Sequential Logic Model Deciphers Dynamic Transcriptional Control of Gene Expressions

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Yeo, Zhen Xuan; Wong, Sum Thai; Arjunan, Satya Nanda Vel; Piras, Vincent; Tomita, Masaru; Selvarajoo, Kumar; Giuliani, Alessandro; Tsuchiya, Masa

2007-01-01

Background Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. Methodology Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. Principal Findings SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. Conclusions/Significance The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological

Sequential logic model deciphers dynamic transcriptional control of gene expressions.

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Zhen Xuan Yeo

Full Text Available BACKGROUND: Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. METHODOLOGY: Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. PRINCIPAL FINDINGS: SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. CONCLUSIONS/SIGNIFICANCE: The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet

Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

KAUST Repository

Dhavala, Soma S.

2010-09-01

Massively Parallel Signature Sequencing (MPSS) is a high-throughput, counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA type model for this count data using a zero-inflatedPoisson distribution, different from existing methods that assume continuous densities. We adopt two Bayesian hierarchical models-one parametric and the other semiparametric with a Dirichlet process prior that has the ability to "borrow strength" across related signatures, where a signature is a specific arrangement of the nucleotides, usually 16-21 base pairs long. We utilize the discreteness of Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using nonparametric approaches, while controlling the false discovery rate. We identify several differentially expressed genes that have important biological significance and conclude with a summary of the biological discoveries. This article has supplementary materials online. © 2010 American Statistical Association.

Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

KAUST Repository

Dhavala, Soma S.; Datta, Sujay; Mallick, Bani K.; Carroll, Raymond J.; Khare, Sangeeta; Lawhon, Sara D.; Adams, L. Garry

2010-01-01

Massively Parallel Signature Sequencing (MPSS) is a high-throughput, counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA type model for this count data using a zero-inflatedPoisson distribution, different from existing methods that assume continuous densities. We adopt two Bayesian hierarchical models-one parametric and the other semiparametric with a Dirichlet process prior that has the ability to "borrow strength" across related signatures, where a signature is a specific arrangement of the nucleotides, usually 16-21 base pairs long. We utilize the discreteness of Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using nonparametric approaches, while controlling the false discovery rate. We identify several differentially expressed genes that have important biological significance and conclude with a summary of the biological discoveries. This article has supplementary materials online. © 2010 American Statistical Association.

Imaging gene expression in gene therapy

International Nuclear Information System (INIS)

Wiebe, Leonard I.

1997-01-01

Full text. Gene therapy can be used to introduce new genes, or to supplement the function of indigenous genes. At the present time, however, there is non-invasive test to demonstrate efficacy of the gene transfer and expression processes. It has been postulated that scintigraphic imaging can offer unique information on both the site at which the transferred gene is expressed, and the degree of expression, both of which are critical issue for safety and clinical efficacy. Many current studies are based on 'suicide gene therapy' of cancer. Cells modified to express these genes commit metabolic suicide in the presence of an enzyme encoded by the transferred gene and a specifically-convertible pro drug. Pro drug metabolism can lead to selective metabolic trapping, required for scintigraphy. Herpes simplex virus type-1 thymidine kinase (H S V-1 t k + ) has been use for 'suicide' in vivo tumor gene therapy. It has been proposed that radiolabelled nucleosides can be used as radiopharmaceuticals to detect H S V-1 t k + gene expression where the H S V-1 t k + gene serves a reporter or therapeutic function. Animal gene therapy models have been studied using purine-([ 18 F]F H P G; [ 18 F]-A C V), and pyrimidine- ([ 123 / 131 I]I V R F U; [ 124 / 131I ]) antiviral nucleosides. Principles of gene therapy and gene therapy imaging will be reviewed and experimental data for [ 123 / 131I ]I V R F U imaging with the H S V-1 t k + reporter gene will be presented

A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.

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Ye, Meixia; Wang, Zhong; Wang, Yaqun; Wu, Rongling

2015-03-01

Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

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Seo, Minseok; Shin, Su-kyung; Kwon, Eun-Young; Kim, Sung-Eun; Bae, Yun-Jung; Lee, Seungyeoun; Sung, Mi-Kyung; Choi, Myung-Sook; Park, Taesung

2016-01-01

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes.

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Samuel Sunghwan Cho

Full Text Available Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs. However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods

Differential cytokine gene expression according to outcome in a hamster model of leptospirosis.

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Frédérique Vernel-Pauillac

Full Text Available BACKGROUND: Parameters predicting the evolution of leptospirosis would be useful for clinicians, as well as to better understand severe leptospirosis, but are scarce and rarely validated. Because severe leptospirosis includes septic shock, similarities with predictors evidenced for sepsis and septic shock were studied in a hamster model. METHODOLOGY/PRINCIPAL FINDINGS: Using an LD50 model of leptospirosis in hamsters, we first determined that 3 days post-infection was a time-point that allowed studying the regulation of immune gene expression and represented the onset of the clinical signs of the disease. In the absence of tools to assess serum concentrations of immune effectors in hamsters, we determined mRNA levels of various immune genes, especially cytokines, together with leptospiraemia at this particular time-point. We found differential expression of both pro- and anti-inflammatory mediators, with significantly higher expression levels of tumor necrosis factor alpha, interleukin 1alpha, cyclo-oxygenase 2 and interleukin 10 genes in nonsurvivors compared to survivors. Higher leptospiraemia was also observed in nonsurvivors. Lastly, we demonstrated the relevance of these results by comparing their respective expression levels using a LD100 model or an isogenic high-passage nonvirulent variant. CONCLUSIONS/SIGNIFICANCE: Up-regulated gene expression of both pro- and anti-inflammatory immune effectors in hamsters with fatal outcome in an LD50 model of leptospirosis, together with a higher Leptospira burden, suggest that these gene expression levels could be predictors of adverse outcome in leptospirosis.

Quantifying intrinsic and extrinsic variability in stochastic gene expression models.

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Singh, Abhyudai; Soltani, Mohammad

2013-01-01

Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters.

Imaging gene expression in gene therapy

Energy Technology Data Exchange (ETDEWEB)

Wiebe, Leonard I. [Alberta Univ., Edmonton (Canada). Noujaim Institute for Pharmaceutical Oncology Research

1997-12-31

Full text. Gene therapy can be used to introduce new genes, or to supplement the function of indigenous genes. At the present time, however, there is non-invasive test to demonstrate efficacy of the gene transfer and expression processes. It has been postulated that scintigraphic imaging can offer unique information on both the site at which the transferred gene is expressed, and the degree of expression, both of which are critical issue for safety and clinical efficacy. Many current studies are based on `suicide gene therapy` of cancer. Cells modified to express these genes commit metabolic suicide in the presence of an enzyme encoded by the transferred gene and a specifically-convertible pro drug. Pro drug metabolism can lead to selective metabolic trapping, required for scintigraphy. Herpes simplex virus type-1 thymidine kinase (H S V-1 t k{sup +}) has been use for `suicide` in vivo tumor gene therapy. It has been proposed that radiolabelled nucleosides can be used as radiopharmaceuticals to detect H S V-1 t k{sup +} gene expression where the H S V-1 t k{sup +} gene serves a reporter or therapeutic function. Animal gene therapy models have been studied using purine-([{sup 18} F]F H P G; [{sup 18} F]-A C V), and pyrimidine- ([{sup 123}/{sup 131} I]I V R F U; [{sup 124}/{sup 131I}]) antiviral nucleosides. Principles of gene therapy and gene therapy imaging will be reviewed and experimental data for [{sup 123}/{sup 131I}]I V R F U imaging with the H S V-1 t k{sup +} reporter gene will be presented

A three-gene expression signature model for risk stratification of patients with neuroblastoma.

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Garcia, Idoia; Mayol, Gemma; Ríos, José; Domenech, Gema; Cheung, Nai-Kong V; Oberthuer, André; Fischer, Matthias; Maris, John M; Brodeur, Garrett M; Hero, Barbara; Rodríguez, Eva; Suñol, Mariona; Galvan, Patricia; de Torres, Carmen; Mora, Jaume; Lavarino, Cinzia

2012-04-01

Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P model was an independent marker for survival (P model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems. ©2012 AACR.

Characterization of differentially expressed genes using high-dimensional co-expression networks

DEFF Research Database (Denmark)

Coelho Goncalves de Abreu, Gabriel; Labouriau, Rodrigo S.

2010-01-01

We present a technique to characterize differentially expressed genes in terms of their position in a high-dimensional co-expression network. The set-up of Gaussian graphical models is used to construct representations of the co-expression network in such a way that redundancy and the propagation...... that allow to make effective inference in problems with high degree of complexity (e.g. several thousands of genes) and small number of observations (e.g. 10-100) as typically occurs in high throughput gene expression studies. Taking advantage of the internal structure of decomposable graphical models, we...... construct a compact representation of the co-expression network that allows to identify the regions with high concentration of differentially expressed genes. It is argued that differentially expressed genes located in highly interconnected regions of the co-expression network are less informative than...

Inferring gene networks from discrete expression data

KAUST Repository

Zhang, L.

2013-07-18

The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.

Gene expression inference with deep learning.

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Chen, Yifei; Li, Yi; Narayan, Rajiv; Subramanian, Aravind; Xie, Xiaohui

2016-06-15

Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. D-GEX is available at https://github.com/uci-cbcl/D-GEX CONTACT: xhx@ics.uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Predicting spatial and temporal gene expression using an integrative model of transcription factor occupancy and chromatin state.

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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

Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data

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Keita Mori

2013-01-01

Full Text Available Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided.

Mining gene expression data of multiple sclerosis.

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Pi Guo

Full Text Available Microarray produces a large amount of gene expression data, containing various biological implications. The challenge is to detect a panel of discriminative genes associated with disease. This study proposed a robust classification model for gene selection using gene expression data, and performed an analysis to identify disease-related genes using multiple sclerosis as an example.Gene expression profiles based on the transcriptome of peripheral blood mononuclear cells from a total of 44 samples from 26 multiple sclerosis patients and 18 individuals with other neurological diseases (control were analyzed. Feature selection algorithms including Support Vector Machine based on Recursive Feature Elimination, Receiver Operating Characteristic Curve, and Boruta algorithms were jointly performed to select candidate genes associating with multiple sclerosis. Multiple classification models categorized samples into two different groups based on the identified genes. Models' performance was evaluated using cross-validation methods, and an optimal classifier for gene selection was determined.An overlapping feature set was identified consisting of 8 genes that were differentially expressed between the two phenotype groups. The genes were significantly associated with the pathways of apoptosis and cytokine-cytokine receptor interaction. TNFSF10 was significantly associated with multiple sclerosis. A Support Vector Machine model was established based on the featured genes and gave a practical accuracy of ∼86%. This binary classification model also outperformed the other models in terms of Sensitivity, Specificity and F1 score.The combined analytical framework integrating feature ranking algorithms and Support Vector Machine model could be used for selecting genes for other diseases.

Altered expression pattern of clock genes in a rat model of depression

DEFF Research Database (Denmark)

Christiansen, Sofie; Bouzinova, Elena; Fahrenkrug, Jan

2016-01-01

BACKGROUND: Abnormalities in circadian rhythms may be causal factors in development of major depressive disorder. The biology underlying a causal relationship between circadian rhythm disturbances and depression is slowly being unraveled. Although there is no direct evidence of dysregulation...... of clock gene expression in depressive patients many studies have reported single-nucleotide polymorphisms in clock genes in these patients. METHODS: In the present study we investigated whether a depression-like state in rats associates with alternations of the diurnal expression of clock genes....... The validated chronic mild stress (CMS) animal model of depression was used to investigate rhythmic expression of three clock genes; Per1, Per2 and Bmal1. Brain and liver tissue was collected from 96 animals after 3.5 weeks of CMS (48 control and 48 depression-like rats) at 4 h sampling interval within 24 h. We...

Connecting protein and mRNA burst distributions for stochastic models of gene expression

International Nuclear Information System (INIS)

Elgart, Vlad; Jia, Tao; Fenley, Andrew T; Kulkarni, Rahul

2011-01-01

The intrinsic stochasticity of gene expression can lead to large variability in protein levels for genetically identical cells. Such variability in protein levels can arise from infrequent synthesis of mRNAs which in turn give rise to bursts of protein expression. Protein expression occurring in bursts has indeed been observed experimentally and recent studies have also found evidence for transcriptional bursting, i.e. production of mRNAs in bursts. Given that there are distinct experimental techniques for quantifying the noise at different stages of gene expression, it is of interest to derive analytical results connecting experimental observations at different levels. In this work, we consider stochastic models of gene expression for which mRNA and protein production occurs in independent bursts. For such models, we derive analytical expressions connecting protein and mRNA burst distributions which show how the functional form of the mRNA burst distribution can be inferred from the protein burst distribution. Additionally, if gene expression is repressed such that observed protein bursts arise only from single mRNAs, we show how observations of protein burst distributions (repressed and unrepressed) can be used to completely determine the mRNA burst distribution. Assuming independent contributions from individual bursts, we derive analytical expressions connecting means and variances for burst and steady-state protein distributions. Finally, we validate our general analytical results by considering a specific reaction scheme involving regulation of protein bursts by small RNAs. For a range of parameters, we derive analytical expressions for regulated protein distributions that are validated using stochastic simulations. The analytical results obtained in this work can thus serve as useful inputs for a broad range of studies focusing on stochasticity in gene expression

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

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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.

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

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

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Lemieux Sébastien

2006-08-01

Full Text Available Abstract Background The identification of differentially expressed genes (DEGs from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. Results On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. Conclusion The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition.

Gene expression and gene therapy imaging

International Nuclear Information System (INIS)

Rome, Claire; Couillaud, Franck; Moonen, Chrit T.W.

2007-01-01

The fast growing field of molecular imaging has achieved major advances in imaging gene expression, an important element of gene therapy. Gene expression imaging is based on specific probes or contrast agents that allow either direct or indirect spatio-temporal evaluation of gene expression. Direct evaluation is possible with, for example, contrast agents that bind directly to a specific target (e.g., receptor). Indirect evaluation may be achieved by using specific substrate probes for a target enzyme. The use of marker genes, also called reporter genes, is an essential element of MI approaches for gene expression in gene therapy. The marker gene may not have a therapeutic role itself, but by coupling the marker gene to a therapeutic gene, expression of the marker gene reports on the expression of the therapeutic gene. Nuclear medicine and optical approaches are highly sensitive (detection of probes in the picomolar range), whereas MRI and ultrasound imaging are less sensitive and require amplification techniques and/or accumulation of contrast agents in enlarged contrast particles. Recently developed MI techniques are particularly relevant for gene therapy. Amongst these are the possibility to track gene therapy vectors such as stem cells, and the techniques that allow spatiotemporal control of gene expression by non-invasive heating (with MRI guided focused ultrasound) and the use of temperature sensitive promoters. (orig.)

Implementation of plaid model biclustering method on microarray of carcinoma and adenoma tumor gene expression data

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Ardaneswari, Gianinna; Bustamam, Alhadi; Sarwinda, Devvi

2017-10-01

A Tumor is an abnormal growth of cells that serves no purpose. Carcinoma is a tumor that grows from the top of the cell membrane and the organ adenoma is a benign tumor of the gland-like cells or epithelial tissue. In the field of molecular biology, the development of microarray technology is used in the data store of disease genetic expression. For each of microarray gene, an amount of information is stored for each trait or condition. In gene expression data clustering can be done with a bicluster algorithm, thats clustering method which not only the objects to be clustered, but also the properties or condition of the object. This research proposed Plaid Model Biclustering as one of biclustering method. In this study, we discuss the implementation of Plaid Model Biclustering Method on microarray of Carcinoma and Adenoma tumor gene expression data. From the experimental results, we found three biclusters are formed by Carcinoma gene expression data and four biclusters are formed by Adenoma gene expression data.

Selection for the compactness of highly expressed genes in Gallus gallus

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Zhou Ming

2010-05-01

Full Text Available Abstract Background Coding sequence (CDS length, gene size, and intron length vary within a genome and among genomes. Previous studies in diverse organisms, including human, D. Melanogaster, C. elegans, S. cerevisiae, and Arabidopsis thaliana, indicated that there are negative relationships between expression level and gene size, CDS length as well as intron length. Different models such as selection for economy model, genomic design model, and mutational bias hypotheses have been proposed to explain such observation. The debate of which model is a superior one to explain the observation has not been settled down. The chicken (Gallus gallus is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. As D. Melanogaster, chicken has a larger effective population size, selection for chicken genome is expected to be more effective in increasing protein synthesis efficiency. Therefore, in this study the chicken was used as a model organism to elucidate the interaction between gene features and expression pattern upon selection pressure. Results Based on different technologies, we gathered expression data for nuclear protein coding, single-splicing genes from Gallus gallus genome and compared them with gene parameters. We found that gene size, CDS length, first intron length, average intron length, and total intron length are negatively correlated with expression level and expression breadth significantly. The tissue specificity is positively correlated with the first intron length but negatively correlated with the average intron length, and not correlated with the CDS length and protein domain numbers. Comparison analyses showed that ubiquitously expressed genes and narrowly expressed genes with the similar expression levels do not differ in compactness. Our data provided evidence that the genomic design model can not, at least in part, explain our observations. We grouped all somatic-tissue-specific genes

Selection of reference genes in different myocardial regions of an in vivo ischemia/reperfusion rat model for normalization of antioxidant gene expression

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Vesentini Nicoletta

2012-02-01

Full Text Available Abstract Background Changes in cardiac gene expression due to myocardial injury are usually assessed in whole heart tissue. However, as the heart is a heterogeneous system, spatial and temporal heterogeneity is expected in gene expression. Results In an ischemia/reperfusion (I/R rat model we evaluated gene expression of mitochondrial and cytoplasmatic superoxide dismutase (MnSod, Cu-ZnSod and thioredoxin reductase (trxr1 upon short (4 h and long (72 h reperfusion times in the right ventricle (RV, and in the ischemic/reperfused (IRR and the remote region (RR of the left ventricle. Gene expression was assessed by Real-time reverse-transcription quantitative PCR (RT-qPCR. In order to select most stable reference genes suitable for normalization purposes, in each myocardial region we tested nine putative reference genes by geNorm analysis. The genes investigated were: Actin beta (actb, Glyceraldehyde-3-P-dehydrogenase (gapdh, Ribosomal protein L13A (rpl13a, Tyrosine 3-monooxygenase (ywhaz, Beta-glucuronidase (gusb, Hypoxanthine guanine Phosphoribosyltransferase 1 (hprt, TATA binding box protein (tbp, Hydroxymethylbilane synthase (hmbs, Polyadenylate-binding protein 1 (papbn1. According to our findings, most stable reference genes in the RV and RR were hmbs/hprt and hmbs/tbp/hprt respectively. In the IRR, six reference genes were recommended for normalization purposes; however, in view of experimental feasibility limitations, target gene expression could be normalized against the three most stable reference genes (ywhaz/pabp/hmbs without loss of sensitivity. In all cases MnSod and Cu-ZnSod expression decreased upon long reperfusion, the former in all myocardial regions and the latter in IRR alone. trxr1 expression did not vary. Conclusions This study provides a validation of reference genes in the RV and in the anterior and posterior wall of the LV of cardiac ischemia/reperfusion model and shows that gene expression should be assessed separately in

Serial analysis of gene expression (SAGE)

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van Ruissen, Fred; Baas, Frank

2007-01-01

In 1995, serial analysis of gene expression (SAGE) was developed as a versatile tool for gene expression studies. SAGE technology does not require pre-existing knowledge of the genome that is being examined and therefore SAGE can be applied to many different model systems. In this chapter, the SAGE

Gene expression

International Nuclear Information System (INIS)

Hildebrand, C.E.; Crawford, B.D.; Walters, R.A.; Enger, M.D.

1983-01-01

We prepared probes for isolating functional pieces of the metallothionein locus. The probes enabled a variety of experiments, eventually revealing two mechanisms for metallothionein gene expression, the order of the DNA coding units at the locus, and the location of the gene site in its chromosome. Once the switch regulating metallothionein synthesis was located, it could be joined by recombinant DNA methods to other, unrelated genes, then reintroduced into cells by gene-transfer techniques. The expression of these recombinant genes could then be induced by exposing the cells to Zn 2+ or Cd 2+ . We would thus take advantage of the clearly defined switching properties of the metallothionein gene to manipulate the expression of other, perhaps normally constitutive, genes. Already, despite an incomplete understanding of how the regulatory switch of the metallothionein locus operates, such experiments have been performed successfully

Evaluation of phenoxybenzamine in the CFA model of pain following gene expression studies and connectivity mapping.

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Chang, Meiping; Smith, Sarah; Thorpe, Andrew; Barratt, Michael J; Karim, Farzana

2010-09-16

We have previously used the rat 4 day Complete Freund's Adjuvant (CFA) model to screen compounds with potential to reduce osteoarthritic pain. The aim of this study was to identify genes altered in this model of osteoarthritic pain and use this information to infer analgesic potential of compounds based on their own gene expression profiles using the Connectivity Map approach. Using microarrays, we identified differentially expressed genes in L4 and L5 dorsal root ganglia (DRG) from rats that had received intraplantar CFA for 4 days compared to matched, untreated control animals. Analysis of these data indicated that the two groups were distinguishable by differences in genes important in immune responses, nerve growth and regeneration. This list of differentially expressed genes defined a "CFA signature". We used the Connectivity Map approach to identify pharmacologic agents in the Broad Institute Build02 database that had gene expression signatures that were inversely related ('negatively connected') with our CFA signature. To test the predictive nature of the Connectivity Map methodology, we tested phenoxybenzamine (an alpha adrenergic receptor antagonist) - one of the most negatively connected compounds identified in this database - for analgesic activity in the CFA model. Our results indicate that at 10 mg/kg, phenoxybenzamine demonstrated analgesia comparable to that of Naproxen in this model. Evaluation of phenoxybenzamine-induced analgesia in the current study lends support to the utility of the Connectivity Map approach for identifying compounds with analgesic properties in the CFA model.

cis sequence effects on gene expression

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Jacobs Kevin

2007-08-01

Full Text Available Abstract Background Sequence and transcriptional variability within and between individuals are typically studied independently. The joint analysis of sequence and gene expression variation (genetical genomics provides insight into the role of linked sequence variation in the regulation of gene expression. We investigated the role of sequence variation in cis on gene expression (cis sequence effects in a group of genes commonly studied in cancer research in lymphoblastoid cell lines. We estimated the proportion of genes exhibiting cis sequence effects and the proportion of gene expression variation explained by cis sequence effects using three different analytical approaches, and compared our results to the literature. Results We generated gene expression profiling data at N = 697 candidate genes from N = 30 lymphoblastoid cell lines for this study and used available candidate gene resequencing data at N = 552 candidate genes to identify N = 30 candidate genes with sufficient variance in both datasets for the investigation of cis sequence effects. We used two additive models and the haplotype phylogeny scanning approach of Templeton (Tree Scanning to evaluate association between individual SNPs, all SNPs at a gene, and diplotypes, with log-transformed gene expression. SNPs and diplotypes at eight candidate genes exhibited statistically significant (p cis sequence effects in our study, respectively. Conclusion Based on analysis of our results and the extant literature, one in four genes exhibits significant cis sequence effects, and for these genes, about 30% of gene expression variation is accounted for by cis sequence variation. Despite diverse experimental approaches, the presence or absence of significant cis sequence effects is largely supported by previously published studies.

The relationship among gene expression, the evolution of gene dosage, and the rate of protein evolution.

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Jean-François Gout

2010-05-01

Full Text Available The understanding of selective constraints affecting genes is a major issue in biology. It is well established that gene expression level is a major determinant of the rate of protein evolution, but the reasons for this relationship remain highly debated. Here we demonstrate that gene expression is also a major determinant of the evolution of gene dosage: the rate of gene losses after whole genome duplications in the Paramecium lineage is negatively correlated to the level of gene expression, and this relationship is not a byproduct of other factors known to affect the fate of gene duplicates. This indicates that changes in gene dosage are generally more deleterious for highly expressed genes. This rule also holds for other taxa: in yeast, we find a clear relationship between gene expression level and the fitness impact of reduction in gene dosage. To explain these observations, we propose a model based on the fact that the optimal expression level of a gene corresponds to a trade-off between the benefit and cost of its expression. This COSTEX model predicts that selective pressure against mutations changing gene expression level or affecting the encoded protein should on average be stronger in highly expressed genes and hence that both the frequency of gene loss and the rate of protein evolution should correlate negatively with gene expression. Thus, the COSTEX model provides a simple and common explanation for the general relationship observed between the level of gene expression and the different facets of gene evolution.

Bayesian assignment of gene ontology terms to gene expression experiments

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Sykacek, P.

2012-01-01

Motivation: Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment. Results: This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways. Availability: Source code under GPL license is available from the author. Contact: peter.sykacek@boku.ac.at PMID:22962488

Bayesian assignment of gene ontology terms to gene expression experiments.

Science.gov (United States)

Sykacek, P

2012-09-15

Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment. This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways. Source code under GPL license is available from the author. peter.sykacek@boku.ac.at.

Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

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Liu Yufeng

2011-01-01

Full Text Available Abstract Background Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR to neoadjuvant chemotherapy were also built using this approach. Results We identified statistically significant prognostic models for relapse-free survival (RFS at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR predictions for the entire population. Conclusions Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA

Neighboring Genes Show Correlated Evolution in Gene Expression

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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

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

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Gao Shouguo

2011-08-01

Full Text Available Abstract Background Bayesian Network (BN is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable. Results We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information. Conclusion our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.

Stress, and pathogen response gene expression in modeled microgravity

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Sundaresan, Alamelu; Pellis, Neal R.

2006-01-01

Purpose: Immune suppression in microgravity has been well documented. With the advent of human exploration and long-term space travel, the immune system of the astronaut must be optimally maintained. It is important to investigate the expression patterns of cytokine genes, because they are directly related to immune response. Heat shock proteins (HSPs), also called stress proteins, are a group of proteins that are present in the cells of every life form. These proteins are induced when a cell responds to stressors such as heat, cold and oxygen deprivation. Microgravity is another stressor that may regulate HSPs. Heat shock proteins trigger immune response through activities that occur both inside the cell (intracellular) and outside the cell (extracellular). Knowledge about these two gene groups could lead to establishment of a blueprint of the immune response and adaptation-related genes in the microgravity environment. Methods: Human peripheral blood cells were cultured in 1g (T flask) and modeled microgravity (MMG, rotating-wall vessel) for 24 and 72 hours. Cell samples were collected and subjected to gene array analysis using the Affymetrix HG_U95 array. Data was collected and subjected to a two-way analysis of variance. The genes related to immune and stress responses were analyzed. Results and Conclusions: HSP70 was up-regulated by more than two fold in microgravity culture, while HSP90 was significantly down-regulated. HSP70 is not typically expressed in all kinds of cells, but it is expressed at high levels in stress conditions. HSP70 participates in translation, protein translocation, proteolysis and protein folding, suppressing aggregation and reactivating denatured proteins. Increased serum HSP70 levels correlate with a better outcome for heat-stroke or severe trauma patients. At the same time, elevated serum levels of HSP70 have been detected in patients with peripheral or renal vascular disease. HSP90 has been identified in the cytosol, nucleus and

Long-term transfer and expression of the human beta-globin gene in a mouse transplant model.

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Raftopoulos, H; Ward, M; Leboulch, P; Bank, A

1997-11-01

Somatic gene therapy of hemoglobinopathies depends initially on the demonstration of safe, efficient gene transfer and long-term, high-level expression of the transferred human beta-globin gene in animal models. We have used a beta-globin gene/beta-locus control region retroviral vector containing several modifications to optimize gene transfer and expression in a mouse transplant model. In this report we show that transplantation of beta-globin-transduced hematopoietic cells into lethally irradiated mice leads to the continued presence of the gene up to 8 months posttransplantation. The transferred human beta-globin gene is detected in 3 of 5 mice surviving long term (>4 months) transplanted with bone marrow cells transduced with high-titer virus. Southern blotting confirms the presence of the unrearranged 5.1-kb human beta-globin gene-containing provirus in 2 of these mice. In addition, long-term expression of the transferred gene is seen in 2 mice at levels of 5% and 20% that of endogenous murine beta-globin at 6 and 8 months posttransplantation. We further document stem cell transduction by the successful transfer and high-level expression of the human beta-globin gene from mice transduced 9 months earlier into irradiated secondary recipient mice. These results demonstrate high-level, long-term somatic human beta-globin gene transfer into the hematopoietic stem cells of an animal for the first time, and suggest the potential feasibility of a retroviral gene therapy approach to sickle cell disease and the beta thalassemias.

GeneCAT--novel webtools that combine BLAST and co-expression analyses

DEFF Research Database (Denmark)

Mutwil, Marek; Obro, Jens; Willats, William G T

2008-01-01

The gene co-expression analysis toolbox (GeneCAT) introduces several novel microarray data analyzing tools. First, the multigene co-expression analysis, combined with co-expressed gene networks, provides a more powerful data mining technique than standard, single-gene co-expression analysis. Second...... orthologs in the plant model organisms Arabidopsis thaliana and Hordeum vulgare (Barley). GeneCAT is equipped with expression data for the model plant A. thaliana, and first to introduce co-expression mining tools for the monocot Barley. GeneCAT is available at http://genecat.mpg.de....

Changes in gene expression and cellular architecture in an ovarian cancer progression model.

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Amy L Creekmore

Full Text Available BACKGROUND: Ovarian cancer is the fifth leading cause of cancer deaths among women. Early stage disease often remains undetected due the lack of symptoms and reliable biomarkers. The identification of early genetic changes could provide insights into novel signaling pathways that may be exploited for early detection and treatment. METHODOLOGY/PRINCIPAL FINDINGS: Mouse ovarian surface epithelial (MOSE cells were used to identify stage-dependent changes in gene expression levels and signal transduction pathways by mouse whole genome microarray analyses and gene ontology. These cells have undergone spontaneous transformation in cell culture and transitioned from non-tumorigenic to intermediate and aggressive, malignant phenotypes. Significantly changed genes were overrepresented in a number of pathways, most notably the cytoskeleton functional category. Concurrent with gene expression changes, the cytoskeletal architecture became progressively disorganized, resulting in aberrant expression or subcellular distribution of key cytoskeletal regulatory proteins (focal adhesion kinase, α-actinin, and vinculin. The cytoskeletal disorganization was accompanied by altered patterns of serine and tyrosine phosphorylation as well as changed expression and subcellular localization of integral signaling intermediates APC and PKCβII. CONCLUSIONS/SIGNIFICANCE: Our studies have identified genes that are aberrantly expressed during MOSE cell neoplastic progression. We show that early stage dysregulation of actin microfilaments is followed by progressive disorganization of microtubules and intermediate filaments at later stages. These stage-specific, step-wise changes provide further insights into the time and spatial sequence of events that lead to the fully transformed state since these changes are also observed in aggressive human ovarian cancer cell lines independent of their histological type. Moreover, our studies support a link between aberrant cytoskeleton

Learning Gene Expression Through Modelling and Argumentation. A Case Study Exploring the Connections Between the Worlds of Knowledge

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Puig, Blanca; Ageitos, Noa; Jiménez-Aleixandre, María Pilar

2017-12-01

There is emerging interest on the interactions between modelling and argumentation in specific contexts, such as genetics learning. It has been suggested that modelling might help students understand and argue on genetics. We propose modelling gene expression as a way to learn molecular genetics and diseases with a genetic component. The study is framed in Tiberghien's (2000) two worlds of knowledge, the world of "theories & models" and the world of "objects & events", adding a third component, the world of representations. We seek to examine how modelling and argumentation interact and connect the three worlds of knowledge while modelling gene expression. It is a case study of 10th graders learning about diseases with a genetic component. The research questions are as follows: (1) What argumentative and modelling operations do students enact in the process of modelling gene expression? Specifically, which operations allow connecting the three worlds of knowledge? (2) What are the interactions between modelling and argumentation in modelling gene expression? To what extent do these interactions help students connect the three worlds of knowledge and modelling gene expression? The argumentative operation of using evidence helps students to relate the three worlds of knowledge, enacted in all the connections. It seems to be a relationship among the number of interactions between modelling and argumentation, the connections between world of knowledge and students' capacity to develop a more sophisticated representation. Despite this is a case study, this approach of analysis reveals potentialities for a deeper understanding of learning genetics though scientific practices.

Microarray analysis of gene expression by skeletal muscle of three mouse models of Kennedy disease/spinal bulbar muscular atrophy.

