Gene therapy: a lipofection approach for gene transfer into primary endothelial cells.

Science.gov (United States)

Young, A T L; Lakey, J R T; Murray, A G; Moore, R B

2002-01-01

Despite the great potential of gene therapy to become a new treatment modality in future medicine, there are still many limitations to overcome before this gene approach can pass to the stage of human trial. The foremost obstacle is the development of a safe, efficient, and efficacious vector system for in vivo gene application. This study evaluated the efficacy of lipofection as a gene delivery vehicle into primary endothelial cells. Transfection efficiency of several lipid-based reagents (Effectene, Fugene 6, DOTAP) was examined at experimental temperatures of 37 degrees C, 24 degrees C, and 6 degrees C. Human umbilical vein endothelial cells (HUVECs) were transfected with the enhanced green fluorescent protein (EGFP) using precise amounts of DNA (Effectene, 0.2 microg; Fugene 6, 0.5 microg; DOTAP, 2.5 microg) and lipids (Effectene, 10 microl; Fugene 6, 6 microl; DOTAP, 15 microl) optimized in our laboratory. Duration of incubation in the DNA/lipid transfection mixture varied for each lipid transfectant as follows: 5 h for both Fugene 6 and DOTAP and 3 h for Effectene. Efficiency of transfection was quantified by microscopic evaluation of EFGP expression in a minimum of 100 cells per group. Transfection efficiencies achieved with these lipofection agents were 34 +/- 1.3% (mean +/- SEM), 33 +/- 1.4%, and 18 +/- 1.5% for Effectene, Fugene 6, and DOTAP, respectively, at 37 degrees C. Transfection results were lower at 24 degrees C with mean efficiencies of 26 +/- 2.4% for Effectene, 14 +/- 2.9% for Fugene 6, and 15 +/- 3.2% for DOTAP. Furthermore, mean efficiencies at 6 degrees C were 6 +/- 0.5%, 8 +/- 1.5%, and 6 +/- 0.0% for Effectene, Fugene 6, and DOTAP, respectively. Efficiency of transfection appeared to be temperature dependent (ANOVA; p lipofection a potential gene delivery strategy for in vivo gene therapy.

Modeling gene expression measurement error: a quasi-likelihood approach

Directory of Open Access Journals (Sweden)

Strimmer Korbinian

2003-03-01

Full Text Available Abstract Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale. Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood. Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic variance structure of the data. As the quasi-likelihood behaves (almost like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also

Imaging reporter gene for monitoring gene therapy

International Nuclear Information System (INIS)

Beco, V. de; Baillet, G.; Tamgac, F.; Tofighi, M.; Weinmann, P.; Vergote, J.; Moretti, J.L.; Tamgac, G.

2002-01-01

Scintigraphic images can be obtained to document gene function at cellular level. This approach is presented here and the use of a reporter gene to monitor gene therapy is described. Two main ways are presented: either the use of a reporter gene coding for an enzyme the action of which will be monitored by radiolabeled pro-drug, or a cellular receptor gene, the action of which is documented by a radio labeled cognate receptor ligand. (author)

Gene variants associated with antisocial behaviour: a latent variable approach.

Science.gov (United States)

Bentley, Mary Jane; Lin, Haiqun; Fernandez, Thomas V; Lee, Maria; Yrigollen, Carolyn M; Pakstis, Andrew J; Katsovich, Liliya; Olds, David L; Grigorenko, Elena L; Leckman, James F

2013-10-01

The aim of this study was to determine if a latent variable approach might be useful in identifying shared variance across genetic risk alleles that is associated with antisocial behaviour at age 15 years. Using a conventional latent variable approach, we derived an antisocial phenotype in 328 adolescents utilizing data from a 15-year follow-up of a randomized trial of a prenatal and infancy nurse-home visitation programme in Elmira, New York. We then investigated, via a novel latent variable approach, 450 informative genetic polymorphisms in 71 genes previously associated with antisocial behaviour, drug use, affiliative behaviours and stress response in 241 consenting individuals for whom DNA was available. Haplotype and Pathway analyses were also performed. Eight single-nucleotide polymorphisms (SNPs) from eight genes contributed to the latent genetic variable that in turn accounted for 16.0% of the variance within the latent antisocial phenotype. The number of risk alleles was linearly related to the latent antisocial variable scores. Haplotypes that included the putative risk alleles for all eight genes were also associated with higher latent antisocial variable scores. In addition, 33 SNPs from 63 of the remaining genes were also significant when added to the final model. Many of these genes interact on a molecular level, forming molecular networks. The results support a role for genes related to dopamine, norepinephrine, serotonin, glutamate, opioid and cholinergic signalling as well as stress response pathways in mediating susceptibility to antisocial behaviour. This preliminary study supports use of relevant behavioural indicators and latent variable approaches to study the potential 'co-action' of gene variants associated with antisocial behaviour. It also underscores the cumulative relevance of common genetic variants for understanding the aetiology of complex behaviour. If replicated in future studies, this approach may allow the identification of a

Positron emission tomography and gene therapy: basic concepts and experimental approaches for in vivo gene expression imaging.

Science.gov (United States)

Peñuelas, Iván; Boán, JoséF; Martí-Climent, Josep M; Sangro, Bruno; Mazzolini, Guillermo; Prieto, Jesús; Richter, José A

2004-01-01

More than two decades of intense research have allowed gene therapy to move from the laboratory to the clinical setting, where its use for the treatment of human pathologies has been considerably increased in the last years. However, many crucial questions remain to be solved in this challenging field. In vivo imaging with positron emission tomography (PET) by combination of the appropriate PET reporter gene and PET reporter probe could provide invaluable qualitative and quantitative information to answer multiple unsolved questions about gene therapy. PET imaging could be used to define parameters not available by other techniques that are of substantial interest not only for the proper understanding of the gene therapy process, but also for its future development and clinical application in humans. This review focuses on the molecular biology basis of gene therapy and molecular imaging, describing the fundamentals of in vivo gene expression imaging by PET, and the application of PET to gene therapy, as a technology that can be used in many different ways. It could be applied to avoid invasive procedures for gene therapy monitoring; accurately diagnose the pathology for better planning of the most adequate therapeutic approach; as treatment evaluation to image the functional effects of gene therapy at the biochemical level; as a quantitative noninvasive way to monitor the location, magnitude and persistence of gene expression over time; and would also help to a better understanding of vector biology and pharmacology devoted to the development of safer and more efficient vectors.

GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.

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Zhang, Song; Cao, Jing; Kong, Y Megan; Scheuermann, Richard H

2010-04-01

A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy. We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.

Integrating Ontological Knowledge and Textual Evidence in Estimating Gene and Gene Product Similarity

Energy Technology Data Exchange (ETDEWEB)

Sanfilippo, Antonio P.; Posse, Christian; Gopalan, Banu; Tratz, Stephen C.; Gregory, Michelle L.

2006-06-08

With the rising influence of the Gene On-tology, new approaches have emerged where the similarity between genes or gene products is obtained by comparing Gene Ontology code annotations associ-ated with them. So far, these approaches have solely relied on the knowledge en-coded in the Gene Ontology and the gene annotations associated with the Gene On-tology database. The goal of this paper is to demonstrate that improvements to these approaches can be obtained by integrating textual evidence extracted from relevant biomedical literature.

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.

In silico approach to identification of a novel gene responsive to ...

African Journals Online (AJOL)

Submergence is one of the major constraints to rice production. Bioinformatics approach has been widely used to identify candidate genes on many biological aspects. In the present study, a novel gene involved in submergence stress in rice, Os07g47670 was identified by in silico approach. The amino acid sequence of ...

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

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Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

2015-01-01

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Suicide genes or p53 gene and p53 target genes as targets for cancer gene therapy by ionizing radiation

International Nuclear Information System (INIS)

Liu Bing; Chinese Academy of Sciences, Beijing; Zhang Hong

2005-01-01

Radiotherapy has some disadvantages due to the severe side-effect on the normal tissues at a curative dose of ionizing radiation (IR). Similarly, as a new developing approach, gene therapy also has some disadvantages, such as lack of specificity for tumors, limited expression of therapeutic gene, potential biological risk. To certain extent, above problems would be solved by the suicide genes or p53 gene and its target genes therapies targeted by ionizing radiation. This strategy not only makes up the disadvantage from radiotherapy or gene therapy alone, but also promotes success rate on the base of lower dose. By present, there have been several vectors measuring up to be reaching clinical trials. This review focused on the development of the cancer gene therapy through suicide genes or p53 and its target genes mediated by IR. (authors)

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

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

2014-01-01

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

GSEH: A Novel Approach to Select Prostate Cancer-Associated Genes Using Gene Expression Heterogeneity.

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Kim, Hyunjin; Choi, Sang-Min; Park, Sanghyun

2018-01-01

When a gene shows varying levels of expression among normal people but similar levels in disease patients or shows similar levels of expression among normal people but different levels in disease patients, we can assume that the gene is associated with the disease. By utilizing this gene expression heterogeneity, we can obtain additional information that abets discovery of disease-associated genes. In this study, we used collaborative filtering to calculate the degree of gene expression heterogeneity between classes and then scored the genes on the basis of the degree of gene expression heterogeneity to find "differentially predicted" genes. Through the proposed method, we discovered more prostate cancer-associated genes than 10 comparable methods. The genes prioritized by the proposed method are potentially significant to biological processes of a disease and can provide insight into them.

GeneTopics - interpretation of gene sets via literature-driven topic models

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2013-01-01

Background Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set. Methods Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research. Results We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner. Conclusions Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly

Rapid approach for cloning bacterial single-genes directly from soils ...

African Journals Online (AJOL)

Obtaining functional genes of bacteria from environmental samples usually depends on library-based approach which is not favored as its large amount of work with small possibility of positive clones. A kind of bacterial single-gene encoding glutamine synthetase (GS) was selected as example to detect the efficiency of ...

Clustering based gene expression feature selection method: A computational approach to enrich the classifier efficiency of differentially expressed genes

KAUST Repository

Abusamra, Heba

2016-07-20

The native nature of high dimension low sample size of gene expression data make the classification task more challenging. Therefore, feature (gene) selection become an apparent need. Selecting a meaningful and relevant genes for classifier not only decrease the computational time and cost, but also improve the classification performance. Among different approaches of feature selection methods, however most of them suffer from several problems such as lack of robustness, validation issues etc. Here, we present a new feature selection technique that takes advantage of clustering both samples and genes. Materials and methods We used leukemia gene expression dataset [1]. The effectiveness of the selected features were evaluated by four different classification methods; support vector machines, k-nearest neighbor, random forest, and linear discriminate analysis. The method evaluate the importance and relevance of each gene cluster by summing the expression level for each gene belongs to this cluster. The gene cluster consider important, if it satisfies conditions depend on thresholds and percentage otherwise eliminated. Results Initial analysis identified 7120 differentially expressed genes of leukemia (Fig. 15a), after applying our feature selection methodology we end up with specific 1117 genes discriminating two classes of leukemia (Fig. 15b). Further applying the same method with more stringent higher positive and lower negative threshold condition, number reduced to 58 genes have be tested to evaluate the effectiveness of the method (Fig. 15c). The results of the four classification methods are summarized in Table 11. Conclusions The feature selection method gave good results with minimum classification error. Our heat-map result shows distinct pattern of refines genes discriminating between two classes of leukemia.

A network approach to predict pathogenic genes for Fusarium graminearum.

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

2010-10-04

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

A network approach to predict pathogenic genes for Fusarium graminearum.

Directory of Open Access Journals (Sweden)

Xiaoping Liu

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

Bioinformatics approach of salt tolerance gene in mangrove plant Rhizophora stylosa

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Basyuni, M.; Sumardi

2017-01-01

This study descibes bioinformatics approach on the analyze of the salt tolerance genes in mangrove plant, Rhizophora stylosa on DDBJ/EMBL/GenBank as well as similarity, phylogenetic, potential peptide, and subcellular localization. The DNA sequence between salt tolerance gene from R. stylosa exhibited 42-11% between themselves The target peptide value of mitochondria varied from 0.163 to 0.430, indicated it was possible to exist. These results suggested the importance of understanding the diversity and functional of properties of the different amino acids in mangrove OSC genes. To clarify the relationship among the salt-tolerant genes in R. stylosa, a phylogenetic tree was constructed. The phylogenetic tree shows that there are three clusters, first branch of Cu/Zn SOD and reverse transcriptase genes, the second branch consists of the majority genes and the last group was MAP3K alpha protein kinase only. The present study, therefore, suggested that salt tolerance genes form distinct clusters in the tree.

Gene selection for the reconstruction of stem cell differentiation trees: a linear programming approach.

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Ghadie, Mohamed A; Japkowicz, Nathalie; Perkins, Theodore J

2015-08-15

Stem cell differentiation is largely guided by master transcriptional regulators, but it also depends on the expression of other types of genes, such as cell cycle genes, signaling genes, metabolic genes, trafficking genes, etc. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering can organize cell types into a tree, but in general this tree is different from the differentiation hierarchy itself. Given the differentiation hierarchy and gene expression data at each node, we construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming approach to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a weighted Euclidean metric that uses just 175 genes. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. We then report on the selected genes and their biological functions. Our approach offers a new way to identify genes that may have important roles in stem cell differentiation. tperkins@ohri.ca Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Radionuclide reporter gene imaging for cardiac gene therapy

International Nuclear Information System (INIS)

Inubushi, Masayuki; Tamaki, Nagara

2007-01-01

In the field of cardiac gene therapy, angiogenic gene therapy has been most extensively investigated. The first clinical trial of cardiac angiogenic gene therapy was reported in 1998, and at the peak, more than 20 clinical trial protocols were under evaluation. However, most trials have ceased owing to the lack of decisive proof of therapeutic effects and the potential risks of viral vectors. In order to further advance cardiac angiogenic gene therapy, remaining open issues need to be resolved: there needs to be improvement of gene transfer methods, regulation of gene expression, development of much safer vectors and optimisation of therapeutic genes. For these purposes, imaging of gene expression in living organisms is of great importance. In radionuclide reporter gene imaging, ''reporter genes'' transferred into cell nuclei encode for a protein that retains a complementary ''reporter probe'' of a positron or single-photon emitter; thus expression of the reporter genes can be imaged with positron emission tomography or single-photon emission computed tomography. Accordingly, in the setting of gene therapy, the location, magnitude and duration of the therapeutic gene co-expression with the reporter genes can be monitored non-invasively. In the near future, gene therapy may evolve into combination therapy with stem/progenitor cell transplantation, so-called cell-based gene therapy or gene-modified cell therapy. Radionuclide reporter gene imaging is now expected to contribute in providing evidence on the usefulness of this novel therapeutic approach, as well as in investigating the molecular mechanisms underlying neovascularisation and safety issues relevant to further progress in conventional gene therapy. (orig.)

Discovering genes underlying QTL

Energy Technology Data Exchange (ETDEWEB)

Vanavichit, Apichart [Kasetsart University, Kamphaengsaen, Nakorn Pathom (Thailand)

2002-02-01

A map-based approach has allowed scientists to discover few genes at a time. In addition, the reproductive barrier between cultivated rice and wild relatives has prevented us from utilizing the germ plasm by a map-based approach. Most genetic traits important to agriculture or human diseases are manifested as observable, quantitative phenotypes called Quantitative Trait Loci (QTL). In many instances, the complexity of the phenotype/genotype interaction and the general lack of clearly identifiable gene products render the direct molecular cloning approach ineffective, thus additional strategies like genome mapping are required to identify the QTL in question. Genome mapping requires no prior knowledge of the gene function, but utilizes statistical methods to identify the most likely gene location. To completely characterize genes of interest, the initially mapped region of a gene location will have to be narrowed down to a size that is suitable for cloning and sequencing. Strategies for gene identification within the critical region have to be applied after the sequencing of a potentially large clone or set of clones that contains this gene(s). Tremendous success of positional cloning has been shown for cloning many genes responsible for human diseases, including cystic fibrosis and muscular dystrophy as well as plant disease resistance genes. Genome and QTL mapping, positional cloning: the pre-genomics era, comparative approaches to gene identification, and positional cloning: the genomics era are discussed in the report. (M. Suetake)

Serious limitations of the QTL/Microarray approach for QTL gene discovery

Directory of Open Access Journals (Sweden)

Warden Craig H

2010-07-01

Full Text Available Abstract Background It has been proposed that the use of gene expression microarrays in nonrecombinant parental or congenic strains can accelerate the process of isolating individual genes underlying quantitative trait loci (QTL. However, the effectiveness of this approach has not been assessed. Results Thirty-seven studies that have implemented the QTL/microarray approach in rodents were reviewed. About 30% of studies showed enrichment for QTL candidates, mostly in comparisons between congenic and background strains. Three studies led to the identification of an underlying QTL gene. To complement the literature results, a microarray experiment was performed using three mouse congenic strains isolating the effects of at least 25 biometric QTL. Results show that genes in the congenic donor regions were preferentially selected. However, within donor regions, the distribution of differentially expressed genes was homogeneous once gene density was accounted for. Genes within identical-by-descent (IBD regions were less likely to be differentially expressed in chromosome 2, but not in chromosomes 11 and 17. Furthermore, expression of QTL regulated in cis (cis eQTL showed higher expression in the background genotype, which was partially explained by the presence of single nucleotide polymorphisms (SNP. Conclusions The literature shows limited successes from the QTL/microarray approach to identify QTL genes. Our own results from microarray profiling of three congenic strains revealed a strong tendency to select cis-eQTL over trans-eQTL. IBD regions had little effect on rate of differential expression, and we provide several reasons why IBD should not be used to discard eQTL candidates. In addition, mismatch probes produced false cis-eQTL that could not be completely removed with the current strains genotypes and low probe density microarrays. The reviewed studies did not account for lack of coverage from the platforms used and therefore removed genes

Research progress in machine learning methods for gene-gene interaction detection.

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Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

2018-03-20

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

Therapeutic genes for anti-HIV/AIDS gene therapy.

Science.gov (United States)

Bovolenta, Chiara; Porcellini, Simona; Alberici, Luca

2013-01-01

The multiple therapeutic approaches developed so far to cope HIV-1 infection, such as anti-retroviral drugs, germicides and several attempts of therapeutic vaccination have provided significant amelioration in terms of life-quality and survival rate of AIDS patients. Nevertheless, no approach has demonstrated efficacy in eradicating this lethal, if untreated, infection. The curative power of gene therapy has been proven for the treatment of monogenic immunodeficiensies, where permanent gene modification of host cells is sufficient to correct the defect for life-time. No doubt, a similar concept is not applicable for gene therapy of infectious immunodeficiensies as AIDS, where there is not a single gene to be corrected; rather engineered cells must gain immunotherapeutic or antiviral features to grant either short- or long-term efficacy mostly by acquisition of antiviral genes or payloads. Anti-HIV/AIDS gene therapy is one of the most promising strategy, although challenging, to eradicate HIV-1 infection. In fact, genetic modification of hematopoietic stem cells with one or multiple therapeutic genes is expected to originate blood cell progenies resistant to viral infection and thereby able to prevail on infected unprotected cells. Ultimately, protected cells will re-establish a functional immune system able to control HIV-1 replication. More than hundred gene therapy clinical trials against AIDS employing different viral vectors and transgenes have been approved or are currently ongoing worldwide. This review will overview anti-HIV-1 infection gene therapy field evaluating strength and weakness of the transgenes and payloads used in the past and of those potentially exploitable in the future.

Gene Circuit Analysis of the Terminal Gap Gene huckebein

Science.gov (United States)

Ashyraliyev, Maksat; Siggens, Ken; Janssens, Hilde; Blom, Joke; Akam, Michael; Jaeger, Johannes

2009-01-01

The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb) on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb) domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network. PMID:19876378

Establishing gene models from the Pinus pinaster genome using gene capture and BAC sequencing.

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Seoane-Zonjic, Pedro; Cañas, Rafael A; Bautista, Rocío; Gómez-Maldonado, Josefa; Arrillaga, Isabel; Fernández-Pozo, Noé; Claros, M Gonzalo; Cánovas, Francisco M; Ávila, Concepción

2016-02-27

In the era of DNA throughput sequencing, assembling and understanding gymnosperm mega-genomes remains a challenge. Although drafts of three conifer genomes have recently been published, this number is too low to understand the full complexity of conifer genomes. Using techniques focused on specific genes, gene models can be established that can aid in the assembly of gene-rich regions, and this information can be used to compare genomes and understand functional evolution. In this study, gene capture technology combined with BAC isolation and sequencing was used as an experimental approach to establish de novo gene structures without a reference genome. Probes were designed for 866 maritime pine transcripts to sequence genes captured from genomic DNA. The gene models were constructed using GeneAssembler, a new bioinformatic pipeline, which reconstructed over 82% of the gene structures, and a high proportion (85%) of the captured gene models contained sequences from the promoter regulatory region. In a parallel experiment, the P. pinaster BAC library was screened to isolate clones containing genes whose cDNA sequence were already available. BAC clones containing the asparagine synthetase, sucrose synthase and xyloglucan endotransglycosylase gene sequences were isolated and used in this study. The gene models derived from the gene capture approach were compared with the genomic sequences derived from the BAC clones. This combined approach is a particularly efficient way to capture the genomic structures of gene families with a small number of members. The experimental approach used in this study is a valuable combined technique to study genomic gene structures in species for which a reference genome is unavailable. It can be used to establish exon/intron boundaries in unknown gene structures, to reconstruct incomplete genes and to obtain promoter sequences that can be used for transcriptional studies. A bioinformatics algorithm (GeneAssembler) is also provided as a

Prostate cancer metastasis-driving genes: hurdles and potential approaches in their identification

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Yan Ting Chiang

2014-08-01

Full Text Available Metastatic prostate cancer is currently incurable. Metastasis is thought to result from changes in the expression of specific metastasis-driving genes in nonmetastatic prostate cancer tissue, leading to a cascade of activated downstream genes that set the metastatic process in motion. Such genes could potentially serve as effective therapeutic targets for improved management of the disease. They could be identified by comparative analysis of gene expression profiles of patient-derived metastatic and nonmetastatic prostate cancer tissues to pinpoint genes showing altered expression, followed by determining whether silencing of such genes can lead to inhibition of metastatic properties. Various hurdles encountered in this approach are discussed, including (i the need for clinically relevant, nonmetastatic and metastatic prostate cancer tissues such as xenografts of patients' prostate cancers developed via subrenal capsule grafting technology and (ii limitations in the currently available methodology for identification of master regulatory genes.

Characterization of Genes for Beef Marbling Based on Applying Gene Coexpression Network

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Dajeong Lim

2014-01-01

Full Text Available Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in M. longissimus with divergent marbling phenotype (marbling scores 2 to 7 using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60 and dihydropyrimidine dehydrogenase (DPYD are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness.

A Nonlinear Model for Gene-Based Gene-Environment Interaction

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Jian Sa

2016-06-01

Full Text Available A vast amount of literature has confirmed the role of gene-environment (G×E interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.

Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine.

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Kim, Jihye; Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kang, Jaewoo; Tan, Aik Choon

2018-01-01

Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis.

Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies.

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Schaid, Daniel J; Sinnwell, Jason P; Jenkins, Gregory D; McDonnell, Shannon K; Ingle, James N; Kubo, Michiaki; Goss, Paul E; Costantino, Joseph P; Wickerham, D Lawrence; Weinshilboum, Richard M

2012-01-01

Gene-set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene-set, have plagued current approaches, often leading to ad hoc "fixes." To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene-level and gene-set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene-sets. However, other types of gene-set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene-sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene-sets, we use an efficient computational strategy that accounts for LD and provides accurate step-down adjusted P-values for each gene-set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene-set analyses. © 2011 Wiley Periodicals, Inc.

A Multiomics Approach to Identify Genes Associated with Childhood Asthma Risk and Morbidity.

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Forno, Erick; Wang, Ting; Yan, Qi; Brehm, John; Acosta-Perez, Edna; Colon-Semidey, Angel; Alvarez, Maria; Boutaoui, Nadia; Cloutier, Michelle M; Alcorn, John F; Canino, Glorisa; Chen, Wei; Celedón, Juan C

2017-10-01

Childhood asthma is a complex disease. In this study, we aim to identify genes associated with childhood asthma through a multiomics "vertical" approach that integrates multiple analytical steps using linear and logistic regression models. In a case-control study of childhood asthma in Puerto Ricans (n = 1,127), we used adjusted linear or logistic regression models to evaluate associations between several analytical steps of omics data, including genome-wide (GW) genotype data, GW methylation, GW expression profiling, cytokine levels, asthma-intermediate phenotypes, and asthma status. At each point, only the top genes/single-nucleotide polymorphisms/probes/cytokines were carried forward for subsequent analysis. In step 1, asthma modified the gene expression-protein level association for 1,645 genes; pathway analysis showed an enrichment of these genes in the cytokine signaling system (n = 269 genes). In steps 2-3, expression levels of 40 genes were associated with intermediate phenotypes (asthma onset age, forced expiratory volume in 1 second, exacerbations, eosinophil counts, and skin test reactivity); of those, methylation of seven genes was also associated with asthma. Of these seven candidate genes, IL5RA was also significant in analytical steps 4-8. We then measured plasma IL-5 receptor α levels, which were associated with asthma age of onset and moderate-severe exacerbations. In addition, in silico database analysis showed that several of our identified IL5RA single-nucleotide polymorphisms are associated with transcription factors related to asthma and atopy. This approach integrates several analytical steps and is able to identify biologically relevant asthma-related genes, such as IL5RA. It differs from other methods that rely on complex statistical models with various assumptions.

Linking Genes and Brain Development of Honeybee Workers: A Whole-Transcriptome Approach.

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Christina Vleurinck

Full Text Available Honeybees live in complex societies whose capabilities far exceed those of the sum of their single members. This social synergism is achieved mainly by the worker bees, which form a female caste. The worker bees display diverse collaborative behaviors and engage in different behavioral tasks, which are controlled by the central nervous system (CNS. The development of the worker brain is determined by the female sex and the worker caste determination signal. Here, we report on genes that are controlled by sex or by caste during differentiation of the worker's pupal brain. We sequenced and compared transcriptomes from the pupal brains of honeybee workers, queens and drones. We detected 333 genes that are differently expressed and 519 genes that are differentially spliced between the sexes, and 1760 genes that are differentially expressed and 692 genes that are differentially spliced between castes. We further found that 403 genes are differentially regulated by both the sex and caste signals, providing evidence of the integration of both signals through differential gene regulation. In this gene set, we found that the molecular processes of restructuring the cell shape and cell-to-cell signaling are overrepresented. Our approach identified candidate genes that may be involved in brain differentiation that ensures the various social worker behaviors.

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life.

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Kannan, Lavanya; Li, Hua; Rubinstein, Boris; Mushegian, Arcady

2013-12-19

The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes and the record of orthologous relationships between their genes to infer the gene content of LUCA, the Last Universal Common Ancestor of all currently living cellular organisms. The crucial parameter, the ratio of gene losses and gene gains, was estimated from the data and was higher in models that take account of the number of in-paralogs in genomes than in models that treat gene presences and absences as a binary trait. While the numbers of genes that are placed confidently into LUCA are similar in the ML methods and in previously published methods that use various parsimony-based approaches, the identities of genes themselves are different. Most of the models of either kind treat the genes found in many existing genomes in a similar way, assigning to them high probabilities of being ancestral ("high ancestrality"). The ML models are more likely than others to assign high ancestrality to the genes that are relatively rare in the present-day genomes.

Tumor targeted gene therapy

International Nuclear Information System (INIS)

Kang, Joo Hyun

2006-01-01

Knowledge of molecular mechanisms governing malignant transformation brings new opportunities for therapeutic intervention against cancer using novel approaches. One of them is gene therapy based on the transfer of genetic material to an organism with the aim of correcting a disease. The application of gene therapy to the cancer treatment had led to the development of new experimental approaches such as suicidal gene therapy, inhibition of oncogenes and restoration of tumor-suppressor genes. Suicidal gene therapy is based on the expression in tumor cells of a gene encoding an enzyme that converts a prodrug into a toxic product. Representative suicidal genes are Herpes simplex virus type 1 thymidine kinase (HSV1-tk) and cytosine deaminase (CD). Especially, physicians and scientists of nuclear medicine field take an interest in suicidal gene therapy because they can monitor the location and magnitude, and duration of expression of HSV1-tk and CD by PET scanner

Clustering gene expression regulators: new approach to disease subtyping.

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Mikhail Pyatnitskiy

Full Text Available One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms, that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient.

Down-Regulation of Gene Expression by RNA-Induced Gene Silencing

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Travella, Silvia; Keller, Beat

Down-regulation of endogenous genes via post-transcriptional gene silencing (PTGS) is a key to the characterization of gene function in plants. Many RNA-based silencing mechanisms such as post-transcriptional gene silencing, co-suppression, quelling, and RNA interference (RNAi) have been discovered among species of different kingdoms (plants, fungi, and animals). One of the most interesting discoveries was RNAi, a sequence-specific gene-silencing mechanism initiated by the introduction of double-stranded RNA (dsRNA), homologous in sequence to the silenced gene, which triggers degradation of mRNA. Infection of plants with modified viruses can also induce RNA silencing and is referred to as virus-induced gene silencing (VIGS). In contrast to insertional mutagenesis, these emerging new reverse genetic approaches represent a powerful tool for exploring gene function and for manipulating gene expression experimentally in cereal species such as barley and wheat. We examined how RNAi and VIGS have been used to assess gene function in barley and wheat, including molecular mechanisms involved in the process and available methodological elements, such as vectors, inoculation procedures, and analysis of silenced phenotypes.

Novel gene sets improve set-level classification of prokaryotic gene expression data.

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Holec, Matěj; Kuželka, Ondřej; Železný, Filip

2015-10-28

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

Efficient strategy for detecting gene × gene joint action and its application in schizophrenia.

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Won, Sungho; Kwon, Min-Seok; Mattheisen, Manuel; Park, Suyeon; Park, Changsoon; Kihara, Daisuke; Cichon, Sven; Ophoff, Roel; Nöthen, Markus M; Rietschel, Marcella; Baur, Max; Uitterlinden, Andre G; Hofmann, A; Lange, Christoph

2014-01-01

We propose a new approach to detect gene × gene joint action in genome-wide association studies (GWASs) for case-control designs. This approach offers an exhaustive search for all two-way joint action (including, as a special case, single gene action) that is computationally feasible at the genome-wide level and has reasonable statistical power under most genetic models. We found that the presence of any gene × gene joint action may imply differences in three types of genetic components: the minor allele frequencies and the amounts of Hardy-Weinberg disequilibrium may differ between cases and controls, and between the two genetic loci the degree of linkage disequilibrium may differ between cases and controls. Using Fisher's method, it is possible to combine the different sources of genetic information in an overall test for detecting gene × gene joint action. The proposed statistical analysis is efficient and its simplicity makes it applicable to GWASs. In the current study, we applied the proposed approach to a GWAS on schizophrenia and found several potential gene × gene interactions. Our application illustrates the practical advantage of the proposed method. © 2013 WILEY PERIODICALS, INC.

Coexpression landscape in ATTED-II: usage of gene list and gene network for various types of pathways.

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Obayashi, Takeshi; Kinoshita, Kengo

2010-05-01

Gene coexpression analyses are a powerful method to predict the function of genes and/or to identify genes that are functionally related to query genes. The basic idea of gene coexpression analyses is that genes with similar functions should have similar expression patterns under many different conditions. This approach is now widely used by many experimental researchers, especially in the field of plant biology. In this review, we will summarize recent successful examples obtained by using our gene coexpression database, ATTED-II. Specifically, the examples will describe the identification of new genes, such as the subunits of a complex protein, the enzymes in a metabolic pathway and transporters. In addition, we will discuss the discovery of a new intercellular signaling factor and new regulatory relationships between transcription factors and their target genes. In ATTED-II, we provide two basic views of gene coexpression, a gene list view and a gene network view, which can be used as guide gene approach and narrow-down approach, respectively. In addition, we will discuss the coexpression effectiveness for various types of gene sets.

A synbio approach for selection of highly expressed gene variants in Gram-positive bacteria

DEFF Research Database (Denmark)

Ferro, Roberto; Rennig, Maja; Hernández Rollán, Cristina

2018-01-01

with a long history in food fermentation. We have developed a synbio approach for increasing gene expression in two Gram-positive bacteria. First of all, the gene of interest was coupled to an antibiotic resistance gene to create a growth-based selection system. We then randomised the translation initiation...... region (TIR) preceding the gene of interest and selected clones that produced high protein titres, as judged by their ability to survive on high concentrations of antibiotic. Using this approach, we were able to significantly increase production of two industrially relevant proteins; sialidase in B....... subtilis and tyrosine ammonia lyase in L. lactis. Gram-positive bacteria are widely used to produce industrial enzymes. High titres are necessary to make the production economically feasible. The synbio approach presented here is a simple and inexpensive way to increase protein titres, which can be carried...

Gene editing in hematopoietic stem cells: a potential therapeutic approach for Fanconi anemia

International Nuclear Information System (INIS)

Diez Cabezas, B.

2015-01-01

Gene therapy nowadays constitutes a safe and efficient treatment for a number of monogenic diseases affecting the hematopoietic system. Risks of insertional mutagenesis derived from the use of integrative vectors cannot, however, be completely excluded. Therefore, gene targeting has been proposed as a safer alternative, since the insertion of the herapeutic gene is driven to a specific locus in the genome. Gene targeting approaches are based on the use of specific nucleases which generate double strand breaks (DSBs) in a specific site of the genome,markedly enhancing the efficacy of homologous recombination (HR) with donor constructs harboring the gene of interest flanked by the corresponding homology arms. In this study we have optimized the conditions to target human lymphoblastic cell lines (LCLs) and also hematopoietic stem cells (HSCs) from healthy donors, with the final aim of correcting by gene editing the hematopoietic progenitor cells from Fanconi anemia subtype A (FA-A) patients. In particular, we have established a robust method to target both LCLs and HSCs in a safe harbor site in the genome, the AAVS1 locus. Our approach is based on the transduction of these cells with integrase-defective lentiviral vectors carrying a donor with the gene of interest, followed by the nucleofection of these cells with zinc finger nucleases used as mRNA. Using a control donor vector carrying the GFP reporter gene we have obtained, on average, 9.43% gene targeting efficiency in cord blood CD34+ cells from healthy donors. Moreover, we confirmed that gene targeting was also efficient in HSCs with long term and multipotent repopulation capacity, as demonstrated by transplants into immunodeficient mice. To improve the gene targeting efficiency, we investigated the feasibility of using gold nanoparticles, which were shown to improve the transduction efficiency of integrase-defective and competent lentiviral vectors in HSCs. This increment, however, did not lead to a higher gene

Gene editing in hematopoietic stem cells: a potential therapeutic approach for Fanconi anemia

Energy Technology Data Exchange (ETDEWEB)

Diez Cabezas, B.

