related to hepatotoxicity were identified. The liver toxicants profile contained the ARE-bla and relevant PubChem assays. Potential toxicophores for well-known toxicants were created by identifying chemical features that existed only in compounds with high IVIVCs. Profiling chemical IVIVCs created an opportunity to fully explore the source-to-outcome continuum of modern experimental toxicology using cheminformatics approaches and big data sources. Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H. 2016. Mechanism profiling of hepatotoxicity caused by oxidative stress using antioxidant response element reporter gene assay models and big data. Environ Health Perspect 124:634-641;

  12. A Microchip for Integrated Single-Cell Gene Expression Profiling and Genotoxicity Detection

    Directory of Open Access Journals (Sweden)

    Hui Dong

    2016-09-01

    Full Text Available Microfluidics-based single-cell study is an emerging approach in personalized treatment or precision medicine studies. Single-cell gene expression holds a potential to provide treatment selections with maximized efficacy to help cancer patients based on a genetic understanding of their disease. This work presents a multi-layer microchip for single-cell multiplexed gene expression profiling and genotoxicity detection. Treated by three drug reagents (i.e., methyl methanesulfonate, docetaxel and colchicine with varied concentrations and time lengths, individual human cancer cells (MDA-MB-231 are lysed on-chip, and the released mRNA templates are captured and reversely transcribed into single strand DNA. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, cyclin-dependent kinase inhibitor 1A (CDKN1A, and aurora kinase A (AURKA genes from single cells are amplified and real-time quantified through multiplex polymerase chain reaction. The microchip is capable of integrating all steps of single-cell multiplexed gene expression profiling, and providing precision detection of drug induced genotoxic stress. Throughput has been set to be 18, and can be further increased following the same approach. Numerical simulation of on-chip single cell trapping and heat transfer has been employed to evaluate the chip design and operation.

  13. Gene expression profiling demonstrates WNT/β-catenin pathway genes alteration in Mexican patients with colorectal cancer and diabetes mellitus.

    Science.gov (United States)

    Ivonne Wence-Chavez, Laura; Palomares-Chacon, Ulises; Pablo Flores-Gutierrez, Juan; Felipe Jave-Suarez, Luis; Del Carmen Aguilar-Lemarroy, Adriana; Barros-Nunez, Patricio; Esperanza Flores-Martinez, Silvia; Sanchez-Corona, Jose; Alejandra Rosales-Reynoso, Monica

    2017-01-01

    Several studies have shown a strong association between diabetes mellitus (DM) and increased risk of colorectal cancer (CRC). The fundamental mechanisms that support this association are not entirely understood; however, it is believed that hyperinsulinemia and hyperglycemia may be involved. Some proposed mechanisms include upregulation of mitogenic signaling pathways like MAPK, PI3K, mTOR, and WNT, which are involved in cell proliferation, growth, and cancer cell survival. The purpose of this study was to evaluate the gene expression profile and identify differently expressed genes involved in mitogenic pathways in CRC patients with and without DM. In this study, microarray analysis of gene expression followed by quantitative PCR (qPCR) was performed in cancer tissue from CRC patients with and without DM to identify the gene expression profiles and validate the differently expressed genes. Among the study groups, some differently expressed genes were identified. However, when bioinformatics clustering tools were used, a significant modulation of genes involved in the WNT pathway was evident. Therefore, we focused on genes participating in this pathway, such as WNT3A, LRP6, TCF7L2, and FRA-1. Validation of the expression levels of those genes by qPCR showed that CRC patients without type 2 diabetes mellitus (T2DM) expressed significantly more WNT3Ay LRP6, but less TCF7L2 and FRA-1 compared to controls, while in CRC patients with DM the expression levels of WNT3A, LRP6, TCF7L2, and FRA-1 were significantly higher compared to controls. Our results suggest that WNT/β-catenin pathway is upregulated in patients with CRC and DM, demonstrating its importance and involvement in both pathologies.

  14. Tumour metastasis-associated gene profiling using one-dimensional microfluidic beads array

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Great efforts have been made on the early diagnosis and molecular mechanism research of tumour metastasis in recent years. In this paper, based on the one-dimensional microfluidic beads array, a novel platform for tumour metastasis-associated genes profiling has been developed by depositing nucleic acids functional beads in the microchannel. This platform is sensitive (limit of detection: 0.02 nmol/L) and can perform mRNAs analysis without PCR. Two human colon cancer cell lines (primary and metastatic) from the same patient were used as a model, and transcriptional expression profiling of multiple tumour metastasis-associated genes in these two cell lines was successfully achieved. Furthermore, the results obtained on the beads array were validated by RT-PCR. This novel beads array has advantages of high sensitivity, little sample consumption, short assay time, low cost and high throughput capability. It holds the potential in early diagnosis and mechanism research of tumour metastasis.

  15. MicroRNA profiling reveals dysregulated microRNAs and their target gene regulatory networks in cemento-ossifying fibroma.

    Science.gov (United States)

    Pereira, Thaís Dos Santos Fontes; Brito, João Artur Ricieri; Guimarães, André Luiz Sena; Gomes, Carolina Cavaliéri; de Lacerda, Júlio Cesar Tanos; de Castro, Wagner Henriques; Coimbra, Roney Santos; Diniz, Marina Gonçalves; Gomez, Ricardo Santiago

    2018-01-01

    Cemento-ossifying fibroma (COF) is a benign fibro-osseous neoplasm of uncertain pathogenesis, and its treatment results in morbidity. MicroRNAs (miRNA) are small non-coding RNAs that regulate gene expression and may represent therapeutic targets. The purpose of the study was to generate a comprehensive miRNA profile of COF compared to normal bone. Additionally, the most relevant pathways and target genes of differentially expressed miRNA were investigated by in silico analysis. Nine COF and ten normal bone samples were included in the study. miRNA profiling was carried out by using TaqMan® OpenArray® Human microRNA panel containing 754 validated human miRNAs. We identified the most relevant miRNAs target genes through the leader gene approach, using STRING and Cytoscape software. Pathways enrichment analysis was performed using DIANA-miRPath. Eleven miRNAs were downregulated (hsa-miR-95-3p, hsa-miR-141-3p, hsa-miR-205-5p, hsa-miR-223-3p, hsa-miR-31-5p, hsa-miR-944, hsa-miR-200b-3p, hsa-miR-135b-5p, hsa-miR-31-3p, hsa-miR-223-5p and hsa-miR-200c-3p), and five were upregulated (hsa-miR-181a-5p, hsa-miR-181c-5p, hsa-miR-149-5p, hsa-miR-138-5p and hsa-miR-199a-3p) in COF compared to normal bone. Eighteen common target genes were predicted, and the leader genes approach identified the following genes involved in human COF: EZH2, XIAP, MET and TGFBR1. According to the biology of bone and COF, the most relevant KEGG pathways revealed by enrichment analysis were proteoglycans in cancer, miRNAs in cancer, pathways in cancer, p53-, PI3K-Akt-, FoxO- and TGF-beta signalling pathways, which were previously found to be differentially regulated in bone neoplasms, odontogenic tumours and osteogenesis. miRNA dysregulation occurs in COF, and EZH2, XIAP, MET and TGFBR1 are potential targets for functional analysis validation. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  16. Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

    Directory of Open Access Journals (Sweden)

    Mark Burton

    2012-01-01

    Full Text Available Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.

  17. Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

    Directory of Open Access Journals (Sweden)

    Kim Seon-Young

    2009-05-01

    Full Text Available Abstract Background Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature. Results A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+ patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures Conclusion Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.

  18. Prognostic Gene Expression Profiles in Breast Cancer

    DEFF Research Database (Denmark)

    Sørensen, Kristina Pilekær

    Each year approximately 4,800 Danish women are diagnosed with breast cancer. Several clinical and pathological factors are used as prognostic and predictive markers to categorize the patients into groups of high or low risk. Around 90% of all patients are allocated to the high risk group...... clinical courses, and they may be useful as novel prognostic biomarkers in breast cancer. The aim of the present project was to predict the development of metastasis in lymph node negative breast cancer patients by RNA profiling. We collected and analyzed 82 primary breast tumors from patients who...... and the time of event. Previous findings have shown that high expression of the lncRNA HOTAIR is correlated with poor survival in breast cancer. We validated this finding by demonstrating that high HOTAIR expression in our primary tumors was significantly associated with worse prognosis independent...

  19. Gene Prediction in Metagenomic Fragments with Deep Learning

    Directory of Open Access Journals (Sweden)

    Shao-Wu Zhang

    2017-01-01

    Full Text Available Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features and using deep stacking networks learning model, we present a novel method (called Meta-MFDL to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

  20. Validation of predicted exponential concentration profiles of chemicals in soils

    International Nuclear Information System (INIS)

    Hollander, Anne; Baijens, Iris; Ragas, Ad; Huijbregts, Mark; Meent, Dik van de

    2007-01-01

    Multimedia mass balance models assume well-mixed homogeneous compartments. Particularly for soils, this does not correspond to reality, which results in potentially large uncertainties in estimates of transport fluxes from soils. A theoretically expected exponential decrease model of chemical concentrations with depth has been proposed, but hardly tested against empirical data. In this paper, we explored the correspondence between theoretically predicted soil concentration profiles and 84 field measured profiles. In most cases, chemical concentrations in soils appear to decline exponentially with depth, and values for the chemical specific soil penetration depth (d p ) are predicted within one order of magnitude. Over all, the reliability of multimedia models will improve when they account for depth-dependent soil concentrations, so we recommend to take into account the described theoretical exponential decrease model of chemical concentrations with depth in chemical fate studies. In this model the d p -values should estimated be either based on local conditions or on a fixed d p -value, which we recommend to be 10 cm for chemicals with a log K ow > 3. - Multimedia mass model predictions will improve when taking into account depth dependent soil concentrations

  1. Bioinformatics analysis of the predicted polyprenol reductase genes in higher plants

    Science.gov (United States)

    Basyuni, M.; Wati, R.

    2018-03-01

    The present study evaluates the bioinformatics methods to analyze twenty-four predicted polyprenol reductase genes from higher plants on GenBank as well as predicted the structure, composition, similarity, subcellular localization, and phylogenetic. The physicochemical properties of plant polyprenol showed diversity among the observed genes. The percentage of the secondary structure of plant polyprenol genes followed the ratio order of α helix > random coil > extended chain structure. The values of chloroplast but not signal peptide were too low, indicated that few chloroplast transit peptide in plant polyprenol reductase genes. The possibility of the potential transit peptide showed variation among the plant polyprenol reductase, suggested the importance of understanding the variety of peptide components of plant polyprenol genes. To clarify this finding, a phylogenetic tree was drawn. The phylogenetic tree shows several branches in the tree, suggested that plant polyprenol reductase genes grouped into divergent clusters in the tree.

  2. Embryonic stem cell-like features of testicular carcinoma in situ revealed by genome-wide gene expression profiling

    DEFF Research Database (Denmark)

    Almstrup, Kristian; Hoei-Hansen, Christina E; Wirkner, Ute

    2004-01-01

    in their stoichiometry on progression into embryonic carcinoma. We compared the CIS expression profile with patterns reported in embryonic stem cells (ESCs), which revealed a substantial overlap that may be as high as 50%. We also demonstrated an over-representation of expressed genes in regions of 17q and 12, reported......Carcinoma in situ (CIS) is the common precursor of histologically heterogeneous testicular germ cell tumors (TGCTs), which in recent decades have markedly increased and now are the most common malignancy of young men. Using genome-wide gene expression profiling, we identified >200 genes highly...

  3. MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer.

    LENUS (Irish Health Repository)

    Chang, Kah Hoong

    2010-01-01

    BACKGROUND: Advances in high-throughput technologies and bioinformatics have transformed gene expression profiling methodologies. The results of microarray experiments are often validated using reverse transcription quantitative PCR (RT-qPCR), which is the most sensitive and reproducible method to quantify gene expression. Appropriate normalisation of RT-qPCR data using stably expressed reference genes is critical to ensure accurate and reliable results. Mi(cro)RNA expression profiles have been shown to be more accurate in disease classification than mRNA expression profiles. However, few reports detailed a robust identification and validation strategy for suitable reference genes for normalisation in miRNA RT-qPCR studies. METHODS: We adopt and report a systematic approach to identify the most stable reference genes for miRNA expression studies by RT-qPCR in colorectal cancer (CRC). High-throughput miRNA profiling was performed on ten pairs of CRC and normal tissues. By using the mean expression value of all expressed miRNAs, we identified the most stable candidate reference genes for subsequent validation. As such the stability of a panel of miRNAs was examined on 35 tumour and 39 normal tissues. The effects of normalisers on the relative quantity of established oncogenic (miR-21 and miR-31) and tumour suppressor (miR-143 and miR-145) target miRNAs were assessed. RESULTS: In the array experiment, miR-26a, miR-345, miR-425 and miR-454 were identified as having expression profiles closest to the global mean. From a panel of six miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) and two small nucleolar RNA genes (RNU48 and Z30), miR-16 and miR-345 were identified as the most stably expressed reference genes. The combined use of miR-16 and miR-345 to normalise expression data enabled detection of a significant dysregulation of all four target miRNAs between tumour and normal colorectal tissue. CONCLUSIONS: Our study demonstrates that the top six most

  4. MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer

    LENUS (Irish Health Repository)

    Chang, Kah Hoong

    2010-04-29

    Abstract Background Advances in high-throughput technologies and bioinformatics have transformed gene expression profiling methodologies. The results of microarray experiments are often validated using reverse transcription quantitative PCR (RT-qPCR), which is the most sensitive and reproducible method to quantify gene expression. Appropriate normalisation of RT-qPCR data using stably expressed reference genes is critical to ensure accurate and reliable results. Mi(cro)RNA expression profiles have been shown to be more accurate in disease classification than mRNA expression profiles. However, few reports detailed a robust identification and validation strategy for suitable reference genes for normalisation in miRNA RT-qPCR studies. Methods We adopt and report a systematic approach to identify the most stable reference genes for miRNA expression studies by RT-qPCR in colorectal cancer (CRC). High-throughput miRNA profiling was performed on ten pairs of CRC and normal tissues. By using the mean expression value of all expressed miRNAs, we identified the most stable candidate reference genes for subsequent validation. As such the stability of a panel of miRNAs was examined on 35 tumour and 39 normal tissues. The effects of normalisers on the relative quantity of established oncogenic (miR-21 and miR-31) and tumour suppressor (miR-143 and miR-145) target miRNAs were assessed. Results In the array experiment, miR-26a, miR-345, miR-425 and miR-454 were identified as having expression profiles closest to the global mean. From a panel of six miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) and two small nucleolar RNA genes (RNU48 and Z30), miR-16 and miR-345 were identified as the most stably expressed reference genes. The combined use of miR-16 and miR-345 to normalise expression data enabled detection of a significant dysregulation of all four target miRNAs between tumour and normal colorectal tissue. Conclusions Our study demonstrates that the top six most

  5. Radiogenomics: predicting clinical normal tissue radiosensitivity

    DEFF Research Database (Denmark)

    Alsner, Jan

    2006-01-01

    Studies on the genetic basis of normal tissue radiosensitivity, or  'radiogenomics', aims at predicting clinical radiosensitivity and optimize treatment from individual genetic profiles. Several studies have now reported links between variations in certain genes related to the biological response...... to radiation injury and risk of normal tissue morbidity in cancer patients treated with radiotherapy. However, after these initial association studies including few genes, we are still far from being able to predict clinical radiosensitivity on an individual level. Recent data from our own studies on risk...

  6. Multiway real-time PCR gene expression profiling in yeast Saccharomyces cerevisiae reveals altered transcriptional response of ADH-genes to glucose stimuli.

    Science.gov (United States)

    Ståhlberg, Anders; Elbing, Karin; Andrade-Garda, José Manuel; Sjögreen, Björn; Forootan, Amin; Kubista, Mikael

    2008-04-16

    The large sensitivity, high reproducibility and essentially unlimited dynamic range of real-time PCR to measure gene expression in complex samples provides the opportunity for powerful multivariate and multiway studies of biological phenomena. In multiway studies samples are characterized by their expression profiles to monitor changes over time, effect of treatment, drug dosage etc. Here we perform a multiway study of the temporal response of four yeast Saccharomyces cerevisiae strains with different glucose uptake rates upon altered metabolic conditions. We measured the expression of 18 genes as function of time after addition of glucose to four strains of yeast grown in ethanol. The data are analyzed by matrix-augmented PCA, which is a generalization of PCA for 3-way data, and the results are confirmed by hierarchical clustering and clustering by Kohonen self-organizing map. Our approach identifies gene groups that respond similarly to the change of nutrient, and genes that behave differently in mutant strains. Of particular interest is our finding that ADH4 and ADH6 show a behavior typical of glucose-induced genes, while ADH3 and ADH5 are repressed after glucose addition. Multiway real-time PCR gene expression profiling is a powerful technique which can be utilized to characterize functions of new genes by, for example, comparing their temporal response after perturbation in different genetic variants of the studied subject. The technique also identifies genes that show perturbed expression in specific strains.

  7. Multiway real-time PCR gene expression profiling in yeast Saccharomyces cerevisiae reveals altered transcriptional response of ADH-genes to glucose stimuli

    Directory of Open Access Journals (Sweden)

    Andrade-Garda José

    2008-04-01

    Full Text Available Abstract Background The large sensitivity, high reproducibility and essentially unlimited dynamic range of real-time PCR to measure gene expression in complex samples provides the opportunity for powerful multivariate and multiway studies of biological phenomena. In multiway studies samples are characterized by their expression profiles to monitor changes over time, effect of treatment, drug dosage etc. Here we perform a multiway study of the temporal response of four yeast Saccharomyces cerevisiae strains with different glucose uptake rates upon altered metabolic conditions. Results We measured the expression of 18 genes as function of time after addition of glucose to four strains of yeast grown in ethanol. The data are analyzed by matrix-augmented PCA, which is a generalization of PCA for 3-way data, and the results are confirmed by hierarchical clustering and clustering by Kohonen self-organizing map. Our approach identifies gene groups that respond similarly to the change of nutrient, and genes that behave differently in mutant strains. Of particular interest is our finding that ADH4 and ADH6 show a behavior typical of glucose-induced genes, while ADH3 and ADH5 are repressed after glucose addition. Conclusion Multiway real-time PCR gene expression profiling is a powerful technique which can be utilized to characterize functions of new genes by, for example, comparing their temporal response after perturbation in different genetic variants of the studied subject. The technique also identifies genes that show perturbed expression in specific strains.

  8. Gene prediction in metagenomic fragments: A large scale machine learning approach

    Directory of Open Access Journals (Sweden)

    Morgenstern Burkhard

    2008-04-01

    Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

  9. Regulators of Long-Term Memory Revealed by Mushroom Body-Specific Gene Expression Profiling in Drosophila melanogaster.

    Science.gov (United States)

    Widmer, Yves F; Bilican, Adem; Bruggmann, Rémy; Sprecher, Simon G

    2018-06-20

    Memory formation is achieved by genetically tightly controlled molecular pathways that result in a change of synaptic strength and synapse organization. While for short-term memory traces rapidly acting biochemical pathways are in place, the formation of long-lasting memories requires changes in the transcriptional program of a cell. Although many genes involved in learning and memory formation have been identified, little is known about the genetic mechanisms required for changing the transcriptional program during different phases of long-term memory formation. With Drosophila melanogaster as a model system we profiled transcriptomic changes in the mushroom body, a memory center in the fly brain, at distinct time intervals during appetitive olfactory long-term memory formation using the targeted DamID technique. We describe the gene expression profiles during these phases and tested 33 selected candidate genes for deficits in long-term memory formation using RNAi knockdown. We identified 10 genes that enhance or decrease memory when knocked-down in the mushroom body. For vajk-1 and hacd1 , the two strongest hits, we gained further support for their crucial role in appetitive learning and forgetting. These findings show that profiling gene expression changes in specific cell-types harboring memory traces provides a powerful entry point to identify new genes involved in learning and memory. The presented transcriptomic data may further be used as resource to study genes acting at different memory phases. Copyright © 2018, Genetics.

  10. Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure.

    Science.gov (United States)

    Bourdon, Julie A; Williams, Andrew; Kuo, Byron; Moffat, Ivy; White, Paul A; Halappanavar, Sabina; Vogel, Ulla; Wallin, Håkan; Yauk, Carole L

    2013-01-07

    New approaches are urgently needed to evaluate potential hazards posed by exposure to nanomaterials. Gene expression profiling provides information on potential modes of action and human relevance, and tools have recently become available for pathway-based quantitative risk assessment. The objective of this study was to use toxicogenomics in the context of human health risk assessment. We explore the utility of toxicogenomics in risk assessment, using published gene expression data from C57BL/6 mice exposed to 18, 54 and 162 μg Printex 90 carbon black nanoparticles (CBNP). Analysis of CBNP-perturbed pathways, networks and transcription factors revealed concomitant changes in predicted phenotypes (e.g., pulmonary inflammation and genotoxicity), that correlated with dose and time. Benchmark doses (BMDs) for apical endpoints were comparable to minimum BMDs for relevant pathway-specific expression changes. Comparison to inflammatory lung disease models (i.e., allergic airway inflammation, bacterial infection and tissue injury and fibrosis) and human disease profiles revealed that induced gene expression changes in Printex 90 exposed mice were similar to those typical for pulmonary injury and fibrosis. Very similar fibrotic pathways were perturbed in CBNP-exposed mice and human fibrosis disease models. Our synthesis demonstrates how toxicogenomic profiles may be used in human health risk assessment of nanoparticles and constitutes an important step forward in the ultimate recognition of toxicogenomic endpoints in human health risk. As our knowledge of molecular pathways, dose-response characteristics and relevance to human disease continues to grow, we anticipate that toxicogenomics will become increasingly useful in assessing chemical toxicities and in human health risk assessment. Crown Copyright © 2012. Published by Elsevier Ireland Ltd. All rights reserved.

  11. Identification of pathogenic genes related to rheumatoid arthritis through integrated analysis of DNA methylation and gene expression profiling.

    Science.gov (United States)

    Zhang, Lei; Ma, Shiyun; Wang, Huailiang; Su, Hang; Su, Ke; Li, Longjie

    2017-11-15

    The purpose of our study was to identify new pathogenic genes used for exploring the pathogenesis of rheumatoid arthritis (RA). To screen pathogenic genes of RA, an integrated analysis was performed by using the microarray datasets in RA derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were further discovered by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Afterwards, the integrated analysis of DNA methylation and gene expression profiling was used to screen crucial genes. In addition, we used RT-PCR and MSP to verify the expression levels and methylation status of these crucial genes in 20 synovial biopsy samples obtained from 10 RA model mice and 10 normal mice. BCL11B, CCDC88C, FCRLA and APOL6 were both up-regulated and hypomethylated in RA according to integrated analysis, RT-PCR and MSP verification. Four crucial genes (BCL11B, CCDC88C, FCRLA and APOL6) identified and analyzed in this study might be closely connected with the pathogenesis of RA. Copyright © 2017. Published by Elsevier B.V.

  12. Twenty putative palmitoyl-acyl transferase genes with distinct ...

    African Journals Online (AJOL)

    There are 20 genes containing DHHC domain predicted to encode putative palmitoyltransferase in Arabidopsis thaliana genome. However, little is known about their characteristics such as genetic relationship and expression profile. Here, we present an overview of the putative PAT genes in A. thaliana focusing on their ...

  13. Molecular cloning, sequence identification and expression profile of domestic guinea pig (Cavia porcellus UGT1A1 gene

    Directory of Open Access Journals (Sweden)

    Yang Deming

    2016-01-01

    Full Text Available Domestic guinea pig is a model animal for human disease research. Uridine diphosphate glucuronosyltransferase 1 family, polypeptide A1 (UGT1A1 is an important human disease-related gene. In this study, the complete coding sequence of domestic guinea pig gene UGT1A1 was amplified by reverse transcription-polymerase chain reaction. The open reading frame of the domestic guinea pig UGT1A1 gene is 1602 bp in length and was found to encode a protein of 533 amino acids. Sequence analysis revealed that the UGT1A1 protein of domestic guinea pig shared high homology with the UGT1A1 proteins of degu (84%, damara mole-rat (84%, human (80%, northern white-cheeked gibbon (80%, Colobus angolensis palliatus (80% and golden snub-nosed monkey (79%. This gene contains five exons and four introns, as revealed by the computer-assisted analysis. The results also showed that the domestic guinea pig UGT1A1 gene had a close genetic relationship with the UGT1A1 gene of degu. The prediction of transmembrane helices showed that domestic guinea pig UGT1A1 might be a transmembrane protein. Expression profile analysis indicated that the domestic guinea pig UGT1A1 gene was differentially expressed in detected domestic guinea pig tissues. Our experiment laid a primary foundation for using the domestic guinea pig as a model animal to study the UGT1A1-related human diseases.

  14. Citrus plastid-related gene profiling based on expressed sequence tag analyses

    Directory of Open Access Journals (Sweden)

    Tercilio Calsa Jr.

    2007-01-01

    Full Text Available Plastid-related sequences, derived from putative nuclear or plastome genes, were searched in a large collection of expressed sequence tags (ESTs and genomic sequences from the Citrus Biotechnology initiative in Brazil. The identified putative Citrus chloroplast gene sequences were compared to those from Arabidopsis, Eucalyptus and Pinus. Differential expression profiling for plastid-directed nuclear-encoded proteins and photosynthesis-related gene expression variation between Citrus sinensis and Citrus reticulata, when inoculated or not with Xylella fastidiosa, were also analyzed. Presumed Citrus plastome regions were more similar to Eucalyptus. Some putative genes appeared to be preferentially expressed in vegetative tissues (leaves and bark or in reproductive organs (flowers and fruits. Genes preferentially expressed in fruit and flower may be associated with hypothetical physiological functions. Expression pattern clustering analysis suggested that photosynthesis- and carbon fixation-related genes appeared to be up- or down-regulated in a resistant or susceptible Citrus species after Xylella inoculation in comparison to non-infected controls, generating novel information which may be helpful to develop novel genetic manipulation strategies to control Citrus variegated chlorosis (CVC.

  15. Comparative transcriptional profiling of the axolotl limb identifies a tripartite regeneration-specific gene program.