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Kaiguo Mo

2010-09-01

Full Text Available Emerging evidence implicates altered gene expression within skeletal muscle in the pathogenesis of Kennedy disease/spinal bulbar muscular atrophy (KD/SBMA. We therefore broadly characterized gene expression in skeletal muscle of three independently generated mouse models of this disease. The mouse models included a polyglutamine expanded (polyQ AR knock-in model (AR113Q, a polyQ AR transgenic model (AR97Q, and a transgenic mouse that overexpresses wild type AR solely in skeletal muscle (HSA-AR. HSA-AR mice were included because they substantially reproduce the KD/SBMA phenotype despite the absence of polyQ AR.We performed microarray analysis of lower hindlimb muscles taken from these three models relative to wild type controls using high density oligonucleotide arrays. All microarray comparisons were made with at least 3 animals in each condition, and only those genes having at least 2-fold difference and whose coefficient of variance was less than 100% were considered to be differentially expressed. When considered globally, there was a similar overlap in gene changes between the 3 models: 19% between HSA-AR and AR97Q, 21% between AR97Q and AR113Q, and 17% between HSA-AR and AR113Q, with 8% shared by all models. Several patterns of gene expression relevant to the disease process were observed. Notably, patterns of gene expression typical of loss of AR function were observed in all three models, as were alterations in genes involved in cell adhesion, energy balance, muscle atrophy and myogenesis. We additionally measured changes similar to those observed in skeletal muscle of a mouse model of Huntington's Disease, and to those common to muscle atrophy from diverse causes.By comparing patterns of gene expression in three independent models of KD/SBMA, we have been able to identify candidate genes that might mediate the core myogenic features of KD/SBMA.

Validation of suitable reference genes for expression studies in different pilocarpine-induced models of mesial temporal lobe epilepsy.

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Thalita Ewellyn Batista Sales Marques

Full Text Available It is well recognized that the reference gene in a RT-qPCR should be properly validated to ensure that gene expression is unaffected by the experimental condition. We investigated eight potential reference genes in two different pilocarpine PILO-models of mesial temporal lobe epilepsy (MTLE performing a stability expression analysis using geNorm, NormFinder and BestKepeer softwares. Then, as a validation strategy, we conducted a relative expression analysis of the Gfap gene. Our results indicate that in the systemic PILO-model Actb, Gapdh, Rplp1, Tubb2a and Polr1a mRNAs were highly stable in hippocampus of rats from all experimental and control groups, whereas Gusb revealed to be the most variable one. In fact, we observed that using Gusb for normalization, the relative mRNA levels of the Gfap gene differed from those obtained with stable genes. On the contrary, in the intrahippocampal PILO-model, all softwares included Gusb as a stable gene, whereas B2m was indicated as the worst candidate gene. The results obtained for the other reference genes were comparable to those observed for the systemic Pilo-model. The validation of these data by the analysis of the relative expression of Gfap showed that the upregulation of the Gfap gene in the hippocampus of rats sacrificed 24 hours after status epilepticus (SE was undetected only when B2m was used as the normalizer. These findings emphasize that a gene that is stable in one pathology model may not be stable in a different experimental condition related to the same pathology and therefore, the choice of reference genes depends on study design.

Roles of Solvent Accessibility and Gene Expression in Modeling Protein Sequence Evolution

OpenAIRE

Kuangyu Wang; Shuhui Yu; Xiang Ji; Clemens Lakner; Alexander Griffing; Jeffrey L. Thorne

2015-01-01

Models of protein evolution tend to ignore functional constraints, although structural constraints are sometimes incorporated. Here we propose a probabilistic framework for codon substitution that evaluates joint effects of relative solvent accessibility (RSA), a structural constraint; and gene expression, a functional constraint. First, we explore the relationship between RSA and codon usage at the genomic scale as well as at the individual gene scale. Motivated by these results, we construc...

Reference gene selection for quantitative gene expression studies during biological invasions: A test on multiple genes and tissues in a model ascidian Ciona savignyi.

Science.gov (United States)

Huang, Xuena; Gao, Yangchun; Jiang, Bei; Zhou, Zunchun; Zhan, Aibin

2016-01-15

As invasive species have successfully colonized a wide range of dramatically different local environments, they offer a good opportunity to study interactions between species and rapidly changing environments. Gene expression represents one of the primary and crucial mechanisms for rapid adaptation to local environments. Here, we aim to select reference genes for quantitative gene expression analysis based on quantitative Real-Time PCR (qRT-PCR) for a model invasive ascidian, Ciona savignyi. We analyzed the stability of ten candidate reference genes in three tissues (siphon, pharynx and intestine) under two key environmental stresses (temperature and salinity) in the marine realm based on three programs (geNorm, NormFinder and delta Ct method). Our results demonstrated only minor difference for stability rankings among the three methods. The use of different single reference gene might influence the data interpretation, while multiple reference genes could minimize possible errors. Therefore, reference gene combinations were recommended for different tissues - the optimal reference gene combination for siphon was RPS15 and RPL17 under temperature stress, and RPL17, UBQ and TubA under salinity treatment; for pharynx, TubB, TubA and RPL17 were the most stable genes under temperature stress, while TubB, TubA and UBQ were the best under salinity stress; for intestine, UBQ, RPS15 and RPL17 were the most reliable reference genes under both treatments. Our results suggest that the necessity of selection and test of reference genes for different tissues under varying environmental stresses. The results obtained here are expected to reveal mechanisms of gene expression-mediated invasion success using C. savignyi as a model species. Copyright © 2015 Elsevier B.V. All rights reserved.

Identifying Regulatory Patterns at the 3'end Regions of Over-expressed and Under-expressed Genes

KAUST Repository

Othoum, Ghofran K

2013-05-01

Promoters, neighboring regulatory regions and those extending further upstream of the 5’end of genes, are considered one of the main components affecting the expression status of genes in a specific phenotype. More recently research by Chen et al. (2006, 2012) and Mapendano et al. (2010) demonstrated that the 3’end regulatory regions of genes also influence gene expression. However, the association between the regulatory regions surrounding 3’end of genes and their over- or under-expression status in a particular phenotype has not been systematically studied. The aim of this study is to ascertain if regulatory regions surrounding the 3’end of genes contain sufficient regulatory information to correlate genes with their expression status in a particular phenotype. Over- and under-expressed ovarian cancer (OC) genes were used as a model. Exploratory analysis of the 3’end regions were performed by transforming the annotated regions using principal component analysis (PCA), followed by clustering the transformed data thereby achieving a clear separation of genes with different expression status. Additionally, several classification algorithms such as Naïve Bayes, Random Forest and Support Vector Machine (SVM) were tested with different parameter settings to analyze the discriminatory capacity of the 3’end regions of genes related to their gene expression status. The best performance was achieved using the SVM classification model with 10-fold cross-validation that yielded an accuracy of 98.4%, sensitivity of 99.5% and specificity of 92.5%. For gene expression status for newly available instances, based on information derived from the 3’end regions, an SVM predictive model was developed with 10-fold cross-validation that yielded an accuracy of 67.0%, sensitivity of 73.2% and specificity of 61.0%. Moreover, building an SVM with polynomial kernel model to PCA transformed data yielded an accuracy of 83.1%, sensitivity of 92.5% and specificity of 74.8% using

Identifying Regulatory Patterns at the 3'end Regions of Over-expressed and Under-expressed Genes

KAUST Repository

Othoum, Ghofran K

2013-01-01

Promoters, neighboring regulatory regions and those extending further upstream of the 5’end of genes, are considered one of the main components affecting the expression status of genes in a specific phenotype. More recently research by Chen et al. (2006, 2012) and Mapendano et al. (2010) demonstrated that the 3’end regulatory regions of genes also influence gene expression. However, the association between the regulatory regions surrounding 3’end of genes and their over- or under-expression status in a particular phenotype has not been systematically studied. The aim of this study is to ascertain if regulatory regions surrounding the 3’end of genes contain sufficient regulatory information to correlate genes with their expression status in a particular phenotype. Over- and under-expressed ovarian cancer (OC) genes were used as a model. Exploratory analysis of the 3’end regions were performed by transforming the annotated regions using principal component analysis (PCA), followed by clustering the transformed data thereby achieving a clear separation of genes with different expression status. Additionally, several classification algorithms such as Naïve Bayes, Random Forest and Support Vector Machine (SVM) were tested with different parameter settings to analyze the discriminatory capacity of the 3’end regions of genes related to their gene expression status. The best performance was achieved using the SVM classification model with 10-fold cross-validation that yielded an accuracy of 98.4%, sensitivity of 99.5% and specificity of 92.5%. For gene expression status for newly available instances, based on information derived from the 3’end regions, an SVM predictive model was developed with 10-fold cross-validation that yielded an accuracy of 67.0%, sensitivity of 73.2% and specificity of 61.0%. Moreover, building an SVM with polynomial kernel model to PCA transformed data yielded an accuracy of 83.1%, sensitivity of 92.5% and specificity of 74.8% using

Stochastic modeling for the expression of a gene regulated by competing transcription factors.

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Hsih-Te Yang

Full Text Available It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data.

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data

OpenAIRE

Ezer, Daphne; Moignard, Victoria; G?ttgens, Berthold; Adryan, Boris

2016-01-01

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete ...

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression

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Mao Yu

2009-07-01

Full Text Available Abstract Background The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression. Results In this investigation we studied gene pair co-expression change by using a stochastic process model for approximating the underlying dynamic procedure of the co-expression change during cancer progression. Also, we presented a novel analytical method named 'Stochastic process model for Identifying differentially co-expressed Gene pair' (SIG method. This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous. From these data sets, 428,582 gene pairs and 303,992 gene pairs were identified respectively. Afterwards, we used two different current statistical methods to the same data sets, which were developed to identify gene pair differential co-expression and did not consider cancer progression in algorithm. We then compared these results from three different perspectives: progression analysis, gene pair identification effectiveness analysis, and pathway enrichment analysis. Statistical methods were used to quantify the quality and performance of these different perspectives. They included: Re-identification Scale (RS and Progression Score (PS in progression analysis, True Positive Rate (TPR in gene pair analysis, and Pathway Enrichment Score (PES in pathway analysis. Our results show small values of RS and large values of PS, TPR, and PES; thus, suggesting that gene pairs identified by the SIG method are highly correlated with cancer progression, and highly enriched in disease-specific pathways. From

Global gene expression analysis in a mouse model for Norrie disease: late involvement of photoreceptor cells.

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Lenzner, Steffen; Prietz, Sandra; Feil, Silke; Nuber, Ulrike A; Ropers, H-Hilger; Berger, Wolfgang

2002-09-01

Mutations in the NDP gene give rise to a variety of eye diseases, including classic Norrie disease (ND), X-linked exudative vitreoretinopathy (EVRX), retinal telangiectasis (Coats disease), and advanced retinopathy of prematurity (ROP). The gene product is a cystine-knot-containing extracellular signaling molecule of unknown function. In the current study, gene expression was determined in a mouse model of ND, to unravel disease-associated mechanisms at the molecular level. Gene transcription in the eyes of 2-year-old Ndp knockout mice was compared with that in the eyes of age-matched wild-type control animals, by means of cDNA subtraction and microarrays. Clones (n = 3072) from the cDNA subtraction libraries were spotted onto glass slides and hybridized with fluorescently labeled RNA-derived targets. More than 230 differentially expressed clones were sequenced, and their expression patterns were verified by virtual Northern blot analysis. Numerous gene transcripts that are absent or downregulated in the eye of Ndp knockout mice are photoreceptor cell specific. In younger Ndp knockout mice (up to 1 year old), however, all these transcripts were found to be expressed at normal levels. The identification of numerous photoreceptor cell-specific transcripts with a reduced expression in 2-year-old, but not in young, Ndp knockout mice indicates that normal gene expression in these light-sensitive cells of mutant mice is established and maintained over a long period and that rods and cones are affected relatively late in the mouse model of ND. Obviously, the absence of the Ndp gene product is not compatible with long-term survival of photoreceptor cells in the mouse.

Backward-stochastic-differential-equation approach to modeling of gene expression.

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Shamarova, Evelina; Chertovskih, Roman; Ramos, Alexandre F; Aguiar, Paulo

2017-03-01

In this article, we introduce a backward method to model stochastic gene expression and protein-level dynamics. The protein amount is regarded as a diffusion process and is described by a backward stochastic differential equation (BSDE). Unlike many other SDE techniques proposed in the literature, the BSDE method is backward in time; that is, instead of initial conditions it requires the specification of end-point ("final") conditions, in addition to the model parametrization. To validate our approach we employ Gillespie's stochastic simulation algorithm (SSA) to generate (forward) benchmark data, according to predefined gene network models. Numerical simulations show that the BSDE method is able to correctly infer the protein-level distributions that preceded a known final condition, obtained originally from the forward SSA. This makes the BSDE method a powerful systems biology tool for time-reversed simulations, allowing, for example, the assessment of the biological conditions (e.g., protein concentrations) that preceded an experimentally measured event of interest (e.g., mitosis, apoptosis, etc.).

Normalisation genes for expression analyses in the brown alga model Ectocarpus siliculosus

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Rousvoal Sylvie

2008-08-01

Full Text Available Abstract Background Brown algae are plant multi-cellular organisms occupying most of the world coasts and are essential actors in the constitution of ecological niches at the shoreline. Ectocarpus siliculosus is an emerging model for brown algal research. Its genome has been sequenced, and several tools are being developed to perform analyses at different levels of cell organization, including transcriptomic expression analyses. Several topics, including physiological responses to osmotic stress and to exposure to contaminants and solvents are being studied in order to better understand the adaptive capacity of brown algae to pollution and environmental changes. A series of genes that can be used to normalise expression analyses is required for these studies. Results We monitored the expression of 13 genes under 21 different culture conditions. These included genes encoding proteins and factors involved in protein translation (ribosomal protein 26S, EF1alpha, IF2A, IF4E and protein degradation (ubiquitin, ubiquitin conjugating enzyme or folding (cyclophilin, and proteins involved in both the structure of the cytoskeleton (tubulin alpha, actin, actin-related proteins and its trafficking function (dynein, as well as a protein implicated in carbon metabolism (glucose 6-phosphate dehydrogenase. The stability of their expression level was assessed using the Ct range, and by applying both the geNorm and the Normfinder principles of calculation. Conclusion Comparisons of the data obtained with the three methods of calculation indicated that EF1alpha (EF1a was the best reference gene for normalisation. The normalisation factor should be calculated with at least two genes, alpha tubulin, ubiquitin-conjugating enzyme or actin-related proteins being good partners of EF1a. Our results exclude actin as a good normalisation gene, and, in this, are in agreement with previous studies in other organisms.

A stochastic approach to multi-gene expression dynamics

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Ochiai, T.; Nacher, J.C.; Akutsu, T.

2005-01-01

In the last years, tens of thousands gene expression profiles for cells of several organisms have been monitored. Gene expression is a complex transcriptional process where mRNA molecules are translated into proteins, which control most of the cell functions. In this process, the correlation among genes is crucial to determine the specific functions of genes. Here, we propose a novel multi-dimensional stochastic approach to deal with the gene correlation phenomena. Interestingly, our stochastic framework suggests that the study of the gene correlation requires only one theoretical assumption-Markov property-and the experimental transition probability, which characterizes the gene correlation system. Finally, a gene expression experiment is proposed for future applications of the model

On theoretical models of gene expression evolution with random genetic drift and natural selection.

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Osamu Ogasawara

2009-11-01

Full Text Available The relative contributions of natural selection and random genetic drift are a major source of debate in the study of gene expression evolution, which is hypothesized to serve as a bridge from molecular to phenotypic evolution. It has been suggested that the conflict between views is caused by the lack of a definite model of the neutral hypothesis, which can describe the long-run behavior of evolutionary change in mRNA abundance. Therefore previous studies have used inadequate analogies with the neutral prediction of other phenomena, such as amino acid or nucleotide sequence evolution, as the null hypothesis of their statistical inference.In this study, we introduced two novel theoretical models, one based on neutral drift and the other assuming natural selection, by focusing on a common property of the distribution of mRNA abundance among a variety of eukaryotic cells, which reflects the result of long-term evolution. Our results demonstrated that (1 our models can reproduce two independently found phenomena simultaneously: the time development of gene expression divergence and Zipf's law of the transcriptome; (2 cytological constraints can be explicitly formulated to describe long-term evolution; (3 the model assuming that natural selection optimized relative mRNA abundance was more consistent with previously published observations than the model of optimized absolute mRNA abundances.The models introduced in this study give a formulation of evolutionary change in the mRNA abundance of each gene as a stochastic process, on the basis of previously published observations. This model provides a foundation for interpreting observed data in studies of gene expression evolution, including identifying an adequate time scale for discriminating the effect of natural selection from that of random genetic drift of selectively neutral variations.

Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

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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

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.

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Boris P Hejblum

2015-06-01

Full Text Available Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial, and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.

High-level transfer and long-term expression of the human beta-globin gene in a mouse transplant model.

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Raftopoulos, H; Ward, M; Bank, A

1998-06-30

Insertion of a normally functioning human beta-globin gene into the hematopoietic stem cells (HSC) of patients with beta-thalassemia may be an effective approach to the therapy of this disorder. Safe, efficient gene transfer and long-term, high-level expression of the transferred human beta-globin gene in animal models are prerequisites for HSC somatic gene therapy. We have recently shown for the first time that, using a modified beta-globin retroviral vector in a mouse transplant model, long-term, high-level expression of a transferred human beta-globin gene is possible. The human beta-globin gene continues to be detected up to eight months post-transplantation of beta-globin-transduced hematopoietic cells into lethally irradiated mice. The transferred human beta-globin gene is detected in three of five mice surviving long-term (> 4 months) transplanted with bone marrow cells transduced with high-titer virus. The unrearranged 5.1 kb human beta-globin gene-containing provirus is seen by Southern blotting in two of these mice. More importantly, long-term expression of the transferred gene is seen in two mice at levels of 5% and 20% that of endogenous murine beta-globin. We document stem cell transduction by showing continued high-level expression of the human beta-globin gene in secondarily transplanted recipient mice. These results provide evidence of HSC transduction with a human beta-globin gene in animals and demonstrate that retroviral-mediated unrearranged human beta-globin gene transfer leads to a high level of human beta-globin gene expression in the long term for the first time. A gene therapy strategy may be a feasible therapeutic approach to the beta-thalassemias if consistent human beta-globin gene transfer and expression into HSC can be achieved.

The functional landscape of mouse gene expression

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Zhang Wen

2004-12-01

Full Text Available Abstract Background Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function. Results We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis. Conclusions We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics.

Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.

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Hu, Ming; Zhu, Yu; Taylor, Jeremy M G; Liu, Jun S; Qin, Zhaohui S

2012-01-01

RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. In particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize RNA-Seq data and quantify gene expressions, ineffective. In this study, we propose a Poisson mixed-effects (POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Since the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level. POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html. yuzhu@purdue.edu; zhaohui.qin@emory.edu Supplementary data are available at Bioinformatics online.

CDX2 gene expression in acute lymphoblastic leukemia

International Nuclear Information System (INIS)

Arnaoaut, H.H.; Mokhtar, D.A.; Samy, R.M.; Omar, Sh.A.; Khames, S.A.

2014-01-01

CDX genes are classically known as regulators of axial elongation during early embryogenesis. An unsuspected role for CDX genes has been revealed during hematopoietic development. The CDX gene family member CDX2 belongs to the most frequent aberrantly expressed proto-oncogenes in human acute leukemias and is highly leukemogenic in experimental models. We used reversed transcriptase polymerase chain reaction (RT-PCR) to determine the expression level of CDX2 gene in 30 pediatric patients with acute lymphoblastic leukemia (ALL) at diagnosis and 30 healthy volunteers. ALL patients were followed up to detect minimal residual disease (MRD) on days 15 and 42 of induction. We found that CDX2 gene was expressed in 50% of patients and not expressed in controls. Associations between gene expression and different clinical and laboratory data of patients revealed no impact on different findings. With follow up, we could not confirm that CDX2 expression had a prognostic significance.

Heterotopic expression of class B floral homeotic genes supports a modified ABC model for tulip (Tulipa gesneriana).

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Kanno, Akira; Saeki, Hiroshi; Kameya, Toshiaki; Saedler, Heinz; Theissen, Günter

2003-07-01

In higher eudicotyledonous angiosperms the floral organs are typically arranged in four different whorls, containing sepals, petals, stamens and carpels. According to the ABC model, the identity of these organs is specified by floral homeotic genes of class A, A+B, B+C and C, respectively. In contrast to the sepal and petal whorls of eudicots, the perianths of many plants from the Liliaceae family have two outer whorls of almost identical petaloid organs, called tepals. To explain the Liliaceae flower morphology, van Tunen et al. (1993) proposed a modified ABC model, exemplified with tulip. According to this model, class B genes are not only expressed in whorls 2 and 3, but also in whorl 1. Thus the organs of both whorls 1 and 2 express class A plus class B genes and, therefore, get the same petaloid identity. To test this modified ABC model we have cloned and characterized putative class B genes from tulip. Two DEF- and one GLO-like gene were identified, named TGDEFA, TGDEFB and TGGLO. Northern hybridization analysis showed that all of these genes are expressed in whorls 1, 2 and 3 (outer and inner tepals and stamens), thus corroborating the modified ABC model. In addition, these experiments demonstrated that TGGLO is also weakly expressed in carpels, leaves, stems and bracts. Gel retardation assays revealed that TGGLO alone binds to DNA as a homodimer. In contrast, TGDEFA and TGDEFB cannot homodimerize, but make heterodimers with PI. Homodimerization of GLO-like protein has also been reported for lily, suggesting that this phenomenon is conserved within Liliaceae plants or even monocot species.

Analytical results for a stochastic model of gene expression with arbitrary partitioning of proteins

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Tschirhart, Hugo; Platini, Thierry

2018-05-01

In biophysics, the search for analytical solutions of stochastic models of cellular processes is often a challenging task. In recent work on models of gene expression, it was shown that a mapping based on partitioning of Poisson arrivals (PPA-mapping) can lead to exact solutions for previously unsolved problems. While the approach can be used in general when the model involves Poisson processes corresponding to creation or degradation, current applications of the method and new results derived using it have been limited to date. In this paper, we present the exact solution of a variation of the two-stage model of gene expression (with time dependent transition rates) describing the arbitrary partitioning of proteins. The methodology proposed makes full use of the PPA-mapping by transforming the original problem into a new process describing the evolution of three biological switches. Based on a succession of transformations, the method leads to a hierarchy of reduced models. We give an integral expression of the time dependent generating function as well as explicit results for the mean, variance, and correlation function. Finally, we discuss how results for time dependent parameters can be extended to the three-stage model and used to make inferences about models with parameter fluctuations induced by hidden stochastic variables.

A comparative gene expression database for invertebrates

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Ormestad Mattias

2011-08-01

Full Text Available Abstract Background As whole genome and transcriptome sequencing gets cheaper and faster, a great number of 'exotic' animal models are emerging, rapidly adding valuable data to the ever-expanding Evo-Devo field. All these new organisms serve as a fantastic resource for the research community, but the sheer amount of data, some published, some not, makes detailed comparison of gene expression patterns very difficult to summarize - a problem sometimes even noticeable within a single lab. The need to merge existing data with new information in an organized manner that is publicly available to the research community is now more necessary than ever. Description In order to offer a homogenous way of storing and handling gene expression patterns from a variety of organisms, we have developed the first web-based comparative gene expression database for invertebrates that allows species-specific as well as cross-species gene expression comparisons. The database can be queried by gene name, developmental stage and/or expression domains. Conclusions This database provides a unique tool for the Evo-Devo research community that allows the retrieval, analysis and comparison of gene expression patterns within or among species. In addition, this database enables a quick identification of putative syn-expression groups that can be used to initiate, among other things, gene regulatory network (GRN projects.

Stably Expressed Genes Involved in Basic Cellular Functions.

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Kejian Wang

Full Text Available Stably Expressed Genes (SEGs whose expression varies within a narrow range may be involved in core cellular processes necessary for basic functions. To identify such genes, we re-analyzed existing RNA-Seq gene expression profiles across 11 organs at 4 developmental stages (from immature to old age in both sexes of F344 rats (n = 4/group; 320 samples. Expression changes (calculated as the maximum expression / minimum expression for each gene of >19000 genes across organs, ages, and sexes ranged from 2.35 to >109-fold, with a median of 165-fold. The expression of 278 SEGs was found to vary ≤4-fold and these genes were significantly involved in protein catabolism (proteasome and ubiquitination, RNA transport, protein processing, and the spliceosome. Such stability of expression was further validated in human samples where the expression variability of the homologous human SEGs was significantly lower than that of other genes in the human genome. It was also found that the homologous human SEGs were generally less subject to non-synonymous mutation than other genes, as would be expected of stably expressed genes. We also found that knockout of SEG homologs in mouse models was more likely to cause complete preweaning lethality than non-SEG homologs, corroborating the fundamental roles played by SEGs in biological development. Such stably expressed genes and pathways across life-stages suggest that tight control of these processes is important in basic cellular functions and that perturbation by endogenous (e.g., genetics or exogenous agents (e.g., drugs, environmental factors may cause serious adverse effects.

Multitarget Effects of Danqi Pill on Global Gene Expression Changes in Myocardial Ischemia

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Qiyan Wang

2018-01-01

Full Text Available Danqi pill (DQP is a widely prescribed traditional Chinese medicine (TCM in the treatment of cardiovascular diseases. The objective of this study is to systematically characterize altered gene expression pattern induced by myocardial ischemia (MI in a rat model and to investigate the effects of DQP on global gene expression. Global mRNA expression was measured. Differentially expressed genes among the sham group, model group, and DQP group were analyzed. The gene ontology enrichment analysis and pathway analysis of differentially expressed genes were carried out. We quantified 10,813 genes. Compared with the sham group, expressions of 339 genes were upregulated and 177 genes were downregulated in the model group. The upregulated genes were enriched in extracellular matrix organization, response to wounding, and defense response pathways. Downregulated genes were enriched in fatty acid metabolism, pyruvate metabolism, PPAR signaling pathways, and so forth. This indicated that energy metabolic disorders occurred in rats with MI. In the DQP group, expressions of genes in the altered pathways were regulated back towards normal levels. DQP reversed expression of 313 of the 516 differentially expressed genes in the model group. This study provides insight into the multitarget mechanism of TCM in the treatment of complex diseases.

Hypothalamic gene expression of appetite regulators in a cancer-cachectic mouse model [Dataset 1

NARCIS (Netherlands)

Dwarkasing, Jvalini; Dijk, Francina J.; Boekschoten, Mark; Faber, Joyce; Argilès, Josep M.; Lavianio, Alessandro; Muller, Michael; Witkamp, Renger; Norren, van Klaske

2013-01-01

Appetite is frequently affected in cancer patients, leading to anorexia and consequently insufficient food intake. In this study, we report on hypothalamic gene expression profile of a cancer cachectic mouse model with increased food intake. In this model, mice bearing C26 colon adenocarcinoma have

Hypothalamic gene expression of appetite regulators in a cancer-cachectic mouse model [Dataset 2

NARCIS (Netherlands)

Dwarkasing, Jvalini; Dijk, Francina J.; Boekschoten, Mark; Faber, Joyce; Argilès, Josep M.; Lavianio, Alessandro; Muller, Michael; Witkamp, Renger; Norren, van Klaske

2013-01-01

Appetite is frequently affected in cancer patients, leading to anorexia and consequently insufficient food intake. In this study, we report on hypothalamic gene expression profile of a cancer cachectic mouse model with increased food intake. In this model, mice bearing C26 colon adenocarcinoma have

Gene expression profiling via LongSAGE in a non-model plant species: a case study in seeds of Brassica napus

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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.

RT-PCR detection of Candida albicans ALS gene expression in the reconstituted human epithelium (RHE) model of oral candidiasis and in model biofilms.

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Green, Clayton B; Cheng, Georgina; Chandra, Jyotsna; Mukherjee, Pranab; Ghannoum, Mahmoud A; Hoyer, Lois L

2004-02-01

An RT-PCR assay was developed to analyse expression patterns of genes in the Candida albicans ALS (agglutinin-like sequence) family. Inoculation of a reconstituted human buccal epithelium (RHE) model of mucocutaneous candidiasis with strain SC5314 showed destruction of the epithelial layer by C. albicans and also formation of an upper fungal layer that had characteristics similar to a biofilm. RT-PCR analysis of total RNA samples extracted from C. albicans-inoculated buccal RHE showed that ALS1, ALS2, ALS3, ALS4, ALS5 and ALS9 were consistently detected over time as destruction of the RHE progressed. Detection of transcripts from ALS7, and particularly from ALS6, was more sporadic, but not associated with a strictly temporal pattern. The expression pattern of ALS genes in C. albicans cultures used to inoculate the RHE was similar to that observed in the RHE model, suggesting that contact of C. albicans with buccal RHE does little to alter ALS gene expression. RT-PCR analysis of RNA samples extracted from model denture and catheter biofilms showed similar gene expression patterns to the buccal RHE specimens. Results from the RT-PCR analysis of biofilm RNA specimens were consistent between various C. albicans strains during biofilm development and were comparable to gene expression patterns in planktonic cells. The RT-PCR assay described here will be useful for analysis of human clinical specimens and samples from other disease models. The method will provide further insight into the role of ALS genes and their encoded proteins in the diverse interactions between C. albicans and its host.

Amyloid protein-mediated differential DNA methylation status regulates gene expression in Alzheimer’s disease model cell line

International Nuclear Information System (INIS)

Sung, Hye Youn; Choi, Eun Nam; Ahn Jo, Sangmee; Oh, Seikwan; Ahn, Jung-Hyuck

2011-01-01

Highlights: ► Genome-wide DNA methylation pattern in Alzheimer’s disease model cell line. ► Integrated analysis of CpG methylation and mRNA expression profiles. ► Identify three Swedish mutant target genes; CTIF, NXT2 and DDR2 gene. ► The effect of Swedish mutation on alteration of DNA methylation and gene expression. -- Abstract: The Swedish mutation of amyloid precursor protein (APP-sw) has been reported to dramatically increase beta amyloid production through aberrant cleavage at the beta secretase site, causing early-onset Alzheimer’s disease (AD). DNA methylation has been reported to be associated with AD pathogenesis, but the underlying molecular mechanism of APP-sw-mediated epigenetic alterations in AD pathogenesis remains largely unknown. We analyzed genome-wide interplay between promoter CpG DNA methylation and gene expression in an APP-sw-expressing AD model cell line. To identify genes whose expression was regulated by DNA methylation status, we performed integrated analysis of CpG methylation and mRNA expression profiles, and identified three target genes of the APP-sw mutant; hypomethylated CTIF (CBP80/CBP20-dependent translation initiation factor) and NXT2 (nuclear exporting factor 2), and hypermethylated DDR2 (discoidin domain receptor 2). Treatment with the demethylating agent 5-aza-2′-deoxycytidine restored mRNA expression of these three genes, implying methylation-dependent transcriptional regulation. The profound alteration in the methylation status was detected at the −435, −295, and −271 CpG sites of CTIF, and at the −505 to −341 region in the promoter of DDR2. In the promoter region of NXT2, only one CpG site located at −432 was differentially unmethylated in APP-sw cells. Thus, we demonstrated the effect of the APP-sw mutation on alteration of DNA methylation and subsequent gene expression. This epigenetic regulatory mechanism may contribute to the pathogenesis of AD.

Analysis of changes in hepatic gene expression in a murine model of tolerance to acetaminophen hepatotoxicity (autoprotection)

International Nuclear Information System (INIS)

O'Connor, Meeghan A.; Koza-Taylor, Petra; Campion, Sarah N.; Aleksunes, Lauren M.; Gu, Xinsheng; Enayetallah, Ahmed E.; Lawton, Michael P.; Manautou, José E.

