WorldWideScience

Sample records for gene expression networks

  1. Inferring gene networks from discrete expression data

    KAUST Repository

    Zhang, L.

    2013-07-18

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

  2. Network Completion for Static Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Natsu Nakajima

    2014-01-01

    Full Text Available We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network completion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent with the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we present a new method for network completion using dynamic programming and least-squares fitting. This method can find an optimal solution in polynomial time if the maximum indegree of the network is bounded by a constant. We evaluate the effectiveness of our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method can distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal gene expression data.

  3. Integration of biological networks and gene expression data using Cytoscape

    DEFF Research Database (Denmark)

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

    2007-01-01

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

  4. Cancer classification based on gene expression using neural networks.

    Science.gov (United States)

    Hu, H P; Niu, Z J; Bai, Y P; Tan, X H

    2015-12-21

    Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.

  5. Gene Expression Network Reconstruction by LEP Method Using Microarray Data

    Directory of Open Access Journals (Sweden)

    Na You

    2012-01-01

    Full Text Available Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix. Due to the high dimensionality and sparsity, we utilize the LEP method to estimate it in this paper. Compared to the existing methods, the LEP reaches the highest PPV with the sensitivity controlled at the satisfactory level. A set of gene expression data from the HapMap project is analyzed for illustration.

  6. Annotation of gene function in citrus using gene expression information and co-expression networks

    OpenAIRE

    Wong, Darren CJ; Sweetman, Crystal; Ford, Christopher M.

    2014-01-01

    Background The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world’s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a “guilt-by-association” principle whereby genes encoding proteins involved in similar and/or related bi...

  7. Annotation of gene function in citrus using gene expression information and co-expression networks

    OpenAIRE

    Wong, Darren CJ; Sweetman, Crystal; Ford, Christopher M.

    2014-01-01

    Background The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world’s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a “guilt-by-association” principle whereby genes encoding proteins involved in similar and/or related bi...

  8. Annotation of gene function in citrus using gene expression information and co-expression networks.

    Science.gov (United States)

    Wong, Darren C J; Sweetman, Crystal; Ford, Christopher M

    2014-07-15

    The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world's most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a "guilt-by-association" principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed. We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit. Integration of citrus gene co-expression networks, functional enrichment analysis and gene

  9. Brief isoflurane anaesthesia affects differential gene expression, gene ontology and gene networks in rat brain.

    Science.gov (United States)

    Lowes, Damon A; Galley, Helen F; Moura, Alessandro P S; Webster, Nigel R

    2017-01-15

    Much is still unknown about the mechanisms of effects of even brief anaesthesia on the brain and previous studies have simply compared differential expression profiles with and without anaesthesia. We hypothesised that network analysis, in addition to the traditional differential gene expression and ontology analysis, would enable identification of the effects of anaesthesia on interactions between genes. Rats (n=10 per group) were randomised to anaesthesia with isoflurane in oxygen or oxygen only for 15min, and 6h later brains were removed. Differential gene expression and gene ontology analysis of microarray data was performed. Standard clustering techniques and principal component analysis with Bayesian rules were used along with social network analysis methods, to quantitatively model and describe the gene networks. Anaesthesia had marked effects on genes in the brain with differential regulation of 416 probe sets by at least 2 fold. Gene ontology analysis showed 23 genes were functionally related to the anaesthesia and of these, 12 were involved with neurotransmitter release, transport and secretion. Gene network analysis revealed much greater connectivity in genes from brains from anaesthetised rats compared to controls. Other importance measures were also altered after anaesthesia; median [range] closeness centrality (shortest path) was lower in anaesthetized animals (0.07 [0-0.30]) than controls (0.39 [0.30-0.53], pgenes after anaesthesia and suggests future targets for investigation. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Network Security via Biometric Recognition of Patterns of Gene Expression

    Science.gov (United States)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time expression and assay of gene expression products.