    Directory of Open Access Journals (Sweden)

    Dunja Knapp

    Full Text Available Understanding how the limb blastema is established after the initial wound healing response is an important aspect of regeneration research. Here we performed parallel expression profile time courses of healing lateral wounds versus amputated limbs in axolotl. This comparison between wound healing and regeneration allowed us to identify amputation-specific genes. By clustering the expression profiles of these samples, we could detect three distinguishable phases of gene expression - early wound healing followed by a transition-phase leading to establishment of the limb development program, which correspond to the three phases of limb regeneration that had been defined by morphological criteria. By focusing on the transition-phase, we identified 93 strictly amputation-associated genes many of which are implicated in oxidative-stress response, chromatin modification, epithelial development or limb development. We further classified the genes based on whether they were or were not significantly expressed in the developing limb bud. The specific localization of 53 selected candidates within the blastema was investigated by in situ hybridization. In summary, we identified a set of genes that are expressed specifically during regeneration and are therefore, likely candidates for the regulation of blastema formation.

  16. Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

    Science.gov (United States)

    Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

    2012-07-15

    Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of EOperon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

  17. Analysis and prediction of gene splice sites in four Aspergillus genomes

    DEFF Research Database (Denmark)

    Wang, Kai; Ussery, David; Brunak, Søren

    2009-01-01

    Several Aspergillus fungal genomic sequences have been published, with many more in progress. Obviously, it is essential to have high-quality, consistently annotated sets of proteins from each of the genomes, in order to make meaningful comparisons. We have developed a dedicated, publicly available......, splice site prediction program called NetAspGene, for the genus Aspergillus. Gene sequences from Aspergillus fumigatus, the most common mould pathogen, were used to build and test our model. Compared to many animals and plants, Aspergillus contains smaller introns; thus we have applied a larger window...... better splice site prediction than other available tools. NetAspGene will be very helpful for the study in Aspergillus splice sites and especially in alternative splicing. A webpage for NetAspGene is publicly available at http://www.cbs.dtu.dk/services/NetAspGene....

  18. Alteration of the gene expression profile of T-cell receptor αβ-modified T-cells with diffuse large B-cell lymphoma specificity.

    Science.gov (United States)

    Zha, Xianfeng; Yin, Qingsong; Tan, Huo; Wang, Chunyan; Chen, Shaohua; Yang, Lijian; Li, Bo; Wu, Xiuli; Li, Yangqiu

    2013-05-01

    Antigen-specific, T-cell receptor (TCR)-modified cytotoxic T lymphocytes (CTLs) that target tumors are an attractive strategy for specific adoptive immunotherapy. Little is known about whether there are any alterations in the gene expression profile after TCR gene transduction in T cells. We constructed TCR gene-redirected CTLs with specificity for diffuse large B-cell lymphoma (DLBCL)-associated antigens to elucidate the gene expression profiles of TCR gene-redirected T-cells, and we further analyzed the gene expression profile pattern of these redirected T-cells by Affymetrix microarrays. The resulting data were analyzed using Bioconductor software, a two-fold cut-off expression change was applied together with anti-correlation of the profile ratios to render the microarray analysis set. The fold change of all genes was calculated by comparing the three TCR gene-modified T-cells and a negative control counterpart. The gene pathways were analyzed using Bioconductor and Kyoto Encyclopedia of Genes and Genomes. Identical genes whose fold change was greater than or equal to 2.0 in all three TCR gene-redirected T-cell groups in comparison with the negative control were identified as the differentially expressed genes. The differentially expressed genes were comprised of 33 up-regulated genes and 1 down-regulated gene including JUNB, FOS, TNF, INF-γ, DUSP2, IL-1B, CXCL1, CXCL2, CXCL9, CCL2, CCL4, and CCL8. These genes are mainly involved in the TCR signaling, mitogen-activated protein kinase signaling, and cytokine-cytokine receptor interaction pathways. In conclusion, we characterized the gene expression profile of DLBCL-specific TCR gene-redirected T-cells. The changes corresponded to an up-regulation in the differentiation and proliferation of the T-cells. These data may help to explain some of the characteristics of the redirected T-cells.

  19. Specific gene expression profiles and chromosomal abnormalities are associated with infant disseminated neuroblastoma

    Directory of Open Access Journals (Sweden)

    Kushner Brian

    2009-02-01

    Full Text Available Abstract Background Neuroblastoma (NB tumours have the highest incidence of spontaneous remission, especially among the stage 4s NB subgroup affecting infants. Clinical distinction of stage 4s from lethal stage 4 can be difficult, but critical for therapeutic decisions. The aim of this study was to investigate chromosomal alterations and differential gene expression amongst infant disseminated NB subgroups. Methods Thirty-five NB tumours from patients diagnosed at Results All stage 4s patients underwent spontaneous remission, only 48% stage 4 patients survived despite combined modality therapy. Stage 4 tumours were 90% near-diploid/tetraploid, 44% MYCN amplified, 77% had 1p LOH (50% 1p36, 23% 11q and/or 14q LOH (27% and 47% had 17q gain. Stage 4s were 90% near-triploid, none MYCN amplified and LOH was restricted to 11q. Initial comparison analyses between stage 4s and 4 P P = 0.0054, 91% with higher expression in stage 4. Less definite expression profiles were observed between stage 4s and 4 P P = 0.005 was maintained. Distinct gene expression profiles but no significant association with specific chromosomal region localization was observed between stage 4s and stage 4 Conclusion Specific chromosomal aberrations are associated with distinct gene expression profiles which characterize spontaneously regressing or aggressive infant NB, providing the biological basis for the distinct clinical behaviour.

  20. Bioinformatic prediction and functional characterization of human KIAA0100 gene

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    He Cui

    2017-02-01

    Full Text Available Our previous study demonstrated that human KIAA0100 gene was a novel acute monocytic leukemia-associated antigen (MLAA gene. But the functional characterization of human KIAA0100 gene has remained unknown to date. Here, firstly, bioinformatic prediction of human KIAA0100 gene was carried out using online softwares; Secondly, Human KIAA0100 gene expression was downregulated by the clustered regularly interspaced short palindromic repeats (CRISPR/CRISPR-associated (Cas 9 system in U937 cells. Cell proliferation and apoptosis were next evaluated in KIAA0100-knockdown U937 cells. The bioinformatic prediction showed that human KIAA0100 gene was located on 17q11.2, and human KIAA0100 protein was located in the secretory pathway. Besides, human KIAA0100 protein contained a signalpeptide, a transmembrane region, three types of secondary structures (alpha helix, extended strand, and random coil , and four domains from mitochondrial protein 27 (FMP27. The observation on functional characterization of human KIAA0100 gene revealed that its downregulation inhibited cell proliferation, and promoted cell apoptosis in U937 cells. To summarize, these results suggest human KIAA0100 gene possibly comes within mitochondrial genome; moreover, it is a novel anti-apoptotic factor related to carcinogenesis or progression in acute monocytic leukemia, and may be a potential target for immunotherapy against acute monocytic leukemia.

  1. Gene expression profiling leads to discovery of correlation of matrix metalloproteinase 11 and heparanase 2 in breast cancer progression

    International Nuclear Information System (INIS)

    Fu, Junjie; Khaybullin, Ravil; Zhang, Yanping; Xia, Amy; Qi, Xin

    2015-01-01

    In order to identify biomarkers involved in breast cancer, gene expression profiling was conducted using human breast cancer tissues. Total RNAs were extracted from 150 clinical patient tissues covering three breast cancer subtypes (Luminal A, Luminal B, and Triple negative) as well as normal tissues. The expression profiles of a total of 50,739 genes were established from a training set of 32 samples using the Agilent Sure Print G3 Human Gene Expression Microarray technology. Data were analyzed using Agilent Gene Spring GX 12.6 software. The expression of several genes was validated using real-time RT-qPCR. Data analysis with Agilent GeneSpring GX 12.6 software showed distinct expression patterns between cancer and normal tissue samples. A group of 28 promising genes were identified with ≥ 10-fold changes of expression level and p-values < 0.05. In particular, MMP11 and HPSE2 were closely examined due to the important roles they play in cancer cell growth and migration. Real-time RT-qPCR analyses of both training and testing sets validated the gene expression profiles of MMP11 and HPSE2. Our findings identified these 2 genes as a novel breast cancer biomarker gene set, which may facilitate the diagnosis and treatment in breast cancer clinical therapies

  2. HMMEditor: a visual editing tool for profile hidden Markov model

    Directory of Open Access Journals (Sweden)

    Cheng Jianlin

    2008-03-01

    Full Text Available Abstract Background Profile Hidden Markov Model (HMM is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available. Results We develop a visual editor for profile Hidden Markov Models (HMMEditor. HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path. Conclusion HMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.

  3. A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma.

    Directory of Open Access Journals (Sweden)

    Jeran K Stratford

    2010-07-01

    Full Text Available Pancreatic ductal adenocarcinoma (PDAC remains a lethal disease. For patients with localized PDAC, surgery is the best option, but with a median survival of less than 2 years and a difficult and prolonged postoperative course for most, there is an urgent need to better identify patients who have the most aggressive disease.We analyzed the gene expression profiles of primary tumors from patients with localized compared to metastatic disease and identified a six-gene signature associated with metastatic disease. We evaluated the prognostic potential of this signature in a training set of 34 patients with localized and resected PDAC and selected a cut-point associated with outcome using X-tile. We then applied this cut-point to an independent test set of 67 patients with localized and resected PDAC and found that our signature was independently predictive of survival and superior to established clinical prognostic factors such as grade, tumor size, and nodal status, with a hazard ratio of 4.1 (95% confidence interval [CI] 1.7-10.0. Patients defined to be high-risk patients by the six-gene signature had a 1-year survival rate of 55% compared to 91% in the low-risk group.Our six-gene signature may be used to better stage PDAC patients and assist in the difficult treatment decisions of surgery and to select patients whose tumor biology may benefit most from neoadjuvant therapy. The use of this six-gene signature should be investigated in prospective patient cohorts, and if confirmed, in future PDAC clinical trials, its potential as a biomarker should be investigated. Genes in this signature, or the pathways that they fall into, may represent new therapeutic targets. Please see later in the article for the Editors' Summary.

  4. Gene trio signatures as molecular markers to predict response to doxorubicin cyclophosphamide neoadjuvant chemotherapy in breast cancerpatients

    Directory of Open Access Journals (Sweden)

    M.C. Barros Filho

    2010-12-01

    Full Text Available In breast cancer patients submitted to neoadjuvant chemotherapy (4 cycles of doxorubicin and cyclophosphamide, AC, expression of groups of three genes (gene trio signatures could distinguish responsive from non-responsive tumors, as demonstrated by cDNA microarray profiling in a previous study by our group. In the current study, we determined if the expression of the same genes would retain the predictive strength, when analyzed by a more accessible technique (real-time RT-PCR. We evaluated 28 samples already analyzed by cDNA microarray, as a technical validation procedure, and 14 tumors, as an independent biological validation set. All patients received neoadjuvant chemotherapy (4 AC. Among five trio combinations previously identified, defined by nine genes individually investigated (BZRP, CLPTM1,MTSS1, NOTCH1, NUP210, PRSS11, RPL37A, SMYD2, and XLHSRF-1, the most accurate were established by RPL37A, XLHSRF-1based trios, with NOTCH1 or NUP210. Both trios correctly separated 86% of tumors (87% sensitivity and 80% specificity for predicting response, according to their response to chemotherapy (82% in a leave-one-out cross-validation method. Using the pre-established features obtained by linear discriminant analysis, 71% samples from the biological validation set were also correctly classified by both trios (72% sensitivity; 66% specificity. Furthermore, we explored other gene combinations to achieve a higher accuracy in the technical validation group (as a training set. A new trio, MTSS1, RPL37 and SMYD2, correctly classified 93% of samples from the technical validation group (95% sensitivity and 80% specificity; 86% accuracy by the cross-validation method and 79% from the biological validation group (72% sensitivity and 100% specificity. Therefore, the combined expression of MTSS1, RPL37 and SMYD2, as evaluated by real-time RT-PCR, is a potential candidate to predict response to neoadjuvant doxorubicin and cyclophosphamide in breast cancer

  5. Alteration of gene expression profiling including GPR174 and GNG2 is associated with vasovagal syncope.

    Science.gov (United States)

    Huang, Yu-Juan; Zhou, Zai-wei; Xu, Miao; Ma, Qing-wen; Yan, Jing-bin; Wang, Jian-yi; Zhang, Quo-qin; Huang, Min; Bao, Liming

    2015-03-01

    Vasovagal syncope (VVS) causes accidental harm for susceptible patients. However, pathophysiology of this disorder remains largely unknown. In an effort to understanding of molecular mechanism for VVS, genome-wide gene expression profiling analyses were performed on VVS patients at syncope state. A total of 66 Type 1 VVS child patients and the same number healthy controls were enrolled in this study. Peripheral blood RNAs were isolated from all subjects, of which 10 RNA samples were randomly selected from each groups for gene expression profile analysis using Gene ST 1.0 arrays (Affymetrix). The results revealed that 103 genes were differently expressed between the patients and controls. Significantly, two G-proteins related genes, GPR174 and GNG2 that have not been related to VVS were among the differently expressed genes. The microarray results were confirmed by qRT-PCR in all the tested individuals. Ingenuity pathway analysis and gene ontology annotation study showed that the differently expressed genes are associated with stress response and apoptosis, suggesting that the alteration of some gene expression including G-proteins related genes is associated with VVS. This study provides new insight into the molecular mechanism of VVS and would be helpful to further identify new molecular biomarkers for the disease.

  6. Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

    Science.gov (United States)

    Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

    2017-12-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

  7. Computational prediction and experimental validation of Ciona intestinalis microRNA genes

    Directory of Open Access Journals (Sweden)

    Pasquinelli Amy E

    2007-11-01

    Full Text Available Abstract Background This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development. Results In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling. Conclusion The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.

  8. Whole Blood Transcriptional Profiling of Interferon-Inducible Genes Identifies Highly Upregulated IFI27 in Primary Myelofibrosis

    DEFF Research Database (Denmark)

    Skov, Vibe; Larsen, Thomas Stauffer; Thomassen, Mads

    2011-01-01

    focused upon the transcriptional profiling of interferon-associated genes in patients with essential thrombocythemia (ET) (n = 19), polycythemia vera (PV) (n = 41), and primary myelofibrosis (PMF) (n = 9). Using whole-blood transcriptional profiling and accordingly obtaining an integrated signature...

  9. Whole-blood transcriptional profiling of interferon-inducible genes identifies highly upregulated IFI27 in primary myelofibrosis

    DEFF Research Database (Denmark)

    Skov, Vibe; Larsen, Thomas Stauffer; Thomassen, Mads

    2011-01-01

    focused upon the transcriptional profiling of interferon-associated genes in patients with essential thrombocythemia (ET) (n = 19), polycythemia vera (PV) (n = 41), and primary myelofibrosis (PMF) (n = 9). Using whole-blood transcriptional profiling and accordingly obtaining an integrated signature...

  10. Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information.

    Science.gov (United States)

    Zhang, Ao; Tian, Suyan

    2018-05-01

    Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. GOPET: A tool for automated predictions of Gene Ontology terms

    Directory of Open Access Journals (Sweden)

    Glatting Karl-Heinz

    2006-03-01

    Full Text Available Abstract Background Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. Description We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO. Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool. It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar Conclusion Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.

  12. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

    Directory of Open Access Journals (Sweden)

    Zhili He

    2018-02-01

    Full Text Available Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN, representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5 increased significantly (P < 0.05 as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.

  13. An ensemble method to predict target genes and pathways in uveal melanoma

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    Wei Chao

    2018-04-01

    Full Text Available This work proposes to predict target genes and pathways for uveal melanoma (UM based on an ensemble method and pathway analyses. Methods: The ensemble method integrated a correlation method (Pearson correlation coefficient, PCC, a causal inference method (IDA and a regression method (Lasso utilizing the Borda count election method. Subsequently, to validate the performance of PIL method, comparisons between confirmed database and predicted miRNA targets were performed. Ultimately, pathway enrichment analysis was conducted on target genes in top 1000 miRNA-mRNA interactions to identify target pathways for UM patients. Results: Thirty eight of the predicted interactions were matched with the confirmed interactions, indicating that the ensemble method was a suitable and feasible approach to predict miRNA targets. We obtained 50 seed miRNA-mRNA interactions of UM patients and extracted target genes from these interactions, such as ASPG, BSDC1 and C4BP. The 601 target genes in top 1,000 miRNA-mRNA interactions were enriched in 12 target pathways, of which Phototransduction was the most significant one. Conclusion: The target genes and pathways might provide a new way to reveal the molecular mechanism of UM and give hand for target treatments and preventions of this malignant tumor.

  14. Phylogenomic detection and functional prediction of genes potentially important for plant meiosis.

    Science.gov (United States)

    Zhang, Luoyan; Kong, Hongzhi; Ma, Hong; Yang, Ji

    2018-02-15

    Meiosis is a specialized type of cell division necessary for sexual reproduction in eukaryotes. A better understanding of the cytological procedures of meiosis has been achieved by comprehensive cytogenetic studies in plants, while the genetic mechanisms regulating meiotic progression remain incompletely understood. The increasing accumulation of complete genome sequences and large-scale gene expression datasets has provided a powerful resource for phylogenomic inference and unsupervised identification of genes involved in plant meiosis. By integrating sequence homology and expression data, 164, 131, 124 and 162 genes potentially important for meiosis were identified in the genomes of Arabidopsis thaliana, Oryza sativa, Selaginella moellendorffii and Pogonatum aloides, respectively. The predicted genes were assigned to 45 meiotic GO terms, and their functions were related to different processes occurring during meiosis in various organisms. Most of the predicted meiotic genes underwent lineage-specific duplication events during plant evolution, with about 30% of the predicted genes retaining only a single copy in higher plant genomes. The results of this study provided clues to design experiments for better functional characterization of meiotic genes in plants, promoting the phylogenomic approach to the evolutionary dynamics of the plant meiotic machineries. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. The integration of weighted human gene association networks based on link prediction.

    Science.gov (United States)

    Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

    2017-01-31

    Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

  16. Gene expression profile associated with radioresistance and malignancy in melanoma

    International Nuclear Information System (INIS)

    Ibañez, I.L.; Molinari, B.; Notcovich, C.; García, F.M.; Bracalente, C.; Zuccato, C.F.; Durán, H.

    2015-01-01

    The incidence of melanoma has substantially increased over the last decades. Melanomas respond poorly to treatments and no effective therapy exists to inhibit its metastatic spread. The aim of this study was to evaluate the association between radioresistance of melanoma cells and malignancy. A melanoma model developed in our laboratory from A375 human amelanotic melanoma cells was used. It consists in two catalase-overexpressing cell lines with the same genetic background, but with different phenotypes: A375-A7, melanotic and non-invasive and A375-G10, amelanotic and metastatic; and A375-PCDNA3 (transfected with empty plasmid) as control. Radiosensitivity was determined by clonogenic assay after irradiating these cells with a “1”3”7 Cs gamma source. Survival curves were fitted to the linear-quadratic model and surviving fraction at 2 Gy (SF2) was calculated. Results showed that A375-G10 cells were significantly more radioresistant than both A375-A7 and control cells, demonstrated by SF2 and α parameter of survival curves: SF2=0.32±0.03, 0.43±0.16 and 0.89±0.05 and α=0.45±0.05, 0.20±0.05 and 0 for A375-PCDNA3, A375-A7 and A375-G10 respectively. Bioinformatic analysis of whole genome expression microarrays data (Affymetrix) from these cells was performed. A priori defined gene sets associated with cell cycle, apoptosis and MAPK signaling pathway were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) to evaluate significant differences in gene set expression between cells by GSEA (Gene Set Enrichment Analysis). A375-G10 showed significant decrease in the expression of genes related to DNA damage response (ATM, TP53BP1 and MRE11A) compared to A375-A7 and controls. Moreover, A375-G10 exhibited down-regulated gene sets that are involved in DNA repair, checkpoint and negative regulation of cell cycle and apoptosis. In conclusion, A375-G10 gene expression profile could be involved in radioresistance mechanisms of these cells. Thus, this expression

  17. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

    Science.gov (United States)

    Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

    2018-02-01

    Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

  18. Neonatal maternal deprivation response and developmental changes in gene expression revealed by hypothalamic gene expression profiling in mice.

    Directory of Open Access Journals (Sweden)

    Feng Ding

    Full Text Available Neonatal feeding problems are observed in several genetic diseases including Prader-Willi syndrome (PWS. Later in life, individuals with PWS develop hyperphagia and obesity due to lack of appetite control. We hypothesized that failure to thrive in infancy and later-onset hyperphagia are related and could be due to a defect in the hypothalamus. In this study, we performed gene expression microarray analysis of the hypothalamic response to maternal deprivation in neonatal wild-type and Snord116del mice, a mouse model for PWS in which a cluster of imprinted C/D box snoRNAs is deleted. The neonatal starvation response in both strains was dramatically different from that reported in adult rodents. Genes that are affected by adult starvation showed no expression change in the hypothalamus of 5 day-old pups after 6 hours of maternal deprivation. Unlike in adult rodents, expression levels of Nanos2 and Pdk4 were increased, and those of Pgpep1, Ndp, Brms1l, Mett10d, and Snx1 were decreased after neonatal deprivation. In addition, we compared hypothalamic gene expression profiles at postnatal days 5 and 13 and observed significant developmental changes. Notably, the gene expression profiles of Snord116del deletion mice and wild-type littermates were very similar at all time points and conditions, arguing against a role of Snord116 in feeding regulation in the neonatal period.

  19. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

    Science.gov (United States)

    Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

    Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

  20. Genome-Wide Transcriptional Profiling of Clostridium perfringens SM101 during Sporulation Extends the Core of Putative Sporulation Genes and Genes Determining Spore Properties and Germination Characteristics.

    Science.gov (United States)

    Xiao, Yinghua; van Hijum, Sacha A F T; Abee, Tjakko; Wells-Bennik, Marjon H J

    2015-01-01

    The formation of bacterial spores is a highly regulated process and the ultimate properties of the spores are determined during sporulation and subsequent maturation. A wide variety of genes that are expressed during sporulation determine spore properties such as resistance to heat and other adverse environmental conditions, dormancy and germination responses. In this study we characterized the sporulation phases of C. perfringens enterotoxic strain SM101 based on morphological characteristics, biomass accumulation (OD600), the total viable counts of cells plus spores, the viable count of heat resistant spores alone, the pH of the supernatant, enterotoxin production and dipicolinic acid accumulation. Subsequently, whole-genome expression profiling during key phases of the sporulation process was performed using DNA microarrays, and genes were clustered based on their time-course expression profiles during sporulation. The majority of previously characterized C. perfringens germination genes showed upregulated expression profiles in time during sporulation and belonged to two main clusters of genes. These clusters with up-regulated genes contained a large number of C. perfringens genes which are homologs of Bacillus genes with roles in sporulation and germination; this study therefore suggests that those homologs are functional in C. perfringens. A comprehensive homology search revealed that approximately half of the upregulated genes in the two clusters are conserved within a broad range of sporeforming Firmicutes. Another 30% of upregulated genes in the two clusters were found only in Clostridium species, while the remaining 20% appeared to be specific for C. perfringens. These newly identified genes may add to the repertoire of genes with roles in sporulation and determining spore properties including germination behavior. Their exact roles remain to be elucidated in future studies.

  1. Gene expression profiling of cutaneous wound healing

    Directory of Open Access Journals (Sweden)

    Wang Ena

    2007-02-01

    Full Text Available Abstract Background Although the sequence of events leading to wound repair has been described at the cellular and, to a limited extent, at the protein level this process has yet to be fully elucidated. Genome wide transcriptional analysis tools promise to further define the global picture of this complex progression of events. Study Design This study was part of a placebo-controlled double-blind clinical trial in which basal cell carcinomas were treated topically with an immunomodifier – toll-like receptor 7 agonist: imiquimod. The fourteen patients with basal cell carcinoma in the placebo arm of the trial received placebo treatment consisting solely of vehicle cream. A skin punch biopsy was obtained immediately before treatment and at the end of the placebo treatment (after 2, 4 or 8 days. 17.5K cDNA microarrays were utilized to profile the biopsy material. Results Four gene signatures whose expression changed relative to baseline (before wound induction by the pre-treatment biopsy were identified. The largest group was comprised predominantly of inflammatory genes whose expression was increased throughout the study. Two additional signatures were observed which included preferentially pro-inflammatory genes in the early post-treatment biopsies (2 days after pre-treatment biopsies and repair and angiogenesis genes in the later (4 to 8 days biopsies. The fourth and smallest set of genes was down-regulated throughout the study. Early in wound healing the expression of markers of both M1 and M2 macrophages were increased, but later M2 markers predominated. Conclusion The initial response to a cutaneous wound induces powerful transcriptional activation of pro-inflammatory stimuli which may alert the host defense. Subsequently and in the absence of infection, inflammation subsides and it is replaced by angiogenesis and remodeling. Understanding this transition which may be driven by a change from a mixed macrophage population to predominately M2

  2. Prediction of novel target genes and pathways involved in irinotecan-resistant colorectal cancer.

    Directory of Open Access Journals (Sweden)

    Precious Takondwa Makondi

    Full Text Available Acquired drug resistance to the chemotherapeutic drug irinotecan (the active metabolite of which is SN-38 is one of the significant obstacles in the treatment of advanced colorectal cancer (CRC. The molecular mechanism or targets mediating irinotecan resistance are still unclear. It is urgent to find the irinotecan response biomarkers to improve CRC patients' therapy.Genetic Omnibus Database GSE42387 which contained the gene expression profiles of parental and irinotecan-resistant HCT-116 cell lines was used. Differentially expressed genes (DEGs between parental and irinotecan-resistant cells, protein-protein interactions (PPIs, gene ontologies (GOs and pathway analysis were performed to identify the overall biological changes. The most common DEGs in the PPIs, GOs and pathways were identified and were validated clinically by their ability to predict overall survival and disease free survival. The gene-gene expression correlation and gene-resistance correlation was also evaluated in CRC patients using The Cancer Genomic Atlas data (TCGA.The 135 DEGs were identified of which 36 were upregulated and 99 were down regulated. After mapping the PPI networks, the GOs and the pathways, nine genes (GNAS, PRKACB, MECOM, PLA2G4C, BMP6, BDNF, DLG4, FGF2 and FGF9 were found to be commonly enriched. Signal transduction was the most significant GO and MAPK pathway was the most significant pathway. The five genes (FGF2, FGF9, PRKACB, MECOM and PLA2G4C in the MAPK pathway were all contained in the signal transduction and the levels of those genes were upregulated. The FGF2, FGF9 and MECOM expression were highly associated with CRC patients' survival rate but not PRKACB and PLA2G4C. In addition, FGF9 was also associated with irinotecan resistance and poor disease free survival. FGF2, FGF9 and PRKACB were positively correlated with each other while MECOM correlated positively with FGF9 and PLA2G4C, and correlated negatively with FGF2 and PRKACB after doing gene-gene

  3. PanCoreGen – profiling, detecting, annotating protein-coding genes in microbial genomes

    Science.gov (United States)

    Bhardwaj, Archana; Bag, Sumit K; Sokurenko, Evgeni V.