2014-01-01

Pretreatment of mice with a low hepatotoxic dose of acetaminophen (APAP) results in resistance to a subsequent, higher dose of APAP. This mouse model, termed APAP autoprotection was used here to identify differentially expressed genes and cellular pathways that could contribute to this development of resistance to hepatotoxicity. Male C57BL/6J mice were pretreated with APAP (400 mg/kg) and then challenged 48 h later with 600 mg APAP/kg. Livers were obtained 4 or 24 h later and total hepatic RNA was isolated and hybridized to Affymetrix Mouse Genome MU430 2 GeneChip. Statistically significant genes were determined and gene expression changes were also interrogated using the Causal Reasoning Engine (CRE). Extensive literature review narrowed our focus to methionine adenosyl transferase-1 alpha (MAT1A), nuclear factor (erythroid-derived 2)-like 2 (Nrf2), flavin-containing monooxygenase 3 (Fmo3) and galectin-3 (Lgals3). Down-regulation of MAT1A could lead to decreases in S-adenosylmethionine (SAMe), which is known to protect against APAP toxicity. Nrf2 activation is expected to play a role in protective adaptation. Up-regulation of Lgals3, one of the genes supporting the Nrf2 hypothesis, can lead to suppression of apoptosis and reduced mitochondrial dysfunction. Fmo3 induction suggests the involvement of an enzyme not known to metabolize APAP in the development of tolerance to APAP toxicity. Subsequent quantitative RT-PCR and immunochemical analysis confirmed the differential expression of some of these genes in the APAP autoprotection model. In conclusion, our genomics strategy identified cellular pathways that might further explain the molecular basis for APAP autoprotection. - Highlights: • Differential expression of genes in mice resistant to acetaminophen hepatotoxicity. • Increased gene expression of Flavin-containing monooxygenase 3 and Galectin-3. • Decrease in MAT1A expression and compensatory hepatocellular regeneration. • Two distinct gene expression

Analysis of changes in hepatic gene expression in a murine model of tolerance to acetaminophen hepatotoxicity (autoprotection)

Energy Technology Data Exchange (ETDEWEB)

O' Connor, Meeghan A., E-mail: meeghan.oconnor@boehringer-ingelheim.com [Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269-3092 (United States); Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, CT 06877-0368 (United States); Koza-Taylor, Petra, E-mail: petra.h.koza-taylor@pfizer.com [Pfizer Inc., Groton, CT 06340 (United States); Campion, Sarah N., E-mail: sarah.campion@pfizer.com [Pfizer Inc., Groton, CT 06340 (United States); Aleksunes, Lauren M., E-mail: aleksunes@eohsi.rutgers.edu [Rutgers University, Department of Pharmacology and Toxicology, Environmental and Occupational Health Sciences Institute, Piscataway, NJ 08854 (United States); Gu, Xinsheng, E-mail: xinsheng.gu@uconn.edu [Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269-3092 (United States); Enayetallah, Ahmed E., E-mail: ahmed.enayetallah@pfizer.com [Pfizer Inc., Groton, CT 06340 (United States); Lawton, Michael P., E-mail: michael.lawton@pfizer.com [Pfizer Inc., Groton, CT 06340 (United States); Manautou, José E., E-mail: jose.manautou@uconn.edu [Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269-3092 (United States)

2014-01-01

Pretreatment of mice with a low hepatotoxic dose of acetaminophen (APAP) results in resistance to a subsequent, higher dose of APAP. This mouse model, termed APAP autoprotection was used here to identify differentially expressed genes and cellular pathways that could contribute to this development of resistance to hepatotoxicity. Male C57BL/6J mice were pretreated with APAP (400 mg/kg) and then challenged 48 h later with 600 mg APAP/kg. Livers were obtained 4 or 24 h later and total hepatic RNA was isolated and hybridized to Affymetrix Mouse Genome MU430{sub 2} GeneChip. Statistically significant genes were determined and gene expression changes were also interrogated using the Causal Reasoning Engine (CRE). Extensive literature review narrowed our focus to methionine adenosyl transferase-1 alpha (MAT1A), nuclear factor (erythroid-derived 2)-like 2 (Nrf2), flavin-containing monooxygenase 3 (Fmo3) and galectin-3 (Lgals3). Down-regulation of MAT1A could lead to decreases in S-adenosylmethionine (SAMe), which is known to protect against APAP toxicity. Nrf2 activation is expected to play a role in protective adaptation. Up-regulation of Lgals3, one of the genes supporting the Nrf2 hypothesis, can lead to suppression of apoptosis and reduced mitochondrial dysfunction. Fmo3 induction suggests the involvement of an enzyme not known to metabolize APAP in the development of tolerance to APAP toxicity. Subsequent quantitative RT-PCR and immunochemical analysis confirmed the differential expression of some of these genes in the APAP autoprotection model. In conclusion, our genomics strategy identified cellular pathways that might further explain the molecular basis for APAP autoprotection. - Highlights: • Differential expression of genes in mice resistant to acetaminophen hepatotoxicity. • Increased gene expression of Flavin-containing monooxygenase 3 and Galectin-3. • Decrease in MAT1A expression and compensatory hepatocellular regeneration. • Two distinct gene

Genetic Variants Contribute to Gene Expression Variability in Humans

Science.gov (United States)

Hulse, Amanda M.; Cai, James J.

2013-01-01

Expression quantitative trait loci (eQTL) studies have established convincing relationships between genetic variants and gene expression. Most of these studies focused on the mean of gene expression level, but not the variance of gene expression level (i.e., gene expression variability). In the present study, we systematically explore genome-wide association between genetic variants and gene expression variability in humans. We adapt the double generalized linear model (dglm) to simultaneously fit the means and the variances of gene expression among the three possible genotypes of a biallelic SNP. The genomic loci showing significant association between the variances of gene expression and the genotypes are termed expression variability QTL (evQTL). Using a data set of gene expression in lymphoblastoid cell lines (LCLs) derived from 210 HapMap individuals, we identify cis-acting evQTL involving 218 distinct genes, among which 8 genes, ADCY1, CTNNA2, DAAM2, FERMT2, IL6, PLOD2, SNX7, and TNFRSF11B, are cross-validated using an extra expression data set of the same LCLs. We also identify ∼300 trans-acting evQTL between >13,000 common SNPs and 500 randomly selected representative genes. We employ two distinct scenarios, emphasizing single-SNP and multiple-SNP effects on expression variability, to explain the formation of evQTL. We argue that detecting evQTL may represent a novel method for effectively screening for genetic interactions, especially when the multiple-SNP influence on expression variability is implied. The implication of our results for revealing genetic mechanisms of gene expression variability is discussed. PMID:23150607

Experimental and Modeling Approaches for Understanding the Effect of Gene Expression Noise in Biological Development

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David M. Holloway

2018-04-01

Full Text Available Biological development involves numerous chemical and physical processes which must act in concert to reliably produce a cell, a tissue, or a body. To be successful, the developing organism must be robust to variability at many levels, such as the environment (e.g., temperature, moisture, upstream information (such as long-range positional information gradients, or intrinsic noise due to the stochastic nature of low concentration chemical kinetics. The latter is especially relevant to the regulation of gene expression in cell differentiation. The temporal stochasticity of gene expression has been studied in single celled organisms for nearly two decades, but only recently have techniques become available to gather temporally-resolved data across spatially-distributed gene expression patterns in developing multicellular organisms. These demonstrate temporal noisy “bursting” in the number of gene transcripts per cell, raising the question of how the transcript number defining a particular cell type is produced, such that one cell type can reliably be distinguished from a neighboring cell of different type along a tissue boundary. Stochastic spatio-temporal modeling of tissue-wide expression patterns can identify signatures for specific types of gene regulation, which can be used to extract regulatory mechanism information from experimental time series. This Perspective focuses on using this type of approach to study gene expression noise during the anterior-posterior segmentation of the fruit fly embryo. Advances in experimental and theoretical techniques will lead to an increasing quantification of expression noise that can be used to understand how regulatory mechanisms contribute to embryonic robustness across a range of developmental processes.

GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature

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Ning Ye

2015-01-01

Full Text Available The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data

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List Markus

2014-06-01

Full Text Available Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks.

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Daifeng Wang

2016-10-01

Full Text Available Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs, cellular growth factors and microRNAs. A subsystem's gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally-e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the "state" and "control" in the model refer to its own (internal and another subsystem's (external gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model's parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs, seeing the degree to which these can be accounted for by orthologous (internal versus species-specific (external TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with

Modelling time course gene expression data with finite mixtures of linear additive models.

Science.gov (United States)

Grün, Bettina; Scharl, Theresa; Leisch, Friedrich

2012-01-15

A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time. The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).

Noise minimization in eukaryotic gene expression.

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Hunter B Fraser

2004-06-01

Full Text Available All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or "noise." Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.

Noise minimization in eukaryotic gene expression

Energy Technology Data Exchange (ETDEWEB)

Fraser, Hunter B.; Hirsh, Aaron E.; Giaever, Guri; Kumm, Jochen; Eisen, Michael B.

2004-01-15

All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or noise. Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.

Noise minimization in eukaryotic gene expression

International Nuclear Information System (INIS)

Fraser, Hunter B.; Hirsh, Aaron E.; Giaever, Guri; Kumm, Jochen; Eisen, Michael B.

2004-01-01

All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or noise. Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection

Differential in vivo gene expression of major Leptospira proteins in resistant or susceptible animal models.

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Matsui, Mariko; Soupé, Marie-Estelle; Becam, Jérôme; Goarant, Cyrille

2012-09-01

Transcripts of Leptospira 16S rRNA, FlaB, LigB, LipL21, LipL32, LipL36, LipL41, and OmpL37 were quantified in the blood of susceptible (hamsters) and resistant (mice) animal models of leptospirosis. We first validated adequate reference genes and then evaluated expression patterns in vivo compared to in vitro cultures. LipL32 expression was downregulated in vivo and differentially regulated in resistant and susceptible animals. FlaB expression was also repressed in mice but not in hamsters. In contrast, LigB and OmpL37 were upregulated in vivo. Thus, we demonstrated that a virulent strain of Leptospira differentially adapts its gene expression in the blood of infected animals.

A compendium of canine normal tissue gene expression.

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Joseph Briggs

Full Text Available BACKGROUND: Our understanding of disease is increasingly informed by changes in gene expression between normal and abnormal tissues. The release of the canine genome sequence in 2005 provided an opportunity to better understand human health and disease using the dog as clinically relevant model. Accordingly, we now present the first genome-wide, canine normal tissue gene expression compendium with corresponding human cross-species analysis. METHODOLOGY/PRINCIPAL FINDINGS: The Affymetrix platform was utilized to catalogue gene expression signatures of 10 normal canine tissues including: liver, kidney, heart, lung, cerebrum, lymph node, spleen, jejunum, pancreas and skeletal muscle. The quality of the database was assessed in several ways. Organ defining gene sets were identified for each tissue and functional enrichment analysis revealed themes consistent with known physio-anatomic functions for each organ. In addition, a comparison of orthologous gene expression between matched canine and human normal tissues uncovered remarkable similarity. To demonstrate the utility of this dataset, novel canine gene annotations were established based on comparative analysis of dog and human tissue selective gene expression and manual curation of canine probeset mapping. Public access, using infrastructure identical to that currently in use for human normal tissues, has been established and allows for additional comparisons across species. CONCLUSIONS/SIGNIFICANCE: These data advance our understanding of the canine genome through a comprehensive analysis of gene expression in a diverse set of tissues, contributing to improved functional annotation that has been lacking. Importantly, it will be used to inform future studies of disease in the dog as a model for human translational research and provides a novel resource to the community at large.

Widespread ectopic expression of olfactory receptor genes

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Yanai Itai

2006-05-01

Full Text Available Abstract Background Olfactory receptors (ORs are the largest gene family in the human genome. Although they are expected to be expressed specifically in olfactory tissues, some ectopic expression has been reported, with special emphasis on sperm and testis. The present study systematically explores the expression patterns of OR genes in a large number of tissues and assesses the potential functional implication of such ectopic expression. Results We analyzed the expression of hundreds of human and mouse OR transcripts, via EST and microarray data, in several dozens of human and mouse tissues. Different tissues had specific, relatively small OR gene subsets which had particularly high expression levels. In testis, average expression was not particularly high, and very few highly expressed genes were found, none corresponding to ORs previously implicated in sperm chemotaxis. Higher expression levels were more common for genes with a non-OR genomic neighbor. Importantly, no correlation in expression levels was detected for human-mouse orthologous pairs. Also, no significant difference in expression levels was seen between intact and pseudogenized ORs, except for the pseudogenes of subfamily 7E which has undergone a human-specific expansion. Conclusion The OR superfamily as a whole, show widespread, locus-dependent and heterogeneous expression, in agreement with a neutral or near neutral evolutionary model for transcription control. These results cannot reject the possibility that small OR subsets might play functional roles in different tissues, however considerable care should be exerted when offering a functional interpretation for ectopic OR expression based only on transcription information.

Multiobjective optimization in Gene Expression Programming for Dew Point

OpenAIRE

Shroff, Siddharth; Dabhi, Vipul

2013-01-01

The processes occurring in climatic change evolution and their variations play a major role in environmental engineering. Different techniques are used to model the relationship between temperatures, dew point and relative humidity. Gene expression programming is capable of modelling complex realities with great accuracy, allowing, at the same time, the extraction of knowledge from the evolved models compared to other learning algorithms. This research aims to use Gene Expression Programming ...

Transcriptome analyses and differential gene expression in a non-model fish species with alternative mating tactics.

Science.gov (United States)

Schunter, Celia; Vollmer, Steven V; Macpherson, Enrique; Pascual, Marta

2014-02-28

Social dominance is important for the reproductive success of males in many species. In the black-faced blenny (Tripterygion delaisi) during the reproductive season, some males change color and invest in nest making and defending a territory, whereas others do not change color and 'sneak' reproductions when females lay their eggs. Using RNAseq, we profiled differential gene expression between the brains of territorial males, sneaker males, and females to study the molecular signatures of male dimorphism. We found that more genes were differentially expressed between the two male phenotypes than between males and females, suggesting that during the reproductive period phenotypic plasticity is a more important factor in differential gene expression than sexual dimorphism. The territorial male overexpresses genes related to synaptic plasticity and the sneaker male overexpresses genes involved in differentiation and development. Previously suggested candidate genes for social dominance in the context of alternative mating strategies seem to be predominantly species-specific. We present a list of novel genes which are differentially expressed in Tripterygion delaisi. This is the first genome-wide study for a molecular non-model species in the context of alternative mating strategies and provides essential information for further studies investigating the molecular basis of social dominance.

A random variance model for detection of differential gene expression in small microarray experiments.

Science.gov (United States)

Wright, George W; Simon, Richard M

2003-12-12

Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true. We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives. This method is incorporated into BRB-ArrayTools version 3.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html). ftp://linus.nci.nih.gov/pub/techreport/RVM_supplement.pdf

Cross-species comparison of biological themes and underlying genes on a global gene expression scale in a mouse model of colorectal liver metastasis and in clinical specimens

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Schirmacher Peter

2008-09-01

Full Text Available Abstract Background Invasion-related genes over-expressed by tumor cells as well as by reacting host cells represent promising drug targets for anti-cancer therapy. Such candidate genes need to be validated in appropriate animal models. Results This study examined the suitability of a murine model (CT26/Balb/C of colorectal liver metastasis to represent clinical liver metastasis specimens using a global gene expression approach. Cross-species similarity was examined between pure liver, liver invasion, tumor invasion and pure tumor compartments through overlap of up-regulated genes and gene ontology (GO-based biological themes on the level of single GO-terms and of condensed GO-term families. Three out of four GO-term families were conserved in a compartment-specific way between the species: secondary metabolism (liver, invasion (invasion front, and immune response (invasion front and liver. Among the individual GO-terms over-represented in the invasion compartments in both species were "extracellular matrix", "cell motility", "cell adhesion" and "antigen presentation" indicating that typical invasion related processes are operating in both species. This was reflected on the single gene level as well, as cross-species overlap of potential target genes over-expressed in the combined invasion front compartments reached up to 36.5%. Generally, histopathology and gene expression correlated well as the highest single gene overlap was found to be 44% in syn-compartmental comparisons (liver versus liver whereas cross-compartmental overlaps were much lower (e.g. liver versus tumor: 9.7%. However, single gene overlap was surprisingly high in some cross-compartmental comparisons (e.g. human liver invasion compartment and murine tumor invasion compartment: 9.0% despite little histolopathologic similarity indicating that invasion relevant genes are not necessarily confined to histologically defined compartments. Conclusion In summary, cross

What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast.

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Artémis Llamosi

2016-02-01

Full Text Available Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals. Here, we propose a quantitative modeling framework that attributes specific parameter values to single cells for a standard model of gene expression. We combine high quality single-cell measurements of the response of yeast cells to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population, and then derive specific parameters for individual cells. The analysis of single-cell parameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growth rate, mother-daughter relationships is, at least partially, captured by the parameter values of gene expression models (e.g. rates of transcription, translation and degradation. Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity.

Mathematical model of flagella gene expression dynamics in Salmonella enterica serovar typhimurium

OpenAIRE

Jain, Kirti; Pradhan, Amit; Mokashi, Chaitanya; Saini, Supreet

2015-01-01

Flagellar assembly in Salmonella is controlled by an intricate genetic and biochemical network. This network comprises of a number of inter-connected feedback loops, which control the assembly process dynamically. Critical among these are the FliA–FlgM feedback, FliZ-mediated positive feedback, and FliT-mediated negative feedback. In this work, we develop a mathematical model to track the dynamics of flagellar gene expression in Salmonella. Analysis of our model demonstrates that the network ...

Identification of Human HK Genes and Gene Expression Regulation Study in Cancer from Transcriptomics Data Analysis

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Zhang, Zhang; Liu, Jingxing; Wu, Jiayan; Yu, Jun

2013-01-01

The regulation of gene expression is essential for eukaryotes, as it drives the processes of cellular differentiation and morphogenesis, leading to the creation of different cell types in multicellular organisms. RNA-Sequencing (RNA-Seq) provides researchers with a powerful toolbox for characterization and quantification of transcriptome. Many different human tissue/cell transcriptome datasets coming from RNA-Seq technology are available on public data resource. The fundamental issue here is how to develop an effective analysis method to estimate expression pattern similarities between different tumor tissues and their corresponding normal tissues. We define the gene expression pattern from three directions: 1) expression breadth, which reflects gene expression on/off status, and mainly concerns ubiquitously expressed genes; 2) low/high or constant/variable expression genes, based on gene expression level and variation; and 3) the regulation of gene expression at the gene structure level. The cluster analysis indicates that gene expression pattern is higher related to physiological condition rather than tissue spatial distance. Two sets of human housekeeping (HK) genes are defined according to cell/tissue types, respectively. To characterize the gene expression pattern in gene expression level and variation, we firstly apply improved K-means algorithm and a gene expression variance model. We find that cancer-associated HK genes (a HK gene is specific in cancer group, while not in normal group) are expressed higher and more variable in cancer condition than in normal condition. Cancer-associated HK genes prefer to AT-rich genes, and they are enriched in cell cycle regulation related functions and constitute some cancer signatures. The expression of large genes is also avoided in cancer group. These studies will help us understand which cell type-specific patterns of gene expression differ among different cell types, and particularly for cancer. PMID:23382867

High-resolution gene expression profiling using RNA sequencing in patients with inflammatory bowel disease and in mouse models of colitis

DEFF Research Database (Denmark)

Holgersen, Kristine; Kutlu, Burak; Fox, Brian

2015-01-01

pathways and assess the similarity between the experimental models and human disease. RNA sequencing was performed on colon biopsies from CD patients, UC patients and non-IBD controls. Genes shown to be significantly dysregulated in human IBD were used to study gene expression in colons from a piroxicam......Proper interpretation of data from preclinical animal studies requires a thorough knowledge about the pathophysiology of both the human disease and animal models. In this study, the expression of IBD-associated genes was characterised in mouse models of colitis to examine the underlying molecular......-accelerated colitis interleukin-10 knockout (PAC IL-10 k.o.), an adoptive transfer (AdTr) and a dextran sulfate sodium (DSS) colitis mouse model. 92 out of 115 literature-defined genes linked to IBD were significantly differentially expressed in inflamed mucosa of CD and/or UC patients compared with non-IBD controls...

FARO server: Meta-analysis of gene expression by matching gene expression signatures to a compendium of public gene expression data

DEFF Research Database (Denmark)

Manijak, Mieszko P.; Nielsen, Henrik Bjørn

2011-01-01

circumvented by instead matching gene expression signatures to signatures of other experiments. FINDINGS: To facilitate this we present the Functional Association Response by Overlap (FARO) server, that match input signatures to a compendium of 242 gene expression signatures, extracted from more than 1700...... Arabidopsis microarray experiments. CONCLUSIONS: Hereby we present a publicly available tool for robust characterization of Arabidopsis gene expression experiments which can point to similar experimental factors in other experiments. The server is available at http://www.cbs.dtu.dk/services/faro/....

Conditional gene expression in the mouse using a Sleeping Beauty gene-trap transposon

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Hackett Perry B

2006-06-01

Full Text Available Abstract Background Insertional mutagenesis techniques with transposable elements have been popular among geneticists studying model organisms from E. coli to Drosophila and, more recently, the mouse. One such element is the Sleeping Beauty (SB transposon that has been shown in several studies to be an effective insertional mutagen in the mouse germline. SB transposon vector studies have employed different functional elements and reporter molecules to disrupt and report the expression of endogenous mouse genes. We sought to generate a transposon system that would be capable of reporting the expression pattern of a mouse gene while allowing for conditional expression of a gene of interest in a tissue- or temporal-specific pattern. Results Here we report the systematic development and testing of a transposon-based gene-trap system incorporating the doxycycline-repressible Tet-Off (tTA system that is capable of activating the expression of genes under control of a Tet response element (TRE promoter. We demonstrate that the gene trap system is fully functional in vitro by introducing the "gene-trap tTA" vector into human cells by transposition and identifying clones that activate expression of a TRE-luciferase transgene in a doxycycline-dependent manner. In transgenic mice, we mobilize gene-trap tTA vectors, discover parameters that can affect germline mobilization rates, and identify candidate gene insertions to demonstrate the in vivo functionality of the vector system. We further demonstrate that the gene-trap can act as a reporter of endogenous gene expression and it can be coupled with bioluminescent imaging to identify genes with tissue-specific expression patterns. Conclusion Akin to the GAL4/UAS system used in the fly, we have made progress developing a tool for mutating and revealing the expression of mouse genes by generating the tTA transactivator in the presence of a secondary TRE-regulated reporter molecule. A vector like the gene

Digital gene expression analysis of gene expression differences within Brassica diploids and allopolyploids.

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Jiang, Jinjin; Wang, Yue; Zhu, Bao; Fang, Tingting; Fang, Yujie; Wang, Youping

2015-01-27

Brassica includes many successfully cultivated crop species of polyploid origin, either by ancestral genome triplication or by hybridization between two diploid progenitors, displaying complex repetitive sequences and transposons. The U's triangle, which consists of three diploids and three amphidiploids, is optimal for the analysis of complicated genomes after polyploidization. Next-generation sequencing enables the transcriptome profiling of polyploids on a global scale. We examined the gene expression patterns of three diploids (Brassica rapa, B. nigra, and B. oleracea) and three amphidiploids (B. napus, B. juncea, and B. carinata) via digital gene expression analysis. In total, the libraries generated between 5.7 and 6.1 million raw reads, and the clean tags of each library were mapped to 18547-21995 genes of B. rapa genome. The unambiguous tag-mapped genes in the libraries were compared. Moreover, the majority of differentially expressed genes (DEGs) were explored among diploids as well as between diploids and amphidiploids. Gene ontological analysis was performed to functionally categorize these DEGs into different classes. The Kyoto Encyclopedia of Genes and Genomes analysis was performed to assign these DEGs into approximately 120 pathways, among which the metabolic pathway, biosynthesis of secondary metabolites, and peroxisomal pathway were enriched. The non-additive genes in Brassica amphidiploids were analyzed, and the results indicated that orthologous genes in polyploids are frequently expressed in a non-additive pattern. Methyltransferase genes showed differential expression pattern in Brassica species. Our results provided an understanding of the transcriptome complexity of natural Brassica species. The gene expression changes in diploids and allopolyploids may help elucidate the morphological and physiological differences among Brassica species.

Expression and localization of regenerating gene I in a rat liver regeneration model

International Nuclear Information System (INIS)

Wang Jingshu; Koyota, Souichi; Zhou, Xiaoping; Ueno, Yasuharu; Ma Li; Kawagoe, Masami; Koizumi, Yukio; Okamoto, Hiroshi; Sugiyama, Toshihiro

2009-01-01

Regenerating gene (Reg) I has been identified as a regenerative/proliferative factor for pancreatic islet cells. We examined Reg I expression in the regenerating liver of a rat model that had been administered 2-acetylaminofluorene and treated with 70% partial hepatectomy (2-AAF/PH model), where hepatocyte and cholangiocyte proliferation was suppressed and the hepatic stem cells and/or hepatic progenitor cells were activated. In a detailed time course study of activation of hepatic stem cells in the 2-AAF/PH model, utilizing immunofluorescence staining with antibodies of Reg I and other cell-type-specific markers, we found that Reg I-expressing cells are present in the bile ductules and increased during regeneration. Reg I-expressing cells were colocalized with CK19, OV6, and AFP. These results demonstrate that Reg I is significantly upregulated in the liver of the 2-AAF/PH rat model, accompanied by the formation of bile ductules during liver regeneration.

Three gene expression vector sets for concurrently expressing multiple genes in Saccharomyces cerevisiae.

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Ishii, Jun; Kondo, Takashi; Makino, Harumi; Ogura, Akira; Matsuda, Fumio; Kondo, Akihiko

2014-05-01

Yeast has the potential to be used in bulk-scale fermentative production of fuels and chemicals due to its tolerance for low pH and robustness for autolysis. However, expression of multiple external genes in one host yeast strain is considerably labor-intensive due to the lack of polycistronic transcription. To promote the metabolic engineering of yeast, we generated systematic and convenient genetic engineering tools to express multiple genes in Saccharomyces cerevisiae. We constructed a series of multi-copy and integration vector sets for concurrently expressing two or three genes in S. cerevisiae by embedding three classical promoters. The comparative expression capabilities of the constructed vectors were monitored with green fluorescent protein, and the concurrent expression of genes was monitored with three different fluorescent proteins. Our multiple gene expression tool will be helpful to the advanced construction of genetically engineered yeast strains in a variety of research fields other than metabolic engineering. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Bayesian median regression for temporal gene expression data

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Yu, Keming; Vinciotti, Veronica; Liu, Xiaohui; 't Hoen, Peter A. C.

2007-09-01

Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.

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

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Zhu Jin

2009-07-01

Full Text Available Abstract Background Liver cancr is a heterogeneous disease in terms of etiology, biologic and clinical behavior. Very little is known about how many genes concur at the molecular level of tumor development, progression and aggressiveness. To explore the key genes involved in the development of liver cancer, we established a rat model induced by diethylnitrosamine to investigate the gene expression profiles of liver tissues during the transition to cirrhosis and carcinoma. Methods A rat model of liver cancer induced by diethylnitrosamine was established. The cirrhotic tissue, the dysplasia nodules, the early cancerous nodules and the cancerous nodules from the rats with lung metastasis were chosen to compare with liver tissue of normal rats to investigate the differential expression genes between them. Affymetrix GeneChip Rat 230 2.0 arrays were used throughout. The real-time quantity PCR was used to verify the expression of some differential expression genes in tissues. Results The pathological changes that occurred in the livers of diethylnitrosamine-treated rats included non-specific injury, fibrosis and cirrhosis, dysplastic nodules, early cancerous nodules and metastasis. There are 349 upregulated and 345 downregulated genes sharing among the above chosen tissues when compared with liver tissue of normal rats. The deregulated genes play various roles in diverse processes such as metabolism, transport, cell proliferation, apoptosis, cell adhesion, angiogenesis and so on. Among which, 41 upregulated and 27 downregulated genes are associated with inflammatory response, immune response and oxidative stress. Twenty-four genes associated with glutathione metabolism majorly participating oxidative stress were deregulated in the development of liver cancer. There were 19 members belong to CYP450 family downregulated, except CYP2C40 upregulated. Conclusion In this study, we provide the global gene expression profiles during the development and

Validating Internal Control Genes for the Accurate Normalization of qPCR Expression Analysis of the Novel Model Plant Setaria viridis.

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Julia Lambret-Frotté

Full Text Available Employing reference genes to normalize the data generated with quantitative PCR (qPCR can increase the accuracy and reliability of this method. Previous results have shown that no single housekeeping gene can be universally applied to all experiments. Thus, the identification of a suitable reference gene represents a critical step of any qPCR analysis. Setaria viridis has recently been proposed as a model system for the study of Panicoid grasses, a crop family of major agronomic importance. Therefore, this paper aims to identify suitable S. viridis reference genes that can enhance the analysis of gene expression in this novel model plant. The first aim of this study was the identification of a suitable RNA extraction method that could retrieve a high quality and yield of RNA. After this, two distinct algorithms were used to assess the gene expression of fifteen different candidate genes in eighteen different samples, which were divided into two major datasets, the developmental and the leaf gradient. The best-ranked pair of reference genes from the developmental dataset included genes that encoded a phosphoglucomutase and a folylpolyglutamate synthase; genes that encoded a cullin and the same phosphoglucomutase as above were the most stable genes in the leaf gradient dataset. Additionally, the expression pattern of two target genes, a SvAP3/PI MADS-box transcription factor and the carbon-fixation enzyme PEPC, were assessed to illustrate the reliability of the chosen reference genes. This study has shown that novel reference genes may perform better than traditional housekeeping genes, a phenomenon which has been previously reported. These results illustrate the importance of carefully validating reference gene candidates for each experimental set before employing them as universal standards. Additionally, the robustness of the expression of the target genes may increase the utility of S. viridis as a model for Panicoid grasses.

Large scale gene expression meta-analysis reveals tissue-specific, sex-biased gene expression in humans

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Benjamin Mayne

2016-10-01

Full Text Available The severity and prevalence of many diseases are known to differ between the sexes. Organ specific sex-biased gene expression may underpin these and other sexually dimorphic traits. To further our understanding of sex differences in transcriptional regulation, we performed meta-analyses of sex biased gene expression in multiple human tissues. We analysed 22 publicly available human gene expression microarray data sets including over 2500 samples from 15 different tissues and 9 different organs. Briefly, by using an inverse-variance method we determined the effect size difference of gene expression between males and females. We found the greatest sex differences in gene expression in the brain, specifically in the anterior cingulate cortex, (1818 genes, followed by the heart (375 genes, kidney (224 genes, colon (218 genes and thyroid (163 genes. More interestingly, we found different parts of the brain with varying numbers and identity of sex-biased genes, indicating that specific cortical regions may influence sexually dimorphic traits. The majority of sex-biased genes in other tissues such as the bladder, liver, lungs and pancreas were on the sex chromosomes or involved in sex hormone production. On average in each tissue, 32% of autosomal genes that were expressed in a sex-biased fashion contained androgen or estrogen hormone response elements. Interestingly, across all tissues, we found approximately two-thirds of autosomal genes that were sex-biased were not under direct influence of sex hormones. To our knowledge this is the largest analysis of sex-biased gene expression in human tissues to date. We identified many sex-biased genes that were not under the direct influence of sex chromosome genes or sex hormones. These may provide targets for future development of sex-specific treatments for diseases.

Expression of venom gene homologs in diverse python tissues suggests a new model for the evolution of snake venom.

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Reyes-Velasco, Jacobo; Card, Daren C; Andrew, Audra L; Shaney, Kyle J; Adams, Richard H; Schield, Drew R; Casewell, Nicholas R; Mackessy, Stephen P; Castoe, Todd A

2015-01-01

Snake venom gene evolution has been studied intensively over the past several decades, yet most previous studies have lacked the context of complete snake genomes and the full context of gene expression across diverse snake tissues. We took a novel approach to studying snake venom evolution by leveraging the complete genome of the Burmese python, including information from tissue-specific patterns of gene expression. We identified the orthologs of snake venom genes in the python genome, and conducted detailed analysis of gene expression of these venom homologs to identify patterns that differ between snake venom gene families and all other genes. We found that venom gene homologs in the python are expressed in many different tissues outside of oral glands, which illustrates the pitfalls of using transcriptomic data alone to define "venom toxins." We hypothesize that the python may represent an ancestral state prior to major venom development, which is supported by our finding that the expansion of venom gene families is largely restricted to highly venomous caenophidian snakes. Therefore, the python provides insight into biases in which genes were recruited for snake venom systems. Python venom homologs are generally expressed at lower levels, have higher variance among tissues, and are expressed in fewer organs compared with all other python genes. We propose a model for the evolution of snake venoms in which venom genes are recruited preferentially from genes with particular expression profile characteristics, which facilitate a nearly neutral transition toward specialized venom system expression. © 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.

Aging and Gene Expression in the Primate Brain

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Fraser, Hunter B.; Khaitovich, Philipp; Plotkin, Joshua B.; Paabo, Svante; Eisen, Michael B.

2005-02-18

It is well established that gene expression levels in many organisms change during the aging process, and the advent of DNA microarrays has allowed genome-wide patterns of transcriptional changes associated with aging to be studied in both model organisms and various human tissues. Understanding the effects of aging on gene expression in the human brain is of particular interest, because of its relation to both normal and pathological neurodegeneration. Here we show that human cerebral cortex, human cerebellum, and chimpanzee cortex each undergo different patterns of age-related gene expression alterations. In humans, many more genes undergo consistent expression changes in the cortex than in the cerebellum; in chimpanzees, many genes change expression with age in cortex, but the pattern of changes in expression bears almost no resemblance to that of human cortex. These results demonstrate the diversity of aging patterns present within the human brain, as well as how rapidly genome-wide patterns of aging can evolve between species; they may also have implications for the oxidative free radical theory of aging, and help to improve our understanding of human neurodegenerative diseases.

Aging and gene expression in the primate brain.

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Hunter B Fraser

2005-09-01

Full Text Available It is well established that gene expression levels in many organisms change during the aging process, and the advent of DNA microarrays has allowed genome-wide patterns of transcriptional changes associated with aging to be studied in both model organisms and various human tissues. Understanding the effects of aging on gene expression in the human brain is of particular interest, because of its relation to both normal and pathological neurodegeneration. Here we show that human cerebral cortex, human cerebellum, and chimpanzee cortex each undergo different patterns of age-related gene expression alterations. In humans, many more genes undergo consistent expression changes in the cortex than in the cerebellum; in chimpanzees, many genes change expression with age in cortex, but the pattern of changes in expression bears almost no resemblance to that of human cortex. These results demonstrate the diversity of aging patterns present within the human brain, as well as how rapidly genome-wide patterns of aging can evolve between species; they may also have implications for the oxidative free radical theory of aging, and help to improve our understanding of human neurodegenerative diseases.