  11. Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networks

    Directory of Open Access Journals (Sweden)

    Chris eGaiteri

    2011-08-01

    Full Text Available The structure of gene coexpression networks reflects the activation and interaction of multiple cellular systems. Since the pathology of neuropsychiatric disorders is influenced by diverse cellular systems and pathways, we investigated gene coexpression networks in major depression, and searched for putative unifying themes in network connectivity across neuropsychiatric disorders. Specifically, based on the prevalence of the lethality-centrality relationship in disease-related networks, we hypothesized that network changes between control and major depression-related networks would be centered around coexpression hubs, and secondly, that differentially expressed (DE genes would have a characteristic position and connectivity level in those networks. Mathematically, the first hypothesis tests the relationship of differential coexpression to network connectivity, while the second hybrid expression-and-network hypothesis tests the relationship of differential expression to network connectivity. To answer these questions about the potential interaction of coexpression network structure with differential expression, we utilized all available human post-mortem depression-related datasets appropriate for coexpression analysis, which spanned different microarray platforms, cohorts, and brain regions. Similar studies were also performed in an animal model of depression and in schizophrenia and bipolar disorder microarray datasets. We now provide results which consistently support (1 that genes assemble into small-world and scale-free networks in control subjects, (2 that this efficient network topology is largely resilient to changes in depressed subjects, and (3 that DE genes are positioned on the periphery of coexpression networks. Similar results were observed in a mouse model of depression, and in selected bipolar- and schizophrenia-related networks. Finally, we show that baseline expression variability contributes to the propensity of genes to be

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

    DEFF Research Database (Denmark)

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

    2010-01-01

    of spurious information along the network are avoided. The proposed inference procedure is based on the minimization of the Bayesian Information Criterion (BIC) in the class of decomposable graphical models. This class of models can be used to represent complex relationships and has suitable properties...... construct a compact representation of the co-expression network that allows to identify the regions with high concentration of differentially expressed genes. It is argued that differentially expressed genes located in highly interconnected regions of the co-expression network are less informative than...

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

    Science.gov (United States)

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

    2015-06-01

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

  14. MicroRNAs and deregulated gene expression networks in neurodegeneration.

    Science.gov (United States)

    Sonntag, Kai-Christian

    2010-06-18

    Neurodegeneration is characterized by the progressive loss of neuronal cell types in the nervous system. Although the main cause of cell dysfunction and death in many neurodegenerative diseases is not known, there is increasing evidence that their demise is a result of a combination of genetic and environmental factors which affect key signaling pathways in cell function. This view is supported by recent observations that disease-compromised cells in late-stage neurodegeneration exhibit profound dysregulation of gene expression. MicroRNAs (miRNAs) introduce a novel concept of regulatory control over gene expression and there is increasing evidence that they play a profound role in neuronal cell identity as well as multiple aspects of disease pathogenesis. Here, we review the molecular properties of brain cells derived from patients with neurodegenerative diseases, and discuss how deregulated miRNA/mRNA expression networks could be a mechanism in neurodegeneration. In addition, we emphasize that the dysfunction of these regulatory networks might overlap between different cell systems and suggest that miRNA functions might be common between neurodegeneration and other disease entities.

  15. Using gene expression programming to infer gene regulatory networks from time-series data.

    Science.gov (United States)

    Zhang, Yongqing; Pu, Yifei; Zhang, Haisen; Su, Yabo; Zhang, Lifang; Zhou, Jiliu

    2013-12-01

    Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. How molecular competition influences fluxes in gene expression networks.