2015-07-01

Gene therapy nowadays constitutes a safe and efficient treatment for a number of monogenic diseases affecting the hematopoietic system. Risks of insertional mutagenesis derived from the use of integrative vectors cannot, however, be completely excluded. Therefore, gene targeting has been proposed as a safer alternative, since the insertion of the herapeutic gene is driven to a specific locus in the genome. Gene targeting approaches are based on the use of specific nucleases which generate double strand breaks (DSBs) in a specific site of the genome,markedly enhancing the efficacy of homologous recombination (HR) with donor constructs harboring the gene of interest flanked by the corresponding homology arms. In this study we have optimized the conditions to target human lymphoblastic cell lines (LCLs) and also hematopoietic stem cells (HSCs) from healthy donors, with the final aim of correcting by gene editing the hematopoietic progenitor cells from Fanconi anemia subtype A (FA-A) patients. In particular, we have established a robust method to target both LCLs and HSCs in a safe harbor site in the genome, the AAVS1 locus. Our approach is based on the transduction of these cells with integrase-defective lentiviral vectors carrying a donor with the gene of interest, followed by the nucleofection of these cells with zinc finger nucleases used as mRNA. Using a control donor vector carrying the GFP reporter gene we have obtained, on average, 9.43% gene targeting efficiency in cord blood CD34+ cells from healthy donors. Moreover, we confirmed that gene targeting was also efficient in HSCs with long term and multipotent repopulation capacity, as demonstrated by transplants into immunodeficient mice. To improve the gene targeting efficiency, we investigated the feasibility of using gold nanoparticles, which were shown to improve the transduction efficiency of integrase-defective and competent lentiviral vectors in HSCs. This increment, however, did not lead to a higher gene

Efficient strategy for detecting gene × gene joint action and its application in schizophrenia

NARCIS (Netherlands)

Won, Sungho; Kwon, Min-Seok; Mattheisen, Manuel; Park, Suyeon; Park, Changsoon; Kihara, Daisuke; Cichon, Sven; Ophoff, Roel; Nöthen, Markus M.; Rietschel, Marcella; Baur, Max; Uitterlinden, Andre G.; Hofmann, A.; Lange, Christoph; Kahn, René S.; Linszen, Don H.; van Os, Jim; Wiersma, Durk; Bruggeman, Richard; Cahn, Wiepke; de Haan, Lieuwe; Krabbendam, Lydia; Myin-Germeys, Inez

2014-01-01

We propose a new approach to detect gene × gene joint action in genome-wide association studies (GWASs) for case-control designs. This approach offers an exhaustive search for all two-way joint action (including, as a special case, single gene action) that is computationally feasible at the

Gene prediction in metagenomic fragments: A large scale machine learning approach

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Morgenstern Burkhard

2008-04-01

Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

Radiopharmaceuticals to monitor the expression of transferred genes in gene transfer therapy

International Nuclear Information System (INIS)

Wiebe, L. I.

1997-01-01

The development and application of radiopharmaceuticals has, in many instances, been based on the pharmacological properties of therapeutic agents. The molecular biology-biotechnology revolution has had an important impact on treatment of diseases, in part through the reduced toxicity of 'biologicals', in part because of their specificity for interaction at unique molecular sites and in part because of their selective delivery to the target site. Immunotherapeutic approaches include the use of monoclonal antibodies (MABs), MAB-fragments and chemotactic peptides. Such agents currently form the basis of both diagnostic and immunotherapeutic radiopharmaceuticals. More recently, gene transfer techniques have been advanced to the point that a new molecular approach, gene therapy, has become a reality. Gene therapy offers an opportunity to attack disease at its most fundamental level. The therapeutic mechanism is based on the expression of a specific gene or genes, the product of which will invoke immunological, receptor-based or enzyme-based therapeutic modalities. Several approaches to gene therapy of cancer have been envisioned, the most clinically-advanced concepts involving the introduction of genes that will encode for molecular targets nor normally found in healthy mammalian cells. A number of gene therapy clinical trials are based on the introduction of the Herpes simplex virus type-1 (HSV-1) gene that encodes for viral thymidine kinase (tk+). Once HSV-1 tk+ is expressed in the target (cancer) cell, therapy can be effected by the administration of a highly molecularly-targeted and systemically non-toxic antiviral drug such as ganciclovir. The development of radiodiagnostic imaging in gene therapy will be reviewed, using HSV-1 tk+ and radioiodinated IVFRU as a basis for development of the theme. Molecular targets that could be exploited in gene therapy, other than tk+, will be identified

Radiopharmaceuticals to monitor the expression of transferred genes in gene transfer therapy

Energy Technology Data Exchange (ETDEWEB)

Wiebe, L I [University of Alberta, Edmonton (Canada). Noujaim Institute for Pharmaceutical Oncology Research

1997-10-01

The development and application of radiopharmaceuticals has, in many instances, been based on the pharmacological properties of therapeutic agents. The molecular biology-biotechnology revolution has had an important impact on treatment of diseases, in part through the reduced toxicity of `biologicals`, in part because of their specificity for interaction at unique molecular sites and in part because of their selective delivery to the target site. Immunotherapeutic approaches include the use of monoclonal antibodies (MABs), MAB-fragments and chemotactic peptides. Such agents currently form the basis of both diagnostic and immunotherapeutic radiopharmaceuticals. More recently, gene transfer techniques have been advanced to the point that a new molecular approach, gene therapy, has become a reality. Gene therapy offers an opportunity to attack disease at its most fundamental level. The therapeutic mechanism is based on the expression of a specific gene or genes, the product of which will invoke immunological, receptor-based or enzyme-based therapeutic modalities. Several approaches to gene therapy of cancer have been envisioned, the most clinically-advanced concepts involving the introduction of genes that will encode for molecular targets nor normally found in healthy mammalian cells. A number of gene therapy clinical trials are based on the introduction of the Herpes simplex virus type-1 (HSV-1) gene that encodes for viral thymidine kinase (tk+). Once HSV-1 tk+ is expressed in the target (cancer) cell, therapy can be effected by the administration of a highly molecularly-targeted and systemically non-toxic antiviral drug such as ganciclovir. The development of radiodiagnostic imaging in gene therapy will be reviewed, using HSV-1 tk+ and radioiodinated IVFRU as a basis for development of the theme. Molecular targets that could be exploited in gene therapy, other than tk+, will be identified

A re-assessment of gene-tag classification approaches for describing var gene expression patterns during human Plasmodium falciparum malaria parasite infections.

Science.gov (United States)

Githinji, George; Bull, Peter C

2017-01-01

PfEMP1 are variant parasite antigens that are inserted on the surface of Plasmodium falciparum infected erythrocytes (IE). Through interactions with various host molecules, PfEMP1 mediate IE sequestration in tissues and play a key role in the pathology of severe malaria. PfEMP1 is encoded by a diverse multi-gene family called var . Previous studies have shown that that expression of specific subsets of var genes are associated with low levels of host immunity and severe malaria. However, in most clinical studies to date, full-length var gene sequences were unavailable and various approaches have been used to make comparisons between var gene expression profiles in different parasite isolates using limited information. Several studies have relied on the classification of a 300 - 500 base-pair "DBLα tag" region in the DBLα domain located at the 5' end of most var genes. We assessed the relationship between various DBLα tag classification methods, and sequence features that are only fully assessable through full-length var gene sequences. We compared these different sequence features in full-length var gene from six fully sequenced laboratory isolates. These comparisons show that despite a long history of recombination,   DBLα sequence tag classification can provide functional information on important features of full-length var genes. Notably, a specific subset of DBLα tags previously defined as "group A-like" is associated with CIDRα1 domains proposed to bind to endothelial protein C receptor. This analysis helps to bring together different sources of data that have been used to assess var gene expression in clinical parasite isolates.

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life

Science.gov (United States)

2013-01-01

Background The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. Results We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes and the record of orthologous relationships between their genes to infer the gene content of LUCA, the Last Universal Common Ancestor of all currently living cellular organisms. The crucial parameter, the ratio of gene losses and gene gains, was estimated from the data and was higher in models that take account of the number of in-paralogs in genomes than in models that treat gene presences and absences as a binary trait. Conclusion While the numbers of genes that are placed confidently into LUCA are similar in the ML methods and in previously published methods that use various parsimony-based approaches, the identities of genes themselves are different. Most of the models of either kind treat the genes found in many existing genomes in a similar way, assigning to them high probabilities of being ancestral (“high ancestrality”). The ML models are more likely than others to assign high ancestrality to the genes that are relatively rare in the present-day genomes. Reviewers This article was reviewed by Martijn A Huynen, Toni Gabaldón and Fyodor Kondrashov. PMID:24354654

A guide to approaching regulatory considerations for lentiviral-mediated gene therapies.

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White, Michael; Whittaker, Roger; Stoll, Elizabeth Ann

2017-06-12

Lentiviral vectors are increasingly the gene transfer tool of choice for gene or cell therapies, with multiple clinical investigations showing promise for this viral vector in terms of both safety and efficacy. The third-generation vector system is well-characterized, effectively delivers genetic material and maintains long-term stable expression in target cells, delivers larger amounts of genetic material than other methods, is non-pathogenic and does not cause an inflammatory response in the recipient. This report aims to help academic scientists and regulatory managers negotiate the governance framework to achieve successful translation of a lentiviral vector-based gene therapy. The focus is on European regulations, and how they are administered in the United Kingdom, although many of the principles will be similar for other regions including the United States. The report justifies the rationale for using third-generation lentiviral vectors to achieve gene delivery for in vivo and ex vivo applications; briefly summarises the extant regulatory guidance for gene therapies, categorised as advanced therapeutic medicinal products (ATMPs); provides guidance on specific regulatory issues regarding gene therapies; presents an overview of the key stakeholders to be approached when pursuing clinical trials authorization for an ATMP; and includes a brief catalogue of the documentation required to submit an application for regulatory approval of a new gene therapy.

Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

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Liang Jinghang

2012-08-01

network inferred from a T cell immune response dataset. An SBN can also implement the function of an asynchronous PBN and is potentially useful in a hybrid approach in combination with a continuous or single-molecule level stochastic model. Conclusions Stochastic Boolean networks (SBNs are proposed as an efficient approach to modelling gene regulatory networks (GRNs. The SBN approach is able to recover biologically-proven regulatory behaviours, such as the oscillatory dynamics of the p53-Mdm2 network and the dynamic attractors in a T cell immune response network. The proposed approach can further predict the network dynamics when the genes are under perturbation, thus providing biologically meaningful insights for a better understanding of the dynamics of GRNs. The algorithms and methods described in this paper have been implemented in Matlab packages, which are attached as Additional files.

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 ...

Efficacious and safe tissue-selective controlled gene therapy approaches for the cornea.

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Rajiv R Mohan

2011-04-01

Full Text Available Untargeted and uncontrolled gene delivery is a major cause of gene therapy failure. This study aimed to define efficient and safe tissue-selective targeted gene therapy approaches for delivering genes into keratocytes of the cornea in vivo using a normal or diseased rabbit model. New Zealand White rabbits, adeno-associated virus serotype 5 (AAV5, and a minimally invasive hair-dryer based vector-delivery technique were used. Fifty microliters of AAV5 titer (6.5×10(12 vg/ml expressing green fluorescent protein gene (GFP was topically applied onto normal or diseased (fibrotic or neovascularized rabbit corneas for 2-minutes with a custom vector-delivery technique. Corneal fibrosis and neovascularization in rabbit eyes were induced with photorefractive keratectomy using excimer laser and VEGF (630 ng using micropocket assay, respectively. Slit-lamp biomicroscopy and immunocytochemistry were used to confirm fibrosis and neovascularization in rabbit corneas. The levels, location and duration of delivered-GFP gene expression in the rabbit stroma were measured with immunocytochemistry and/or western blotting. Slot-blot measured delivered-GFP gene copy number. Confocal microscopy performed in whole-mounts of cornea and thick corneal sections determined geometric and spatial localization of delivered-GFP in three-dimensional arrangement. AAV5 toxicity and safety were evaluated with clinical eye exam, stereomicroscopy, slit-lamp biomicroscopy, and H&E staining. A single 2-minute AAV5 topical application via custom delivery-technique efficiently and selectively transduced keratocytes in the anterior stroma of normal and diseased rabbit corneas as evident from immunocytochemistry and confocal microscopy. Transgene expression was first detected at day 3, peaked at day 7, and was maintained up to 16 weeks (longest tested time point. Clinical and slit-lamp eye examination in live rabbits and H&E staining did not reveal any significant changes between AAV5

Bioinformatics approach of three partial polyprenol reductase genes in Kandelia obovata

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Basyuni, M.; Wati, R.; Sagami, H.; Oku, H.; Baba, S.

2018-03-01

This present study describesthe bioinformatics approach to analyze three partial polyprenol reductase genes from mangrove plant, Kandeliaobovataas well aspredictedphysical and chemical properties, potential peptide, subcellular localization, and phylogenetic. The diversity was noted in the physical and chemical properties of three partial polyprenol reductase genes. The values of chloroplast were relatively high, showed that chloroplast transit peptide occurred in mangrove polyprenol reductase. The target peptide value of mitochondria varied from 0.088 to 0.198 indicated it was possible to be present. These results suggested the importance of understanding the diversity of physicochemical properties of the different amino acids in polyprenol reductase. The subcellular localization of two partial genes located in the plasma membrane. To confirm the homology among the polyprenol reductase in the database, a dendrogram was drawn. The phylogenetic tree depicts that there are three clusters, the partial genes of K. obovata joined the largest one: C23157 was close to Ricinus communis polyprenol reductase. Whereas, C23901 and C24171 were grouped with Ipomoea nil polyprenol reductase, suggested that these polyprenol reductase genes form distinct separation into tropical habitat plants.

Clustering approaches to identifying gene expression patterns from DNA microarray data.

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Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

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

Effect of the absolute statistic on gene-sampling gene-set analysis methods.

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Nam, Dougu

2017-06-01

Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.

A robust approach based on Weibull distribution for clustering gene expression data

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Gong Binsheng

2011-05-01

Full Text Available Abstract Background Clustering is a widely used technique for analysis of gene expression data. Most clustering methods group genes based on the distances, while few methods group genes according to the similarities of the distributions of the gene expression levels. Furthermore, as the biological annotation resources accumulated, an increasing number of genes have been annotated into functional categories. As a result, evaluating the performance of clustering methods in terms of the functional consistency of the resulting clusters is of great interest. Results In this paper, we proposed the WDCM (Weibull Distribution-based Clustering Method, a robust approach for clustering gene expression data, in which the gene expressions of individual genes are considered as the random variables following unique Weibull distributions. Our WDCM is based on the concept that the genes with similar expression profiles have similar distribution parameters, and thus the genes are clustered via the Weibull distribution parameters. We used the WDCM to cluster three cancer gene expression data sets from the lung cancer, B-cell follicular lymphoma and bladder carcinoma and obtained well-clustered results. We compared the performance of WDCM with k-means and Self Organizing Map (SOM using functional annotation information given by the Gene Ontology (GO. The results showed that the functional annotation ratios of WDCM are higher than those of the other methods. We also utilized the external measure Adjusted Rand Index to validate the performance of the WDCM. The comparative results demonstrate that the WDCM provides the better clustering performance compared to k-means and SOM algorithms. The merit of the proposed WDCM is that it can be applied to cluster incomplete gene expression data without imputing the missing values. Moreover, the robustness of WDCM is also evaluated on the incomplete data sets. Conclusions The results demonstrate that our WDCM produces clusters

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life

OpenAIRE

Kannan, Lavanya; Li, Hua; Rubinstein, Boris; Mushegian, Arcady

2013-01-01

Background The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. Results We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes...

Transgene traceability in transgenic mice: a bioanalytical approach for potential gene-doping analysis.

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Bogani, Patrizia; Spiriti, Maria Michela; Lazzarano, Stefano; Arcangeli, Annarosa; Buiatti, Marcello; Minunni, Maria

2011-11-01

The World Anti-Doping Agency fears the use of gene doping to enhance athletic performances. Thus, a bioanalytical approach based on end point PCR for detecting markers' of transgenesis traceability was developed. A few sequences from two different vectors using an animal model were selected and traced in different tissues and at different times. In particular, enhanced green fluorescent protein gene and a construct-specific new marker were targeted in the analysis. To make the developed detection approach open to future routine doping analysis, matrices such as urine and tears as well blood were also tested. This study will have impact in evaluating the vector transgenes traceability for the detection of a gene doping event by non-invasive sampling.

From gene networks to drugs: systems pharmacology approaches for AUD.

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Ferguson, Laura B; Harris, R Adron; Mayfield, Roy Dayne

2018-06-01

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks.

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Saik, Olga V; Demenkov, Pavel S; Ivanisenko, Timofey V; Bragina, Elena Yu; Freidin, Maxim B; Goncharova, Irina A; Dosenko, Victor E; Zolotareva, Olga I; Hofestaedt, Ralf; Lavrik, Inna N; Rogaev, Evgeny I; Ivanisenko, Vladimir A

2018-02-13

Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in

Identification of Hematopoietic Stem Cell Engraftment Genes in Gene Therapy Studies.

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Powers, John M; Trobridge, Grant D

2013-09-01

Hematopoietic stem cell (HSC) therapy using replication-incompetent retroviral vectors is a promising approach to provide life-long correction for genetic defects. HSC gene therapy clinical studies have resulted in functional cures for several diseases, but in some studies clonal expansion or leukemia has occurred. This is due to the dyregulation of endogenous host gene expression from vector provirus insertional mutagenesis. Insertional mutagenesis screens using replicating retroviruses have been used extensively to identify genes that influence oncogenesis. However, retroviral mutagenesis screens can also be used to determine the role of genes in biological processes such as stem cell engraftment. The aim of this review is to describe the potential for vector insertion site data from gene therapy studies to provide novel insights into mechanisms of HSC engraftment. In HSC gene therapy studies dysregulation of host genes by replication-incompetent vector proviruses may lead to enrichment of repopulating clones with vector integrants near genes that influence engraftment. Thus, data from HSC gene therapy studies can be used to identify novel candidate engraftment genes. As HSC gene therapy use continues to expand, the vector insertion site data collected will be of great interest to help identify novel engraftment genes and may ultimately lead to new therapies to improve engraftment.

Coalitional game theory as a promising approach to identify candidate autism genes.

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Gupta, Anika; Sun, Min Woo; Paskov, Kelley Marie; Stockham, Nate Tyler; Jung, Jae-Yoon; Wall, Dennis Paul

2018-01-01

Despite mounting evidence for the strong role of genetics in the phenotypic manifestation of Autism Spectrum Disorder (ASD), the specific genes responsible for the variable forms of ASD remain undefined. ASD may be best explained by a combinatorial genetic model with varying epistatic interactions across many small effect mutations. Coalitional or cooperative game theory is a technique that studies the combined effects of groups of players, known as coalitions, seeking to identify players who tend to improve the performance--the relationship to a specific disease phenotype--of any coalition they join. This method has been previously shown to boost biologically informative signal in gene expression data but to-date has not been applied to the search for cooperative mutations among putative ASD genes. We describe our approach to highlight genes relevant to ASD using coalitional game theory on alteration data of 1,965 fully sequenced genomes from 756 multiplex families. Alterations were encoded into binary matrices for ASD (case) and unaffected (control) samples, indicating likely gene-disrupting, inherited mutations in altered genes. To determine individual gene contributions given an ASD phenotype, a "player" metric, referred to as the Shapley value, was calculated for each gene in the case and control cohorts. Sixty seven genes were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Using network and cross-study analysis, we found that these genes are involved in biological pathways known to be affected in the autism cases and that a subset directly interact with several genes known to have strong associations to autism. These findings suggest that coalitional game theory can be applied to large-scale genomic data to identify hidden yet influential players in complex polygenic disorders such as autism.

Twenty Years of European Union Support to Gene Therapy and Gene Transfer.

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Gancberg, David

2017-11-01

For 20 years and throughout its research programmes, the European Union has supported the entire innovation chain for gene transfer and gene therapy. The fruits of this investment are ripening as gene therapy products are reaching the European market and as clinical trials are demonstrating the safety of this approach to treat previously untreatable diseases.

Genes and Gene Therapy

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... correctly, a child can have a genetic disorder. Gene therapy is an experimental technique that uses genes to ... or prevent disease. The most common form of gene therapy involves inserting a normal gene to replace an ...

A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes

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Larremore, Daniel B.; Clauset, Aaron; Buckee, Caroline O.

2013-01-01

The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. PMID:24130474

A network approach to analyzing highly recombinant malaria parasite genes.

Science.gov (United States)

Larremore, Daniel B; Clauset, Aaron; Buckee, Caroline O

2013-01-01

The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.

A network approach to analyzing highly recombinant malaria parasite genes.

Directory of Open Access Journals (Sweden)

Daniel B Larremore

Full Text Available The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs, and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences.

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis

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Akira Ishikawa

2017-11-01

Full Text Available Large numbers of quantitative trait loci (QTL affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis.

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Ishikawa, Akira

2017-11-27

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

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.

Gene-wide analysis detects two new susceptibility genes for Alzheimer's Disease

OpenAIRE

Escott-Price, Valentina; Bellenguez, Céline; Wang, Li-San; Choi, Seung-Hoan; Harold, Denise; Jones, Lesley; Holmans, Peter Alan; Gerrish, Amy; Vedernikov, Alexey; Richards, Alexander; DeStefano, Anita L.; Lambert, Jean-Charles; Ibrahim-Verbaas, Carla A.; Naj, Adam C.; Sims, Rebecca

2014-01-01

PUBLISHED BACKGROUND: Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over...

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

Directory of Open Access Journals (Sweden)

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.

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

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

2006-01-01

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

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

Directory of Open Access Journals (Sweden)

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

Gene therapy: An overview

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Sudip Indu

2013-01-01

Full Text Available Gene therapy "the use of genes as medicine" involves the transfer of a therapeutic or working copy of a gene into specific cells of an individual in order to repair a faulty gene copy. The technique may be used to replace a faulty gene, or to introduce a new gene whose function is to cure or to favorably modify the clinical course of a condition. The objective of gene therapy is to introduce new genetic material into target cells while causing no damage to the surrounding healthy cells and tissues, hence the treatment related morbidity is decreased. The delivery system includes a vector that delivers a therapeutic gene into the patient′s target cell. Functional proteins are created from the therapeutic gene causing the cell to return to a normal stage. The vectors used in gene therapy can be viral and non-viral. Gene therapy, an emerging field of biomedicine, is still at infancy and much research remains to be done before this approach to the treatment of condition will realize its full potential.

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

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

2018-02-23

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

'Omics' approaches in tomato aimed at identifying candidate genes ...

African Journals Online (AJOL)

adriana

2013-12-04

Dec 4, 2013 ... approaches could be combined in order to identify candidate genes for the genetic control of ascorbic ..... applied to other traits under the complex control of many ... Engineering increased vitamin C levels in ... Chem. Biol. 13:532–538. Giovannucci E, Rimm EB, Liu Y, Stampfer MJ, Willett WC (2002). A.

Candidate gene linkage approach to identify DNA variants that predispose to preterm birth

DEFF Research Database (Denmark)

Bream, Elise N A; Leppellere, Cara R; Cooper, Margaret E

2013-01-01

Background:The aim of this study was to identify genetic variants contributing to preterm birth (PTB) using a linkage candidate gene approach.Methods:We studied 99 single-nucleotide polymorphisms (SNPs) for 33 genes in 257 families with PTBs segregating. Nonparametric and parametric analyses were...... through the infant and/or the mother in the etiology of PTB....

A synbio approach for selection of highly expressed gene variants in Gram-positive bacteria.

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Ferro, Roberto; Rennig, Maja; Hernández-Rollán, Cristina; Daley, Daniel O; Nørholm, Morten H H

2018-03-08

The market for recombinant proteins is on the rise, and Gram-positive strains are widely exploited for this purpose. Bacillus subtilis is a profitable host for protein production thanks to its ability to secrete large amounts of proteins, and Lactococcus lactis is an attractive production organism with a long history in food fermentation. We have developed a synbio approach for increasing gene expression in two Gram-positive bacteria. First of all, the gene of interest was coupled to an antibiotic resistance gene to create a growth-based selection system. We then randomised the translation initiation region (TIR) preceding the gene of interest and selected clones that produced high protein titres, as judged by their ability to survive on high concentrations of antibiotic. Using this approach, we were able to significantly increase production of two industrially relevant proteins; sialidase in B. subtilis and tyrosine ammonia lyase in L. lactis. Gram-positive bacteria are widely used to produce industrial enzymes. High titres are necessary to make the production economically feasible. The synbio approach presented here is a simple and inexpensive way to increase protein titres, which can be carried out in any laboratory within a few days. It could also be implemented as a tool for applications beyond TIR libraries, such as screening of synthetic, homologous or domain-shuffled genes.

Gene therapy and its implications in Periodontics

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Mahale, Swapna; Dani, Nitin; Ansari, Shumaila S.; Kale, Triveni

2009-01-01

Gene therapy is a field of Biomedicine. With the advent of gene therapy in dentistry, significant progress has been made in the control of periodontal diseases and reconstruction of dento-alveolar apparatus. Implementation in periodontics include: -As a mode of tissue engineering with three approaches: cell, protein-based and gene delivery approach. -Genetic approach to Biofilm Antibiotic Resistance. Future strategies of gene therapy in preventing periodontal diseases: -Enhances host defense mechanism against infection by transfecting host cells with an antimicrobial peptide protein-encoding gene. -Periodontal vaccination. Gene therapy is one of the recent entrants and its applications in the field of periodontics are reviewed in general here. PMID:20376232

A new measure for functional similarity of gene products based on Gene Ontology

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Lengauer Thomas

2006-06-01

Full Text Available Abstract Background Gene Ontology (GO is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role. Results We present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; simRel and funSim. One measure (simRel is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families. Conclusion The approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.

Uropathogenic Escherichia coli virulence genes: invaluable approaches for designing DNA microarray probes.

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Jahandeh, Nadia; Ranjbar, Reza; Behzadi, Payam; Behzadi, Elham

2015-01-01

The pathotypes of uropathogenic Escherichia coli (UPEC) cause different types of urinary tract infections (UTIs). The presence of a wide range of virulence genes in UPEC enables us to design appropriate DNA microarray probes. These probes, which are used in DNA microarray technology, provide us with an accurate and rapid diagnosis and definitive treatment in association with UTIs caused by UPEC pathotypes. The main goal of this article is to introduce the UPEC virulence genes as invaluable approaches for designing DNA microarray probes. Main search engines such as Google Scholar and databases like NCBI were searched to find and study several original pieces of literature, review articles, and DNA gene sequences. In parallel with in silico studies, the experiences of the authors were helpful for selecting appropriate sources and writing this review article. There is a significant variety of virulence genes among UPEC strains. The DNA sequences of virulence genes are fabulous patterns for designing microarray probes. The location of virulence genes and their sequence lengths influence the quality of probes. The use of selected virulence genes for designing microarray probes gives us a wide range of choices from which the best probe candidates can be chosen. DNA microarray technology provides us with an accurate, rapid, cost-effective, sensitive, and specific molecular diagnostic method which is facilitated by designing microarray probes. Via these tools, we are able to have an accurate diagnosis and a definitive treatment regarding UTIs caused by UPEC pathotypes.

So many genes, so little time: A practical approach to divergence-time estimation in the genomic era.

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Smith, Stephen A; Brown, Joseph W; Walker, Joseph F

2018-01-01

Phylogenomic datasets have been successfully used to address questions involving evolutionary relationships, patterns of genome structure, signatures of selection, and gene and genome duplications. However, despite the recent explosion in genomic and transcriptomic data, the utility of these data sources for efficient divergence-time inference remains unexamined. Phylogenomic datasets pose two distinct problems for divergence-time estimation: (i) the volume of data makes inference of the entire dataset intractable, and (ii) the extent of underlying topological and rate heterogeneity across genes makes model mis-specification a real concern. "Gene shopping", wherein a phylogenomic dataset is winnowed to a set of genes with desirable properties, represents an alternative approach that holds promise in alleviating these issues. We implemented an approach for phylogenomic datasets (available in SortaDate) that filters genes by three criteria: (i) clock-likeness, (ii) reasonable tree length (i.e., discernible information content), and (iii) least topological conflict with a focal species tree (presumed to have already been inferred). Such a winnowing procedure ensures that errors associated with model (both clock and topology) mis-specification are minimized, therefore reducing error in divergence-time estimation. We demonstrated the efficacy of this approach through simulation and applied it to published animal (Aves, Diplopoda, and Hymenoptera) and plant (carnivorous Caryophyllales, broad Caryophyllales, and Vitales) phylogenomic datasets. By quantifying rate heterogeneity across both genes and lineages we found that every empirical dataset examined included genes with clock-like, or nearly clock-like, behavior. Moreover, many datasets had genes that were clock-like, exhibited reasonable evolutionary rates, and were mostly compatible with the species tree. We identified overlap in age estimates when analyzing these filtered genes under strict clock and uncorrelated

Gene-based interaction analysis shows GABAergic genes interacting with parenting in adolescent depressive symptoms

NARCIS (Netherlands)

Van Assche, Evelien; Moons, Tim; Cinar, Ozan; Viechtbauer, Wolfgang; Oldehinkel, Albertine J.; Van Leeuwen, Karla; Verschueren, Karine; Colpin, Hilde; Lambrechts, Diether; Van den Noortgate, Wim; Goossens, Luc; Claes, Stephan; van Winkel, Ruud

2017-01-01

BACKGROUND: Most gene-environment interaction studies (G × E) have focused on single candidate genes. This approach is criticized for its expectations of large effect sizes and occurrence of spurious results. We describe an approach that accounts for the polygenic nature of most psychiatric

Targeted delivery of genes to endothelial cells and cell- and gene-based therapy in pulmonary vascular diseases.

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Suen, Colin M; Mei, Shirley H J; Kugathasan, Lakshmi; Stewart, Duncan J

2013-10-01

Pulmonary arterial hypertension (PAH) is a devastating disease that, despite significant advances in medical therapies over the last several decades, continues to have an extremely poor prognosis. Gene therapy is a method to deliver therapeutic genes to replace defective or mutant genes or supplement existing cellular processes to modify disease. Over the last few decades, several viral and nonviral methods of gene therapy have been developed for preclinical PAH studies with varying degrees of efficacy. However, these gene delivery methods face challenges of immunogenicity, low transduction rates, and nonspecific targeting which have limited their translation to clinical studies. More recently, the emergence of regenerative approaches using stem and progenitor cells such as endothelial progenitor cells (EPCs) and mesenchymal stem cells (MSCs) have offered a new approach to gene therapy. Cell-based gene therapy is an approach that augments the therapeutic potential of EPCs and MSCs and may deliver on the promise of reversal of established PAH. These new regenerative approaches have shown tremendous potential in preclinical studies; however, large, rigorously designed clinical studies will be necessary to evaluate clinical efficacy and safety. © 2013 American Physiological Society. Compr Physiol 3:1749-1779, 2013.

Vaccine-induced modulation of gene expression in turbot peritoneal cells. A microarray approach.

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Fontenla, Francisco; Blanco-Abad, Verónica; Pardo, Belén G; Folgueira, Iria; Noia, Manuel; Gómez-Tato, Antonio; Martínez, Paulino; Leiro, José M; Lamas, Jesús

2016-07-01

We used a microarray approach to examine changes in gene expression in turbot peritoneal cells after injection of the fish with vaccines containing the ciliate parasite Philasterides dicentrarchi as antigen and one of the following adjuvants: chitosan-PVMMA microspheres, Freund́s complete adjuvant, aluminium hydroxide gel or Matrix-Q (Isconova, Sweden). We identified 374 genes that were differentially expressed in all groups of fish. Forty-two genes related to tight junctions and focal adhesions and/or actin cytoskeleton were differentially expressed in free peritoneal cells. The profound changes in gene expression related to cell adherence and cytoskeleton may be associated with cell migration and also with the formation of cell-vaccine masses and their attachment to the peritoneal wall. Thirty-five genes related to apoptosis were differentially expressed. Although most of the proteins coded by these genes have a proapoptotic effect, others are antiapoptotic, indicating that both types of signals occur in peritoneal leukocytes of vaccinated fish. Interestingly, many of the genes related to lymphocytes and lymphocyte activity were downregulated in the groups injected with vaccine. We also observed decreased expression of genes related to antigen presentation, suggesting that macrophages (which were abundant in the peritoneal cavity after vaccination) did not express these during the early inflammatory response in the peritoneal cavity. Finally, several genes that participate in the inflammatory response were differentially expressed, and most participated in resolution of inflammation, indicating that an M2 macrophage response is generated in the peritoneal cavity of fish one day post vaccination. Copyright © 2016 Elsevier Ltd. All rights reserved.

An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series.

Science.gov (United States)

Wang, Zidong; Liu, Xiaohui; Liu, Yurong; Liang, Jinling; Vinciotti, Veronica

2009-01-01

In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.

Identification of conserved drought-adaptive genes using a cross-species meta-analysis approach.

Science.gov (United States)

Shaar-Moshe, Lidor; Hübner, Sariel; Peleg, Zvi

2015-05-03

Drought is the major environmental stress threatening crop-plant productivity worldwide. Identification of new genes and metabolic pathways involved in plant adaptation to progressive drought stress at the reproductive stage is of great interest for agricultural research. We developed a novel Cross-Species meta-Analysis of progressive Drought stress at the reproductive stage (CSA:Drought) to identify key drought adaptive genes and mechanisms and to test their evolutionary conservation. Empirically defined filtering criteria were used to facilitate a robust integration of 17 deposited microarray experiments (148 arrays) of Arabidopsis, rice, wheat and barley. By prioritizing consistency over intensity, our approach was able to identify 225 differentially expressed genes shared across studies and taxa. Gene ontology enrichment and pathway analyses classified the shared genes into functional categories involved predominantly in metabolic processes (e.g. amino acid and carbohydrate metabolism), regulatory function (e.g. protein degradation and transcription) and response to stimulus. We further investigated drought related cis-acting elements in the shared gene promoters, and the evolutionary conservation of shared genes. The universal nature of the identified drought-adaptive genes was further validated in a fifth species, Brachypodium distachyon that was not included in the meta-analysis. qPCR analysis of 27, randomly selected, shared orthologs showed similar expression pattern as was found by the CSA:Drought.In accordance, morpho-physiological characterization of progressive drought stress, in B. distachyon, highlighted the key role of osmotic adjustment as evolutionary conserved drought-adaptive mechanism. Our CSA:Drought strategy highlights major drought-adaptive genes and metabolic pathways that were only partially, if at all, reported in the original studies included in the meta-analysis. These genes include a group of unclassified genes that could be involved

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

Science.gov (United States)

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

2004-03-05

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

A novel approach to simulate gene-environment interactions in complex diseases

Directory of Open Access Journals (Sweden)

Nicodemi Mario

2010-01-01

Full Text Available Abstract Background Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.. Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS, a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte

Convergent functional genomics in addiction research - a translational approach to study candidate genes and gene networks.