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks

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Gerstein, Mark

2016-01-01

Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem’s gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally–e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the “state” and “control” in the model refer to its own (internal) and another subsystem’s (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model’s parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with

Differential Gene Expression and Aging

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Laurent Seroude

2002-01-01

Full Text Available It has been established that an intricate program of gene expression controls progression through the different stages in development. The equally complex biological phenomenon known as aging is genetically determined and environmentally modulated. This review focuses on the genetic component of aging, with a special emphasis on differential gene expression. At least two genetic pathways regulating organism longevity act by modifying gene expression. Many genes are also subjected to age-dependent transcriptional regulation. Some age-related gene expression changes are prevented by caloric restriction, the most robust intervention that slows down the aging process. Manipulating the expression of some age-regulated genes can extend an organism's life span. Remarkably, the activity of many transcription regulatory elements is linked to physiological age as opposed to chronological age, indicating that orderly and tightly controlled regulatory pathways are active during aging.

Alpha-fetoprotein-targeted reporter gene expression imaging in hepatocellular carcinoma.

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Kim, Kwang Il; Chung, Hye Kyung; Park, Ju Hui; Lee, Yong Jin; Kang, Joo Hyun

2016-07-21

Hepatocellular carcinoma (HCC) is one of the most common cancers in Eastern Asia, and its incidence is increasing globally. Numerous experimental models have been developed to better our understanding of the pathogenic mechanism of HCC and to evaluate novel therapeutic approaches. Molecular imaging is a convenient and up-to-date biomedical tool that enables the visualization, characterization and quantification of biologic processes in a living subject. Molecular imaging based on reporter gene expression, in particular, can elucidate tumor-specific events or processes by acquiring images of a reporter gene's expression driven by tumor-specific enhancers/promoters. In this review, we discuss the advantages and disadvantages of various experimental HCC mouse models and we present in vivo images of tumor-specific reporter gene expression driven by an alpha-fetoprotein (AFP) enhancer/promoter system in a mouse model of HCC. The current mouse models of HCC development are established by xenograft, carcinogen induction and genetic engineering, representing the spectrum of tumor-inducing factors and tumor locations. The imaging analysis approach of reporter genes driven by AFP enhancer/promoter is presented for these different HCC mouse models. Such molecular imaging can provide longitudinal information about carcinogenesis and tumor progression. We expect that clinical application of AFP-targeted reporter gene expression imaging systems will be useful for the detection of AFP-expressing HCC tumors and screening of increased/decreased AFP levels due to disease or drug treatment.

Aberrant Behaviours of Reaction Diffusion Self-organisation Models on Growing Domains in the Presence of Gene Expression Time Delays

KAUST Repository

Seirin Lee, S.

2010-03-23

Turing\\'s pattern formation mechanism exhibits sensitivity to the details of the initial conditions suggesting that, in isolation, it cannot robustly generate pattern within noisy biological environments. Nonetheless, secondary aspects of developmental self-organisation, such as a growing domain, have been shown to ameliorate this aberrant model behaviour. Furthermore, while in-situ hybridisation reveals the presence of gene expression in developmental processes, the influence of such dynamics on Turing\\'s model has received limited attention. Here, we novelly focus on the Gierer-Meinhardt reaction diffusion system considering delays due the time taken for gene expression, while incorporating a number of different domain growth profiles to further explore the influence and interplay of domain growth and gene expression on Turing\\'s mechanism. We find extensive pathological model behaviour, exhibiting one or more of the following: temporal oscillations with no spatial structure, a failure of the Turing instability and an extreme sensitivity to the initial conditions, the growth profile and the duration of gene expression. This deviant behaviour is even more severe than observed in previous studies of Schnakenberg kinetics on exponentially growing domains in the presence of gene expression (Gaffney and Monk in Bull. Math. Biol. 68:99-130, 2006). Our results emphasise that gene expression dynamics induce unrealistic behaviour in Turing\\'s model for multiple choices of kinetics and thus such aberrant modelling predictions are likely to be generic. They also highlight that domain growth can no longer ameliorate the excessive sensitivity of Turing\\'s mechanism in the presence of gene expression time delays. The above, extensive, pathologies suggest that, in the presence of gene expression, Turing\\'s mechanism would generally require a novel and extensive secondary mechanism to control reaction diffusion patterning. © 2010 Society for Mathematical Biology.

Identification and Expression Profiling of Radiation-sensitive Genes Using Plant Model System, Arabidopsis thaliana

International Nuclear Information System (INIS)

Kim, Dong-Sub; Kang, Si-Yong; Lee, Geung-Joo; Kim, Jin-Baek

2008-06-01

The purpose of this study is to characterize genes specifically expressed in response to ionizing energy (gamma-rays) of acute irradiation and elucidate signalling mechanisms via functional analysis of isolated genes in Arabidopsis thaliana. Recent improvements in DNA microarray technologies and bioinformatics have made it possible to look for common features of ionizing radiation-responsive genes and their regulatory regions. It has produced massive quantities of gene expression and other functional genomics data, and its application will increase in plant genomics. In this study, we used oligonucleotide microarrays to detect the Arabidopsis genes expressed differentially by a gamma-irradiation during the vegetative (VT, 21 DAG) and reproductive (RT, 28 DAG) stages. Wild-type (Ler) Arabidopsis was irradiated with gamma-rays with 100 and 800 Gy doses. Among the 21,500 genes represented in the Agilent chip, approximately 13,500 ( ∼ 61.4 %) responsive genes to ν -irradiation were expressed with signal intensity greater than 192 when compared to the combined control (non-irradiated vegetative and reproductive pool). Expression patterns of several radiation inducible genes were confirmed by RT-PCR and Northern blotting. Our microarray results may contribute to an overall understanding of the type and quantities of genes that are expressed by an acute gamma-irradiation. In addition, to investigate the oxidative damage caused by irradiation, RT-PCR analysis for the expression of antioxidant isoenzyme genes, and a Transmission Electron Microscope (TEM) observation for visualizing the H 2 O 2 scavenging activity in leaves were applied

Polycistronic gene expression in Aspergillus niger.

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Schuetze, Tabea; Meyer, Vera

2017-09-25

Genome mining approaches predict dozens of biosynthetic gene clusters in each of the filamentous fungal genomes sequenced so far. However, the majority of these gene clusters still remain cryptic because they are not expressed in their natural host. Simultaneous expression of all genes belonging to a biosynthetic pathway in a heterologous host is one approach to activate biosynthetic gene clusters and to screen the metabolites produced for bioactivities. Polycistronic expression of all pathway genes under control of a single and tunable promoter would be the method of choice, as this does not only simplify cloning procedures, but also offers control on timing and strength of expression. However, polycistronic gene expression is a feature not commonly found in eukaryotic host systems, such as Aspergillus niger. In this study, we tested the suitability of the viral P2A peptide for co-expression of three genes in A. niger. Two genes descend from Fusarium oxysporum and are essential to produce the secondary metabolite enniatin (esyn1, ekivR). The third gene (luc) encodes the reporter luciferase which was included to study position effects. Expression of the polycistronic gene cassette was put under control of the Tet-On system to ensure tunable gene expression in A. niger. In total, three polycistronic expression cassettes which differed in the position of luc were constructed and targeted to the pyrG locus in A. niger. This allowed direct comparison of the luciferase activity based on the position of the luciferase gene. Doxycycline-mediated induction of the Tet-On expression cassettes resulted in the production of one long polycistronic mRNA as proven by Northern analyses, and ensured comparable production of enniatin in all three strains. Notably, gene position within the polycistronic expression cassette matters, as, luciferase activity was lowest at position one and had a comparable activity at positions two and three. The P2A peptide can be used to express at

Inferring gene networks from discrete expression data

KAUST Repository

Zhang, L.; Mallick, B. K.

2013-01-01

graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which

The consequences of chromosomal aneuploidy on gene expression profiles in a cell line model for prostate carcinogenesis.

Science.gov (United States)

Phillips, J L; Hayward, S W; Wang, Y; Vasselli, J; Pavlovich, C; Padilla-Nash, H; Pezullo, J R; Ghadimi, B M; Grossfeld, G D; Rivera, A; Linehan, W M; Cunha, G R; Ried, T

2001-11-15

Here we report the genetic characterization of immortalized prostate epithelial cells before and after conversion to tumorigenicity using molecular cytogenetics and microarray technology. We were particularly interested to analyze the consequences of acquired chromosomal aneuploidies with respect to modifications of gene expression profiles. Compared with nontumorigenic but immortalized prostate epithelium, prostate tumor cell lines showed high levels of chromosomal rearrangements that led to gains of 1p, 5, 11q, 12p, 16q, and 20q and losses of 1pter, 11p, 17, 20p, 21, 22, and Y. Of 5700 unique targets on a 6.5K cDNA microarray, approximately 3% were subject to modification in expression levels; these included GRO-1, -2, IAP-1,- 2, MMP-9, and cyclin D1, which showed increased expression, and TRAIL, BRCA1, and CTNNA, which showed decreased expression. Thirty % of expression changes occurred in regions the genomic copy number of which remained balanced. Of the remainder, 42% of down-regulated and 51% of up-regulated genes mapped to regions present in decreased or increased genomic copy numbers, respectively. A relative gain or loss of a chromosome or chromosomal arm usually resulted in a statistically significant increase or decrease, respectively, in the average expression level of all of the genes on the chromosome. However, of these genes, very few (e.g., 5 of 101 genes on chromosome 11q), and in some instances only two genes (MMP-9 and PROCR on chromosome 20q), were overexpressed by > or =1.7-fold when scored individually. Cluster analysis by gene function suggests that prostate tumorigenesis in these cell line models involves alterations in gene expression that may favor invasion, prevent apoptosis, and promote growth.

Gene expression results in lipopolysaccharide-stimulated monocytes depend significantly on the choice of reference genes

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Øvstebø Reidun

2010-05-01

Full Text Available Abstract Background Gene expression in lipopolysaccharide (LPS-stimulated monocytes is mainly studied by quantitative real-time reverse transcription PCR (RT-qPCR using GAPDH (glyceraldehyde 3-phosphate dehydrogenase or ACTB (beta-actin as reference gene for normalization. Expression of traditional reference genes has been shown to vary substantially under certain conditions leading to invalid results. To investigate whether traditional reference genes are stably expressed in LPS-stimulated monocytes or if RT-qPCR results are dependent on the choice of reference genes, we have assessed and evaluated gene expression stability of twelve candidate reference genes in this model system. Results Twelve candidate reference genes were quantified by RT-qPCR in LPS-stimulated, human monocytes and evaluated using the programs geNorm, Normfinder and BestKeeper. geNorm ranked PPIB (cyclophilin B, B2M (beta-2-microglobulin and PPIA (cyclophilin A as the best combination for gene expression normalization in LPS-stimulated monocytes. Normfinder suggested TBP (TATA-box binding protein and B2M as the best combination. Compared to these combinations, normalization using GAPDH alone resulted in significantly higher changes of TNF-α (tumor necrosis factor-alpha and IL10 (interleukin 10 expression. Moreover, a significant difference in TNF-α expression between monocytes stimulated with equimolar concentrations of LPS from N. meningitides and E. coli, respectively, was identified when using the suggested combinations of reference genes for normalization, but stayed unrecognized when employing a single reference gene, ACTB or GAPDH. Conclusions Gene expression levels in LPS-stimulated monocytes based on RT-qPCR results differ significantly when normalized to a single gene or a combination of stably expressed reference genes. Proper evaluation of reference gene stabiliy is therefore mandatory before reporting RT-qPCR results in LPS-stimulated monocytes.

In silico method for modelling metabolism and gene product expression at genome scale

Energy Technology Data Exchange (ETDEWEB)

Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem; Portnoy, Vasiliy A.; Lewis, Nathan E.; Orth, Jeffrey D.; Rutledge, Alexandra C.; Smith, Richard D.; Adkins, Joshua N.; Zengler, Karsten; Palsson, Bernard O.

2012-07-03

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

Gene expression and 18FDG uptake in atherosclerotic carotid plaques

DEFF Research Database (Denmark)

Pedersen, Sune Folke; Graebe, Martin; Fisker Hag, Anne Mette

2010-01-01

) and an additional ipsilateral internal carotid artery stenosis of greater than 60% were recruited. FDG uptake in the carotids was determined by PET/computed tomography and expressed as mean and maximal standardized uptake values (SUVmean and SUVmax). The atherosclerotic plaques were subsequently recovered...... by carotid endarterectomy. The gene expression of markers of vulnerability - CD68, IL-18, matrix metalloproteinase 9, cathepsin K, GLUT-1, and hexokinase type II (HK2) - were measured in plaques by quantitative PCR. RESULTS: In a multivariate linear regression model, GLUT-1, CD68, cathepsin K, and HK2 gene...... expression remained in the final model as predictive variables of FDG accumulation calculated as SUVmean (R=0.26, PK, and HK2 gene expression as independent predictive variables of FDG accumulation calculated...

Dynamic gene expression analysis in a H1N1 influenza virus mouse pneumonia model.

Science.gov (United States)

Bao, Yanyan; Gao, Yingjie; Shi, Yujing; Cui, Xiaolan

2017-06-01

H1N1, a major pathogenic subtype of influenza A virus, causes a respiratory infection in humans and livestock that can range from a mild infection to more severe pneumonia associated with acute respiratory distress syndrome. Understanding the dynamic changes in the genome and the related functional changes induced by H1N1 influenza virus infection is essential to elucidating the pathogenesis of this virus and thereby determining strategies to prevent future outbreaks. In this study, we filtered the significantly expressed genes in mouse pneumonia using mRNA microarray analysis. Using STC analysis, seven significant gene clusters were revealed, and using STC-GO analysis, we explored the significant functions of these seven gene clusters. The results revealed GOs related to H1N1 virus-induced inflammatory and immune functions, including innate immune response, inflammatory response, specific immune response, and cellular response to interferon-beta. Furthermore, the dynamic regulation relationships of the key genes in mouse pneumonia were revealed by dynamic gene network analysis, and the most important genes were filtered, including Dhx58, Cxcl10, Cxcl11, Zbp1, Ifit1, Ifih1, Trim25, Mx2, Oas2, Cd274, Irgm1, and Irf7. These results suggested that during mouse pneumonia, changes in the expression of gene clusters and the complex interactions among genes lead to significant changes in function. Dynamic gene expression analysis revealed key genes that performed important functions. These results are a prelude to advancements in mouse H1N1 influenza virus infection biology, as well as the use of mice as a model organism for human H1N1 influenza virus infection studies.

Identification and Expression Profiling of Radiation-sensitive Genes Using Plant Model System, Arabidopsis thaliana

Energy Technology Data Exchange (ETDEWEB)

Kim, Dong-Sub; Kang, Si-Yong; Lee, Geung-Joo; Kim, Jin-Baek

2008-06-15

The purpose of this study is to characterize genes specifically expressed in response to ionizing energy (gamma-rays) of acute irradiation and elucidate signalling mechanisms via functional analysis of isolated genes in Arabidopsis thaliana. Recent improvements in DNA microarray technologies and bioinformatics have made it possible to look for common features of ionizing radiation-responsive genes and their regulatory regions. It has produced massive quantities of gene expression and other functional genomics data, and its application will increase in plant genomics. In this study, we used oligonucleotide microarrays to detect the Arabidopsis genes expressed differentially by a gamma-irradiation during the vegetative (VT, 21 DAG) and reproductive (RT, 28 DAG) stages. Wild-type (Ler) Arabidopsis was irradiated with gamma-rays with 100 and 800 Gy doses. Among the 21,500 genes represented in the Agilent chip, approximately 13,500 ({sup {approx}}61.4 %) responsive genes to {nu} -irradiation were expressed with signal intensity greater than 192 when compared to the combined control (non-irradiated vegetative and reproductive pool). Expression patterns of several radiation inducible genes were confirmed by RT-PCR and Northern blotting. Our microarray results may contribute to an overall understanding of the type and quantities of genes that are expressed by an acute gamma-irradiation. In addition, to investigate the oxidative damage caused by irradiation, RT-PCR analysis for the expression of antioxidant isoenzyme genes, and a Transmission Electron Microscope (TEM) observation for visualizing the H{sub 2}O{sub 2} scavenging activity in leaves were applied.

With Reference to Reference Genes: A Systematic Review of Endogenous Controls in Gene Expression Studies.

Science.gov (United States)

Chapman, Joanne R; Waldenström, Jonas

2015-01-01

The choice of reference genes that are stably expressed amongst treatment groups is a crucial step in real-time quantitative PCR gene expression studies. Recent guidelines have specified that a minimum of two validated reference genes should be used for normalisation. However, a quantitative review of the literature showed that the average number of reference genes used across all studies was 1.2. Thus, the vast majority of studies continue to use a single gene, with β-actin (ACTB) and/or glyceraldehyde 3-phosphate dehydrogenase (GAPDH) being commonly selected in studies of vertebrate gene expression. Few studies (15%) tested a panel of potential reference genes for stability of expression before using them to normalise data. Amongst studies specifically testing reference gene stability, few found ACTB or GAPDH to be optimal, whereby these genes were significantly less likely to be chosen when larger panels of potential reference genes were screened. Fewer reference genes were tested for stability in non-model organisms, presumably owing to a dearth of available primers in less well characterised species. Furthermore, the experimental conditions under which real-time quantitative PCR analyses were conducted had a large influence on the choice of reference genes, whereby different studies of rat brain tissue showed different reference genes to be the most stable. These results highlight the importance of validating the choice of normalising reference genes before conducting gene expression studies.

Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

Science.gov (United States)

Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.

2018-03-01

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

Gene co-expression networks in liver and muscle transcriptome reveal sex-specific gene expression in lambs fed with a mix of essential oils

DEFF Research Database (Denmark)

Sabino, Marcella; Carmelo, Victor Adriano Okstoft; Mazzoni, Gianluca

2018-01-01

the potential of RNA-Sequencing data in order to evaluate the effect of an EO supplementary diet on gene expression in both lamb liver and muscle. Using a treatment and sex interaction model, 13 and 4 differentially expressed genes were identified in liver and muscle respectively. Sex-specific differentially...... on the expression profile of both liver and muscle tissues. We hypothesize that the presence of EOs could have beneficial effects on wellness of male lamb and further analyses are needed to understand the biological mechanisms behind the different effect of EO metabolites based on sex. Using lamb as a model...

Of mice and men: divergence of gene expression patterns in kidney.

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Lydie Cheval

Full Text Available Since the development of methods for homologous gene recombination, mouse models have played a central role in research in renal pathophysiology. However, many published and unpublished results show that mice with genetic changes mimicking human pathogenic mutations do not display the human phenotype. These functional differences may stem from differences in gene expression between mouse and human kidneys. However, large scale comparison of gene expression networks revealed conservation of gene expression among a large panel of human and mouse tissues including kidneys. Because renal functions result from the spatial integration of elementary processes originating in the glomerulus and the successive segments constituting the nephron, we hypothesized that differences in gene expression profiles along the human and mouse nephron might account for different behaviors. Analysis of SAGE libraries generated from the glomerulus and seven anatomically defined nephron segments from human and mouse kidneys allowed us to identify 4644 pairs of gene orthologs expressed in either one or both species. Quantitative analysis shows that many transcripts are present at different levels in the two species. It also shows poor conservation of gene expression profiles, with less than 10% of the 4644 gene orthologs displaying a higher conservation of expression profiles than the neutral expectation (p<0.05. Accordingly, hierarchical clustering reveals a higher degree of conservation of gene expression patterns between functionally unrelated kidney structures within a given species than between cognate structures from the two species. Similar findings were obtained for sub-groups of genes with either kidney-specific or housekeeping functions. Conservation of gene expression at the scale of the whole organ and divergence at the level of its constituting sub-structures likely account for the fact that although kidneys assume the same global function in the two species

Mutual repression enhances the steepness and precision of gene expression boundaries.

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Thomas R Sokolowski

Full Text Available Embryonic development is driven by spatial patterns of gene expression that determine the fate of each cell in the embryo. While gene expression is often highly erratic, embryonic development is usually exceedingly precise. In particular, gene expression boundaries are robust not only against intra-embryonic fluctuations such as noise in gene expression and protein diffusion, but also against embryo-to-embryo variations in the morphogen gradients, which provide positional information to the differentiating cells. How development is robust against intra- and inter-embryonic variations is not understood. A common motif in the gene regulation networks that control embryonic development is mutual repression between pairs of genes. To assess the role of mutual repression in the robust formation of gene expression patterns, we have performed large-scale stochastic simulations of a minimal model of two mutually repressing gap genes in Drosophila, hunchback (hb and knirps (kni. Our model includes not only mutual repression between hb and kni, but also the stochastic and cooperative activation of hb by the anterior morphogen Bicoid (Bcd and of kni by the posterior morphogen Caudal (Cad, as well as the diffusion of Hb and Kni between neighboring nuclei. Our analysis reveals that mutual repression can markedly increase the steepness and precision of the gap gene expression boundaries. In contrast to other mechanisms such as spatial averaging and cooperative gene activation, mutual repression thus allows for gene-expression boundaries that are both steep and precise. Moreover, mutual repression dramatically enhances their robustness against embryo-to-embryo variations in the morphogen levels. Finally, our simulations reveal that diffusion of the gap proteins plays a critical role not only in reducing the width of the gap gene expression boundaries via the mechanism of spatial averaging, but also in repairing patterning errors that could arise because of the

GEM2Net: from gene expression modeling to -omics networks, a new CATdb module to investigate Arabidopsis thaliana genes involved in stress response.

Science.gov (United States)

Zaag, Rim; Tamby, Jean Philippe; Guichard, Cécile; Tariq, Zakia; Rigaill, Guillem; Delannoy, Etienne; Renou, Jean-Pierre; Balzergue, Sandrine; Mary-Huard, Tristan; Aubourg, Sébastien; Martin-Magniette, Marie-Laure; Brunaud, Véronique

2015-01-01

CATdb (http://urgv.evry.inra.fr/CATdb) is a database providing a public access to a large collection of transcriptomic data, mainly for Arabidopsis but also for other plants. This resource has the rare advantage to contain several thousands of microarray experiments obtained with the same technical protocol and analyzed by the same statistical pipelines. In this paper, we present GEM2Net, a new module of CATdb that takes advantage of this homogeneous dataset to mine co-expression units and decipher Arabidopsis gene functions. GEM2Net explores 387 stress conditions organized into 18 biotic and abiotic stress categories. For each one, a model-based clustering is applied on expression differences to identify clusters of co-expressed genes. To characterize functions associated with these clusters, various resources are analyzed and integrated: Gene Ontology, subcellular localization of proteins, Hormone Families, Transcription Factor Families and a refined stress-related gene list associated to publications. Exploiting protein-protein interactions and transcription factors-targets interactions enables to display gene networks. GEM2Net presents the analysis of the 18 stress categories, in which 17,264 genes are involved and organized within 681 co-expression clusters. The meta-data analyses were stored and organized to compose a dynamic Web resource. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Global gene expression profile progression in Gaucher disease mouse models

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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.

Creating and validating cis-regulatory maps of tissue-specific gene expression regulation

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O'Connor, Timothy R.; Bailey, Timothy L.

2014-01-01

Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules–CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for ‘other’ tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a ‘nearest neighbor’ heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps. PMID:25200088

Dlx homeobox gene family expression in osteoclasts.

Science.gov (United States)

Lézot, F; Thomas, B L; Blin-Wakkach, C; Castaneda, B; Bolanos, A; Hotton, D; Sharpe, P T; Heymann, D; Carles, G F; Grigoriadis, A E; Berdal, A

2010-06-01

Skeletal growth and homeostasis require the finely orchestrated secretion of mineralized tissue matrices by highly specialized cells, balanced with their degradation by osteoclasts. Time- and site-specific expression of Dlx and Msx homeobox genes in the cells secreting these matrices have been identified as important elements in the regulation of skeletal morphology. Such specific expression patterns have also been reported in osteoclasts for Msx genes. The aim of the present study was to establish the expression patterns of Dlx genes in osteoclasts and identify their function in regulating skeletal morphology. The expression patterns of all Dlx genes were examined during the whole osteoclastogenesis using different in vitro models. The results revealed that Dlx1 and Dlx2 are the only Dlx family members with a possible function in osteoclastogenesis as well as in mature osteoclasts. Dlx5 and Dlx6 were detected in the cultures but appear to be markers of monocytes and their derivatives. In vivo, Dlx2 expression in osteoclasts was examined using a Dlx2/LacZ transgenic mouse. Dlx2 is expressed in a subpopulation of osteoclasts in association with tooth, brain, nerve, and bone marrow volumetric growths. Altogether the present data suggest a role for Dlx2 in regulation of skeletal morphogenesis via functions within osteoclasts. (c) 2010 Wiley-Liss, Inc.

Directed natural product biosynthesis gene cluster capture and expression in the model bacterium Bacillus subtilis

KAUST Repository

Li, Yongxin

2015-03-24

Bacilli are ubiquitous low G+C environmental Gram-positive bacteria that produce a wide assortment of specialized small molecules. Although their natural product biosynthetic potential is high, robust molecular tools to support the heterologous expression of large biosynthetic gene clusters in Bacillus hosts are rare. Herein we adapt transformation-associated recombination (TAR) in yeast to design a single genomic capture and expression vector for antibiotic production in Bacillus subtilis. After validating this direct cloning plug-and-playa approach with surfactin, we genetically interrogated amicoumacin biosynthetic gene cluster from the marine isolate Bacillus subtilis 1779. Its heterologous expression allowed us to explore an unusual maturation process involving the N-acyl-asparagine pro-drug intermediates preamicoumacins, which are hydrolyzed by the asparagine-specific peptidase into the active component amicoumacin A. This work represents the first direct cloning based heterologous expression of natural products in the model organism B. subtilis and paves the way to the development of future genome mining efforts in this genus.

Directed natural product biosynthesis gene cluster capture and expression in the model bacterium Bacillus subtilis

Science.gov (United States)

Li, Yongxin; Li, Zhongrui; Yamanaka, Kazuya; Xu, Ying; Zhang, Weipeng; Vlamakis, Hera; Kolter, Roberto; Moore, Bradley S.; Qian, Pei-Yuan

2015-03-01

Bacilli are ubiquitous low G+C environmental Gram-positive bacteria that produce a wide assortment of specialized small molecules. Although their natural product biosynthetic potential is high, robust molecular tools to support the heterologous expression of large biosynthetic gene clusters in Bacillus hosts are rare. Herein we adapt transformation-associated recombination (TAR) in yeast to design a single genomic capture and expression vector for antibiotic production in Bacillus subtilis. After validating this direct cloning ``plug-and-play'' approach with surfactin, we genetically interrogated amicoumacin biosynthetic gene cluster from the marine isolate Bacillus subtilis 1779. Its heterologous expression allowed us to explore an unusual maturation process involving the N-acyl-asparagine pro-drug intermediates preamicoumacins, which are hydrolyzed by the asparagine-specific peptidase into the active component amicoumacin A. This work represents the first direct cloning based heterologous expression of natural products in the model organism B. subtilis and paves the way to the development of future genome mining efforts in this genus.

Scaling of gene expression data allowing the comparison of different gene expression platforms

NARCIS (Netherlands)

van Ruissen, Fred; Schaaf, Gerben J.; Kool, Marcel; Baas, Frank; Ruijter, Jan M.

2008-01-01

Serial analysis of gene expression (SAGE) and microarrays have found a widespread application, but much ambiguity exists regarding the amalgamation of the data resulting from these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce

Identification of Reference Genes for Quantitative Gene Expression Studies in a Non-Model Tree Pistachio (Pistacia vera L..

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Maryam Moazzam Jazi

Full Text Available The tree species, Pistacia vera (P. vera is an important commercial product that is salt-tolerant and long-lived, with a possible lifespan of over one thousand years. Gene expression analysis is an efficient method to explore the possible regulatory mechanisms underlying these characteristics. Therefore, having the most suitable set of reference genes is required for transcript level normalization under different conditions in P. vera. In the present study, we selected eight widely used reference genes, ACT, EF1α, α-TUB, β-TUB, GAPDH, CYP2, UBQ10, and 18S rRNA. Using qRT-PCR their expression was assessed in 54 different samples of three cultivars of P. vera. The samples were collected from different organs under various abiotic treatments (cold, drought, and salt across three time points. Several statistical programs (geNorm, NormFinder, and BestKeeper were applied to estimate the expression stability of candidate reference genes. Results obtained from the statistical analysis were then exposed to Rank aggregation package to generate a consensus gene rank. Based on our results, EF1α was found to be the superior reference gene in all samples under all abiotic treatments. In addition to EF1α, ACT and β-TUB were the second best reference genes for gene expression analysis in leaf and root. We recommended β-TUB as the second most stable gene for samples under the cold and drought treatments, while ACT holds the same position in samples analyzed under salt treatment. This report will benefit future research on the expression profiling of P. vera and other members of the Anacardiaceae family.

Identification of Reference Genes for Quantitative Gene Expression Studies in a Non-Model Tree Pistachio (Pistacia vera L.).

Science.gov (United States)

Moazzam Jazi, Maryam; Ghadirzadeh Khorzoghi, Effat; Botanga, Christopher; Seyedi, Seyed Mahdi

2016-01-01

The tree species, Pistacia vera (P. vera) is an important commercial product that is salt-tolerant and long-lived, with a possible lifespan of over one thousand years. Gene expression analysis is an efficient method to explore the possible regulatory mechanisms underlying these characteristics. Therefore, having the most suitable set of reference genes is required for transcript level normalization under different conditions in P. vera. In the present study, we selected eight widely used reference genes, ACT, EF1α, α-TUB, β-TUB, GAPDH, CYP2, UBQ10, and 18S rRNA. Using qRT-PCR their expression was assessed in 54 different samples of three cultivars of P. vera. The samples were collected from different organs under various abiotic treatments (cold, drought, and salt) across three time points. Several statistical programs (geNorm, NormFinder, and BestKeeper) were applied to estimate the expression stability of candidate reference genes. Results obtained from the statistical analysis were then exposed to Rank aggregation package to generate a consensus gene rank. Based on our results, EF1α was found to be the superior reference gene in all samples under all abiotic treatments. In addition to EF1α, ACT and β-TUB were the second best reference genes for gene expression analysis in leaf and root. We recommended β-TUB as the second most stable gene for samples under the cold and drought treatments, while ACT holds the same position in samples analyzed under salt treatment. This report will benefit future research on the expression profiling of P. vera and other members of the Anacardiaceae family.

Hypothalamic gene expression of appetite regulators in a cancer-cachectic mouse model [Dataset 2

OpenAIRE

Dwarkasing, Jvalini; Dijk, Francina J.; Boekschoten, Mark; Faber, Joyce; Argilès, Josep M.; Lavianio, Alessandro; Muller, Michael; Witkamp, Renger; Norren, van, Klaske

2013-01-01

Appetite is frequently affected in cancer patients, leading to anorexia and consequently insufficient food intake. In this study, we report on hypothalamic gene expression profile of a cancer cachectic mouse model with increased food intake. In this model, mice bearing C26 colon adenocarcinoma have an increased food intake subsequently to the loss of body weight. We hypothesize that in this model, appetite regulating systems in the hypothalamus, which apparently fail in anorexia, are still ab...

Hypothalamic gene expression of appetite regulators in a cancer-cachectic mouse model [Dataset 1

OpenAIRE

Dwarkasing, Jvalini; Dijk, Francina J.; Boekschoten, Mark; Faber, Joyce; Argilès, Josep M.; Lavianio, Alessandro; Muller, Michael; Witkamp, Renger; Norren, van, Klaske

2013-01-01

Appetite is frequently affected in cancer patients, leading to anorexia and consequently insufficient food intake. In this study, we report on hypothalamic gene expression profile of a cancer cachectic mouse model with increased food intake. In this model, mice bearing C26 colon adenocarcinoma have an increased food intake subsequently to the loss of body weight. We hypothesize that in this model, appetite regulating systems in the hypothalamus, which apparently fail in anorexia, are still ab...

Analysis of Epidermal Growth Factor Receptor Related Gene Expression Changes in a Cellular and Animal Model of Parkinson’s Disease

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In-Su Kim

2017-02-01

Full Text Available We employed transcriptome analysis of epidermal growth factor receptor related gene expression changes in cellular and animal models of Parkinson’s disease (PD. We used a well-known Parkinsonian toxin 1-methyl-4-phenylpyridine (MPP+ to induce neuronal apoptosis in the human neuroblastoma SH-SY5Y cell line. The MPP+-treatment of SH-SY5Y cells was capable of inducing neuro-apoptosis, but it remains unclear what kinds of transcriptional genes are affected by MPP+ toxicity. Therefore the pathways that were significantly perturbed in MPP+ treated human neuroblastoma SH-SY5Y cells were identified based on genome-wide gene expression data at two time points (24 and 48 h. We found that the Epidermal Growth Factor Receptor (EGFR pathway-related genes showed significantly differential expression at all time points. The EGFR pathway has been linked to diverse cellular events such as proliferation, differentiation, and apoptosis. Further, to evaluate the functional significance of the altered EGFR related gene expression observed in MPP+-treated SH-SY5Y cells, the EGFR related GJB2 (Cx26 gene expression was analyzed in an MPP+-intoxicated animal PD model. Our findings identify that the EGFR signaling pathway and its related genes, such as Cx26, might play a significant role in dopaminergic (DAergic neuronal cell death during the process of neuro-apoptosis and therefore can be focused on as potential targets for therapeutic intervention.