    2015-01-01

    A large amount of genomic data, especially from multiple isolates of a single species, has opened new vistas for microbial genomics analysis. Analyzing pan-genome (i.e. the sum of genetic repertoire) of microbial species is crucial in understanding the dynamics of molecular evolution, where virulence evolution is of major interest. Here we present PanCoreGen – a standalone application for pan- and core-genomic profiling of microbial protein-coding genes. PanCoreGen overcomes key limitations of the existing pan-genomic analysis tools, and develops an integrated annotation-structure for species-specific pan-genomic profile. It provides important new features for annotating draft genomes/contigs and detecting unidentified genes in annotated genomes. It also generates user-defined group-specific datasets within the pan-genome. Interestingly, analyzing an example-set of Salmonella genomes, we detect potential footprints of adaptive convergence of horizontally transferred genes in two human-restricted pathogenic serovars – Typhi and Paratyphi A. Overall, PanCoreGen represents a state-of-the-art tool for microbial phylogenomics and pathogenomics study. PMID:26456591

  4. Predictive value of MSH2 gene expression in colorectal cancer treated with capecitabine

    DEFF Research Database (Denmark)

    Jensen, Lars H; Danenberg, Kathleen D; Danenberg, Peter V

    2007-01-01

    was associated with a hazard ratio of 0.5 (95% confidence interval, 0.23-1.11; P = 0.083) in survival analysis. CONCLUSION: The higher gene expression of MSH2 in responders and the trend for predicting overall survival indicates a predictive value of this marker in the treatment of advanced CRC with capecitabine.......PURPOSE: The objective of the present study was to evaluate the gene expression of the DNA mismatch repair gene MSH2 as a predictive marker in advanced colorectal cancer (CRC) treated with first-line capecitabine. PATIENTS AND METHODS: Microdissection of paraffin-embedded tumor tissue, RNA...

  5. Predicting Post-Editor Profiles from the Translation Process

    DEFF Research Database (Denmark)

    Singla, Karan; Orrego-Carmona, David; Gonzales, Ashleigh Rhea

    2014-01-01

    The purpose of the current investigation is to predict post-editor profiles based on user behaviour and demographics using machine learning techniques to gain a better understanding of post-editor styles. Our study extracts process unit features from the CasMaCat LS14 database from the CRITT...... of translation process features. The classification and clustering of participants resulting from our study suggest this type of exploration could be used as a tool to develop new translation tool features or customization possibilities....

  6. Soybean (Glycine max) SWEET gene family: insights through comparative genomics, transcriptome profiling and whole genome re-sequence analysis.

    Science.gov (United States)

    Patil, Gunvant; Valliyodan, Babu; Deshmukh, Rupesh; Prince, Silvas; Nicander, Bjorn; Zhao, Mingzhe; Sonah, Humira; Song, Li; Lin, Li; Chaudhary, Juhi; Liu, Yang; Joshi, Trupti; Xu, Dong; Nguyen, Henry T

    2015-07-11

    SWEET (MtN3_saliva) domain proteins, a recently identified group of efflux transporters, play an indispensable role in sugar efflux, phloem loading, plant-pathogen interaction and reproductive tissue development. The SWEET gene family is predominantly studied in Arabidopsis and members of the family are being investigated in rice. To date, no transcriptome or genomics analysis of soybean SWEET genes has been reported. In the present investigation, we explored the evolutionary aspect of the SWEET gene family in diverse plant species including primitive single cell algae to angiosperms with a major emphasis on Glycine max. Evolutionary features showed expansion and duplication of the SWEET gene family in land plants. Homology searches with BLAST tools and Hidden Markov Model-directed sequence alignments identified 52 SWEET genes that were mapped to 15 chromosomes in the soybean genome as tandem duplication events. Soybean SWEET (GmSWEET) genes showed a wide range of expression profiles in different tissues and developmental stages. Analysis of public transcriptome data and expression profiling using quantitative real time PCR (qRT-PCR) showed that a majority of the GmSWEET genes were confined to reproductive tissue development. Several natural genetic variants (non-synonymous SNPs, premature stop codons and haplotype) were identified in the GmSWEET genes using whole genome re-sequencing data analysis of 106 soybean genotypes. A significant association was observed between SNP-haplogroup and seed sucrose content in three gene clusters on chromosome 6. Present investigation utilized comparative genomics, transcriptome profiling and whole genome re-sequencing approaches and provided a systematic description of soybean SWEET genes and identified putative candidates with probable roles in the reproductive tissue development. Gene expression profiling at different developmental stages and genomic variation data will aid as an important resource for the soybean research

  7. Alternative life histories shape brain gene expression profiles in males of the same population.

    Science.gov (United States)

    Aubin-Horth, Nadia; Landry, Christian R; Letcher, Benjamin H; Hofmann, Hans A

    2005-08-22

    Atlantic salmon (Salmo salar) undergo spectacular marine migrations before homing to spawn in natal rivers. However, males that grow fastest early in life can adopt an alternative 'sneaker' tactic by maturing earlier at greatly reduced size without leaving freshwater. While the ultimate evolutionary causes have been well studied, virtually nothing is known about the molecular bases of this developmental plasticity. We investigate the nature and extent of coordinated molecular changes that accompany such a fundamental transformation by comparing the brain transcription profiles of wild mature sneaker males to age-matched immature males (future large anadromous males) and immature females. Of the ca. 3000 genes surveyed, 15% are differentially expressed in the brains of the two male types. These genes are involved in a wide range of processes, including growth, reproduction and neural plasticity. Interestingly, despite the potential for wide variation in gene expression profiles among individuals sampled in nature, consistent patterns of gene expression were found for individuals of the same reproductive tactic. Notably, gene expression patterns in immature males were different both from immature females and sneakers, indicating that delayed maturation and sea migration by immature males, the 'default' life cycle, may actually result from an active inhibition of development into a sneaker.

  8. Global analysis of gene expression profiles in physic nut (Jatropha curcas L.) seedlings exposed to salt stress.

    Science.gov (United States)

    Zhang, Lin; Zhang, Chao; Wu, Pingzhi; Chen, Yaping; Li, Meiru; Jiang, Huawu; Wu, Guojiang

    2014-01-01

    Salt stress interferes with plant growth and production. Plants have evolved a series of molecular and morphological adaptations to cope with this abiotic stress, and overexpression of salt response genes reportedly enhances the productivity of various crops. However, little is known about the salt responsive genes in the energy plant physic nut (Jatropha curcas L.). Thus, excavate salt responsive genes in this plant are informative in uncovering the molecular mechanisms for the salt response in physic nut. We applied next-generation Illumina sequencing technology to analyze global gene expression profiles of physic nut plants (roots and leaves) 2 hours, 2 days and 7 days after the onset of salt stress. A total of 1,504 and 1,115 genes were significantly up and down-regulated in roots and leaves, respectively, under salt stress condition. Gene ontology (GO) analysis of physiological process revealed that, in the physic nut, many "biological processes" were affected by salt stress, particular those categories belong to "metabolic process", such as "primary metabolism process", "cellular metabolism process" and "macromolecule metabolism process". The gene expression profiles indicated that the associated genes were responsible for ABA and ethylene signaling, osmotic regulation, the reactive oxygen species scavenging system and the cell structure in physic nut. The major regulated genes detected in this transcriptomic data were related to trehalose synthesis and cell wall structure modification in roots, while related to raffinose synthesis and reactive oxygen scavenger in leaves. The current study shows a comprehensive gene expression profile of physic nut under salt stress. The differential expression genes detected in this study allows the underling the salt responsive mechanism in physic nut with the aim of improving its salt resistance in the future.

  9. A Classification Framework Applied to Cancer Gene Expression Profiles

    Directory of Open Access Journals (Sweden)

    Hussein Hijazi

    2013-01-01

    Full Text Available Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM, bagging, and random forest on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression increase the prediction accuracy as compared to using gene expression alone.

  10. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

    Science.gov (United States)

    Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

    2017-11-24

    Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

  11. Bovine mammary gene expression profiling during the onset of lactation.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Gao

    Full Text Available BACKGROUND: Lactogenesis includes two stages. Stage I begins a few weeks before parturition. Stage II is initiated around the time of parturition and extends for several days afterwards. METHODOLOGY/PRINCIPAL FINDINGS: To better understand the molecular events underlying these changes, genome-wide gene expression profiling was conducted using digital gene expression (DGE on bovine mammary tissue at three time points (on approximately day 35 before parturition (-35 d, day 7 before parturition (-7 d and day 3 after parturition (+3 d. Approximately 6.2 million (M, 5.8 million (M and 6.1 million (M 21-nt cDNA tags were sequenced in the three cDNA libraries (-35 d, -7 d and +3 d, respectively. After aligning to the reference sequences, the three cDNA libraries included 8,662, 8,363 and 8,359 genes, respectively. With a fold change cutoff criteria of ≥ 2 or ≤-2 and a false discovery rate (FDR of ≤ 0.001, a total of 812 genes were significantly differentially expressed at -7 d compared with -35 d (stage I. Gene ontology analysis showed that those significantly differentially expressed genes were mainly associated with cell cycle, lipid metabolism, immune response and biological adhesion. A total of 1,189 genes were significantly differentially expressed at +3 d compared with -7 d (stage II, and these genes were mainly associated with the immune response and cell cycle. Moreover, there were 1,672 genes significantly differentially expressed at +3 d compared with -35 d. Gene ontology analysis showed that the main differentially expressed genes were those associated with metabolic processes. CONCLUSIONS: The results suggest that the mammary gland begins to lactate not only by a gain of function but also by a broad suppression of function to effectively push most of the cell's resources towards lactation.

  12. Predictive modelling of gene expression from transcriptional regulatory elements.

    Science.gov (United States)

    Budden, David M; Hurley, Daniel G; Crampin, Edmund J

    2015-07-01

    Predictive modelling of gene expression provides a powerful framework for exploring the regulatory logic underpinning transcriptional regulation. Recent studies have demonstrated the utility of such models in identifying dysregulation of gene and miRNA expression associated with abnormal patterns of transcription factor (TF) binding or nucleosomal histone modifications (HMs). Despite the growing popularity of such approaches, a comparative review of the various modelling algorithms and feature extraction methods is lacking. We define and compare three methods of quantifying pairwise gene-TF/HM interactions and discuss their suitability for integrating the heterogeneous chromatin immunoprecipitation (ChIP)-seq binding patterns exhibited by TFs and HMs. We then construct log-linear and ϵ-support vector regression models from various mouse embryonic stem cell (mESC) and human lymphoblastoid (GM12878) data sets, considering both ChIP-seq- and position weight matrix- (PWM)-derived in silico TF-binding. The two algorithms are evaluated both in terms of their modelling prediction accuracy and ability to identify the established regulatory roles of individual TFs and HMs. Our results demonstrate that TF-binding and HMs are highly predictive of gene expression as measured by mRNA transcript abundance, irrespective of algorithm or cell type selection and considering both ChIP-seq and PWM-derived TF-binding. As we encourage other researchers to explore and develop these results, our framework is implemented using open-source software and made available as a preconfigured bootable virtual environment. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  13. Gene expression profiling via LongSAGE in a non-model plant species: a case study in seeds of Brassica napus

    Directory of Open Access Journals (Sweden)

    Friedt Wolfgang

    2009-07-01

    Full Text Available Abstract Background Serial analysis of gene expression (LongSAGE was applied for gene expression profiling in seeds of oilseed rape (Brassica napus ssp. napus. The usefulness of this technique for detailed expression profiling in a non-model organism was demonstrated for the highly complex, neither fully sequenced nor annotated genome of B. napus by applying a tag-to-gene matching strategy based on Brassica ESTs and the annotated proteome of the closely related model crucifer A. thaliana. Results Transcripts from 3,094 genes were detected at two time-points of seed development, 23 days and 35 days after pollination (DAP. Differential expression showed a shift from gene expression involved in diverse developmental processes including cell proliferation and seed coat formation at 23 DAP to more focussed metabolic processes including storage protein accumulation and lipid deposition at 35 DAP. The most abundant transcripts at 23 DAP were coding for diverse protease inhibitor proteins and proteases, including cysteine proteases involved in seed coat formation and a number of lipid transfer proteins involved in embryo pattern formation. At 35 DAP, transcripts encoding napin, cruciferin and oleosin storage proteins were most abundant. Over both time-points, 18.6% of the detected genes were matched by Brassica ESTs identified by LongSAGE tags in antisense orientation. This suggests a strong involvement of antisense transcript expression in regulatory processes during B. napus seed development. Conclusion This study underlines the potential of transcript tagging approaches for gene expression profiling in Brassica crop species via EST matching to annotated A. thaliana genes. Limits of tag detection for low-abundance transcripts can today be overcome by ultra-high throughput sequencing approaches, so that tag-based gene expression profiling may soon become the method of choice for global expression profiling in non-model species.

  14. Digital Gene Expression Profiling Analysis of Aged Mice under Moxibustion Treatment

    Directory of Open Access Journals (Sweden)

    Nan Liu

    2018-01-01

    Full Text Available Aging is closely connected with death, progressive physiological decline, and increased risk of diseases, such as cancer, arteriosclerosis, heart disease, hypertension, and neurodegenerative diseases. It is reported that moxibustion can treat more than 300 kinds of diseases including aging related problems and can improve immune function and physiological functions. The digital gene expression profiling of aged mice with or without moxibustion treatment was investigated and the mechanisms of moxibustion in aged mice were speculated by gene ontology and pathway analysis in the study. Almost 145 million raw reads were obtained by digital gene expression analysis and about 140 million (96.55% were clean reads. Five differentially expressed genes with an adjusted P value 1 were identified between the control and moxibustion groups. They were Gm6563, Gm8116, Rps26-ps1, Nat8f4, and Igkv3-12. Gene ontology analysis was carried out by the GOseq R package and functional annotations of the differentially expressed genes related to translation, mRNA export from nucleus, mRNA transport, nuclear body, acetyltransferase activity, and so on. Kyoto Encyclopedia of Genes and Genomes database was used for pathway analysis and ribosome was the most significantly enriched pathway term.

  15. Gene expression profiles in cervical cancer with radiation therapy alone and chemo-radiation therapy

    International Nuclear Information System (INIS)

    Lee, Kyu Chan; Kim, Joo Young; Hwang, You Jin; Kim, Meyoung Kon; Choi, Myung Sun; Kim, Chul Young

    2003-01-01

    To analyze the gene expression profiles of uterine cervical cancer, and its variation after radiation therapy, with or without concurrent chemotherapy, using a cDNA microarray. Sixteen patients, 8 with squamous cell carcinomas of the uterine cervix, who were treated with radiation alone, and the other 8 treated with concurrent chemo-radiation, were included in the study. Before the starting of the treatment, tumor biopsies were carried out, and the second time biopsies were performed after a radiation dose of 16.2-27 Gy. Three normal cervix tissues were used as a control group. The microarray experiments were performed with 5 groups of the total RNAs extracted individually and then admixed as control, pre-radiation therapy alone, during-radiation therapy alone, pre-chemoradiation therapy, and during chemoradiation therapy. The 33P-labeled cDNAs were synthesized from the total RNAs of each group, by reverse transcription, and then they were hybridized to the cDNA microarray membrane. The gene expression of each microarrays was captured by the intensity of each spot produced by the radioactive isotopes. The pixels per spot were counted with an Arrayguage, and were exported to Microsoft Excel. The data were normalized by the Z transformation, and the comparisons were performed on the Z-ratio values calculated. The expressions of 15 genes, including integrin linked kinase (ILK), CDC28 protein kinase 2, Spry 2, and ERK 3, were increased with the Z-ratio values of over 2.0 for the cervix cancer tissues compared to those for the normal controls. Those genes were involved in cell growth and proliferation, cell cycle control, or signal transduction. The expressions of the other 6 genes, including G protein coupled receptor kinase 6, were decreased with the Z-ratio values of below -2.0. After the radiation therapy, most of the genes, with a previously increase expressions, represented the decreased expression profiles, and the genes, with the Z-ratio values of over 2.0, were

  16. Clustering gene expression data based on predicted differential effects of GV interaction.

    Science.gov (United States)

    Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

    2005-02-01

    Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

  17. Prediction of Clinically Relevant Safety Signals of Nephrotoxicity through Plasma Metabolite Profiling

    Directory of Open Access Journals (Sweden)

    W. B. Mattes

    2013-01-01

    Full Text Available Addressing safety concerns such as drug-induced kidney injury (DIKI early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC. The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound’s well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity, not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.

  18. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

  19. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

  20. Genome-wide characterization and expression profiling of immune genes in the diamondback moth, Plutella xylostella (L.).

    Science.gov (United States)

    Xia, Xiaofeng; Yu, Liying; Xue, Minqian; Yu, Xiaoqiang; Vasseur, Liette; Gurr, Geoff M; Baxter, Simon W; Lin, Hailan; Lin, Junhan; You, Minsheng

    2015-05-06

    The diamondback moth, Plutella xylostella (L.), is a destructive pest that attacks cruciferous crops worldwide. Immune responses are important for interactions between insects and pathogens and information on these underpins the development of strategies for biocontrol-based pest management. Little, however, is known about immune genes and their regulation patterns in P. xylostella. A total of 149 immune-related genes in 20 gene families were identified through comparison of P. xylostella genome with the genomes of other insects. Complete and conserved Toll, IMD and JAK-STAT signaling pathways were found in P. xylostella. Genes involved in pathogen recognition were expanded and more diversified than genes associated with intracellular signal transduction. Gene expression profiles showed that the IMD pathway may regulate expression of antimicrobial peptide (AMP) genes in the midgut, and be related to an observed down-regulation of AMPs in experimental lines of insecticide-resistant P. xylostella. A bacterial feeding study demonstrated that P. xylostella could activate different AMPs in response to bacterial infection. This study has established a framework of comprehensive expression profiles that highlight cues for immune regulation in a major pest. Our work provides a foundation for further studies on the functions of P. xylostella immune genes and mechanisms of innate immunity.

  1. Liver gene expression profiles of rats treated with clofibric acid: comparison of whole liver and laser capture microdissected liver.

    Science.gov (United States)

    Michel, Cécile; Desdouets, Chantal; Sacre-Salem, Béatrice; Gautier, Jean-Charles; Roberts, Ruth; Boitier, Eric

    2003-12-01

    Clofibric acid (CLO) is a peroxisome proliferator (PP) that acts through the peroxisome proliferator activated receptor alpha, leading to hepatocarcinogenesis in rodents. CLO-induced hepatocarcinogenesis is a multi-step process, first transforming normal liver cells into foci. The combination of laser capture microdissection (LCM) and genomics has the potential to provide expression profiles from such small cell clusters, giving an opportunity to understand the process of cancer development in response to PPs. To our knowledge, this is the first evaluation of the impact of the successive steps of LCM procedure on gene expression profiling by comparing profiles from LCM samples to those obtained with non-microdissected liver samples collected after a 1 month CLO treatment in the rat. We showed that hematoxylin and eosin (H&E) staining and laser microdissection itself do not impact on RNA quality. However, the overall process of the LCM procedure affects the RNA quality, resulting in a bias in the gene profiles. Nonetheless, this bias did not prevent accurate determination of a CLO-specific molecular signature. Thus, gene-profiling analysis of microdissected foci, identified by H&E staining may provide insight into the mechanisms underlying non-genotoxic hepatocarcinogenesis in the rat by allowing identification of specific genes that are regulated by CLO in early pre-neoplastic foci.

  2. Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data

    Directory of Open Access Journals (Sweden)

    Turk Rolf

    2006-04-01

    Full Text Available Abstract Background The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other. Results We validate the temporal Hotelling T2-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections. Conclusion The temporal Hotelling T2-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it

  3. A polymorphism upstream MIR1279 gene is associated with pericarditis development in Systemic Lupus Erythematosus and contributes to definition of a genetic risk profile for this complication.

    Science.gov (United States)

    Ciccacci, C; Perricone, C; Politi, C; Rufini, S; Ceccarelli, F; Cipriano, E; Alessandri, C; Latini, A; Valesini, G; Novelli, G; Conti, F; Borgiani, P

    2017-07-01

    Recently, a study has shown that a polymorphism in the region of MIR1279 modulates the expression of the TRAF3IP2 gene. Since polymorphisms in the TRAF3IP2 gene have been described in association with systemic lupus erithematosus (SLE) susceptibility and with the development of pericarditis, our aim is to verify if the MIR1279 gene variability could also be involved. The rs1463335 SNP, located upstream MIR1279 gene, was analyzed by allelic discrimination assay in 315 Italian SLE patients and 201 healthy controls. Moreover, the MIR1279 gene was full sequenced in 50 patients. A case/control association study and a genotype/phenotype correlation analysis were performed. We also constructed a pericarditis genetic risk profile for patients with SLE. The full sequencing of the MIR1279 gene in patients with SLE did not reveal any novel or known variation. The variant allele of the rs1463335 SNP was significantly associated with susceptibility to pericarditis ( P = 0.017 and OR = 1.67). A risk profile model for pericarditis considering the risk alleles of MIR1279 and three other genes (STAT4, PTPN2 and TRAF3IP2) showed that patients with 4 or 5 risk alleles have a higher risk of developing pericarditis ( OR = 4.09 with P = 0.001 and OR = 6.04 with P = 0.04 respectively). In conclusion, we describe for the first time the contribution of a MIR1279 SNP in pericarditis development in patients with SLE and a genetic risk profile model that could be useful to identify patients more susceptible to developing pericarditis in SLE. This approach could help to improve the prediction and the management of this complication.

  4. Recrudescence mechanisms and gene expression profile of the reproductive tracts from chickens during the molting period.

    Directory of Open Access Journals (Sweden)

    Wooyoung Jeong

    Full Text Available The reproductive system of chickens undergoes dynamic morphological and functional tissue remodeling during the molting period. The present study identified global gene expression profiles following oviductal tissue regression and regeneration in laying hens in which molting was induced by feeding high levels of zinc in the diet. During the molting and recrudescence processes, progressive morphological and physiological changes included regression and re-growth of reproductive organs and fluctuations in concentrations of testosterone, progesterone, estradiol and corticosterone in blood. The cDNA microarray analysis of oviductal tissues revealed the biological significance of gene expression-based modulation in oviductal tissue during its remodeling. Based on the gene expression profiles, expression patterns of selected genes such as, TF, ANGPTL3, p20K, PTN, AvBD11 and SERPINB3 exhibited similar patterns in expression with gradual decreases during regression of the oviduct and sequential increases during resurrection of the functional oviduct. Also, miR-1689* inhibited expression of Sp1, while miR-17-3p, miR-22* and miR-1764 inhibited expression of STAT1. Similarly, chicken miR-1562 and miR-138 reduced the expression of ANGPTL3 and p20K, respectively. These results suggest that these differentially regulated genes are closely correlated with the molecular mechanism(s for development and tissue remodeling of the avian female reproductive tract, and that miRNA-mediated regulation of key genes likely contributes to remodeling of the avian reproductive tract by controlling expression of those genes post-transcriptionally. The discovered global gene profiles provide new molecular candidates responsible for regulating morphological and functional recrudescence of the avian reproductive tract, and provide novel insights into understanding the remodeling process at the genomic and epigenomic levels.

  5. ESTs, cDNA microarrays, and gene expression profiling: tools for dissecting plant physiology and development.

    Science.gov (United States)

    Alba, Rob; Fei, Zhangjun; Payton, Paxton; Liu, Yang; Moore, Shanna L; Debbie, Paul; Cohn, Jonathan; D'Ascenzo, Mark; Gordon, Jeffrey S; Rose, Jocelyn K C; Martin, Gregory; Tanksley, Steven D; Bouzayen, Mondher; Jahn, Molly M; Giovannoni, Jim

    2004-09-01

    Gene expression profiling holds tremendous promise for dissecting the regulatory mechanisms and transcriptional networks that underlie biological processes. Here we provide details of approaches used by others and ourselves for gene expression profiling in plants with emphasis on cDNA microarrays and discussion of both experimental design and downstream analysis. We focus on methods and techniques emphasizing fabrication of cDNA microarrays, fluorescent labeling, cDNA hybridization, experimental design, and data processing. We include specific examples that demonstrate how this technology can be used to further our understanding of plant physiology and development (specifically fruit development and ripening) and for comparative genomics by comparing transcriptome activity in tomato and pepper fruit.

  6. The euryhaline yeast Debaryomyces hansenii has two catalase genes encoding enzymes with differential activity profile.

    Science.gov (United States)

    Segal-Kischinevzky, Claudia; Rodarte-Murguía, Beatriz; Valdés-López, Victor; Mendoza-Hernández, Guillermo; González, Alicia; Alba-Lois, Luisa

    2011-03-01

    Debaryomyces hansenii is a spoilage yeast able to grow in a variety of ecological niches, from seawater to dairy products. Results presented in this article show that (i) D. hansenii has an inherent resistance to H2O2 which could be attributed to the fact that this yeast has a basal catalase activity which is several-fold higher than that observed in Saccharomyces cerevisiae under the same culture conditions, (ii) D. hansenii has two genes (DhCTA1 and DhCTT1) encoding two catalase isozymes with a differential enzymatic activity profile which is not strictly correlated with a differential expression profile of the encoding genes.