    Directory of Open Access Journals (Sweden)

    Dirk De Vos

    Full Text Available Often, in living cells different molecular species compete for binding to the same molecular target. Typical examples are the competition of genes for the transcription machinery or the competition of mRNAs for the translation machinery. Here we show that such systems have specific regulatory features and how they can be analysed. We derive a theory for molecular competition in parallel reaction networks. Analytical expressions for the response of network fluxes to changes in the total competitor and common target pools indicate the precise conditions for ultrasensitivity and intuitive rules for competitor strength. The calculations are based on measurable concentrations of the competitor-target complexes. We show that kinetic parameters, which are usually tedious to determine, are not required in the calculations. Given their simplicity, the obtained equations are easily applied to networks of any dimension. The new theory is illustrated for competing sigma factors in bacterial transcription and for a genome-wide network of yeast mRNAs competing for ribosomes. We conclude that molecular competition can drastically influence the network fluxes and lead to negative response coefficients and ultrasensitivity. Competitors that bind a large fraction of the target, like bacterial σ(70, tend to influence competing pathways strongly. The less a competitor is saturated by the target, the more sensitive it is to changes in the concentration of the target, as well as to other competitors. As a consequence, most of the mRNAs in yeast turn out to respond ultrasensitively to changes in ribosome concentration. Finally, applying the theory to a genome-wide dataset we observe that high and low response mRNAs exhibit distinct Gene Ontology profiles.

  17. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

    Directory of Open Access Journals (Sweden)

    Gao Haichun

    2007-08-01

    Full Text Available Abstract Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT, which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under

  18. Modeling Gene Networks in Saccharomyces cerevisiae Based on Gene Expression Profiles

    Directory of Open Access Journals (Sweden)

    Yulin Zhang

    2015-01-01

    Full Text Available Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network in Saccharomyces cerevisiae can differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function.

  19. Functional analysis of prognostic gene expression network genes in metastatic breast cancer models.

    Directory of Open Access Journals (Sweden)

    Thomas R Geiger

    Full Text Available Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+ breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease.

  20. Spectral analysis of Gene co-expression network of Zebrafish

    CERN Document Server

    Jalan, S; Bhojwani, J; Li, B; Zhang, L; Lan, S H; Gong, Z

    2012-01-01

    We analyze the gene expression data of Zebrafish under the combined framework of complex networks and random matrix theory. The nearest neighbor spacing distribution of the corresponding matrix spectra follows random matrix predictions of Gaussian orthogonal statistics. Based on the eigenvector analysis we can divide the spectra into two parts, first part for which the eigenvector localization properties match with the random matrix theory predictions, and the second part for which they show deviation from the theory and hence are useful to understand the system dependent properties. Spectra with the localized eigenvectors can be characterized into three groups based on the eigenvalues. We explore the position of localized nodes from these different categories. Using an overlap measure, we find that the top contributing nodes in the different groups carry distinguished structural features. Furthermore, the top contributing nodes of the different localized eigenvectors corresponding to the lower eigenvalue reg...

  1. A general co-expression network-based approach to gene expression analysis: comparison and applications

    Directory of Open Access Journals (Sweden)

    Zhang Weixiong

    2010-02-01

    Full Text Available Abstract Background Co-expression network-based approaches have become popular in analyzing microarray data, such as for detecting functional gene modules. However, co-expression networks are often constructed by ad hoc methods, and network-based analyses have not been shown to outperform the conventional cluster analyses, partially due to the lack of an unbiased evaluation metric. Results Here, we develop a general co-expression network-based approach for analyzing both genes and samples in microarray data. Our approach consists of a simple but robust rank-based network construction method, a parameter-free module discovery algorithm and a novel reference network-based metric for module evaluation. We report some interesting topological properties of rank-based co-expression networks that are very different from that of value-based networks in the literature. Using a large set of synthetic and real microarray data, we demonstrate the superior performance of our approach over several popular existing algorithms. Applications of our approach to yeast, Arabidopsis and human cancer microarray data reveal many interesting modules, including a fatal subtype of lymphoma and a gene module regulating yeast telomere integrity, which were missed by the existing methods. Conclusions We demonstrated that our novel approach is very effective in discovering the modular structures in microarray data, both for genes and for samples. As the method is essentially parameter-free, it may be applied to large data sets where the number of clusters is difficult to estimate. The method is also very general and can be applied to other types of data. A MATLAB implementation of our algorithm can be downloaded from http://cs.utsa.edu/~jruan/Software.html.