Capturing heterogeneity in gene expression studies by surrogate variable analysis.

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Jeffrey T Leek

2007-09-01

Full Text Available It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce "surrogate variable analysis" (SVA to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.

Rat Models of Cardiovascular Disease Demonstrate Distinctive Pulmonary Gene Expressions for Vascular Response Genes: Impact of Ozone Exposure

Science.gov (United States)

Comparative gene expression profiling of multiple tissues from rat strains with genetic predisposition to diverse cardiovascular diseases (CVD) can help decode the transcriptional program that governs organ-specific functions. We examined expressions of CVD genes in the lungs of ...

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

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Paules Richard S

2007-11-01

Full Text Available Abstract Background A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions. Results Through evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratio's, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV or ionizing radiation (IR-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying

Modeling and validation of autoinducer-mediated bacterial gene expression in microfluidic environments

Science.gov (United States)

Austin, Caitlin M.; Stoy, William; Su, Peter; Harber, Marie C.; Bardill, J. Patrick; Hammer, Brian K.; Forest, Craig R.

2014-01-01

Biosensors exploiting communication within genetically engineered bacteria are becoming increasingly important for monitoring environmental changes. Currently, there are a variety of mathematical models for understanding and predicting how genetically engineered bacteria respond to molecular stimuli in these environments, but as sensors have miniaturized towards microfluidics and are subjected to complex time-varying inputs, the shortcomings of these models have become apparent. The effects of microfluidic environments such as low oxygen concentration, increased biofilm encapsulation, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. We report a mathematical model that accurately predicts the biological response of the autoinducer N-acyl homoserine lactone-mediated green fluorescent protein expression in reporter bacteria in microfluidic environments by accommodating these rate constants. This generalized mass action model considers a chain of biomolecular events from input autoinducer chemical to fluorescent protein expression through a series of six chemical species. We have validated this model against experimental data from our own apparatus as well as prior published experimental results. Results indicate accurate prediction of dynamics (e.g., 14% peak time error from a pulse input) and with reduced mean-squared error with pulse or step inputs for a range of concentrations (10 μM–30 μM). This model can help advance the design of genetically engineered bacteria sensors and molecular communication devices. PMID:25379076

Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression.

Science.gov (United States)

Walker, Jeffrey A

2016-01-01

Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori . Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R) methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness) on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set). The original analysis of these data used a linear model (GLS) of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on mean CTRA expression. The standardized effects of Hedonia and Eudaimonia on CTRA gene set expression estimated by GLS were compared to estimates using multivariate (OLS) linear models and generalized estimating equation (GEE) models. The OLS estimates were tested using O'Brien's OLS test, Anderson's permutation [Formula: see text]-test, two permutation F -tests (including GlobalAncova), and a rotation z -test (Roast). The GEE estimates were tested using a Wald test with robust standard errors. The performance (Type I, II, S, and M errors) of all tests was investigated using a Monte Carlo simulation of data explicitly modeled on the re-analyzed dataset. GLS estimates are inconsistent between data sets, and, in each dataset, at least one coefficient is large and highly statistically significant. By contrast, effects estimated by OLS or GEE are very small, especially relative to the standard errors. Bootstrap and permutation GLS distributions suggest that the GLS results in

Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression

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Jeffrey A. Walker

2016-10-01

Full Text Available Background Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori. Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set. The original analysis of these data used a linear model (GLS of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on mean CTRA expression. Methods The standardized effects of Hedonia and Eudaimonia on CTRA gene set expression estimated by GLS were compared to estimates using multivariate (OLS linear models and generalized estimating equation (GEE models. The OLS estimates were tested using O’Brien’s OLS test, Anderson’s permutation ${r}_{F}^{2}$ r F 2 -test, two permutation F-tests (including GlobalAncova, and a rotation z-test (Roast. The GEE estimates were tested using a Wald test with robust standard errors. The performance (Type I, II, S, and M errors of all tests was investigated using a Monte Carlo simulation of data explicitly modeled on the re-analyzed dataset. Results GLS estimates are inconsistent between data sets, and, in each dataset, at least one coefficient is large and highly statistically significant. By contrast, effects estimated by OLS or GEE are very small, especially relative to the standard errors. Bootstrap and permutation GLS

Gene duplication, tissue-specific gene expression and sexual conflict in stalk-eyed flies (Diopsidae).

Science.gov (United States)

Baker, Richard H; Narechania, Apurva; Johns, Philip M; Wilkinson, Gerald S

2012-08-19

Gene duplication provides an essential source of novel genetic material to facilitate rapid morphological evolution. Traits involved in reproduction and sexual dimorphism represent some of the fastest evolving traits in nature, and gene duplication is intricately involved in the origin and evolution of these traits. Here, we review genomic research on stalk-eyed flies (Diopsidae) that has been used to examine the extent of gene duplication and its role in the genetic architecture of sexual dimorphism. Stalk-eyed flies are remarkable because of the elongation of the head into long stalks, with the eyes and antenna laterally displaced at the ends of these stalks. Many species are strongly sexually dimorphic for eyespan, and these flies have become a model system for studying sexual selection. Using both expressed sequence tag and next-generation sequencing, we have established an extensive database of gene expression in the developing eye-antennal imaginal disc, the adult head and testes. Duplicated genes exhibit narrower expression patterns than non-duplicated genes, and the testes, in particular, provide an abundant source of gene duplication. Within somatic tissue, duplicated genes are more likely to be differentially expressed between the sexes, suggesting gene duplication may provide a mechanism for resolving sexual conflict.

Modulation of gene expression made easy

DEFF Research Database (Denmark)

Solem, Christian; Jensen, Peter Ruhdal

2002-01-01

A new approach for modulating gene expression, based on randomization of promoter (spacer) sequences, was developed. The method was applied to chromosomal genes in Lactococcus lactis and shown to generate libraries of clones with broad ranges of expression levels of target genes. In one example...... that the method can be applied to modulating the expression of native genes on the chromosome. We constructed a series of strains in which the expression of the las operon, containing the genes pfk, pyk, and ldh, was modulated by integrating a truncated copy of the pfk gene. Importantly, the modulation affected...

Analysis of telomerase target gene expression effects from murine models in patient cohorts by homology translation and random survival forest modeling

Directory of Open Access Journals (Sweden)

Frederik Otzen Bagger

2016-03-01

Full Text Available Acute myeloid leukemia (AML is an aggressive and rapidly fatal blood cancer that affects patients of any age group. Despite an initial response to standard chemotherapy, most patients relapse and this relapse is mediated by leukemia stem cell (LSC populations. We identified a functional requirement for telomerase in sustaining LSC populations in murine models of AML and validated this requirement using an inhibitor of telomerase in human AML. Here, we describe in detail the contents, quality control and methods of the gene expression analysis used in the published study (Gene Expression Omnibus GSE63242. Additionally, we provide annotated gene lists of telomerase regulated genes in AML and R code snippets to access and analyze the data used in the original manuscript. Keywords: AML, Leukemia, Stem cells, Telomere, Telomerase

Rhythmic diel pattern of gene expression in juvenile maize leaf.

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Maciej Jończyk

Full Text Available BACKGROUND: Numerous biochemical and physiological parameters of living organisms follow a circadian rhythm. Although such rhythmic behavior is particularly pronounced in plants, which are strictly dependent on the daily photoperiod, data on the molecular aspects of the diurnal cycle in plants is scarce and mostly concerns the model species Arabidopsis thaliana. Here we studied the leaf transcriptome in seedlings of maize, an important C4 crop only distantly related to A. thaliana, throughout a cycle of 10 h darkness and 14 h light to look for rhythmic patterns of gene expression. RESULTS: Using DNA microarrays comprising ca. 43,000 maize-specific probes we found that ca. 12% of all genes showed clear-cut diel rhythms of expression. Cluster analysis identified 35 groups containing from four to ca. 1,000 genes, each comprising genes of similar expression patterns. Perhaps unexpectedly, the most pronounced and most common (concerning the highest number of genes expression maxima were observed towards and during the dark phase. Using Gene Ontology classification several meaningful functional associations were found among genes showing similar diel expression patterns, including massive induction of expression of genes related to gene expression, translation, protein modification and folding at dusk and night. Additionally, we found a clear-cut tendency among genes belonging to individual clusters to share defined transcription factor-binding sequences. CONCLUSIONS: Co-expressed genes belonging to individual clusters are likely to be regulated by common mechanisms. The nocturnal phase of the diurnal cycle involves gross induction of fundamental biochemical processes and should be studied more thoroughly than was appreciated in most earlier physiological studies. Although some general mechanisms responsible for the diel regulation of gene expression might be shared among plants, details of the diurnal regulation of gene expression seem to differ

The Medicago truncatula gene expression atlas web server

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Tang Yuhong

2009-12-01

Full Text Available Abstract Background Legumes (Leguminosae or Fabaceae play a major role in agriculture. Transcriptomics studies in the model legume species, Medicago truncatula, are instrumental in helping to formulate hypotheses about the role of legume genes. With the rapid growth of publically available Affymetrix GeneChip Medicago Genome Array GeneChip data from a great range of tissues, cell types, growth conditions, and stress treatments, the legume research community desires an effective bioinformatics system to aid efforts to interpret the Medicago genome through functional genomics. We developed the Medicago truncatula Gene Expression Atlas (MtGEA web server for this purpose. Description The Medicago truncatula Gene Expression Atlas (MtGEA web server is a centralized platform for analyzing the Medicago transcriptome. Currently, the web server hosts gene expression data from 156 Affymetrix GeneChip® Medicago genome arrays in 64 different experiments, covering a broad range of developmental and environmental conditions. The server enables flexible, multifaceted analyses of transcript data and provides a range of additional information about genes, including different types of annotation and links to the genome sequence, which help users formulate hypotheses about gene function. Transcript data can be accessed using Affymetrix probe identification number, DNA sequence, gene name, functional description in natural language, GO and KEGG annotation terms, and InterPro domain number. Transcripts can also be discovered through co-expression or differential expression analysis. Flexible tools to select a subset of experiments and to visualize and compare expression profiles of multiple genes have been implemented. Data can be downloaded, in part or full, in a tabular form compatible with common analytical and visualization software. The web server will be updated on a regular basis to incorporate new gene expression data and genome annotation, and is accessible

Oxygen and tissue culture affect placental gene expression.

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Brew, O; Sullivan, M H F

2017-07-01

Placental explant culture is an important model for studying placental development and functions. We investigated the differences in placental gene expression in response to tissue culture, atmospheric and physiologic oxygen concentrations. Placental explants were collected from normal term (38-39 weeks of gestation) placentae with no previous uterine contractile activity. Placental transcriptomic expressions were evaluated with GeneChip ® Human Genome U133 Plus 2.0 arrays (Affymetrix). We uncovered sub-sets of genes that regulate response to stress, induction of apoptosis programmed cell death, mis-regulation of cell growth, proliferation, cell morphogenesis, tissue viability, and protection from apoptosis in cultured placental explants. We also identified a sub-set of genes with highly unstable pattern of expression after exposure to tissue culture. Tissue culture irrespective of oxygen concentration induced dichotomous increase in significant gene expression and increased enrichment of significant pathways and transcription factor targets (TFTs) including HIF1A. The effect was exacerbated by culture at atmospheric oxygen concentration, where further up-regulation of TFTs including PPARA, CEBPD, HOXA9 and down-regulated TFTs such as JUND/FOS suggest intrinsic heightened key biological and metabolic mechanisms such as glucose use, lipid biosynthesis, protein metabolism; apoptosis, inflammatory responses; and diminished trophoblast proliferation, differentiation, invasion, regeneration, and viability. These findings demonstrate that gene expression patterns differ between pre-culture and cultured explants, and the gene expression of explants cultured at atmospheric oxygen concentration favours stressed, pro-inflammatory and increased apoptotic transcriptomic response. Copyright © 2017 Elsevier Ltd. All rights reserved.

Left Ventricular Gene Expression Profile of Healthy and Cardiovascular Compromised Rat Models Used in Air Pollution Studies

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The link between pollutant exposure and cardiovascular disease (CVD) has prompted mechanistic research with animal models of CVD. We hypothesized that the cardiac gene expression patterns of healthy and genetically compromised, CVD-prone rat models, with or without metabolic impa...

Using gene expression noise to understand gene regulation

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Munsky, B.; Neuert, G.; van Oudenaarden, A.

2012-01-01

Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression "noise," has been suggested as a

Analysis of gene expression in resynthesized Brassica napus Allopolyploids using arabidopsis 70mer oligo microarrays.

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Robert T Gaeta

Full Text Available BACKGROUND: Studies in resynthesized Brassica napus allopolyploids indicate that homoeologous chromosome exchanges in advanced generations (S(5ratio6 alter gene expression through the loss and doubling of homoeologous genes within the rearrangements. Rearrangements may also indirectly affect global gene expression if homoeologous copies of gene regulators within rearrangements have differential affects on the transcription of genes in networks. METHODOLOGY/PRINCIPAL FINDINGS: We utilized Arabidopsis 70mer oligonucleotide microarrays for exploring gene expression in three resynthesized B. napus lineages at the S(0ratio1 and S(5ratio6 generations as well as their diploid progenitors B. rapa and B. oleracea. Differential gene expression between the progenitors and additive (midparent expression in the allopolyploids were tested. The S(5ratio6 lines differed in the number of genetic rearrangements, allowing us to test if the number of genes displaying nonadditive expression was related to the number of rearrangements. Estimates using per-gene and common variance ANOVA models indicated that 6-15% of 26,107 genes were differentially expressed between the progenitors. Individual allopolyploids showed nonadditive expression for 1.6-32% of all genes. Less than 0.3% of genes displayed nonadditive expression in all S(0ratio1 lines and 0.1-0.2% were nonadditive among all S(5ratio6 lines. Differentially expressed genes in the polyploids were over-represented by genes differential between the progenitors. The total number of differentially expressed genes was correlated with the number of genetic changes in S(5ratio6 lines under the common variance model; however, there was no relationship using a per-gene variance model, and many genes showed nonadditive expression in S(0ratio1 lines. CONCLUSIONS/SIGNIFICANCE: Few genes reproducibly demonstrated nonadditive expression among lineages, suggesting few changes resulted from a general response to polyploidization

Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.

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Neutelings Godfrey

2010-04-01

Full Text Available Abstract Background Quantitative real-time PCR (qRT-PCR is currently the most accurate method for detecting differential gene expression. Such an approach depends on the identification of uniformly expressed 'housekeeping genes' (HKGs. Extensive transcriptomic data mining and experimental validation in different model plants have shown that the reliability of these endogenous controls can be influenced by the plant species, growth conditions and organs/tissues examined. It is therefore important to identify the best reference genes to use in each biological system before using qRT-PCR to investigate differential gene expression. In this paper we evaluate different candidate HKGs for developmental transcriptomic studies in the economically-important flax fiber- and oil-crop (Linum usitatissimum L. Results Specific primers were designed in order to quantify the expression levels of 20 different potential housekeeping genes in flax roots, internal- and external-stem tissues, leaves and flowers at different developmental stages. After calculations of PCR efficiencies, 13 HKGs were retained and their expression stabilities evaluated by the computer algorithms geNorm and NormFinder. According to geNorm, 2 Transcriptional Elongation Factors (TEFs and 1 Ubiquitin gene are necessary for normalizing gene expression when all studied samples are considered. However, only 2 TEFs are required for normalizing expression in stem tissues. In contrast, NormFinder identified glyceraldehyde-3-phosphate dehydrogenase (GADPH as the most stably expressed gene when all samples were grouped together, as well as when samples were classed into different sub-groups. qRT-PCR was then used to investigate the relative expression levels of two splice variants of the flax LuMYB1 gene (homologue of AtMYB59. LuMYB1-1 and LuMYB1-2 were highly expressed in the internal stem tissues as compared to outer stem tissues and other samples. This result was confirmed with both ge

Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.).

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Huis, Rudy; Hawkins, Simon; Neutelings, Godfrey

2010-04-19

Quantitative real-time PCR (qRT-PCR) is currently the most accurate method for detecting differential gene expression. Such an approach depends on the identification of uniformly expressed 'housekeeping genes' (HKGs). Extensive transcriptomic data mining and experimental validation in different model plants have shown that the reliability of these endogenous controls can be influenced by the plant species, growth conditions and organs/tissues examined. It is therefore important to identify the best reference genes to use in each biological system before using qRT-PCR to investigate differential gene expression. In this paper we evaluate different candidate HKGs for developmental transcriptomic studies in the economically-important flax fiber- and oil-crop (Linum usitatissimum L). Specific primers were designed in order to quantify the expression levels of 20 different potential housekeeping genes in flax roots, internal- and external-stem tissues, leaves and flowers at different developmental stages. After calculations of PCR efficiencies, 13 HKGs were retained and their expression stabilities evaluated by the computer algorithms geNorm and NormFinder. According to geNorm, 2 Transcriptional Elongation Factors (TEFs) and 1 Ubiquitin gene are necessary for normalizing gene expression when all studied samples are considered. However, only 2 TEFs are required for normalizing expression in stem tissues. In contrast, NormFinder identified glyceraldehyde-3-phosphate dehydrogenase (GADPH) as the most stably expressed gene when all samples were grouped together, as well as when samples were classed into different sub-groups.qRT-PCR was then used to investigate the relative expression levels of two splice variants of the flax LuMYB1 gene (homologue of AtMYB59). LuMYB1-1 and LuMYB1-2 were highly expressed in the internal stem tissues as compared to outer stem tissues and other samples. This result was confirmed with both geNorm-designated- and Norm

Stochastic fluctuations and distributed control of gene expression impact cellular memory.

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Guillaume Corre

Full Text Available Despite the stochastic noise that characterizes all cellular processes the cells are able to maintain and transmit to their daughter cells the stable level of gene expression. In order to better understand this phenomenon, we investigated the temporal dynamics of gene expression variation using a double reporter gene model. We compared cell clones with transgenes coding for highly stable mRNA and fluorescent proteins with clones expressing destabilized mRNA-s and proteins. Both types of clones displayed strong heterogeneity of reporter gene expression levels. However, cells expressing stable gene products produced daughter cells with similar level of reporter proteins, while in cell clones with short mRNA and protein half-lives the epigenetic memory of the gene expression level was completely suppressed. Computer simulations also confirmed the role of mRNA and protein stability in the conservation of constant gene expression levels over several cell generations. These data indicate that the conservation of a stable phenotype in a cellular lineage may largely depend on the slow turnover of mRNA-s and proteins.

Early maternal alcohol consumption alters hippocampal DNA methylation, gene expression and volume in a mouse model.

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Heidi Marjonen

Full Text Available The adverse effects of alcohol consumption during pregnancy are known, but the molecular events that lead to the phenotypic characteristics are unclear. To unravel the molecular mechanisms, we have used a mouse model of gestational ethanol exposure, which is based on maternal ad libitum ingestion of 10% (v/v ethanol for the first 8 days of gestation (GD 0.5-8.5. Early neurulation takes place by the end of this period, which is equivalent to the developmental stage early in the fourth week post-fertilization in human. During this exposure period, dynamic epigenetic reprogramming takes place and the embryo is vulnerable to the effects of environmental factors. Thus, we hypothesize that early ethanol exposure disrupts the epigenetic reprogramming of the embryo, which leads to alterations in gene regulation and life-long changes in brain structure and function. Genome-wide analysis of gene expression in the mouse hippocampus revealed altered expression of 23 genes and three miRNAs in ethanol-exposed, adolescent offspring at postnatal day (P 28. We confirmed this result by using two other tissues, where three candidate genes are known to express actively. Interestingly, we found a similar trend of upregulated gene expression in bone marrow and main olfactory epithelium. In addition, we observed altered DNA methylation in the CpG islands upstream of the candidate genes in the hippocampus. Our MRI study revealed asymmetry of brain structures in ethanol-exposed adult offspring (P60: we detected ethanol-induced enlargement of the left hippocampus and decreased volume of the left olfactory bulb. Our study indicates that ethanol exposure in early gestation can cause changes in DNA methylation, gene expression, and brain structure of offspring. Furthermore, the results support our hypothesis of early epigenetic origin of alcohol-induced disorders: changes in gene regulation may have already taken place in embryonic stem cells and therefore can be seen in

Aberrant neuronal activity-induced signaling and gene expression in a mouse model of RASopathy.

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Franziska Altmüller

2017-03-01

Full Text Available Noonan syndrome (NS is characterized by reduced growth, craniofacial abnormalities, congenital heart defects, and variable cognitive deficits. NS belongs to the RASopathies, genetic conditions linked to mutations in components and regulators of the Ras signaling pathway. Approximately 50% of NS cases are caused by mutations in PTPN11. However, the molecular mechanisms underlying cognitive impairments in NS patients are still poorly understood. Here, we report the generation and characterization of a new conditional mouse strain that expresses the overactive Ptpn11D61Y allele only in the forebrain. Unlike mice with a global expression of this mutation, this strain is viable and without severe systemic phenotype, but shows lower exploratory activity and reduced memory specificity, which is in line with a causal role of disturbed neuronal Ptpn11 signaling in the development of NS-linked cognitive deficits. To explore the underlying mechanisms we investigated the neuronal activity-regulated Ras signaling in brains and neuronal cultures derived from this model. We observed an altered surface expression and trafficking of synaptic glutamate receptors, which are crucial for hippocampal neuronal plasticity. Furthermore, we show that the neuronal activity-induced ERK signaling, as well as the consecutive regulation of gene expression are strongly perturbed. Microarray-based hippocampal gene expression profiling revealed profound differences in the basal state and upon stimulation of neuronal activity. The neuronal activity-dependent gene regulation was strongly attenuated in Ptpn11D61Y neurons. In silico analysis of functional networks revealed changes in the cellular signaling beyond the dysregulation of Ras/MAPK signaling that is nearly exclusively discussed in the context of NS at present. Importantly, changes in PI3K/AKT/mTOR and JAK/STAT signaling were experimentally confirmed. In summary, this study uncovers aberrant neuronal activity

Finding gene regulatory network candidates using the gene expression knowledge base.

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Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

MediPlEx - a tool to combine in silico & experimental gene expression profiles of the model legume Medicago truncatula

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Stutz Leonhard J

2010-10-01

Full Text Available Abstract Background Expressed Sequence Tags (ESTs are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets. Findings Using our new analysis tool, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis, expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips® can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi. Conclusions MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants. MediPlEx can freely be used at http://www.cebitec.uni-bielefeld.de/mediplex.

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

Mitochondrial Gene Expression Profiles and Metabolic Pathways in the Amygdala Associated with Exaggerated Fear in an Animal Model of PTSD.

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Li, He; Li, Xin; Smerin, Stanley E; Zhang, Lei; Jia, Min; Xing, Guoqiang; Su, Yan A; Wen, Jillian; Benedek, David; Ursano, Robert

2014-01-01

The metabolic mechanisms underlying the development of exaggerated fear in post-traumatic stress disorder (PTSD) are not well defined. In the present study, alteration in the expression of genes associated with mitochondrial function in the amygdala of an animal model of PTSD was determined. Amygdala tissue samples were excised from 10 non-stressed control rats and 10 stressed rats, 14 days post-stress treatment. Total RNA was isolated, cDNA was synthesized, and gene expression levels were determined using a cDNA microarray. During the development of the exaggerated fear associated with PTSD, 48 genes were found to be significantly upregulated and 37 were significantly downregulated in the amygdala complex based on stringent criteria (p metabolism, one with transcriptional factors, and one with chromatin remodeling. Thus, informatics of a neuronal gene array allowed us to determine the expression profile of mitochondrial genes in the amygdala complex of an animal model of PTSD. The result is a further understanding of the metabolic and neuronal signaling mechanisms associated with delayed and exaggerated fear.

Gene Expression and Microarray Investigation of Dendrobium ...

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blood glucose > 16.7 mmol/L were used as the model group and treated with Dendrobium mixture. (DEN ... Keywords: Diabetes, Gene expression, Dendrobium mixture, Microarray testing ..... homeostasis in airway smooth muscle. Am J.

Effects of Digested Onion Extracts on Intestinal Gene Expression: An Interspecies Comparison Using Different Intestine Models.

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Nicole J W de Wit

Full Text Available Human intestinal tissue samples are barely accessible to study potential health benefits of nutritional compounds. Numbers of animals used in animal trials, however, need to be minimalized. Therefore, we explored the applicability of in vitro (human Caco-2 cells and ex vivo intestine models (rat precision cut intestine slices and the pig in-situ small intestinal segment perfusion (SISP technique to study the effect of food compounds. In vitro digested yellow (YOd and white onion extracts (WOd were used as model food compounds and transcriptomics was applied to obtain more insight into which extent mode of actions depend on the model. The three intestine models shared 9,140 genes which were used to compare the responses to digested onions between the models. Unsupervised clustering analysis showed that genes up- or down-regulated by WOd in human Caco-2 cells and rat intestine slices were similarly regulated by YOd, indicating comparable modes of action for the two onion species. Highly variable responses to onion were found in the pig SISP model. By focussing only on genes with significant differential expression, in combination with a fold change > 1.5, 15 genes showed similar onion-induced expression in human Caco-2 cells and rat intestine slices and 2 overlapping genes were found between the human Caco-2 and pig SISP model. Pathway analyses revealed that mainly processes related to oxidative stress, and especially the Keap1-Nrf2 pathway, were affected by onions in all three models. Our data fit with previous in vivo studies showing that the beneficial effects of onions are mostly linked to their antioxidant properties. Taken together, our data indicate that each of the in vitro and ex vivo intestine models used in this study, taking into account their limitations, can be used to determine modes of action of nutritional compounds and can thereby reduce the number of animals used in conventional nutritional intervention studies.

In vivo characterization of a reporter gene system for imaging hypoxia-induced gene expression.

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Carlin, Sean; Pugachev, Andrei; Sun, Xiaorong; Burke, Sean; Claus, Filip; O'Donoghue, Joseph; Ling, C Clifton; Humm, John L

2009-10-01

To characterize a tumor model containing a hypoxia-inducible reporter gene and to demonstrate utility by comparison of reporter gene expression to the uptake and distribution of the hypoxia tracer (18)F-fluoromisonidazole ((18)F-FMISO). Three tumors derived from the rat prostate cancer cell line R3327-AT were grown in each of two rats as follows: (1) parental R3327-AT, (2) positive control R3327-AT/PC in which the HSV1-tkeGFP fusion reporter gene was expressed constitutively, (3) R3327-AT/HRE in which the reporter gene was placed under the control of a hypoxia-inducible factor-responsive promoter sequence (HRE). Animals were coadministered a hypoxia-specific marker (pimonidazole) and the reporter gene probe (124)I-2'-fluoro-2'-deoxy-1-beta-d-arabinofuranosyl-5-iodouracil ((124)I-FIAU) 3 h prior to sacrifice. Statistical analysis of the spatial association between (124)I-FIAU uptake and pimonidazole fluorescent staining intensity was then performed on a pixel-by-pixel basis. Utility of this system was demonstrated by assessment of reporter gene expression versus the exogenous hypoxia probe (18)F-FMISO. Two rats, each bearing a single R3327-AT/HRE tumor, were injected with (124)I-FIAU (3 h before sacrifice) and (18)F-FMISO (2 h before sacrifice). Statistical analysis of the spatial association between (18)F-FMISO and (124)I-FIAU on a pixel-by-pixel basis was performed. Correlation coefficients between (124)I-FIAU uptake and pimonidazole staining intensity were: 0.11 in R3327-AT tumors, -0.66 in R3327-AT/PC and 0.76 in R3327-AT/HRE, confirming that only in the R3327-AT/HRE tumor was HSV1-tkeGFP gene expression associated with hypoxia. Correlation coefficients between (18)F-FMISO and (124)I-FIAU uptakes in R3327-AT/HRE tumors were r=0.56, demonstrating good spatial correspondence between the two tracers. We have confirmed hypoxia-specific expression of the HSV1-tkeGFP fusion gene in the R3327-AT/HRE tumor model and demonstrated the utility of this model for the

In vivo characterization of a reporter gene system for imaging hypoxia-induced gene expression

International Nuclear Information System (INIS)

Carlin, Sean; Pugachev, Andrei; Sun Xiaorong; Burke, Sean; Claus, Filip; O'Donoghue, Joseph; Ling, C. Clifton; Humm, John L.

2009-01-01

Purpose: To characterize a tumor model containing a hypoxia-inducible reporter gene and to demonstrate utility by comparison of reporter gene expression to the uptake and distribution of the hypoxia tracer 18 F-fluoromisonidazole ( 18 F-FMISO). Methods: Three tumors derived from the rat prostate cancer cell line R3327-AT were grown in each of two rats as follows: (1) parental R3327-AT, (2) positive control R3327-AT/PC in which the HSV1-tkeGFP fusion reporter gene was expressed constitutively, (3) R3327-AT/HRE in which the reporter gene was placed under the control of a hypoxia-inducible factor-responsive promoter sequence (HRE). Animals were coadministered a hypoxia-specific marker (pimonidazole) and the reporter gene probe 124 I-2'-fluoro-2'-deoxy-1-β-D-arabinofuranosyl-5-iodouracil ( 124 I-FIAU) 3 h prior to sacrifice. Statistical analysis of the spatial association between 124 I-FIAU uptake and pimonidazole fluorescent staining intensity was then performed on a pixel-by-pixel basis. Utility of this system was demonstrated by assessment of reporter gene expression versus the exogenous hypoxia probe 18 F-FMISO. Two rats, each bearing a single R3327-AT/HRE tumor, were injected with 124 I-FIAU (3 h before sacrifice) and 18 F-FMISO (2 h before sacrifice). Statistical analysis of the spatial association between 18 F-FMISO and 124 I-FIAU on a pixel-by-pixel basis was performed. Results: Correlation coefficients between 124 I-FIAU uptake and pimonidazole staining intensity were: 0.11 in R3327-AT tumors, -0.66 in R3327-AT/PC and 0.76 in R3327-AT/HRE, confirming that only in the R3327-AT/HRE tumor was HSV1-tkeGFP gene expression associated with hypoxia. Correlation coefficients between 18 F-FMISO and 124 I-FIAU uptakes in R3327-AT/HRE tumors were r=0.56, demonstrating good spatial correspondence between the two tracers. Conclusions: We have confirmed hypoxia-specific expression of the HSV1-tkeGFP fusion gene in the R3327-AT/HRE tumor model and demonstrated the utility of

Neonatal maternal deprivation response and developmental changes in gene expression revealed by hypothalamic gene expression profiling in mice.

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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.

An incoherent feedforward loop facilitates adaptive tuning of gene expression.

Science.gov (United States)

Hong, Jungeui; Brandt, Nathan; Abdul-Rahman, Farah; Yang, Ally; Hughes, Tim; Gresham, David

2018-04-05

We studied adaptive evolution of gene expression using long-term experimental evolution of Saccharomyces cerevisiae in ammonium-limited chemostats. We found repeated selection for non-synonymous variation in the DNA binding domain of the transcriptional activator, GAT1, which functions with the repressor, DAL80 in an incoherent type-1 feedforward loop (I1-FFL) to control expression of the high affinity ammonium transporter gene, MEP2. Missense mutations in the DNA binding domain of GAT1 reduce its binding to the GATAA consensus sequence. However, we show experimentally, and using mathematical modeling, that decreases in GAT1 binding result in increased expression of MEP2 as a consequence of properties of I1-FFLs. Our results show that I1-FFLs, one of the most commonly occurring network motifs in transcriptional networks, can facilitate adaptive tuning of gene expression through modulation of transcription factor binding affinities. Our findings highlight the importance of gene regulatory architectures in the evolution of gene expression. © 2018, Hong et al.

Expression profiles of genes involved in xenobiotic metabolism and disposition in human renal tissues and renal cell models

Energy Technology Data Exchange (ETDEWEB)

Van der Hauwaert, Cynthia; Savary, Grégoire [EA4483, Université de Lille 2, Faculté de Médecine de Lille, Pôle Recherche, 59045 Lille (France); Buob, David [Institut de Pathologie, Centre de Biologie Pathologie Génétique, Centre Hospitalier Régional Universitaire de Lille, 59037 Lille (France); Leroy, Xavier; Aubert, Sébastien [Institut de Pathologie, Centre de Biologie Pathologie Génétique, Centre Hospitalier Régional Universitaire de Lille, 59037 Lille (France); Institut National de la Santé et de la Recherche Médicale, UMR837, Centre de Recherche Jean-Pierre Aubert, Equipe 5, 59045 Lille (France); Flamand, Vincent [Service d' Urologie, Hôpital Huriez, Centre Hospitalier Régional Universitaire de Lille, 59037 Lille (France); Hennino, Marie-Flore [EA4483, Université de Lille 2, Faculté de Médecine de Lille, Pôle Recherche, 59045 Lille (France); Service de Néphrologie, Hôpital Huriez, Centre Hospitalier Régional Universitaire de Lille, 59037 Lille (France); Perrais, Michaël [Institut National de la Santé et de la Recherche Médicale, UMR837, Centre de Recherche Jean-Pierre Aubert, Equipe 5, 59045 Lille (France); and others

2014-09-15

Numerous xenobiotics have been shown to be harmful for the kidney. Thus, to improve our knowledge of the cellular processing of these nephrotoxic compounds, we evaluated, by real-time PCR, the mRNA expression level of 377 genes encoding xenobiotic-metabolizing enzymes (XMEs), transporters, as well as nuclear receptors and transcription factors that coordinate their expression in eight normal human renal cortical tissues. Additionally, since several renal in vitro models are commonly used in pharmacological and toxicological studies, we investigated their metabolic capacities and compared them with those of renal tissues. The same set of genes was thus investigated in HEK293 and HK2 immortalized cell lines in commercial primary cultures of epithelial renal cells and in proximal tubular cell primary cultures. Altogether, our data offers a comprehensive description of kidney ability to process xenobiotics. Moreover, by hierarchical clustering, we observed large variations in gene expression profiles between renal cell lines and renal tissues. Primary cultures of proximal tubular epithelial cells exhibited the highest similarities with renal tissue in terms of transcript profiling. Moreover, compared to other renal cell models, Tacrolimus dose dependent toxic effects were lower in proximal tubular cell primary cultures that display the highest metabolism and disposition capacity. Therefore, primary cultures appear to be the most relevant in vitro model for investigating the metabolism and bioactivation of nephrotoxic compounds and for toxicological and pharmacological studies. - Highlights: • Renal proximal tubular (PT) cells are highly sensitive to xenobiotics. • Expression of genes involved in xenobiotic disposition was measured. • PT cells exhibited the highest similarities with renal tissue.