  7. Characterization of the global profile of genes expressed in cervical epithelium by Serial Analysis of Gene Expression (SAGE

    Directory of Open Access Journals (Sweden)

    Piña-Sanchez Patricia

    2005-09-01

    Full Text Available Abstract Background Serial Analysis of Gene Expression (SAGE is a new technique that allows a detailed and profound quantitative and qualitative knowledge of gene expression profile, without previous knowledge of sequence of analyzed genes. We carried out a modification of SAGE methodology (microSAGE, useful for the analysis of limited quantities of tissue samples, on normal human cervical tissue obtained from a donor without histopathological lesions. Cervical epithelium is constituted mainly by cervical keratinocytes which are the targets of human papilloma virus (HPV, where persistent HPV infection of cervical epithelium is associated with an increase risk for developing cervical carcinomas (CC. Results We report here a transcriptome analysis of cervical tissue by SAGE, derived from 30,418 sequenced tags that provide a wealth of information about the gene products involved in normal cervical epithelium physiology, as well as genes not previously found in uterine cervix tissue involved in the process of epidermal differentiation. Conclusion This first comprehensive and profound analysis of uterine cervix transcriptome, should be useful for the identification of genes involved in normal cervix uterine function, and candidate genes associated with cervical carcinoma.

  8. Epigenetic modifications and chromatin loop organization explain the different expression profiles of the Tbrg4, WAP and Ramp3 genes

    International Nuclear Information System (INIS)

    Montazer-Torbati, Mohammad Bagher; Hue-Beauvais, Cathy; Droineau, Stephanie; Ballester, Maria; Coant, Nicolas; Aujean, Etienne; Petitbarat, Marie; Rijnkels, Monique; Devinoy, Eve

    2008-01-01

    Whey Acidic Protein (WAP) gene expression is specific to the mammary gland and regulated by lactogenic hormones to peak during lactation. It differs markedly from the more constitutive expression of the two flanking genes, Ramp3 and Tbrg4. Our results show that the tight regulation of WAP gene expression parallels variations in the chromatin structure and DNA methylation profile throughout the Ramp3-WAP-Tbrg4 locus. Three Matrix Attachment Regions (MAR) have been predicted in this locus. Two of them are located between regions exhibiting open and closed chromatin structures in the liver. The third, located around the transcription start site of the Tbrg4 gene, interacts with topoisomerase II in HC11 mouse mammary cells, and in these cells anchors the chromatin loop to the nuclear matrix. Furthermore, if lactogenic hormones are present in these cells, the chromatin loop surrounding the WAP gene is more tightly attached to the nuclear structure, as observed after a high salt treatment of the nuclei and the formation of nuclear halos. Taken together, our results point to a combination of several epigenetic events that may explain the differential expression pattern of the WAP locus in relation to tissue and developmental stages

  9. Abundance profiling of specific gene groups using precomputed gut metagenomes yields novel biological hypotheses.

    Directory of Open Access Journals (Sweden)

    Konstantin Yarygin

    Full Text Available The gut microbiota is essentially a multifunctional bioreactor within a human being. The exploration of its enormous metabolic potential provides insights into the mechanisms underlying microbial ecology and interactions with the host. The data obtained using "shotgun" metagenomics capture information about the whole spectrum of microbial functions. However, each new study presenting new sequencing data tends to extract only a little of the information concerning the metabolic potential and often omits specific functions. A meta-analysis of the available data with an emphasis on biomedically relevant gene groups can unveil new global trends in the gut microbiota. As a step toward the reuse of metagenomic data, we developed a method for the quantitative profiling of user-defined groups of genes in human gut metagenomes. This method is based on the quick analysis of a gene coverage matrix obtained by pre-mapping the metagenomic reads to a global gut microbial catalogue. The method was applied to profile the abundance of several gene groups related to antibiotic resistance, phages, biosynthesis clusters and carbohydrate degradation in 784 metagenomes from healthy populations worldwide and patients with inflammatory bowel diseases and obesity. We discovered country-wise functional specifics in gut resistome and virome compositions. The most distinct features of the disease microbiota were found for Crohn's disease, followed by ulcerative colitis and obesity. Profiling of the genes belonging to crAssphage showed that its abundance varied across the world populations and was not associated with clinical status. We demonstrated temporal resilience of crAssphage and the influence of the sample preparation protocol on its detected abundance. Our approach offers a convenient method to add value to accumulated "shotgun" metagenomic data by helping researchers state and assess novel biological hypotheses.

  10. A predictable Java profile

    DEFF Research Database (Denmark)

    Bøgholm, Thomas; Hansen, Rene Rydhof; Ravn, Anders Peter

    2009-01-01

    A Java profile suitable for development of high integrity embedded systems is presented. It is based on event handlers which are grouped in missions and equipped with respectively private handler memory and shared mission memory. This is a result of our previous work on developing a Java profile......, and is directly inspired by interactions with the Open Group on their on-going work on a safety critical Java profile (JSR-302). The main contribution is an arrangement of the class hierarchy such that the proposal is a generalization of Real-Time Specification for Java (RTSJ). A further contribution...

  11. Integrated analysis of microRNA and gene expression profiles reveals a functional regulatory module associated with liver fibrosis.

    Science.gov (United States)

    Chen, Wei; Zhao, Wenshan; Yang, Aiting; Xu, Anjian; Wang, Huan; Cong, Min; Liu, Tianhui; Wang, Ping; You, Hong

    2017-12-15

    Liver fibrosis, characterized with the excessive accumulation of extracellular matrix (ECM) proteins, represents the final common pathway of chronic liver inflammation. Ever-increasing evidence indicates microRNAs (miRNAs) dysregulation has important implications in the different stages of liver fibrosis. However, our knowledge of miRNA-gene regulation details pertaining to such disease remains unclear. The publicly available Gene Expression Omnibus (GEO) datasets of patients suffered from cirrhosis were extracted for integrated analysis. Differentially expressed miRNAs (DEMs) and genes (DEGs) were identified using GEO2R web tool. Putative target gene prediction of DEMs was carried out using the intersection of five major algorithms: DIANA-microT, TargetScan, miRanda, PICTAR5 and miRWalk. Functional miRNA-gene regulatory network (FMGRN) was constructed based on the computational target predictions at the sequence level and the inverse expression relationships between DEMs and DEGs. DAVID web server was selected to perform KEGG pathway enrichment analysis. Functional miRNA-gene regulatory module was generated based on the biological interpretation. Internal connections among genes in liver fibrosis-related module were determined using String database. MiRNA-gene regulatory modules related to liver fibrosis were experimentally verified in recombinant human TGFβ1 stimulated and specific miRNA inhibitor treated LX-2 cells. We totally identified 85 and 923 dysregulated miRNAs and genes in liver cirrhosis biopsy samples compared to their normal controls. All evident miRNA-gene pairs were identified and assembled into FMGRN which consisted of 990 regulations between 51 miRNAs and 275 genes, forming two big sub-networks that were defined as down-network and up-network, respectively. KEGG pathway enrichment analysis revealed that up-network was prominently involved in several KEGG pathways, in which "Focal adhesion", "PI3K-Akt signaling pathway" and "ECM

  12. Neurocognitive Profiles in Duchenne Muscular Dystrophy and Gene Mutation Site

    Science.gov (United States)

    D’Angelo, Maria Grazia; Lorusso, Maria Luisa; Civati, Federica; Comi, Giacomo Pietro; Magri, Francesca; Del Bo, Roberto; Guglieri, Michela; Molteni, Massimo; Turconi, Anna Carla; Bresolin, Nereo

    2011-01-01

    The presence of nonprogressive cognitive impairment is recognized as a common feature in a substantial proportion of patients with Duchenne muscular dystrophy. To investigate the possible role of mutations along the dystrophin gene affecting different brain dystrophin isoforms and specific cognitive profiles, 42 school-age children affected with Duchenne muscular dystrophy, subdivided according to sites of mutations along the dystrophin gene, underwent a battery of tests tapping a wide range of intellectual, linguistic, and neuropsychologic functions. Full-scale intelligence quotient was approximately 1 S.D. below the population average in the whole group of dystrophic children. Patients with Duchenne muscular dystrophy and mutations located in the distal portion of the dystrophin gene (involving the 140-kDa brain protein isoform, called Dp140) were generally more severely affected and expressed different patterns of strengths and impairments, compared with patients with Duchenne muscular dystrophy and mutations located in the proximal portion of the dystrophin gene (not involving Dp140). Patients with Duchenne muscular dystrophy and distal mutations demonstrated specific impairments in visuospatial functions and visual memory (which seemed intact in proximally mutated patients) and greater impairment in syntactic processing. PMID:22000308

  13. Mesenchymal stem cells display different gene expression profiles compared to hyaline and elastic chondrocytes

    OpenAIRE

    Zhai, Li-Jie; Zhao, Ke-Qing; Wang, Zhi-Qiang; Feng, Ya; Xing, Shuang-Chun

    2011-01-01

    Cartilage has a poor intrinsic repair capacity, requiring surgical intervention to effect biological repair. Tissue engineering technologies or regenerative medicine strategies are currently being employed to address cartilage repair. Mesenchymal stem cells (MSCs) are considered to be an excellent cell source for this application. However, the different gene expression profiles between the MSCs and differentiated cartilage remain unclear. In this report, we first examined the gene expression ...

  14. The importance of the selection of appropriate reference genes for gene expression profiling in adrenal medulla or sympathetic ganglia of spontaneously hypertensive rat

    Czech Academy of Sciences Publication Activity Database

    Vavřínová, Anna; Behuliak, Michal; Zicha, Josef

    2016-01-01

    Roč. 65, č. 3 (2016), s. 401-411 ISSN 0862-8408 R&D Projects: GA ČR(CZ) GP14-16225P Institutional support: RVO:67985823 Keywords : adrenal medulla * gene expression profiling * reference gene selection * sympathetic nervous system Subject RIV: FA - Cardiovascular Diseases incl. Cardiotharic Surgery Impact factor: 1.461, year: 2016

  15. Gene expression profiling of two distinct neuronal populations in the rodent spinal cord

    DEFF Research Database (Denmark)

    Ryge, Jesper; Westerdahl, Ann Charlotte; Alstøm, Preben

    2008-01-01

    Background: In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal p...

  16. Influence of smoking on colonic gene expression profile in Crohn's disease

    DEFF Research Database (Denmark)

    Nielsen, Ole Haagen; Bjerrum, Jacob Tveiten; Csillag, Claudio

    2009-01-01

    the included material: CD smokers (n = 28) or never-smokers (n = 14) as compared to fifteen healthy controls (8 smokers and 7 never-smokers). RNA was isolated and gene expression assessed with Affymetrix GeneChip Human Genome U133 Plus 2.0. Data were analyzed by principal component analysis (PCA), Wilcoxon......BACKGROUND: The development and course of Crohn's disease (CD) is related to both genetic and environmental factors. Smoking has been found to exacerbate the course of CD by increasing the risk of developing fistulas and strictures as well as the need for surgery, possibly because of an interaction...... smokers). AIM: To identify any difference in gene expression of the descending colonic mucosa between smoking and never-smoking CD patients (and controls) by determining genetic expression profiles from microarray analysis. METHODS: Fifty-seven specimens were obtained by routine colonoscopy from...

  17. Comparative gene expression analysis throughout the life cycle of Leishmania braziliensis: diversity of expression profiles among clinical isolates.

    Directory of Open Access Journals (Sweden)

    Vanessa Adaui

    Full Text Available BACKGROUND: Most of the Leishmania genome is reported to be constitutively expressed during the life cycle of the parasite, with a few regulated genes. Inter-species comparative transcriptomics evidenced a low number of species-specific differences related to differentially distributed genes or the differential regulation of conserved genes. It is of uppermost importance to ensure that the observed differences are indeed species-specific and not simply specific of the strains selected for representing the species. The relevance of this concern is illustrated by current study. METHODOLOGY/PRINCIPAL FINDINGS: We selected 5 clinical isolates of L. braziliensis characterized by their diversity of clinical and in vitro phenotypes. Real-time quantitative PCR was performed on promastigote and amastigote life stages to assess gene expression profiles at seven time points covering the whole life cycle. We tested 12 genes encoding proteins with roles in transport, thiol-based redox metabolism, cellular reduction, RNA poly(A-tail metabolism, cytoskeleton function and ribosomal function. The general trend of expression profiles showed that regulation of gene expression essentially occurs around the stationary phase of promastigotes. However, the genes involved in this phenomenon appeared to vary significantly among the isolates considered. CONCLUSION/SIGNIFICANCE: Our results clearly illustrate the unique character of each isolate in terms of gene expression dynamics. Results obtained on an individual strain are not necessarily representative of a given species. Therefore, extreme care should be taken when comparing the profiles of different species and extrapolating functional differences between them.

  18. Gene Expression Profile Change and Associated Physiological and Pathological Effects in Mouse Liver Induced by Fasting and Refeeding

    Science.gov (United States)

    Zhang, Fang; Xu, Xiang; Zhou, Ben; He, Zhishui; Zhai, Qiwei

    2011-01-01

    Food availability regulates basal metabolism and progression of many diseases, and liver plays an important role in these processes. The effects of food availability on digital gene expression profile, physiological and pathological functions in liver are yet to be further elucidated. In this study, we applied high-throughput sequencing technology to detect digital gene expression profile of mouse liver in fed, fasted and refed states. Totally 12162 genes were detected, and 2305 genes were significantly regulated by food availability. Biological process and pathway analysis showed that fasting mainly affected lipid and carboxylic acid metabolic processes in liver. Moreover, the genes regulated by fasting and refeeding in liver were mainly enriched in lipid metabolic process or fatty acid metabolism. Network analysis demonstrated that fasting mainly regulated Drug Metabolism, Small Molecule Biochemistry and Endocrine System Development and Function, and the networks including Lipid Metabolism, Small Molecule Biochemistry and Gene Expression were affected by refeeding. In addition, FunDo analysis showed that liver cancer and diabetes mellitus were most likely to be affected by food availability. This study provides the digital gene expression profile of mouse liver regulated by food availability, and demonstrates the main biological processes, pathways, gene networks and potential hepatic diseases regulated by fasting and refeeding. These results show that food availability mainly regulates hepatic lipid metabolism and is highly correlated with liver-related diseases including liver cancer and diabetes. PMID:22096593

  19. FERAL : Network-based classifier with application to breast cancer outcome prediction

    NARCIS (Netherlands)

    Allahyar, A.; De Ridder, J.

    2015-01-01

    Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.

  20. Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

    Science.gov (United States)

    Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

    2018-04-15

    Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen

  1. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

    Directory of Open Access Journals (Sweden)

    Assaf Gottlieb

    2017-11-01

    Full Text Available Abstract Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort

  2. Whole genome expression array profiling highlights differences in mucosal defense genes in Barrett's esophagus and esophageal adenocarcinoma.

    Directory of Open Access Journals (Sweden)

    Derek J Nancarrow

    Full Text Available Esophageal adenocarcinoma (EAC has become a major concern in Western countries due to rapid rises in incidence coupled with very poor survival rates. One of the key risk factors for the development of this cancer is the presence of Barrett's esophagus (BE, which is believed to form in response to repeated gastro-esophageal reflux. In this study we performed comparative, genome-wide expression profiling (using Illumina whole-genome Beadarrays on total RNA extracted from esophageal biopsy tissues from individuals with EAC, BE (in the absence of EAC and those with normal squamous epithelium. We combined these data with publically accessible raw data from three similar studies to investigate key gene and ontology differences between these three tissue states. The results support the deduction that BE is a tissue with enhanced glycoprotein synthesis machinery (DPP4, ATP2A3, AGR2 designed to provide strong mucosal defenses aimed at resisting gastro-esophageal reflux. EAC exhibits the enhanced extracellular matrix remodeling (collagens, IGFBP7, PLAU effects expected in an aggressive form of cancer, as well as evidence of reduced expression of genes associated with mucosal (MUC6, CA2, TFF1 and xenobiotic (AKR1C2, AKR1B10 defenses. When our results are compared to previous whole-genome expression profiling studies keratin, mucin, annexin and trefoil factor gene groups are the most frequently represented differentially expressed gene families. Eleven genes identified here are also represented in at least 3 other profiling studies. We used these genes to discriminate between squamous epithelium, BE and EAC within the two largest cohorts using a support vector machine leave one out cross validation (LOOCV analysis. While this method was satisfactory for discriminating squamous epithelium and BE, it demonstrates the need for more detailed investigations into profiling changes between BE and EAC.

  3. A 65‑gene signature for prognostic prediction in colon adenocarcinoma.

    Science.gov (United States)

    Jiang, Hui; Du, Jun; Gu, Jiming; Jin, Liugen; Pu, Yong; Fei, Bojian

    2018-04-01

    The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival‑related genes were selected from the DEGs using the Cox regression method. A co‑expression network of survival‑related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan‑Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival‑related genes selected. The co‑expression network of survival‑related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator‑activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine‑cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65‑gene signature was established using this co‑expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e‑12) and the GSE17538 dataset (P=1.67e‑6). The 65‑gene signature included kallikrein‑related peptidase 6 (KLK6), collagen type XI α1 (COL11A1), cartilage

  4. Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients

    DEFF Research Database (Denmark)

    Rapin, Nicolas; Bagger, Frederik Otzen; Jendholm, Johan

    2014-01-01

    Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional programs that differ between cancer and corresponding normal cells and has not been efficient in iden......-karyotype AML, which allowed for the generation of a highly prognostic survival signature. Collectively, our CvN method holds great potential as a tool for the analysis of gene expression profiles of cancer patients....

  5. Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment.

    Science.gov (United States)

    Severgnini, Marco; Bicciato, Silvio; Mangano, Eleonora; Scarlatti, Francesca; Mezzelani, Alessandra; Mattioli, Michela; Ghidoni, Riccardo; Peano, Clelia; Bonnal, Raoul; Viti, Federica; Milanesi, Luciano; De Bellis, Gianluca; Battaglia, Cristina

    2006-06-01

    Meta-analysis of microarray data is increasingly important, considering both the availability of multiple platforms using disparate technologies and the accumulation in public repositories of data sets from different laboratories. We addressed the issue of comparing gene expression profiles from two microarray platforms by devising a standardized investigative strategy. We tested this procedure by studying MDA-MB-231 cells, which undergo apoptosis on treatment with resveratrol. Gene expression profiles were obtained using high-density, short-oligonucleotide, single-color microarray platforms: GeneChip (Affymetrix) and CodeLink (Amersham). Interplatform analyses were carried out on 8414 common transcripts represented on both platforms, as identified by LocusLink ID, representing 70.8% and 88.6% of annotated GeneChip and CodeLink features, respectively. We identified 105 differentially expressed genes (DEGs) on CodeLink and 42 DEGs on GeneChip. Among them, only 9 DEGs were commonly identified by both platforms. Multiple analyses (BLAST alignment of probes with target sequences, gene ontology, literature mining, and quantitative real-time PCR) permitted us to investigate the factors contributing to the generation of platform-dependent results in single-color microarray experiments. An effective approach to cross-platform comparison involves microarrays of similar technologies, samples prepared by identical methods, and a standardized battery of bioinformatic and statistical analyses.

  6. Comparative expression profiling reveals gene functions in female meiosis and gametophyte development in Arabidopsis.

    Science.gov (United States)

    Zhao, Lihua; He, Jiangman; Cai, Hanyang; Lin, Haiyan; Li, Yanqiang; Liu, Renyi; Yang, Zhenbiao; Qin, Yuan

    2014-11-01

    Megasporogenesis is essential for female fertility, and requires the accomplishment of meiosis and the formation of functional megaspores. The inaccessibility and low abundance of female meiocytes make it particularly difficult to elucidate the molecular basis underlying megasporogenesis. We used high-throughput tag-sequencing analysis to identify genes expressed in female meiocytes (FMs) by comparing gene expression profiles from wild-type ovules undergoing megasporogenesis with those from the spl mutant ovules, which lack megasporogenesis. A total of 862 genes were identified as FMs, with levels that are consistently reduced in spl ovules in two biological replicates. Fluorescence-assisted cell sorting followed by RNA-seq analysis of DMC1:GFP-labeled female meiocytes confirmed that 90% of the FMs are indeed detected in the female meiocyte protoplast profiling. We performed reverse genetic analysis of 120 candidate genes and identified four FM genes with a function in female meiosis progression in Arabidopsis. We further revealed that KLU, a putative cytochrome P450 monooxygenase, is involved in chromosome pairing during female meiosis, most likely by affecting the normal expression pattern of DMC1 in ovules during female meiosis. Our studies provide valuable information for functional genomic analyses of plant germline development as well as insights into meiosis. © 2014 The Authors The Plant Journal © 2014 John Wiley & Sons Ltd.

  7. Differential Gene Expression Profiling of Enriched Human Spermatogonia after Short- and Long-Term Culture

    Directory of Open Access Journals (Sweden)

    Sabine Conrad

    2014-01-01

    Full Text Available This study aimed to provide a molecular signature for enriched adult human stem/progenitor spermatogonia during short-term (<2 weeks and long-term culture (up to more than 14 months in comparison to human testicular fibroblasts and human embryonic stem cells. Human spermatogonia were isolated by CD49f magnetic activated cell sorting and collagen−/laminin+ matrix binding from primary testis cultures obtained from ten adult men. For transcriptomic analysis, single spermatogonia-like cells were collected based on their morphology and dimensions using a micromanipulation system from the enriched germ cell cultures. Immunocytochemical, RT-PCR and microarray analyses revealed that the analyzed populations of cells were distinct at the molecular level. The germ- and pluripotency-associated genes and genes of differentiation/spermatogenesis pathway were highly expressed in enriched short-term cultured spermatogonia. After long-term culture, a proportion of cells retained and aggravated the “spermatogonial” gene expression profile with the expression of germ and pluripotency-associated genes, while in the majority of long-term cultured cells this molecular profile, typical for the differentiation pathway, was reduced and more genes related to the extracellular matrix production and attachment were expressed. The approach we provide here to study the molecular status of in vitro cultured spermatogonia may be important to optimize the culture conditions and to evaluate the germ cell plasticity in the future.

  8. The Vitis vinifera sugar transporter gene family: phylogenetic overview and macroarray expression profiling

    Directory of Open Access Journals (Sweden)

    Atanassova Rossitza

    2010-11-01

    Full Text Available Abstract Background In higher plants, sugars are not only nutrients but also important signal molecules. They are distributed through the plant via sugar transporters, which are involved not only in sugar long-distance transport via the loading and the unloading of the conducting complex, but also in sugar allocation into source and sink cells. The availability of the recently released grapevine genome sequence offers the opportunity to identify sucrose and monosaccharide transporter gene families in a woody species and to compare them with those of the herbaceous Arabidopsis thaliana using a phylogenetic analysis. Results In grapevine, one of the most economically important fruit crop in the world, it appeared that sucrose and monosaccharide transporter genes are present in 4 and 59 loci, respectively and that the monosaccharide transporter family can be divided into 7 subfamilies. Phylogenetic analysis of protein sequences has indicated that orthologs exist between Vitis and Arabidospis. A search for cis-regulatory elements in the promoter sequences of the most characterized transporter gene families (sucrose, hexoses and polyols transporters, has revealed that some of them might probably be regulated by sugars. To profile several genes simultaneously, we created a macroarray bearing cDNA fragments specific to 20 sugar transporter genes. This macroarray analysis has revealed that two hexose (VvHT1, VvHT3, one polyol (VvPMT5 and one sucrose (VvSUC27 transporter genes, are highly expressed in most vegetative organs. The expression of one hexose transporter (VvHT2 and two tonoplastic monosaccharide transporter (VvTMT1, VvTMT2 genes are regulated during berry development. Finally, three putative hexose transporter genes show a preferential organ specificity being highly expressed in seeds (VvHT3, VvHT5, in roots (VvHT2 or in mature leaves (VvHT5. Conclusions This study provides an exhaustive survey of sugar transporter genes in Vitis vinifera and

  9. Gene expression profiling in the inductive human hematopoietic microenvironment

    International Nuclear Information System (INIS)

    Zhao Yongjun; Chen, Edwin; Li Liheng; Gong Baiwei; Xie Wei; Nanji, Shaherose; Dube, Ian D.; Hough, Margaret R.

    2004-01-01

    Human hematopoietic stem cells (HSCs) and their progenitors can be maintained in vitro in long-term bone marrow cultures (LTBMCs) in which constituent HSCs can persist within the adherent layers for up to 2 months. Media replenishment of LTBMCs has been shown to induce transition of HSCs from a quiescent state to an active cycling state. We hypothesize that the media replenishment of the LTBMCs leads to the activation of important regulatory genes uniquely involved in HSC proliferation and differentiation. To profile the gene expression changes associated with HSC activation, we performed suppression subtractive hybridization (SSH) on day 14 human LTBMCs following 1-h media replenishment and on unmanipulated controls. The generated SSH library contained 191 differentially up-regulated expressed sequence tags (ESTs), the majority corresponding to known genes related to various intracellular processes, including signal transduction pathways, protein synthesis, and cell cycle regulation. Nineteen ESTs represented previously undescribed sequences encoding proteins of unknown function. Differential up-regulation of representative genes, including IL-8, IL-1, putative cytokine 21/HC21, MAD3, and a novel EST was confirmed by semi-quantitative RT-PCR. Levels of fibronectin, G-CSF, and stem cell factor also increased in the conditioned media of LTBMCs as assessed by ELISA, indicating increased synthesis and secretion of these factors. Analysis of our library provides insights into some of the immediate early gene changes underlying the mechanisms by which the stromal elements within the LTBMCs contribute to the induction of HSC activation and provides the opportunity to identify as yet unrecognized factors regulating HSC activation in the LTBMC milieu

  10. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

    NARCIS (Netherlands)

    Broyl, Annemiek; Hose, Dirk; Lokhorst, Henk; de Knegt, Yvonne; Peeters, Justine; Jauch, Anna; Bertsch, Uta; Buijs, Arjan; Stevens-Kroef, Marian; Beverloo, H. Berna; Vellenga, Edo; Zweegman, Sonja; Kersten, Marie-Josée; van der Holt, Bronno; el Jarari, Laila; Mulligan, George; Goldschmidt, Hartmut; van Duin, Mark; Sonneveld, Pieter

    2010-01-01

    To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138(+) plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6

  11. Development of Gene Expression Signatures for Practical Radiation Biodosimetry

    International Nuclear Information System (INIS)

    Paul, Sunirmal; Amundson, Sally A.