Science.gov (United States)

Spanagel, Rainer

2013-01-01

Convergent functional genomics (CFG) is a translational methodology that integrates in a Bayesian fashion multiple lines of evidence from studies in human and animal models to get a better understanding of the genetics of a disease or pathological behavior. Here the integration of data sets that derive from forward genetics in animals and genetic association studies including genome wide association studies (GWAS) in humans is described for addictive behavior. The aim of forward genetics in animals and association studies in humans is to identify mutations (e.g. SNPs) that produce a certain phenotype; i.e. "from phenotype to genotype". Most powerful in terms of forward genetics is combined quantitative trait loci (QTL) analysis and gene expression profiling in recombinant inbreed rodent lines or genetically selected animals for a specific phenotype, e.g. high vs. low drug consumption. By Bayesian scoring genomic information from forward genetics in animals is then combined with human GWAS data on a similar addiction-relevant phenotype. This integrative approach generates a robust candidate gene list that has to be functionally validated by means of reverse genetics in animals; i.e. "from genotype to phenotype". It is proposed that studying addiction relevant phenotypes and endophenotypes by this CFG approach will allow a better determination of the genetics of addictive behavior.

Evolution by Pervasive Gene Fusion in Antibiotic Resistance and Antibiotic Synthesizing Genes

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Orla Coleman

2015-03-01

Full Text Available Phylogenetic (tree-based approaches to understanding evolutionary history are unable to incorporate convergent evolutionary events where two genes merge into one. In this study, as exemplars of what can be achieved when a tree is not assumed a priori, we have analysed the evolutionary histories of polyketide synthase genes and antibiotic resistance genes and have shown that their history is replete with convergent events as well as divergent events. We demonstrate that the overall histories of these genes more closely resembles the remodelling that might be seen with the children’s toy Lego, than the standard model of the phylogenetic tree. This work demonstrates further that genes can act as public goods, available for re-use and incorporation into other genetic goods.

Gene therapy in periodontics.

Science.gov (United States)

Chatterjee, Anirban; Singh, Nidhi; Saluja, Mini

2013-03-01

GENES are made of DNA - the code of life. They are made up of two types of base pair from different number of hydrogen bonds AT, GC which can be turned into instruction. Everyone inherits genes from their parents and passes them on in turn to their children. Every person's genes are different, and the changes in sequence determine the inherited differences between each of us. Some changes, usually in a single gene, may cause serious diseases. Gene therapy is 'the use of genes as medicine'. It involves the transfer of a therapeutic or working gene copy into specific cells of an individual in order to repair a faulty gene copy. Thus it may be used to replace a faulty gene, or to introduce a new gene whose function is to cure or to favorably modify the clinical course of a condition. It has a promising era in the field of periodontics. Gene therapy has been used as a mode of tissue engineering in periodontics. The tissue engineering approach reconstructs the natural target tissue by combining four elements namely: Scaffold, signaling molecules, cells and blood supply and thus can help in the reconstruction of damaged periodontium including cementum, gingival, periodontal ligament and bone.

Gene expression in cerebral ischemia: a new approach for neuroprotection.

Science.gov (United States)

Millán, Mónica; Arenillas, Juan

2006-01-01

Cerebral ischemia is one of the strongest stimuli for gene induction in the brain. Hundreds of genes have been found to be induced by brain ischemia. Many genes are involved in neurodestructive functions such as excitotoxicity, inflammatory response and neuronal apoptosis. However, cerebral ischemia is also a powerful reformatting and reprogramming stimulus for the brain through neuroprotective gene expression. Several genes may participate in both cellular responses. Thus, isolation of candidate genes for neuroprotection strategies and interpretation of expression changes have been proven difficult. Nevertheless, many studies are being carried out to improve the knowledge of the gene activation and protein expression following ischemic stroke, as well as in the development of new therapies that modify biochemical, molecular and genetic changes underlying cerebral ischemia. Owing to the complexity of the process involving numerous critical genes expressed differentially in time, space and concentration, ongoing therapeutic efforts should be based on multiple interventions at different levels. By modification of the acute gene expression induced by ischemia or the apoptotic gene program, gene therapy is a promising treatment but is still in a very experimental phase. Some hurdles will have to be overcome before these therapies can be introduced into human clinical stroke trials. Copyright 2006 S. Karger AG, Basel.

Functional Associations by Response Overlap (FARO, a functional genomics approach matching gene expression phenotypes.

Directory of Open Access Journals (Sweden)

Henrik Bjørn Nielsen

2007-08-01

Full Text Available The systematic comparison of transcriptional responses of organisms is a powerful tool in functional genomics. For example, mutants may be characterized by comparing their transcript profiles to those obtained in other experiments querying the effects on gene expression of many experimental factors including treatments, mutations and pathogen infections. Similarly, drugs may be discovered by the relationship between the transcript profiles effectuated or impacted by a candidate drug and by the target disease. The integration of such data enables systems biology to predict the interplay between experimental factors affecting a biological system. Unfortunately, direct comparisons of gene expression profiles obtained in independent, publicly available microarray experiments are typically compromised by substantial, experiment-specific biases. Here we suggest a novel yet conceptually simple approach for deriving 'Functional Association(s by Response Overlap' (FARO between microarray gene expression studies. The transcriptional response is defined by the set of differentially expressed genes independent from the magnitude or direction of the change. This approach overcomes the limited comparability between studies that is typical for methods that rely on correlation in gene expression. We apply FARO to a compendium of 242 diverse Arabidopsis microarray experimental factors, including phyto-hormones, stresses and pathogens, growth conditions/stages, tissue types and mutants. We also use FARO to confirm and further delineate the functions of Arabidopsis MAP kinase 4 in disease and stress responses. Furthermore, we find that a large, well-defined set of genes responds in opposing directions to different stress conditions and predict the effects of different stress combinations. This demonstrates the usefulness of our approach for exploiting public microarray data to derive biologically meaningful associations between experimental factors. Finally, our

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 surgical approach appropriate for targeted cochlear gene therapy in the mouse.

Science.gov (United States)

Jero, J; Tseng, C J; Mhatre, A N; Lalwani, A K

2001-01-01

Therapeutic manipulations of the mammalian cochlea, including cochlear gene transfer, have been predominantly studied using the guinea pig as the experimental model. With the significant developments in mouse genomics and the availability of mutant strains of mice with well-characterized hearing loss, the mouse justifiably will be the preferred animal model for therapeutic manipulations. However, the potential advantages of the mouse model have not been fully realized due to the surgical difficulty of accessing its small cochlea. This study describes a ventral approach, instead of the routinely used postauricular approach in other rodents, for accessing the mouse middle and inner ear, and its application in cochlear gene transfer. This ventral approach enabled rapid and direct delivery of liposome-transgene complex to the mouse inner ear while avoiding blood loss, facial nerve morbidity, and mortality. Transgene expression at 3 days was detected in Reissner's membrane, spiral limbus, spiral ligament, and spiral ganglion cells, in a pattern similar to that previously described in the guinea pig. The successful access and delivery of material to the mouse cochlea and the replication of gene expression seen in the guinea pig demonstrated in this study should promote the use of the mouse in future studies investigating targeted cochlear therapy.

Gene Therapy for Parkinson's Disease

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Rachel Denyer

2012-01-01

Full Text Available Current pharmacological and surgical treatments for Parkinson's disease offer symptomatic improvements to those suffering from this incurable degenerative neurological disorder, but none of these has convincingly shown effects on disease progression. Novel approaches based on gene therapy have several potential advantages over conventional treatment modalities. These could be used to provide more consistent dopamine supplementation, potentially providing superior symptomatic relief with fewer side effects. More radically, gene therapy could be used to correct the imbalances in basal ganglia circuitry associated with the symptoms of Parkinson's disease, or to preserve or restore dopaminergic neurons lost during the disease process itself. The latter neuroprotective approach is the most exciting, as it could theoretically be disease modifying rather than simply symptom alleviating. Gene therapy agents using these approaches are currently making the transition from the laboratory to the bedside. This paper summarises the theoretical approaches to gene therapy for Parkinson's disease and the findings of clinical trials in this rapidly changing field.

Constructing an integrated gene similarity network for the identification of disease genes.

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Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

Refining discordant gene trees.

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Górecki, Pawel; Eulenstein, Oliver

2014-01-01

Evolutionary studies are complicated by discordance between gene trees and the species tree in which they evolved. Dealing with discordant trees often relies on comparison costs between gene and species trees, including the well-established Robinson-Foulds, gene duplication, and deep coalescence costs. While these costs have provided credible results for binary rooted gene trees, corresponding cost definitions for non-binary unrooted gene trees, which are frequently occurring in practice, are challenged by biological realism. We propose a natural extension of the well-established costs for comparing unrooted and non-binary gene trees with rooted binary species trees using a binary refinement model. For the duplication cost we describe an efficient algorithm that is based on a linear time reduction and also computes an optimal rooted binary refinement of the given gene tree. Finally, we show that similar reductions lead to solutions for computing the deep coalescence and the Robinson-Foulds costs. Our binary refinement of Robinson-Foulds, gene duplication, and deep coalescence costs for unrooted and non-binary gene trees together with the linear time reductions provided here for computing these costs significantly extends the range of trees that can be incorporated into approaches dealing with discordance.

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

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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.

Imaging gene expression in gene therapy

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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

Using complementary approaches to identify trans-domain nuclear gene transfers in the extremophile Galdieria sulphuraria (Rhodophyta).

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Pandey, Ravi S; Saxena, Garima; Bhattacharya, Debashish; Qiu, Huan; Azad, Rajeev K

2017-02-01

Identification of horizontal gene transfers (HGTs) has primarily relied on phylogenetic tree based methods, which require a rich sampling of sequenced genomes to ensure a reliable inference. Because the success of phylogenetic approaches depends on the breadth and depth of the database, researchers usually apply stringent filters to detect only the most likely gene transfers in the genomes of interest. One such study focused on a highly conservative estimate of trans-domain gene transfers in the extremophile eukaryote, Galdieria sulphuraria (Galdieri) Merola (Rhodophyta), by applying multiple filters in their phylogenetic pipeline. This led to the identification of 75 inter-domain acquisitions from Bacteria or Archaea. Because of the evolutionary, ecological, and potential biotechnological significance of foreign genes in algae, alternative approaches and pipelines complementing phylogenetics are needed for a more comprehensive assessment of HGT. We present here a novel pipeline that uncovered 17 novel foreign genes of prokaryotic origin in G. sulphuraria, results that are supported by multiple lines of evidence including composition-based, comparative data, and phylogenetics. These genes encode a variety of potentially adaptive functions, from metabolite transport to DNA repair. © 2016 Phycological Society of America.

Detection of EPO gene doping in blood.

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Neuberger, Elmo W I; Jurkiewicz, Magdalena; Moser, Dirk A; Simon, Perikles

2012-11-01

Gene doping--or the abuse of gene therapy--will continue to threaten the sports world. History has shown that progress in medical research is likely to be abused in order to enhance human performance. In this review, we critically discuss the progress and the risks associated with the field of erythropoietin (EPO) gene therapy and its applicability to EPO gene doping. We present typical vector systems that are employed in ex vivo and in vivo gene therapy trials. Due to associated risks, gene doping is not a feasible alternative to conventional EPO or blood doping at this time. Nevertheless, it is well described that about half of the elite athlete population is in principle willing to risk its health to gain a competitive advantage. This includes the use of technologies that lack safety approval. Sophisticated detection approaches are a prerequisite for prevention of unapproved and uncontrolled use of gene therapy technology. In this review, we present current detection approaches for EPO gene doping, with a focus on blood-based direct and indirect approaches. Gene doping is detectable in principle, and recent DNA-based detection strategies enable long-term detection of transgenic DNA (tDNA) following in vivo gene transfer. Copyright © 2012 John Wiley & Sons, Ltd.

An oscillopathic approach to developmental dyslexia: From genes to speech processing.

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Jiménez-Bravo, Miguel; Marrero, Victoria; Benítez-Burraco, Antonio

2017-06-30

Developmental dyslexia is a heterogeneous condition entailing problems with reading and spelling. Several genes have been linked or associated to the disease, many of which contribute to the development and function of brain areas important for auditory and phonological processing. Nonetheless, a clear link between genes, the brain, and the symptoms of dyslexia is still pending. The goal of this paper is contributing to bridge this gap. With this aim, we have focused on how the dyslexic brain fails to process speech sounds and reading cues. We have adopted an oscillatory perspective, according to which dyslexia may result from a deficient integration of different brain rhythms during reading/spellings tasks. Moreover, we show that some candidate genes for this condition are related to brain rhythms. This fresh approach is expected to provide a better understanding of the aetiology and the clinical presentation of developmental dyslexia, but also to achieve an earlier and more accurate diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

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Ettore Mosca

2017-09-01

Full Text Available Autism spectrum disorder (ASD is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.

Genes with minimal phylogenetic information are problematic for coalescent analyses when gene tree estimation is biased.

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Xi, Zhenxiang; Liu, Liang; Davis, Charles C

2015-11-01

The development and application of coalescent methods are undergoing rapid changes. One little explored area that bears on the application of gene-tree-based coalescent methods to species tree estimation is gene informativeness. Here, we investigate the accuracy of these coalescent methods when genes have minimal phylogenetic information, including the implementation of the multilocus bootstrap approach. Using simulated DNA sequences, we demonstrate that genes with minimal phylogenetic information can produce unreliable gene trees (i.e., high error in gene tree estimation), which may in turn reduce the accuracy of species tree estimation using gene-tree-based coalescent methods. We demonstrate that this problem can be alleviated by sampling more genes, as is commonly done in large-scale phylogenomic analyses. This applies even when these genes are minimally informative. If gene tree estimation is biased, however, gene-tree-based coalescent analyses will produce inconsistent results, which cannot be remedied by increasing the number of genes. In this case, it is not the gene-tree-based coalescent methods that are flawed, but rather the input data (i.e., estimated gene trees). Along these lines, the commonly used program PhyML has a tendency to infer one particular bifurcating topology even though it is best represented as a polytomy. We additionally corroborate these findings by analyzing the 183-locus mammal data set assembled by McCormack et al. (2012) using ultra-conserved elements (UCEs) and flanking DNA. Lastly, we demonstrate that when employing the multilocus bootstrap approach on this 183-locus data set, there is no strong conflict between species trees estimated from concatenation and gene-tree-based coalescent analyses, as has been previously suggested by Gatesy and Springer (2014). Copyright © 2015 Elsevier Inc. All rights reserved.

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

Gene-wide analysis detects two new susceptibility genes for Alzheimer's disease.

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Escott-Price, Valentina; Bellenguez, Céline; Wang, Li-San; Choi, Seung-Hoan; Harold, Denise; Jones, Lesley; Holmans, Peter; Gerrish, Amy; Vedernikov, Alexey; Richards, Alexander; DeStefano, Anita L; Lambert, Jean-Charles; Ibrahim-Verbaas, Carla A; Naj, Adam C; Sims, Rebecca; Jun, Gyungah; Bis, Joshua C; Beecham, Gary W; Grenier-Boley, Benjamin; Russo, Giancarlo; Thornton-Wells, Tricia A; Denning, Nicola; Smith, Albert V; Chouraki, Vincent; Thomas, Charlene; Ikram, M Arfan; Zelenika, Diana; Vardarajan, Badri N; Kamatani, Yoichiro; Lin, Chiao-Feng; Schmidt, Helena; Kunkle, Brian; Dunstan, Melanie L; Vronskaya, Maria; Johnson, Andrew D; Ruiz, Agustin; Bihoreau, Marie-Thérèse; Reitz, Christiane; Pasquier, Florence; Hollingworth, Paul; Hanon, Olivier; Fitzpatrick, Annette L; Buxbaum, Joseph D; Campion, Dominique; Crane, Paul K; Baldwin, Clinton; Becker, Tim; Gudnason, Vilmundur; Cruchaga, Carlos; Craig, David; Amin, Najaf; Berr, Claudine; Lopez, Oscar L; De Jager, Philip L; Deramecourt, Vincent; Johnston, Janet A; Evans, Denis; Lovestone, Simon; Letenneur, Luc; Hernández, Isabel; Rubinsztein, David C; Eiriksdottir, Gudny; Sleegers, Kristel; Goate, Alison M; Fiévet, Nathalie; Huentelman, Matthew J; Gill, Michael; Brown, Kristelle; Kamboh, M Ilyas; Keller, Lina; Barberger-Gateau, Pascale; McGuinness, Bernadette; Larson, Eric B; Myers, Amanda J; Dufouil, Carole; Todd, Stephen; Wallon, David; Love, Seth; Rogaeva, Ekaterina; Gallacher, John; George-Hyslop, Peter St; Clarimon, Jordi; Lleo, Alberto; Bayer, Anthony; Tsuang, Debby W; Yu, Lei; Tsolaki, Magda; Bossù, Paola; Spalletta, Gianfranco; Proitsi, Petra; Collinge, John; Sorbi, Sandro; Garcia, Florentino Sanchez; Fox, Nick C; Hardy, John; Naranjo, Maria Candida Deniz; Bosco, Paolo; Clarke, Robert; Brayne, Carol; Galimberti, Daniela; Scarpini, Elio; Bonuccelli, Ubaldo; Mancuso, Michelangelo; Siciliano, Gabriele; Moebus, Susanne; Mecocci, Patrizia; Zompo, Maria Del; Maier, Wolfgang; Hampel, Harald; Pilotto, Alberto; Frank-García, Ana; Panza, Francesco; Solfrizzi, Vincenzo; Caffarra, Paolo; Nacmias, Benedetta; Perry, William; Mayhaus, Manuel; Lannfelt, Lars; Hakonarson, Hakon; Pichler, Sabrina; Carrasquillo, Minerva M; Ingelsson, Martin; Beekly, Duane; Alvarez, Victoria; Zou, Fanggeng; Valladares, Otto; Younkin, Steven G; Coto, Eliecer; Hamilton-Nelson, Kara L; Gu, Wei; Razquin, Cristina; Pastor, Pau; Mateo, Ignacio; Owen, Michael J; Faber, Kelley M; Jonsson, Palmi V; Combarros, Onofre; O'Donovan, Michael C; Cantwell, Laura B; Soininen, Hilkka; Blacker, Deborah; Mead, Simon; Mosley, Thomas H; Bennett, David A; Harris, Tamara B; Fratiglioni, Laura; Holmes, Clive; de Bruijn, Renee F A G; Passmore, Peter; Montine, Thomas J; Bettens, Karolien; Rotter, Jerome I; Brice, Alexis; Morgan, Kevin; Foroud, Tatiana M; Kukull, Walter A; Hannequin, Didier; Powell, John F; Nalls, Michael A; Ritchie, Karen; Lunetta, Kathryn L; Kauwe, John S K; Boerwinkle, Eric; Riemenschneider, Matthias; Boada, Mercè; Hiltunen, Mikko; Martin, Eden R; Schmidt, Reinhold; Rujescu, Dan; Dartigues, Jean-François; Mayeux, Richard; Tzourio, Christophe; Hofman, Albert; Nöthen, Markus M; Graff, Caroline; Psaty, Bruce M; Haines, Jonathan L; Lathrop, Mark; Pericak-Vance, Margaret A; Launer, Lenore J; Van Broeckhoven, Christine; Farrer, Lindsay A; van Duijn, Cornelia M; Ramirez, Alfredo; Seshadri, Sudha; Schellenberg, Gerard D; Amouyel, Philippe; Williams, Julie

2014-01-01

Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls. In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10-6) and 14 (IGHV1-67 p = 7.9×10-8) which indexed novel susceptibility loci. The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease.

Gene-wide analysis detects two new susceptibility genes for Alzheimer's disease.

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Valentina Escott-Price

Full Text Available Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls.In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10-6 and 14 (IGHV1-67 p = 7.9×10-8 which indexed novel susceptibility loci.The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease.

Integrative characterization of germ cell-specific genes from mouse spermatocyte UniGene library

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Eddy Edward M

2007-07-01

Full Text Available Abstract Background The primary regulator of spermatogenesis, a highly ordered and tightly regulated developmental process, is an intrinsic genetic program involving male germ cell-specific genes. Results We analyzed the mouse spermatocyte UniGene library containing 2155 gene-oriented transcript clusters. We predict that 11% of these genes are testis-specific and systematically identified 24 authentic genes specifically and abundantly expressed in the testis via in silico and in vitro approaches. Northern blot analysis disclosed various transcript characteristics, such as expression level, size and the presence of isoform. Expression analysis revealed developmentally regulated and stage-specific expression patterns in all of the genes. We further analyzed the genes at the protein and cellular levels. Transfection assays performed using GC-2 cells provided information on the cellular characteristics of the gene products. In addition, antibodies were generated against proteins encoded by some of the genes to facilitate their identification and characterization in spermatogenic cells and sperm. Our data suggest that a number of the gene products are implicated in transcriptional regulation, nuclear integrity, sperm structure and motility, and fertilization. In particular, we found for the first time that Mm.333010, predicted to contain a trypsin-like serine protease domain, is a sperm acrosomal protein. Conclusion We identify 24 authentic genes with spermatogenic cell-specific expression, and provide comprehensive information about the genes. Our findings establish a new basis for future investigation into molecular mechanisms underlying male reproduction.

History of gene therapy.

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Wirth, Thomas; Parker, Nigel; Ylä-Herttuala, Seppo

2013-08-10

Two decades after the initial gene therapy trials and more than 1700 approved clinical trials worldwide we not only have gained much new information and knowledge regarding gene therapy in general, but also learned to understand the concern that has persisted in society. Despite the setbacks gene therapy has faced, success stories have increasingly emerged. Examples for these are the positive recommendation for a gene therapy product (Glybera) by the EMA for approval in the European Union and the positive trials for the treatment of ADA deficiency, SCID-X1 and adrenoleukodystrophy. Nevertheless, our knowledge continues to grow and during the course of time more safety data has become available that helps us to develop better gene therapy approaches. Also, with the increased understanding of molecular medicine, we have been able to develop more specific and efficient gene transfer vectors which are now producing clinical results. In this review, we will take a historical view and highlight some of the milestones that had an important impact on the development of gene therapy. We will also discuss briefly the safety and ethical aspects of gene therapy and address some concerns that have been connected with gene therapy as an important therapeutic modality. Copyright © 2013 Elsevier B.V. All rights reserved.

Positron emission tomography imaging of gene expression

International Nuclear Information System (INIS)

Tang Ganghua

2001-01-01

The merging of molecular biology and nuclear medicine is developed into molecular nuclear medicine. Positron emission tomography (PET) of gene expression in molecular nuclear medicine has become an attractive area. Positron emission tomography imaging gene expression includes the antisense PET imaging and the reporter gene PET imaging. It is likely that the antisense PET imaging will lag behind the reporter gene PET imaging because of the numerous issues that have not yet to be resolved with this approach. The reporter gene PET imaging has wide application into animal experimental research and human applications of this approach will likely be reported soon

Inflammatory gene regulatory networks in amnion cells following cytokine stimulation: translational systems approach to modeling human parturition.

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Ruth Li

Full Text Available A majority of the studies examining the molecular regulation of human labor have been conducted using single gene approaches. While the technology to produce multi-dimensional datasets is readily available, the means for facile analysis of such data are limited. The objective of this study was to develop a systems approach to infer regulatory mechanisms governing global gene expression in cytokine-challenged cells in vitro, and to apply these methods to predict gene regulatory networks (GRNs in intrauterine tissues during term parturition. To this end, microarray analysis was applied to human amnion mesenchymal cells (AMCs stimulated with interleukin-1β, and differentially expressed transcripts were subjected to hierarchical clustering, temporal expression profiling, and motif enrichment analysis, from which a GRN was constructed. These methods were then applied to fetal membrane specimens collected in the absence or presence of spontaneous term labor. Analysis of cytokine-responsive genes in AMCs revealed a sterile immune response signature, with promoters enriched in response elements for several inflammation-associated transcription factors. In comparison to the fetal membrane dataset, there were 34 genes commonly upregulated, many of which were part of an acute inflammation gene expression signature. Binding motifs for nuclear factor-κB were prominent in the gene interaction and regulatory networks for both datasets; however, we found little evidence to support the utilization of pathogen-associated molecular pattern (PAMP signaling. The tissue specimens were also enriched for transcripts governed by hypoxia-inducible factor. The approach presented here provides an uncomplicated means to infer global relationships among gene clusters involved in cellular responses to labor-associated signals.

A systems genetics approach identifies CXCL14, ITGAX, and LPCAT2 as novel aggressive prostate cancer susceptibility genes.

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Kendra A Williams

2014-11-01

Full Text Available Although prostate cancer typically runs an indolent course, a subset of men develop aggressive, fatal forms of this disease. We hypothesize that germline variation modulates susceptibility to aggressive prostate cancer. The goal of this work is to identify susceptibility genes using the C57BL/6-Tg(TRAMP8247Ng/J (TRAMP mouse model of neuroendocrine prostate cancer. Quantitative trait locus (QTL mapping was performed in transgene-positive (TRAMPxNOD/ShiLtJ F2 intercross males (n = 228, which facilitated identification of 11 loci associated with aggressive disease development. Microarray data derived from 126 (TRAMPxNOD/ShiLtJ F2 primary tumors were used to prioritize candidate genes within QTLs, with candidate genes deemed as being high priority when possessing both high levels of expression-trait correlation and a proximal expression QTL. This process enabled the identification of 35 aggressive prostate tumorigenesis candidate genes. The role of these genes in aggressive forms of human prostate cancer was investigated using two concurrent approaches. First, logistic regression analysis in two human prostate gene expression datasets revealed that expression levels of five genes (CXCL14, ITGAX, LPCAT2, RNASEH2A, and ZNF322 were positively correlated with aggressive prostate cancer and two genes (CCL19 and HIST1H1A were protective for aggressive prostate cancer. Higher than average levels of expression of the five genes that were positively correlated with aggressive disease were consistently associated with patient outcome in both human prostate cancer tumor gene expression datasets. Second, three of these five genes (CXCL14, ITGAX, and LPCAT2 harbored polymorphisms associated with aggressive disease development in a human GWAS cohort consisting of 1,172 prostate cancer patients. This study is the first example of using a systems genetics approach to successfully identify novel susceptibility genes for aggressive prostate cancer. Such

More powerful significant testing for time course gene expression data using functional principal component analysis approaches.

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Wu, Shuang; Wu, Hulin

2013-01-16

One of the fundamental problems in time course gene expression data analysis is to identify genes associated with a biological process or a particular stimulus of interest, like a treatment or virus infection. Most of the existing methods for this problem are designed for data with longitudinal replicates. But in reality, many time course gene experiments have no replicates or only have a small number of independent replicates. We focus on the case without replicates and propose a new method for identifying differentially expressed genes by incorporating the functional principal component analysis (FPCA) into a hypothesis testing framework. The data-driven eigenfunctions allow a flexible and parsimonious representation of time course gene expression trajectories, leaving more degrees of freedom for the inference compared to that using a prespecified basis. Moreover, the information of all genes is borrowed for individual gene inferences. The proposed approach turns out to be more powerful in identifying time course differentially expressed genes compared to the existing methods. The improved performance is demonstrated through simulation studies and a real data application to the Saccharomyces cerevisiae cell cycle data.

A recently transferred cluster of bacterial genes in Trichomonas vaginalis - lateral gene transfer and the fate of acquired genes

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2014-01-01

Background Lateral Gene Transfer (LGT) has recently gained recognition as an important contributor to some eukaryote proteomes, but the mechanisms of acquisition and fixation in eukaryotic genomes are still uncertain. A previously defined norm for LGTs in microbial eukaryotes states that the majority are genes involved in metabolism, the LGTs are typically localized one by one, surrounded by vertically inherited genes on the chromosome, and phylogenetics shows that a broad collection of bacterial lineages have contributed to the transferome. Results A unique 34 kbp long fragment with 27 clustered genes (TvLF) of prokaryote origin was identified in the sequenced genome of the protozoan parasite Trichomonas vaginalis. Using a PCR based approach we confirmed the presence of the orthologous fragment in four additional T. vaginalis strains. Detailed sequence analyses unambiguously suggest that TvLF is the result of one single, recent LGT event. The proposed donor is a close relative to the firmicute bacterium Peptoniphilus harei. High nucleotide sequence similarity between T. vaginalis strains, as well as to P. harei, and the absence of homologs in other Trichomonas species, suggests that the transfer event took place after the radiation of the genus Trichomonas. Some genes have undergone pseudogenization and degradation, indicating that they may not be retained in the future. Functional annotations reveal that genes involved in informational processes are particularly prone to degradation. Conclusions We conclude that, although the majority of eukaryote LGTs are single gene occurrences, they may be acquired in clusters of several genes that are subsequently cleansed of evolutionarily less advantageous genes. PMID:24898731

Cross-species transcriptomic approach reveals genes in hamster implantation sites.

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Lei, Wei; Herington, Jennifer; Galindo, Cristi L; Ding, Tianbing; Brown, Naoko; Reese, Jeff; Paria, Bibhash C

2014-12-01

The mouse model has greatly contributed to understanding molecular mechanisms involved in the regulation of progesterone (P4) plus estrogen (E)-dependent blastocyst implantation process. However, little is known about contributory molecular mechanisms of the P4-only-dependent blastocyst implantation process that occurs in species such as hamsters, guineapigs, rabbits, pigs, rhesus monkeys, and perhaps humans. We used the hamster as a model of P4-only-dependent blastocyst implantation and carried out cross-species microarray (CSM) analyses to reveal differentially expressed genes at the blastocyst implantation site (BIS), in order to advance the understanding of molecular mechanisms of implantation. Upregulation of 112 genes and downregulation of 77 genes at the BIS were identified using a mouse microarray platform, while use of the human microarray revealed 62 up- and 38 down-regulated genes at the BIS. Excitingly, a sizable number of genes (30 up- and 11 down-regulated genes) were identified as a shared pool by both CSMs. Real-time RT-PCR and in situ hybridization validated the expression patterns of several up- and down-regulated genes identified by both CSMs at the hamster and mouse BIS to demonstrate the merit of CSM findings across species, in addition to revealing genes specific to hamsters. Functional annotation analysis found that genes involved in the spliceosome, proteasome, and ubiquination pathways are enriched at the hamster BIS, while genes associated with tight junction, SAPK/JNK signaling, and PPARα/RXRα signalings are repressed at the BIS. Overall, this study provides a pool of genes and evidence of their participation in up- and down-regulated cellular functions/pathways at the hamster BIS. © 2014 Society for Reproduction and Fertility.

Principles of gene microarray data analysis.

Science.gov (United States)

Mocellin, Simone; Rossi, Carlo Riccardo

2007-01-01

The development of several gene expression profiling methods, such as comparative genomic hybridization (CGH), differential display, serial analysis of gene expression (SAGE), and gene microarray, together with the sequencing of the human genome, has provided an opportunity to monitor and investigate the complex cascade of molecular events leading to tumor development and progression. The availability of such large amounts of information has shifted the attention of scientists towards a nonreductionist approach to biological phenomena. High throughput technologies can be used to follow changing patterns of gene expression over time. Among them, gene microarray has become prominent because it is easier to use, does not require large-scale DNA sequencing, and allows for the parallel quantification of thousands of genes from multiple samples. Gene microarray technology is rapidly spreading worldwide and has the potential to drastically change the therapeutic approach to patients affected with tumor. Therefore, it is of paramount importance for both researchers and clinicians to know the principles underlying the analysis of the huge amount of data generated with microarray technology.

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...

GoGene: gene annotation in the fast lane.

Science.gov (United States)

Plake, Conrad; Royer, Loic; Winnenburg, Rainer; Hakenberg, Jörg; Schroeder, Michael

2009-07-01

High-throughput screens such as microarrays and RNAi screens produce huge amounts of data. They typically result in hundreds of genes, which are often further explored and clustered via enriched GeneOntology terms. The strength of such analyses is that they build on high-quality manual annotations provided with the GeneOntology. However, the weakness is that annotations are restricted to process, function and location and that they do not cover all known genes in model organisms. GoGene addresses this weakness by complementing high-quality manual annotation with high-throughput text mining extracting co-occurrences of genes and ontology terms from literature. GoGene contains over 4,000,000 associations between genes and gene-related terms for 10 model organisms extracted from more than 18,000,000 PubMed entries. It does not cover only process, function and location of genes, but also biomedical categories such as diseases, compounds, techniques and mutations. By bringing it all together, GoGene provides the most recent and most complete facts about genes and can rank them according to novelty and importance. GoGene accepts keywords, gene lists, gene sequences and protein sequences as input and supports search for genes in PubMed, EntrezGene and via BLAST. Since all associations of genes to terms are supported by evidence in the literature, the results are transparent and can be verified by the user. GoGene is available at http://gopubmed.org/gogene.

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

Science.gov (United States)

Jamal, Salma; Goyal, Sukriti; Shanker, Asheesh; Grover, Abhinav

2016-10-18

Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes

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Reverter Antonio

2008-09-01

Full Text Available Abstract Background The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association. Methods We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported. Results Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes. Conclusion We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing

Environment-Gene interaction in common complex diseases: New approaches

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William A. Toscano, Jr.

2014-10-01

Full Text Available Approximately 100,000 different environmental chemicals that are in use as high production volume chemicals confront us in our daily lives. Many of the chemicals we encounter are persistent and have long half-lives in the environment and our bodies. These compounds are referred to as Persistent Organic Pollutants, or POPS. The total environment however is broader than just toxic pollutants. It includes social capital, social economic status, and other factors that are not commonly considered in traditional approaches to studying environment-human interactions. The mechanism of action of environmental agents in altering the human phenotype from health to disease is more complex than once thought. The focus in public health has shifted away from the study of single-gene rare diseases and has given way to the study of multifactorial complex diseases that are common in the population. To understand common complex diseases, we need teams of scientists from different fields working together with common aims. We review some approaches for studying the action of the environment by discussing use-inspired research, and transdisciplinary research approaches. The Genomic era has yielded new tools for study of gene-environment interactions, including genomics, epigenomics, and systems biology. We use environmentally-driven diabetes mellitus type two as an example of environmental epigenomics and disease. The aim of this review is to start the conversation of how the application of advances in biomedical science can be used to advance public health.

A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization.

Science.gov (United States)

Vafaee Sharbaf, Fatemeh; Mosafer, Sara; Moattar, Mohammad Hossein

2016-06-01

This paper proposes an approach for gene selection in microarray data. The proposed approach consists of a primary filter approach using Fisher criterion which reduces the initial genes and hence the search space and time complexity. Then, a wrapper approach which is based on cellular learning automata (CLA) optimized with ant colony method (ACO) is used to find the set of features which improve the classification accuracy. CLA is applied due to its capability to learn and model complicated relationships. The selected features from the last phase are evaluated using ROC curve and the most effective while smallest feature subset is determined. The classifiers which are evaluated in the proposed framework are K-nearest neighbor; support vector machine and naïve Bayes. The proposed approach is evaluated on 4 microarray datasets. The evaluations confirm that the proposed approach can find the smallest subset of genes while approaching the maximum accuracy. Copyright © 2016 Elsevier Inc. All rights reserved.