  2. Learning gene regulatory networks from gene expression data using weighted consensus

    KAUST Repository

    Fujii, Chisato

    2016-08-25

    An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

  3. 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.

  4. FUMET: A fuzzy network module extraction technique for gene expression data

    Indian Academy of Sciences (India)

    Priyakshi Mahanta; Hasin Afzal Ahmed; Dhruba Kumar Bhattacharyya; Ashish Ghosh

    2014-06-01

    Construction of co-expression network and extraction of network modules have been an appealing area of bioinformatics research. This article presents a co-expression network construction and a biologically relevant network module extraction technique based on fuzzy set theoretic approach. The technique is able to handle both positive and negative correlations among genes. The constructed network for some benchmark gene expression datasets have been validated using topological internal and external measures. The effectiveness of network module extraction technique has been established in terms of well-known p-value, Q-value and topological statistics.

  5. Inferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancer.

    Directory of Open Access Journals (Sweden)

    Xionghui Zhou

    Full Text Available Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer. In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis. Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene

  6. Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.

    Science.gov (United States)

    Gao, Chuan; McDowell, Ian C; Zhao, Shiwen; Brown, Christopher D; Engelhardt, Barbara E

    2016-07-01

    Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.

  7. Identification of therapeutic targets for Alzheimer's disease via differentially expressed gene and weighted gene co-expression network analyses.

    Science.gov (United States)

    Jia, Yujie; Nie, Kun; Li, Jing; Liang, Xinyue; Zhang, Xuezhu

    2016-11-01

    In order to investigate the pathogenic targets and associated biological process of Alzheimer's disease in the present study, mRNA expression profiles (GSE28146) and microRNA (miRNA) expression profiles (GSE16759) were downloaded from the Gene Expression Omnibus database. In GSE28146, eight control samples, and Alzheimer's disease samples comprising seven incipient, eight moderate, seven severe Alzheimer's disease samples, were included. The Affy package in R was used for background correction and normalization of the raw microarray data. The differentially expressed genes (DEGs) and differentially expressed miRNAs were identified using the Limma package. In addition, mRNAs were clustered using weighted gene correlation network analysis, and modules found to be significantly associated with the stages of Alzheimer's disease were screened out. The Database for Annotation, Visualization, and Integrated Discovery was used to perform Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The target genes of the differentially expressed miRNAs were identified using the miRWalk database. Compared with the control samples, 175,59 genes and 90 DEGs were identified in the incipient, moderate and severe Alzheimer's disease samples, respectively. A module, which contained 1,592 genes was found to be closely associated with the stage of Alzheimer's disease and biological processes. In addition, pathways associated with Alzheimer's disease and other neurological diseases were found to be enriched in those genes. A total of 139 overlapped genes were identified between those genes and the DEGs in the three groups. From the miRNA expression profiles, 189 miRNAs were found differentially expressed in the samples from patients with Alzheimer's disease and 1,647 target genes were obtained. In addition, five overlapped genes were identified between those 1,647 target genes and the 139 genes, and these genes may be important pathogenic targets for Alzheimer

  8. Reshaping of global gene expression networks and sex‐biased gene expression by integration of a young gene

    National Research Council Canada - National Science Library

    Chen, Sidi; Ni, Xiaochun; Krinsky, Benjamin H; Zhang, Yong E; Vibranovski, Maria D; White, Kevin P; Long, Manyuan

    2012-01-01

    ...‐biased gene expression in Drosophila . This 4–6 million‐year‐old factor, named Zeus for its role in male fecundity, originated through retroposition of a highly conserved housekeeping gene, Caf40...

  9. 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

  10. The R package FANet: sparse factor analysis model for high dimensional gene co-expression networks

    OpenAIRE

    Blum, Anne; Houee-Bigot, Magalie; Lagarrigue, Sandrine; Causeur, David

    2014-01-01

    Inference on gene regulatory networks from high-throughput expression data turns out to be one of the main current challenges in systems biology. Such interaction networks are very insightful for the deep understanding of biological relationships between genes. In particular, a functional characterization of gene modules of highly interacting genes enables the identification of biological processes underlying complex traits as diseases. Inference on this dependence structure shall...