    2008-01-01

    Purpose: In a large-scale radiologic emergency, estimates of exposure doses and radiation injury would be required for individuals without physical dosimeters. Current methods are inadequate for the task, so we are developing gene expression profiles for radiation biodosimetry. This approach could provide both an estimate of physical radiation dose and an indication of the extent of individual injury or future risk. Methods and Materials: We used whole genome microarray expression profiling as a discovery platform to identify genes with the potential to predict radiation dose across an exposure range relevant for medical decision making in a radiologic emergency. Human peripheral blood from 10 healthy donors was irradiated ex vivo, and global gene expression was measured both 6 and 24 h after exposure. Results: A 74-gene signature was identified that distinguishes between four radiation doses (0.5, 2, 5, and 8 Gy) and controls. More than one third of these genes are regulated by TP53. A nearest centroid classifier using these same 74 genes correctly predicted 98% of samples taken either 6 h or 24 h after treatment as unexposed, exposed to 0.5, 2, or ≥5 Gy. Expression patterns of five genes (CDKN1A, FDXR, SESN1, BBC3, and PHPT1) from this signature were also confirmed by real-time polymerase chain reaction. Conclusion: The ability of a single gene set to predict radiation dose throughout a window of time without need for individual pre-exposure controls represents an important advance in the development of gene expression for biodosimetry

  12. Prediction of Process-Induced Distortions in L-Shaped Composite Profiles Using Path-Dependent Constitutive Law

    Science.gov (United States)

    Ding, Anxin; Li, Shuxin; Wang, Jihui; Ni, Aiqing; Sun, Liangliang; Chang, Lei

    2016-10-01

    In this paper, the corner spring-in angles of AS4/8552 L-shaped composite profiles with different thicknesses are predicted using path-dependent constitutive law with the consideration of material properties variation due to phase change during curing. The prediction accuracy mainly depends on the properties in the rubbery and glassy states obtained by homogenization method rather than experimental measurements. Both analytical and finite element (FE) homogenization methods are applied to predict the overall properties of AS4/8552 composite. The effect of fiber volume fraction on the properties is investigated for both rubbery and glassy states using both methods. And the predicted results are compared with experimental measurements for the glassy state. Good agreement is achieved between the predicted results and available experimental data, showing the reliability of the homogenization method. Furthermore, the corner spring-in angles of L-shaped composite profiles are measured experimentally and the reliability of path-dependent constitutive law is validated as well as the properties prediction by FE homogenization method.

  13. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Science.gov (United States)

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Differential DNA methylation profiles of coding and non-coding genes define hippocampal sclerosis in human temporal lobe epilepsy

    Science.gov (United States)

    Miller-Delaney, Suzanne F.C.; Bryan, Kenneth; Das, Sudipto; McKiernan, Ross C.; Bray, Isabella M.; Reynolds, James P.; Gwinn, Ryder; Stallings, Raymond L.

    2015-01-01

    Temporal lobe epilepsy is associated with large-scale, wide-ranging changes in gene expression in the hippocampus. Epigenetic changes to DNA are attractive mechanisms to explain the sustained hyperexcitability of chronic epilepsy. Here, through methylation analysis of all annotated C-phosphate-G islands and promoter regions in the human genome, we report a pilot study of the methylation profiles of temporal lobe epilepsy with or without hippocampal sclerosis. Furthermore, by comparative analysis of expression and promoter methylation, we identify methylation sensitive non-coding RNA in human temporal lobe epilepsy. A total of 146 protein-coding genes exhibited altered DNA methylation in temporal lobe epilepsy hippocampus (n = 9) when compared to control (n = 5), with 81.5% of the promoters of these genes displaying hypermethylation. Unique methylation profiles were evident in temporal lobe epilepsy with or without hippocampal sclerosis, in addition to a common methylation profile regardless of pathology grade. Gene ontology terms associated with development, neuron remodelling and neuron maturation were over-represented in the methylation profile of Watson Grade 1 samples (mild hippocampal sclerosis). In addition to genes associated with neuronal, neurotransmitter/synaptic transmission and cell death functions, differential hypermethylation of genes associated with transcriptional regulation was evident in temporal lobe epilepsy, but overall few genes previously associated with epilepsy were among the differentially methylated. Finally, a panel of 13, methylation-sensitive microRNA were identified in temporal lobe epilepsy including MIR27A, miR-193a-5p (MIR193A) and miR-876-3p (MIR876), and the differential methylation of long non-coding RNA documented for the first time. The present study therefore reports select, genome-wide DNA methylation changes in human temporal lobe epilepsy that may contribute to the molecular architecture of the epileptic brain. PMID

  15. A Predictive Coexpression Network Identifies Novel Genes Controlling the Seed-to-Seedling Phase Transition in Arabidopsis thaliana.

    Science.gov (United States)

    Silva, Anderson Tadeu; Ribone, Pamela A; Chan, Raquel L; Ligterink, Wilco; Hilhorst, Henk W M

    2016-04-01

    The transition from a quiescent dry seed to an actively growing photoautotrophic seedling is a complex and crucial trait for plant propagation. This study provides a detailed description of global gene expression in seven successive developmental stages of seedling establishment in Arabidopsis (Arabidopsis thaliana). Using the transcriptome signature from these developmental stages, we obtained a coexpression gene network that highlights interactions between known regulators of the seed-to-seedling transition and predicts the functions of uncharacterized genes in seedling establishment. The coexpressed gene data sets together with the transcriptional module indicate biological functions related to seedling establishment. Characterization of the homeodomain leucine zipper I transcription factor AtHB13, which is expressed during the seed-to-seedling transition, demonstrated that this gene regulates some of the network nodes and affects late seedling establishment. Knockout mutants for athb13 showed increased primary root length as compared with wild-type (Columbia-0) seedlings, suggesting that this transcription factor is a negative regulator of early root growth, possibly repressing cell division and/or cell elongation or the length of time that cells elongate. The signal transduction pathways present during the early phases of the seed-to-seedling transition anticipate the control of important events for a vigorous seedling, such as root growth. This study demonstrates that a gene coexpression network together with transcriptional modules can provide insights that are not derived from comparative transcript profiling alone. © 2016 American Society of Plant Biologists. All Rights Reserved.

  16. Identification of differentially expressed genes in cucumber (Cucumis sativus L.) root under waterlogging stress by digital gene expression profile.

    Science.gov (United States)

    Qi, Xiao-Hua; Xu, Xue-Wen; Lin, Xiao-Jian; Zhang, Wen-Jie; Chen, Xue-Hao

    2012-03-01

    High-throughput tag-sequencing (Tag-seq) analysis based on the Solexa Genome Analyzer platform was applied to analyze the gene expression profiling of cucumber plant at 5 time points over a 24h period of waterlogging treatment. Approximately 5.8 million total clean sequence tags per library were obtained with 143013 distinct clean tag sequences. Approximately 23.69%-29.61% of the distinct clean tags were mapped unambiguously to the unigene database, and 53.78%-60.66% of the distinct clean tags were mapped to the cucumber genome database. Analysis of the differentially expressed genes revealed that most of the genes were down-regulated in the waterlogging stages, and the differentially expressed genes mainly linked to carbon metabolism, photosynthesis, reactive oxygen species generation/scavenging, and hormone synthesis/signaling. Finally, quantitative real-time polymerase chain reaction using nine genes independently verified the tag-mapped results. This present study reveals the comprehensive mechanisms of waterlogging-responsive transcription in cucumber. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Exploring Regulatory Mechanisms of Atrial Myocyte Hypertrophy of Mitral Regurgitation through Gene Expression Profiling Analysis: Role of NFAT in Cardiac Hypertrophy.

    Directory of Open Access Journals (Sweden)

    Tzu-Hao Chang

    Full Text Available Left atrial enlargement in mitral regurgitation (MR predicts a poor prognosis. The regulatory mechanisms of atrial myocyte hypertrophy of MR patients remain unknown.This study comprised 14 patients with MR, 7 patients with aortic valve disease (AVD, and 6 purchased samples from normal subjects (NC. We used microarrays, enrichment analysis and quantitative RT-PCR to study the gene expression profiles in the left atria. Microarray results showed that 112 genes were differentially up-regulated and 132 genes were differentially down-regulated in the left atria between MR patients and NC. Enrichment analysis of differentially expressed genes demonstrated that "NFAT in cardiac hypertrophy" pathway was not only one of the significant associated canonical pathways, but also the only one predicted with a non-zero score of 1.34 (i.e. activated through Ingenuity Pathway Analysis molecule activity predictor. Ingenuity Pathway Analysis Global Molecular Network analysis exhibited that the highest score network also showed high association with cardiac related pathways and functions. Therefore, 5 NFAT associated genes (PPP3R1, PPP3CB, CAMK1, MEF2C, PLCE1 were studies for validation. The mRNA expressions of PPP3CB and MEF2C were significantly up-regulated, and CAMK1 and PPP3R1 were significantly down-regulated in MR patients compared to NC. Moreover, MR patients had significantly increased mRNA levels of PPP3CB, MEF2C and PLCE1 compared to AVD patients. The atrial myocyte size of MR patients significantly exceeded that of the AVD patients and NC.Differentially expressed genes in the "NFAT in cardiac hypertrophy" pathway may play a critical role in the atrial myocyte hypertrophy of MR patients.

  18. Transcription profile data of phorbol esters biosynthetic genes during developmental stages in Jatropha curcas.

    Science.gov (United States)

    Jadid, Nurul; Mardika, Rizal Kharisma; Purwani, Kristanti Indah; Permatasari, Erlyta Vivi; Prasetyowati, Indah; Irawan, Mohammad Isa

    2018-06-01

    Jatropha curcas is currently known as an alternative source for biodiesel production. Beside its high free fatty acid content, J. curcas also contains typical diterpenoid-toxic compounds of Euphorbiaceae plant namely phorbol esters. This article present the transcription profile data of genes involved in the biosynthesis of phorbol esters at different developmental stages of leaves, fruit, and seed in Jatropha curcas . Transcriptional profiles were analyzed using reverse transcription-polymerase chain reaction (RT-PCR). We used two genes including GGPPS (Geranylgeranyl diphospate synthase), which is responsible for the formation of common diterpenoid precursor (GGPP) and CS (Casbene Synthase), which functions in the synthesis of casbene. Meanwhile, J. curcas Actin ( ACT ) was used as internal standard. We demonstrated dynamic of GGPPS and CS expression among different stage of development of leaves, fruit and seed in Jatropha .

  19. Altered Gene Expression Profile in Mouse Bladder Cancers Induced by Hydroxybutyl(butylnitrosamine

    Directory of Open Access Journals (Sweden)

    Ruisheng Yao

    2004-09-01

    Full Text Available A variety of genetic alterations and gene expression changes are involved in the pathogenesis of bladder tumor. To explore these changes, oligonucleotide array analysis was performed on RNA obtained from carcinogen-induced mouse bladder tumors and normal mouse bladder epithelia using Affymetrix (Santa Clara, CA MGU74Av2 GeneChips. Analysis yielded 1164 known genes that were changed in the tumors. Certain of the upregulated genes included EGFR-Ras signaling genes, transcription factors, cell cycle-related genes, and intracellular signaling cascade genes. However, downregulated genes include mitogen-activated protein kinases, cell cycle checkpoint genes, Rab subfamily genes, Rho subfamily genes, and SH2 and SH3 domains-related genes. These genes are involved in a broad range of different pathways including control of cell proliferation, differentiation, cell cycle, signal transduction, and apoptosis. Using the pathway visualization tool GenMAPP, we found that several genes, including TbR-l, STAT1, Smad1, Smad2, Jun, NFκB, and so on, in the TGF-β signaling pathway and p115 RhoGEF, RhoGDl3, MEKK4A/MEKK4B, P13KA, and JNK in the G13 signaling pathway were differentially expressed in the tumors. In summary, we have determined the expression profiles of genes differentially expressed during mouse bladder tumorigenesis. Our results suggest that activation of the EGFR-Ras pathway, uncontrolled cell cycle, aberrant transcription factors, and G13 and TGF-β pathways are involved, and the cross-talk between these pathways seems to play important roles in mouse bladder tumorigenesis.

  20. Asthma pharmacogenetics and the development of genetic profiles for personalized medicine

    Directory of Open Access Journals (Sweden)

    Ortega VE

    2015-01-01

    Full Text Available Victor E Ortega, Deborah A Meyers, Eugene R Bleecker Center for Genomics and Personalized Medicine Research, Pulmonary Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA Abstract: Human genetics research will be critical to the development of genetic profiles for personalized or precision medicine in asthma. Genetic profiles will consist of gene variants that predict individual disease susceptibility and risk for progression, predict which pharmacologic therapies will result in a maximal therapeutic benefit, and predict whether a therapy will result in an adverse response and should be avoided in a given individual. Pharmacogenetic studies of the glucocorticoid, leukotriene, and β2-adrenergic receptor pathways have focused on candidate genes within these pathways and, in addition to a small number of genome-wide association studies, have identified genetic loci associated with therapeutic responsiveness. This review summarizes these pharmacogenetic discoveries and the future of genetic profiles for personalized medicine in asthma. The benefit of a personalized, tailored approach to health care delivery is needed in the development of expensive biologic drugs directed at a specific biologic pathway. Prior pharmacogenetic discoveries, in combination with additional variants identified in future studies, will form the basis for future genetic profiles for personalized tailored approaches to maximize therapeutic benefit for an individual asthmatic while minimizing the risk for adverse events. Keywords: asthma, pharmacogenetics, response heterogeneity, single nucleotide polymorphism, genome-wide association study

  1. Digital gene expression profiling of flax (Linum usitatissimum L.) stem peel identifies genes enriched in fiber-bearing phloem tissue.

    Science.gov (United States)

    Guo, Yuan; Qiu, Caisheng; Long, Songhua; Chen, Ping; Hao, Dongmei; Preisner, Marta; Wang, Hui; Wang, Yufu

    2017-08-30

    To better understand the molecular mechanisms and gene expression characteristics associated with development of bast fiber cell within flax stem phloem, the gene expression profiling of flax stem peels and leaves were screened, using Illumina's Digital Gene Expression (DGE) analysis. Four DGE libraries (2 for stem peel and 2 for leaf), ranging from 6.7 to 9.2 million clean reads were obtained, which produced 7.0 million and 6.8 million mapped reads for flax stem peel and leave, respectively. By differential gene expression analysis, a total of 975 genes, of which 708 (73%) genes have protein-coding annotation, were identified as phloem enriched genes putatively involved in the processes of polysaccharide and cell wall metabolism. Differential expression genes (DEGs) was validated using quantitative RT-PCR, the expression pattern of all nine genes determined by qRT-PCR fitted in well with that obtained by sequencing analysis. Cluster and Gene Ontology (GO) analysis revealed that a large number of genes related to metabolic process, catalytic activity and binding category were expressed predominantly in the stem peels. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the phloem enriched genes suggested approximately 111 biological pathways. The large number of genes and pathways produced from DGE sequencing will expand our understanding of the complex molecular and cellular events in flax bast fiber development and provide a foundation for future studies on fiber development in other bast fiber crops. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

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    Teng Shaolei

    2013-01-01

    Full Text Available Abstract Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs and Support Vector Machines (SVMs were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.

  3. Prediction of highly expressed genes in microbes based on chromatin accessibility

    DEFF Research Database (Denmark)

    Willenbrock, Hanni; Ussery, David

    2007-01-01

    BACKGROUND: It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed...

  4. An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

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    Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

    2018-05-14

    A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives

  5. Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

    Science.gov (United States)

    Yang, Jun; Hou, Ziming; Wang, Changjiang; Wang, Hao; Zhang, Hongbing

    2018-04-23

    Adamantinomatous craniopharyngioma (ACP) is an aggressive brain tumor that occurs predominantly in the pediatric population. Conventional diagnosis method and standard therapy cannot treat ACPs effectively. In this paper, we aimed to identify key genes for ACP early diagnosis and treatment. Datasets GSE94349 and GSE68015 were obtained from Gene Expression Omnibus database. Consensus clustering was applied to discover the gene clusters in the expression data of GSE94349 and functional enrichment analysis was performed on gene set in each cluster. The protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes, and hubs were selected. Support vector machine (SVM) model was built based on the signature genes identified from enrichment analysis and PPI network. Dataset GSE94349 was used for training and testing, and GSE68015 was used for validation. Besides, RT-qPCR analysis was performed to analyze the expression of signature genes in ACP samples compared with normal controls. Seven gene clusters were discovered in the differentially expressed genes identified from GSE94349 dataset. Enrichment analysis of each cluster identified 25 pathways that highly associated with ACP. PPI network was built and 46 hubs were determined. Twenty-five pathway-related genes that overlapped with the hubs in PPI network were used as signatures to establish the SVM diagnosis model for ACP. The prediction accuracy of SVM model for training, testing, and validation data were 94, 85, and 74%, respectively. The expression of CDH1, CCL2, ITGA2, COL8A1, COL6A2, and COL6A3 were significantly upregulated in ACP tumor samples, while CAMK2A, RIMS1, NEFL, SYT1, and STX1A were significantly downregulated, which were consistent with the differentially expressed gene analysis. SVM model is a promising classification tool for screening and early diagnosis of ACP. The ACP-related pathways and signature genes will advance our knowledge of ACP pathogenesis

  6. Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

    Science.gov (United States)

    Noar, Roslyn D; Daub, Margaret E

    2016-01-01

    Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana, is a Dothideomycete fungus closely related to fungi that produce polyketides important for plant pathogenicity. We utilized the M. fijiensis genome sequence to predict PKS genes and their gene clusters and make bioinformatics predictions about the types of compounds produced by these clusters. Eight PKS gene clusters were identified in the M. fijiensis genome, placing M. fijiensis into the 23rd percentile for the number of PKS genes compared to other Dothideomycetes. Analysis of the PKS domains identified three of the PKS enzymes as non-reducing and two as highly reducing. Gene clusters contained types of genes frequently found in PKS clusters including genes encoding transporters, oxidoreductases, methyltransferases, and non-ribosomal peptide synthases. Phylogenetic analysis identified a putative PKS cluster encoding melanin biosynthesis. None of the other clusters were closely aligned with genes encoding known polyketides, however three of the PKS genes fell into clades with clusters encoding alternapyrone, fumonisin, and solanapyrone produced by Alternaria and Fusarium species. A search for homologs among available genomic sequences from 103 Dothideomycetes identified close homologs (>80% similarity) for six of the PKS sequences. One of the PKS sequences was not similar (< 60% similarity) to sequences in any of the 103 genomes, suggesting that it encodes a unique compound. Comparison of the M. fijiensis PKS sequences with those of two other banana pathogens, M. musicola and M. eumusae, showed that these two species have close homologs to five of the M. fijiensis PKS sequences, but three others were not found in either species. RT-PCR and RNA-Seq analysis showed that the melanin PKS cluster was down-regulated in infected banana as compared to growth in culture. Three other clusters, however were strongly upregulated during disease development in banana, suggesting that they may encode

  7. Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

    Directory of Open Access Journals (Sweden)

    Roslyn D Noar

    Full Text Available Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana, is a Dothideomycete fungus closely related to fungi that produce polyketides important for plant pathogenicity. We utilized the M. fijiensis genome sequence to predict PKS genes and their gene clusters and make bioinformatics predictions about the types of compounds produced by these clusters. Eight PKS gene clusters were identified in the M. fijiensis genome, placing M. fijiensis into the 23rd percentile for the number of PKS genes compared to other Dothideomycetes. Analysis of the PKS domains identified three of the PKS enzymes as non-reducing and two as highly reducing. Gene clusters contained types of genes frequently found in PKS clusters including genes encoding transporters, oxidoreductases, methyltransferases, and non-ribosomal peptide synthases. Phylogenetic analysis identified a putative PKS cluster encoding melanin biosynthesis. None of the other clusters were closely aligned with genes encoding known polyketides, however three of the PKS genes fell into clades with clusters encoding alternapyrone, fumonisin, and solanapyrone produced by Alternaria and Fusarium species. A search for homologs among available genomic sequences from 103 Dothideomycetes identified close homologs (>80% similarity for six of the PKS sequences. One of the PKS sequences was not similar (< 60% similarity to sequences in any of the 103 genomes, suggesting that it encodes a unique compound. Comparison of the M. fijiensis PKS sequences with those of two other banana pathogens, M. musicola and M. eumusae, showed that these two species have close homologs to five of the M. fijiensis PKS sequences, but three others were not found in either species. RT-PCR and RNA-Seq analysis showed that the melanin PKS cluster was down-regulated in infected banana as compared to growth in culture. Three other clusters, however were strongly upregulated during disease development in banana, suggesting that

  8. Differences in gene expression profiles and signaling pathways in rhabdomyolysis-induced acute kidney injury.

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    Geng, Xiaodong; Wang, Yuanda; Hong, Quan; Yang, Jurong; Zheng, Wei; Zhang, Gang; Cai, Guangyan; Chen, Xiangmei; Wu, Di

    2015-01-01

    Rhabdomyolysis is a threatening syndrome because it causes the breakdown of skeletal muscle. Muscle destruction leads to the release of myoglobin, intracellular proteins, and electrolytes into the circulation. The aim of this study was to investigate the differences in gene expression profiles and signaling pathways upon rhabdomyolysis-induced acute kidney injury (AKI). In this study, we used glycerol-induced renal injury as a model of rhabdomyolysis-induced AKI. We analyzed data and relevant information from the Gene Expression Omnibus database (No: GSE44925). The gene expression data for three untreated mice were compared to data for five mice with rhabdomyolysis-induced AKI. The expression profiling of the three untreated mice and the five rhabdomyolysis-induced AKI mice was performed using microarray analysis. We examined the levels of Cyp3a13, Rela, Aldh7a1, Jun, CD14. And Cdkn1a using RT-PCR to determine the accuracy of the microarray results. The microarray analysis showed that there were 1050 downregulated and 659 upregulated genes in the rhabdomyolysis-induced AKI mice compared to the control group. The interactions of all differentially expressed genes in the Signal-Net were analyzed. Cyp3a13 and Rela had the most interactions with other genes. The data showed that Rela and Aldh7a1 were the key nodes and had important positions in the Signal-Net. The genes Jun, CD14, and Cdkn1a were also significantly upregulated. The pathway analysis classified the differentially expressed genes into 71 downregulated and 48 upregulated pathways including the PI3K/Akt, MAPK, and NF-κB signaling pathways. The results of this study indicate that the NF-κB, MAPK, PI3K/Akt, and apoptotic pathways are regulated in rhabdomyolysis-induced AKI.

  9. Classification of genes and putative biomarker identification using distribution metrics on expression profiles.

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    Hung-Chung Huang

    Full Text Available BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic, and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery.

  10. Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

    Science.gov (United States)

    Patel, Nihir; Wang, Jason T L

    2015-10-01

    Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

  11. A 7-Gene Signature Depicts the Biochemical Profile of Early Prefibrotic Myelofibrosis

    DEFF Research Database (Denmark)

    Skov, Vibe; Burton, Mark; Thomassen, Mads

    2016-01-01

    was performed in 17 and 9 patients diagnosed with ET and PMF, respectively. Using elevated LDH obtained at the time of diagnosis as a marker of prePMF, a 7-gene signature was identified which correctly predicted the prePMF group with a sensitivity of 100% and a specificity of 89%. The 7 genes included MPO......, CEACAM8, CRISP3, MS4A3, CEACAM6, HEMGN, and MMP8, which are genes known to be involved in inflammation, cell adhesion, differentiation and proliferation. Evaluation of bone marrow biopsies and the 7-gene signature showed a concordance rate of 71%, 79%, 62%, and 38%. Our 7-gene signature may be a useful...

  12. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

    Science.gov (United States)

    Zhang, Ping; Wu, Linwei; Rocha, Andrea M.; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D.; Wu, Liyou; Watson, David B.; Adams, Michael W. W.; Alm, Eric J.; Adams, Paul D.; Arkin, Adam P.

    2018-01-01

    ABSTRACT Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. PMID:29463661

  13. Time-course investigation of the gene expression profile during Fasciola hepatica infection: A microarray-based study

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    Jose Rojas-Caraballo

    2015-12-01

    Full Text Available Fasciolosis is listed as one of the most important neglected tropical diseases according with the World Health Organization and is also considered as a reemerging disease in the human beings. Despite there are several studies describing the immune response induced by Fasciola hepatica in the mammalian host, investigations aimed at identifying the expression profile of genes involved in inducing hepatic injury are currently scarce. Data presented here belong to a time-course investigation of the gene expression profile in the liver of BALB/c mice infected with F. hepatica metacercariae at 7 and 21 days after experimental infection. The data published here have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE69588, previously published by Rojas-Caraballo et al. (2015 in PLoS One [1].