Newer Gene Editing Technologies toward HIV Gene Therapy

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Premlata Shankar

2013-11-01

Full Text Available Despite the great success of highly active antiretroviral therapy (HAART in ameliorating the course of HIV infection, alternative therapeutic approaches are being pursued because of practical problems associated with life-long therapy. The eradication of HIV in the so-called “Berlin patient” who received a bone marrow transplant from a CCR5-negative donor has rekindled interest in genome engineering strategies to achieve the same effect. Precise gene editing within the cells is now a realistic possibility with recent advances in understanding the DNA repair mechanisms, DNA interaction with transcription factors and bacterial defense mechanisms. Within the past few years, four novel technologies have emerged that can be engineered for recognition of specific DNA target sequences to enable site-specific gene editing: Homing Endonuclease, ZFN, TALEN, and CRISPR/Cas9 system. The most recent CRISPR/Cas9 system uses a short stretch of complementary RNA bound to Cas9 nuclease to recognize and cleave target DNA, as opposed to the previous technologies that use DNA binding motifs of either zinc finger proteins or transcription activator-like effector molecules fused to an endonuclease to mediate sequence-specific DNA cleavage. Unlike RNA interference, which requires the continued presence of effector moieties to maintain gene silencing, the newer technologies allow permanent disruption of the targeted gene after a single treatment. Here, we review the applications, limitations and future prospects of novel gene-editing strategies for use as HIV therapy.

Whole genome sequencing options for bacterial strain typing and epidemiologic analysis based on single nucleotide polymorphism versus gene-by-gene-based approaches.

Science.gov (United States)

Schürch, A C; Arredondo-Alonso, S; Willems, R J L; Goering, R V

2018-04-01

Whole genome sequence (WGS)-based strain typing finds increasing use in the epidemiologic analysis of bacterial pathogens in both public health as well as more localized infection control settings. This minireview describes methodologic approaches that have been explored for WGS-based epidemiologic analysis and considers the challenges and pitfalls of data interpretation. Personal collection of relevant publications. When applying WGS to study the molecular epidemiology of bacterial pathogens, genomic variability between strains is translated into measures of distance by determining single nucleotide polymorphisms in core genome alignments or by indexing allelic variation in hundreds to thousands of core genes, assigning types to unique allelic profiles. Interpreting isolate relatedness from these distances is highly organism specific, and attempts to establish species-specific cutoffs are unlikely to be generally applicable. In cases where single nucleotide polymorphism or core gene typing do not provide the resolution necessary for accurate assessment of the epidemiology of bacterial pathogens, inclusion of accessory gene or plasmid sequences may provide the additional required discrimination. As with all epidemiologic analysis, realizing the full potential of the revolutionary advances in WGS-based approaches requires understanding and dealing with issues related to the fundamental steps of data generation and interpretation. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Radiopharmaceuticals to monitor gene transfer

International Nuclear Information System (INIS)

Wiebe, L. I.; Morin, K. W.; Knaus, E. E.

1997-01-01

Advances in genetic engineering and molecular biology have opened the door to disease treatment by transferring genes to cells that are responsible for the pathological condition being addressed. These genes can serve to supplement or introduce the function of indigenous genes that are either inadequately expressed or that are congenitally absent in the patient. They can introduce new functions such as drug sensitization to provide a unique therapeutic target. Gene transfer is readily monitored in vitro using a range of histochemical and biochemical tests that are ''built in'' to the therapeutic gene cassette. In vivo, in situ monitoring of the gene transfer and gene expression processes can be achieved with these tests only if biopsy is possible. Scintigraphic imaging can offer unique information on both the extent and location of gene expression, provided that an appropriate reporter gene is included in the therapeutic cassette. This overview includes a brief orientation to gene transfer therapy and is followed by a review of current approaches to gene therapy imaging. The concluding section deals with imaging based on radiolabelled nucleoside substrates for herpes simplex type-1 thymidine kinase, with emphasis on IVFRU, a stable potent and selective HSV-1 TK substrate developed in their laboratories

A copula method for modeling directional dependence of genes

Directory of Open Access Journals (Sweden)

Park Changyi

2008-05-01

Full Text Available Abstract Background Genes interact with each other as basic building blocks of life, forming a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. With the deposition of huge microarray data sets in public domains, study on gene networking is now possible. In recent years, there has been an increasing interest in the reconstruction of gene networks from gene expression data. Recent work includes linear models, Boolean network models, and Bayesian networks. Among them, Bayesian networks seem to be the most effective in constructing gene networks. A major problem with the Bayesian network approach is the excessive computational time. This problem is due to the interactive feature of the method that requires large search space. Since fitting a model by using the copulas does not require iterations, elicitation of the priors, and complicated calculations of posterior distributions, the need for reference to extensive search spaces can be eliminated leading to manageable computational affords. Bayesian network approach produces a discretely expression of conditional probabilities. Discreteness of the characteristics is not required in the copula approach which involves use of uniform representation of the continuous random variables. Our method is able to overcome the limitation of Bayesian network method for gene-gene interaction, i.e. information loss due to binary transformation. Results We analyzed the gene interactions for two gene data sets (one group is eight histone genes and the other group is 19 genes which include DNA polymerases, DNA helicase, type B cyclin genes, DNA primases, radiation sensitive genes, repaire related genes, replication protein A encoding gene, DNA replication initiation factor, securin gene, nucleosome assembly factor, and a subunit of the cohesin complex by adopting a measure of directional dependence based on a copula function. We have compared

Gene cluster statistics with gene families.

Science.gov (United States)

Raghupathy, Narayanan; Durand, Dannie

2009-05-01

Identifying genomic regions that descended from a common ancestor is important for understanding the function and evolution of genomes. In distantly related genomes, clusters of homologous gene pairs are evidence of candidate homologous regions. Demonstrating the statistical significance of such "gene clusters" is an essential component of comparative genomic analyses. However, currently there are no practical statistical tests for gene clusters that model the influence of the number of homologs in each gene family on cluster significance. In this work, we demonstrate empirically that failure to incorporate gene family size in gene cluster statistics results in overestimation of significance, leading to incorrect conclusions. We further present novel analytical methods for estimating gene cluster significance that take gene family size into account. Our methods do not require complete genome data and are suitable for testing individual clusters found in local regions, such as contigs in an unfinished assembly. We consider pairs of regions drawn from the same genome (paralogous clusters), as well as regions drawn from two different genomes (orthologous clusters). Determining cluster significance under general models of gene family size is computationally intractable. By assuming that all gene families are of equal size, we obtain analytical expressions that allow fast approximation of cluster probabilities. We evaluate the accuracy of this approximation by comparing the resulting gene cluster probabilities with cluster probabilities obtained by simulating a realistic, power-law distributed model of gene family size, with parameters inferred from genomic data. Surprisingly, despite the simplicity of the underlying assumption, our method accurately approximates the true cluster probabilities. It slightly overestimates these probabilities, yielding a conservative test. We present additional simulation results indicating the best choice of parameter values for data

Shrinkage covariance matrix approach based on robust trimmed mean in gene sets detection

Science.gov (United States)

Karjanto, Suryaefiza; Ramli, Norazan Mohamed; Ghani, Nor Azura Md; Aripin, Rasimah; Yusop, Noorezatty Mohd

2015-02-01

Microarray involves of placing an orderly arrangement of thousands of gene sequences in a grid on a suitable surface. The technology has made a novelty discovery since its development and obtained an increasing attention among researchers. The widespread of microarray technology is largely due to its ability to perform simultaneous analysis of thousands of genes in a massively parallel manner in one experiment. Hence, it provides valuable knowledge on gene interaction and function. The microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints. Therefore, the sample covariance matrix in Hotelling's T2 statistic is not positive definite and become singular, thus it cannot be inverted. In this research, the Hotelling's T2 statistic is combined with a shrinkage approach as an alternative estimation to estimate the covariance matrix to detect significant gene sets. The use of shrinkage covariance matrix overcomes the singularity problem by converting an unbiased to an improved biased estimator of covariance matrix. Robust trimmed mean is integrated into the shrinkage matrix to reduce the influence of outliers and consequently increases its efficiency. The performance of the proposed method is measured using several simulation designs. The results are expected to outperform existing techniques in many tested conditions.

Gene therapy prospects--intranasal delivery of therapeutic genes.

Science.gov (United States)

Podolska, Karolina; Stachurska, Anna; Hajdukiewicz, Karolina; Małecki, Maciej

2012-01-01

Gene therapy is recognized to be a novel method for the treatment of various disorders. Gene therapy strategies involve gene manipulation on broad biological processes responsible for the spreading of diseases. Cancer, monogenic diseases, vascular and infectious diseases are the main targets of gene therapy. In order to obtain valuable experimental and clinical results, sufficient gene transfer methods are required. Therapeutic genes can be administered into target tissues via gene carriers commonly defined as vectors. The retroviral, adenoviral and adeno-associated virus based vectors are most frequently used in the clinic. So far, gene preparations may be administered directly into target organs or by intravenous, intramuscular, intratumor or intranasal injections. It is common knowledge that the number of gene therapy clinical trials has rapidly increased. However, some limitations such as transfection efficiency and stable and long-term gene expression are still not resolved. Consequently, great effort is focused on the evaluation of new strategies of gene delivery. There are many expectations associated with intranasal delivery of gene preparations for the treatment of diseases. Intranasal delivery of therapeutic genes is regarded as one of the most promising forms of pulmonary gene therapy research. Gene therapy based on inhalation of gene preparations offers an alternative way for the treatment of patients suffering from such lung diseases as cystic fibrosis, alpha-1-antitrypsin defect, or cancer. Experimental and first clinical trials based on plasmid vectors or recombinant viruses have revealed that gene preparations can effectively deliver therapeutic or marker genes to the cells of the respiratory tract. The noninvasive intranasal delivery of gene preparations or conventional drugs seems to be very encouraging, although basic scientific research still has to continue.

Gene-gene interactions and gene polymorphisms of VEGFA and EG-VEGF gene systems in recurrent pregnancy loss.

Science.gov (United States)

Su, Mei-Tsz; Lin, Sheng-Hsiang; Chen, Yi-Chi; Kuo, Pao-Lin

2014-06-01

Both vascular endothelial growth factor A (VEGFA) and endocrine gland-derived vascular endothelial growth factor (EG-VEGF) systems play major roles in angiogenesis. A body of evidence suggests VEGFs regulate critical processes during pregnancy and have been associated with recurrent pregnancy loss (RPL). However, little information is available regarding the interaction of these two major major angiogenesis-related systems in early human pregnancy. This study was conducted to investigate the association of gene polymorphisms and gene-gene interaction among genes in VEGFA and EG-VEGF systems and idiopathic RPL. A total of 98 women with history of idiopathic RPL and 142 controls were included, and 5 functional SNPs selected from VEGFA, KDR, EG-VEGF (PROK1), PROKR1 and PROKR2 were genotyped. We used multifactor dimensionality reduction (MDR) analysis to choose a best model and evaluate gene-gene interactions. Ingenuity pathways analysis (IPA) was introduced to explore possible complex interactions. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL (P<0.01). The MDR test revealed that the KDR (Q472H) polymorphism was the best loci to be associated with RPL (P=0.02). IPA revealed EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3 signaling pathways. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL. EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3.

The use of molecular imaging of gene expression by radiotracers in gene therapy

International Nuclear Information System (INIS)

Richard-Fiardo, P.; Franken, P.R.; Harrington, K.J.; Vassaux, G.; Cambien, B.

2011-01-01

Introduction: Progress with gene-based therapies has been hampered by difficulties in monitoring the biodistribution and kinetics of vector-mediated gene expression. Recent developments in non-invasive imaging have allowed researchers and clinicians to assess the location, magnitude and persistence of gene expression in animals and humans. Such advances should eventually lead to improvement in the efficacy and safety of current clinical protocols for future treatments. Areas Covered: The molecular imaging techniques for monitoring gene therapy in the living subject, with a specific highlight on the key reporter gene approaches that have been developed and validated in preclinical models using the latest imaging modalities. The applications of molecular imaging to biotherapy, with a particular emphasis on monitoring of gene and vector biodistribution and on image-guided radiotherapy. Expert Opinion: Among the reporter gene/probe combinations that have been described so far, one stands out, in our view, as the most versatile and easy to implement: the Na/I symporter. This strategy, exploiting more than 50 years of experience in the treatment of differentiated thyroid carcinomas, has been validated in different types of experimental cancers and with different types of oncolytic viruses and is likely to become a key tool in the implementation of human gene therapy. (authors)

Strategy of gene silencing in cassava for validation of resistance genes

International Nuclear Information System (INIS)

Cortes, Simon; Lopez, Camilo

2010-01-01

Cassava (Manihot esculenta) is a major source of food for more than 1000 million people in the world and constitutes an important staple crop. Cassava bacterial blight, caused by the gram negative bacterium Xanthomonas axonopodis pv. manihotis, is one of the most important constraints for this crop. A candidate resistance gene against cassava bacterial blight, named RXam1, has been identified previously. In this work, we employed the gene silencing approach using the African cassava mosaic virus (ACMV) to validate the function of the RXam1 gene. We used as positive control the su gen, which produce photo blanching in leaves when is silenced. Plants from the SG10735 variety were bombardment with the ACMV-A-SU+ACMV-B y ACMV-A-RXam1+ACMV-B constructions. The silencing efficiency employing the su gene was low, only one of seven plants showed photo blanching. In the putative silenced plants for the RXam1 gene, no presence of siRNAs corresponding to RXam1 was observed; although a low diminution of the RXam1 gene expression was obtained. The growth curves for the Xam strain CIO136 in cassava plants inoculated showing a little but no significance difference in the susceptibility in the silenced plants compared to not silenced

Improvements in algal lipid production: a systems biology and gene editing approach.

Science.gov (United States)

Banerjee, Avik; Banerjee, Chiranjib; Negi, Sangeeta; Chang, Jo-Shu; Shukla, Pratyoosh

2018-05-01

In the wake of rising energy demands, microalgae have emerged as potential sources of sustainable and renewable carbon-neutral fuels, such as bio-hydrogen and bio-oil. For rational metabolic engineering, the elucidation of metabolic pathways in fine detail and their manipulation according to requirements is the key to exploiting the use of microalgae. Emergence of site-specific nucleases have revolutionized applied research leading to biotechnological gains. Genome engineering as well as modulation of the endogenous genome with high precision using CRISPR systems is being gradually employed in microalgal research. Further, to optimize and produce better algal platforms, use of systems biology network analysis and integration of omics data is required. This review discusses two important approaches: systems biology and gene editing strategies used on microalgal systems with a focus on biofuel production and sustainable solutions. It also emphasizes that the integration of such systems would contribute and compliment applied research on microalgae. Recent advances in microalgae are discussed, including systems biology, gene editing approaches in lipid bio-synthesis, and antenna engineering. Lastly, it has been attempted here to showcase how CRISPR/Cas systems are a better editing tool than existing techniques that can be utilized for gene modulation and engineering during biofuel production.

Ratiometric Gas Reporting: A Nondisruptive Approach To Monitor Gene Expression in Soils.

Science.gov (United States)

Cheng, Hsiao-Ying; Masiello, Caroline A; Del Valle, Ilenne; Gao, Xiaodong; Bennett, George N; Silberg, Jonathan J

2018-03-16

Fluorescent proteins are ubiquitous tools that are used to monitor the dynamic functions of natural and synthetic genetic circuits. However, these visual reporters can only be used in transparent settings, a limitation that complicates nondisruptive measurements of gene expression within many matrices, such as soils and sediments. We describe a new ratiometric gas reporting method for nondisruptively monitoring gene expression within hard-to-image environmental matrices. With this approach, C 2 H 4 is continuously synthesized by ethylene forming enzyme to provide information on viable cell number, and CH 3 Br is conditionally synthesized by placing a methyl halide transferase gene under the control of a conditional promoter. We show that ratiometric gas reporting enables the creation of Escherichia coli biosensors that report on acylhomoserine lactone (AHL) autoinducers used for quorum sensing by Gram-negative bacteria. Using these biosensors, we find that an agricultural soil decreases the bioavailable concentration of a long-chain AHL up to 100-fold. We also demonstrate that these biosensors can be used in soil to nondisruptively monitor AHLs synthesized by Rhizobium leguminosarum and degraded by Bacillus thuringiensis. Finally, we show that this new reporting approach can be used in Shewanella oneidensis, a bacterium that lives in sediments.

Gene doping: gene delivery for olympic victory

OpenAIRE

Gould, David

2012-01-01

With one recently recommended gene therapy in Europe and a number of other gene therapy treatments now proving effective in clinical trials it is feasible that the same technologies will soon be adopted in the world of sport by unscrupulous athletes and their trainers in so called ‘gene doping’. In this article an overview of the successful gene therapy clinical trials is provided and the potential targets for gene doping are highlighted. Depending on whether a doping gene product is secreted...

Using the candidate gene approach for detecting genes underlying seed oil concentration and yield in soybean.

Science.gov (United States)

Eskandari, Mehrzad; Cober, Elroy R; Rajcan, Istvan

2013-07-01

Increasing the oil concentration in soybean seeds has been given more attention in recent years because of demand for both edible oil and biodiesel production. Oil concentration in soybean is a complex quantitative trait regulated by many genes as well as environmental conditions. To identify genes governing seed oil concentration in soybean, 16 putative candidate genes of three important gene families (GPAT: acyl-CoA:sn-glycerol-3-phosphate acyltransferase, DGAT: acyl-CoA:diacylglycerol acyltransferase, and PDAT: phospholipid:diacylglycerol acyltransferase) involved in triacylglycerol (TAG) biosynthesis pathways were selected and their sequences retrieved from the soybean database ( http://www.phytozome.net/soybean ). Three sequence mutations were discovered in either coding or noncoding regions of three DGAT soybean isoforms when comparing the parents of a 203 recombinant inbreed line (RIL) population; OAC Wallace and OAC Glencoe. The RIL population was used to study the effects of these mutations on seed oil concentration and other important agronomic and seed composition traits, including seed yield and protein concentration across three field locations in Ontario, Canada, in 2009 and 2010. An insertion/deletion (indel) mutation in the GmDGAT2B gene in OAC Wallace was significantly associated with reduced seed oil concentration across three environments and reduced seed yield at Woodstock in 2010. A mutation in the 3' untranslated (3'UTR) region of GmDGAT2C was associated with seed yield at Woodstock in 2009. A mutation in the intronic region of GmDGAR1B was associated with seed yield and protein concentration at Ottawa in 2010. The genes identified in this study had minor effects on either seed yield or oil concentration, which was in agreement with the quantitative nature of the traits. However, the novel gene-specific markers designed in the present study can be used in soybean breeding for marker-assisted selection aimed at increasing seed yield and oil

A comparative study of mutation screening of sarcomeric genes (MYBPC3, MYH7, TNNT2 using single gene approach versus targeted gene panel next generation sequencing in a cohort of HCM patients in Egypt

Directory of Open Access Journals (Sweden)

Heba Sh. Kassem

2017-10-01

Full Text Available Background: NGS enables simultaneous sequencing of large numbers of associated genes in genetic heterogeneous disorders, in a more rapid and cost-effective manner than traditional technologies. However there have been limited direct comparisons between NGS and more established technologies to assess the sensitivity and false negative rates of this new approach. The scope of the present manuscript is to compare variants detected in MYBPC3, MYH7 and TNNT2 genes using the stepwise dHPLC/Sanger versus targeted NGS. Methods: In this study, we have analysed a group of 150 samples of patients from the Bibliotheca Alexandrina-Aswan Heart Centre National HCM program. The genetic testing was simultaneously undertaken by high throughput denaturing high-performance liquid chromatography (dHPLC followed by Sanger based sequencing and targeted next generation deep sequencing using panel of inherited cardiac genes (ICC. The panel included over 100 genes including the 3 sarcomeric genes. Analysis of the sequencing data of the 3 genes was undertaken in a double blinded strategy. Results: NGS analysis detected all pathogenic and likely pathogenic variants identified by dHPLC (50 in total, some samples had double hits. There was a 0% false negative rate for NGS based analysis. Nineteen variants were missed by dHPLC and detected by NGS, thus increasing the diagnostic yield in this co- analysed cohort from 22.0% (33/150 to 31.3% (47/150.Of interest to note that the mutation spectrum in this Egyptian HCM population revealed a high rate of homozygosity in MYBPC3 and MYH7 genes in comparison to other population studies (6/150, 4%. None of the homozygous samples were detected by dHPLC analysis. Conclusion: NGS provides a useful and rapid tool to allow panoramic screening of several genes simultaneously with a high sensitivity rate amongst genes of known etiologic role allowing high throughput analysis of HCM patients and relevant control series in a less characterised

FunGene: the functional gene pipeline and repository.

Science.gov (United States)

Fish, Jordan A; Chai, Benli; Wang, Qiong; Sun, Yanni; Brown, C Titus; Tiedje, James M; Cole, James R

2013-01-01

Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer. While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/) offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

FunGene: the Functional Gene Pipeline and Repository

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Jordan A. Fish

2013-10-01

Full Text Available Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer.While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/ offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data

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Hu Xiaohua

2011-07-01

Full Text Available Abstract Background The wide use of high-throughput DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. Although biomedical researchers typically design microarray experiments to explore specific biological contexts, the relationships between genes are hard to identified because they are complex and noisy high-dimensional data and are often hindered by low statistical power. The main challenge now is to extract valuable biological information from the colossal amount of data to gain insight into biological processes and the mechanisms of human disease. To overcome the challenge requires mathematical and computational methods that are versatile enough to capture the underlying biological features and simple enough to be applied efficiently to large datasets. Methods Unsupervised machine learning approaches provide new and efficient analysis of gene expression profiles. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA and nonnegative matrix factorization (NMF are integrated to identify significant genes and related pathways in microarray gene expression dataset of Alzheimer’s disease. The advantage of these two approaches is they can be performed as a biclustering method by which genes and conditions can be clustered simultaneously. Furthermore, they can group genes into different categories for identifying related diagnostic pathways and regulatory networks. The difference between these two method lies in ICA assume statistical independence of the expression modes, while NMF need positivity constrains to generate localized gene expression profiles. Results In our work, we performed FastICA and non-smooth NMF methods on DNA microarray gene expression data of Alzheimer’s disease respectively. The simulation results shows that both of the methods can clearly classify severe AD samples from control samples, and

A suicide gene therapy approach to treat epidermolysis bullosa-associated skin cancer

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Gruber, C.

2009-01-01

Recessive dystrophic epidermolysis bullosa (RDEB) is an inherited disease causing extensive blister formation within the basal membrane zone (BMZ) of the skin and mucous membranes. It is caused by premature STOP mutations in the COL7A1 gene, which is indispensable for proper skin assembling. RDEB is associated with the development of a highly malignant skin cancer (squamous cell carcinoma, SCC) in early adulthood that displays a life threatening complication within this patient group. To date, neither chemo- nor radiotherapies showed successful results and due to the high metastatic potential of RDEB SCC wide surgical excision is still favoured. In this study we could reveal a new promising cancer treatment using spliceosome mediated RNA trans-splicing (SMaRT) using a suicide gene therapy approach. First we identified the tumour marker gene MMP-9 expressed by RDEB SCC cells in cell culture which was used to generate various pre-mRNA trans-splicing molecules (PTM). PTMs are able to facilitate trans-splicing between a tumour target gene and a cell death inducing peptide/toxin, encoded by the PTM. As a consequence the toxin is expressed in cancer cells leading to the induction of cell death. This technique offers high specificity in cancer cell targeting compared to other conventional cDNA expression studies. Various trans-splicing molecules were pre-evaluated in a fluorescence screening model for their best trans-splicing efficiency with the target molecule. Herein we identified two potent PTMs (PTM BD0 and PTM BD6), that were further adapted for endogenous suicide studies by inserting the toxin streptolysin O. In two independent in vitro cell culture assays we were able to confirm that the trans-splicing molecules are able to induce expression of the toxin resulting in cell membrane permeabilization and increased cell death induction. The results indicate that SMaRT technology offers a new platform for a suicide gene therapy approach to treat malignant squamous cell

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

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Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

CAR gene cluster and transcript levels of carotenogenic genes in Rhodotorula mucilaginosa.

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Landolfo, Sara; Ianiri, Giuseppe; Camiolo, Salvatore; Porceddu, Andrea; Mulas, Giuliana; Chessa, Rossella; Zara, Giacomo; Mannazzu, Ilaria

2018-01-01

A molecular approach was applied to the study of the carotenoid biosynthetic pathway of Rhodotorula mucilaginosa. At first, functional annotation of the genome of R. mucilaginosa C2.5t1 was carried out and gene ontology categories were assigned to 4033 predicted proteins. Then, a set of genes involved in different steps of carotenogenesis was identified and those coding for phytoene desaturase, phytoene synthase/lycopene cyclase and carotenoid dioxygenase (CAR genes) proved to be clustered within a region of ~10 kb. Quantitative PCR of the genes involved in carotenoid biosynthesis showed that genes coding for 3-hydroxy-3-methylglutharyl-CoA reductase and mevalonate kinase are induced during exponential phase while no clear trend of induction was observed for phytoene synthase/lycopene cyclase and phytoene dehydrogenase encoding genes. Thus, in R. mucilaginosa the induction of genes involved in the early steps of carotenoid biosynthesis is transient and accompanies the onset of carotenoid production, while that of CAR genes does not correlate with the amount of carotenoids produced. The transcript levels of genes coding for carotenoid dioxygenase, superoxide dismutase and catalase A increased during the accumulation of carotenoids, thus suggesting the activation of a mechanism aimed at the protection of cell structures from oxidative stress during carotenoid biosynthesis. The data presented herein, besides being suitable for the elucidation of the mechanisms that underlie carotenoid biosynthesis, will contribute to boosting the biotechnological potential of this yeast by improving the outcome of further research efforts aimed at also exploring other features of interest.

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.

A model of gene-gene and gene-environment interactions and its implications for targeting environmental interventions by genotype

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Wallace Helen M

2006-10-01

Full Text Available Abstract Background The potential public health benefits of targeting environmental interventions by genotype depend on the environmental and genetic contributions to the variance of common diseases, and the magnitude of any gene-environment interaction. In the absence of prior knowledge of all risk factors, twin, family and environmental data may help to define the potential limits of these benefits in a given population. However, a general methodology to analyze twin data is required because of the potential importance of gene-gene interactions (epistasis, gene-environment interactions, and conditions that break the 'equal environments' assumption for monozygotic and dizygotic twins. Method A new model for gene-gene and gene-environment interactions is developed that abandons the assumptions of the classical twin study, including Fisher's (1918 assumption that genes act as risk factors for common traits in a manner necessarily dominated by an additive polygenic term. Provided there are no confounders, the model can be used to implement a top-down approach to quantifying the potential utility of genetic prediction and prevention, using twin, family and environmental data. The results describe a solution space for each disease or trait, which may or may not include the classical twin study result. Each point in the solution space corresponds to a different model of genotypic risk and gene-environment interaction. Conclusion The results show that the potential for reducing the incidence of common diseases using environmental interventions targeted by genotype may be limited, except in special cases. The model also confirms that the importance of an individual's genotype in determining their risk of complex diseases tends to be exaggerated by the classical twin studies method, owing to the 'equal environments' assumption and the assumption of no gene-environment interaction. In addition, if phenotypes are genetically robust, because of epistasis

Why commercialization of gene therapy stalled; examining the life cycles of gene therapy technologies.

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Ledley, F D; McNamee, L M; Uzdil, V; Morgan, I W

2014-02-01

This report examines the commercialization of gene therapy in the context of innovation theories that posit a relationship between the maturation of a technology through its life cycle and prospects for successful product development. We show that the field of gene therapy has matured steadily since the 1980s, with the congruent accumulation of >35 000 papers, >16 000 US patents, >1800 clinical trials and >$4.3 billion in capital investment in gene therapy companies. Gene therapy technologies comprise a series of dissimilar approaches for gene delivery, each of which has introduced a distinct product architecture. Using bibliometric methods, we quantify the maturation of each technology through a characteristic life cycle S-curve, from a Nascent stage, through a Growing stage of exponential advance, toward an Established stage and projected limit. Capital investment in gene therapy is shown to have occurred predominantly in Nascent stage technologies and to be negatively correlated with maturity. Gene therapy technologies are now achieving the level of maturity that innovation research and biotechnology experience suggest may be requisite for efficient product development. Asynchrony between the maturation of gene therapy technologies and capital investment in development-focused business models may have stalled the commercialization of gene therapy.

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

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Hala Alshamlan

2015-01-01

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

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

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Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

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

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

Gene set analysis using variance component tests.

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Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Gene-gene, gene-environment, gene-nutrient interactions and single nucleotide polymorphisms of inflammatory cytokines.

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Nadeem, Amina; Mumtaz, Sadaf; Naveed, Abdul Khaliq; Aslam, Muhammad; Siddiqui, Arif; Lodhi, Ghulam Mustafa; Ahmad, Tausif

2015-05-15

Inflammation plays a significant role in the etiology of type 2 diabetes mellitus (T2DM). The rise in the pro-inflammatory cytokines is the essential step in glucotoxicity and lipotoxicity induced mitochondrial injury, oxidative stress and beta cell apoptosis in T2DM. Among the recognized markers are interleukin (IL)-6, IL-1, IL-10, IL-18, tissue necrosis factor-alpha (TNF-α), C-reactive protein, resistin, adiponectin, tissue plasminogen activator, fibrinogen and heptoglobins. Diabetes mellitus has firm genetic and very strong environmental influence; exhibiting a polygenic mode of inheritance. Many single nucleotide polymorphisms (SNPs) in various genes including those of pro and anti-inflammatory cytokines have been reported as a risk for T2DM. Not all the SNPs have been confirmed by unifying results in different studies and wide variations have been reported in various ethnic groups. The inter-ethnic variations can be explained by the fact that gene expression may be regulated by gene-gene, gene-environment and gene-nutrient interactions. This review highlights the impact of these interactions on determining the role of single nucleotide polymorphism of IL-6, TNF-α, resistin and adiponectin in pathogenesis of T2DM.

Gene transfer technology and genetic radioisotope targeting therapy

International Nuclear Information System (INIS)

Wang Jiaqiong; Wang Zizheng

2004-01-01

With deeper cognition about mechanisms of disease at the cellular and molecular level, gene therapy has become one of the most important research fields in medical molecular biology at present. Gene transfer technology plays an important role during the course of gene therapy, and further improvement should be made about vectors carrying target gene sequences. Also, gene survey is needed during gene therapy, and gene imaging is the most effective method. The combination of gene therapy and targeted radiotherapy, that is, 'Genetic Radioisotope Targeting Therapy', will be a novel approach to tumor gene therapy

From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality

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Jan-Ulrich Kreft

2017-11-01

Full Text Available Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression, stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s underlying it for the specific microbial system and question investigated is essential for selecting the

Association testing to detect gene-gene interactions on sex chromosomes in trio data

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Yeonok eLee

2013-11-01

Full Text Available Autism Spectrum Disorder (ASD occurs more often among males than females in a 4:1 ratio. Among theories used to explain the causes of ASD, the X chromosome and the Y chromosome theories attribute ASD to X-linked mutation and the male-limited gene expressions on the Y chromosome, respectively. Despite the rationale of the theory, studies have failed to attribute the sex-biased ratio to the significant linkage or association on the regions of interest on X chromosome. We further study the gender biased ratio by examining the possible interaction effects between two genes in the sex chromosomes. We propose a logistic regression model with mixed effects to detect gene-gene interactions on sex chromosomes. We investigated the power and type I error rates of the approach for a range of minor allele frequencies and varying linkage disequilibrium between markers and QTLs. We also evaluated the robustness of the model to population stratification. We applied the model to a trio-family data set with an ASD affected male child to study gene-gene interactions on sex chromosomes.

Meta-analysis of Cancer Gene Profiling Data.

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Roy, Janine; Winter, Christof; Schroeder, Michael

2016-01-01

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

Strategies in Gene Therapy for Glioblastoma

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Kwiatkowska, Aneta; Nandhu, Mohan S.; Behera, Prajna; Chiocca, E. Antonio; Viapiano, Mariano S.

2013-01-01

Glioblastoma (GBM) is the most aggressive form of brain cancer, with a dismal prognosis and extremely low percentage of survivors. Novel therapies are in dire need to improve the clinical management of these tumors and extend patient survival. Genetic therapies for GBM have been postulated and attempted for the past twenty years, with variable degrees of success in pre-clinical models and clinical trials. Here we review the most common approaches to treat GBM by gene therapy, including strategies to deliver tumor-suppressor genes, suicide genes, immunomodulatory cytokines to improve immune response, and conditionally-replicating oncolytic viruses. The review focuses on the strategies used for gene delivery, including the most common and widely used vehicles (i.e., replicating and non-replicating viruses) as well as novel therapeutic approaches such as stem cell-mediated therapy and nanotechnologies used for gene delivery. We present an overview of these strategies, their targets, different advantages, and challenges for success. Finally, we discuss the potential of gene therapy-based strategies to effectively attack such a complex genetic target as GBM, alone or in combination with conventional therapy

Strategies in Gene Therapy for Glioblastoma

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Kwiatkowska, Aneta; Nandhu, Mohan S.; Behera, Prajna; Chiocca, E. Antonio; Viapiano, Mariano S., E-mail: mviapiano@partners.org [Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115 (United States)

2013-10-22

Glioblastoma (GBM) is the most aggressive form of brain cancer, with a dismal prognosis and extremely low percentage of survivors. Novel therapies are in dire need to improve the clinical management of these tumors and extend patient survival. Genetic therapies for GBM have been postulated and attempted for the past twenty years, with variable degrees of success in pre-clinical models and clinical trials. Here we review the most common approaches to treat GBM by gene therapy, including strategies to deliver tumor-suppressor genes, suicide genes, immunomodulatory cytokines to improve immune response, and conditionally-replicating oncolytic viruses. The review focuses on the strategies used for gene delivery, including the most common and widely used vehicles (i.e., replicating and non-replicating viruses) as well as novel therapeutic approaches such as stem cell-mediated therapy and nanotechnologies used for gene delivery. We present an overview of these strategies, their targets, different advantages, and challenges for success. Finally, we discuss the potential of gene therapy-based strategies to effectively attack such a complex genetic target as GBM, alone or in combination with conventional therapy.

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

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Ma'ayan Avi

2007-10-01

Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Characterization of the MLO gene family in Rosaceae and gene expression analysis in Malus domestica.

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Pessina, Stefano; Pavan, Stefano; Catalano, Domenico; Gallotta, Alessandra; Visser, Richard G F; Bai, Yuling; Malnoy, Mickael; Schouten, Henk J

2014-07-22

Powdery mildew (PM) is a major fungal disease of thousands of plant species, including many cultivated Rosaceae. PM pathogenesis is associated with up-regulation of MLO genes during early stages of infection, causing down-regulation of plant defense pathways. Specific members of the MLO gene family act as PM-susceptibility genes, as their loss-of-function mutations grant durable and broad-spectrum resistance. We carried out a genome-wide characterization of the MLO gene family in apple, peach and strawberry, and we isolated apricot MLO homologs through a PCR-approach. Evolutionary relationships between MLO homologs were studied and syntenic blocks constructed. Homologs that are candidates for being PM susceptibility genes were inferred by phylogenetic relationships with functionally characterized MLO genes and, in apple, by monitoring their expression following inoculation with the PM causal pathogen Podosphaera leucotricha. Genomic tools available for Rosaceae were exploited in order to characterize the MLO gene family. Candidate MLO susceptibility genes were identified. In follow-up studies it can be investigated whether silencing or a loss-of-function mutations in one or more of these candidate genes leads to PM resistance.