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface

Science.gov (United States)

Rot, Gregor; Parikh, Anup; Curk, Tomaz; Kuspa, Adam; Shaulsky, Gad; Zupan, Blaz

2009-01-01

Background Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. Results We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. Conclusion dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms. PMID:19706156

Regulation of eucaryotic gene expression

Energy Technology Data Exchange (ETDEWEB)

Brent, R.; Ptashne, M.S

1989-05-23

This patent describes a method of regulating the expression of a gene in a eucaryotic cell. The method consists of: providing in the eucaryotic cell, a peptide, derived from or substantially similar to a peptide of a procaryotic cell able to bind to DNA upstream from or within the gene, the amount of the peptide being sufficient to bind to the gene and thereby control expression of the gene.

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

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

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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

Global developmental gene expression and pathway analysis of normal brain development and mouse models of human neuronal migration defects.

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Tiziano Pramparo

2011-03-01

Full Text Available Heterozygous LIS1 mutations are the most common cause of human lissencephaly, a human neuronal migration defect, and DCX mutations are the most common cause of X-linked lissencephaly. LIS1 is part of a protein complex including NDEL1 and 14-3-3ε that regulates dynein motor function and microtubule dynamics, while DCX stabilizes microtubules and cooperates with LIS1 during neuronal migration and neurogenesis. Targeted gene mutations of Lis1, Dcx, Ywhae (coding for 14-3-3ε, and Ndel1 lead to neuronal migration defects in mouse and provide models of human lissencephaly, as well as aid the study of related neuro-developmental diseases. Here we investigated the developing brain of these four mutants and wild-type mice using expression microarrays, bioinformatic analyses, and in vivo/in vitro experiments to address whether mutations in different members of the LIS1 neuronal migration complex lead to similar and/or distinct global gene expression alterations. Consistent with the overall successful development of the mutant brains, unsupervised clustering and co-expression analysis suggested that cell cycle and synaptogenesis genes are similarly expressed and co-regulated in WT and mutant brains in a time-dependent fashion. By contrast, focused co-expression analysis in the Lis1 and Ndel1 mutants uncovered substantial differences in the correlation among pathways. Differential expression analysis revealed that cell cycle, cell adhesion, and cytoskeleton organization pathways are commonly altered in all mutants, while synaptogenesis, cell morphology, and inflammation/immune response are specifically altered in one or more mutants. We found several commonly dysregulated genes located within pathogenic deletion/duplication regions, which represent novel candidates of human mental retardation and neurocognitive disabilities. Our analysis suggests that gene expression and pathway analysis in mouse models of a similar disorder or within a common pathway can

Characterization of human septic sera induced gene expression modulation in human myocytes

Science.gov (United States)

Hussein, Shaimaa; Michael, Paul; Brabant, Danielle; Omri, Abdelwahab; Narain, Ravin; Passi, Kalpdrum; Ramana, Chilakamarti V.; Parrillo, Joseph E.; Kumar, Anand; Parissenti, Amadeo; Kumar, Aseem

2009-01-01

To gain a better understanding of the gene expression changes that occurs during sepsis, we have performed a cDNA microarray study utilizing a tissue culture model that mimics human sepsis. This study utilized an in vitro model of cultured human fetal cardiac myocytes treated with 10% sera from septic patients or 10% sera from healthy volunteers. A 1700 cDNA expression microarray was used to compare the transcription profile from human cardiac myocytes treated with septic sera vs normal sera. Septic sera treatment of myocytes resulted in the down-regulation of 178 genes and the up-regulation of 4 genes. Our data indicate that septic sera induced cell cycle, metabolic, transcription factor and apoptotic gene expression changes in human myocytes. Identification and characterization of gene expression changes that occur during sepsis may lead to the development of novel therapeutics and diagnostics. PMID:19684886

Automatic Control of Gene Expression in Mammalian Cells.

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Fracassi, Chiara; Postiglione, Lorena; Fiore, Gianfranco; di Bernardo, Diego

2016-04-15

Automatic control of gene expression in living cells is paramount importance to characterize both endogenous gene regulatory networks and synthetic circuits. In addition, such a technology can be used to maintain the expression of synthetic circuit components in an optimal range in order to ensure reliable performance. Here we present a microfluidics-based method to automatically control gene expression from the tetracycline-inducible promoter in mammalian cells in real time. Our approach is based on the negative-feedback control engineering paradigm. We validated our method in a monoclonal population of cells constitutively expressing a fluorescent reporter protein (d2EYFP) downstream of a minimal CMV promoter with seven tet-responsive operator motifs (CMV-TET). These cells also constitutively express the tetracycline transactivator protein (tTA). In cells grown in standard growth medium, tTA is able to bind the CMV-TET promoter, causing d2EYFP to be maximally expressed. Upon addition of tetracycline to the culture medium, tTA detaches from the CMV-TET promoter, thus preventing d2EYFP expression. We tested two different model-independent control algorithms (relay and proportional-integral (PI)) to force a monoclonal population of cells to express an intermediate level of d2EYFP equal to 50% of its maximum expression level for up to 3500 min. The control input is either tetracycline-rich or standard growth medium. We demonstrated that both the relay and PI controllers can regulate gene expression at the desired level, despite oscillations (dampened in the case of the PI controller) around the chosen set point.

Pattern Recognition of Gene Expression with Singular Spectrum Analysis

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Hossein Hassani

2014-07-01

Full Text Available Drosophila segmentation as a model organism is one of the most highly studied. Among many maternal segmentation coordinate genes, bicoid protein pattern plays a significant role during Drosophila embryogenesis, since this gradient determines most aspects of head and thorax development. Despite the fact that several models have been proposed to describe the bicoid gradient, due to its association with considerable error, each can only partially explain bicoid characteristics. In this paper, a modified version of singular spectrum analysis is examined for filtering and extracting the bicoid gene expression signal. The results with strong evidence indicate that the proposed technique is able to remove noise more effectively and can be considered as a promising method for filtering gene expression measurements for other applications.

Inferring gene expression dynamics via functional regression analysis

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Leng Xiaoyan

2008-01-01

Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

Synthetic promoter libraries- tuning of gene expression

DEFF Research Database (Denmark)

Hammer, Karin; Mijakovic, Ivan; Jensen, Peter Ruhdal

2006-01-01

knockout and strong overexpression. However, applications such as metabolic optimization and control analysis necessitate a continuous set of expression levels with only slight increments in strength to cover a specific window around the wildtype expression level of the studied gene; this requirement can......The study of gene function often requires changing the expression of a gene and evaluating the consequences. In principle, the expression of any given gene can be modulated in a quasi-continuum of discrete expression levels but the traditional approaches are usually limited to two extremes: gene...

Evaluation of in vitro spermatogenesis system effectiveness to study genes behavior: monitoring the expression of the testis specific 10 (Tsga10) gene as a model.

Science.gov (United States)

Miryounesi, Mohammad; Nayernia, Karim; Mobasheri, Maryam Beigom; Dianatpour, Mahdi; Oko, Richard; Savad, Shahram; Modarressi, Mohammad Hossein

2014-10-01

In vitro generation of germ cells introduces a novel approach to male infertility and provides an effective system in gene tracking studies, however many aspects of this process have remained unclear. We aimed to promote mouse embryonic stem cells (mESC) differentiation into germ cells and evaluate its effectiveness with tracking the expression of the Tsga10 during this process. mESCs were differentiated into germ cells in the presence of Retinoic Acid. Based on developmental schedule of the postnatal testis, samples were taken on the 7th, 12th, and 25th days of the culture and were subjected to expression analysis of a panel of germ cell specific genes. Expression of Tsga10 in RNA and protein levels was then analyzed. Transition from mitosis to meiosis occurred between 7th and 12th days of mESC culture and post-meiotic gene expression did not occur until the 25th day of the culture. Results showed low level of Tsga10expression in undifferentiated stem cells. During transition from meiotic to post-meiotic phase, Tsga10 expression increased in 6.6 folds. This finding is in concordance with in vivo changes during transition from pre-pubertal to pubertal stage. Localization of processed and unprocessed forms of the related protein was similar to those in vivo as well. Expression pattern of Tsga10, as a gene with critical function in spermatogenesis, is similar during in vitro and in vivo germ cell generation. The results suggest that in vitro derived germ cells could be a trusted model to study genes behavior during spermatogenesis.

Site-specific analysis of gene expression in early osteoarthritis using the Pond-Nuki model in dogs

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Kuroki Keiichi

2006-10-01

Full Text Available Abstract Background Osteoarthritis (OA is a progressive and debilitating disease that often develops from a focal lesion and may take years to clinically manifest to a complete loss of joint structure and function. Currently, there is not a cure for OA, but early diagnosis and initiation of treatment may dramatically improve the prognosis and quality of life for affected individuals. This study was designed to determine the feasibility of analyzing changes in gene expression of articular cartilage using the Pond-Nuki model two weeks after ACL-transection in dogs, and to characterize the changes observed at this time point. Methods The ACL of four dogs was completely transected arthroscopically, and the contralateral limb was used as the non-operated control. After two weeks the dogs were euthanatized and tissues harvested from the tibial plateau and femoral condyles of both limbs. Two dogs were used for histologic analysis and Mankin scoring. From the other two dogs the surface of the femoral condyle and tibial plateau were divided into four regions each, and tissues were harvested from each region for biochemical (GAG and HP and gene expression analysis. Significant changes in gene expression were determined using REST-XL, and Mann-Whitney rank sum test was used to analyze biochemical data. Significance was set at (p Results Significant differences were not observed between ACL-X and control limbs for Mankin scores or GAG and HP tissue content. Further, damage to the tissue was not observed grossly by India ink staining. However, significant changes in gene expression were observed between ACL-X and control tissues from each region analyzed, and indicate that a unique regional gene expression profile for impending ACL-X induced joint pathology may be identified in future studies. Conclusion The data obtained from this study lend credence to the research approach and model for the characterization of OA, and the identification and validation of

A high resolution atlas of gene expression in the domestic sheep (Ovis aries).

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Clark, Emily L; Bush, Stephen J; McCulloch, Mary E B; Farquhar, Iseabail L; Young, Rachel; Lefevre, Lucas; Pridans, Clare; Tsang, Hiu G; Wu, Chunlei; Afrasiabi, Cyrus; Watson, Mick; Whitelaw, C Bruce; Freeman, Tom C; Summers, Kim M; Archibald, Alan L; Hume, David A

2017-09-01

Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of 'guilt by association' was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages.

Gene Structures, Classification, and Expression Models of the DREB Transcription Factor Subfamily in Populus trichocarpa

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Yunlin Chen

2013-01-01

Full Text Available We identified 75 dehydration-responsive element-binding (DREB protein genes in Populus trichocarpa. We analyzed gene structures, phylogenies, domain duplications, genome localizations, and expression profiles. The phylogenic construction suggests that the PtrDREB gene subfamily can be classified broadly into six subtypes (DREB A-1 to A-6 in Populus. The chromosomal localizations of the PtrDREB genes indicated 18 segmental duplication events involving 36 genes and six redundant PtrDREB genes were involved in tandem duplication events. There were fewer introns in the PtrDREB subfamily. The motif composition of PtrDREB was highly conserved in the same subtype. We investigated expression profiles of this gene subfamily from different tissues and/or developmental stages. Sixteen genes present in the digital expression analysis had high levels of transcript accumulation. The microarray results suggest that 18 genes were upregulated. We further examined the stress responsiveness of 15 genes by qRT-PCR. A digital northern analysis showed that the PtrDREB17, 18, and 32 genes were highly induced in leaves under cold stress, and the same expression trends were shown by qRT-PCR. Taken together, these observations may lay the foundation for future functional analyses to unravel the biological roles of Populus’ DREB genes.

A rat model of smoke inhalation injury: Influence of combustion smoke on gene expression in the brain

International Nuclear Information System (INIS)

Lee, Heung M.; Greeley, George H.; Herndon, David N.; Sinha, Mala; Luxon, Bruce A.; Englander, Ella W.

2005-01-01

Acute smoke inhalation causes death and injury in victims of home and industrial fires as well as victims of combat situations. The lethal factors in combustion smoke inhalation are toxic gases and oxygen deficiency, with carbon monoxide (CO) as a primary cause of death. In survivors, inhalation of smoke can result in severe immediate and delayed neuropathologies. To gain insight into the progression of molecular events contributing to smoke inhalation sequelae in the brain, we developed a smoke inhalation rat model and conducted a genome-wide analysis of gene expression. Microarray analysis revealed a modified brain transcriptome with changes peaking at 24 h and subsiding within 7 days post-smoke. Overall, smoke inhalation downregulated genes associated with synaptic function, neurotransmission, and neurotrophic support, and upregulated genes associated with stress responses, including nitric oxide synthesis, antioxidant defenses, proteolysis, inflammatory response, and glial activation. Notably, among the affected genes, many have been previously implicated in other types of brain injury, demonstrating the usefulness of microarrays for analysis of changes in gene expression in complex insults. In accord with previously described modulations of nitric oxide homeostasis in CO poisoning, microarray analysis revealed increased brain expression of nitric oxide synthase (NOS) and NOS ligand after inhalation of smoke. Furthermore, immunostaining showed significant elevations in perivascular NOS and in protein nitration, corroborating the involvement of nitric oxide perturbations in post-smoke sequelae in the brain. Thus, the new rat model, in combination with microarray analyses, affords insight into the complex molecular pathophysiology of smoke inhalation in the brain

Adaptive Evolution of Gene Expression in Drosophila.

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Nourmohammad, Armita; Rambeau, Joachim; Held, Torsten; Kovacova, Viera; Berg, Johannes; Lässig, Michael

2017-08-08

Gene expression levels are important quantitative traits that link genotypes to molecular functions and fitness. In Drosophila, population-genetic studies have revealed substantial adaptive evolution at the genomic level, but the evolutionary modes of gene expression remain controversial. Here, we present evidence that adaptation dominates the evolution of gene expression levels in flies. We show that 64% of the observed expression divergence across seven Drosophila species are adaptive changes driven by directional selection. Our results are derived from time-resolved data of gene expression divergence across a family of related species, using a probabilistic inference method for gene-specific selection. Adaptive gene expression is stronger in specific functional classes, including regulation, sensory perception, sexual behavior, and morphology. Moreover, we identify a large group of genes with sex-specific adaptation of expression, which predominantly occurs in males. Our analysis opens an avenue to map system-wide selection on molecular quantitative traits independently of their genetic basis. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Adaptive Evolution of Gene Expression in Drosophila

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Armita Nourmohammad

2017-08-01

Full Text Available Gene expression levels are important quantitative traits that link genotypes to molecular functions and fitness. In Drosophila, population-genetic studies have revealed substantial adaptive evolution at the genomic level, but the evolutionary modes of gene expression remain controversial. Here, we present evidence that adaptation dominates the evolution of gene expression levels in flies. We show that 64% of the observed expression divergence across seven Drosophila species are adaptive changes driven by directional selection. Our results are derived from time-resolved data of gene expression divergence across a family of related species, using a probabilistic inference method for gene-specific selection. Adaptive gene expression is stronger in specific functional classes, including regulation, sensory perception, sexual behavior, and morphology. Moreover, we identify a large group of genes with sex-specific adaptation of expression, which predominantly occurs in males. Our analysis opens an avenue to map system-wide selection on molecular quantitative traits independently of their genetic basis.

A GMM-IG framework for selecting genes as expression panel biomarkers.

Science.gov (United States)

Wang, Mingyi; Chen, Jake Y

2010-01-01

The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker genes from publicly available functional genomics studies. We developed a new framework, Gaussian Mixture Modeling-Coupled Information Gain (GMM-IG). In this framework, we first apply a two-component Gaussian mixture model (GMM) to estimate the conditional probability distributions of gene expression data between two different types of samples, for example, normal versus cancer. An expectation-maximization algorithm is then used to estimate the maximum likelihood parameters of a mixture of two Gaussian models in the feature space and determine the underlying expression levels of genes. Gene expression results from different studies are discretized, based on GMM estimations and then unified. Significantly differentially-expressed genes are filtered and assessed with information gain (IG) measures. DNA microarray experimental data for lung cancers from three different prior studies was processed using the new GMM-IG method. Target gene markers from a gene expression panel were selected and compared with several conventional computational biomarker data analysis methods. GMM-IG showed consistently high accuracy for several classification assessments. A high reproducibility of gene selection results was also determined from statistical validations. Our study shows that the GMM-IG framework can overcome poor reliability issues from single-study DNA microarray experiment while maintaining high accuracies by combining true signals from multiple studies. We present a conceptually simple framework that enables reliable integration of true differential gene expression signals from multiple

Aberrant Behaviours of Reaction Diffusion Self-organisation Models on Growing Domains in the Presence of Gene Expression Time Delays

KAUST Repository

Seirin  Lee, S.; Gaffney, E. A.

2010-01-01

domains in the presence of gene expression (Gaffney and Monk in Bull. Math. Biol. 68:99-130, 2006). Our results emphasise that gene expression dynamics induce unrealistic behaviour in Turing's model for multiple choices of kinetics and thus such aberrant

A power law global error model for the identification of differentially expressed genes in microarray data

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Granucci Francesca

2004-12-01

Full Text Available Abstract Background High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG. Results In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law. This enables us to construct a power law global error model (PLGEM that is applicable to various Affymetrix GeneChip data sets. A new DEG identification method is therefore proposed, consisting of a statistic designed to make explicit use of model-derived measurement spread estimates and a resampling-based hypothesis testing algorithm. Conclusions The new method provides a control of the false positive rate, a good sensitivity vs. specificity trade-off and consistent results with varying number of replicates and even using single samples.

Inflammation and Gli2 suppress gastrin gene expression in a murine model of antral hyperplasia.

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Milena Saqui-Salces

Full Text Available Chronic inflammation in the stomach can lead to gastric cancer. We previously reported that gastrin-deficient (Gast⁻/⁻ mice develop bacterial overgrowth, inflammatory infiltrate, increased Il-1β expression, antral hyperplasia and eventually antral tumors. Since Hedgehog (Hh signaling is active in gastric cancers but its role in precursor lesions is poorly understood, we examined the role of inflammation and Hh signaling in antral hyperplasia. LacZ reporter mice for Sonic hedgehog (Shh, Gli1, and Gli2 expression bred onto the Gast⁻/⁻ background revealed reduced Shh and Gli1 expression in the antra compared to wild type controls (WT. Gli2 expression in the Gast⁻/⁻ corpus was unchanged. However in the hyperplastic Gast⁻/⁻ antra, Gli2 expression increased in both the mesenchyme and epithelium, whereas expression in WT mice remained exclusively mesenchymal. These observations suggested that Gli2 is differentially regulated in the hyperplastic Gast⁻/⁻ antrum versus the corpus and by a Shh ligand-independent mechanism. Moreover, the proinflammatory cytokines Il-1β and Il-11, which promote gastric epithelial proliferation, were increased in the Gast⁻/⁻ stomach along with Infγ. To test if inflammation could account for elevated epithelial Gli2 expression in the Gast⁻/⁻ antra, the human gastric cell line AGS was treated with IL-1β and was found to increase GLI2 but decrease GLI1 levels. IL-1β also repressed human GAST gene expression. Indeed, GLI2 but not GLI1 or GLI3 expression repressed gastrin luciferase reporter activity by ∼50 percent. Moreover, chromatin immunoprecipitation of GLI2 in AGS cells confirmed that GLI2 directly binds to the GAST promoter. Using a mouse model of constitutively active epithelial GLI2 expression, we found that activated GLI2 repressed Gast expression but induced Il-1β gene expression and proliferation in the gastric antrum, along with a reduction of the number of G-cells. In summary

The Influence of Gene Expression Time Delays on Gierer–Meinhardt Pattern Formation Systems

KAUST Repository

Seirin Lee, S.

2010-03-23

There are numerous examples of morphogen gradients controlling long range signalling in developmental and cellular systems. The prospect of two such interacting morphogens instigating long range self-organisation in biological systems via a Turing bifurcation has been explored, postulated, or implicated in the context of numerous developmental processes. However, modelling investigations of cellular systems typically neglect the influence of gene expression on such dynamics, even though transcription and translation are observed to be important in morphogenetic systems. In particular, the influence of gene expression on a large class of Turing bifurcation models, namely those with pure kinetics such as the Gierer-Meinhardt system, is unexplored. Our investigations demonstrate that the behaviour of the Gierer-Meinhardt model profoundly changes on the inclusion of gene expression dynamics and is sensitive to the sub-cellular details of gene expression. Features such as concentration blow up, morphogen oscillations and radical sensitivities to the duration of gene expression are observed and, at best, severely restrict the possible parameter spaces for feasible biological behaviour. These results also indicate that the behaviour of Turing pattern formation systems on the inclusion of gene expression time delays may provide a means of distinguishing between possible forms of interaction kinetics. Finally, this study also emphasises that sub-cellular and gene expression dynamics should not be simply neglected in models of long range biological pattern formation via morphogens. © 2010 Society for Mathematical Biology.

Gene expression profiles of human dendritic cells interacting with Aspergillus fumigatus in a bilayer model of the alveolar epithelium/endothelium interface.

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Charles Oliver Morton

Full Text Available The initial stages of the interaction between the host and Aspergillus fumigatus at the alveolar surface of the human lung are critical in the establishment of aspergillosis. Using an in vitro bilayer model of the alveolus, including both the epithelium (human lung adenocarcinoma epithelial cell line, A549 and endothelium (human pulmonary artery epithelial cells, HPAEC on transwell membranes, it was possible to closely replicate the in vivo conditions. Two distinct sub-groups of dendritic cells (DC, monocyte-derived DC (moDC and myeloid DC (mDC, were included in the model to examine immune responses to fungal infection at the alveolar surface. RNA in high quantity and quality was extracted from the cell layers on the transwell membrane to allow gene expression analysis using tailored custom-made microarrays, containing probes for 117 immune-relevant genes. This microarray data indicated minimal induction of immune gene expression in A549 alveolar epithelial cells in response to germ tubes of A. fumigatus. In contrast, the addition of DC to the system greatly increased the number of differentially expressed immune genes. moDC exhibited increased expression of genes including CLEC7A, CD209 and CCL18 in the absence of A. fumigatus compared to mDC. In the presence of A. fumigatus, both DC subgroups exhibited up-regulation of genes identified in previous studies as being associated with the exposure of DC to A. fumigatus and exhibiting chemotactic properties for neutrophils, including CXCL2, CXCL5, CCL20, and IL1B. This model closely approximated the human alveolus allowing for an analysis of the host pathogen interface that complements existing animal models of IA.

Statistical modelling of transcript profiles of differentially regulated genes

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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

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

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Cumbie, Jason S; Kimbrel, Jeffrey A; Di, Yanming; Schafer, Daniel W; Wilhelm, Larry J; Fox, Samuel E; Sullivan, Christopher M; Curzon, Aron D; Carrington, James C; Mockler, Todd C; Chang, Jeff H

2011-01-01

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

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Jason S Cumbie

Full Text Available GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.

GeneBins: a database for classifying gene expression data, with application to plant genome arrays

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Weiller Georg

2007-03-01

Full Text Available Abstract Background To interpret microarray experiments, several ontological analysis tools have been developed. However, current tools are limited to specific organisms. Results We developed a bioinformatics system to assign the probe set sequences of any organism to a hierarchical functional classification modelled on KEGG ontology. The GeneBins database currently supports the functional classification of expression data from four Affymetrix arrays; Arabidopsis thaliana, Oryza sativa, Glycine max and Medicago truncatula. An online analysis tool to identify relevant functions is also provided. Conclusion GeneBins provides resources to interpret gene expression results from microarray experiments. It is available at http://bioinfoserver.rsbs.anu.edu.au/utils/GeneBins/

Gene expression profiling of the hippocampal dentate gyrus in an adult toxicity study captures a variety of neurodevelopmental dysfunctions in rat models of hypothyroidism.

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Shiraki, Ayako; Saito, Fumiyo; Akane, Hirotoshi; Akahori, Yumi; Imatanaka, Nobuya; Itahashi, Megu; Yoshida, Toshinori; Shibutani, Makoto

2016-01-01

We previously found that developmental hypothyroidism changed the expression of genes in the rat hippocampal dentate gyrus, a brain region where adult neurogenesis is known to occur. In the present study, we performed brain region-specific global gene expression profiling in an adult rat hypothyroidism model to see if it reflected the developmental neurotoxicity we saw in the developmental hypothyroidism model. Starting when male rats were 5 weeks old, we administered 6-propyl-2-thiouracil at a doses of 0, 0.1 and 10 mg kg(-1) body weight by gavage for 28 days. We selected four brain regions to represent both cerebral and cerebellar tissues: hippocampal dentate gyrus, cerebral cortex, corpus callosum and cerebellar vermis. We observed significant alterations in the expression of genes related to neural development (Eph family genes and Robo3) in the cerebral cortex and hippocampal dentate gyrus and in the expression of genes related to myelination (Plp1 and Mbp) in the hippocampal dentate gyrus. We observed only minor changes in the expression of these genes in the corpus callosum and cerebellar vermis. We used real-time reverse-transcription polymerase chain reaction to confirm Chrdl1, Hes5, Mbp, Plp1, Slit1, Robo3 and the Eph family transcript expression changes. The most significant changes in gene expression were found in the dentate gyrus. Considering that the gene expression profile of the adult dentate gyrus closely related to neurogenesis, 28-day toxicity studies looking at gene expression changes in adult hippocampal dentate gyrus may also detect possible developmental neurotoxic effects. Copyright © 2015 John Wiley & Sons, Ltd.

Mechanisms of gap gene expression canalization in the Drosophila blastoderm

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Samsonova Maria G

2011-07-01

Full Text Available Abstract Background Extensive variation in early gap gene expression in the Drosophila blastoderm is reduced over time because of gap gene cross regulation. This phenomenon is a manifestation of canalization, the ability of an organism to produce a consistent phenotype despite variations in genotype or environment. The canalization of gap gene expression can be understood as arising from the actions of attractors in the gap gene dynamical system. Results In order to better understand the processes of developmental robustness and canalization in the early Drosophila embryo, we investigated the dynamical effects of varying spatial profiles of Bicoid protein concentration on the formation of the expression border of the gap gene hunchback. At several positions on the anterior-posterior axis of the embryo, we analyzed attractors and their basins of attraction in a dynamical model describing expression of four gap genes with the Bicoid concentration profile accounted as a given input in the model equations. This model was tested against a family of Bicoid gradients obtained from individual embryos. These gradients were normalized by two independent methods, which are based on distinct biological hypotheses and provide different magnitudes for Bicoid spatial variability. We showed how the border formation is dictated by the biological initial conditions (the concentration gradient of maternal Hunchback protein being attracted to specific attracting sets in a local vicinity of the border. Different types of these attracting sets (point attractors or one dimensional attracting manifolds define several possible mechanisms of border formation. The hunchback border formation is associated with intersection of the spatial gradient of the maternal Hunchback protein and a boundary between the attraction basins of two different point attractors. We demonstrated how the positional variability for hunchback is related to the corresponding variability of the

Gene expression changes with age in skin, adipose tissue, blood and brain.

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Glass, Daniel; Viñuela, Ana; Davies, Matthew N; Ramasamy, Adaikalavan; Parts, Leopold; Knowles, David; Brown, Andrew A; Hedman, Asa K; Small, Kerrin S; Buil, Alfonso; Grundberg, Elin; Nica, Alexandra C; Di Meglio, Paola; Nestle, Frank O; Ryten, Mina; Durbin, Richard; McCarthy, Mark I; Deloukas, Panagiotis; Dermitzakis, Emmanouil T; Weale, Michael E; Bataille, Veronique; Spector, Tim D

2013-07-26

Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes with age in abdominal skin, subcutaneous adipose tissue and lymphoblastoid cell lines in 856 female twins in the age range of 39-85 years. Additionally, we investigated genotypic variants involved in genotype-by-age interactions to understand how the genomic regulation of gene expression alters with age. Using a linear mixed model, differential expression with age was identified in 1,672 genes in skin and 188 genes in adipose tissue. Only two genes expressed in lymphoblastoid cell lines showed significant changes with age. Genes significantly regulated by age were compared with expression profiles in 10 brain regions from 100 postmortem brains aged 16 to 83 years. We identified only one age-related gene common to the three tissues. There were 12 genes that showed differential expression with age in both skin and brain tissue and three common to adipose and brain tissues. Skin showed the most age-related gene expression changes of all the tissues investigated, with many of the genes being previously implicated in fatty acid metabolism, mitochondrial activity, cancer and splicing. A significant proportion of age-related changes in gene expression appear to be tissue-specific with only a few genes sharing an age effect in expression across tissues. More research is needed to improve our understanding of the genetic influences on aging and the relationship with age-related diseases.

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

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Guo Zheng

2006-01-01

Full Text Available Abstract Background It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. Results In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network to address the underlying regulations of genes that can span any unit(s of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. Conclusion We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

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Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb

2016-01-01

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Dynamics of hepatic gene expression and serum cytokine profiles in single and double-hit burn and sepsis animal models

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Rohit Rao

2015-06-01

Full Text Available We simulate the pathophysiology of severe burn trauma and burn-induced sepsis, using rat models of experimental burn injury and cecal ligation and puncture (CLP either individually (singe-hit model or in combination (double-hit model. The experimental burn injury simulates a systemic but sterile pro-inflammatory response, while the CLP simulates the effect of polymicrobial sepsis. Given the liver׳s central role in mediating the host immune response and onset of hypermetabolism after burn injury, elucidating the alterations in hepatic gene expression in response to injury can lead to a better understanding of the regulation of the inflammatory response, whereas circulating cytokine protein expression, reflects key systemic inflammatory mediators. In this article, we present both the hepatic gene expression and circulating cytokine/chemokine protein expression data for the above-mentioned experimental model to gain insights into the temporal dynamics of the inflammatory and hypermetabolic response following burn and septic injury. This data article supports results discussed in research articles (Yang et al., 2012 [1,4]; Mattick et al. 2012, 2013 [2,3]; Nguyen et al., 2014 [5]; Orman et al., 2011, 2012 [6–8].

Combined effects of dietary polyunsaturated fatty acids and parasite exposure on eicosanoid-related gene expression in an invertebrate model.

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Schlotz, Nina; Roulin, Anne; Ebert, Dieter; Martin-Creuzburg, Dominik

2016-11-01

Eicosanoids derive from essential polyunsaturated fatty acids (PUFA) and play crucial roles in immunity, development, and reproduction. However, potential links between dietary PUFA supply and eicosanoid biosynthesis are poorly understood, especially in invertebrates. Using Daphnia magna and its bacterial parasite Pasteuria ramosa as model system, we studied the expression of genes coding for key enzymes in eicosanoid biosynthesis and of genes related to oogenesis in response to dietary arachidonic acid and eicosapentaenoic acid in parasite-exposed and non-exposed animals. Gene expression related to cyclooxygenase activity was especially responsive to the dietary PUFA supply and parasite challenge, indicating a role for prostanoid eicosanoids in immunity and reproduction. Vitellogenin gene expression was induced upon parasite exposure in all food treatments, suggesting infection-related interference with the host's reproductive system. Our findings highlight the potential of dietary PUFA to modulate the expression of key enzymes involved in eicosanoid biosynthesis and reproduction and thus underpin the idea that the dietary PUFA supply can influence invertebrate immune functions and host-parasite interactions. Copyright © 2016 Elsevier Inc. All rights reserved.

Effect of external and internal factors on the expression of reporter genes driven by the N resistance gene promoter.