  14. Specific Tandem 3'UTR Patterns and Gene Expression Profiles in Mouse Thy1+ Germline Stem Cells.

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    Yan Huang

    Full Text Available A recently developed strategy of sequencing alternative polyadenylation (APA sites (SAPAS with second-generation sequencing technology can be used to explore complete genome-wide patterns of tandem APA sites and global gene expression profiles. spermatogonial stem cells (SSCs maintain long-term reproductive abilities in male mammals. The detailed mechanisms by which SSCs self-renew and generate mature spermatozoa are not clear. To understand the specific alternative polyadenylation pattern and global gene expression profile of male germline stem cells (GSCs, mainly referred to SSCs here, we isolated and purified mouse Thy1+ cells from testis by magnetic-activated cell sorting (MACS and then used the SAPAS method for analysis, using pluripotent embryonic stem cells (ESCs and differentiated mouse embryonic fibroblast cells (MEFs as controls. As a result, we obtained 99,944 poly(A sites, approximately 40% of which were newly detected in our experiments. These poly(A sites originated from three mouse cell types and covered 17,499 genes, including 831 long non-coding RNA (lncRNA genes. We observed that GSCs tend to have shorter 3'UTR lengths while MEFs tend towards longer 3'UTR lengths. We also identified 1337 genes that were highly expressed in GSCs, and these genes were highly consistent with the functional characteristics of GSCs. Our detailed bioinformatics analysis identified APA site-switching events at 3'UTRs and many new specifically expressed genes in GSCs, which we experimentally confirmed. Furthermore, qRT-PCR was performed to validate several events of the 334 genes with distal-to-proximal poly(A switch in GSCs. Consistently APA reporter assay confirmed the total 3'UTR shortening in GSCs compared to MEFs. We also analyzed the cis elements around the proximal poly(A site preferentially used in GSCs and found C-rich elements may contribute to this regulation. Overall, our results identified the expression level and polyadenylation site

  15. Specific Tandem 3'UTR Patterns and Gene Expression Profiles in Mouse Thy1+ Germline Stem Cells

    Science.gov (United States)

    Lin, Zhuoheng; Feng, Xuyang; Jiang, Xue; Songyang, Zhou; Huang, Junjiu

    2015-01-01

    A recently developed strategy of sequencing alternative polyadenylation (APA) sites (SAPAS) with second-generation sequencing technology can be used to explore complete genome-wide patterns of tandem APA sites and global gene expression profiles. spermatogonial stem cells (SSCs) maintain long-term reproductive abilities in male mammals. The detailed mechanisms by which SSCs self-renew and generate mature spermatozoa are not clear. To understand the specific alternative polyadenylation pattern and global gene expression profile of male germline stem cells (GSCs, mainly referred to SSCs here), we isolated and purified mouse Thy1+ cells from testis by magnetic-activated cell sorting (MACS) and then used the SAPAS method for analysis, using pluripotent embryonic stem cells (ESCs) and differentiated mouse embryonic fibroblast cells (MEFs) as controls. As a result, we obtained 99,944 poly(A) sites, approximately 40% of which were newly detected in our experiments. These poly(A) sites originated from three mouse cell types and covered 17,499 genes, including 831 long non-coding RNA (lncRNA) genes. We observed that GSCs tend to have shorter 3'UTR lengths while MEFs tend towards longer 3'UTR lengths. We also identified 1337 genes that were highly expressed in GSCs, and these genes were highly consistent with the functional characteristics of GSCs. Our detailed bioinformatics analysis identified APA site-switching events at 3'UTRs and many new specifically expressed genes in GSCs, which we experimentally confirmed. Furthermore, qRT-PCR was performed to validate several events of the 334 genes with distal-to-proximal poly(A) switch in GSCs. Consistently APA reporter assay confirmed the total 3'UTR shortening in GSCs compared to MEFs. We also analyzed the cis elements around the proximal poly(A) site preferentially used in GSCs and found C-rich elements may contribute to this regulation. Overall, our results identified the expression level and polyadenylation site profiles and

  16. Paired hormone response elements predict caveolin-1 as a glucocorticoid target gene.

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    Marinus F van Batenburg

    2010-01-01

    Full Text Available Glucocorticoids act in part via glucocorticoid receptor binding to hormone response elements (HREs, but their direct target genes in vivo are still largely unknown. We developed the criterion that genomic occurrence of paired HREs at an inter-HRE distance less than 200 bp predicts hormone responsiveness, based on synergy of multiple HREs, and HRE information from known target genes. This criterion predicts a substantial number of novel responsive genes, when applied to genomic regions 10 kb upstream of genes. Multiple-tissue in situ hybridization showed that mRNA expression of 6 out of 10 selected genes was induced in a tissue-specific manner in mice treated with a single dose of corticosterone, with the spleen being the most responsive organ. Caveolin-1 was strongly responsive in several organs, and the HRE pair in its upstream region showed increased occupancy by glucocorticoid receptor in response to corticosterone. Our approach allowed for discovery of novel tissue specific glucocorticoid target genes, which may exemplify responses underlying the permissive actions of glucocorticoids.

  17. Gene expression profiling during asexual development of the late blight pathogen Phytophthora infestans reveals a highly dynamic transcriptome.

    Science.gov (United States)

    Judelson, Howard S; Ah-Fong, Audrey M V; Aux, George; Avrova, Anna O; Bruce, Catherine; Cakir, Cahid; da Cunha, Luis; Grenville-Briggs, Laura; Latijnhouwers, Maita; Ligterink, Wilco; Meijer, Harold J G; Roberts, Samuel; Thurber, Carrie S; Whisson, Stephen C; Birch, Paul R J; Govers, Francine; Kamoun, Sophien; van West, Pieter; Windass, John

    2008-04-01

    Much of the pathogenic success of Phytophthora infestans, the potato and tomato late blight agent, relies on its ability to generate from mycelia large amounts of sporangia, which release zoospores that encyst and form infection structures. To better understand these stages, Affymetrix GeneChips based on 15,650 unigenes were designed and used to profile the life cycle. Approximately half of P. infestans genes were found to exhibit significant differential expression between developmental transitions, with approximately (1)/(10) being stage-specific and most changes occurring during zoosporogenesis. Quantitative reverse-transcription polymerase chain reaction assays confirmed the robustness of the array results and showed that similar patterns of differential expression were obtained regardless of whether hyphae were from laboratory media or infected tomato. Differentially expressed genes encode potential cellular regulators, especially protein kinases; metabolic enzymes such as those involved in glycolysis, gluconeogenesis, or the biosynthesis of amino acids or lipids; regulators of DNA synthesis; structural proteins, including predicted flagellar proteins; and pathogenicity factors, including cell-wall-degrading enzymes, RXLR effector proteins, and enzymes protecting against plant defense responses. Curiously, some stage-specific transcripts do not appear to encode functional proteins. These findings reveal many new aspects of oomycete biology, as well as potential targets for crop protection chemicals.

  18. Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma.

    Science.gov (United States)

    Grinchuk, Oleg V; Yenamandra, Surya P; Iyer, Ramakrishnan; Singh, Malay; Lee, Hwee Kuan; Lim, Kiat Hon; Chow, Pierce Kah-Hoe; Kuznetsov, Vladamir A

    2018-01-01

    Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro-oncogenic pathways in primary tumors (PT) and adjacent non-malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery. We examined the genome-wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24-ribosomal gene-based HCC classifier (RGC), which is prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (hazard ratio = 8.2, P = 9.4 × 10 -6 ) and cross-cohort validation (hazard ratio = 2.63, P = 0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (hazard ratio = 5.0, P = 0.03) and cross-validation (hazard ratio = 1.9, P = 0.03) HCC groups, confirming the accuracy and robustness of the RGC. Our experimental and bioinformatics analyses suggested a key role for c-MYC in the pro-oncogenic pattern of ribosomal biogenesis co-regulation in PT and AT. Microarray, quantitative RT-PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co-transcriptional pattern of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, as supported by the model of tumor-like metabolic redirection/assimilation in non-malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for

  19. Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data [version 1; referees: 2 approved

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    Linh Nguyen

    2016-12-01

    Full Text Available Background: Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data. Methods: Here we present this systematic comparison using Random Forest (RF classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC50 measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than K-fold cross-validation. Results and Discussion: Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug. Regarding overall classification performance, about two thirds of the drugs are better predicted by multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG. Conclusions: We now know that this type of models can predict in vitro tumour response to these drugs. These models can thus be further investigated on in vivo tumour models.

  20. Oral cancer cells with different potential of lymphatic metastasis displayed distinct biologic behaviors and gene expression profiles.

    Science.gov (United States)

    Zhuang, Zhang; Jian, Pan; Longjiang, Li; Bo, Han; Wenlin, Xiao

    2010-02-01

    Oral squamous cell carcinoma (OSCC) often spreads from the primary tumor to regional lymph nodes in the early stage. Better understanding of the biology of lymphatic spread of oral cancer cells is important for improving the survival rate of cancer patients. We established the cell line LNMTca8113 by repeated injections in foot pads of nude mice, which had a much higher lymphatic metastasis rate than its parental cell line Tca8113. Then, we compared the biologic behaviors of cancer cells between them. Moreover, microarray-based expression profiles between them were also compared, and a panel of differential genes was validated using real-time-PCR. In contrast to Tca8113 cells, LNMTca8113 cells were more proliferative and resistant to apoptosis in the absence of serum, and had enhanced ability of inducing capillary-like structures. Moreover, microarray-based expression profiles between them identified 1341 genes involved in cell cycle, cell adhesion, lymphangiogenesis, regulation of apoptosis, and so on. Some genes dedicating to the metastatic potential, including JAM2, TNC, CTSC, LAMB1, VEGFC, HAPLN1, ACPP, GDF9 and FGF11, were upregulated in LNMTca8113 cells. These results suggested that LNMTca8113 and Tca8113 cells were proper models for lymphatic metastasis study because there were differences in biologic behaviors and metastasis-related genes between them. Additionally, the differentially expressed gene profiles in cancer progression may be helpful in exploring therapeutic targets and provide the foundation for further functional validation of these specific candidate genes for OSCC.

  1. Expression profile of cell cycle genes in the fish CATLA CATLA (Ham.) exposed to gamma radiation

    International Nuclear Information System (INIS)

    Anbumani, S.; Mohankumar Mary, N.

    2012-01-01

    The International Commission on Radiological Protection (ICRP) emphasized the need to protect non-human biota from the potential effects of ionizing radiation and proposed to include molecular effects such as DNA damage as endpoints. Molecular effects of ionizing radiation exposure in representative non-humans are largely unexplored and sufficient data is not available in fishes. Gene expression is a fast and sensitive end point in detecting the molecular cues as a result of ionizing radiation exposure in a wide variety of aquatic organisms under suspected environmental contamination. Exposure to ionizing radiation transiently alters gene expression profiles as cells regulate certain genes to protect cellular structures and repair damage. The present study focused on genes like Gadd45á, Cdk1 and Bcl-2 in DNA damage repair and cell cycle machinery and its implication as molecular markers of radiation exposure. This study is first of its kind showing the in vivo expression profile of cell cycle genes in fish exposed to gamma radiation. Although this preliminary investigation points to certain molecular markers of ionizing radiation, elaborate studies with various doses and dose-rates are required before these markers find application as prospective molecular markers in aquatic radiation biodosimetry

  2. Reconstructing Dynamic Promoter Activity Profiles from Reporter Gene Data.

    Science.gov (United States)

    Kannan, Soumya; Sams, Thomas; Maury, Jérôme; Workman, Christopher T

    2018-03-16

    Accurate characterization of promoter activity is important when designing expression systems for systems biology and metabolic engineering applications. Promoters that respond to changes in the environment enable the dynamic control of gene expression without the necessity of inducer compounds, for example. However, the dynamic nature of these processes poses challenges for estimating promoter activity. Most experimental approaches utilize reporter gene expression to estimate promoter activity. Typically the reporter gene encodes a fluorescent protein that is used to infer a constant promoter activity despite the fact that the observed output may be dynamic and is a number of steps away from the transcription process. In fact, some promoters that are often thought of as constitutive can show changes in activity when growth conditions change. For these reasons, we have developed a system of ordinary differential equations for estimating dynamic promoter activity for promoters that change their activity in response to the environment that is robust to noise and changes in growth rate. Our approach, inference of dynamic promoter activity (PromAct), improves on existing methods by more accurately inferring known promoter activity profiles. This method is also capable of estimating the correct scale of promoter activity and can be applied to quantitative data sets to estimate quantitative rates.

  3. Versatile Gene-Specific Sequence Tags for Arabidopsis Functional Genomics: Transcript Profiling and Reverse Genetics Applications

    Science.gov (United States)

    Hilson, Pierre; Allemeersch, Joke; Altmann, Thomas; Aubourg, Sébastien; Avon, Alexandra; Beynon, Jim; Bhalerao, Rishikesh P.; Bitton, Frédérique; Caboche, Michel; Cannoot, Bernard; Chardakov, Vasil; Cognet-Holliger, Cécile; Colot, Vincent; Crowe, Mark; Darimont, Caroline; Durinck, Steffen; Eickhoff, Holger; de Longevialle, Andéol Falcon; Farmer, Edward E.; Grant, Murray; Kuiper, Martin T.R.; Lehrach, Hans; Léon, Céline; Leyva, Antonio; Lundeberg, Joakim; Lurin, Claire; Moreau, Yves; Nietfeld, Wilfried; Paz-Ares, Javier; Reymond, Philippe; Rouzé, Pierre; Sandberg, Goran; Segura, Maria Dolores; Serizet, Carine; Tabrett, Alexandra; Taconnat, Ludivine; Thareau, Vincent; Van Hummelen, Paul; Vercruysse, Steven; Vuylsteke, Marnik; Weingartner, Magdalena; Weisbeek, Peter J.; Wirta, Valtteri; Wittink, Floyd R.A.; Zabeau, Marc; Small, Ian

    2004-01-01

    Microarray transcript profiling and RNA interference are two new technologies crucial for large-scale gene function studies in multicellular eukaryotes. Both rely on sequence-specific hybridization between complementary nucleic acid strands, inciting us to create a collection of gene-specific sequence tags (GSTs) representing at least 21,500 Arabidopsis genes and which are compatible with both approaches. The GSTs were carefully selected to ensure that each of them shared no significant similarity with any other region in the Arabidopsis genome. They were synthesized by PCR amplification from genomic DNA. Spotted microarrays fabricated from the GSTs show good dynamic range, specificity, and sensitivity in transcript profiling experiments. The GSTs have also been transferred to bacterial plasmid vectors via recombinational cloning protocols. These cloned GSTs constitute the ideal starting point for a variety of functional approaches, including reverse genetics. We have subcloned GSTs on a large scale into vectors designed for gene silencing in plant cells. We show that in planta expression of GST hairpin RNA results in the expected phenotypes in silenced Arabidopsis lines. These versatile GST resources provide novel and powerful tools for functional genomics. PMID:15489341

  4. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

    Directory of Open Access Journals (Sweden)

    Han Kyungsook

    2010-06-01

    Full Text Available Abstract Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI. First, a high-coverage and high-precision functional gene network (FGN is constructed by integrating protein-protein interaction (PPI, protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM, on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%. Noticeably, the SSL method is more efficient than SVM, especially for

  5. Global analysis of gene expression profiles in developing physic nut (Jatropha curcas L.) seeds.

    Science.gov (United States)

    Jiang, Huawu; Wu, Pingzhi; Zhang, Sheng; Song, Chi; Chen, Yaping; Li, Meiru; Jia, Yongxia; Fang, Xiaohua; Chen, Fan; Wu, Guojiang

    2012-01-01

    Physic nut (Jatropha curcas L.) is an oilseed plant species with high potential utility as a biofuel. Furthermore, following recent sequencing of its genome and the availability of expressed sequence tag (EST) libraries, it is a valuable model plant for studying carbon assimilation in endosperms of oilseed plants. There have been several transcriptomic analyses of developing physic nut seeds using ESTs, but they have provided limited information on the accumulation of stored resources in the seeds. We applied next-generation Illumina sequencing technology to analyze global gene expression profiles of developing physic nut seeds 14, 19, 25, 29, 35, 41, and 45 days after pollination (DAP). The acquired profiles reveal the key genes, and their expression timeframes, involved in major metabolic processes including: carbon flow, starch metabolism, and synthesis of storage lipids and proteins in the developing seeds. The main period of storage reserves synthesis in the seeds appears to be 29-41 DAP, and the fatty acid composition of the developing seeds is consistent with relative expression levels of different isoforms of acyl-ACP thioesterase and fatty acid desaturase genes. Several transcription factor genes whose expression coincides with storage reserve deposition correspond to those known to regulate the process in Arabidopsis. The results will facilitate searches for genes that influence de novo lipid synthesis, accumulation and their regulatory networks in developing physic nut seeds, and other oil seeds. Thus, they will be helpful in attempts to modify these plants for efficient biofuel production.

  6. Behavioral Profile Predicts Dominance Status in Mountain Chickadees.

    Science.gov (United States)

    Fox, Rebecca A; Ladage, Lara D; Roth, Timothy C; Pravosudov, Vladimir V

    2009-06-01

    Individual variation in stable behavioral traits may explain variation in ecologically-relevant behaviors such as foraging, dispersal, anti-predator behavior, and dominance. We investigated behavioral variation in mountain chickadees (Poecile gambeli), a North American parid that lives in dominance-structured winter flocks, using two common measures of behavioral profile: exploration of a novel room and novel object exploration. We related those behavioral traits to dominance status in male chickadees following brief, pair-wise encounters. Low-exploring birds (birds that visited less than four locations in the novel room) were significantly more likely to become dominant in brief, pairwise encounters with high-exploring birds (i.e., birds that visited all perching locations within a novel room). On the other hand, there was no relationship between novel object exploration and dominance. Interestingly, novel room exploration was also not correlated with novel object exploration. These results suggest that behavioral profile may predict the social status of group-living individuals. Moreover, our results contradict the idea that novel object exploration and novel room exploration are always interchangeable measures of individuals' sensitivity to environmental novelty.

  7. Periodontal profile classes predict periodontal disease progression and tooth loss.

    Science.gov (United States)

    Morelli, Thiago; Moss, Kevin L; Preisser, John S; Beck, James D; Divaris, Kimon; Wu, Di; Offenbacher, Steven

    2018-02-01

    Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived

  8. Integrative analyses of leprosy susceptibility genes indicate a common autoimmune profile.

    Science.gov (United States)

    Zhang, Deng-Feng; Wang, Dong; Li, Yu-Ye; Yao, Yong-Gang

    2016-04-01

    Leprosy is an ancient chronic infection in the skin and peripheral nerves caused by Mycobacterium leprae. The development of leprosy depends on genetic background and the immune status of the host. However, there is no systematic view focusing on the biological pathways, interaction networks and overall expression pattern of leprosy-related immune and genetic factors. To identify the hub genes in the center of leprosy genetic network and to provide an insight into immune and genetic factors contributing to leprosy. We retrieved all reported leprosy-related genes and performed integrative analyses covering gene expression profiling, pathway analysis, protein-protein interaction network, and evolutionary analyses. A list of 123 differentially expressed leprosy related genes, which were enriched in activation and regulation of immune response, was obtained in our analyses. Cross-disorder analysis showed that the list of leprosy susceptibility genes was largely shared by typical autoimmune diseases such as lupus erythematosus and arthritis, suggesting that similar pathways might be affected in leprosy and autoimmune diseases. Protein-protein interaction (PPI) and positive selection analyses revealed a co-evolution network of leprosy risk genes. Our analyses showed that leprosy associated genes constituted a co-evolution network and might undergo positive selection driven by M. leprae. We suggested that leprosy may be a kind of autoimmune disease and the development of leprosy is a matter of defect or over-activation of body immunity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  9. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

    Science.gov (United States)

    Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

    2015-06-25

    Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

  10. Correlation between Group B Streptococcal Genotypes, Their Antimicrobial Resistance Profiles, and Virulence Genes among Pregnant Women in Lebanon

    Directory of Open Access Journals (Sweden)

    Antoine Hannoun

    2009-01-01

    Full Text Available The antimicrobial susceptibility profiles of 76 Streptococcus agalactiae (Group B Streptococci [GBS] isolates from vaginal specimens of pregnant women near term were correlated to their genotypes generated by Random Amplified Polymorphic DNA analysis and their virulence factors encoding genes cylE, lmb, scpB, rib, and bca by PCR. Based on the distribution of the susceptibility patterns, six profiles were generated. RAPD analysis detected 7 clusters of genotypes. The cylE gene was present in 99% of the isolates, the lmb in 96%, scpB in 94.7%, rib in 33%, and bca in 56.5% of isolates. The isolates demonstrated a significant correlation between antimicrobial resistance and genotype clusters denoting the distribution of particular clones with different antimicrobial resistance profiles, entailing the practice of caution in therapeutic options. All virulence factors encoding genes were detected in all seven genotypic clusters with rib and bca not coexisting in the same genome.

  11. Gene Profiling in Late Blight Resistance in Potato Genotype SD20

    Directory of Open Access Journals (Sweden)

    Xiaohui Yang

    2018-06-01

    Full Text Available Late blight caused by the oomycete fungus Phytophthora infestans (Pi is the most serious obstacle to potato (Solanum tuberosum production in the world. A super race isolate, CN152, which was identified from Sichuan Province, China, could overcome nearly all known late blight resistance genes and caused serious damage in China. The potato genotype SD20 was verified to be highly resistant to CN152; however, the molecular regulation network underlying late blight resistance pathway remains unclear in SD20. Here, we performed a time-course experiment to systematically profile the late blight resistance response genes using RNA-sequencing in SD20. We identified 3354 differentially expressed genes (DEGs, which mainly encoded transcription factors and protein kinases, and also included four NBS-LRR genes. The late blight responsive genes showed time-point-specific induction/repression. Multi-signaling pathways of salicylic acid, jasmonic acid, and ethylene signaling pathways involved in resistance and defense against Pi in SD20. Gene Ontology and KEGG analyses indicated that the DEGs were significantly enriched in metabolic process, protein serine/threonine kinase activity, and biosynthesis of secondary metabolites. Forty-three DEGs were involved in immune response, of which 19 were enriched in hypersensitive response reaction, which could play an important role in broad-spectrum resistance to Pi infection. Experimental verification confirmed the induced expression of the responsive genes in the late blight resistance signaling pathway, such as WRKY, ERF, MAPK, and NBS-LRR family genes. Our results provided valuable information for understanding late blight resistance mechanism of potato.

  12. Longitudinal prediction and concurrent functioning of adolescent girls demonstrating various profiles of dating violence and victimization.

    Science.gov (United States)

    Chiodo, Debbie; Crooks, Claire V; Wolfe, David A; McIsaac, Caroline; Hughes, Ray; Jaffe, Peter G

    2012-08-01

    Adolescent girls are involved in physical dating violence as both perpetrators and victims, and there are negative consequences associated with each of these behaviors. This article used a prospective design with 519 girls dating in grade 9 to predict profiles of dating violence in grade 11 based on relationships with families of origin (child maltreatment experiences, harsh parenting), and peers (harassment, delinquency, relational aggression). In addition, dating violence profiles were compared on numerous indices of adjustment (school connectedness, grades, self-efficacy and community connectedness) and maladjustment (suicide attempts, distress, delinquency, sexual behavior) for descriptive purposes. The most common profile was no dating violence (n = 367) followed by mutual violence (n = 81). Smaller numbers of girls reported victimization or perpetration only (ns = 39 and 32, respectively). Predicting grade 11 dating violence profile membership from grade 9 relationships was limited, although delinquency, parental rejection, and sexual harassment perpetration predicted membership to the mutually violent group, and delinquency predicted the perpetrator-only group. Compared to the non-violent group, the mutually violent girls in grade 11 had lower grades, poorer self-efficacy, and lower school connectedness and community involvement. Furthermore, they had higher rates of peer aggression and delinquency, were less likely to use condoms and were much more likely to have considered suicide. There were fewer differences among the profiles for girls involved with dating violence. In addition, the victims-only group reported higher rates of sexual intercourse, comparable to the mutually violent group and those involved in nonviolent relationships. Implications for prevention and intervention are highlighted.

  13. Dissecting miRNA gene repression on single cell level with an advanced fluorescent reporter system

    Science.gov (United States)

    Lemus-Diaz, Nicolas; Böker, Kai O.; Rodriguez-Polo, Ignacio; Mitter, Michael; Preis, Jasmin; Arlt, Maximilian; Gruber, Jens

    2017-01-01

    Despite major advances on miRNA profiling and target predictions, functional readouts for endogenous miRNAs are limited and frequently lead to contradicting conclusions. Numerous approaches including functional high-throughput and miRISC complex evaluations suggest that the functional miRNAome differs from the predictions based on quantitative sRNA profiling. To resolve the apparent contradiction of expression versus function, we generated and applied a fluorescence reporter gene assay enabling single cell analysis. This approach integrates and adapts a mathematical model for miRNA-driven gene repression. This model predicts three distinct miRNA-groups with unique repression activities (low, mid and high) governed not just by expression levels but also by miRNA/target-binding capability. Here, we demonstrate the feasibility of the system by applying controlled concentrations of synthetic siRNAs and in parallel, altering target-binding capability on corresponding reporter-constructs. Furthermore, we compared miRNA-profiles with the modeled predictions of 29 individual candidates. We demonstrate that expression levels only partially reflect the miRNA function, fitting to the model-projected groups of different activities. Furthermore, we demonstrate that subcellular localization of miRNAs impacts functionality. Our results imply that miRNA profiling alone cannot define their repression activity. The gene regulatory function is a dynamic and complex process beyond a minimalistic conception of “highly expressed equals high repression”. PMID:28338079

  14. Gene expression profiling of mucolipidosis type IV fibroblasts reveals deregulation of genes with relevant functions in lysosome physiology.

    Science.gov (United States)

    Bozzato, Andrea; Barlati, Sergio; Borsani, Giuseppe

    2008-04-01

    Mucolipidosis type IV (MLIV, MIM 252650) is an autosomal recessive lysosomal storage disorder that causes mental and motor retardation as well as visual impairment. The lysosomal storage defect in MLIV is consistent with abnormalities of membrane traffic and organelle dynamics in the late endocytic pathway. MLIV is caused by mutations in the MCOLN1 gene, which codes for mucolipin-1 (MLN1), a member of the large family of transient receptor potential (TRP) cation channels. Although a number of studies have been performed on mucolipin-1, the pathological mechanisms underlying MLIV are not fully understood. To identify genes that characterize pathogenic changes in mucolipidosis type IV, we compared the expression profiles of three MLIV and three normal skin fibroblasts cell lines using oligonucleotide microarrays. Genes that were differentially expressed in patients' cells were identified. 231 genes were up-regulated, and 116 down-regulated. Real-Time RT-PCR performed on selected genes in six independent MLIV fibroblasts cell lines was generally consistent with the microarray findings. This study allowed to evidence the modulation at the transcriptional level of a discrete number of genes relevant in biological processes which are altered in the disease such as endosome/lysosome trafficking, lysosome biogenesis, organelle acidification and lipid metabolism.

  15. Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification.