Exploring the Optimal Strategy to Predict Essential Genes in Microbes

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Yao Lu

2011-12-01

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

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

Pollen Sterility—A Promising Approach to Gene Confinement and Breeding for Genetically Modified Bioenergy Crops

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Albert P. Kausch

2012-10-01

Full Text Available Advanced genetic and biotechnology tools will be required to realize the full potential of food and bioenergy crops. Given current regulatory concerns, many transgenic traits might never be deregulated for commercial release without a robust gene confinement strategy in place. The potential for transgene flow from genetically modified (GM crops is widely known. Pollen-mediated transfer is a major component of gene flow in flowering plants and therefore a potential avenue for the escape of transgenes from GM crops. One approach for preventing and/or mitigating transgene flow is the production of trait linked pollen sterility. To evaluate the feasibility of generating pollen sterility lines for gene confinement and breeding purposes we tested the utility of a promoter (Zm13Pro from a maize pollen-specific gene (Zm13 for driving expression of the reporter gene GUS and the cytotoxic gene barnase in transgenic rice (Oryza sativa ssp. Japonica cv. Nipponbare as a monocot proxy for bioenergy grasses. This study demonstrates that the Zm13 promoter can drive pollen-specific expression in stably transformed rice and may be useful for gametophytic transgene confinement and breeding strategies by pollen sterility in food and bioenergy crops.

Cell based-gene delivery approaches for the treatment of spinal cord injury and neurodegenerative disorders.

Science.gov (United States)

Taha, Masoumeh Fakhr

2010-03-01

Cell based-gene delivery has provided an important therapeutic strategy for different disorders in the recent years. This strategy is based on the transplantation of genetically modified cells to express specific genes and to target the delivery of therapeutic factors, especially for the treatment of cancers and neurological, immunological, cardiovascular and heamatopoietic disorders. Although, preliminary reports are encouraging, and experimental studies indicate functionally and structurally improvements in the animal models of different disorders, universal application of this strategy for human diseases requires more evidence. There are a number of parameters that need to be evaluated, including the optimal cell source, the most effective gene/genes to be delivered, the optimal vector and method of gene delivery into the cells and the most efficient route for the delivery of genetically modified cells into the patient. Also, some obstacles have to be overcome, including the safety and usefulness of the approaches and the stability of the improvements. Here, recent studies concerning with the cell-based gene delivery for spinal cord injury and some neurodegenerative disorders such as amyotrophic lateral sclerosis, Parkinson's disease and Alzheimer's disease are briefly reviewed, and their exciting consequences are discussed.

Problem-Solving Test: Targeted Gene Disruption

Science.gov (United States)

Szeberenyi, Jozsef

2008-01-01

Mutational inactivation of a specific gene is the most powerful technique to analyze the biological function of the gene. This approach has been used for a long time in viruses, bacteria, yeast, and fruit fly, but looked quite hopeless in more complex organisms. Targeted inactivation of specific genes (also known as knock-out mutation) in mice is…

Conditional RNAi: towards a silent gene therapy.

Science.gov (United States)

Lee, Sang-Kyung; Kumar, Priti

2009-07-02

RNA interference (RNAi) has the potential to permit the downregulation of virtually any gene. While transgenic RNAi enables stable propagation of the resulting phenotype to progeny, the dominant nature of RNAi limits its use to applications where the continued suppression of gene expression does not disturb normal cell functioning. This is of particular importance when the target gene product is essential for cell survival, development or differentiation. It is therefore desirable that knockdown be externally regulatable. This review is aimed at providing an overview of the approaches for conditional RNAi in mammalian systems, with a special mention of studies employing these approaches to target therapeutically/biologically relevant molecules, their advantages and disadvantages, and a pointer towards approaches best suited for RNAi-based gene therapy.

The sociobiology of genes: the gene's eye view as a unifying behavioural-ecological framework for biological evolution.

Science.gov (United States)

De Tiège, Alexis; Van de Peer, Yves; Braeckman, Johan; Tanghe, Koen B

2017-11-22

Although classical evolutionary theory, i.e., population genetics and the Modern Synthesis, was already implicitly 'gene-centred', the organism was, in practice, still generally regarded as the individual unit of which a population is composed. The gene-centred approach to evolution only reached a logical conclusion with the advent of the gene-selectionist or gene's eye view in the 1960s and 1970s. Whereas classical evolutionary theory can only work with (genotypically represented) fitness differences between individual organisms, gene-selectionism is capable of working with fitness differences among genes within the same organism and genome. Here, we explore the explanatory potential of 'intra-organismic' and 'intra-genomic' gene-selectionism, i.e., of a behavioural-ecological 'gene's eye view' on genetic, genomic and organismal evolution. First, we give a general outline of the framework and how it complements the-to some extent-still 'organism-centred' approach of classical evolutionary theory. Secondly, we give a more in-depth assessment of its explanatory potential for biological evolution, i.e., for Darwin's 'common descent with modification' or, more specifically, for 'historical continuity or homology with modular evolutionary change' as it has been studied by evolutionary developmental biology (evo-devo) during the last few decades. In contrast with classical evolutionary theory, evo-devo focuses on 'within-organism' developmental processes. Given the capacity of gene-selectionism to adopt an intra-organismal gene's eye view, we outline the relevance of the latter model for evo-devo. Overall, we aim for the conceptual integration between the gene's eye view on the one hand, and more organism-centred evolutionary models (both classical evolutionary theory and evo-devo) on the other.

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

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Mary Qu Yang

Full Text Available Clear cell renal cell carcinoma (ccRCC is the most common and most aggressive form of renal cell cancer (RCC. The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways. Keywords: ccRCC, Causative mutation, Pathways, Protein-protein interaction, Gene module, eQTL

Application of HSVtk suicide gene to X-SCID gene therapy: Ganciclovir treatment offsets gene corrected X-SCID B cells

International Nuclear Information System (INIS)

Uchiyama, Toru; Kumaki, Satoru; Ishikawa, Yoshinori; Onodera, Masafumi; Sato, Miki; Du, Wei; Sasahara, Yoji; Tanaka, Nobuyuki; Sugamura, Kazuo; Tsuchiya, Shigeru

2006-01-01

Recently, a serious adverse effect of uncontrolled clonal T cell proliferation due to insertional mutagenesis of retroviral vector was reported in X-SCID gene therapy clinical trial. To offset the side effect, we have incorporated a suicide gene into therapeutic retroviral vector for selective elimination of transduced cells. In this study, B-cell lines from two X-SCID patients were transduced with bicistronic retroviral vector carrying human γc chain cDNA and Herpes simplex virus thymidine kinase gene. After confirmation of functional reconstitution of the γc chain, the cells were treated with ganciclovir (GCV). The γc chain positive cells were eliminated under low concentration without cytotoxicity on untransduced cells and have not reappeared at least for 5 months. Furthermore, the γc chain transduced cells were still sensitive to GCV after five months. These results demonstrated the efficacy of the suicide gene therapy although further in vivo studies are required to assess feasibility of this approach in clinical trial

Gene Therapy

Science.gov (United States)

Gene therapy Overview Gene therapy involves altering the genes inside your body's cells in an effort to treat or stop disease. Genes contain your ... that don't work properly can cause disease. Gene therapy replaces a faulty gene or adds a new ...

Cis-regulatory element based targeted gene finding: genome-wide identification of abscisic acid- and abiotic stress-responsive genes in Arabidopsis thaliana.

Science.gov (United States)

Zhang, Weixiong; Ruan, Jianhua; Ho, Tuan-Hua David; You, Youngsook; Yu, Taotao; Quatrano, Ralph S

2005-07-15

A fundamental problem of computational genomics is identifying the genes that respond to certain endogenous cues and environmental stimuli. This problem can be referred to as targeted gene finding. Since gene regulation is mainly determined by the binding of transcription factors and cis-regulatory DNA sequences, most existing gene annotation methods, which exploit the conservation of open reading frames, are not effective in finding target genes. A viable approach to targeted gene finding is to exploit the cis-regulatory elements that are known to be responsible for the transcription of target genes. Given such cis-elements, putative target genes whose promoters contain the elements can be identified. As a case study, we apply the above approach to predict the genes in model plant Arabidopsis thaliana which are inducible by a phytohormone, abscisic acid (ABA), and abiotic stress, such as drought, cold and salinity. We first construct and analyze two ABA specific cis-elements, ABA-responsive element (ABRE) and its coupling element (CE), in A.thaliana, based on their conservation in rice and other cereal plants. We then use the ABRE-CE module to identify putative ABA-responsive genes in A.thaliana. Based on RT-PCR verification and the results from literature, this method has an accuracy rate of 67.5% for the top 40 predictions. The cis-element based targeted gene finding approach is expected to be widely applicable since a large number of cis-elements in many species are available.

A New Two-Step Approach for Hands-On Teaching of Gene Technology: Effects on Students' Activities during Experimentation in an Outreach Gene Technology Lab

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Scharfenberg, Franz-Josef; Bogner, Franz X.

2011-01-01

Emphasis on improving higher level biology education continues. A new two-step approach to the experimental phases within an outreach gene technology lab, derived from cognitive load theory, is presented. We compared our approach using a quasi-experimental design with the conventional one-step mode. The difference consisted of additional focused…

A Systems’ Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

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Xin Lai

2013-01-01

Full Text Available MicroRNAs (miRNAs are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

Gene-Gene and Gene-Environment Interactions in the Etiology of Breast Cancer

National Research Council Canada - National Science Library

Adegoke, Olufemi

2003-01-01

The objective of this CDA is to evaluate the gene-gene and gene-environment interactions in the etiology of breast cancer in two ongoing case-control studies, the Shanghai Breast Cancer Study (SBCS...

Synthetic sustained gene delivery systems.

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Agarwal, Ankit; Mallapragada, Surya K

2008-01-01

Gene therapy today is hampered by the need of a safe and efficient gene delivery system that can provide a sustained therapeutic effect without cytotoxicity or unwanted immune responses. Bolus gene delivery in solution results in the loss of delivered factors via lymphatic system and may cause undesired effects by the escape of bioactive molecules to distant sites. Controlled gene delivery systems, acting as localized depot of genes, provide an extended sustained release of genes, giving prolonged maintenance of the therapeutic level of encoded proteins. They also limit the DNA degradation in the nuclease rich extra-cellular environment. While attempts have been made to adapt existing controlled drug delivery technologies, more novel approaches are being investigated for controlled gene delivery. DNA encapsulated in nano/micro spheres of polymers have been administered systemically/orally to be taken up by the targeted tissues and provide sustained release once internalized. Alternatively, DNA entrapped in hydrogels or scaffolds have been injected/implanted in tissues/cavities as platforms for gene delivery. The present review examines these different modalities for sustained delivery of viral and non-viral gene-delivery vectors. Design parameters and release mechanisms of different systems made with synthetic or natural polymers are presented along with their prospective applications and opportunities for continuous development.

Gene Fusion Markup Language: a prototype for exchanging gene fusion data.

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Kalyana-Sundaram, Shanker; Shanmugam, Achiraman; Chinnaiyan, Arul M

2012-10-16

An avalanche of next generation sequencing (NGS) studies has generated an unprecedented amount of genomic structural variation data. These studies have also identified many novel gene fusion candidates with more detailed resolution than previously achieved. However, in the excitement and necessity of publishing the observations from this recently developed cutting-edge technology, no community standardization approach has arisen to organize and represent the data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future. Here we propose a prototype, Gene Fusion Markup Language (GFML) as an initiative to provide a standard format for organizing and representing the significant features of gene fusion data. GFML will offer the advantage of representing the data in a machine-readable format to enable data exchange, automated analysis interpretation, and independent verification. As this database-independent exchange initiative evolves it will further facilitate the formation of related databases, repositories, and analysis tools. The GFML prototype is made available at http://code.google.com/p/gfml-prototype/. The Gene Fusion Markup Language (GFML) presented here could facilitate the development of a standard format for organizing, integrating and representing the significant features of gene fusion data in an inter-operable and query-able fashion that will enable biologically intuitive access to gene fusion findings and expedite functional characterization. A similar model is envisaged for other NGS data analyses.

Gene doping: gene delivery for olympic victory.

Science.gov (United States)

Gould, David

2013-08-01

With one recently recommended gene therapy in Europe and a number of other gene therapy treatments now proving effective in clinical trials it is feasible that the same technologies will soon be adopted in the world of sport by unscrupulous athletes and their trainers in so called 'gene doping'. In this article an overview of the successful gene therapy clinical trials is provided and the potential targets for gene doping are highlighted. Depending on whether a doping gene product is secreted from the engineered cells or is retained locally to, or inside engineered cells will, to some extent, determine the likelihood of detection. It is clear that effective gene delivery technologies now exist and it is important that detection and prevention plans are in place. © 2012 The Author. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.

Systems approach identifies an organic nitrogen-responsive gene network that is regulated by the master clock control gene CCA1.

Science.gov (United States)

Gutiérrez, Rodrigo A; Stokes, Trevor L; Thum, Karen; Xu, Xiaodong; Obertello, Mariana; Katari, Manpreet S; Tanurdzic, Milos; Dean, Alexis; Nero, Damion C; McClung, C Robertson; Coruzzi, Gloria M

2008-03-25

Understanding how nutrients affect gene expression will help us to understand the mechanisms controlling plant growth and development as a function of nutrient availability. Nitrate has been shown to serve as a signal for the control of gene expression in Arabidopsis. There is also evidence, on a gene-by-gene basis, that downstream products of nitrogen (N) assimilation such as glutamate (Glu) or glutamine (Gln) might serve as signals of organic N status that in turn regulate gene expression. To identify genome-wide responses to such organic N signals, Arabidopsis seedlings were transiently treated with ammonium nitrate in the presence or absence of MSX, an inhibitor of glutamine synthetase, resulting in a block of Glu/Gln synthesis. Genes that responded to organic N were identified as those whose response to ammonium nitrate treatment was blocked in the presence of MSX. We showed that some genes previously identified to be regulated by nitrate are under the control of an organic N-metabolite. Using an integrated network model of molecular interactions, we uncovered a subnetwork regulated by organic N that included CCA1 and target genes involved in N-assimilation. We validated some of the predicted interactions and showed that regulation of the master clock control gene CCA1 by Glu or a Glu-derived metabolite in turn regulates the expression of key N-assimilatory genes. Phase response curve analysis shows that distinct N-metabolites can advance or delay the CCA1 phase. Regulation of CCA1 by organic N signals may represent a novel input mechanism for N-nutrients to affect plant circadian clock function.

Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

KAUST Repository

Fujii, Chisato

2015-04-16

Gene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and non-linear which makes learning the networks a challenging problem and existing gene network inference methods do not give consistent results. Current state-of-the-art study uses the average-ranking-based consensus method to combine and average the ranked predictions from individual methods. However each individual method has an equal contribution to the consen- sus prediction. We have developed a linear programming-based consensus approach which uses learned weights from linear programming among individual methods such that the methods have di↵erent weights depending on their performance. Our result reveals that assigning di↵erent weights to individual methods rather than giving them equal weights improves the performance of the consensus. The linear programming- based consensus method is evaluated and it had the best performance on in silico and Saccharomyces cerevisiae networks, and the second best on the Escherichia coli network outperformed by Inferelator Pipeline method which gives inconsistent results across a wide range of microarray data sets.

Sorted gene genealogies and species-specific nonsynonymous substitutions point to putative postmating prezygotic isolation genes in Allonemobius crickets

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Suegene Noh

2016-02-01

Full Text Available In the Allonemobius socius complex of crickets, reproductive isolation is primarily accomplished via postmating prezygotic barriers. We tested seven protein-coding genes expressed in the male ejaculate for patterns of evolution consistent with a putative role as postmating prezygotic isolation genes. Our recently diverged species generally lacked sequence variation. As a result, ω-based tests were only mildly successful. Some of our genes showed evidence of elevated ω values on the internal branches of gene trees. In a couple of genes, these internal branches coincided with both species branching events of the species tree, between A. fasciatus and the other two species, and between A. socius and A. sp. nov. Tex. In comparison, more successful approaches were those that took advantage of the varying degrees of lineage sorting and allele sharing among our young species. These approaches were particularly powerful within the contact zone. Among the genes we tested we found genes with genealogies that indicated relatively advanced degrees of lineage sorting across both allopatric and contact zone alleles. Within a contact zone between two members of the species complex, only a subset of genes maintained allelic segregation despite evidence of ongoing gene flow in other genes. The overlap in these analyses was arginine kinase (AK and apolipoprotein A-1 binding protein (APBP. These genes represent two of the first examples of sperm maturation, capacitation, and motility proteins with fixed non-synonymous substitutions between species-specific alleles that may lead to postmating prezygotic isolation. Both genes express ejaculate proteins transferred to females during copulation and were previously identified through comparative proteomics. We discuss the potential function of these genes in the context of the specific postmating prezygotic isolation phenotype among our species, namely conspecific sperm precedence and the superior ability of

Analysis of multiplex gene expression maps obtained by voxelation

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Smith Desmond J

2009-04-01

Full Text Available Abstract Background Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions. Results To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in

Analysis of multiplex gene expression maps obtained by voxelation.

Science.gov (United States)

An, Li; Xie, Hongbo; Chin, Mark H; Obradovic, Zoran; Smith, Desmond J; Megalooikonomou, Vasileios

2009-04-29

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions. To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum. The experimental

Using RNA-Seq Data to Evaluate Reference Genes Suitable for Gene Expression Studies in Soybean.

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Aldrin Kay-Yuen Yim

Full Text Available Differential gene expression profiles often provide important clues for gene functions. While reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR is an important tool, the validity of the results depends heavily on the choice of proper reference genes. In this study, we employed new and published RNA-sequencing (RNA-Seq datasets (26 sequencing libraries in total to evaluate reference genes reported in previous soybean studies. In silico PCR showed that 13 out of 37 previously reported primer sets have multiple targets, and 4 of them have amplicons with different sizes. Using a probabilistic approach, we identified new and improved candidate reference genes. We further performed 2 validation tests (with 26 RNA samples on 8 commonly used reference genes and 7 newly identified candidates, using RT-qPCR. In general, the new candidate reference genes exhibited more stable expression levels under the tested experimental conditions. The three newly identified candidate reference genes Bic-C2, F-box protein2, and VPS-like gave the best overall performance, together with the commonly used ELF1b. It is expected that the proposed probabilistic model could serve as an important tool to identify stable reference genes when more soybean RNA-Seq data from different growth stages and treatments are used.

Looking into flowering time in almond (Prunus dulcis (Mill) D. A. Webb): the candidate gene approach.

Science.gov (United States)

Silva, C; Garcia-Mas, J; Sánchez, A M; Arús, P; Oliveira, M M

2005-03-01

Blooming time is one of the most important agronomic traits in almond. Biochemical and molecular events underlying flowering regulation must be understood before methods to stimulate late flowering can be developed. Attempts to elucidate the genetic control of this process have led to the identification of a major gene (Lb) and quantitative trait loci (QTLs) linked to observed phenotypic differences, but although this gene and these QTLs have been placed on the Prunus reference genetic map, their sequences and specific functions remain unknown. The aim of our investigation was to associate these loci with known genes using a candidate gene approach. Two almond cDNAs and eight Prunus expressed sequence tags were selected as candidate genes (CGs) since their sequences were highly identical to those of flowering regulatory genes characterized in other species. The CGs were amplified from both parental lines of the mapping population using specific primers. Sequence comparison revealed DNA polymorphisms between the parental lines, mainly of the single nucleotide type. Polymorphisms were used to develop co-dominant cleaved amplified polymorphic sequence markers or length polymorphisms based on insertion/deletion events for mapping the candidate genes on the Prunus reference map. Ten candidate genes were assigned to six linkage groups in the Prunus genome. The positions of two of these were compatible with the regions where two QTLs for blooming time were detected. One additional candidate was localized close to the position of the Evergrowing gene, which determines a non-deciduous behaviour in peach.

Genome-wide local ancestry approach identifies genes and variants associated with chemotherapeutic susceptibility in African Americans.

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Heather E Wheeler

Full Text Available Chemotherapeutic agents are used in the treatment of many cancers, yet variable resistance and toxicities among individuals limit successful outcomes. Several studies have indicated outcome differences associated with ancestry among patients with various cancer types. Using both traditional SNP-based and newly developed gene-based genome-wide approaches, we investigated the genetics of chemotherapeutic susceptibility in lymphoblastoid cell lines derived from 83 African Americans, a population for which there is a disparity in the number of genome-wide studies performed. To account for population structure in this admixed population, we incorporated local ancestry information into our association model. We tested over 2 million SNPs and identified 325, 176, 240, and 190 SNPs that were suggestively associated with cytarabine-, 5'-deoxyfluorouridine (5'-DFUR-, carboplatin-, and cisplatin-induced cytotoxicity, respectively (p≤10(-4. Importantly, some of these variants are found only in populations of African descent. We also show that cisplatin-susceptibility SNPs are enriched for carboplatin-susceptibility SNPs. Using a gene-based genome-wide association approach, we identified 26, 11, 20, and 41 suggestive candidate genes for association with cytarabine-, 5'-DFUR-, carboplatin-, and cisplatin-induced cytotoxicity, respectively (p≤10(-3. Fourteen of these genes showed evidence of association with their respective chemotherapeutic phenotypes in the Yoruba from Ibadan, Nigeria (p<0.05, including TP53I11, COPS5 and GAS8, which are known to be involved in tumorigenesis. Although our results require further study, we have identified variants and genes associated with chemotherapeutic susceptibility in African Americans by using an approach that incorporates local ancestry information.

Genes2FANs: connecting genes through functional association networks

Science.gov (United States)

2012-01-01

Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in

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

Science.gov (United States)

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.).

Gene therapy in animal models of autosomal dominant retinitis pigmentosa

Science.gov (United States)

Rossmiller, Brian; Mao, Haoyu

2012-01-01

Gene therapy for dominantly inherited genetic disease is more difficult than gene-based therapy for recessive disorders, which can be treated with gene supplementation. Treatment of dominant disease may require gene supplementation partnered with suppression of the expression of the mutant gene either at the DNA level, by gene repair, or at the RNA level by RNA interference or transcriptional repression. In this review, we examine some of the gene delivery approaches used to treat animal models of autosomal dominant retinitis pigmentosa, focusing on those models associated with mutations in the gene for rhodopsin. We conclude that combinatorial approaches have the greatest promise for success. PMID:23077406

Center for Fetal Monkey Gene Transfer for Heart, Lung, and Blood Diseases: An NHLBI Resource for the Gene Therapy Community

Science.gov (United States)

Skarlatos, Sonia I.

2012-01-01

Abstract The goals of the National Heart, Lung, and Blood Institute (NHLBI) Center for Fetal Monkey Gene Transfer for Heart, Lung, and Blood Diseases are to conduct gene transfer studies in monkeys to evaluate safety and efficiency; and to provide NHLBI-supported investigators with expertise, resources, and services to actively pursue gene transfer approaches in monkeys in their research programs. NHLBI-supported projects span investigators throughout the United States and have addressed novel approaches to gene delivery; “proof-of-principle”; assessed whether findings in small-animal models could be demonstrated in a primate species; or were conducted to enable new grant or IND submissions. The Center for Fetal Monkey Gene Transfer for Heart, Lung, and Blood Diseases successfully aids the gene therapy community in addressing regulatory barriers, and serves as an effective vehicle for advancing the field. PMID:22974119

Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data

Directory of Open Access Journals (Sweden)

Simpson David

2006-03-01

Full Text Available Abstract Background Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. Results Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE, this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model performed significantly better than relatively more complex algorithms, e.g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re

An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

Science.gov (United States)

Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related

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Vasieva Olga

2011-01-01

Full Text Available Abstract Background The ageing of the worldwide population means there is a growing need for research on the biology of ageing. DNA damage is likely a key contributor to the ageing process and elucidating the role of different DNA repair systems in ageing is of great interest. In this paper we propose a data mining approach, based on classification methods (decision trees and Naive Bayes, for analysing data about human DNA repair genes. The goal is to build classification models that allow us to discriminate between ageing-related and non-ageing-related DNA repair genes, in order to better understand their different properties. Results The main patterns discovered by the classification methods are as follows: (a the number of protein-protein interactions was a predictor of DNA repair proteins being ageing-related; (b the use of predictor attributes based on protein-protein interactions considerably increased predictive accuracy of attributes based on Gene Ontology (GO annotations; (c GO terms related to "response to stimulus" seem reasonably good predictors of ageing-relatedness for DNA repair genes; (d interaction with the XRCC5 (Ku80 protein is a strong predictor of ageing-relatedness for DNA repair genes; and (e DNA repair genes with a high expression in T lymphocytes are more likely to be ageing-related. Conclusions The above patterns are broadly integrated in an analysis discussing relations between Ku, the non-homologous end joining DNA repair pathway, ageing and lymphocyte development. These patterns and their analysis support non-homologous end joining double strand break repair as central to the ageing-relatedness of DNA repair genes. Our work also showcases the use of protein interaction partners to improve accuracy in data mining methods and our approach could be applied to other ageing-related pathways.

GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data.

Science.gov (United States)

Rue-Albrecht, Kévin; McGettigan, Paul A; Hernández, Belinda; Nalpas, Nicolas C; Magee, David A; Parnell, Andrew C; Gordon, Stephen V; MacHugh, David E

2016-03-11

Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.

The roles of segmental and tandem gene duplication in the evolution of large gene families in Arabidopsis thaliana

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Baumgarten Andrew

2004-06-01

Full Text Available Abstract Background Most genes in Arabidopsis thaliana are members of gene families. How do the members of gene families arise, and how are gene family copy numbers maintained? Some gene families may evolve primarily through tandem duplication and high rates of birth and death in clusters, and others through infrequent polyploidy or large-scale segmental duplications and subsequent losses. Results Our approach to understanding the mechanisms of gene family evolution was to construct phylogenies for 50 large gene families in Arabidopsis thaliana, identify large internal segmental duplications in Arabidopsis, map gene duplications onto the segmental duplications, and use this information to identify which nodes in each phylogeny arose due to segmental or tandem duplication. Examples of six gene families exemplifying characteristic modes are described. Distributions of gene family sizes and patterns of duplication by genomic distance are also described in order to characterize patterns of local duplication and copy number for large gene families. Both gene family size and duplication by distance closely follow power-law distributions. Conclusions Combining information about genomic segmental duplications, gene family phylogenies, and gene positions provides a method to evaluate contributions of tandem duplication and segmental genome duplication in the generation and maintenance of gene families. These differences appear to correspond meaningfully to differences in functional roles of the members of the gene families.

Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning

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Tsatsoulis Costas

2010-05-01

Full Text Available Abstract Background There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives us insight into a gene's functionality by informing us how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, we analyzed if we could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. Results We performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the gene's location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours should be included around each gene to provide better classification rules. Our results show that by just incorporating neighbour information from each gene's two-nearest neighbours, the percentage of correctly classified genes to their correct Gene Ontology Slim term for each ontology reaches over 80% with high accuracy (reflected in F-measures over 0.80 of the classification rules produced. Conclusions We confirmed that in classifying genes to their correct Gene Ontology Slim term, the inclusion of neighbour information from those genes is beneficial. Knowing the location of a gene and the Gene Ontology Slim information from neighbouring genes gives us insight into that gene's functionality. This benefit is seen by just including information from a gene's two-nearest neighbouring genes.

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records.

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Jiang, Li; Edwards, Stefan M; Thomsen, Bo; Workman, Christopher T; Guldbrandtsen, Bernt; Sørensen, Peter

2014-09-24

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization. We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance. We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data

Beyond main effects of gene-sets: harsh parenting moderates the association between a dopamine gene-set and child externalizing behavior.

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Windhorst, Dafna A; Mileva-Seitz, Viara R; Rippe, Ralph C A; Tiemeier, Henning; Jaddoe, Vincent W V; Verhulst, Frank C; van IJzendoorn, Marinus H; Bakermans-Kranenburg, Marian J

2016-08-01

In a longitudinal cohort study, we investigated the interplay of harsh parenting and genetic variation across a set of functionally related dopamine genes, in association with children's externalizing behavior. This is one of the first studies to employ gene-based and gene-set approaches in tests of Gene by Environment (G × E) effects on complex behavior. This approach can offer an important alternative or complement to candidate gene and genome-wide environmental interaction (GWEI) studies in the search for genetic variation underlying individual differences in behavior. Genetic variants in 12 autosomal dopaminergic genes were available in an ethnically homogenous part of a population-based cohort. Harsh parenting was assessed with maternal (n = 1881) and paternal (n = 1710) reports at age 3. Externalizing behavior was assessed with the Child Behavior Checklist (CBCL) at age 5 (71 ± 3.7 months). We conducted gene-set analyses of the association between variation in dopaminergic genes and externalizing behavior, stratified for harsh parenting. The association was statistically significant or approached significance for children without harsh parenting experiences, but was absent in the group with harsh parenting. Similarly, significant associations between single genes and externalizing behavior were only found in the group without harsh parenting. Effect sizes in the groups with and without harsh parenting did not differ significantly. Gene-environment interaction tests were conducted for individual genetic variants, resulting in two significant interaction effects (rs1497023 and rs4922132) after correction for multiple testing. Our findings are suggestive of G × E interplay, with associations between dopamine genes and externalizing behavior present in children without harsh parenting, but not in children with harsh parenting experiences. Harsh parenting may overrule the role of genetic factors in externalizing behavior. Gene-based and gene

Gene Overexpression Resources in Cereals for Functional Genomics and Discovery of Useful Genes

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Kiyomi Abe

2016-09-01

Full Text Available Identification and elucidation of functions of plant genes is valuable for both basic and applied research. In addition to natural variation in model plants, numerous loss-of-function resources have been produced by mutagenesis with chemicals, irradiation, or insertions of transposable elements or T-DNA. However, we may be unable to observe loss-of-function phenotypes for genes with functionally redundant homologs, and for those essential for growth and development. To offset such disadvantages, gain-of-function transgenic resources have been exploited. Activation-tagged lines have been generated using obligatory overexpression of endogenous genes by random insertion of an enhancer. Recent progress in DNA sequencing technology and bioinformatics has enabled the preparation of genomewide collections of full-length cDNAs (fl-cDNAs in some model species. Using the fl-cDNA clones, a novel gain-of-function strategy, Fl-cDNA OvereXpressor gene (FOX-hunting system, has been developed. A mutant phenotype in a FOX line can be directly attributed to the overexpressed fl-cDNA. Investigating a large population of FOX lines could reveal important genes conferring favorable phenotypes for crop breeding. Alternatively, a unique loss-of-function approach Chimeric REpressor gene Silencing Technology (CRES-T has been developed. In CRES-T, overexpression of a chimeric repressor, composed of the coding sequence of a transcription factor (TF and short peptide designated as the repression domain, could interfere with the action of endogenous TF in plants. Although plant TFs usually consist of gene families, CRES-T is effective, in principle, even for the TFs with functional redundancy. In this review, we focus on the current status of the gene-overexpression strategies and resources for identifying and elucidating novel functions of cereal genes. We discuss the potential of these research tools for identifying useful genes and phenotypes for application in crop

Gene analogue finder: a GRID solution for finding functionally analogous gene products

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Licciulli Flavio

2007-09-01

Full Text Available Abstract Background To date more than 2,1 million gene products from more than 100000 different species have been described specifying their function, the processes they are involved in and their cellular localization using a very well defined and structured vocabulary, the gene ontology (GO. Such vast, well defined knowledge opens the possibility of compare gene products at the level of functionality, finding gene products which have a similar function or are involved in similar biological processes without relying on the conventional sequence similarity approach. Comparisons within such a large space of knowledge are highly data and computing intensive. For this reason this project was based upon the use of the computational GRID, a technology offering large computing and storage resources. Results We have developed a tool, GENe AnaloGue FINdEr (ENGINE that parallelizes the search process and distributes the calculation and data over the computational GRID, splitting the process into many sub-processes and joining the calculation and the data on the same machine and therefore completing the whole search in about 3 days instead of occupying one single machine for more than 5 CPU years. The results of the functional comparison contain potential functional analogues for more than 79000 gene products from the most important species. 46% of the analyzed gene products are well enough described for such an analysis to individuate functional analogues, such as well-known members of the same gene family, or gene products with similar functions which would never have been associated by standard methods. Conclusion ENGINE has produced a list of potential functionally analogous relations between gene products within and between species using, in place of the sequence, the gene description of the GO, thus demonstrating the potential of the GO. However, the current limiting factor is the quality of the associations of many gene products from non

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...

Immune genes undergo more adaptive evolution than non-immune system genes in Daphnia pulex

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McTaggart Seanna J

2012-05-01

Full Text Available Abstract Background Understanding which parts of the genome have been most influenced by adaptive evolution remains an unsolved puzzle. Some evidence suggests that selection has the greatest impact on regions of the genome that interact with other evolving genomes, including loci that are involved in host-parasite co-evolutionary processes. In this study, we used a population genetic approach to test this hypothesis by comparing DNA sequences of 30 putative immune system genes in the crustacean Daphnia pulex with 24 non-immune system genes. Results In support of the hypothesis, results from a multilocus extension of the McDonald-Kreitman (MK test indicate that immune system genes as a class have experienced more adaptive evolution than non-immune system genes. However, not all immune system genes show evidence of adaptive evolution. Additionally, we apply single locus MK tests and calculate population genetic parameters at all loci in order to characterize the mode of selection (directional versus balancing in the genes that show the greatest deviation from neutral evolution. Conclusions Our data are consistent with the hypothesis that immune system genes undergo more adaptive evolution than non-immune system genes, possibly as a result of host-parasite arms races. The results of these analyses highlight several candidate loci undergoing adaptive evolution that could be targeted in future studies.

Gene doping: possibilities and practicalities.

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Wells, Dominic J

2009-01-01

Our ever-increasing understanding of the genetic control of cardiovascular and musculoskeletal function together with recent technical improvements in genetic manipulation generates mounting concern over the possibility of such technology being abused by athletes in their quest for improved performance. Genetic manipulation in the context of athletic performance is commonly referred to as gene doping. A review of the literature was performed to identify the genes and methodologies most likely to be used for gene doping and the technologies that might be used to identify such doping. A large number of candidate performance-enhancing genes have been identified from animal studies, many of them using transgenic mice. Only a limited number have been shown to be effective following gene transfer into adults. Those that seem most likely to be abused are genes that exert their effects locally and leave little, if any, trace in blood or urine. There is currently no evidence that gene doping has yet been undertaken in competitive athletes but the anti-doping authorities will need to remain vigilant in reviewing this rapidly emerging technology. The detection of gene doping involves some different challenges from other agents and a number of promising approaches are currently being explored. 2009 S. Karger AG, Basel

Tracking microorganisms and gene in the environment

International Nuclear Information System (INIS)

Atlas, R.M.; Sayler, G.S.