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Kathiria, Palak; Sidler, Corinne; Woycicki, Rafal; Yao, Youli; Kovalchuk, Igor

2013-07-01

The role of resistance (R) genes in plant pathogen interaction has been studied extensively due to its economical impact on agriculture. Interaction between tobacco mosaic virus (TMV) and the N protein from tobacco is one of the most widely used models to understand various aspects of pathogen resistance. The transcription activity governed by N gene promoter is one of the least understood elements of the model. In this study, the N gene promoter was cloned and fused with two different reporter genes, one encoding β-glucuronidase (N::GUS) and another, luciferase (N::LUC). Tobacco plants transformed with the N::GUS or N::LUC reporter constructs were screened for homozygosity and stable expression. Histochemical analysis of N::GUS tobacco plants revealed that the expression is organ specific and developmentally regulated. Whereas two week old plants expressed GUS in midveins only, 6-wk-old plants also expressed GUS in leaf lamella. Roots did not show GUS expression at any time during development. Experiments to address effects of external stress were performed using N::LUC tobacco plants. These experiments showed that N gene promoter expression was suppressed when plants were exposed to high but not low temperatures. Expression was also upregulated in response to TMV, but no changes were observed in plants treated with SA.

Developmental expression of the alpha-skeletal actin gene

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Vonk Freek J

2008-06-01

Full Text Available Abstract Background Actin is a cytoskeletal protein which exerts a broad range of functions in almost all eukaryotic cells. In higher vertebrates, six primary actin isoforms can be distinguished: alpha-skeletal, alpha-cardiac, alpha-smooth muscle, gamma-smooth muscle, beta-cytoplasmic and gamma-cytoplasmic isoactin. Expression of these actin isoforms during vertebrate development is highly regulated in a temporal and tissue-specific manner, but the mechanisms and the specific differences are currently not well understood. All members of the actin multigene family are highly conserved, suggesting that there is a high selective pressure on these proteins. Results We present here a model for the evolution of the genomic organization of alpha-skeletal actin and by molecular modeling, illustrate the structural differences of actin proteins of different phyla. We further describe and compare alpha-skeletal actin expression in two developmental stages of five vertebrate species (mouse, chicken, snake, salamander and fish. Our findings confirm that alpha-skeletal actin is expressed in skeletal muscle and in the heart of all five species. In addition, we identify many novel non-muscular expression domains including several in the central nervous system. Conclusion Our results show that the high sequence homology of alpha-skeletal actins is reflected by similarities of their 3 dimensional protein structures, as well as by conserved gene expression patterns during vertebrate development. Nonetheless, we find here important differences in 3D structures, in gene architectures and identify novel expression domains for this structural and functional important gene.

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

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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

Interplay of bistable kinetics of gene expression during cellular growth

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Zhdanov, Vladimir P

2009-01-01

In cells, the bistable kinetics of gene expression can be observed on the level of (i) one gene with positive feedback between protein and mRNA production, (ii) two genes with negative mutual feedback between protein and mRNA production, or (iii) in more complex cases. We analyse the interplay of two genes of type (ii) governed by a gene of type (i) during cellular growth. In particular, using kinetic Monte Carlo simulations, we show that in the case where gene 1, operating in the bistable regime, regulates mutually inhibiting genes 2 and 3, also operating in the bistable regime, the latter genes may eventually be trapped either to the state with high transcriptional activity of gene 2 and low activity of gene 3 or to the state with high transcriptional activity of gene 3 and low activity of gene 2. The probability to get to one of these states depends on the values of the model parameters. If genes 2 and 3 are kinetically equivalent, the probability is equal to 0.5. Thus, our model illustrates how different intracellular states can be chosen at random with predetermined probabilities. This type of kinetics of gene expression may be behind complex processes occurring in cells, e.g., behind the choice of the fate by stem cells

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

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Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data

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List, Markus; Hauschild, Anne-Christin; Tan, Qihua

2014-01-01

expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene...... on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10...

Actin gene identification from selected medicinal plants for their use as internal controls for gene expression studies

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Mufti, F.U.D.; Banaras, S.

2015-01-01

Internal control genes are the constitutive genes which maintain the basic cellular functions and regularly express in both normal and stressed conditions in living organisms. They are used in normalization of gene expression studies in comparative analysis of target genes, as their expression remains comparatively unchanged in all varied conditions. Among internal control genes, actin is considered as a candidate gene for expression studies due to its vital role in shaping cytoskeleton and plant physiology. Unfortunately most of such knowledge is limited to only model plants or crops, not much is known about important medicinal plants. Therefore, we selected seven important medicinal wild plants for molecular identification of actin gene. We used gene specific primers designed from the conserved regions of several known orthologues or homologues of actin genes from other plants. The amplified products of 370-380 bp were sequenced and submitted to GeneBank after their confirmation using different bioinformatics tools. All the novel partial sequences of putative actin genes were submitted to GeneBank (Parthenium hysterophorus (KJ774023), Fagonia indica (KJ774024), Rhazya stricta (KJ774025), Whithania coagulans (KJ774026), Capparis decidua (KJ774027), Verbena officinalis (KJ774028) and Aerva javanica (KJ774029)). The comparisons of these partial sequences by Basic Local Alignment Search Tool (BLAST) and phylogenetic trees demonstrated high similarity with known actin genes of other plants. Our findings illustrated highly conserved nature of actin gene among these selected plants. These novel partial fragments of actin genes from these wild medicinal plants can be used as internal controls for future gene expression studies of these important plants after precise validations of their stable expression in such plants. (author)

Identification of Region-Specific Myocardial Gene Expression Patterns in a Chronic Swine Model of Repaired Tetralogy of Fallot.

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Sabine Charron

Full Text Available Surgical repair of Tetralogy of Fallot (TOF is highly successful but may be complicated in adulthood by arrhythmias, sudden death, and right ventricular or biventricular dysfunction. To better understand the molecular and cellular mechanisms of these delayed cardiac events, a chronic animal model of postoperative TOF was studied using microarrays to perform cardiac transcriptomic studies. The experimental study included 12 piglets (7 rTOF and 5 controls that underwent surgery at age 2 months and were further studied after 23 (+/- 1 weeks of postoperative recovery. Two distinct regions (endocardium and epicardium from both ventricles were analyzed. Expression levels from each localization were compared in order to decipher mechanisms and signaling pathways leading to ventricular dysfunction and arrhythmias in surgically repaired TOF. Several genes were confirmed to participate in ventricular remodeling and cardiac failure and some new candidate genes were described. In particular, these data pointed out FRZB as a heart failure marker. Moreover, calcium handling and contractile function genes (SLN, ACTC1, PLCD4, PLCZ, potential arrhythmia-related genes (MYO5B, KCNA5, and cytoskeleton and cellular organization-related genes (XIRP2, COL8A1, KCNA6 were among the most deregulated genes in rTOF ventricles. To our knowledge, this is the first comprehensive report on global gene expression profiling in the heart of a long-term swine model of repaired TOF.

A comparative analysis of biclustering algorithms for gene expression data

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Eren, Kemal; Deveci, Mehmet; Küçüktunç, Onur; Çatalyürek, Ümit V.

2013-01-01

The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters. PMID:22772837

Muscle wasting and the temporal gene expression pattern in a novel rat intensive care unit model

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Llano-Diez Monica

2011-12-01

Full Text Available Abstract Background Acute quadriplegic myopathy (AQM or critical illness myopathy (CIM is frequently observed in intensive care unit (ICU patients. To elucidate duration-dependent effects of the ICU intervention on molecular and functional networks that control the muscle wasting and weakness associated with AQM, a gene expression profile was analyzed at time points varying from 6 hours to 14 days in a unique experimental rat model mimicking ICU conditions, i.e., post-synaptically paralyzed, mechanically ventilated and extensively monitored animals. Results During the observation period, 1583 genes were significantly up- or down-regulated by factors of two or greater. A significant temporal gene expression pattern was constructed at short (6 h-4 days, intermediate (5-8 days and long (9-14 days durations. A striking early and maintained up-regulation (6 h-14d of muscle atrogenes (muscle ring-finger 1/tripartite motif-containing 63 and F-box protein 32/atrogin-1 was observed, followed by an up-regulation of the proteolytic systems at intermediate and long durations (5-14d. Oxidative stress response genes and genes that take part in amino acid catabolism, cell cycle arrest, apoptosis, muscle development, and protein synthesis together with myogenic factors were significantly up-regulated from 5 to 14 days. At 9-14 d, genes involved in immune response and the caspase cascade were up-regulated. At 5-14d, genes related to contractile (myosin heavy chain and myosin binding protein C, regulatory (troponin, tropomyosin, developmental, caveolin-3, extracellular matrix, glycolysis/gluconeogenesis, cytoskeleton/sarcomere regulation and mitochondrial proteins were down-regulated. An activation of genes related to muscle growth and new muscle fiber formation (increase of myogenic factors and JunB and down-regulation of myostatin and up-regulation of genes that code protein synthesis and translation factors were found from 5 to 14 days. Conclusions Novel

Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.

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Hensman, James; Lawrence, Neil D; Rattray, Magnus

2013-08-20

Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications. We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications. The hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.

The rules of gene expression in plants: Organ identity and gene body methylation are key factors for regulation of gene expression in Arabidopsis thaliana

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Gutiérrez Rodrigo A

2008-09-01

Full Text Available Abstract Background Microarray technology is a widely used approach for monitoring genome-wide gene expression. For Arabidopsis, there are over 1,800 microarray hybridizations representing many different experimental conditions on Affymetrix™ ATH1 gene chips alone. This huge amount of data offers a unique opportunity to infer the principles that govern the regulation of gene expression in plants. Results We used bioinformatics methods to analyze publicly available data obtained using the ATH1 chip from Affymetrix. A total of 1887 ATH1 hybridizations were normalized and filtered to eliminate low-quality hybridizations. We classified and compared control and treatment hybridizations and determined differential gene expression. The largest differences in gene expression were observed when comparing samples obtained from different organs. On average, ten-fold more genes were differentially expressed between organs as compared to any other experimental variable. We defined "gene responsiveness" as the number of comparisons in which a gene changed its expression significantly. We defined genes with the highest and lowest responsiveness levels as hypervariable and housekeeping genes, respectively. Remarkably, housekeeping genes were best distinguished from hypervariable genes by differences in methylation status in their transcribed regions. Moreover, methylation in the transcribed region was inversely correlated (R2 = 0.8 with gene responsiveness on a genome-wide scale. We provide an example of this negative relationship using genes encoding TCA cycle enzymes, by contrasting their regulatory responsiveness to nitrate and methylation status in their transcribed regions. Conclusion Our results indicate that the Arabidopsis transcriptome is largely established during development and is comparatively stable when faced with external perturbations. We suggest a novel functional role for DNA methylation in the transcribed region as a key determinant

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

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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

Expression of Sox genes in tooth development.

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Kawasaki, Katsushige; Kawasaki, Maiko; Watanabe, Momoko; Idrus, Erik; Nagai, Takahiro; Oommen, Shelly; Maeda, Takeyasu; Hagiwara, Nobuko; Que, Jianwen; Sharpe, Paul T; Ohazama, Atsushi

2015-01-01

Members of the Sox gene family play roles in many biological processes including organogenesis. We carried out comparative in situ hybridization analysis of seventeen sox genes (Sox1-14, 17, 18, 21) during murine odontogenesis from the epithelial thickening to the cytodifferentiation stages. Localized expression of five Sox genes (Sox6, 9, 13, 14 and 21) was observed in tooth bud epithelium. Sox13 showed restricted expression in the primary enamel knots. At the early bell stage, three Sox genes (Sox8, 11, 17 and 21) were expressed in pre-ameloblasts, whereas two others (Sox5 and 18) showed expression in odontoblasts. Sox genes thus showed a dynamic spatio-temporal expression during tooth development.

Identification of human circadian genes based on time course gene expression profiles by using a deep learning method.

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Cui, Peng; Zhong, Tingyan; Wang, Zhuo; Wang, Tao; Zhao, Hongyu; Liu, Chenglin; Lu, Hui

2018-06-01

Circadian genes express periodically in an approximate 24-h period and the identification and study of these genes can provide deep understanding of the circadian control which plays significant roles in human health. Although many circadian gene identification algorithms have been developed, large numbers of false positives and low coverage are still major problems in this field. In this study we constructed a novel computational framework for circadian gene identification using deep neural networks (DNN) - a deep learning algorithm which can represent the raw form of data patterns without imposing assumptions on the expression distribution. Firstly, we transformed time-course gene expression data into categorical-state data to denote the changing trend of gene expression. Two distinct expression patterns emerged after clustering of the state data for circadian genes from our manually created learning dataset. DNN was then applied to discriminate the aperiodic genes and the two subtypes of periodic genes. In order to assess the performance of DNN, four commonly used machine learning methods including k-nearest neighbors, logistic regression, naïve Bayes, and support vector machines were used for comparison. The results show that the DNN model achieves the best balanced precision and recall. Next, we conducted large scale circadian gene detection using the trained DNN model for the remaining transcription profiles. Comparing with JTK_CYCLE and a study performed by Möller-Levet et al. (doi: https://doi.org/10.1073/pnas.1217154110), we identified 1132 novel periodic genes. Through the functional analysis of these novel circadian genes, we found that the GTPase superfamily exhibits distinct circadian expression patterns and may provide a molecular switch of circadian control of the functioning of the immune system in human blood. Our study provides novel insights into both the circadian gene identification field and the study of complex circadian-driven biological

Effects of strain and age on hepatic gene expression profiles in murine models of HFE-associated hereditary hemochromatosis.

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Lee, Seung-Min; Loguinov, Alexandre; Fleming, Robert E; Vulpe, Christopher D

2015-01-01

Hereditary hemochromatosis is an iron overload disorder most commonly caused by a defect in the HFE gene. While the genetic defect is highly prevalent, the majority of individuals do not develop clinically significant iron overload, suggesting the importance of genetic modifiers. Murine hfe knockout models have demonstrated that strain background has a strong effect on the severity of iron loading. We noted that hepatic iron loading in hfe-/- mice occurs primarily over the first postnatal weeks (loading phase) followed by a timeframe of relatively static iron concentrations (plateau phase). We thus evaluated the effects of background strain and of age on hepatic gene expression in Hfe knockout mice (hfe-/-). Hepatic gene expression profiles were examined using cDNA microarrays in 4- and 8-week-old hfe-/- and wild-type mice on two different genetic backgrounds, C57BL/6J (C57) and AKR/J (AKR). Genes differentially regulated in all hfe-/- mice groups, compared with wild-type mice, including those involved in cell survival, stress and damage responses and lipid metabolism. AKR strain-specific changes in lipid metabolism genes and C57 strain-specific changes in cell adhesion and extracellular matrix protein genes were detected in hfe-/- mice. Mouse strain and age are each significantly associated with hepatic gene expression profiles in hfe-/- mice. These affects may underlie or reflect differences in iron loading in these mice.

Profiling Gene Expression in Germinating Brassica Roots.

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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.

Functional categorization of gene expression changes in the cerebellum of a Cln3-knockout mouse model for Batten disease.

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Brooks, Andrew I; Chattopadhyay, Subrata; Mitchison, Hannah M; Nussbaum, Robert L; Pearce, David A

2003-01-01

Juvenile neuronal ceroid lipofuscinosis (JNCL or Batten Disease) is the most common progressive neurodegenerative disorder of childhood. The disease is inherited in an autosomal recessive manner and is the result of mutations in the CLN3 gene. One brain region severely affected in Batten disease is the cerebellum. Using a mouse model for Batten disease which shares pathological similarities to the disease in humans we have used oligonucleotide arrays to profile approximately 19000 mRNAs in the cerebellum. We have identified reproducible changes of twofold or more in the expression of 756 gene products in the cerebellum of 10-week-old Cln3-knockout mice as compared to wild-type controls. We have subsequently divided these genes with altered expression into 14 functional categories. We report a significant alteration in expression of genes associated with neurotransmission, neuronal cell structure and development, immune response and inflammation, and lipid metabolism. An apparent shift in metabolism toward gluconeogenesis is also evident in Cln3-knockout mice. Further experimentation will be necessary to understand the contribution of these changes in expression to a disease state. Detailed analysis of the functional consequences of altered expression of genes in the cerebellum of the Cln3-knockout mice may provide valuable clues in understanding the molecular basis of the pathological mechanisms underlying Batten disease.

A model of gene expression based on random dynamical systems reveals modularity properties of gene regulatory networks.

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Antoneli, Fernando; Ferreira, Renata C; Briones, Marcelo R S

2016-06-01

Here we propose a new approach to modeling gene expression based on the theory of random dynamical systems (RDS) that provides a general coupling prescription between the nodes of any given regulatory network given the dynamics of each node is modeled by a RDS. The main virtues of this approach are the following: (i) it provides a natural way to obtain arbitrarily large networks by coupling together simple basic pieces, thus revealing the modularity of regulatory networks; (ii) the assumptions about the stochastic processes used in the modeling are fairly general, in the sense that the only requirement is stationarity; (iii) there is a well developed mathematical theory, which is a blend of smooth dynamical systems theory, ergodic theory and stochastic analysis that allows one to extract relevant dynamical and statistical information without solving the system; (iv) one may obtain the classical rate equations form the corresponding stochastic version by averaging the dynamic random variables (small noise limit). It is important to emphasize that unlike the deterministic case, where coupling two equations is a trivial matter, coupling two RDS is non-trivial, specially in our case, where the coupling is performed between a state variable of one gene and the switching stochastic process of another gene and, hence, it is not a priori true that the resulting coupled system will satisfy the definition of a random dynamical system. We shall provide the necessary arguments that ensure that our coupling prescription does indeed furnish a coupled regulatory network of random dynamical systems. Finally, the fact that classical rate equations are the small noise limit of our stochastic model ensures that any validation or prediction made on the basis of the classical theory is also a validation or prediction of our model. We illustrate our framework with some simple examples of single-gene system and network motifs. Copyright © 2016 Elsevier Inc. All rights reserved.

Unstable Expression of Commonly Used Reference Genes in Rat Pancreatic Islets Early after Isolation Affects Results of Gene Expression Studies.

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Lucie Kosinová

Full Text Available The use of RT-qPCR provides a powerful tool for gene expression studies; however, the proper interpretation of the obtained data is crucially dependent on accurate normalization based on stable reference genes. Recently, strong evidence has been shown indicating that the expression of many commonly used reference genes may vary significantly due to diverse experimental conditions. The isolation of pancreatic islets is a complicated procedure which creates severe mechanical and metabolic stress leading possibly to cellular damage and alteration of gene expression. Despite of this, freshly isolated islets frequently serve as a control in various gene expression and intervention studies. The aim of our study was to determine expression of 16 candidate reference genes and one gene of interest (F3 in isolated rat pancreatic islets during short-term cultivation in order to find a suitable endogenous control for gene expression studies. We compared the expression stability of the most commonly used reference genes and evaluated the reliability of relative and absolute quantification using RT-qPCR during 0-120 hrs after isolation. In freshly isolated islets, the expression of all tested genes was markedly depressed and it increased several times throughout the first 48 hrs of cultivation. We observed significant variability among samples at 0 and 24 hrs but substantial stabilization from 48 hrs onwards. During the first 48 hrs, relative quantification failed to reflect the real changes in respective mRNA concentrations while in the interval 48-120 hrs, the relative expression generally paralleled the results determined by absolute quantification. Thus, our data call into question the suitability of relative quantification for gene expression analysis in pancreatic islets during the first 48 hrs of cultivation, as the results may be significantly affected by unstable expression of reference genes. However, this method could provide reliable information

Comparative gene expression analysis of two mouse models of autism:transcriptome profiling of the BTBR and En2-/- hippocampus

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Giovanni Provenzano

2016-08-01

Full Text Available Autism spectrum disorders (ASD are characterized by a high degree of genetic heterogeneity. Genomic studies identified common pathological processes underlying the heterogeneous clinical manifestations of ASD, and transcriptome analyses revealed that gene networks involved in synapse development, neuronal activity and immune function are deregulated in ASD. Mouse models provide unique tools to investigate the neurobiological basis of ASD; however, a comprehensive approach to identify transcriptional abnormalities in different ASD models has never been performed. Here we used two well-recognized ASD mouse models, BTBR T+ Itpr3tf/J (BTBR and Engrailed-2 knockout (En2-/-, to identify conserved ASD-related molecular signatures. En2-/- mice bear a mutation within the EN2 transcription factor homeobox, while BTBR is an inbred strain with unknown genetic defects. Hippocampal RNA samples from BTBR, En2-/- and respective control (C57Bl/6J and En2+/+ adult mice were assessed for differential gene expression using microarrays. A total of 153 genes were similarly deregulated in the BTBR and En2-/- hippocampus. Mouse phenotype and gene ontology enrichment analyses were performed on BTBR and En2-/- hippocampal differentially expressed genes (DEGs. Pathways represented in both BTBR and En2-/- hippocampal DEGs included abnormal behavioral response and chemokine/MAP kinase signaling. Genes involved in abnormal function of the immune system and abnormal synaptic transmission/seizures were significantly represented among BTBR and En2-/- DEGs, respectively. Interestingly, both BTBR and En2-/- hippocampal DEGs showed a significant enrichment of ASD and schizophrenia (SCZ-associated genes. Specific gene sets were enriched in the two models: microglial genes were significantly enriched among BTBR DEGs, whereas GABAergic/glutamatergic postsynaptic genes, FMRP-interacting genes and epilepsy-related genes were significantly enriched among En2-/- DEGs. Weighted

Establishment of canine hemangiosarcoma xenograft models expressing endothelial growth factors, their receptors, and angiogenesis-associated homeobox genes

International Nuclear Information System (INIS)

Kodama, Atsushi; Yanai, Tokuma; Sakai, Hiroki; Matsuura, Satoko; Murakami, Mami; Murai, Atsuko; Mori, Takashi; Maruo, Kouji; Kimura, Tohru; Masegi, Toshiaki

2009-01-01

Human hemangiosarcoma (HSA) tends to have a poor prognosis; its tumorigenesis has not been elucidated, as there is a dearth of HSA clinical specimens and no experimental model for HSA. However, the incidence of spontaneous HSA is relatively high in canines; therefore, canine HSA has been useful in the study of human HSA. Recently, the production of angiogenic growth factors and their receptors in human and canine HSA has been reported. Moreover, the growth-factor environment of HSA is very similar to that of pathophysiological angiogenesis, which some homeobox genes regulate in the transcription of angiogenic molecules. In the present study, we established 6 xenograft canine HSA tumors and detected the expression of growth factors, their receptors, and angiogenic homeobox genes. Six primary canine HSAs were xenografted to nude mice subcutaneously and serially transplanted. Subsequently, the expressions of vascular endothelial growth factor (VEGF)-A, basic fibroblast growth factors (bFGF), flt-1 and flk-1 (receptors of VEGF-A), FGFR-1, and angiogenic homeobox genes HoxA9, HoxB3, HoxB7, HoxD3, Pbx1, and Meis1 were investigated in original and xenograft tumors by histopathology, immunostaining, and reverse transcription polymerase chain reaction (RT-PCR), using canine-specific primer sets. Histopathologically, xenograft tumors comprised a proliferation of neoplastic cells that were varied in shape, from spindle-shaped and polygonal to ovoid; some vascular-like structures and vascular clefts of channels were observed, similar to those in the original tumors. The expression of endothelial markers (CD31 and vWF) was detected in xenograft tumors by immunohistochemistry and RT-PCR. Moreover, the expression of VEGF-A, bFGF, flt-1, flk-1, FGFR-1, HoxA9, HoxB3, HoxB7, HoxD3, Pbx1, and Meis1 was detected in xenograft tumors. Interestingly, expressions of bFGF tended to be higher in 3 of the xenograft HSA tumors than in the other tumors. We established 6 xenograft canine HSA

Detection of transgenerational spermatogenic inheritance of adult male acquired CNS gene expression characteristics using a Drosophila systems model.

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Abhay Sharma

Full Text Available Available instances of inheritance of epigenetic transgenerational phenotype are limited to environmental exposures during embryonic and adult gonadal development. Adult exposures can also affect gametogenesis and thereby potentially result in reprogramming of the germline. Although examples of epigenetic effects on gametogenesis exist, it is notable that transgenerational inheritance of environment-induced adult phenotype has not yet been reported. Epigenetic codes are considered to be critical in neural plasticity. A Drosophila systems model of pentylenetetrazole (PTZ induced long-term brain plasticity has recently been described. In this model, chronic PTZ treatment of adult males causes alterations in CNS transcriptome. Here, we describe our search for transgenerational spermatogenic inheritance of PTZ induced gene expression phenotype acquired by adult Drosophila males. We generated CNS transcriptomic profiles of F(1 adults after treating F(0 adult males with PTZ and of F(2 adults resulting from a cross between F(1 males and normal females. Surprisingly, microarray clustering showed F(1 male profile as closest to F(1 female and F(0 male profile closest to F(2 male. Differentially expressed genes in F(1 males, F(1 females and F(2 males showed significant overlap with those caused by PTZ. Interestingly, microarray evidence also led to the identification of upregulated rRNA in F(2 males. Next, we generated microarray expression profiles of adult testis from F(0 and F(1 males. Further surprising, clustering of CNS and testis profiles and matching of differentially expressed genes in them provided evidence of a spermatogenic mechanism in the transgenerational effect observed. To our knowledge, we report for the first time detection of transgenerational spermatogenic inheritance of adult acquired somatic gene expression characteristic. The Drosophila systems model offers an excellent opportunity to understand the epigenetic mechanisms underlying

Detection of transgenerational spermatogenic inheritance of adult male acquired CNS gene expression characteristics using a Drosophila systems model.

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Sharma, Abhay; Singh, Priyanka

2009-06-02

Available instances of inheritance of epigenetic transgenerational phenotype are limited to environmental exposures during embryonic and adult gonadal development. Adult exposures can also affect gametogenesis and thereby potentially result in reprogramming of the germline. Although examples of epigenetic effects on gametogenesis exist, it is notable that transgenerational inheritance of environment-induced adult phenotype has not yet been reported. Epigenetic codes are considered to be critical in neural plasticity. A Drosophila systems model of pentylenetetrazole (PTZ) induced long-term brain plasticity has recently been described. In this model, chronic PTZ treatment of adult males causes alterations in CNS transcriptome. Here, we describe our search for transgenerational spermatogenic inheritance of PTZ induced gene expression phenotype acquired by adult Drosophila males. We generated CNS transcriptomic profiles of F(1) adults after treating F(0) adult males with PTZ and of F(2) adults resulting from a cross between F(1) males and normal females. Surprisingly, microarray clustering showed F(1) male profile as closest to F(1) female and F(0) male profile closest to F(2) male. Differentially expressed genes in F(1) males, F(1) females and F(2) males showed significant overlap with those caused by PTZ. Interestingly, microarray evidence also led to the identification of upregulated rRNA in F(2) males. Next, we generated microarray expression profiles of adult testis from F(0) and F(1) males. Further surprising, clustering of CNS and testis profiles and matching of differentially expressed genes in them provided evidence of a spermatogenic mechanism in the transgenerational effect observed. To our knowledge, we report for the first time detection of transgenerational spermatogenic inheritance of adult acquired somatic gene expression characteristic. The Drosophila systems model offers an excellent opportunity to understand the epigenetic mechanisms underlying the

Structure and expression of thyroglobulin gene

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Vassart, G; Brocas, H; Christophe, D; de Martynoff, G; Leriche, A; Mercken, L; Pohl, V; van Heuverswyn, B [Institut de Recherche Interdisciplinaire en Biologie Humaine et Nucleaire (IRIBHN), Faculte de Medecine, Universite libre de Bruxelles, Campus Hopital Erasme, Brussels (Belgium)

1982-01-01

Thyroglobulin is composed of two 300000 dalton polypeptide chains, translated from an 8000 base mRNA. Preparation of a full length cDNA and its cloning in E. coli have lead to the demonstration that the polypeptides of thyroglobulin protomers were identical. Used as molecular probes, the cloned cDNA allowed the isolation of a fragment of thyroglobulin gene. Electron microscopic studies have demonstrated that this gene contains more than 90 % intronic material separating small size exons (<200 bp). Sequencing of bovine thyroglobulin structural gene is in progress. Preliminary results show evidence for the existence of repetitive segments. Availability of cloned DNA complementary to bovine and human thyroglobulin mRNA allows the study of genetic defects of thyroglobulin gene expression in the human and in various animal models.

Aldehyde Dehydrogenase Gene Superfamily in Populus: Organization and Expression Divergence between Paralogous Gene Pairs.

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Tian, Feng-Xia; Zang, Jian-Lei; Wang, Tan; Xie, Yu-Li; Zhang, Jin; Hu, Jian-Jun

2015-01-01

Aldehyde dehydrogenases (ALDHs) constitute a superfamily of NAD(P)+-dependent enzymes that catalyze the irreversible oxidation of a wide range of reactive aldehydes to their corresponding nontoxic carboxylic acids. ALDHs have been studied in many organisms from bacteria to mammals; however, no systematic analyses incorporating genome organization, gene structure, expression profiles, and cis-acting elements have been conducted in the model tree species Populus trichocarpa thus far. In this study, a comprehensive analysis of the Populus ALDH gene superfamily was performed. A total of 26 Populus ALDH genes were found to be distributed across 12 chromosomes. Genomic organization analysis indicated that purifying selection may have played a pivotal role in the retention and maintenance of PtALDH gene families. The exon-intron organizations of PtALDHs were highly conserved within the same family, suggesting that the members of the same family also may have conserved functionalities. Microarray data and qRT-PCR analysis indicated that most PtALDHs had distinct tissue-specific expression patterns. The specificity of cis-acting elements in the promoter regions of the PtALDHs and the divergence of expression patterns between nine paralogous PtALDH gene pairs suggested that gene duplications may have freed the duplicate genes from the functional constraints. The expression levels of some ALDHs were up- or down-regulated by various abiotic stresses, implying that the products of these genes may be involved in the adaptation of Populus to abiotic stresses. Overall, the data obtained from our investigation contribute to a better understanding of the complexity of the Populus ALDH gene superfamily and provide insights into the function and evolution of ALDH gene families in vascular plants.

Aldehyde Dehydrogenase Gene Superfamily in Populus: Organization and Expression Divergence between Paralogous Gene Pairs.

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Feng-Xia Tian

Full Text Available Aldehyde dehydrogenases (ALDHs constitute a superfamily of NAD(P+-dependent enzymes that catalyze the irreversible oxidation of a wide range of reactive aldehydes to their corresponding nontoxic carboxylic acids. ALDHs have been studied in many organisms from bacteria to mammals; however, no systematic analyses incorporating genome organization, gene structure, expression profiles, and cis-acting elements have been conducted in the model tree species Populus trichocarpa thus far. In this study, a comprehensive analysis of the Populus ALDH gene superfamily was performed. A total of 26 Populus ALDH genes were found to be distributed across 12 chromosomes. Genomic organization analysis indicated that purifying selection may have played a pivotal role in the retention and maintenance of PtALDH gene families. The exon-intron organizations of PtALDHs were highly conserved within the same family, suggesting that the members of the same family also may have conserved functionalities. Microarray data and qRT-PCR analysis indicated that most PtALDHs had distinct tissue-specific expression patterns. The specificity of cis-acting elements in the promoter regions of the PtALDHs and the divergence of expression patterns between nine paralogous PtALDH gene pairs suggested that gene duplications may have freed the duplicate genes from the functional constraints. The expression levels of some ALDHs were up- or down-regulated by various abiotic stresses, implying that the products of these genes may be involved in the adaptation of Populus to abiotic stresses. Overall, the data obtained from our investigation contribute to a better understanding of the complexity of the Populus ALDH gene superfamily and provide insights into the function and evolution of ALDH gene families in vascular plants.

Expression studies of the obesity candidate gene FTO in pig

DEFF Research Database (Denmark)

Madsen, Majbritt Busk; Birck, Malene Muusfeldt; Fredholm, Merete

2010-01-01

Obesity is an increasing problem worldwide and research on candidate genes in good animal models is highly needed. The pig is an excellent model as its metabolism, organ size, and eating habits resemble that of humans. The present study is focused on the characterization of the fat mass and obesity...... associated gene (FTO) in pig. This gene has recently been associated with increased body mass index in several human populations. To establish information on the expression profile of FTO in the pig we performed quantitative PCR in a panel of adult pig tissues and in tissues sampled at different...... and cerebellum). Additionally, in order to see the involvement of the FTO gene in obesity, the changes in expression level were investigated in a nutritional study in brain of Gottingen minipigs under a high cholesterol diet. Significantly higher (P

Dynamic sporulation gene co-expression networks for Bacillus subtilis 168 and the food-borne isolate Bacillus amyloliquefaciens: a transcriptomic model.

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Omony, Jimmy; de Jong, Anne; Krawczyk, Antonina O; Eijlander, Robyn T; Kuipers, Oscar P

2018-02-09

Sporulation is a survival strategy, adapted by bacterial cells in response to harsh environmental adversities. The adaptation potential differs between strains and the variations may arise from differences in gene regulation. Gene networks are a valuable way of studying such regulation processes and establishing associations between genes. We reconstructed and compared sporulation gene co-expression networks (GCNs) of the model laboratory strain Bacillus subtilis 168 and the food-borne industrial isolate Bacillus amyloliquefaciens. Transcriptome data obtained from samples of six stages during the sporulation process were used for network inference. Subsequently, a gene set enrichment analysis was performed to compare the reconstructed GCNs of B. subtilis 168 and B. amyloliquefaciens with respect to biological functions, which showed the enriched modules with coherent functional groups associated with sporulation. On basis of the GCNs and time-evolution of differentially expressed genes, we could identify novel candidate genes strongly associated with sporulation in B. subtilis 168 and B. amyloliquefaciens. The GCNs offer a framework for exploring transcription factors, their targets, and co-expressed genes during sporulation. Furthermore, the methodology described here can conveniently be applied to other species or biological processes.