    Science.gov (United States)

    Ferris, Laura K; Farberg, Aaron S; Middlebrook, Brooke; Johnson, Clare E; Lassen, Natalie; Oelschlager, Kristen M; Maetzold, Derek J; Cook, Robert W; Rigel, Darrell S; Gerami, Pedram

    2017-05-01

    A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  16. Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

    Science.gov (United States)

    Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

    2016-02-19

    Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

  17. Gene Expression Profiling in Lung Tissues from Rat Exposed to Lunar Dust Particles

    Science.gov (United States)

    Zhang, Ye; Lam, Chiu-Wing; Zalesak, Selina M.; Kidane, Yared H.; Feiveson, Alan H.; Ploutz-Snyder, Robert; Scully, Robert R.; Williams, Kyle; Wu, Honglu; James, John T.

    2014-01-01

    The Moon's surface is covered by a layer of fine, reactive dust. Lunar dust contain about 1-2% of very fine dust (gene expression changes in lung tissues from rats exposed to lunar dust particles. F344 rats were exposed for 4 weeks (6h/d; 5d/wk) in nose-only inhalation chambers to concentrations of 0 (control air), 2.1, 6.8, 21, and 61 mg/m(exp 3) of lunar dust. Five rats per group were euthanized 1 day, and 3 months after the last inhalation exposure. The total RNAs were isolated from lung tissues after being lavaged. The Agilent Rat GE v3 microarray was used to profile global gene expression (44K). The genes with significant expression changes are identified and the gene expression data were further analyzed using various statistical tools.

  18. Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

    Directory of Open Access Journals (Sweden)

    Lee Yun-Shien

    2008-03-01

    Full Text Available Abstract Background The hierarchical clustering tree (HCT with a dendrogram 1 and the singular value decomposition (SVD with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.

  19. Methods for small RNA preparation for digital gene expression profiling by next-generation sequencing

    NARCIS (Netherlands)

    Linsen, S.E.V.; Cuppen, E.

    2012-01-01

    Digital gene expression (DGE) profiling techniques are playing an eminent role in the detection, localization, and differential expression quantification of many small RNA species, including microRNAs (1-3). Procedures in small RNA library preparation techniques typically include adapter ligation by

  20. Predicting spatial and temporal gene expression using an integrative model of transcription factor occupancy and chromatin state.

    Directory of Open Access Journals (Sweden)

    Bartek Wilczynski

    Full Text Available Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal

  1. Predicting Hydrologic Function With Aquatic Gene Fragments

    Science.gov (United States)

    Good, S. P.; URycki, D. R.; Crump, B. C.

    2018-03-01

    Recent advances in microbiology techniques, such as genetic sequencing, allow for rapid and cost-effective collection of large quantities of genetic information carried within water samples. Here we posit that the unique composition of aquatic DNA material within a water sample contains relevant information about hydrologic function at multiple temporal scales. In this study, machine learning was used to develop discharge prediction models trained on the relative abundance of bacterial taxa classified into operational taxonomic units (OTUs) based on 16S rRNA gene sequences from six large arctic rivers. We term this approach "genohydrology," and show that OTU relative abundances can be used to predict river discharge at monthly and longer timescales. Based on a single DNA sample from each river, the average Nash-Sutcliffe efficiency (NSE) for predicted mean monthly discharge values throughout the year was 0.84, while the NSE for predicted discharge values across different return intervals was 0.67. These are considerable improvements over predictions based only on the area-scaled mean specific discharge of five similar rivers, which had average NSE values of 0.64 and -0.32 for seasonal and recurrence interval discharge values, respectively. The genohydrology approach demonstrates that genetic diversity within the aquatic microbiome is a large and underutilized data resource with benefits for prediction of hydrologic function.

  2. Transcriptome profiling of the Plutella xylostella (Lepidoptera: Plutellidae) ovary reveals genes involved in oogenesis.

    Science.gov (United States)

    Peng, Lu; Wang, Lei; Yang, Yi-Fan; Zou, Ming-Min; He, Wei-Yi; Wang, Yue; Wang, Qing; Vasseur, Liette; You, Min-Sheng

    2017-12-30

    As a specialized organ, the insect ovary performs valuable functions by ensuring fecundity and population survival. Oogenesis is the complex physiological process resulting in the production of mature eggs, which are involved in epigenetic programming, germ cell behavior, cell cycle regulation, etc. Identification of the genes involved in ovary development and oogenesis is critical to better understand the reproductive biology and screening for the potential molecular targets in Plutella xylostella, a worldwide destructive pest of economically major crops. Based on transcriptome sequencing, a total of 7.88Gb clean nucleotides was obtained, with 19,934 genes and 1861 new transcripts being identified. Expression profiling indicated that 61.7% of the genes were expressed (FPKM≥1) in the P. xylostella ovary. GO annotation showed that the pathways of multicellular organism reproduction and multicellular organism reproduction process, as well as gamete generation and chorion were significantly enriched. Processes that were most likely relevant to reproduction included the spliceosome, ubiquitin mediated proteolysis, endocytosis, PI3K-Akt signaling pathway, insulin signaling pathway, cAMP signaling pathway, and focal adhesion were identified in the top 20 'highly represented' KEGG pathways. Functional genes involved in oogenesis were further analyzed and validated by qRT-PCR to show their potential predominant roles in P. xylostella reproduction. Our newly developed P. xylostella ovary transcriptome provides an overview of the gene expression profiling in this specialized tissue and the functional gene network closely related to the ovary development and oogenesis. This is the first genome-wide transcriptome dataset of P. xylostella ovary that includes a subset of functionally activated genes. This global approach will be the basis for further studies on molecular mechanisms of P. xylostella reproduction aimed at screening potential molecular targets for integrated pest

  3. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression.

    Science.gov (United States)

    Guilloux, Jean-Philippe; Bassi, Sabrina; Ding, Ying; Walsh, Chris; Turecki, Gustavo; Tseng, George; Cyranowski, Jill M; Sibille, Etienne

    2015-02-01

    Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).

  4. Gene expression profiling in human precision cut liver slices in response to the FXR agonist obeticholic acid.

    Science.gov (United States)

    Ijssennagger, Noortje; Janssen, Aafke W F; Milona, Alexandra; Ramos Pittol, José M; Hollman, Danielle A A; Mokry, Michal; Betzel, Bark; Berends, Frits J; Janssen, Ignace M; van Mil, Saskia W C; Kersten, Sander

    2016-05-01

    The bile acid-activated farnesoid X receptor (FXR) is a nuclear receptor regulating bile acid, glucose and cholesterol homeostasis. Obeticholic acid (OCA), a promising drug for the treatment of non-alcoholic steatohepatitis (NASH) and type 2 diabetes, activates FXR. Mouse studies demonstrated that FXR activation by OCA alters hepatic expression of many genes. However, no data are available on the effects of OCA in the human liver. Here we generated gene expression profiles in human precision cut liver slices (hPCLS) after treatment with OCA. hPCLS were incubated with OCA for 24 h. Wild-type or FXR(-/-) mice received OCA or vehicle by oral gavage for 7 days. Transcriptomic analysis showed that well-known FXR target genes, including NR0B2 (SHP), ABCB11 (BSEP), SLC51A (OSTα) and SLC51B (OSTβ), and ABCB4 (MDR3) are regulated by OCA in hPCLS. Ingenuity pathway analysis confirmed that 'FXR/RXR activation' is the most significantly changed pathway upon OCA treatment. Comparison of gene expression profiles in hPCLS and mouse livers identified 18 common potential FXR targets. ChIP-sequencing in mouse liver confirmed FXR binding to IR1 sequences of Akap13, Cgnl1, Dyrk3, Pdia5, Ppp1r3b and Tbx6. Our study shows that hPCLS respond to OCA treatment by upregulating well-known FXR target genes, demonstrating its suitability to study FXR-mediated gene regulation. We identified six novel bona-fide FXR target genes in both mouse and human liver. Finally, we discuss a possible explanation for changes in high or low density lipoprotein observed in NASH and primary biliary cholangitis patients treated with OCA based on the genomic expression profile in hPCLS. Copyright © 2016 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

  5. The gene expression profile of resistant and susceptible Bombyx mori strains reveals cypovirus-associated variations in host gene transcript levels.

    Science.gov (United States)

    Guo, Rui; Wang, Simei; Xue, Renyu; Cao, Guangli; Hu, Xiaolong; Huang, Moli; Zhang, Yangqi; Lu, Yahong; Zhu, Liyuan; Chen, Fei; Liang, Zi; Kuang, Sulan; Gong, Chengliang

    2015-06-01

    High-throughput paired-end RNA sequencing (RNA-Seq) was performed to investigate the gene expression profile of a susceptible Bombyx mori strain, Lan5, and a resistant B. mori strain, Ou17, which were both orally infected with B. mori cypovirus (BmCPV) in the midgut. There were 330 and 218 up-regulated genes, while there were 147 and 260 down-regulated genes in the Lan5 and Ou17 strains, respectively. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment for differentially expressed genes (DEGs) were carried out. Moreover, gene interaction network (STRING) analyses were performed to analyze the relationships among the shared DEGs. Some of these genes were related and formed a large network, in which the genes for B. mori cuticular protein RR-2 motif 123 (BmCPR123) and the gene for B. mori DNA replication licensing factor Mcm2-like (BmMCM2) were key genes among the common up-regulated DEGs, whereas the gene for B. mori heat shock protein 20.1 (Bmhsp20.1) was the central gene among the shared down-regulated DEGs between Lan5 vs Lan5-CPV and Ou17 vs Ou17-CPV. These findings established a comprehensive database of genes that are differentially expressed in response to BmCPV infection between silkworm strains that differed in resistance to BmCPV and implied that these DEGs might be involved in B. mori immune responses against BmCPV infection.

  6. Gene expression in early stage cervical cancer

    NARCIS (Netherlands)

    Biewenga, Petra; Buist, Marrije R.; Moerland, Perry D.; van Thernaat, Emiel Ver Loren; van Kampen, Antoine H. C.; ten Kate, Fiebo J. W.; Baas, Frank

    2008-01-01

    Objective. Pelvic lymph node metastases are the main prognostic factor for survival in early stage cervical cancer, yet accurate detection methods before surgery are lacking. In this study, we examined whether gene expression profiling can predict the presence of lymph node metastasis in early stage

  7. Dose and temporal effects on gene expression profiles of urothelial cells from rats exposed to diuron

    International Nuclear Information System (INIS)

    Ihlaseh-Catalano, Shadia M.; Bailey, Kathryn A.; Cardoso, Ana Paula F.; Ren, Hongzu; Fry, Rebecca C.; Camargo, João Lauro V.de; Wolf, Douglas C.

    2014-01-01

    Diuron (3-(3,4-dichlorophenyl)-1,1-dimethylurea) is a substituted urea herbicide that at high dietary levels (2500 ppm) induces rat urinary bladder hyperplasia after 20 weeks of exposure and neoplasia after 2 years. The effects on the urothelium after short-term exposure have not been described. The present 7-day study evaluated the dose-dependency of urothelial alterations in the urinary bladder using light microscopy, scanning electron microscopy, and genome-wide transcriptional profiling. Male Wistar rats were fed 0, 125, 500, 2500 ppm diuron for 7 days. The urinary bladder and isolated urothelial cells of these animals were processed for microscopic examination and gene expression profiling, respectively. No significant treatment-related morphologic effects were observed. The number of differentially expressed genes (DEGs) in the exposed groups increased with diuron levels. Diuron-altered genes involved in cell-to-cell interactions and tissue organization were identified in all treatment groups. After 7 days of diuron exposure, transcriptional responses were observed in the urothelium in the absence of clear morphologic changes. These morphological findings are different from those observed in a previous study in which 20 weeks of diuron exposure was associated with simple hyperplasia secondary to the persistent cytotoxicity and necrosis associated with continuous cellular regeneration. Comparison of the gene expression profiles of rats exposed to the 2500 ppm carcinogenic diuron dose for 7 days versus 20 weeks revealed few similarities between these two time points at the gene or pathway level. Taken together, these data provide insight into the dose- and temporal-dependent morphological and transcriptional changes associated with diuron exposure that may lead to the development of tumors in the rat urinary bladder

  8. Identifying Tmem59 related gene regulatory network of mouse neural stem cell from a compendium of expression profiles

    Directory of Open Access Journals (Sweden)

    Guo Xiuyun

    2011-09-01

    Full Text Available Abstract Background Neural stem cells offer potential treatment for neurodegenerative disorders, such like Alzheimer's disease (AD. While much progress has been made in understanding neural stem cell function, a precise description of the molecular mechanisms regulating neural stem cells is not yet established. This lack of knowledge is a major barrier holding back the discovery of therapeutic uses of neural stem cells. In this paper, the regulatory mechanism of mouse neural stem cell (NSC differentiation by tmem59 is explored on the genome-level. Results We identified regulators of tmem59 during the differentiation of mouse NSCs from a compendium of expression profiles. Based on the microarray experiment, we developed the parallelized SWNI algorithm to reconstruct gene regulatory networks of mouse neural stem cells. From the inferred tmem59 related gene network including 36 genes, pou6f1 was identified to regulate tmem59 significantly and might play an important role in the differentiation of NSCs in mouse brain. There are four pathways shown in the gene network, indicating that tmem59 locates in the downstream of the signalling pathway. The real-time RT-PCR results shown that the over-expression of pou6f1 could significantly up-regulate tmem59 expression in C17.2 NSC line. 16 out of 36 predicted genes in our constructed network have been reported to be AD-related, including Ace, aqp1, arrdc3, cd14, cd59a, cds1, cldn1, cox8b, defb11, folr1, gdi2, mmp3, mgp, myrip, Ripk4, rnd3, and sncg. The localization of tmem59 related genes and functional-related gene groups based on the Gene Ontology (GO annotation was also identified. Conclusions Our findings suggest that the expression of tmem59 is an important factor contributing to AD. The parallelized SWNI algorithm increased the efficiency of network reconstruction significantly. This study enables us to highlight novel genes that may be involved in NSC differentiation and provides a shortcut to

  9. Clues to pathogenesis of spondyloarthropathy derived from synovial fluid mononuclear cell gene expression profiles

    NARCIS (Netherlands)

    Gu, Jieruo; Rihl, Markus; Märker-Hermann, Elisabeth; Baeten, Dominique; Kuipers, Jens G.; Song, Yeong Wook; Maksymowych, Walter P.; Burgos-Vargas, Ruben; Veys, Eric M.; de Keyser, Filip; Deister, Helmuth; Xiong, Momiao; Huang, Feng; Tsai, Wen Chan; Yu, David Tak Yan

    2002-01-01

    OBJECTIVE: To use gene expression profiles of spondyloarthropathy (SpA) synovial fluid mononuclear cells (SFMC) to determine if there are transcripts that support the unfolded protein response (UPR) hypothesis, and to identify which cytokines/chemokines are being expressed and which cell fractions

  10. Prediction of epigenetically regulated genes in breast cancer cell lines

    Energy Technology Data Exchange (ETDEWEB)

    Loss, Leandro A; Sadanandam, Anguraj; Durinck, Steffen; Nautiyal, Shivani; Flaucher, Diane; Carlton, Victoria EH; Moorhead, Martin; Lu, Yontao; Gray, Joe W; Faham, Malek; Spellman, Paul; Parvin, Bahram

    2010-05-04

    panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

  11. Gene expression profiling in limb-girdle muscular dystrophy 2A.

    Directory of Open Access Journals (Sweden)

    Amets Sáenz

    Full Text Available Limb-girdle muscular dystrophy type 2A (LGMD2A is a recessive genetic disorder caused by mutations in calpain 3 (CAPN3. Calpain 3 plays different roles in muscular cells, but little is known about its functions or in vivo substrates. The aim of this study was to identify the genes showing an altered expression in LGMD2A patients and the possible pathways they are implicated in. Ten muscle samples from LGMD2A patients with in which molecular diagnosis was ascertained were investigated using array technology to analyze gene expression profiling as compared to ten normal muscle samples. Upregulated genes were mostly those related to extracellular matrix (different collagens, cell adhesion (fibronectin, muscle development (myosins and melusin and signal transduction. It is therefore suggested that different proteins located or participating in the costameric region are implicated in processes regulated by calpain 3 during skeletal muscle development. Genes participating in the ubiquitin proteasome degradation pathway were found to be deregulated in LGMD2A patients, suggesting that regulation of this pathway may be under the control of calpain 3 activity. As frizzled-related protein (FRZB is upregulated in LGMD2A muscle samples, it could be hypothesized that beta-catenin regulation is also altered at the Wnt signaling pathway, leading to an incorrect myogenesis. Conversely, expression of most transcription factor genes was downregulated (MYC, FOS and EGR1. Finally, the upregulation of IL-32 and immunoglobulin genes may induce the eosinophil chemoattraction explaining the inflammatory findings observed in presymptomatic stages. The obtained results try to shed some light on identification of novel therapeutic targets for limb-girdle muscular dystrophies.

  12. Predicting lower third molar eruption on panoramic radiographs after cephalometric comparison of profile and panoramic radiographs

    DEFF Research Database (Denmark)

    Begtrup, Anders; Grønastøð, Halldis Á; Christensen, Ib Jarle

    2012-01-01

    and to find a simple and reliable method for predicting the eruption of the mandibular third molar by measurements on panoramic radiographs. The material consisted of profile and panoramic radiographs, taken before orthodontic treatment, of 30 males and 23 females (median age 22, range 18-48 years......Previous studies have suggested methods for predicting third molar tooth eruption radiographically. Still, this prediction is associated with uncertainty. The aim of the present study was to elucidate the association between cephalometric measurements on profile and panoramic radiographs...... the length from the ramus to the incisors (olr-id) showed a statistically significant correlation. By combining this length with the mesiodistal width of the lower second molar, the prediction of eruption of the lower third molar was strengthened. A new formula for calculating the probability of eruption...

  13. Early and long-standing rheumatoid arthritis: distinct molecular signatures identified by gene-expression profiling in synovia

    Science.gov (United States)

    Lequerré, Thierry; Bansard, Carine; Vittecoq, Olivier; Derambure, Céline; Hiron, Martine; Daveau, Maryvonne; Tron, François; Ayral, Xavier; Biga, Norman; Auquit-Auckbur, Isabelle; Chiocchia, Gilles; Le Loët, Xavier; Salier, Jean-Philippe

    2009-01-01

    Introduction Rheumatoid arthritis (RA) is a heterogeneous disease and its underlying molecular mechanisms are still poorly understood. Because previous microarray studies have only focused on long-standing (LS) RA compared to osteoarthritis, we aimed to compare the molecular profiles of early and LS RA versus control synovia. Methods Synovial biopsies were obtained by arthroscopy from 15 patients (4 early untreated RA, 4 treated LS RA and 7 controls, who had traumatic or mechanical lesions). Extracted mRNAs were used for large-scale gene-expression profiling. The different gene-expression combinations identified by comparison of profiles of early, LS RA and healthy synovia were linked to the biological processes involved in each situation. Results Three combinations of 719, 116 and 52 transcripts discriminated, respectively, early from LS RA, and early or LS RA from healthy synovia. We identified several gene clusters and distinct molecular signatures specifically expressed during early or LS RA, thereby suggesting the involvement of different pathophysiological mechanisms during the course of RA. Conclusions Early and LS RA have distinct molecular signatures with different biological processes participating at different times during the course of the disease. These results suggest that better knowledge of the main biological processes involved at a given RA stage might help to choose the most appropriate treatment. PMID:19563633

  14. Gene Expression Profile in the Early Stage of Angiotensin II-induced Cardiac Remodeling: a Time Series Microarray Study in a Mouse Model

    Directory of Open Access Journals (Sweden)

    Meng-Qiu Dang

    2015-01-01

    Full Text Available Background/Aims: Angiotensin II (Ang II plays a critical role in the cardiac remodeling contributing to heart failure. However, the gene expression profiles induced by Ang II in the early stage of cardiac remodeling remain unknown. Methods: Wild-type male mice (C57BL/6 background, 10-weeek-old were infused with Ang II (1500 ng/kg/min for 7 days. Blood pressure was measured. Cardiac function and remodeling were examined by echocardiography, H&E and Masson staining. The time series microarrays were then conducted to detected gene expression profiles. Results: Microarray results identified that 1,489 genes were differentially expressed in the hearts at day 1, 3 and 7 of Ang II injection. These genes were further classified into 26 profiles by hierarchical cluster analysis. Of them, 4 profiles were significant (No. 19, 8, 21 and 22 and contained 904 genes. Gene Ontology showed that these genes mainly participate in metabolic process, oxidation-reduction process, extracellular matrix organization, apoptotic process, immune response, and others. Significant pathways included focal adhesion, ECM-receptor interaction, cytokine-cytokine receptor interaction, MAPK and insulin signaling pathways, which were known to play important roles in Ang II-induced cardiac remodeling. Moreover, gene co-expression networks analysis suggested that serine/cysteine peptidase inhibitor, member 1 (Serpine1, also known as PAI-1 localized in the core of the network. Conclusions: Our results indicate that many genes are mainly involved in metabolism, inflammation, cardiac fibrosis and hypertrophy. Serpine1 may play a central role in the development of Ang II-induced cardiac remodeling at the early stage.

  15. Gene Expression Profiling Identifies Important Genes Affected by R2 Compound Disrupting FAK and P53 Complex

    International Nuclear Information System (INIS)

    Golubovskaya, Vita M.; Ho, Baotran; Conroy, Jeffrey; Liu, Song; Wang, Dan; Cance, William G.

    2014-01-01

    Focal Adhesion Kinase (FAK) is a non-receptor kinase that plays an important role in many cellular processes: adhesion, proliferation, invasion, angiogenesis, metastasis and survival. Recently, we have shown that Roslin 2 or R2 (1-benzyl-15,3,5,7-tetraazatricyclo[3.3.1.1~3,7~]decane) compound disrupts FAK and p53 proteins, activates p53 transcriptional activity, and blocks tumor growth. In this report we performed a microarray gene expression analysis of R2-treated HCT116 p53 +/+ and p53 −/− cells and detected 1484 genes that were significantly up- or down-regulated (p < 0.05) in HCT116 p53 +/+ cells but not in p53 −/− cells. Among up-regulated genes in HCT p53 +/+ cells we detected critical p53 targets: Mdm-2, Noxa-1, and RIP1. Among down-regulated genes, Met, PLK2, KIF14, BIRC2 and other genes were identified. In addition, a combination of R2 compound with M13 compound that disrupts FAK and Mmd-2 complex or R2 and Nutlin-1 that disrupts Mdm-2 and p53 decreased clonogenicity of HCT116 p53 +/+ colon cancer cells more significantly than each agent alone in a p53-dependent manner. Thus, the report detects gene expression profile in response to R2 treatment and demonstrates that the combination of drugs targeting FAK, Mdm-2, and p53 can be a novel therapy approach

  16. Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

    International Nuclear Information System (INIS)

    Gevaert, Olivier; De Smet, Frank; Van Gorp, Toon; Pochet, Nathalie; Engelen, Kristof; Amant, Frederic; De Moor, Bart; Timmerman, Dirk; Vergote, Ignace

    2008-01-01

    In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs). In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs. The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study – although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours – were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models. We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology

  17. Slurry discharge management-beach profile prediction

    Energy Technology Data Exchange (ETDEWEB)

    Bravo, R.; Nawrot, J.R. [Southern Illinois University at Carbondale, Carbondale, IL (United States). Dept. of Civil Engineering

    1996-11-01

    Mine tailings dams are embankments used by the mining industry to retain the tailings products after the mineral preparation process. Based on the acid-waste stereotype that all coal slurry is acid producing, current reclamation requires a four foot soil cover for inactive slurry disposal areas. Compliance with this requirement is both difficult and costly and in some case unnecessary, as not all the slurry, or portions of slurry impoundments are acid producing. Reduced costs and recent popularity of wetland development has prompted many operators to request reclamation variances for slurry impoundments. Waiting to address slurry reclamation until after the impoundment is full, limits the flexibility of reclamation opportunities. This paper outlines a general methodology to predict the formation of the beach profile for mine tailings dams, by the discharge volume and location of the slurry into the impoundment. The review is presented under the perspective of geotechnical engineering and waste disposal management emphasizing the importance of pre-planning slurry disposal land reclamation. 4 refs., 5 figs.

  18. Global analysis of gene expression profiles in developing physic nut (Jatropha curcas L. seeds.

    Directory of Open Access Journals (Sweden)

    Huawu Jiang

    Full Text Available BACKGROUND: Physic nut (Jatropha curcas L. is an oilseed plant species with high potential utility as a biofuel. Furthermore, following recent sequencing of its genome and the availability of expressed sequence tag (EST libraries, it is a valuable model plant for studying carbon assimilation in endosperms of oilseed plants. There have been several transcriptomic analyses of developing physic nut seeds using ESTs, but they have provided limited information on the accumulation of stored resources in the seeds. METHODOLOGY/PRINCIPAL FINDINGS: We applied next-generation Illumina sequencing technology to analyze global gene expression profiles of developing physic nut seeds 14, 19, 25, 29, 35, 41, and 45 days after pollination (DAP. The acquired profiles reveal the key genes, and their expression timeframes, involved in major metabolic processes including: carbon flow, starch metabolism, and synthesis of storage lipids and proteins in the developing seeds. The main period of storage reserves synthesis in the seeds appears to be 29-41 DAP, and the fatty acid composition of the developing seeds is consistent with relative expression levels of different isoforms of acyl-ACP thioesterase and fatty acid desaturase genes. Several transcription factor genes whose expression coincides with storage reserve deposition correspond to those known to regulate the process in Arabidopsis. CONCLUSIONS/SIGNIFICANCE: The results will facilitate searches for genes that influence de novo lipid synthesis, accumulation and their regulatory networks in developing physic nut seeds, and other oil seeds. Thus, they will be helpful in attempts to modify these plants for efficient biofuel production.