1988-01-01

Studies have been conducted to determine the sensitivities and limitations of various methods for determining the fate of genetically engineered microorganisms (GEMs) and their genes in the environment. Selective viable plate count procedures can be designed to detect the introduced organisms with high sensitivity; but they are restricted by potential mutations affecting the expression of the selective characteristic in the introduced organism, the occurrence of the particular selective characteristic in the indigenous organisms, and the need to culture the organism. The accuracy of this approach is greatly improved by colony hybridization procedures that use a specific gene probe to detect the introduced genes, but this approach is still only as sensitive as the plating procedure. Direct extraction of DNA from environmental samples, coupled with dot blot hybridization with radiolabeled probe DNA or solution hybridization, gives a high degree of both sensitivity and precision. This approach does not require culturing of the organism; and even if an introduced gene moves into a new organism or if the introduced organism is viable but nonculturable, the gene probe methods will detect the persistence of the introduced genes in the environment. Efficient direct DNA extraction methods have been developed and tested following in vitro experimental additions of GEMs to sediment and water samples

In-silico human genomics with GeneCards

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Stelzer Gil

2011-10-01

Full Text Available Abstract Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org. This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of tools (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.

Snapshot of the eukaryotic gene expression in muskoxen rumen--a metatranscriptomic approach.

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Meng Qi

Full Text Available BACKGROUND: Herbivores rely on digestive tract lignocellulolytic microorganisms, including bacteria, fungi and protozoa, to derive energy and carbon from plant cell wall polysaccharides. Culture independent metagenomic studies have been used to reveal the genetic content of the bacterial species within gut microbiomes. However, the nature of the genes encoded by eukaryotic protozoa and fungi within these environments has not been explored using metagenomic or metatranscriptomic approaches. METHODOLOGY/PRINCIPAL FINDINGS: In this study, a metatranscriptomic approach was used to investigate the functional diversity of the eukaryotic microorganisms within the rumen of muskoxen (Ovibos moschatus, with a focus on plant cell wall degrading enzymes. Polyadenylated RNA (mRNA was sequenced on the Illumina Genome Analyzer II system and 2.8 gigabases of sequences were obtained and 59129 contigs assembled. Plant cell wall degrading enzyme modules including glycoside hydrolases, carbohydrate esterases and polysaccharide lyases were identified from over 2500 contigs. These included a number of glycoside hydrolase family 6 (GH6, GH48 and swollenin modules, which have rarely been described in previous gut metagenomic studies. CONCLUSIONS/SIGNIFICANCE: The muskoxen rumen metatranscriptome demonstrates a much higher percentage of cellulase enzyme discovery and an 8.7x higher rate of total carbohydrate active enzyme discovery per gigabase of sequence than previous rumen metagenomes. This study provides a snapshot of eukaryotic gene expression in the muskoxen rumen, and identifies a number of candidate genes coding for potentially valuable lignocellulolytic enzymes.

Snapshot of the Eukaryotic Gene Expression in Muskoxen Rumen—A Metatranscriptomic Approach

Science.gov (United States)

O'Toole, Nicholas; Barboza, Perry S.; Ungerfeld, Emilio; Leigh, Mary Beth; Selinger, L. Brent; Butler, Greg; Tsang, Adrian; McAllister, Tim A.; Forster, Robert J.

2011-01-01

Background Herbivores rely on digestive tract lignocellulolytic microorganisms, including bacteria, fungi and protozoa, to derive energy and carbon from plant cell wall polysaccharides. Culture independent metagenomic studies have been used to reveal the genetic content of the bacterial species within gut microbiomes. However, the nature of the genes encoded by eukaryotic protozoa and fungi within these environments has not been explored using metagenomic or metatranscriptomic approaches. Methodology/Principal Findings In this study, a metatranscriptomic approach was used to investigate the functional diversity of the eukaryotic microorganisms within the rumen of muskoxen (Ovibos moschatus), with a focus on plant cell wall degrading enzymes. Polyadenylated RNA (mRNA) was sequenced on the Illumina Genome Analyzer II system and 2.8 gigabases of sequences were obtained and 59129 contigs assembled. Plant cell wall degrading enzyme modules including glycoside hydrolases, carbohydrate esterases and polysaccharide lyases were identified from over 2500 contigs. These included a number of glycoside hydrolase family 6 (GH6), GH48 and swollenin modules, which have rarely been described in previous gut metagenomic studies. Conclusions/Significance The muskoxen rumen metatranscriptome demonstrates a much higher percentage of cellulase enzyme discovery and an 8.7x higher rate of total carbohydrate active enzyme discovery per gigabase of sequence than previous rumen metagenomes. This study provides a snapshot of eukaryotic gene expression in the muskoxen rumen, and identifies a number of candidate genes coding for potentially valuable lignocellulolytic enzymes. PMID:21655220

Genetic architecture of gene expression in the chicken

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Stanley Dragana

2013-01-01

Full Text Available Abstract Background The annotation of many genomes is limited, with a large proportion of identified genes lacking functional assignments. The construction of gene co-expression networks is a powerful approach that presents a way of integrating information from diverse gene expression datasets into a unified analysis which allows inferences to be drawn about the role of previously uncharacterised genes. Using this approach, we generated a condition-free gene co-expression network for the chicken using data from 1,043 publically available Affymetrix GeneChip Chicken Genome Arrays. This data was generated from a diverse range of experiments, including different tissues and experimental conditions. Our aim was to identify gene co-expression modules and generate a tool to facilitate exploration of the functional chicken genome. Results Fifteen modules, containing between 24 and 473 genes, were identified in the condition-free network. Most of the modules showed strong functional enrichment for particular Gene Ontology categories. However, a few showed no enrichment. Transcription factor binding site enrichment was also noted. Conclusions We have demonstrated that this chicken gene co-expression network is a useful tool in gene function prediction and the identification of putative novel transcription factors and binding sites. This work highlights the relevance of this methodology for functional prediction in poorly annotated genomes such as the chicken.

Human gene therapy and imaging: cardiology

International Nuclear Information System (INIS)

Wu, Joseph C.; Yla-Herttuala, Seppo

2005-01-01

This review discusses the basics of cardiovascular gene therapy, the results of recent human clinical trials, and the rapid progress in imaging techniques in cardiology. Improved understanding of the molecular and genetic basis of coronary heart disease has made gene therapy a potential new alternative for the treatment of cardiovascular diseases. Experimental studies have established the proof-of-principle that gene transfer to the cardiovascular system can achieve therapeutic effects. First human clinical trials provided initial evidence of feasibility and safety of cardiovascular gene therapy. However, phase II/III clinical trials have so far been rather disappointing and one of the major problems in cardiovascular gene therapy has been the inability to verify gene expression in the target tissue. New imaging techniques could significantly contribute to the development of better gene therapeutic approaches. Although the exact choice of imaging modality will depend on the biological question asked, further improvement in image resolution and detection sensitivity will be needed for all modalities as we move from imaging of organs and tissues to imaging of cells and genes. (orig.)

'Omics' approaches in tomato aimed at identifying candidate genes ...

African Journals Online (AJOL)

adriana

2013-12-04

Dec 4, 2013 ... importance for human health and nutrition. This species has ... function to genes, proteins and metabolites is still a daunting task. Major challenges ... relation of the expression pattern of genes with the accu- mulation pattern of ..... M, Gordon JS, Rose, JKC, Martin G, Tanksley SD, Bouzayen M,. Jahn MM ...

A new approach to enhance the performance of decision tree for classifying gene expression data.

Science.gov (United States)

Hassan, Md; Kotagiri, Ramamohanarao

2013-12-20

Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

Gene therapy for lipid disorders

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Rader Daniel J

2000-10-01

Full Text Available Abstract Lipid disorders are associated with atherosclerotic vascular disease, and therapy is associated with a substantial reduction in cardiovascular events. Current approaches to the treatment of lipid disorders are ineffective in a substantial number of patients. New therapies for refractory hypercholesterolemia, severe hypertriglyceridemia, and low levels of high-density lipoprotein cholesterol are needed: somatic gene therapy is one viable approach. The molecular etiology and pathophysiology of most of the candidate diseases are well understood. Animal models exist for the diseases and in many cases preclinical proof-of-principle studies have already been performed. There has been progress in the development of vectors that provide long-term gene expression. New clinical gene therapy trials for lipid disorders are likely to be initiated within the next few years.

Gene-knockdown in the honey bee mite Varroa destructor by a non-invasive approach: studies on a glutathione S-transferase

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Campbell Ewan M

2010-08-01

Full Text Available Abstract Background The parasitic mite Varroa destructor is considered the major pest of the European honey bee (Apis mellifera and responsible for declines in honey bee populations worldwide. Exploiting the full potential of gene sequences becoming available for V. destructor requires adaptation of modern molecular biology approaches to this non-model organism. Using a mu-class glutathione S-transferase (VdGST-mu1 as a candidate gene we investigated the feasibility of gene knockdown in V. destructor by double-stranded RNA-interference (dsRNAi. Results Intra-haemocoelic injection of dsRNA-VdGST-mu1 resulted in 97% reduction in VdGST-mu1 transcript levels 48 h post-injection compared to mites injected with a bolus of irrelevant dsRNA (LacZ. This gene suppression was maintained to, at least, 72 h. Total GST catalytic activity was reduced by 54% in VdGST-mu1 gene knockdown mites demonstrating the knockdown was effective at the translation step as well as the transcription steps. Although near total gene knockdown was achieved by intra-haemocoelic injection, only half of such treated mites survived this traumatic method of dsRNA administration and less invasive methods were assessed. V. destructor immersed overnight in 0.9% NaCl solution containing dsRNA exhibited excellent reduction in VdGST-mu1 transcript levels (87% compared to mites immersed in dsRNA-LacZ. Importantly, mites undergoing the immersion approach had greatly improved survival (75-80% over 72 h, approaching that of mites not undergoing any treatment. Conclusions Our findings on V. destructor are the first report of gene knockdown in any mite species and demonstrate that the small size of such organisms is not a major impediment to applying gene knockdown approaches to the study of such parasitic pests. The immersion in dsRNA solution method provides an easy, inexpensive, relatively high throughput method of gene silencing suitable for studies in V. destructor, other small mites and

A comprehensive candidate gene approach identifies genetic variation associated with osteosarcoma

International Nuclear Information System (INIS)

Mirabello, Lisa; Grotmol, Tom; Douglass, Chester; Hayes, Richard B; Hoover, Robert N; Savage, Sharon A; Yu, Kai; Berndt, Sonja I; Burdett, Laurie; Wang, Zhaoming; Chowdhury, Salma; Teshome, Kedest; Uzoka, Arinze; Hutchinson, Amy

2011-01-01

Osteosarcoma (OS) is a bone malignancy which occurs primarily in adolescents. Since it occurs during a period of rapid growth, genes important in bone formation and growth are plausible modifiers of risk. Genes involved in DNA repair and ribosomal function may contribute to OS pathogenesis, because they maintain the integrity of critical cellular processes. We evaluated these hypotheses in an OS association study of genes from growth/hormone, bone formation, DNA repair, and ribosomal pathways. We evaluated 4836 tag-SNPs across 255 candidate genes in 96 OS cases and 1426 controls. Logistic regression models were used to estimate the odds ratios (OR) and 95% confidence intervals (CI). Twelve SNPs in growth or DNA repair genes were significantly associated with OS after Bonferroni correction. Four SNPs in the DNA repair gene FANCM (ORs 1.9-2.0, P = 0.003-0.004) and 2 SNPs downstream of the growth hormone gene GH1 (OR 1.6, P = 0.002; OR 0.5, P = 0.0009) were significantly associated with OS. One SNP in the region of each of the following genes was significant: MDM2, MPG, FGF2, FGFR3, GNRH2, and IGF1. Our results suggest that several SNPs in biologically plausible pathways are associated with OS. Larger studies are required to confirm our findings

Radiation-modulated gene expression in C. elegans

International Nuclear Information System (INIS)

Nelson, G.A.; Bayeta, E.; Perez, C.; Lloyd, E.; Jones, T.; Smith, A.; Tian, J.

2003-01-01

Full text: We use the nematode C. elegans to characterize the genotoxic and cytotoxic effects of ionizing radiation with emphasis effects of charged particle radiation and have described the fluence vs. response relationships for mutation, chromosome aberration and certain developmental errors. These endpoints quantify the biological after repair and compensation pathways have completed their work. In order to address the control of these reactions we have turned to gene expression profiling to identify genes that uniquely respond to high LET species or respond differentially as a function of radiation properties. We have employed whole genome microarray methods to map gene expression following exposure to gamma rays, protons and accelerated iron ions. We found that 599 of 17871 genes analyzed showed differential expression 3 hrs after exposure to 3 Gy of at least one radiation types. 193 were up-regulated, 406 were down-regulated, and 90% were affected by only one species of radiation. Genes whose transcription levels responded significantly mapped to definite statistical clusters that were unique for each radiation type. We are now trying to establish the functional relationships of the genes their relevance to mitigation of radiation-induced damage. Three approaches are being used. First, bioinformatics tools are being used to determine the roles of genes in co-regulated gene sets. Second, we are applying the technique of RNA interference to determine whether our radiation-induced genes affect cell survival (measured in terms of embryo survival) and chromosome aberration (intestinal anaphase bridges). Finally we are focussing on the response of the most strongly-regulated gene in our data set. This is the autosomal gene, F36D3.9, whose predicted structure is that of a cysteine protease resembling cathepsin B. An enzymological approach is being used to characterize this gene at the protein level. This work was supported by NASA Cooperative Agreement NCC9-149

In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer

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Colaprico, Antonio; Bontempi, Gianluca; Castiglioni, Isabella

2018-01-01

Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC. PMID:29562723

An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

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Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

2018-05-14

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives

Gene prioritization for livestock diseases by data integration

DEFF Research Database (Denmark)

Jiang, Li; Sørensen, Peter; Thomsen, Bo Stjerne

2012-01-01

in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to Escherichia coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes...... underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis...

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

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Liu, Gang; Lee, Seunggeun; Lee, Alice W

2018-01-01

test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides power gain compared to the standard logistic regression analysis and better control of Type I error when compared to the analysis......There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances the power for testing multiplicative interaction in case......-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated Type I error in the corresponding tests can occur. This paper extends the empirical Bayes (EB) approach previously developed for multiplicative interaction that trades off between bias and efficiency...

A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes

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de Jong, Simone; Boks, Marco P M; Fuller, Tova F

2012-01-01

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood...... of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co......, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes...

Rapid isolation of gene homologs across taxa: Efficient identification and isolation of gene orthologs from non-model organism genomes, a technical report

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Heffer Alison

2011-03-01

Full Text Available Abstract Background Tremendous progress has been made in the field of evo-devo through comparisons of related genes from diverse taxa. While the vast number of species in nature precludes a complete analysis of the molecular evolution of even one single gene family, this would not be necessary to understand fundamental mechanisms underlying gene evolution if experiments could be designed to systematically sample representative points along the path of established phylogenies to trace changes in regulatory and coding gene sequence. This isolation of homologous genes from phylogenetically diverse, representative species can be challenging, especially if the gene is under weak selective pressure and evolving rapidly. Results Here we present an approach - Rapid Isolation of Gene Homologs across Taxa (RIGHT - to efficiently isolate specific members of gene families. RIGHT is based upon modification and a combination of degenerate polymerase chain reaction (PCR and gene-specific amplified fragment length polymorphism (AFLP. It allows targeted isolation of specific gene family members from any organism, only requiring genomic DNA. We describe this approach and how we used it to isolate members of several different gene families from diverse arthropods spanning millions of years of evolution. Conclusions RIGHT facilitates systematic isolation of one gene from large gene families. It allows for efficient gene isolation without whole genome sequencing, RNA extraction, or culturing of non-model organisms. RIGHT will be a generally useful method for isolation of orthologs from both distant and closely related species, increasing sample size and facilitating the tracking of molecular evolution of gene families and regulatory networks across the tree of life.

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

Systematic identification of human housekeeping genes possibly useful as references in gene expression studies.

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Caracausi, Maria; Piovesan, Allison; Antonaros, Francesca; Strippoli, Pierluigi; Vitale, Lorenza; Pelleri, Maria Chiara

2017-09-01

The ideal reference, or control, gene for the study of gene expression in a given organism should be expressed at a medium‑high level for easy detection, should be expressed at a constant/stable level throughout different cell types and within the same cell type undergoing different treatments, and should maintain these features through as many different tissues of the organism. From a biological point of view, these theoretical requirements of an ideal reference gene appear to be best suited to housekeeping (HK) genes. Recent advancements in the quality and completeness of human expression microarray data and in their statistical analysis may provide new clues toward the quantitative standardization of human gene expression studies in biology and medicine, both cross‑ and within‑tissue. The systematic approach used by the present study is based on the Transcriptome Mapper tool and exploits the automated reassignment of probes to corresponding genes, intra‑ and inter‑sample normalization, elaboration and representation of gene expression values in linear form within an indexed and searchable database with a graphical interface recording quantitative levels of expression, expression variability and cross‑tissue width of expression for more than 31,000 transcripts. The present study conducted a meta‑analysis of a pool of 646 expression profile data sets from 54 different human tissues and identified actin γ 1 as the HK gene that best fits the combination of all the traditional criteria to be used as a reference gene for general use; two ribosomal protein genes, RPS18 and RPS27, and one aquaporin gene, POM121 transmembrane nucleporin C, were also identified. The present study provided a list of tissue‑ and organ‑specific genes that may be most suited for the following individual tissues/organs: Adipose tissue, bone marrow, brain, heart, kidney, liver, lung, ovary, skeletal muscle and testis; and also provides in these cases a representative

Bacterial Toxins for Oncoleaking Suicidal Cancer Gene Therapy.

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Pahle, Jessica; Walther, Wolfgang

For suicide gene therapy, initially prodrug-converting enzymes (gene-directed enzyme-producing therapy, GDEPT) were employed to intracellularly metabolize non-toxic prodrugs into toxic compounds, leading to the effective suicidal killing of the transfected tumor cells. In this regard, the suicide gene therapy has demonstrated its potential for efficient tumor eradication. Numerous suicide genes of viral or bacterial origin were isolated, characterized, and extensively tested in vitro and in vivo, demonstrating their therapeutic potential even in clinical trials to treat cancers of different entities. Apart from this, growing efforts are made to generate more targeted and more effective suicide gene systems for cancer gene therapy. In this regard, bacterial toxins are an alternative to the classical GDEPT strategy, which add to the broad spectrum of different suicide approaches. In this context, lytic bacterial toxins, such as streptolysin O (SLO) or the claudin-targeted Clostridium perfringens enterotoxin (CPE) represent attractive new types of suicide oncoleaking genes. They permit as pore-forming proteins rapid and also selective toxicity toward a broad range of cancers. In this chapter, we describe the generation and use of SLO as well as of CPE-based gene therapies for the effective tumor cell eradication as promising, novel suicide gene approach particularly for treatment of therapy refractory tumors.

Pediatric Multiple Sclerosis: Genes, Environment, and a Comprehensive Therapeutic Approach.

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Cappa, Ryan; Theroux, Liana; Brenton, J Nicholas

2017-10-01

Pediatric multiple sclerosis is an increasingly recognized and studied disorder that accounts for 3% to 10% of all patients with multiple sclerosis. The risk for pediatric multiple sclerosis is thought to reflect a complex interplay between environmental and genetic risk factors. Environmental exposures, including sunlight (ultraviolet radiation, vitamin D levels), infections (Epstein-Barr virus), passive smoking, and obesity, have been identified as potential risk factors in youth. Genetic predisposition contributes to the risk of multiple sclerosis, and the major histocompatibility complex on chromosome 6 makes the single largest contribution to susceptibility to multiple sclerosis. With the use of large-scale genome-wide association studies, other non-major histocompatibility complex alleles have been identified as independent risk factors for the disease. The bridge between environment and genes likely lies in the study of epigenetic processes, which are environmentally-influenced mechanisms through which gene expression may be modified. This article will review these topics to provide a framework for discussion of a comprehensive approach to counseling and ultimately treating the pediatric patient with multiple sclerosis. Copyright © 2017 Elsevier Inc. All rights reserved.

Selection of Reliable Reference Genes for Gene Expression Studies on Rhododendron molle G. Don.

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Xiao, Zheng; Sun, Xiaobo; Liu, Xiaoqing; Li, Chang; He, Lisi; Chen, Shangping; Su, Jiale

2016-01-01

The quantitative real-time polymerase chain reaction (qRT-PCR) approach has become a widely used method to analyze expression patterns of target genes. The selection of an optimal reference gene is a prerequisite for the accurate normalization of gene expression in qRT-PCR. The present study constitutes the first systematic evaluation of potential reference genes in Rhododendron molle G. Don. Eleven candidate reference genes in different tissues and flowers at different developmental stages of R. molle were assessed using the following three software packages: GeNorm, NormFinder, and BestKeeper. The results showed that EF1- α (elongation factor 1-alpha), 18S (18s ribosomal RNA), and RPL3 (ribosomal protein L3) were the most stable reference genes in developing rhododendron flowers and, thus, in all of the tested samples, while tublin ( TUB ) was the least stable. ACT5 (actin), RPL3 , 18S , and EF1- α were found to be the top four choices for different tissues, whereas TUB was not found to favor qRT-PCR normalization in these tissues. Three stable reference genes are recommended for the normalization of qRT-PCR data in R. molle . Furthermore, the expression profiles of RmPSY (phytoene synthase) and RmPDS (phytoene dehydrogenase) were assessed using EF1- α, 18S , ACT5 , RPL3 , and their combination as internals. Similar trends were found, but these trends varied when the least stable reference gene TUB was used. The results further prove that it is necessary to validate the stability of reference genes prior to their use for normalization under different experimental conditions. This study provides useful information for reliable qRT-PCR data normalization in gene studies of R. molle .

Selection of Reliable Reference Genes for Gene Expression Studies on Rhododendron molle G. Don

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Zheng Xiao

2016-10-01

Full Text Available The quantitative real-time polymerase chain reaction (qRT-PCR approach has become a widely used method to analyze expression patterns of target genes. The selection of an optimal reference gene is a prerequisite for the accurate normalization of gene expression in qRT-PCR. The present study constitutes the first systematic evaluation of potential reference genes in Rhododendron molle G. Don. Eleven candidate reference genes in different tissues and flowers at different developmental stages of R. molle were assessed using the following three software packages: GeNorm, NormFinder and BestKeeper. The results showed that EF1-α (elongation factor 1-alpha, 18S (18s ribosomal RNA and RPL3 (ribosomal protein L3 were the most stable reference genes in developing rhododendron flowers and, thus, in all of the tested samples, while tublin (TUB was the least stable. ACT5 (actin, RPL3, 18S and EF1-α were found to be the top four choices for different tissues, whereas TUB was not found to favor qRT-PCR normalization in these tissues. Three stable reference genes are recommended for the normalization of qRT-PCR data in R. molle. Furthermore, the expression profiles of RmPSY (phytoene synthase and RmPDS (phytoene dehydrogenase were assessed using EF1-α, 18S, ACT5, and RPL3 and their combination as internals. Similar trends were found, but these trends varied when the least stable reference gene TUB was used. The results further prove that it is necessary to validate the stability of reference genes prior to their use for normalization under different experimental conditions. This study provides useful information for reliable qRT-PCR data normalization in gene studies of R. molle.

A feature selection approach for identification of signature genes from SAGE data

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Silva Paulo JS

2007-05-01

Full Text Available Abstract Background One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. Results A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. Conclusion The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS or the recent Sequencing-By-Synthesis (SBS technique. Some of such genes identified by the proposed method may be useful to generate classifiers.

Gene editing as a promising approach for respiratory diseases.

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Bai, Yichun; Liu, Yang; Su, Zhenlei; Ma, Yana; Ren, Chonghua; Zhao, Runzhen; Ji, Hong-Long

2018-03-01

Respiratory diseases, which are leading causes of mortality and morbidity in the world, are dysfunctions of the nasopharynx, the trachea, the bronchus, the lung and the pleural cavity. Symptoms of chronic respiratory diseases, such as cough, sneezing and difficulty breathing, may seriously affect the productivity, sleep quality and physical and mental well-being of patients, and patients with acute respiratory diseases may have difficulty breathing, anoxia and even life-threatening respiratory failure. Respiratory diseases are generally heterogeneous, with multifaceted causes including smoking, ageing, air pollution, infection and gene mutations. Clinically, a single pulmonary disease can exhibit more than one phenotype or coexist with multiple organ disorders. To correct abnormal function or repair injured respiratory tissues, one of the most promising techniques is to correct mutated genes by gene editing, as some gene mutations have been clearly demonstrated to be associated with genetic or heterogeneous respiratory diseases. Zinc finger nucleases (ZFN), transcription activator-like effector nucleases (TALEN) and clustered regulatory interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) systems are three innovative gene editing technologies developed recently. In this short review, we have summarised the structure and operating principles of the ZFNs, TALENs and CRISPR/Cas9 systems and their preclinical and clinical applications in respiratory diseases. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Link-Based Cluster Ensemble Approach For Improved Gene Expression Data Analysis

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P.Balaji

2015-01-01

Full Text Available Abstract It is difficult from possibilities to select a most suitable effective way of clustering algorithm and its dataset for a defined set of gene expression data because we have a huge number of ways and huge number of gene expressions. At present many researchers are preferring to use hierarchical clustering in different forms this is no more totally optimal. Cluster ensemble research can solve this type of problem by automatically merging multiple data partitions from a wide range of different clusterings of any dimensions to improve both the quality and robustness of the clustering result. But we have many existing ensemble approaches using an association matrix to condense sample-cluster and co-occurrence statistics and relations within the ensemble are encapsulated only at raw level while the existing among clusters are totally discriminated. Finding these missing associations can greatly expand the capability of those ensemble methodologies for microarray data clustering. We propose general K-means cluster ensemble approach for the clustering of general categorical data into required number of partitions.

Sexy gene conversions: locating gene conversions on the X-chromosome.

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Lawson, Mark J; Zhang, Liqing

2009-08-01

Gene conversion can have a profound impact on both the short- and long-term evolution of genes and genomes. Here, we examined the gene families that are located on the X-chromosomes of human (Homo sapiens), chimpanzee (Pan troglodytes), mouse (Mus musculus) and rat (Rattus norvegicus) for evidence of gene conversion. We identified seven gene families (WD repeat protein family, Ferritin Heavy Chain family, RAS-related Protein RAB-40 family, Diphosphoinositol polyphosphate phosphohydrolase family, Transcription Elongation Factor A family, LDOC1-related family, Zinc Finger Protein ZIC, and GLI family) that show evidence of gene conversion. Through phylogenetic analyses and synteny evidence, we show that gene conversion has played an important role in the evolution of these gene families and that gene conversion has occurred independently in both primates and rodents. Comparing the results with those of two gene conversion prediction programs (GENECONV and Partimatrix), we found that both GENECONV and Partimatrix have very high false negative rates (i.e. failed to predict gene conversions), which leads to many undetected gene conversions. The combination of phylogenetic analyses with physical synteny evidence exhibits high resolution in the detection of gene conversions.

Optimal Reference Genes for Gene Expression Normalization in Trichomonas vaginalis

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dos Santos, Odelta; de Vargas Rigo, Graziela; Frasson, Amanda Piccoli; Macedo, Alexandre José; Tasca, Tiana

2015-01-01

Trichomonas vaginalis is the etiologic agent of trichomonosis, the most common non-viral sexually transmitted disease worldwide. This infection is associated with several health consequences, including cervical and prostate cancers and HIV acquisition. Gene expression analysis has been facilitated because of available genome sequences and large-scale transcriptomes in T. vaginalis, particularly using quantitative real-time polymerase chain reaction (qRT-PCR), one of the most used methods for molecular studies. Reference genes for normalization are crucial to ensure the accuracy of this method. However, to the best of our knowledge, a systematic validation of reference genes has not been performed for T. vaginalis. In this study, the transcripts of nine candidate reference genes were quantified using qRT-PCR under different cultivation conditions, and the stability of these genes was compared using the geNorm and NormFinder algorithms. The most stable reference genes were α-tubulin, actin and DNATopII, and, conversely, the widely used T. vaginalis reference genes GAPDH and β-tubulin were less stable. The PFOR gene was used to validate the reliability of the use of these candidate reference genes. As expected, the PFOR gene was upregulated when the trophozoites were cultivated with ferrous ammonium sulfate when the DNATopII, α-tubulin and actin genes were used as normalizing gene. By contrast, the PFOR gene was downregulated when the GAPDH gene was used as an internal control, leading to misinterpretation of the data. These results provide an important starting point for reference gene selection and gene expression analysis with qRT-PCR studies of T. vaginalis. PMID:26393928

Gene expression, nucleotide composition and codon usage bias of genes associated with human Y chromosome.

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Choudhury, Monisha Nath; Uddin, Arif; Chakraborty, Supriyo

2017-06-01

Analysis of codon usage pattern is important to understand the genetic and evolutionary characteristics of genomes. We have used bioinformatic approaches to analyze the codon usage bias (CUB) of the genes located in human Y chromosome. Codon bias index (CBI) indicated that the overall extent of codon usage bias was low. The relative synonymous codon usage (RSCU) analysis suggested that approximately half of the codons out of 59 synonymous codons were most frequently used, and possessed a T or G at the third codon position. The codon usage pattern was different in different genes as revealed from correspondence analysis (COA). A significant correlation between effective number of codons (ENC) and various GC contents suggests that both mutation pressure and natural selection affect the codon usage pattern of genes located in human Y chromosome. In addition, Y-linked genes have significant difference in GC contents at the second and third codon positions, expression level, and codon usage pattern of some codons like the SPANX genes in X chromosome.

Classification of gene expression data: A hubness-aware semi-supervised approach.

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Buza, Krisztian

2016-04-01

Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data. Gene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of self-training is the certainty score, we propose a new hubness-aware certainty score. We performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the components (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. Our results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubness-aware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model

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Mickael Orgeur

2018-01-01

Full Text Available The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads, and the gene annotation that defines gene features, must also be taken into account. A partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.

New tools for Mendelian disease gene identification: PhenoDB variant analysis module; and GeneMatcher, a web-based tool for linking investigators with an interest in the same gene.

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Sobreira, Nara; Schiettecatte, François; Boehm, Corinne; Valle, David; Hamosh, Ada

2015-04-01

Identifying the causative variant from among the thousands identified by whole-exome sequencing or whole-genome sequencing is a formidable challenge. To make this process as efficient and flexible as possible, we have developed a Variant Analysis Module coupled to our previously described Web-based phenotype intake tool, PhenoDB (http://researchphenodb.net and http://phenodb.org). When a small number of candidate-causative variants have been identified in a study of a particular patient or family, a second, more difficult challenge becomes proof of causality for any given variant. One approach to this problem is to find other cases with a similar phenotype and mutations in the same candidate gene. Alternatively, it may be possible to develop biological evidence for causality, an approach that is assisted by making connections to basic scientists studying the gene of interest, often in the setting of a model organism. Both of these strategies benefit from an open access, online site where individual clinicians and investigators could post genes of interest. To this end, we developed GeneMatcher (http://genematcher.org), a freely accessible Website that enables connections between clinicians and researchers across the world who share an interest in the same gene(s). © 2015 WILEY PERIODICALS, INC.

A network of genes, genetic disorders, and brain areas.

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Satoru Hayasaka

Full Text Available The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

Patenting human genes: Chinese academic articles' portrayal of gene patents.

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Du, Li

2018-04-24

The patenting of human genes has been the subject of debate for decades. While China has gradually come to play an important role in the global genomics-based testing and treatment market, little is known about Chinese scholars' perspectives on patent protection for human genes. A content analysis of academic literature was conducted to identify Chinese scholars' concerns regarding gene patents, including benefits and risks of patenting human genes, attitudes that researchers hold towards gene patenting, and any legal and policy recommendations offered for the gene patent regime in China. 57.2% of articles were written by law professors, but scholars from health sciences, liberal arts, and ethics also participated in discussions on gene patent issues. While discussions of benefits and risks were relatively balanced in the articles, 63.5% of the articles favored gene patenting in general and, of the articles (n = 41) that explored gene patents in the Chinese context, 90.2% supported patent protections for human genes in China. The patentability of human genes was discussed in 33 articles, and 75.8% of these articles reached the conclusion that human genes are patentable. Chinese scholars view the patent regime as an important legal tool to protect the interests of inventors and inventions as well as the genetic resources of China. As such, many scholars support a gene patent system in China. These attitudes towards gene patents remain unchanged following the court ruling in the Myriad case in 2013, but arguments have been raised about the scope of gene patents, in particular that the increasing numbers of gene patents may negatively impact public health in China.

Queer Genes: Realism, Sexuality and Science.

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Griffiths, David Andrew

2016-10-19

What are 'gay genes' and are they real? This article looks at key research into these hypothesized gay genes, made possible, in part, by the Human Genome Project. I argue that the complexity of both genetics and human sexuality demands a truly critical approach: one that takes into account feminist epistemologies of science and queer approaches to the body, while putting into conversation resources from agential realism and critical realism. This approach is able to maintain the agential complexity of genetic materiality, while also critically challenging the seemingly stable relationships between sex, gender and sexuality.

Scuba: scalable kernel-based gene prioritization.

Science.gov (United States)

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Nature versus nurture: A systematic approach to elucidate gene-environment interactions in the development of myopic refractive errors.

Science.gov (United States)

Miraldi Utz, Virginia

2017-01-01

Myopia is the most common eye disorder and major cause of visual impairment worldwide. As the incidence of myopia continues to rise, the need to further understand the complex roles of molecular and environmental factors controlling variation in refractive error is of increasing importance. Tkatchenko and colleagues applied a systematic approach using a combination of gene set enrichment analysis, genome-wide association studies, and functional analysis of a murine model to identify a myopia susceptibility gene, APLP2. Differential expression of refractive error was associated with time spent reading for those with low frequency variants in this gene. This provides support for the longstanding hypothesis of gene-environment interactions in refractive error development.

Comparative study on gene set and pathway topology-based enrichment methods.

Science.gov (United States)

Bayerlová, Michaela; Jung, Klaus; Kramer, Frank; Klemm, Florian; Bleckmann, Annalen; Beißbarth, Tim

2015-10-22

Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis. We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods. In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower. We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both

Construction of functional linkage gene networks by data integration.

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Linghu, Bolan; Franzosa, Eric A; Xia, Yu

2013-01-01

Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.

A multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors for functional gene analysis.