Comprehensive analysis of gene expression patterns of hedgehog-related genes

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Baillie David

2006-10-01

Full Text Available Abstract Background The Caenorhabditis elegans genome encodes ten proteins that share sequence similarity with the Hedgehog signaling molecule through their C-terminal autoprocessing Hint/Hog domain. These proteins contain novel N-terminal domains, and C. elegans encodes dozens of additional proteins containing only these N-terminal domains. These gene families are called warthog, groundhog, ground-like and quahog, collectively called hedgehog (hh-related genes. Previously, the expression pattern of seventeen genes was examined, which showed that they are primarily expressed in the ectoderm. Results With the completion of the C. elegans genome sequence in November 2002, we reexamined and identified 61 hh-related ORFs. Further, we identified 49 hh-related ORFs in C. briggsae. ORF analysis revealed that 30% of the genes still had errors in their predictions and we improved these predictions here. We performed a comprehensive expression analysis using GFP fusions of the putative intergenic regulatory sequence with one or two transgenic lines for most genes. The hh-related genes are expressed in one or a few of the following tissues: hypodermis, seam cells, excretory duct and pore cells, vulval epithelial cells, rectal epithelial cells, pharyngeal muscle or marginal cells, arcade cells, support cells of sensory organs, and neuronal cells. Using time-lapse recordings, we discovered that some hh-related genes are expressed in a cyclical fashion in phase with molting during larval development. We also generated several translational GFP fusions, but they did not show any subcellular localization. In addition, we also studied the expression patterns of two genes with similarity to Drosophila frizzled, T23D8.1 and F27E11.3A, and the ortholog of the Drosophila gene dally-like, gpn-1, which is a heparan sulfate proteoglycan. The two frizzled homologs are expressed in a few neurons in the head, and gpn-1 is expressed in the pharynx. Finally, we compare the

Changes in Hepatic TRβ Protein Expression, Lipogenic Gene Expression, and Long-Chain Acylcarnitine Levels During Chronic Hyperthyroidism and Triiodothyronine Withdrawal in a Mouse Model.

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Ohba, Kenji; Sinha, Rohit Anthony; Singh, Brijesh Kumar; Iannucci, Liliana Felicia; Zhou, Jin; Kovalik, Jean-Paul; Liao, Xiao-Hui; Refetoff, Samuel; Sng, Judy Chia Ghee; Leow, Melvin Khee-Shing; Yen, Paul Michael

2017-06-01

Thyroid hormone (TH) has important roles in regulating hepatic metabolism. It was previously reported that most hepatic genes activated by a single triiodothyronine (T3) injection became desensitized after multiple injections, and that approximately 10% of target genes did not return to basal expression levels after T3 withdrawal, despite normalization of serum TH and thyrotropin (TSH) levels. To determine the possible mechanism(s) for desensitization and incomplete recovery of hepatic target gene transcription and their effects on metabolism, mRNA and/or protein expression levels of key regulators of TH action were measured, as well as metabolomic changes after chronic T3 treatment and withdrawal. Adult male mice were treated with daily injections of T3 (20 μg/100 g body weight) for 14 days followed by the cessation of T3 for 10 days. Livers were harvested at 6 hours, 24 hours, and 14 days after the first T3 injection, and at 10 days after withdrawal, and then analyzed by quantitative reverse transcription polymerase chain reaction, Western blotting, and metabolomics. Although TH receptor (TRα and TRβ) mRNAs decreased slightly after chronic T3 treatment, only TRβ protein decreased before returning to basal expression level after withdrawal. The expression of other regulators of TH action was unchanged. TRβ protein expression was also decreased in adult male monocarboxylate transporter-8 (Mct8)-knockout mice, an in vivo model of chronic intrahepatic hyperthyroidism. Previously, increased hepatic long-chain acylcarnitine levels were found after acute TH treatment. However, in this study, long-chain acylcarnitine levels were unchanged after chronic T3, and paradoxically increased after T3 withdrawal. Pathway analyses of the previous microarray results showed upregulation of lipogenic genes after acute T3 treatment and withdrawal. Phosphorylation of acetyl-CoA carboxylase also decreased after T3 withdrawal. Decreased hepatic TRβ protein expression occurred

Simple Comparative Analyses of Differentially Expressed Gene Lists May Overestimate Gene Overlap.

Science.gov (United States)

Lawhorn, Chelsea M; Schomaker, Rachel; Rowell, Jonathan T; Rueppell, Olav

2018-04-16

Comparing the overlap between sets of differentially expressed genes (DEGs) within or between transcriptome studies is regularly used to infer similarities between biological processes. Significant overlap between two sets of DEGs is usually determined by a simple test. The number of potentially overlapping genes is compared to the number of genes that actually occur in both lists, treating every gene as equal. However, gene expression is controlled by transcription factors that bind to a variable number of transcription factor binding sites, leading to variation among genes in general variability of their expression. Neglecting this variability could therefore lead to inflated estimates of significant overlap between DEG lists. With computer simulations, we demonstrate that such biases arise from variation in the control of gene expression. Significant overlap commonly arises between two lists of DEGs that are randomly generated, assuming that the control of gene expression is variable among genes but consistent between corresponding experiments. More overlap is observed when transcription factors are specific to their binding sites and when the number of genes is considerably higher than the number of different transcription factors. In contrast, overlap between two DEG lists is always lower than expected when the genetic architecture of expression is independent between the two experiments. Thus, the current methods for determining significant overlap between DEGs are potentially confounding biologically meaningful overlap with overlap that arises due to variability in control of expression among genes, and more sophisticated approaches are needed.

Spatial gene expression quantification: a tool for analysis of in situ hybridizations in sea anemone Nematostella vectensis

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Botman Daniel

2012-10-01

Full Text Available Abstract Background Spatial gene expression quantification is required for modeling gene regulation in developing organisms. The fruit fly Drosophila melanogaster is the model system most widely applied for spatial gene expression analysis due to its unique embryonic properties: the shape does not change significantly during its early cleavage cycles and most genes are differentially expressed along a straight axis. This system of development is quite exceptional in the animal kingdom. In the sea anemone Nematostella vectensis the embryo changes its shape during early development; there are cell divisions and cell movement, like in most other metazoans. Nematostella is an attractive case study for spatial gene expression since its transparent body wall makes it accessible to various imaging techniques. Findings Our new quantification method produces standardized gene expression profiles from raw or annotated Nematostella in situ hybridizations by measuring the expression intensity along its cell layer. The procedure is based on digital morphologies derived from high-resolution fluorescence pictures. Additionally, complete descriptions of nonsymmetric expression patterns have been constructed by transforming the gene expression images into a three-dimensional representation. Conclusions We created a standard format for gene expression data, which enables quantitative analysis of in situ hybridizations from embryos with various shapes in different developmental stages. The obtained expression profiles are suitable as input for optimization of gene regulatory network models, and for correlation analysis of genes from dissimilar Nematostella morphologies. This approach is potentially applicable to many other metazoan model organisms and may also be suitable for processing data from three-dimensional imaging techniques.

Methods for monitoring multiple gene expression

Energy Technology Data Exchange (ETDEWEB)

Berka, Randy [Davis, CA; Bachkirova, Elena [Davis, CA; Rey, Michael [Davis, CA

2012-05-01

The present invention relates to methods for monitoring differential expression of a plurality of genes in a first filamentous fungal cell relative to expression of the same genes in one or more second filamentous fungal cells using microarrays containing Trichoderma reesei ESTs or SSH clones, or a combination thereof. The present invention also relates to computer readable media and substrates containing such array features for monitoring expression of a plurality of genes in filamentous fungal cells.

Methods for monitoring multiple gene expression

Energy Technology Data Exchange (ETDEWEB)

Berka, Randy; Bachkirova, Elena; Rey, Michael

2013-10-01

The present invention relates to methods for monitoring differential expression of a plurality of genes in a first filamentous fungal cell relative to expression of the same genes in one or more second filamentous fungal cells using microarrays containing Trichoderma reesei ESTs or SSH clones, or a combination thereof. The present invention also relates to computer readable media and substrates containing such array features for monitoring expression of a plurality of genes in filamentous fungal cells.

Integration of biological networks and gene expression data using Cytoscape

DEFF Research Database (Denmark)

Cline, M.S.; Smoot, M.; Cerami, E.

2007-01-01

of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules......Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context...... and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape....

Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease.

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Peter R Sinnaeve

Full Text Available Systemic and local inflammation plays a prominent role in the pathogenesis of atherosclerotic coronary artery disease, but the relationship of whole blood gene expression changes with coronary disease remains unclear. We have investigated whether gene expression patterns in peripheral blood correlate with the severity of coronary disease and whether these patterns correlate with the extent of atherosclerosis in the vascular wall. Patients were selected according to their coronary artery disease index (CADi, a validated angiographical measure of the extent of coronary atherosclerosis that correlates with outcome. RNA was extracted from blood of 120 patients with at least a stenosis greater than 50% (CADi > or = 23 and from 121 controls without evidence of coronary stenosis (CADi = 0. 160 individual genes were found to correlate with CADi (rho > 0.2, P<0.003. Prominent differential expression was observed especially in genes involved in cell growth, apoptosis and inflammation. Using these 160 genes, a partial least squares multivariate regression model resulted in a highly predictive model (r(2 = 0.776, P<0.0001. The expression pattern of these 160 genes in aortic tissue also predicted the severity of atherosclerosis in human aortas, showing that peripheral blood gene expression associated with coronary atherosclerosis mirrors gene expression changes in atherosclerotic arteries. In conclusion, the simultaneous expression pattern of 160 genes in whole blood correlates with the severity of coronary artery disease and mirrors expression changes in the atherosclerotic vascular wall.

Determinants of human adipose tissue gene expression

DEFF Research Database (Denmark)

Viguerie, Nathalie; Montastier, Emilie; Maoret, Jean-José

2012-01-01

weight maintenance diets. For 175 genes, opposite regulation was observed during calorie restriction and weight maintenance phases, independently of variations in body weight. Metabolism and immunity genes showed inverse profiles. During the dietary intervention, network-based analyses revealed strong...... interconnection between expression of genes involved in de novo lipogenesis and components of the metabolic syndrome. Sex had a marked influence on AT expression of 88 transcripts, which persisted during the entire dietary intervention and after control for fat mass. In women, the influence of body mass index...... on expression of a subset of genes persisted during the dietary intervention. Twenty-two genes revealed a metabolic syndrome signature common to men and women. Genetic control of AT gene expression by cis signals was observed for 46 genes. Dietary intervention, sex, and cis genetic variants independently...

LINE FUSION GENES: a database of LINE expression in human genes

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Park Hong-Seog

2006-06-01

Full Text Available Abstract Background Long Interspersed Nuclear Elements (LINEs are the most abundant retrotransposons in humans. About 79% of human genes are estimated to contain at least one segment of LINE per transcription unit. Recent studies have shown that LINE elements can affect protein sequences, splicing patterns and expression of human genes. Description We have developed a database, LINE FUSION GENES, for elucidating LINE expression throughout the human gene database. We searched the 28,171 genes listed in the NCBI database for LINE elements and analyzed their structures and expression patterns. The results show that the mRNA sequences of 1,329 genes were affected by LINE expression. The LINE expression types were classified on the basis of LINEs in the 5' UTR, exon or 3' UTR sequences of the mRNAs. Our database provides further information, such as the tissue distribution and chromosomal location of the genes, and the domain structure that is changed by LINE integration. We have linked all the accession numbers to the NCBI data bank to provide mRNA sequences for subsequent users. Conclusion We believe that our work will interest genome scientists and might help them to gain insight into the implications of LINE expression for human evolution and disease. Availability http://www.primate.or.kr/line

Gene organization in rice revealed by full-length cDNA mapping and gene expression analysis through microarray.

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Kouji Satoh

Full Text Available Rice (Oryza sativa L. is a model organism for the functional genomics of monocotyledonous plants since the genome size is considerably smaller than those of other monocotyledonous plants. Although highly accurate genome sequences of indica and japonica rice are available, additional resources such as full-length complementary DNA (FL-cDNA sequences are also indispensable for comprehensive analyses of gene structure and function. We cross-referenced 28.5K individual loci in the rice genome defined by mapping of 578K FL-cDNA clones with the 56K loci predicted in the TIGR genome assembly. Based on the annotation status and the presence of corresponding cDNA clones, genes were classified into 23K annotated expressed (AE genes, 33K annotated non-expressed (ANE genes, and 5.5K non-annotated expressed (NAE genes. We developed a 60mer oligo-array for analysis of gene expression from each locus. Analysis of gene structures and expression levels revealed that the general features of gene structure and expression of NAE and ANE genes were considerably different from those of AE genes. The results also suggested that the cloning efficiency of rice FL-cDNA is associated with the transcription activity of the corresponding genetic locus, although other factors may also have an effect. Comparison of the coverage of FL-cDNA among gene families suggested that FL-cDNA from genes encoding rice- or eukaryote-specific domains, and those involved in regulatory functions were difficult to produce in bacterial cells. Collectively, these results indicate that rice genes can be divided into distinct groups based on transcription activity and gene structure, and that the coverage bias of FL-cDNA clones exists due to the incompatibility of certain eukaryotic genes in bacteria.

Alterations in gene expression profiles between radioresistant and radiosensitive cell lines

International Nuclear Information System (INIS)

Zhou Fuxiang; Zhou Yunfeng; Xie Conghua; Dai Jing; Cao Zhen; Yu Haijun; Liao Zhengkai; Luo Zhiguo

2007-01-01

Objective: To study the-difference of gene expressions by the contrastive model including the cells with same pathological origin and genetic background, but definitely different radioresponse, and to find the main molecular targets related to radiosensitivity. Methods: Human larynx squamous carcinoma cell, Hep -2 was irradiated with dose of 637 cGy repeatedly to establish a radioresistant daughter cell line. The radiobiology characteristics were obtained using clone forming assay. The difference of gene expression between parent and daughter cells was detected by cDNA microarray using two different arrays including 14000 genes respectively. Results: A radioresistant cell strain Hep-2R was isolated from its parental strain Hep-2 cell. The SF 2 , D 0 , α, β for Hep-2R cell line were 0.6798, 3.24, 0.2951 and 0.0363, respectively, while 0.4148, 2.06, 0.1074 and 0.0405 for Hep-2, respectively (for SF 2 , χ 2 =63.957, P<0.001). Compared with Hep-2 cells, the expressions of 41 genes were significantly altered in the radioresistant Hep-2R cells, including 22 genes up-regulated and 19 genes down-regulated, which were involved in DNA repair, regulation of the cell cycle, cell proliferation, cytoskeleton, protein synthesis, cellular metabolism and especially apoptosis which is responsible for the different radiosensitivity between these two larynx cancer cells. The telomere protection protein gene, POT1, was the mostly up-regulated by 3.348 times. Conclusions: There is difference of gene expression between the radioresistant contrastive models. POT1 gene may be the target of radiosensitization. (authors)

Design parameters to control synthetic gene expression in Escherichia coli.

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Mark Welch

Full Text Available BACKGROUND: Production of proteins as therapeutic agents, research reagents and molecular tools frequently depends on expression in heterologous hosts. Synthetic genes are increasingly used for protein production because sequence information is easier to obtain than the corresponding physical DNA. Protein-coding sequences are commonly re-designed to enhance expression, but there are no experimentally supported design principles. PRINCIPAL FINDINGS: To identify sequence features that affect protein expression we synthesized and expressed in E. coli two sets of 40 genes encoding two commercially valuable proteins, a DNA polymerase and a single chain antibody. Genes differing only in synonymous codon usage expressed protein at levels ranging from undetectable to 30% of cellular protein. Using partial least squares regression we tested the correlation of protein production levels with parameters that have been reported to affect expression. We found that the amount of protein produced in E. coli was strongly dependent on the codons used to encode a subset of amino acids. Favorable codons were predominantly those read by tRNAs that are most highly charged during amino acid starvation, not codons that are most abundant in highly expressed E. coli proteins. Finally we confirmed the validity of our models by designing, synthesizing and testing new genes using codon biases predicted to perform well. CONCLUSION: The systematic analysis of gene design parameters shown in this study has allowed us to identify codon usage within a gene as a critical determinant of achievable protein expression levels in E. coli. We propose a biochemical basis for this, as well as design algorithms to ensure high protein production from synthetic genes. Replication of this methodology should allow similar design algorithms to be empirically derived for any expression system.

Global expression differences and tissue specific expression differences in rice evolution result in two contrasting types of differentially expressed genes

KAUST Repository

Horiuchi, Youko

2015-12-23

Background Since the development of transcriptome analysis systems, many expression evolution studies characterized evolutionary forces acting on gene expression, without explicit discrimination between global expression differences and tissue specific expression differences. However, different types of gene expression alteration should have different effects on an organism, the evolutionary forces that act on them might be different, and different types of genes might show different types of differential expression between species. To confirm this, we studied differentially expressed (DE) genes among closely related groups that have extensive gene expression atlases, and clarified characteristics of different types of DE genes including the identification of regulating loci for differential expression using expression quantitative loci (eQTL) analysis data. Results We detected differentially expressed (DE) genes between rice subspecies in five homologous tissues that were verified using japonica and indica transcriptome atlases in public databases. Using the transcriptome atlases, we classified DE genes into two types, global DE genes and changed-tissues DE genes. Global type DE genes were not expressed in any tissues in the atlas of one subspecies, however changed-tissues type DE genes were expressed in both subspecies with different tissue specificity. For the five tissues in the two japonica-indica combinations, 4.6 ± 0.8 and 5.9 ± 1.5 % of highly expressed genes were global and changed-tissues DE genes, respectively. Changed-tissues DE genes varied in number between tissues, increasing linearly with the abundance of tissue specifically expressed genes in the tissue. Molecular evolution of global DE genes was rapid, unlike that of changed-tissues DE genes. Based on gene ontology, global and changed-tissues DE genes were different, having no common GO terms. Expression differences of most global DE genes were regulated by cis-eQTLs. Expression

Social Regulation of Gene Expression in Threespine Sticklebacks.

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Anna K Greenwood

Full Text Available Identifying genes that are differentially expressed in response to social interactions is informative for understanding the molecular basis of social behavior. To address this question, we described changes in gene expression as a result of differences in the extent of social interactions. We housed threespine stickleback (Gasterosteus aculeatus females in either group conditions or individually for one week, then measured levels of gene expression in three brain regions using RNA-sequencing. We found that numerous genes in the hindbrain/cerebellum had altered expression in response to group or individual housing. However, relatively few genes were differentially expressed in either the diencephalon or telencephalon. The list of genes upregulated in fish from social groups included many genes related to neural development and cell adhesion as well as genes with functions in sensory signaling, stress, and social and reproductive behavior. The list of genes expressed at higher levels in individually-housed fish included several genes previously identified as regulated by social interactions in other animals. The identified genes are interesting targets for future research on the molecular mechanisms of normal social interactions.

Elimination of contaminating cap genes in AAV vector virions reduces immune responses and improves transgene expression in a canine gene therapy model.

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Wang, Z; Halbert, C L; Lee, D; Butts, T; Tapscott, S J; Storb, R; Miller, A D

2014-04-01

Animal and human gene therapy studies utilizing AAV vectors have shown that immune responses to AAV capsid proteins can severely limit transgene expression. The main source of capsid antigen is that associated with the AAV vectors, which can be reduced by stringent vector purification. A second source of AAV capsid proteins is that expressed from cap genes aberrantly packaged into AAV virions during vector production. This antigen source can be eliminated by the use of a cap gene that is too large to be incorporated into an AAV capsid, such as a cap gene containing a large intron (captron gene). Here, we investigated the effects of elimination of cap gene transfer and of vector purification by CsCl gradient centrifugation on AAV vector immunogenicity and expression following intramuscular injection in dogs. We found that both approaches reduced vector immunogenicity and that combining the two produced the lowest immune responses and highest transgene expression. This combined approach enabled the use of a relatively mild immunosuppressive regimen to promote robust micro-dystrophin gene expression in Duchenne muscular dystrophy-affected dogs. Our study shows the importance of minimizing AAV cap gene impurities and indicates that this improvement in AAV vector production may benefit human applications.

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.

Gene Expression Contributes to the Recent Evolution of Host Resistance in a Model Host Parasite System

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Brian K. Lohman

2017-09-01

Full Text Available Heritable population differences in immune gene expression following infection can reveal mechanisms of host immune evolution. We compared gene expression in infected and uninfected threespine stickleback (Gasterosteus aculeatus from two natural populations that differ in resistance to a native cestode parasite, Schistocephalus solidus. Genes in both the innate and adaptive immune system were differentially expressed as a function of host population, infection status, and their interaction. These genes were enriched for loci controlling immune functions known to differ between host populations or in response to infection. Coexpression network analysis identified two distinct processes contributing to resistance: parasite survival and suppression of growth. Comparing networks between populations showed resistant fish have a dynamic expression profile while susceptible fish are static. In summary, recent evolutionary divergence between two vertebrate populations has generated population-specific gene expression responses to parasite infection, affecting parasite establishment and growth.

Gene expression in a paleopolyploid: a transcriptome resource for the ciliate Paramecium tetraurelia

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Kapusta Aurélie

2010-10-01

Full Text Available Abstract Background The genome of Paramecium tetraurelia, a unicellular model that belongs to the ciliate phylum, has been shaped by at least 3 successive whole genome duplications (WGD. These dramatic events, which have also been documented in plants, animals and fungi, are resolved over evolutionary time by the loss of one duplicate for the majority of genes. Thanks to a low rate of large scale genome rearrangement in Paramecium, an unprecedented large number of gene duplicates of different ages have been identified, making this organism an outstanding model to investigate the evolutionary consequences of polyploidization. The most recent WGD, with 51% of pre-duplication genes still in 2 copies, provides a snapshot of a phase of rapid gene loss that is not accessible in more ancient polyploids such as yeast. Results We designed a custom oligonucleotide microarray platform for P. tetraurelia genome-wide expression profiling and used the platform to measure gene expression during 1 the sexual cycle of autogamy, 2 growth of new cilia in response to deciliation and 3 biogenesis of secretory granules after massive exocytosis. Genes that are differentially expressed during these time course experiments have expression patterns consistent with a very low rate of subfunctionalization (partition of ancestral functions between duplicated genes in particular since the most recent polyploidization event. Conclusions A public transcriptome resource is now available for Paramecium tetraurelia. The resource has been integrated into the ParameciumDB model organism database, providing searchable access to the data. The microarray platform, freely available through NimbleGen Systems, provides a robust, cost-effective approach for genome-wide expression profiling in P. tetraurelia. The expression data support previous studies showing that at short evolutionary times after a whole genome duplication, gene dosage balance constraints and not functional change are

A constructive approach to gene expression dynamics

International Nuclear Information System (INIS)

Ochiai, T.; Nacher, J.C.; Akutsu, T.

2004-01-01

Recently, experiments on mRNA abundance (gene expression) have revealed that gene expression shows a stationary organization described by a scale-free distribution. Here we propose a constructive approach to gene expression dynamics which restores the scale-free exponent and describes the intermediate state dynamics. This approach requires only one assumption: Markov property

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection.

Science.gov (United States)

Carey, Michelle; Ramírez, Juan Camilo; Wu, Shuang; Wu, Hulin

2018-07-01

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

Effects of lithium and valproic acid on gene expression and phenotypic markers in an NT2 neurosphere model of neural development.

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Eric J Hill

Full Text Available Mood stabilising drugs such as lithium (LiCl and valproic acid (VPA are the first line agents for treating conditions such as Bipolar disorder and Epilepsy. However, these drugs have potential developmental effects that are not fully understood. This study explores the use of a simple human neurosphere-based in vitro model to characterise the pharmacological and toxicological effects of LiCl and VPA using gene expression changes linked to phenotypic alterations in cells. Treatment with VPA and LiCl resulted in the differential expression of 331 and 164 genes respectively. In the subset of VPA targeted genes, 114 were downregulated whilst 217 genes were upregulated. In the subset of LiCl targeted genes, 73 were downregulated and 91 were upregulated. Gene ontology (GO term enrichment analysis was used to highlight the most relevant GO terms associated with a given gene list following toxin exposure. In addition, in order to phenotypically anchor the gene expression data, changes in the heterogeneity of cell subtype populations and cell cycle phase were monitored using flow cytometry. Whilst LiCl exposure did not significantly alter the proportion of cells expressing markers for stem cells/undifferentiated cells (Oct4, SSEA4, neurons (Neurofilament M, astrocytes (GFAP or cell cycle phase, the drug caused a 1.4-fold increase in total cell number. In contrast, exposure to VPA resulted in significant upregulation of Oct4, SSEA, Neurofilament M and GFAP with significant decreases in both G2/M phase cells and cell number. This neurosphere model might provide the basis of a human-based cellular approach for the regulatory exploration of developmental impact of potential toxic chemicals.

Lithium ions induce prestalk-associated gene expression and inhibit prespore gene expression in Dictyostelium discoideum

NARCIS (Netherlands)

Peters, Dorien J.M.; Lookeren Campagne, Michiel M. van; Haastert, Peter J.M. van; Spek, Wouter; Schaap, Pauline

1989-01-01

We investigated the effect of Li+ on two types of cyclic AMP-regulated gene expression and on basal and cyclic AMP-stimulated inositol 1,4,5-trisphosphate (Ins(1,4,5)P3) levels. Li+ effectively inhibits cyclic AMP-induced prespore gene expression, half-maximal inhibition occurring at about 2mM-LiCl.

Fresh Frozen Plasma Modulates Brain Gene Expression in a Swine Model of Traumatic Brain Injury and Shock

DEFF Research Database (Denmark)

Sillesen, Martin; Bambakidis, Ted; Dekker, Simone E

2017-01-01

BACKGROUND: Resuscitation with fresh frozen plasma (FFP) decreases brain lesion size and swelling in a swine model of traumatic brain injury and hemorrhagic shock. We hypothesized that brain gene expression profiles after traumatic brain injury and hemorrhagic shock would be modulated by FFP resu...

An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data.

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Daniel Ramsköld

2009-12-01

Full Text Available The parts of the genome transcribed by a cell or tissue reflect the biological processes and functions it carries out. We characterized the features of mammalian tissue transcriptomes at the gene level through analysis of RNA deep sequencing (RNA-Seq data across human and mouse tissues and cell lines. We observed that roughly 8,000 protein-coding genes were ubiquitously expressed, contributing to around 75% of all mRNAs by message copy number in most tissues. These mRNAs encoded proteins that were often intracellular, and tended to be involved in metabolism, transcription, RNA processing or translation. In contrast, genes for secreted or plasma membrane proteins were generally expressed in only a subset of tissues. The distribution of expression levels was broad but fairly continuous: no support was found for the concept of distinct expression classes of genes. Expression estimates that included reads mapping to coding exons only correlated better with qRT-PCR data than estimates which also included 3' untranslated regions (UTRs. Muscle and liver had the least complex transcriptomes, in that they expressed predominantly ubiquitous genes and a large fraction of the transcripts came from a few highly expressed genes, whereas brain, kidney and testis expressed more complex transcriptomes with the vast majority of genes expressed and relatively small contributions from the most expressed genes. mRNAs expressed in brain had unusually long 3'UTRs, and mean 3'UTR length was higher for genes involved in development, morphogenesis and signal transduction, suggesting added complexity of UTR-based regulation for these genes. Our results support a model in which variable exterior components feed into a large, densely connected core composed of ubiquitously expressed intracellular proteins.

Validation of commonly used reference genes for sleep-related gene expression studies

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Castro Rosa MRPS

2009-05-01

Full Text Available Abstract Background Sleep is a restorative process and is essential for maintenance of mental and physical health. In an attempt to understand the complexity of sleep, multidisciplinary strategies, including genetic approaches, have been applied to sleep research. Although quantitative real time PCR has been used in previous sleep-related gene expression studies, proper validation of reference genes is currently lacking. Thus, we examined the effect of total or paradoxical sleep deprivation (TSD or PSD on the expression stability of the following frequently used reference genes in brain and blood: beta-actin (b-actin, beta-2-microglobulin (B2M, glyceraldehyde-3-phosphate dehydrogenase (GAPDH, and hypoxanthine guanine phosphoribosyl transferase (HPRT. Results Neither TSD nor PSD affected the expression stability of all tested genes in both tissues indicating that b-actin, B2M, GAPDH and HPRT are appropriate reference genes for the sleep-related gene expression studies. In order to further verify these results, the relative expression of brain derived neurotrophic factor (BDNF and glycerol-3-phosphate dehydrogenase1 (GPD1 was evaluated in brain and blood, respectively. The normalization with each of four reference genes produced similar pattern of expression in control and sleep deprived rats, but subtle differences in the magnitude of expression fold change were observed which might affect the statistical significance. Conclusion This study demonstrated that sleep deprivation does not alter the expression stability of commonly used reference genes in brain and blood. Nonetheless, the use of multiple reference genes in quantitative RT-PCR is required for the accurate results.

Sex-biased gene expression in dioecious garden asparagus (Asparagus officinalis).

Science.gov (United States)

Harkess, Alex; Mercati, Francesco; Shan, Hong-Yan; Sunseri, Francesco; Falavigna, Agostino; Leebens-Mack, Jim

2015-08-01

Sex chromosomes have evolved independently in phylogenetically diverse flowering plant lineages. The genes governing sex determination in dioecious species remain unknown, but theory predicts that the linkage of genes influencing male and female function will spur the origin and early evolution of sex chromosomes. For example, in an XY system, the origin of an active Y may be spurred by the linkage of female suppressing and male promoting genes. Garden asparagus (Asparagus officinalis) serves as a model for plant sex chromosome evolution, given that it has recently evolved an XX/XY sex chromosome system. In order to elucidate the molecular basis of gender differences and sex determination, we used RNA-sequencing (RNA-Seq) to identify differentially expressed genes between female (XX), male (XY) and supermale (YY) individuals. We identified 570 differentially expressed genes, and showed that significantly more genes exhibited male-biased than female-biased expression in garden asparagus. In the context of anther development, we identified genes involved in pollen microspore and tapetum development that were specifically expressed in males and supermales. Comparative analysis of genes in the Arabidopsis thaliana, Zea mays and Oryza sativa anther development pathways shows that anther sterility in females probably occurs through interruption of tapetum development before microspore meiosis. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Estimating intrinsic and extrinsic noise from single-cell gene expression measurements

Science.gov (United States)

Fu, Audrey Qiuyan; Pachter, Lior

2017-01-01

Gene expression is stochastic and displays variation (“noise”) both within and between cells. Intracellular (intrinsic) variance can be distinguished from extracellular (extrinsic) variance by applying the law of total variance to data from two-reporter assays that probe expression of identically regulated gene pairs in single cells. We examine established formulas [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.] for the estimation of intrinsic and extrinsic noise and provide interpretations of them in terms of a hierarchical model. This allows us to derive alternative estimators that minimize bias or mean squared error. We provide a geometric interpretation of these results that clarifies the interpretation in [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.]. We also demonstrate through simulation and re-analysis of published data that the distribution assumptions underlying the hierarchical model have to be satisfied for the estimators to produce sensible results, which highlights the importance of normalization. PMID:27875323

Patterns of gene expression in a scleractinian coral undergoing natural bleaching.

Science.gov (United States)

Seneca, Francois O; Forêt, Sylvain; Ball, Eldon E; Smith-Keune, Carolyn; Miller, David J; van Oppen, Madeleine J H

2010-10-01

Coral bleaching is a major threat to coral reefs worldwide and is predicted to intensify with increasing global temperature. This study represents the first investigation of gene expression in an Indo-Pacific coral species undergoing natural bleaching which involved the loss of algal symbionts. Quantitative real-time polymerase chain reaction experiments were conducted to select and evaluate coral internal control genes (ICGs), and to investigate selected coral genes of interest (GOIs) for changes in gene expression in nine colonies of the scleractinian coral Acropora millepora undergoing bleaching at Magnetic Island, Great Barrier Reef, Australia. Among the six ICGs tested, glyceraldehyde 3-phosphate dehydrogenase and the ribosomal protein genes S7 and L9 exhibited the most constant expression levels between samples from healthy-looking colonies and samples from the same colonies when severely bleached a year later. These ICGs were therefore utilised for normalisation of expression data for seven selected GOIs. Of the seven GOIs, homologues of catalase, C-type lectin and chromoprotein genes were significantly up-regulated as a result of bleaching by factors of 1.81, 1.46 and 1.61 (linear mixed models analysis of variance, P coral bleaching response genes. In contrast, three genes, including one putative ICG, showed highly variable levels of expression between coral colonies. Potential variation in microhabitat, gene function unrelated to the stress response and individualised stress responses may influence such differences between colonies and need to be better understood when designing and interpreting future studies of gene expression in natural coral populations.