  19. Body composition indices and predicted cardiovascular disease risk profile among urban dwellers in Malaysia.

    Science.gov (United States)

    Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Dahlui, Maznah; Majid, Hazreen Abdul

    2015-01-01

    This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD) risk profile in an urban population in Kuala Lumpur, Malaysia. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

  20. Body Composition Indices and Predicted Cardiovascular Disease Risk Profile among Urban Dwellers in Malaysia

    Directory of Open Access Journals (Sweden)

    Tin Tin Su

    2015-01-01

    Full Text Available Objectives. This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD risk profile in an urban population in Kuala Lumpur, Malaysia. Methods. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Results. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. Conclusions. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

  1. Microarray analysis of the gene expression profile in triethylene glycol dimethacrylate-treated human dental pulp cells.

    Science.gov (United States)

    Torun, D; Torun, Z Ö; Demirkaya, K; Sarper, M; Elçi, M P; Avcu, F

    2017-11-01

    Triethylene glycol dimethacrylate (TEGDMA) is an important resin monomer commonly used in the structure of dental restorative materials. Recent studies have shown that unpolymerized resin monomers may be released into the oral environment and cause harmful biological effects. We investigated changes in the gene expression profiles of TEGDMA-treated human dental pulp cells (hDPCs) following short- (1-day) and long-term (7-days) exposure. HDPCs were exposed to a noncytotoxic concentration of TEGDMA, and gene expression profiles were evaluated by microarray analysis. The results were confirmed by quantitative reverse-transcriptase PCR (qRT PCR). In total, 1282 and 1319 genes (up- or down-regulated) were differentially expressed compared with control group after the 1- and 7-day incubation periods, respectively. Biological ontology-based analyses revealed that metabolic, cellular, and developmental processes constituted the largest groups of biological functional processes. qRT-PCR analysis on bone morphogenetic protein-2 (BMP-2), BMP-4, secreted protein, acidic, cysteine-rich, collagen type I alpha 1, oxidative stress-induced growth inhibitor 1, MMP3, interleukin-6, and heme oxygenase-1 genes confirmed the changes in expression observed in the microarray analysis. Our results suggest that TEGDMA can change the many functions of hDPCs through large changes in gene expression levels and complex interactions with different signaling pathways.

  2. Gene expression profiling in cervical cancer: identification of novel markers for disease diagnosis and therapy.

    LENUS (Irish Health Repository)

    Martin, Cara M

    2012-02-01

    Cervical cancer, a potentially preventable disease, remains the second most common malignancy in women worldwide. Human papillomavirus is the single most important etiological agent in cervical cancer. HPV contributes to neoplastic progression through the action of two viral oncoproteins E6 and E7, which interfere with critical cell cycle pathways, p53, and retinoblastoma. However, evidence suggests that HPV infection alone is insufficient to induce malignant changes and other host genetic variations are important in the development of cervical cancer. Advances in molecular biology and high throughput gene expression profiling technologies have heralded a new era in biomarker discovery and identification of molecular targets related to carcinogenesis. These advancements have improved our understanding of carcinogenesis and will facilitate screening, early detection, management, and personalised targeted therapy. In this chapter, we have described the use of high density microarrays to assess gene expression profiles in cervical cancer. Using this approach we have identified a number of novel genes which are differentially expressed in cervical cancer, including several genes involved in cell cycle regulation. These include p16ink4a, MCM 3 and 5, CDC6, Geminin, Cyclins A-D, TOPO2A, CDCA1, and BIRC5. We have validated expression of mRNA using real-time PCR and protein by immunohistochemistry.

  3. Gene expression analysis of precision-cut human liver slices indicates stable expression of ADME-Tox related genes

    NARCIS (Netherlands)

    Elferink, M. G. L.; Olinga, P.; van Leeuwen, E. M.; Bauerschmidt, S.; Polman, J.; Schoonen, W. G.; Heisterkamp, S. H.; Groothuis, G. M. M.

    2011-01-01

    In the process of drug development it is of high importance to test the safety of new drugs with predictive value for human toxicity. A promising approach of toxicity testing is based on shifts in gene expression profiling of the liver. Toxicity screening based on animal liver cells cannot be

  4. Age-Specific Gene Expression Profiles of Rhesus Monkey Ovaries Detected by Microarray Analysis

    Directory of Open Access Journals (Sweden)

    Hengxi Wei

    2015-01-01

    Full Text Available The biological function of human ovaries declines with age. To identify the potential molecular changes in ovarian aging, we performed genome-wide gene expression analysis by microarray of ovaries from young, middle-aged, and old rhesus monkeys. Microarray data was validated by quantitative real-time PCR. Results showed that a total of 503 (60 upregulated, 443 downregulated and 84 (downregulated genes were differentially expressed in old ovaries compared to young and middle-aged groups, respectively. No difference in gene expression was found between middle-aged and young groups. Differentially expressed genes were mainly enriched in cell and organelle, cellular and physiological process, binding, and catalytic activity. These genes were primarily associated with KEGG pathways of cell cycle, DNA replication and repair, oocyte meiosis and maturation, MAPK, TGF-beta, and p53 signaling pathway. Genes upregulated were involved in aging, defense response, oxidation reduction, and negative regulation of cellular process; genes downregulated have functions in reproduction, cell cycle, DNA and RNA process, macromolecular complex assembly, and positive regulation of macromolecule metabolic process. These findings show that monkey ovary undergoes substantial change in global transcription with age. Gene expression profiles are useful in understanding the mechanisms underlying ovarian aging and age-associated infertility in primates.

  5. Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data

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    Osamu Komori

    2013-01-01

    Full Text Available This paper discusses mathematical and statistical aspects in analysis methods applied to microarray gene expressions. We focus on pattern recognition to extract informative features embedded in the data for prediction of phenotypes. It has been pointed out that there are severely difficult problems due to the unbalance in the number of observed genes compared with the number of observed subjects. We make a reanalysis of microarray gene expression published data to detect many other gene sets with almost the same performance. We conclude in the current stage that it is not possible to extract only informative genes with high performance in the all observed genes. We investigate the reason why this difficulty still exists even though there are actively proposed analysis methods and learning algorithms in statistical machine learning approaches. We focus on the mutual coherence or the absolute value of the Pearson correlations between two genes and describe the distributions of the correlation for the selected set of genes and the total set. We show that the problem of finding informative genes in high dimensional data is ill-posed and that the difficulty is closely related with the mutual coherence.

  6. Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

    Science.gov (United States)

    Wan, Cen; Lees, Jonathan G; Minneci, Federico; Orengo, Christine A; Jones, David T

    2017-10-01

    Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

  7. Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

    Directory of Open Access Journals (Sweden)

    Cen Wan

    2017-10-01

    Full Text Available Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

  8. Comparison of gene expression and fatty acid profiles in concentrate and forage finished beef.

    Science.gov (United States)

    Buchanan, J W; Garmyn, A J; Hilton, G G; VanOverbeke, D L; Duan, Q; Beitz, D C; Mateescu, R G

    2013-01-01

    Fatty acid profiles and intramuscular expression of genes involved in fatty acid metabolism were characterized in concentrate- (CO) and forage- (FO) based finishing systems. Intramuscular samples from the adductor were taken at slaughter from 99 heifers finished on a CO diet and 58 heifers finished on a FO diet. Strip loins were obtained at fabrication to evaluate fatty acid profiles of LM muscle for all 157 heifers by using gas chromatography fatty acid methyl ester analysis. Composition was analyzed for differences by using the General Linear Model (GLM) procedure in SAS. Differences in fatty acid profile included a greater atherogenic index, greater percentage total MUFA, decreased omega-3 to omega-6 ratio, decreased percentage total PUFA, and decreased percentage omega-3 fatty acids in CO- compared with FO-finished heifers (P0.05). Upregulation was observed for PPARγ, fatty acid synthase (FASN), and fatty acid binding protein 4 (FABP4) in FO-finished compared with CO-finished heifers in both atherogenic index categories (P<0.05). Upregulation of diglyceride acyl transferase 2 (DGAT2) was observed in FO-finished heifers with a HAI (P<0.05). Expression of steroyl Co-A desaturase (SCD) was upregulated in CO-finished heifers with a LAI, and downregulated in FO-finished heifers with a HAI (P<0.05). Expression of adiponectin (ADIPOQ) was significantly downregulated in CO-finished heifers with a HAI compared with all other categories (P<0.05). The genes identified in this study which exhibit differential regulation in response to diet or in animals with extreme fatty acid profiles may provide genetic markers for selecting desirable fatty acid profiles in future selection programs.

  9. Detection of growth hormone doping by gene expression profiling of peripheral blood.

    Science.gov (United States)

    Mitchell, Christopher J; Nelson, Anne E; Cowley, Mark J; Kaplan, Warren; Stone, Glenn; Sutton, Selina K; Lau, Amie; Lee, Carol M Y; Ho, Ken K Y

    2009-12-01

    GH abuse is a significant problem in many sports, and there is currently no robust test that allows detection of doping beyond a short window after administration. Our objective was to evaluate gene expression profiling in peripheral blood leukocytes in-vivo as a test for GH doping in humans. Seven men and thirteen women were administered GH, 2 mg/d sc for 8 wk. Blood was collected at baseline and at 8 wk. RNA was extracted from the white cell fraction. Microarray analysis was undertaken using Agilent 44K G4112F arrays using a two-color design. Quantitative RT-PCR using TaqMan gene expression assays was performed for validation of selected differentially expressed genes. GH induced an approximately 2-fold increase in circulating IGF-I that was maintained throughout the 8 wk of the study. GH induced significant changes in gene expression with 353 in women and 41 in men detected with a false discovery rate of less than 5%. None of the differentially expressed genes were common between men and women. The maximal changes were a doubling for up-regulated or halving for down-regulated genes, similar in magnitude to the variation between individuals. Quantitative RT-PCR for seven target genes showed good concordance between microarray and quantitative PCR data in women but not in men. Gene expression analysis of peripheral blood leukocytes is unlikely to be a viable approach for the detection of GH doping.

  10. Whole-genome gene expression profiling of formalin-fixed, paraffin-embedded tissue samples.

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    Craig April

    2009-12-01

    Full Text Available We have developed a gene expression assay (Whole-Genome DASL, capable of generating whole-genome gene expression profiles from degraded samples such as formalin-fixed, paraffin-embedded (FFPE specimens.We demonstrated a similar level of sensitivity in gene detection between matched fresh-frozen (FF and FFPE samples, with the number and overlap of probes detected in the FFPE samples being approximately 88% and 95% of that in the corresponding FF samples, respectively; 74% of the differentially expressed probes overlapped between the FF and FFPE pairs. The WG-DASL assay is also able to detect 1.3-1.5 and 1.5-2 -fold changes in intact and FFPE samples, respectively. The dynamic range for the assay is approximately 3 logs. Comparing the WG-DASL assay with an in vitro transcription-based labeling method yielded fold-change correlations of R(2 approximately 0.83, while fold-change comparisons with quantitative RT-PCR assays yielded R(2 approximately 0.86 and R(2 approximately 0.55 for intact and FFPE samples, respectively. Additionally, the WG-DASL assay yielded high self-correlations (R(2>0.98 with low intact RNA inputs ranging from 1 ng to 100 ng; reproducible expression profiles were also obtained with 250 pg total RNA (R(2 approximately 0.92, with approximately 71% of the probes detected in 100 ng total RNA also detected at the 250 pg level. When FFPE samples were assayed, 1 ng total RNA yielded self-correlations of R(2 approximately 0.80, while still maintaining a correlation of R(2 approximately 0.75 with standard FFPE inputs (200 ng.Taken together, these results show that WG-DASL assay provides a reliable platform for genome-wide expression profiling in archived materials. It also possesses utility within clinical settings where only limited quantities of samples may be available (e.g. microdissected material or when minimally invasive procedures are performed (e.g. biopsied specimens.

  11. Host gene expression profiles in ferrets infected with genetically distinct henipavirus strains.

    Directory of Open Access Journals (Sweden)

    Alberto J Leon

    2018-03-01

    Full Text Available Henipavirus infection causes severe respiratory and neurological disease in humans that can be fatal. To characterize the pathogenic mechanisms of henipavirus infection in vivo, we performed experimental infections in ferrets followed by genome-wide gene expression analysis of lung and brain tissues. The Hendra, Nipah-Bangladesh, and Nipah-Malaysia strains caused severe respiratory and neurological disease with animals succumbing around 7 days post infection. Despite the presence of abundant viral shedding, animal-to-animal transmission did not occur. The host gene expression profiles of the lung tissue showed early activation of interferon responses and subsequent expression of inflammation-related genes that coincided with the clinical deterioration. Additionally, the lung tissue showed unchanged levels of lymphocyte markers and progressive downregulation of cell cycle genes and extracellular matrix components. Infection in the brain resulted in a limited breadth of the host responses, which is in accordance with the immunoprivileged status of this organ. Finally, we propose a model of the pathogenic mechanisms of henipavirus infection that integrates multiple components of the host responses.

  12. Gene expression profile of Bombyx mori hemocyte under the stress of destruxin A.

    Directory of Open Access Journals (Sweden)

    Liang Gong

    Full Text Available Destruxin A (DA is a cyclo-peptidic mycotoxin from the entomopathogenic fungus Metarhizium anisopliae. To uncover potential genes associated with its molecular mechanisms, a digital gene expression (DGE profiling analysis was used to compare differentially expressed genes in the hemocytes of silkworm larvae treated with DA. Ten DGE libraries were constructed, sequenced, and assembled, and the unigenes with least 2.0-fold difference were further analyzed. The numbers of up-regulated genes were 10, 20, 18, 74 and 8, as well as the numbers of down-regulated genes were 0, 1, 8, 13 and 3 at 1, 4, 8, 12 and 24 h post treatment, respectively. Totally, the expression of 132 genes were significantly changed, among them, 1, 3 and 12 genes were continually up-regulated at 4, 3 and 2 different time points, respectively, while 1 gene was either up or down-regulated continually at 2 different time points. Furthermore, 68 genes were assigned to one or multiple gene ontology (GO terms and 89 genes were assigned to specific Kyoto Encyclopedia of Genes and Genomes (KEGG Orthology. In-depth analysis identified that these genes putatively involved in insecticide resistance, cell apoptosis, and innate immune defense. Finally, twenty differentially expressed genes were randomly chosen and validated by quantitative real-time PCR (qRT-PCR. Our studies provide insights into the toxic effect of this microbial insecticide on silkworm's hemocytes, and are helpful to better understanding of the molecular mechanisms of DA as a biological insecticide.

  13. Identification of Early Response Genes in Human Peripheral Leukocytes Infected with Orientia tsutsugamushi: The Emergent of a Unique Gene Expression Profile for Diagnosis of O. tsutsugamush Infection

    Science.gov (United States)

    2010-01-01

    all found in Homo sapiens and the biological processes were assigned based on human protein reference database (HPRD, www.hprd.org). Gene names in...the following: i) whether infection by O. tsutsugamushi is accompanied by distinct gene expression profiles; ii) which features of the host

  14. Quantitative high-throughput gene expression profiling of human striatal development to screen stem cell–derived medium spiny neurons

    Directory of Open Access Journals (Sweden)

    Marco Straccia

    Full Text Available A systematic characterization of the spatio-temporal gene expression during human neurodevelopment is essential to understand brain function in both physiological and pathological conditions. In recent years, stem cell technology has provided an in vitro tool to recapitulate human development, permitting also the generation of human models for many diseases. The correct differentiation of human pluripotent stem cell (hPSC into specific cell types should be evaluated by comparison with specific cells/tissue profiles from the equivalent adult in vivo organ. Here, we define by a quantitative high-throughput gene expression analysis the subset of specific genes of the whole ganglionic eminence (WGE and adult human striatum. Our results demonstrate that not only the number of specific genes is crucial but also their relative expression levels between brain areas. We next used these gene profiles to characterize the differentiation of hPSCs. Our findings demonstrate a temporal progression of gene expression during striatal differentiation of hPSCs from a WGE toward an adult striatum identity. Present results establish a gene expression profile to qualitatively and quantitatively evaluate the telencephalic hPSC-derived progenitors eventually used for transplantation and mature striatal neurons for disease modeling and drug-screening.

  15. A network-based predictive gene-expression signature for adjuvant chemotherapy benefit in stage II colorectal cancer.

    Science.gov (United States)

    Cao, Bangrong; Luo, Liping; Feng, Lin; Ma, Shiqi; Chen, Tingqing; Ren, Yuan; Zha, Xiao; Cheng, Shujun; Zhang, Kaitai; Chen, Changmin

    2017-12-13

    The clinical benefit of adjuvant chemotherapy for stage II colorectal cancer (CRC) is controversial. This study aimed to explore novel gene signature to predict outcome benefit of postoperative 5-Fu-based therapy in stage II CRC. Gene-expression profiles of stage II CRCs from two datasets with 5-Fu-based adjuvant chemotherapy (training dataset, n = 212; validation dataset, n = 85) were analyzed to identify the indicator. A systemic approach by integrating gene-expression and protein-protein interaction (PPI) network was implemented to develop the predictive signature. Kaplan-Meier curves and Cox proportional hazards model were used to determine the survival benefit of adjuvant chemotherapy. Experiments with shRNA knock-down were carried out to confirm the signature identified in this study. In the training dataset, we identified 44 PPI sub-modules, by which we separate patients into two clusters (1 and 2) having different chemotherapeutic benefit. A predictor of 11 PPI sub-modules (11-PPI-Mod) was established to discriminate the two sub-groups, with an overall accuracy of 90.1%. This signature was independently validated in an external validation dataset. Kaplan-Meier curves showed an improved outcome for patients who received adjuvant chemotherapy in Cluster 1 sub-group, but even worse survival for those in Cluster 2 sub-group. Similar results were found in both the training and the validation dataset. Multivariate Cox regression revealed an interaction effect between 11-PPI-Mod signature and adjuvant therapy treatment in the training dataset (RFS, p = 0.007; OS, p = 0.006) and the validation dataset (RFS, p = 0.002). From the signature, we found that PTGES gene was up-regulated in CRC cells which were more resistant to 5-Fu. Knock-down of PTGES indicated a growth inhibition and up-regulation of apoptotic markers induced by 5-Fu in CRC cells. Only a small proportion of stage II CRC patients could benefit from adjuvant therapy. The 11-PPI-Mod as

  16. Ecological transition predictably associated with gene degeneration.

    Science.gov (United States)

    Wessinger, Carolyn A; Rausher, Mark D

    2015-02-01

    Gene degeneration or loss can significantly contribute to phenotypic diversification, but may generate genetic constraints on future evolutionary trajectories, potentially restricting phenotypic reversal. Such constraints may manifest as directional evolutionary trends when parallel phenotypic shifts consistently involve gene degeneration or loss. Here, we demonstrate that widespread parallel evolution in Penstemon from blue to red flowers predictably involves the functional inactivation and degeneration of the enzyme flavonoid 3',5'-hydroxylase (F3'5'H), an anthocyanin pathway enzyme required for the production of blue floral pigments. Other types of genetic mutations do not consistently accompany this phenotypic shift. This pattern may be driven by the relatively large mutational target size of degenerative mutations to this locus and the apparent lack of associated pleiotropic effects. The consistent degeneration of F3'5'H may provide a mechanistic explanation for the observed asymmetry in the direction of flower color evolution in Penstemon: Blue to red transitions are common, but reverse transitions have not been observed. Although phenotypic shifts in this system are likely driven by natural selection, internal constraints may generate predictable genetic outcomes and may restrict future evolutionary trajectories. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  17. Gene expression profiling reveals different molecular patterns in G-protein coupled receptor signaling pathways between early- and late-onset preeclampsia.

    Science.gov (United States)

    Liang, Mengmeng; Niu, Jianmin; Zhang, Liang; Deng, Hua; Ma, Jian; Zhou, Weiping; Duan, Dongmei; Zhou, Yuheng; Xu, Huikun; Chen, Longding

    2016-04-01

    Early-onset preeclampsia and late-onset preeclampsia have been regarded as two different phenotypes with heterogeneous manifestations; To gain insights into the pathogenesis of the two traits, we analyzed the gene expression profiles in preeclamptic placentas. A whole genome-wide microarray was used to determine the gene expression profiles in placental tissues from patients with early-onset (n = 7; 36 weeks) preeclampsia and their controls who delivered preterm (n = 5; 36 weeks). Genes were termed differentially expressed if they showed a fold-change ≥ 2 and q-value preeclampsia (177 genes were up-regulated and 450 were down-regulated). Gene ontology analysis identified significant alterations in several biological processes; the top two were immune response and cell surface receptor linked signal transduction. Among the cell surface receptor linked signal transduction-related, differentially expressed genes, those involved in the G-protein coupled receptor protein signaling pathway were significantly enriched. G-protein coupled receptor signaling pathway related genes, such as GPR124 and MRGPRF, were both found to be down-regulated in early-onset preeclampsia. The results were consistent with those of western blotting that the abundance of GPR124 was lower in early-onset compared with late-onset preeclampsia. The different gene expression profiles reflect the different levels of transcription regulation between the two conditions and supported the hypothesis that they are separate disease entities. Moreover, the G-protein coupled receptor signaling pathway related genes may contribute to the mechanism underlying early- and late-onset preeclampsia. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Gene expression profiling reveals candidate genes related to residual feed intake in duodenum of laying ducks.

    Science.gov (United States)

    Zeng, T; Huang, L; Ren, J; Chen, L; Tian, Y; Huang, Y; Zhang, H; Du, J; Lu, L

    2017-12-01

    Feed represents two-thirds of the total costs of poultry production, especially in developing countries. Improvement in feed efficiency would reduce the amount of feed required for production (growth or laying), the production cost, and the amount of nitrogenous waste. The most commonly used measures for feed efficiency are feed conversion ratio (FCR) and residual feed intake (RFI). As a more suitable indicator assessing feed efficiency, RFI is defined as the difference between observed and expected feed intake based on maintenance and growth or laying. However, the genetic and biological mechanisms regulating RFI are largely unknown. Identifying molecular mechanisms explaining divergence in RFI in laying ducks would lead to the development of early detection methods for the selection of more efficient breeding poultry. The objective of this study was to identify duodenum genes and pathways through transcriptional profiling in 2 extreme RFI phenotypes (HRFI and LRFI) of the duck population. Phenotypic aspects of feed efficiency showed that RFI was strongly positive with FCR and feed intake (FI). Transcriptomic analysis identified 35 differentially expressed genes between LRFI and HRFI ducks. These genes play an important role in metabolism, digestibility, secretion, and innate immunity including (), (), (), β (), and (). These results improve our knowledge of the biological basis underlying RFI, which would be useful for further investigations of key candidate genes for RFI and for the development of biomarkers.

  19. Using RNA-Seq for gene identification, polymorphism detection and transcript profiling in two alfalfa genotypes with divergent cell wall composition in stems

    Science.gov (United States)

    2011-01-01

    Background Alfalfa, [Medicago sativa (L.) sativa], a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy. The recent advent of RNA-Seq, a massively parallel sequencing method for transcriptome analysis, provides an opportunity to expand the identification of alfalfa genes and polymorphisms, and conduct in-depth transcript profiling. Results Cell walls in stems of alfalfa genotype 708 have higher cellulose and lower lignin concentrations compared to cell walls in stems of genotype 773. Using the Illumina GA-II platform, a total of 198,861,304 expression sequence tags (ESTs, 76 bp in length) were generated from cDNA libraries derived from elongating stem (ES) and post-elongation stem (PES) internodes of 708 and 773. In addition, 341,984 ESTs were generated from ES and PES internodes of genotype 773 using the GS FLX Titanium platform. The first alfalfa (Medicago sativa) gene index (MSGI 1.0) was assembled using the Sanger ESTs available from GenBank, the GS FLX Titanium EST sequences, and the de novo assembled Illumina sequences. MSGI 1.0 contains 124,025 unique sequences including 22,729 tentative consensus sequences (TCs), 22,315 singletons and 78,981 pseudo-singletons. We identified a total of 1,294 simple sequence repeats (SSR) among the sequences in MSGI 1.0. In addition, a total of 10,826 single nucleotide polymorphisms (SNPs) were predicted between the two genotypes. Out of 55 SNPs randomly selected for experimental validation, 47 (85%) were polymorphic between the two genotypes. We also identified numerous allelic variations within each genotype. Digital gene expression analysis identified numerous candidate genes that may play a role in stem development as well as candidate genes that may contribute to the differences in cell wall composition in stems of the two genotypes. Conclusions Our results demonstrate that RNA-Seq can be

  20. A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence

    International Nuclear Information System (INIS)

    Reis, Patricia P; Simpson, Colleen; Goldstein, David; Brown, Dale; Gilbert, Ralph; Gullane, Patrick; Irish, Jonathan; Jurisica, Igor; Kamel-Reid, Suzanne; Waldron, Levi; Perez-Ordonez, Bayardo; Pintilie, Melania; Galloni, Natalie Naranjo; Xuan, Yali; Cervigne, Nilva K; Warner, Giles C; Makitie, Antti A

    2011-01-01

    Oral Squamous Cell Carcinoma (OSCC) is a major cause of cancer death worldwide, which is mainly due to recurrence leading to treatment failure and patient death. Histological status of surgical margins is a currently available assessment for recurrence risk in OSCC; however histological status does not predict recurrence, even in patients with histologically negative margins. Therefore, molecular analysis of histologically normal resection margins and the corresponding OSCC may aid in identifying a gene signature predictive of recurrence. We used a meta-analysis of 199 samples (OSCCs and normal oral tissues) from five public microarray datasets, in addition to our microarray analysis of 96 OSCCs and histologically normal margins from 24 patients, to train a gene signature for recurrence. Validation was performed by quantitative real-time PCR using 136 samples from an independent cohort of 30 patients. We identified 138 significantly over-expressed genes (> 2-fold, false discovery rate of 0.01) in OSCC. By penalized likelihood Cox regression, we identified a 4-gene signature with prognostic value for recurrence in our training set. This signature comprised the invasion-related genes MMP1, COL4A1, P4HA2, and THBS2. Over-expression of this 4-gene signature in histologically normal margins was associated with recurrence in our training cohort (p = 0.0003, logrank test) and in our independent validation cohort (p = 0.04, HR = 6.8, logrank test). Gene expression alterations occur in histologically normal margins in OSCC. Over-expression of the 4-gene signature in histologically normal surgical margins was validated and highly predictive of recurrence in an independent patient cohort. Our findings may be applied to develop a molecular test, which would be clinically useful to help predict which patients are at a higher risk of local recurrence