Science.gov (United States)

Weber, Kristoffer; Bartsch, Udo; Stocking, Carol; Fehse, Boris

2008-04-01

Functional gene analysis requires the possibility of overexpression, as well as downregulation of one, or ideally several, potentially interacting genes. Lentiviral vectors are well suited for this purpose as they ensure stable expression of complementary DNAs (cDNAs), as well as short-hairpin RNAs (shRNAs), and can efficiently transduce a wide spectrum of cell targets when packaged within the coat proteins of other viruses. Here we introduce a multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors designed according to the "building blocks" principle. Using a wide spectrum of different fluorescent markers, including drug-selectable enhanced green fluorescent protein (eGFP)- and dTomato-blasticidin-S resistance fusion proteins, LeGO vectors allow simultaneous analysis of multiple genes and shRNAs of interest within single, easily identifiable cells. Furthermore, each functional module is flanked by unique cloning sites, ensuring flexibility and individual optimization. The efficacy of these vectors for analyzing multiple genes in a single cell was demonstrated in several different cell types, including hematopoietic, endothelial, and neural stem and progenitor cells, as well as hepatocytes. LeGO vectors thus represent a valuable tool for investigating gene networks using conditional ectopic expression and knock-down approaches simultaneously.

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

GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes [version 2; referees: 2 approved

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Evert van den Broek

2017-07-01

Full Text Available Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH or by (low-pass whole genome sequencing (WGS. First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html.

Evaluating historical candidate genes for schizophrenia

DEFF Research Database (Denmark)

Farrell, M S; Werge, T; Sklar, P

2015-01-01

Prior to the genome-wide association era, candidate gene studies were a major approach in schizophrenia genetics. In this invited review, we consider the current status of 25 historical candidate genes for schizophrenia (for example, COMT, DISC1, DTNBP1 and NRG1). The initial study for 24 of thes...

Chassis organism from Corynebacterium glutamicum--a top-down approach to identify and delete irrelevant gene clusters.

Science.gov (United States)

Unthan, Simon; Baumgart, Meike; Radek, Andreas; Herbst, Marius; Siebert, Daniel; Brühl, Natalie; Bartsch, Anna; Bott, Michael; Wiechert, Wolfgang; Marin, Kay; Hans, Stephan; Krämer, Reinhard; Seibold, Gerd; Frunzke, Julia; Kalinowski, Jörn; Rückert, Christian; Wendisch, Volker F; Noack, Stephan

2015-02-01

For synthetic biology applications, a robust structural basis is required, which can be constructed either from scratch or in a top-down approach starting from any existing organism. In this study, we initiated the top-down construction of a chassis organism from Corynebacterium glutamicum ATCC 13032, aiming for the relevant gene set to maintain its fast growth on defined medium. We evaluated each native gene for its essentiality considering expression levels, phylogenetic conservation, and knockout data. Based on this classification, we determined 41 gene clusters ranging from 3.7 to 49.7 kbp as target sites for deletion. 36 deletions were successful and 10 genome-reduced strains showed impaired growth rates, indicating that genes were hit, which are relevant to maintain biological fitness at wild-type level. In contrast, 26 deleted clusters were found to include exclusively irrelevant genes for growth on defined medium. A combinatory deletion of all irrelevant gene clusters would, in a prophage-free strain, decrease the size of the native genome by about 722 kbp (22%) to 2561 kbp. Finally, five combinatory deletions of irrelevant gene clusters were investigated. The study introduces the novel concept of relevant genes and demonstrates general strategies to construct a chassis suitable for biotechnological application. © 2014 The Authors. Biotechnology Journal published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the terms of the Creative Commons Attribution-Non-Commercial-NoDerivs Licence, which permits use and distribution in any medium, provided the original work is properly cited, the use is non- commercial and no modifications or adaptations are made.

Genes from scratch--the evolutionary fate of de novo genes.

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Schlötterer, Christian

2015-04-01

Although considered an extremely unlikely event, many genes emerge from previously noncoding genomic regions. This review covers the entire life cycle of such de novo genes. Two competing hypotheses about the process of de novo gene birth are discussed as well as the high death rate of de novo genes. Despite the high death rate, some de novo genes are retained and remain functional, even in distantly related species, through their integration into gene networks. Further studies combining gene expression with ribosome profiling in multiple populations across different species will be instrumental for an improved understanding of the evolutionary processes operating on de novo genes. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

International Nuclear Information System (INIS)

Zulfiqar, Asma; Paulose, Bibin; Chhikara, Sudesh; Dhankher, Om Parkash

2011-01-01

Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: → Molecular mechanism of Cr uptake and detoxification in plants is not well known. → We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. → 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. → Pathways linked to stress, ion transport, and sulfur assimilation were affected. → This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

Energy Technology Data Exchange (ETDEWEB)

Zulfiqar, Asma, E-mail: asmazulfiqar08@yahoo.com [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Paulose, Bibin, E-mail: bpaulose@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Chhikara, Sudesh, E-mail: sudesh@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Dhankher, Om Parkash, E-mail: parkash@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States)

2011-10-15

Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: > Molecular mechanism of Cr uptake and detoxification in plants is not well known. > We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. > 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. > Pathways linked to stress, ion transport, and sulfur assimilation were affected. > This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

Gene Duplicability of Core Genes Is Highly Consistent across All Angiosperms.

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Li, Zhen; Defoort, Jonas; Tasdighian, Setareh; Maere, Steven; Van de Peer, Yves; De Smet, Riet

2016-02-01

Gene duplication is an important mechanism for adding to genomic novelty. Hence, which genes undergo duplication and are preserved following duplication is an important question. It has been observed that gene duplicability, or the ability of genes to be retained following duplication, is a nonrandom process, with certain genes being more amenable to survive duplication events than others. Primarily, gene essentiality and the type of duplication (small-scale versus large-scale) have been shown in different species to influence the (long-term) survival of novel genes. However, an overarching view of "gene duplicability" is lacking, mainly due to the fact that previous studies usually focused on individual species and did not account for the influence of genomic context and the time of duplication. Here, we present a large-scale study in which we investigated duplicate retention for 9178 gene families shared between 37 flowering plant species, referred to as angiosperm core gene families. For most gene families, we observe a strikingly consistent pattern of gene duplicability across species, with gene families being either primarily single-copy or multicopy in all species. An intermediate class contains gene families that are often retained in duplicate for periods extending to tens of millions of years after whole-genome duplication, but ultimately appear to be largely restored to singleton status, suggesting that these genes may be dosage balance sensitive. The distinction between single-copy and multicopy gene families is reflected in their functional annotation, with single-copy genes being mainly involved in the maintenance of genome stability and organelle function and multicopy genes in signaling, transport, and metabolism. The intermediate class was overrepresented in regulatory genes, further suggesting that these represent putative dosage-balance-sensitive genes. © 2016 American Society of Plant Biologists. All rights reserved.

Model-based gene set analysis for Bioconductor.

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Bauer, Sebastian; Robinson, Peter N; Gagneur, Julien

2011-07-01

Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach. The mgsa package has been made available as part of Bioconductor 2.8. It is released under the conditions of the Artistic license 2.0. peter.robinson@charite.de; julien.gagneur@embl.de.

A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals

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Qihua Tan

2009-01-01

Full Text Available Different from significant gene expression analysis which looks for genes that are differentially regulated, feature selection in the microarray-based prognostic gene expression analysis aims at finding a subset of marker genes that are not only differentially expressed but also informative for prediction. Unfortunately feature selection in literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significances. We introduce a combinatory feature selection strategy that integrates differential gene expression analysis with the Gram-Schmidt process to identify prognostic genes that are both statistically significant and highly informative for predicting tumour survival outcomes. Empirical application to leukemia and ovarian cancer survival data through-within- and cross-study validations shows that the feature space can be largely reduced while achieving improved testing performances.

Advances in study of reporter gene imaging for monitoring gene therapy

International Nuclear Information System (INIS)

Mu Chuanjie; Zhou Jiwen

2003-01-01

To evaluate the efficiency of gene therapy, it is requisite to monitor localization and expression of the therapeutic gene in vivo. Monitoring expression of reporter gene using radionuclide reporter gene technique is the best method. Adenoviral vectors expressing reporter gene are constructed using gene fusion, bicistronic, double promoter or bidirectional transcriptional recombination techniques, and transferred into target cells and tissues, then injected radiolabeled reporter probes which couple to the reporter genes. The reporter genes can be imaged invasively, repeatedly, quantitatively with γ-camera, PET and SPECT. Recently, several reporter gene and reporter probe systems have been used in studies of gene therapy. The part of them has been used for clinic trials

Dictionary-driven prokaryotic gene finding

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Shibuya, Tetsuo; Rigoutsos, Isidore

2002-01-01

Gene identification, also known as gene finding or gene recognition, is among the important problems of molecular biology that have been receiving increasing attention with the advent of large scale sequencing projects. Previous strategies for solving this problem can be categorized into essentially two schools of thought: one school employs sequence composition statistics, whereas the other relies on database similarity searches. In this paper, we propose a new gene identification scheme that combines the best characteristics from each of these two schools. In particular, our method determines gene candidates among the ORFs that can be identified in a given DNA strand through the use of the Bio-Dictionary, a database of patterns that covers essentially all of the currently available sample of the natural protein sequence space. Our approach relies entirely on the use of redundant patterns as the agents on which the presence or absence of genes is predicated and does not employ any additional evidence, e.g. ribosome-binding site signals. The Bio-Dictionary Gene Finder (BDGF), the algorithm’s implementation, is a single computational engine able to handle the gene identification task across distinct archaeal and bacterial genomes. The engine exhibits performance that is characterized by simultaneous very high values of sensitivity and specificity, and a high percentage of correctly predicted start sites. Using a collection of patterns derived from an old (June 2000) release of the Swiss-Prot/TrEMBL database that contained 451 602 proteins and fragments, we demonstrate our method’s generality and capabilities through an extensive analysis of 17 complete archaeal and bacterial genomes. Examples of previously unreported genes are also shown and discussed in detail. PMID:12060689

Molecular genetic approach to human meningioma: loss of genes on chromosome 22

International Nuclear Information System (INIS)

Seizinger, B.R.; De La Monte, S.; Atkins, L.; Gusella, J.F.; Martuza, R.L.

1987-01-01

A molecular genetic approach employing polymorphic DNA markers has been used to investigate the role of chromosomal aberrations in meningioma, one of the most common tumors of the human nervous system. Comparison of the alleles detected by DNA markers in tumor DNA versus DNA from normal tissue revealed chromosomal alterations present in primary surgical specimens. In agreement with cytogenetic studies of cultured meningiomas, the most frequent alteration detected was loss of heterozygosity on chromosome 22. Forty of 51 patients were constitutionally heterozygous for at least one chromosome 22 DNA marker. Seventeen of the 40 constitutionally heterozygotic patients (43%) displayed hemizygosity for the corresponding marker in their meningioma tumor tissues. Loss of heterozygosity was also detected at a significantly lower frequency for markers on several other autosomes. In view of the striking association between acoustic neuroma and meningioma in bilateral acoustic neurofibromatosis and the discovery that acoustic neuromas display specific loss of genes on chromosome 22, the authors propose that a common mechanism involving chromosome 22 is operative in the development of both tumor types. Fine-structure mapping to reveal partial deletions in meningiomas may provide the means to clone and characterize a gene (or genes) of importance for tumorigenesis in this and possibly other clinically associated tumors of the human nervous system

Gene replacement in Penicillium roqueforti.

Science.gov (United States)

Goarin, Anne; Silar, Philippe; Malagnac, Fabienne

2015-05-01

Most cheese-making filamentous fungi lack suitable molecular tools to improve their biotechnology potential. Penicillium roqueforti, a species of high industrial importance, would benefit from functional data yielded by molecular genetic approaches. This work provides the first example of gene replacement by homologous recombination in P. roqueforti, demonstrating that knockout experiments can be performed in this fungus. To do so, we improved the existing transformation method to integrate transgenes into P. roqueforti genome. In the meantime, we cloned the PrNiaD gene, which encodes a NADPH-dependent nitrate reductase that reduces nitrate to nitrite. Then, we performed a deletion of the PrNiaD gene from P. roqueforti strain AGO. The ΔPrNiaD mutant strain is more resistant to chlorate-containing medium than the wild-type strain, but did not grow on nitrate-containing medium. Because genomic data are now available, we believe that generating selective deletions of candidate genes will be a key step to open the way for a comprehensive exploration of gene function in P. roqueforti.

Keratinocyte Growth Factor Gene Electroporation into Skeletal Muscle as a Novel Gene Therapeutic Approach for Elastase-Induced Pulmonary Emphysema in Mice

International Nuclear Information System (INIS)

Tobinaga, Shuichi; Matsumoto, Keitaro; Nagayasu, Takeshi; Furukawa, Katsuro; Abo, Takafumi; Yamasaki, Naoya; Tsuchiya, Tomoshi; Miyazaki, Takuro; Koji, Takehiko

2015-01-01

Pulmonary emphysema is a progressive disease with airspace destruction and an effective therapy is needed. Keratinocyte growth factor (KGF) promotes pulmonary epithelial proliferation and has the potential to induce lung regeneration. The aim of this study was to determine the possibility of using KGF gene therapy for treatment of a mouse emphysema model induced by porcine pancreatic elastase (PPE). Eight-week-old BALB/c male mice treated with intra-tracheal PPE administration were transfected with 80 μg of a recombinant human KGF (rhKGF)-expressing FLAG-CMV14 plasmid (pKGF-FLAG gene), or with the pFLAG gene expressing plasmid as a control, into the quadriceps muscle by electroporation. In the lung, the expression of proliferating cell nuclear antigen (PCNA) was augmented, and surfactant protein A (SP-A) and KGF receptor (KGFR) were co-expressed in PCNA-positive cells. Moreover, endogenous KGF and KGFR gene expression increased significantly by pKGF-FLAG gene transfection. Arterial blood gas analysis revealed that the PaO 2 level was not significantly reduced on day 14 after PPE instillation with pKGF-FLAG gene transfection compared to that of normal mice. These results indicated that KGF gene therapy with electroporation stimulated lung epithelial proliferation and protected depression of pulmonary function in a mouse emphysema model, suggesting a possible method of treating pulmonary emphysema

Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks.

Science.gov (United States)

Fischer, Martin; Grossmann, Patrick; Padi, Megha; DeCaprio, James A

2016-07-27

Cell cycle (CC) and TP53 regulatory networks are frequently deregulated in cancer. While numerous genome-wide studies of TP53 and CC-regulated genes have been performed, significant variation between studies has made it difficult to assess regulation of any given gene of interest. To overcome the limitation of individual studies, we developed a meta-analysis approach to identify high confidence target genes that reflect their frequency of identification in independent datasets. Gene regulatory networks were generated by comparing differential expression of TP53 and CC-regulated genes with chromatin immunoprecipitation studies for TP53, RB1, E2F, DREAM, B-MYB, FOXM1 and MuvB. RNA-seq data from p21-null cells revealed that gene downregulation by TP53 generally requires p21 (CDKN1A). Genes downregulated by TP53 were also identified as CC genes bound by the DREAM complex. The transcription factors RB, E2F1 and E2F7 bind to a subset of DREAM target genes that function in G1/S of the CC while B-MYB, FOXM1 and MuvB control G2/M gene expression. Our approach yields high confidence ranked target gene maps for TP53, DREAM, MMB-FOXM1 and RB-E2F and enables prediction and distinction of CC regulation. A web-based atlas at www.targetgenereg.org enables assessing the regulation of any human gene of interest. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Gene therapy for carcinoma of the breast: Genetic toxins

International Nuclear Information System (INIS)

Vassaux, Georges; Lemoine, Nick R

2000-01-01

Gene therapy was initially envisaged as a potential treatment for genetically inherited, monogenic disorders. The applications of gene therapy have now become wider, however, and include cardiovascular diseases, vaccination and cancers in which conventional therapies have failed. With regard to oncology, various gene therapy approaches have been developed. Among them, the use of genetic toxins to kill cancer cells selectively is emerging. Two different types of genetic toxins have been developed so far: the metabolic toxins and the dominant-negative class of toxins. This review describes these two different approaches, and discusses their potential applications in cancer gene therapy

Developing strategies for detection of gene doping.

Science.gov (United States)

Baoutina, Anna; Alexander, Ian E; Rasko, John E J; Emslie, Kerry R

2008-01-01

It is feared that the use of gene transfer technology to enhance athletic performance, the practice that has received the term 'gene doping', may soon become a real threat to the world of sport. As recognised by the anti-doping community, gene doping, like doping in any form, undermines principles of fair play in sport and most importantly, involves major health risks to athletes who partake in gene doping. One attraction of gene doping for such athletes and their entourage lies in the apparent difficulty of detecting its use. Since the realisation of the threat of gene doping to sport in 2001, the anti-doping community and scientists from different disciplines concerned with potential misuse of gene therapy technologies for performance enhancement have focused extensive efforts on developing robust methods for gene doping detection which could be used by the World Anti-Doping Agency to monitor athletes and would meet the requirements of a legally defensible test. Here we review the approaches and technologies which are being evaluated for the detection of gene doping, as well as for monitoring the efficacy of legitimate gene therapy, in relation to the detection target, the type of sample required for analysis and detection methods. We examine the accumulated knowledge on responses of the body, at both cellular and systemic levels, to gene transfer and evaluate strategies for gene doping detection based on current knowledge of gene technology, immunology, transcriptomics, proteomics, biochemistry and physiology. (c) 2008 John Wiley & Sons, Ltd.

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

Directory of Open Access Journals (Sweden)

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.

Screening for single nucleotide variants, small indels and exon deletions with a next-generation sequencing based gene panel approach for Usher syndrome.

Science.gov (United States)

Krawitz, Peter M; Schiska, Daniela; Krüger, Ulrike; Appelt, Sandra; Heinrich, Verena; Parkhomchuk, Dmitri; Timmermann, Bernd; Millan, Jose M; Robinson, Peter N; Mundlos, Stefan; Hecht, Jochen; Gross, Manfred

2014-09-01

Usher syndrome is an autosomal recessive disorder characterized both by deafness and blindness. For the three clinical subtypes of Usher syndrome causal mutations in altogether 12 genes and a modifier gene have been identified. Due to the genetic heterogeneity of Usher syndrome, the molecular analysis is predestined for a comprehensive and parallelized analysis of all known genes by next-generation sequencing (NGS) approaches. We describe here the targeted enrichment and deep sequencing for exons of Usher genes and compare the costs and workload of this approach compared to Sanger sequencing. We also present a bioinformatics analysis pipeline that allows us to detect single-nucleotide variants, short insertions and deletions, as well as copy number variations of one or more exons on the same sequence data. Additionally, we present a flexible in silico gene panel for the analysis of sequence variants, in which newly identified genes can easily be included. We applied this approach to a cohort of 44 Usher patients and detected biallelic pathogenic mutations in 35 individuals and monoallelic mutations in eight individuals of our cohort. Thirty-nine of the sequence variants, including two heterozygous deletions comprising several exons of USH2A, have not been reported so far. Our NGS-based approach allowed us to assess single-nucleotide variants, small indels, and whole exon deletions in a single test. The described diagnostic approach is fast and cost-effective with a high molecular diagnostic yield.

Discovering implicit entity relation with the gene-citation-gene network.

Directory of Open Access Journals (Sweden)

Min Song

Full Text Available In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner.

Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data.

Directory of Open Access Journals (Sweden)

Pugalendhi Ganesh Kumar

Full Text Available This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection such as filtering and embedding for selection of potential genes and the most relevant genes associated with cancer, respectively. The filter procedure was implemented by developing a fuzzy rough set (FR-based method for redefining the criterion function of f-information (FI to identify the potential genes without discretizing the continuous gene expression values. The embedded procedure is implemented by means of a water swirl algorithm (WSA, which attempts to optimize the rule set and membership function required to classify samples using a fuzzy-rule-based multiclassification system (FRBMS. Two novel update equations are proposed in WSA, which have better exploration and exploitation abilities while designing a self-learning FRBMS. The efficiency of our new approach was evaluated on 13 multicategory and 9 binary datasets of cancer gene expression. Additionally, the performance of the proposed FRFI-WSA method in designing an FRBMS was compared with existing methods for gene selection and optimization such as genetic algorithm (GA, particle swarm optimization (PSO, and artificial bee colony algorithm (ABC on all the datasets. In the global cancer map with repeated measurements (GCM_RM dataset, the FRFI-WSA showed the smallest number of 16 most relevant genes associated with cancer using a minimal number of 26 compact rules with the highest classification accuracy (96.45%. In addition, the statistical validation used in this study revealed that the biological relevance of the most relevant genes associated with cancer and their linguistics detected by the proposed FRFI-WSA approach are better than those in the other methods. The simple interpretable rules with most relevant genes and effectively

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

Science.gov (United States)

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.

Strategies used for genetically modifying bacterial genome: ite-directed mutagenesis, gene inactivation, and gene over-expression*

Science.gov (United States)

Xu, Jian-zhong; Zhang, Wei-guo

2016-01-01

With the availability of the whole genome sequence of Escherichia coli or Corynebacterium glutamicum, strategies for directed DNA manipulation have developed rapidly. DNA manipulation plays an important role in understanding the function of genes and in constructing novel engineering bacteria according to requirement. DNA manipulation involves modifying the autologous genes and expressing the heterogenous genes. Two alternative approaches, using electroporation linear DNA or recombinant suicide plasmid, allow a wide variety of DNA manipulation. However, the over-expression of the desired gene is generally executed via plasmid-mediation. The current review summarizes the common strategies used for genetically modifying E. coli and C. glutamicum genomes, and discusses the technical problem of multi-layered DNA manipulation. Strategies for gene over-expression via integrating into genome are proposed. This review is intended to be an accessible introduction to DNA manipulation within the bacterial genome for novices and a source of the latest experimental information for experienced investigators. PMID:26834010

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

DEFF Research Database (Denmark)

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

2004-01-01

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

Reduced rates of gene loss, gene silencing, and gene mutation in Dnmt1-deficient embryonic stem cells

NARCIS (Netherlands)

Chan, M.F.; van Amerongen, R.; Nijjar, T.; Cuppen, E.; Jones, P.A.; Laird, P.W.

2001-01-01

Tumor suppressor gene inactivation is a crucial event in oncogenesis. Gene inactivation mechanisms include events resulting in loss of heterozygosity (LOH), gene mutation, and transcriptional silencing. The contribution of each of these different pathways varies among tumor suppressor genes and by

Theranostic Imaging of Cancer Gene Therapy.

Science.gov (United States)

Sekar, Thillai V; Paulmurugan, Ramasamy

2016-01-01

Gene-directed enzyme prodrug therapy (GDEPT) is a promising therapeutic approach for treating cancers of various phenotypes. This strategy is independent of various other chemotherapeutic drugs used for treating cancers where the drugs are mainly designed to target endogenous cellular mechanisms, which are different in various cancer subtypes. In GDEPT an external enzyme, which is different from the cellular proteins, is expressed to convert the injected prodrug in to a toxic metabolite, that normally kill cancer cells express this protein. Theranostic imaging is an approach used to directly monitor the expression of these gene therapy enzymes while evaluating therapeutic effect. We recently developed a dual-GDEPT system where we combined mutant human herpes simplex thymidine kinase (HSV1sr39TK) and E. coli nitroreductase (NTR) enzyme, to improve therapeutic efficiency of cancer gene therapy by simultaneously injecting two prodrugs at a lower dose. In this approach we use two different prodrugs such as ganciclovir (GCV) and CB1954 to target two different cellular mechanisms to kill cancer cells. The developed dual GDEPT system was highly efficacious than that of either of the system used independently. In this chapter, we describe the complete protocol involved for in vitro and in vivo imaging of therapeutic cancer gene therapy evaluation.

Gene therapy for carcinoma of the breast: Therapeutic genetic correction strategies

International Nuclear Information System (INIS)

Obermiller, Patrice S; Tait, David L; Holt, Jeffrey T

2000-01-01

Gene therapy is a therapeutic approach that is designed to correct specific molecular defects that contribute to the cause or progression of cancer. Genes that are mutated or deleted in cancers include the cancer susceptibility genes p53 and BRCA1. Because mutational inactivation of gene function is specific to tumor cells in these settings, cancer gene correction strategies may provide an opportunity for selective targeting without significant toxicity for normal nontumor cells. Both p53 and BRCA1 appear to inhibit cancer cells that lack mutations in these genes, suggesting that the so-called gene correction strategies may have broader potential than initially believed. Increasing knowledge of cancer genetics has identified these and other genes as potential targets for gene replacement therapy. Initial patient trials of p53 and BRCA1 gene therapy have provided some indications of potential efficacy, but have also identified areas of basic and clinical research that are needed before these approaches may be widely used in patient care

Progress in Gene Therapy for Prostate Cancer

International Nuclear Information System (INIS)

Ahmed, Kamran A.; Davis, Brian J.; Wilson, Torrence M.; Wiseman, Gregory A.; Federspiel, Mark J.; Morris, John C.

2012-01-01

Gene therapy has held promise to correct various disease processes. Prostate cancer represents the second leading cause of cancer death in American men. A number of clinical trials involving gene therapy for the treatment of prostate cancer have been reported. The ability to efficiently transduce tumors with effective levels of therapeutic genes has been identified as a fundamental barrier to effective cancer gene therapy. The approach utilizing gene therapy in prostate cancer patients at our institution attempts to address this deficiency. The sodium-iodide symporter (NIS) is responsible for the ability of the thyroid gland to transport and concentrate iodide. The characteristics of the NIS gene suggest that it could represent an ideal therapeutic gene for cancer therapy. Published results from Mayo Clinic researchers have indicated several important successes with the use of the NIS gene and prostate gene therapy. Studies have demonstrated that transfer of the human NIS gene into prostate cancer using adenovirus vectors in vitro and in vivo results in efficient uptake of radioactive iodine and significant tumor growth delay with prolongation of survival. Preclinical successes have culminated in the opening of a phase I trial for patients with advanced prostate disease which is currently accruing patients. Further study will reveal the clinical promise of NIS gene therapy in the treatment of prostate as well as other malignancies.

Progress in Gene Therapy for Prostate Cancer

Energy Technology Data Exchange (ETDEWEB)

Ahmed, Kamran A.; Davis, Brian J. [Department of Radiation Oncology, Mayo Clinic, Rochester, MN (United States); Wilson, Torrence M. [Department of Urology, Mayo Clinic, Rochester, MN (United States); Wiseman, Gregory A. [Division of Nuclear Medicine, Mayo Clinic, Rochester, MN (United States); Federspiel, Mark J. [Department of Molecular Medicine, Mayo Clinic, Rochester, MN (United States); Morris, John C., E-mail: davis.brian@mayo.edu [Division of Endocrinology, Mayo Clinic, Rochester, MN (United States)

2012-11-19

Gene therapy has held promise to correct various disease processes. Prostate cancer represents the second leading cause of cancer death in American men. A number of clinical trials involving gene therapy for the treatment of prostate cancer have been reported. The ability to efficiently transduce tumors with effective levels of therapeutic genes has been identified as a fundamental barrier to effective cancer gene therapy. The approach utilizing gene therapy in prostate cancer patients at our institution attempts to address this deficiency. The sodium-iodide symporter (NIS) is responsible for the ability of the thyroid gland to transport and concentrate iodide. The characteristics of the NIS gene suggest that it could represent an ideal therapeutic gene for cancer therapy. Published results from Mayo Clinic researchers have indicated several important successes with the use of the NIS gene and prostate gene therapy. Studies have demonstrated that transfer of the human NIS gene into prostate cancer using adenovirus vectors in vitro and in vivo results in efficient uptake of radioactive iodine and significant tumor growth delay with prolongation of survival. Preclinical successes have culminated in the opening of a phase I trial for patients with advanced prostate disease which is currently accruing patients. Further study will reveal the clinical promise of NIS gene therapy in the treatment of prostate as well as other malignancies.

Computational Identification of Novel Genes: Current and Future Perspectives.

Science.gov (United States)

Klasberg, Steffen; Bitard-Feildel, Tristan; Mallet, Ludovic

2016-01-01

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

Gene expression inference with deep learning.

Science.gov (United States)

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.

Norrie disease gene is distinct from the monoamine oxidase genes

OpenAIRE

Sims, Katherine B.; Ozelius, Laurie; Corey, Timothy; Rinehart, William B.; Liberfarb, Ruth; Haines, Jonathan; Chen, Wei Jane; Norio, Reijo; Sankila, Eeva; de la Chapelle, Albert; Murphy, Dennis L.; Gusella, James; Breakefield, Xandra O.

1989-01-01

The genes for MAO-A and MAO-B appear to be very close to the Norrie disease gene, on the basis of loss and /or disruption of the MAO genes and activities in atypical Norrie disease patients deleted for the DXS7 locus; linkage among the MAO genes, the Norrie disease gene, and the DXS7 locus; and mapping of all these loci to the chromosomal region Xp11. The present study provides evidence that the MAO genes are not disrupted in “classic” Norrie disease patients. Genomic DNA from these “nondelet...

Functional clustering of time series gene expression data by Granger causality

Science.gov (United States)

2012-01-01

Background A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them. PMID:23107425

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.

Gene therapy strategy for long-term myocardial protection using adeno-associated virus-mediated delivery of heme oxygenase gene.

Science.gov (United States)

Melo, Luis G; Agrawal, Reitu; Zhang, Lunan; Rezvani, Mojgan; Mangi, Abeel A; Ehsan, Afshin; Griese, Daniel P; Dell'Acqua, Giorgio; Mann, Michael J; Oyama, Junichi; Yet, Shaw-Fang; Layne, Matthew D; Perrella, Mark A; Dzau, Victor J

2002-02-05

Ischemia and oxidative stress are the leading mechanisms for tissue injury. An ideal strategy for preventive/protective therapy would be to develop an approach that could confer long-term transgene expression and, consequently, tissue protection from repeated ischemia/reperfusion injury with a single administration of a therapeutic gene. In the present study, we used recombinant adeno-associated virus (rAAV) as a vector for direct delivery of the cytoprotective gene heme oxygenase-1 (HO-1) into the rat myocardium, with the purpose of evaluating this strategy as a therapeutic approach for long-term protection from ischemia-induced myocardial injury. Human HO-1 gene (hHO-1) was delivered to normal rat hearts by intramyocardial injection. AAV-mediated transfer of the hHO-1 gene 8 weeks before acute coronary artery ligation and release led to a dramatic reduction (>75%) in left ventricular myocardial infarction. The reduction in infarct size was accompanied by decreases in myocardial lipid peroxidation and in proapoptotic Bax and proinflammatory interleukin-1beta protein abundance, concomitant with an increase in antiapoptotic Bcl-2 protein level. This suggested that the transgene exerts its cardioprotective effects in part by reducing oxidative stress and associated inflammation and apoptotic cell death. This study documents the beneficial therapeutic effect of rAAV-mediated transfer, before myocardial injury, of a cytoprotective gene that confers long-term myocardial protection from ischemia/reperfusion injury. Our data suggest that this novel "pre-event" gene transfer approach may provide sustained tissue protection from future repeated episodes of injury and may be beneficial as preventive therapy for patients with or at risk of developing coronary ischemic events.

Gene targeting approaches to complex genetic diseases: atherosclerosis and essential hypertension.

OpenAIRE

Smithies, O; Maeda, N

1995-01-01

Gene targeting allows precise, predetermined changes to be made in a chosen gene in the mouse genome. To date, targeting has been used most often for generation of animals completely lacking the product of a gene of interest. The resulting "knockout" mice have confirmed some hypotheses, have upset others, but have rarely been uninformative. Models of several human genetic diseases have been produced by targeting--including Gaucher disease, cystic fibrosis, and the fragile X syndrome. These di...

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

Science.gov (United States)

Ghanat Bari, Mehrab; Ung, Choong Yong; Zhang, Cheng; Zhu, Shizhen; Li, Hu

2017-08-01

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 10 8 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

Prodrug encapsulated albumin nanoparticles as an alternative approach to manifest anti-proliferative effects of suicide gene therapy

International Nuclear Information System (INIS)

Tirkey, Bulbul; Bhushan, Bharat; Uday Kumar, S.; Gopinath, P.

2017-01-01

Conventional anticancer agents are associated with limited therapeutic efficacy and substantial nonspecific cytotoxicity. Thus, there is an imminent need for an alternative approach that can specifically annihilate the cancer cells with minimal side effects. Among such alternative approaches, CD::UPRT (cytosine deaminase uracil phosphoribosyl transferase) suicide gene therapy has tremendous potential due to its high efficacy. Prodrug 5-Fluorocytosine (5-FC) used in combination with CD::UPRT suicide gene suffers from limited solubility which subsequently leads to decline in therapeutic efficacy. In order to overcome this, 5-FC encapsulated bovine serum albumin nanoparticles (BSA-5-FC NPs) were prepared in this work by desolvation method. Physico-chemical characterizations studies revealed amorphous nature of BSA-5-FC NPs with uniform spherical morphology. Apart from increase in solubility, encapsulated 5-FC followed slow and sustained release profile. Suicide gene expressing stable clone of L-132 cells were adapted for investigating therapeutic potential of BSA-5-FC NPs. These nanoparticles were readily taken up by the cells in a concentration dependent manner and subsequently manifested apoptosis, which was further confirmed by morphological examination and gene expression analysis. These findings clearly illustrate that CD::UPRT suicide gene therapy can be efficiently utilized in combination with this nanosystem for improved suicide gene therapy and tumor eradication. - Highlights: • In this work, BSA-5-FC NPs has been prepared to achieve its sustained release and also facilitate its uptake by cells. • A protein based system has been realized for the first time to deliver prodrug for cancer therapy. • Physico-chemical characterizations further validate the formation of spherical, monodispersed and stable nanoparticles. • The therapeutic efficacy of BSA-5-FC NPs has been validated against CD::UPRT expressing stable cells.

Prodrug encapsulated albumin nanoparticles as an alternative approach to manifest anti-proliferative effects of suicide gene therapy

Energy Technology Data Exchange (ETDEWEB)

Tirkey, Bulbul [Nanobiotechnology Laboratory, Department of Biotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 (India); Bhushan, Bharat; Uday Kumar, S. [Nanobiotechnology Laboratory, Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 (India); Gopinath, P., E-mail: pgopifnt@iitr.ernet.in [Nanobiotechnology Laboratory, Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 (India); Nanobiotechnology Laboratory, Department of Biotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 (India)

2017-04-01

Conventional anticancer agents are associated with limited therapeutic efficacy and substantial nonspecific cytotoxicity. Thus, there is an imminent need for an alternative approach that can specifically annihilate the cancer cells with minimal side effects. Among such alternative approaches, CD::UPRT (cytosine deaminase uracil phosphoribosyl transferase) suicide gene therapy has tremendous potential due to its high efficacy. Prodrug 5-Fluorocytosine (5-FC) used in combination with CD::UPRT suicide gene suffers from limited solubility which subsequently leads to decline in therapeutic efficacy. In order to overcome this, 5-FC encapsulated bovine serum albumin nanoparticles (BSA-5-FC NPs) were prepared in this work by desolvation method. Physico-chemical characterizations studies revealed amorphous nature of BSA-5-FC NPs with uniform spherical morphology. Apart from increase in solubility, encapsulated 5-FC followed slow and sustained release profile. Suicide gene expressing stable clone of L-132 cells were adapted for investigating therapeutic potential of BSA-5-FC NPs. These nanoparticles were readily taken up by the cells in a concentration dependent manner and subsequently manifested apoptosis, which was further confirmed by morphological examination and gene expression analysis. These findings clearly illustrate that CD::UPRT suicide gene therapy can be efficiently utilized in combination with this nanosystem for improved suicide gene therapy and tumor eradication. - Highlights: • In this work, BSA-5-FC NPs has been prepared to achieve its sustained release and also facilitate its uptake by cells. • A protein based system has been realized for the first time to deliver prodrug for cancer therapy. • Physico-chemical characterizations further validate the formation of spherical, monodispersed and stable nanoparticles. • The therapeutic efficacy of BSA-5-FC NPs has been validated against CD::UPRT expressing stable cells.

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

Directory of Open Access Journals (Sweden)

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.).

Science.gov (United States)

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

The Mycoplasma hominis vaa gene displays a mosaic gene structure

DEFF Research Database (Denmark)

Boesen, Thomas; Emmersen, Jeppe M. G.; Jensen, Lise T.

1998-01-01

Mycoplasma hominis contains a variable adherence-associated (vaa) gene. To classify variants of the vaa genes, we examined 42 M. hominis isolated by PCR, DNA sequencing and immunoblotting. This uncovered the existence of five gene categories. Comparison of the gene types revealed a modular...

A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity.

Science.gov (United States)

Ultsch, Alfred; Kringel, Dario; Kalso, Eija; Mogil, Jeffrey S; Lötsch, Jörn

2016-12-01

The increasing availability of "big data" enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 535 genes identified empirically as relevant to pain with the knowledge about the functions of thousands of genes. Starting from an accepted description of chronic pain as displaying systemic features described by the terms "learning" and "neuronal plasticity," a functional genomics analysis proposed that among the functions of the 535 "pain genes," the biological processes "learning or memory" (P = 8.6 × 10) and "nervous system development" (P = 2.4 × 10) are statistically significantly overrepresented as compared with the annotations to these processes expected by chance. After establishing that the hypothesized biological processes were among important functional genomics features of pain, a subset of n = 34 pain genes were found to be annotated with both Gene Ontology terms. Published empirical evidence supporting their involvement in chronic pain was identified for almost all these genes, including 1 gene identified in March 2016 as being involved in pain. By contrast, such evidence was virtually absent in a randomly selected set of 34 other human genes. Hence, the present computational functional genomics-based method can be used for candidate gene selection, providing an alternative to established methods.

Animal models of gene-environment interactions in schizophrenia.

Science.gov (United States)

Ayhan, Yavuz; Sawa, Akira; Ross, Christopher A; Pletnikov, Mikhail V

2009-12-07

The pathogenesis of schizophrenia and related mental illnesses likely involves multiple interactions between susceptibility genes of small effects and environmental factors. Gene-environment interactions occur across different stages of neurodevelopment to produce heterogeneous clinical and pathological manifestations of the disease. The main obstacle for mechanistic studies of gene-environment interplay has been the paucity of appropriate experimental systems for elucidating the molecular pathways that mediate gene-environment interactions relevant to schizophrenia. Recent advances in psychiatric genetics and a plethora of experimental data from animal studies allow us to suggest a new approach to gene-environment interactions in schizophrenia. We propose that animal models based on identified genetic mutations and measurable environment factors will help advance studies of the molecular mechanisms of gene-environment interplay.

High-throughput analysis of candidate imprinted genes and allele-specific gene expression in the human term placenta

Directory of Open Access Journals (Sweden)

Clark Taane G

2010-04-01

Full Text Available Abstract Background Imprinted genes show expression from one parental allele only and are important for development and behaviour. This extreme mode of allelic imbalance has been described for approximately 56 human genes. Imprinting status is often disrupted in cancer and dysmorphic syndromes. More subtle variation of gene expression, that is not parent-of-origin specific, termed 'allele-specific gene expression' (ASE is more common and may give rise to milder phenotypic differences. Using two allele-specific high-throughput technologies alongside bioinformatics predictions, normal term human placenta was screened to find new imprinted genes and to ascertain the extent of ASE in this tissue. Results Twenty-three family trios of placental cDNA, placental genomic DNA (gDNA and gDNA from both parents were tested for 130 candidate genes with the Sequenom MassArray system. Six genes were found differentially expressed but none imprinted. The Illumina ASE BeadArray platform was then used to test 1536 SNPs in 932 genes. The array was enriched for the human orthologues of 124 mouse candidate genes from bioinformatics predictions and 10 human candidate imprinted genes from EST database mining. After quality control pruning, a total of 261 informative SNPs (214 genes remained for analysis. Imprinting with maternal expression was demonstrated for the lymphocyte imprinted gene ZNF331 in human placenta. Two potential differentially methylated regions (DMRs were found in the vicinity of ZNF331. None of the bioinformatically predicted candidates tested showed imprinting except for a skewed allelic expression in a parent-specific manner observed for PHACTR2, a neighbour of the imprinted PLAGL1 gene. ASE was detected for two or more individuals in 39 candidate genes (18%. Conclusions Both Sequenom and Illumina assays were sensitive enough to study imprinting and strong allelic bias. Previous bioinformatics approaches were not predictive of new imprinted genes

Combining Human Epigenetics and Sleep Studies in Caenorhabditis elegans: A Cross-Species Approach for Finding Conserved Genes Regulating Sleep.

Science.gov (United States)

Huang, Huiyan; Zhu, Yong; Eliot, Melissa N; Knopik, Valerie S; McGeary, John E; Carskadon, Mary A; Hart, Anne C

2017-06-01

We aimed to test a combined approach to identify conserved genes regulating sleep and to explore the association between DNA methylation and sleep length. We identified candidate genes associated with shorter versus longer sleep duration in college students based on DNA methylation using Illumina Infinium HumanMethylation450 BeadChip arrays. Orthologous genes in Caenorhabditis elegans were identified, and we examined whether their loss of function affected C. elegans sleep. For genes whose perturbation affected C. elegans sleep, we subsequently undertook a small pilot study to re-examine DNA methylation in an independent set of human participants with shorter versus longer sleep durations. Eighty-seven out of 485,577 CpG sites had significant differential methylation in young adults with shorter versus longer sleep duration, corresponding to 52 candidate genes. We identified 34 C. elegans orthologs, including NPY/flp-18 and flp-21, which are known to affect sleep. Loss of five additional genes alters developmentally timed C. elegans sleep (B4GALT6/bre-4, DOCK180/ced-5, GNB2L1/rack-1, PTPRN2/ida-1, ZFYVE28/lst-2). For one of these genes, ZFYVE28 (also known as hLst2), the pilot replication study again found decreased DNA methylation associated with shorter sleep duration at the same two CpG sites in the first intron of ZFYVE28. Using an approach that combines human epigenetics and C. elegans sleep studies, we identified five genes that play previously unidentified roles in C. elegans sleep. We suggest sleep duration in humans may be associated with differential DNA methylation at specific sites and that the conserved genes identified here likely play roles in C. elegans sleep and in other species. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Identification of housekeeping genes as references for quantitative ...

Indian Academy of Sciences (India)

Navya

2017-01-20

Jan 20, 2017 ... approach to identify genes suited for normalization, applied to bladder and colon cancer ... Cloning of Atlantic halibut growth hormone receptor genes and .... messenger and ribosomal RNA content in rat mammary tumors.

Candidate gene approach for parasite resistance in sheep--variation in immune pathway genes and association with fecal egg count.

Directory of Open Access Journals (Sweden)

Kathiravan Periasamy

Full Text Available Sheep chromosome 3 (Oar3 has the largest number of QTLs reported to be significantly associated with resistance to gastro-intestinal nematodes. This study aimed to identify single nucleotide polymorphisms (SNPs within candidate genes located in sheep chromosome 3 as well as genes involved in major immune pathways. A total of 41 SNPs were identified across 38 candidate genes in a panel of unrelated sheep and genotyped in 713 animals belonging to 22 breeds across Asia, Europe and South America. The variations and evolution of immune pathway genes were assessed in sheep populations across these macro-environmental regions that significantly differ in the diversity and load of pathogens. The mean minor allele frequency (MAF did not vary between Asian and European sheep reflecting the absence of ascertainment bias. Phylogenetic analysis revealed two major clusters with most of South Asian, South East Asian and South West Asian breeds clustering together while European and South American sheep breeds clustered together distinctly. Analysis of molecular variance revealed strong phylogeographic structure at loci located in immune pathway genes, unlike microsatellite and genome wide SNP markers. To understand the influence of natural selection processes, SNP loci located in chromosome 3 were utilized to reconstruct haplotypes, the diversity of which showed significant deviations from selective neutrality. Reduced Median network of reconstructed haplotypes showed balancing selection in force at these loci. Preliminary association of SNP genotypes with phenotypes recorded 42 days post challenge revealed significant differences (P<0.05 in fecal egg count, body weight change and packed cell volume at two, four and six SNP loci respectively. In conclusion, the present study reports strong phylogeographic structure and balancing selection operating at SNP loci located within immune pathway genes. Further, SNP loci identified in the study were found to have

Recent trends in the gene therapy of β-thalassemia

Science.gov (United States)

Finotti, Alessia; Breda, Laura; Lederer, Carsten W; Bianchi, Nicoletta; Zuccato, Cristina; Kleanthous, Marina; Rivella, Stefano; Gambari, Roberto

2015-01-01

The β-thalassemias are a group of hereditary hematological diseases caused by over 300 mutations of the adult β-globin gene. Together with sickle cell anemia, thalassemia syndromes are among the most impactful diseases in developing countries, in which the lack of genetic counseling and prenatal diagnosis have contributed to the maintenance of a very high frequency of these genetic diseases in the population. Gene therapy for β-thalassemia has recently seen steadily accelerating progress and has reached a crossroads in its development. Presently, data from past and ongoing clinical trials guide the design of further clinical and preclinical studies based on gene augmentation, while fundamental insights into globin switching and new technology developments have inspired the investigation of novel gene-therapy approaches. Moreover, human erythropoietic stem cells from β-thalassemia patients have been the cellular targets of choice to date whereas future gene-therapy studies might increasingly draw on induced pluripotent stem cells. Herein, we summarize the most significant developments in β-thalassemia gene therapy over the last decade, with a strong emphasis on the most recent findings, for β-thalassemia model systems; for β-, γ-, and anti-sickling β-globin gene addition and combinatorial approaches including the latest results of clinical trials; and for novel approaches, such as transgene-mediated activation of γ-globin and genome editing using designer nucleases. PMID:25737641

Spectral biclustering of microarray data: coclustering genes and conditions.

Science.gov (United States)

Kluger, Yuval; Basri, Ronen; Chang, Joseph T; Gerstein, Mark

2003-04-01

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

Comparative Genomic Analysis of Soybean Flowering Genes

Science.gov (United States)

Jung, Chol-Hee; Wong, Chui E.; Singh, Mohan B.; Bhalla, Prem L.

2012-01-01

Flowering is an important agronomic trait that determines crop yield. Soybean is a major oilseed legume crop used for human and animal feed. Legumes have unique vegetative and floral complexities. Our understanding of the molecular basis of flower initiation and development in legumes is limited. Here, we address this by using a computational approach to examine flowering regulatory genes in the soybean genome in comparison to the most studied model plant, Arabidopsis. For this comparison, a genome-wide analysis of orthologue groups was performed, followed by an in silico gene expression analysis of the identified soybean flowering genes. Phylogenetic analyses of the gene families highlighted the evolutionary relationships among these candidates. Our study identified key flowering genes in soybean and indicates that the vernalisation and the ambient-temperature pathways seem to be the most variant in soybean. A comparison of the orthologue groups containing flowering genes indicated that, on average, each Arabidopsis flowering gene has 2-3 orthologous copies in soybean. Our analysis highlighted that the CDF3, VRN1, SVP, AP3 and PIF3 genes are paralogue-rich genes in soybean. Furthermore, the genome mapping of the soybean flowering genes showed that these genes are scattered randomly across the genome. A paralogue comparison indicated that the soybean genes comprising the largest orthologue group are clustered in a 1.4 Mb region on chromosome 16 of soybean. Furthermore, a comparison with the undomesticated soybean (Glycine soja) revealed that there are hundreds of SNPs that are associated with putative soybean flowering genes and that there are structural variants that may affect the genes of the light-signalling and ambient-temperature pathways in soybean. Our study provides a framework for the soybean flowering pathway and insights into the relationship and evolution of flowering genes between a short-day soybean and the long-day plant, Arabidopsis. PMID:22679494

Induced pluripotency with endogenous and inducible genes

International Nuclear Information System (INIS)

Duinsbergen, Dirk; Eriksson, Malin; Hoen, Peter A.C. 't; Frisen, Jonas; Mikkers, Harald

2008-01-01

The recent discovery that two partly overlapping sets of four genes induce nuclear reprogramming of mouse and even human cells has opened up new possibilities for cell replacement therapies. Although the combination of genes that induce pluripotency differs to some extent, Oct4 and Sox2 appear to be a prerequisite. The introduction of four genes, several of which been linked with cancer, using retroviral approaches is however unlikely to be suitable for future clinical applications. Towards developing a safer reprogramming protocol, we investigated whether cell types that express one of the most critical reprogramming genes endogenously are predisposed to reprogramming. We show here that three of the original four pluripotency transcription factors (Oct4, Klf4 and c-Myc or MYCER TAM ) induced reprogramming of mouse neural stem (NS) cells exploiting endogenous SoxB1 protein levels in these cells. The reprogrammed neural stem cells differentiated into cells of each germ layer in vitro and in vivo, and contributed to mouse development in vivo. Thus a combinatorial approach taking advantage of endogenously expressed genes and inducible transgenes may contribute to the development of improved reprogramming protocols

Genetic Approaches to Study Meiosis and Meiosis-Specific Gene Expression in Saccharomyces cerevisiae.

Science.gov (United States)

Kassir, Yona; Stuart, David T

2017-01-01

The budding yeast Saccharomyces cerevisiae has a long history as a model organism for studies of meiosis and the cell cycle. The popularity of this yeast as a model is in large part due to the variety of genetic and cytological approaches that can be effectively performed with the cells. Cultures of the cells can be induced to synchronously progress through meiosis and sporulation allowing large-scale gene expression and biochemical studies to be performed. Additionally, the spore tetrads resulting from meiosis make it possible to characterize the haploid products of meiosis allowing investigation of meiotic recombination and chromosome segregation. Here we describe genetic methods for analysis progression of S. cerevisiae through meiosis and sporulation with an emphasis on strategies for the genetic analysis of regulators of meiosis-specific genes.

Gene Duplicability of Core Genes Is Highly Consistent across All Angiosperms[OPEN

Science.gov (United States)

Li, Zhen; Van de Peer, Yves; De Smet, Riet

2016-01-01

Gene duplication is an important mechanism for adding to genomic novelty. Hence, which genes undergo duplication and are preserved following duplication is an important question. It has been observed that gene duplicability, or the ability of genes to be retained following duplication, is a nonrandom process, with certain genes being more amenable to survive duplication events than others. Primarily, gene essentiality and the type of duplication (small-scale versus large-scale) have been shown in different species to influence the (long-term) survival of novel genes. However, an overarching view of “gene duplicability” is lacking, mainly due to the fact that previous studies usually focused on individual species and did not account for the influence of genomic context and the time of duplication. Here, we present a large-scale study in which we investigated duplicate retention for 9178 gene families shared between 37 flowering plant species, referred to as angiosperm core gene families. For most gene families, we observe a strikingly consistent pattern of gene duplicability across species, with gene families being either primarily single-copy or multicopy in all species. An intermediate class contains gene families that are often retained in duplicate for periods extending to tens of millions of years after whole-genome duplication, but ultimately appear to be largely restored to singleton status, suggesting that these genes may be dosage balance sensitive. The distinction between single-copy and multicopy gene families is reflected in their functional annotation, with single-copy genes being mainly involved in the maintenance of genome stability and organelle function and multicopy genes in signaling, transport, and metabolism. The intermediate class was overrepresented in regulatory genes, further suggesting that these represent putative dosage-balance-sensitive genes. PMID:26744215

Heterologous reconstitution of the intact geodin gene cluster in Aspergillus nidulans through a simple and versatile PCR based approach.

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Morten Thrane Nielsen

Full Text Available Fungal natural products are a rich resource for bioactive molecules. To fully exploit this potential it is necessary to link genes to metabolites. Genetic information for numerous putative biosynthetic pathways has become available in recent years through genome sequencing. However, the lack of solid methodology for genetic manipulation of most species severely hampers pathway characterization. Here we present a simple PCR based approach for heterologous reconstitution of intact gene clusters. Specifically, the putative gene cluster responsible for geodin production from Aspergillus terreus was transferred in a two step procedure to an expression platform in A. nidulans. The individual cluster fragments were generated by PCR and assembled via efficient USER fusion prior to transformation and integration via re-iterative gene targeting. A total of 13 open reading frames contained in 25 kb of DNA were successfully transferred between the two species enabling geodin synthesis in A. nidulans. Subsequently, functions of three genes in the cluster were validated by genetic and chemical analyses. Specifically, ATEG_08451 (gedC encodes a polyketide synthase, ATEG_08453 (gedR encodes a transcription factor responsible for activation of the geodin gene cluster and ATEG_08460 (gedL encodes a halogenase that catalyzes conversion of sulochrin to dihydrogeodin. We expect that our approach for transferring intact biosynthetic pathways to a fungus with a well developed genetic toolbox will be instrumental in characterizing the many exciting pathways for secondary metabolite production that are currently being uncovered by the fungal genome sequencing projects.

Discovery of Cationic Polymers for Non-viral Gene Delivery using Combinatorial Approaches

Science.gov (United States)

Barua, Sutapa; Ramos, James; Potta, Thrimoorthy; Taylor, David; Huang, Huang-Chiao; Montanez, Gabriela; Rege, Kaushal

2015-01-01

Gene therapy is an attractive treatment option for diseases of genetic origin, including several cancers and cardiovascular diseases. While viruses are effective vectors for delivering exogenous genes to cells, concerns related to insertional mutagenesis, immunogenicity, lack of tropism, decay and high production costs necessitate the discovery of non-viral methods. Significant efforts have been focused on cationic polymers as non-viral alternatives for gene delivery. Recent studies have employed combinatorial syntheses and parallel screening methods for enhancing the efficacy of gene delivery, biocompatibility of the delivery vehicle, and overcoming cellular level barriers as they relate to polymer-mediated transgene uptake, transport, transcription, and expression. This review summarizes and discusses recent advances in combinatorial syntheses and parallel screening of cationic polymer libraries for the discovery of efficient and safe gene delivery systems. PMID:21843141

Shrinkage Approach for Gene Expression Data Analysis

Czech Academy of Sciences Publication Activity Database

Haman, Jiří; Valenta, Zdeněk; Kalina, Jan

2013-01-01

Roč. 1, č. 1 (2013), s. 65-65 ISSN 1805-8698. [EFMI 2013 Special Topic Conference. 17.04.2013-19.04.2013, Prague] Institutional support: RVO:67985807 Keywords : shrinkage estimation * covariance matrix * high dimensional data * gene expression Subject RIV: IN - Informatics, Computer Science

Translational approach for gene therapy in epilepsy

DEFF Research Database (Denmark)

Ledri, Litsa Nikitidou; Melin, Esbjörn; Christiansen, Søren H.

2016-01-01

clinical trial for gene therapy of temporal lobe epilepsy was explored: We investigated (i) whether the post intrahippocampal kainate-induced status epilepticus (SE) model of chronic epilepsy in rats could be clinically relevant; and (ii) whether a translationally designed neuropeptide Y (NPY)/Y2 receptor...

Deduction of Novel Genes Potentially Involved in Osteoblasts of Rheumatoid Arthritis Using Next-Generation Sequencing and Bioinformatic Approaches

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Yi-Jen Chen

2017-11-01

Full Text Available The role of osteoblasts in peri-articular bone loss and bone erosion in rheumatoid arthritis (RA has gained much attention, and microRNAs are hypothesized to play critical roles in the regulation of osteoblast function in RA. The aim of this study is to explore novel microRNAs differentially expressed in RA osteoblasts and to identify genes potentially involved in the dysregulated bone homeostasis in RA. RNAs were extracted from cultured normal and RA osteoblasts for sequencing. Using the next generation sequencing and bioinformatics approaches, we identified 35 differentially expressed microRNAs and 13 differentially expressed genes with potential microRNA–mRNA interactions in RA osteoblasts. The 13 candidate genes were involved mainly in cell–matrix adhesion, as classified by the Gene Ontology. Two genes of interest identified from RA osteoblasts, A-kinase anchoring protein 12 (AKAP12 and leucin rich repeat containing 15 (LRRC15, were found to express more consistently in the related RA synovial tissue arrays in the Gene Expression Omnibus database, with the predicted interactions with miR-183-5p and miR-146a-5p, respectively. The Ingenuity Pathway Analysis identified AKAP12 as one of the genes involved in protein kinase A signaling and the function of chemotaxis, interconnecting with molecules related to neovascularization. The findings indicate new candidate genes as the potential indicators in evaluating therapies targeting chemotaxis and neovascularization to control joint destruction in RA.

A Novel Phytophthora sojae Resistance Rps12 Gene Mapped to a Genomic Region That Contains Several Rps Genes.

Science.gov (United States)

Sahoo, Dipak K; Abeysekara, Nilwala S; Cianzio, Silvia R; Robertson, Alison E; Bhattacharyya, Madan K

2017-01-01

Phytophthora sojae Kaufmann and Gerdemann, which causes Phytophthora root rot, is a widespread pathogen that limits soybean production worldwide. Development of Phytophthora resistant cultivars carrying Phytophthora resistance Rps genes is a cost-effective approach in controlling this disease. For this mapping study of a novel Rps gene, 290 recombinant inbred lines (RILs) (F7 families) were developed by crossing the P. sojae resistant cultivar PI399036 with the P. sojae susceptible AR2 line, and were phenotyped for responses to a mixture of three P. sojae isolates that overcome most of the known Rps genes. Of these 290 RILs, 130 were homozygous resistant, 12 heterzygous and segregating for Phytophthora resistance, and 148 were recessive homozygous and susceptible. From this population, 59 RILs homozygous for Phytophthora sojae resistance and 61 susceptible to a mixture of P. sojae isolates R17 and Val12-11 or P7074 that overcome resistance encoded by known Rps genes mapped to Chromosome 18 were selected for mapping novel Rps gene. A single gene accounted for the 1:1 segregation of resistance and susceptibility among the RILs. The gene encoding the Phytophthora resistance mapped to a 5.8 cM interval between the SSR markers BARCSOYSSR_18_1840 and Sat_064 located in the lower arm of Chromosome 18. The gene is mapped 2.2 cM proximal to the NBSRps4/6-like sequence that was reported to co-segregate with the Phytophthora resistance genes Rps4 and Rps6. The gene is mapped to a highly recombinogenic, gene-rich genomic region carrying several nucleotide binding site-leucine rich repeat (NBS-LRR)-like genes. We named this novel gene as Rps12, which is expected to be an invaluable resource in breeding soybeans for Phytophthora resistance.

A Novel Phytophthora sojae Resistance Rps12 Gene Mapped to a Genomic Region That Contains Several Rps Genes.

Directory of Open Access Journals (Sweden)

Dipak K Sahoo

Full Text Available Phytophthora sojae Kaufmann and Gerdemann, which causes Phytophthora root rot, is a widespread pathogen that limits soybean production worldwide. Development of Phytophthora resistant cultivars carrying Phytophthora resistance Rps genes is a cost-effective approach in controlling this disease. For this mapping study of a novel Rps gene, 290 recombinant inbred lines (RILs (F7 families were developed by crossing the P. sojae resistant cultivar PI399036 with the P. sojae susceptible AR2 line, and were phenotyped for responses to a mixture of three P. sojae isolates that overcome most of the known Rps genes. Of these 290 RILs, 130 were homozygous resistant, 12 heterzygous and segregating for Phytophthora resistance, and 148 were recessive homozygous and susceptible. From this population, 59 RILs homozygous for Phytophthora sojae resistance and 61 susceptible to a mixture of P. sojae isolates R17 and Val12-11 or P7074 that overcome resistance encoded by known Rps genes mapped to Chromosome 18 were selected for mapping novel Rps gene. A single gene accounted for the 1:1 segregation of resistance and susceptibility among the RILs. The gene encoding the Phytophthora resistance mapped to a 5.8 cM interval between the SSR markers BARCSOYSSR_18_1840 and Sat_064 located in the lower arm of Chromosome 18. The gene is mapped 2.2 cM proximal to the NBSRps4/6-like sequence that was reported to co-segregate with the Phytophthora resistance genes Rps4 and Rps6. The gene is mapped to a highly recombinogenic, gene-rich genomic region carrying several nucleotide binding site-leucine rich repeat (NBS-LRR-like genes. We named this novel gene as Rps12, which is expected to be an invaluable resource in breeding soybeans for Phytophthora resistance.

Inductive matrix completion for predicting gene-disease associations.

Science.gov (United States)

Natarajan, Nagarajan; Dhillon, Inderjit S

2014-06-15

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

Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario

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Onay Venus

2007-08-01

Full Text Available Abstract Background There is growing evidence that gene-gene interactions are ubiquitous in determining the susceptibility to common human diseases. The investigation of such gene-gene interactions presents new statistical challenges for studies with relatively small sample sizes as the number of potential interactions in the genome can be large. Breast cancer provides a useful paradigm to study genetically complex diseases because commonly occurring single nucleotide polymorphisms (SNPs may additively or synergistically disturb the system-wide communication of the cellular processes leading to cancer development. Methods In this study, we systematically studied SNP-SNP interactions among 19 SNPs from 18 key genes involved in major cancer pathways in a sample of 398 breast cancer cases and 372 controls from Ontario. We discuss the methodological issues associated with the detection of SNP-SNP interactions in this dataset by applying and comparing three commonly used methods: the logistic regression model, classification and regression trees (CART, and the multifactor dimensionality reduction (MDR method. Results Our analyses show evidence for several simple (two-way and complex (multi-way SNP-SNP interactions associated with breast cancer. For example, all three methods identified XPD-[Lys751Gln]*IL10-[G(-1082A] as the most significant two-way interaction. CART and MDR identified the same critical SNPs participating in complex interactions. Our results suggest that the use of multiple statistical approaches (or an integrated approach rather than a single methodology could be the best strategy to elucidate complex gene interactions that have generally very different patterns. Conclusion The strategy used here has the potential to identify complex biological relationships among breast cancer genes and processes. This will lead to the discovery of novel biological information, which will improve breast cancer risk management.

Human Gene Therapy: Genes without Frontiers?

Science.gov (United States)

Simon, Eric J.

2002-01-01

Describes the latest advancements and setbacks in human gene therapy to provide reference material for biology teachers to use in their science classes. Focuses on basic concepts such as recombinant DNA technology, and provides examples of human gene therapy such as severe combined immunodeficiency syndrome, familial hypercholesterolemia, and…

The approaches to mathematical modeling of recA, umuD genes expression in bacteria Escherichia coli after UV-irradiation

International Nuclear Information System (INIS)

Belov, O.V.

2006-01-01

The modern data of recA, umuD genes expression of the system of SOS-repair at classical object of radiation genetic researches - bacteria Escherichia coli, after ultraviolet irradiation are presented. Essentially a new method of analysis of SOS-genes expression is considered. It was shown that using this method it is possible to determine the character of induction of some SOS-genes more precisely. The possible approach to the mathematical description of SOS-response of cells by construction of the system of the differential equations is presented

Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS.

Science.gov (United States)

Pan, David Z; Garske, Kristina M; Alvarez, Marcus; Bhagat, Yash V; Boocock, James; Nikkola, Elina; Miao, Zong; Raulerson, Chelsea K; Cantor, Rita M; Civelek, Mete; Glastonbury, Craig A; Small, Kerrin S; Boehnke, Michael; Lusis, Aldons J; Sinsheimer, Janet S; Mohlke, Karen L; Laakso, Markku; Pajukanta, Päivi; Ko, Arthur

2018-04-17

Increased adiposity is a hallmark of obesity and overweight, which affect 2.2 billion people world-wide. Understanding the genetic and molecular mechanisms that underlie obesity-related phenotypes can help to improve treatment options and drug development. Here we perform promoter Capture Hi-C in human adipocytes to investigate interactions between gene promoters and distal elements as a transcription-regulating mechanism contributing to these phenotypes. We find that promoter-interacting elements in human adipocytes are enriched for adipose-related transcription factor motifs, such as PPARG and CEBPB, and contribute to heritability of cis-regulated gene expression. We further intersect these data with published genome-wide association studies for BMI and BMI-related metabolic traits to identify the genes that are under genetic cis regulation in human adipocytes via chromosomal interactions. This integrative genomics approach identifies four cis-eQTL-eGene relationships associated with BMI or obesity-related traits, including rs4776984 and MAP2K5, which we further confirm by EMSA, and highlights 38 additional candidate genes.

Learning gene networks under SNP perturbations using eQTL datasets.

Directory of Open Access Journals (Sweden)

Lingxue Zhang

2014-02-01

Full Text Available The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network

Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease

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Maria V. Fernández

2018-04-01

Full Text Available Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families (N = 1,235 with late-onset Alzheimer disease (LOAD. After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, and MAS1L as candidate genes for familial LOAD.

Identification and Analysis of Jasmonate Pathway Genes in Coffea canephora (Robusta Coffee) by In Silico Approach.

Science.gov (United States)

Bharathi, Kosaraju; Sreenath, H L

2017-07-01

bioinformatic approaches confirming the conserved nature of the pathway in coffee. The findings are useful to understand the defense mechanisms of C. canephora and coffee breeding in the long run. JA is a plant hormone that plays an important role in plant defense against insect pests. Genes coding for the 4 key enzymes involved in the production of JA viz., LOX, AOS, AOC and OPR were identified and analyzed in C. canephora (robusta coffee) by in silico approach. The study has confirmed the conserved nature of JA pathway in coffee; the findings are useful to further explore the defense mechanisms of coffee plants. Abbreviations used: C. canephora : Coffea canephora ; C. arabica : Coffea arabica ; JA: Jasmonic acid; CGH: Coffee Genome Hub; NCBI: National Centre for Biotechnology Information; BLAST: Basic Local Alignment Search Tool; A. thaliana : Arabidopsis thaliana ; LOX: Lipoxygenase, AOS: Allene oxide synthase; AOC: Allene oxide cyclase; OPR: 12 oxo phytodienoic reductase.

Identification of human disease genes from interactome network using graphlet interaction.

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Xiao-Dong Wang

Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

Science.gov (United States)

Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

2014-01-01

Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

Candidate genes in panic disorder

DEFF Research Database (Denmark)

Howe, A. S.; Buttenschön, Henriette N; Bani-Fatemi, A.

2016-01-01

The utilization of molecular genetics approaches in examination of panic disorder (PD) has implicated several variants as potential susceptibility factors for panicogenesis. However, the identification of robust PD susceptibility genes has been complicated by phenotypic diversity, underpowered...... association studies and ancestry-specific effects. In the present study, we performed a succinct review of case-control association studies published prior to April 2015. Meta-analyses were performed for candidate gene variants examined in at least three studies using the Cochrane Mantel-Haenszel fixed......-effect model. Secondary analyses were also performed to assess the influences of sex, agoraphobia co-morbidity and ancestry-specific effects on panicogenesis. Meta-analyses were performed on 23 variants in 20 PD candidate genes. Significant associations after correction for multiple testing were observed...

Gene Therapy with the Sleeping Beauty Transposon System.

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Kebriaei, Partow; Izsvák, Zsuzsanna; Narayanavari, Suneel A; Singh, Harjeet; Ivics, Zoltán

2017-11-01

The widespread clinical implementation of gene therapy requires the ability to stably integrate genetic information through gene transfer vectors in a safe, effective, and economical manner. The latest generation of Sleeping Beauty (SB) transposon vectors fulfills these requirements, and may overcome limitations associated with viral gene transfer vectors and transient nonviral gene delivery approaches that are prevalent in ongoing clinical trials. The SB system enables high-level stable gene transfer and sustained transgene expression in multiple primary human somatic cell types, thereby representing a highly attractive gene transfer strategy for clinical use. Here, we review the most important aspects of using SB for gene therapy, including vectorization as well as genomic integration features. We also illustrate the path to successful clinical implementation by highlighting the application of chimeric antigen receptor (CAR)-modified T cells in cancer immunotherapy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

OpenAIRE

Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

2009-01-01

Abstract Background Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori assumptions about the interactions, which all simulate the observed patterns. It is important to analyze the properties of the circuits. Findings We have analyzed the simulated gene expression ...

A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes.

Directory of Open Access Journals (Sweden)

Simone de Jong

Full Text Available Despite large-scale genome-wide association studies (GWAS, the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1, is located in, and regulated by the major histocompatibility (MHC complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network.

A Probabilistic Genome-Wide Gene Reading Frame Sequence Model

DEFF Research Database (Denmark)

Have, Christian Theil; Mørk, Søren

We introduce a new type of probabilistic sequence model, that model the sequential composition of reading frames of genes in a genome. Our approach extends gene finders with a model of the sequential composition of genes at the genome-level -- effectively producing a sequential genome annotation...... as output. The model can be used to obtain the most probable genome annotation based on a combination of i: a gene finder score of each gene candidate and ii: the sequence of the reading frames of gene candidates through a genome. The model --- as well as a higher order variant --- is developed and tested...... and are evaluated by the effect on prediction performance. Since bacterial gene finding to a large extent is a solved problem it forms an ideal proving ground for evaluating the explicit modeling of larger scale gene sequence composition of genomes. We conclude that the sequential composition of gene reading frames...

A metaheuristic optimization framework for informative gene selection

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Kaberi Das

Full Text Available This paper presents a metaheuristic framework using Harmony Search (HS with Genetic Algorithm (GA for gene selection. The internal architecture of the proposed model broadly works in two phases, in the first phase, the model allows the hybridization of HS with GA to compute and evaluate the fitness of the randomly selected solutions of binary strings and then HS ranks the solutions in descending order of their fitness. In the second phase, the offsprings are generated using crossover and mutation operations of GA and finally, those offsprings were selected for the next generation whose fitness value is more than their parents evaluated by SVM classifier. The accuracy of the final gene subsets obtained from this model has been evaluated using SVM classifiers. The merit of this approach is analyzed by experimental results on five benchmark datasets and the results showed an impressive accuracy over existing feature selection approaches. The occurrence of gene subsets selected from this model have also been computed and the most often selected gene subsets with the probability of [0.1–0.9] have been chosen as optimal sets of informative genes. Finally, the performance of those selected informative gene subsets have been measured and established through probabilistic measures. Keywords: Gene Selection, Metaheuristic, Harmony Search Algorithm, Genetic Algorithm, SVM

Vertebrate gene predictions and the problem of large genes

DEFF Research Database (Denmark)

Wang, Jun; Li, ShengTing; Zhang, Yong

2003-01-01

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

Inferring gene expression dynamics via functional regression analysis

Directory of Open Access Journals (Sweden)

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.

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

Directory of Open Access Journals (Sweden)

Pereira Fernando CN

2008-10-01

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