  11. Understanding gene expression in coronary artery disease through global profiling, network analysis and independent validation of key candidate genes

    Indian Academy of Sciences (India)

    Prathima Arvind; Shanker Jayashree; Srikarthika Jambunathan; Jiny Nair; Vijay V. Kakkar

    2015-12-01

    Molecular mechanism underlying the patho-physiology of coronary artery disease (CAD) is complex. We used global expression profiling combined with analysis of biological network to dissect out potential genes and pathways associated with CAD in a representative case–control Asian Indian cohort. We initially performed blood transcriptomics profiling in 20 subjects, including 10 CAD patients and 10 healthy controls on the Agilent microarray platform. Data was analysed with Gene Spring Gx12.5, followed by network analysis using David v 6.7 and Reactome databases. The most significant differentially expressed genes from microarray were independently validated by real time PCR in 97 cases and 97 controls. A total of 190 gene transcripts showed significant differential expression (fold change > 2, P < 0.05) between the cases and the controls of which 142 genes were upregulated and 48 genes were downregulated. Genes associated with inflammation, immune response, cell regula- tion, proliferation and apoptotic pathways were enriched, while inflammatory and immune response genes were displayed as hubs in the network, having greater number of interactions with the neighbouring genes. Expression of 1/2/3, 8, 1, 2, 69, , , 4, 42, 58, and 42 genes were independently validated; 1/2/3 and 8 showed >8-fold higher expression in cases relative to the controls implying their important role in CAD. In conclusion, global gene expression profiling combined with network analysis can help in identifying key genes and pathways for CAD.

  12. The impact of gene expression variation on the robustness and evolvability of a developmental gene regulatory network.

    Directory of Open Access Journals (Sweden)

    David A Garfield

    2013-10-01

    Full Text Available Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear, allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.

  13. Building gene co-expression networks using transcriptomics data for systems biology investigations

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Watson-Haigh, Nathan S.

    2012-01-01

    Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four...

  14. Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

    Science.gov (United States)

    Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C

    2013-01-01

    The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

  15. Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

    Directory of Open Access Journals (Sweden)

    Scott M Gibson

    Full Text Available The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT, is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens, rice (Oryza sativa and budding yeast (Saccharomyces cerevisiae. We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

  16. Bagging statistical network inference from large-scale gene expression data.

    OpenAIRE

    Ricardo de Matos Simoes; Frank Emmert-Streib

    2012-01-01

    Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-sc...

  17. Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

    Directory of Open Access Journals (Sweden)

    Benjamin A Logsdon

    Full Text Available Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL, which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1, and genes involved in endocytosis (RCY1, the spindle checkpoint (BUB2, sulfonate catabolism (JLP1, and cell-cell communication (PRM7. Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.

  18. Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

    Directory of Open Access Journals (Sweden)

    2009-03-01

    Full Text Available Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

  19. Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Lodowski Kerrie H

    2009-01-01

    Full Text Available Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

  20. Incorporating gene co-expression network in identification of cancer prognosis markers

    Directory of Open Access Journals (Sweden)

    Li Yang

    2010-05-01

    Full Text Available Abstract Background Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them. Results We adopt the weighted co-expression network to describe the interplay among genes. Although there are several different ways of defining gene networks, the weighted co-expression network may be preferred because of its computational simplicity, satisfactory empirical performance, and because it does not demand additional biological experiments. For cancer prognosis studies with gene expression measurements, we propose a new marker selection method that can properly incorporate the network connectivity of genes. We analyze six prognosis studies on breast cancer and lymphoma. We find that the proposed approach can identify genes that are significantly different from those using alternatives. We search published literature and find that genes identified using the proposed approach are biologically meaningful. In addition, they have better prediction performance and reproducibility than genes identified using alternatives. Conclusions The network contains important information on the functionality of genes. Incorporating the network structure can improve cancer marker identification.

  1. Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers.

    Science.gov (United States)

    Pramparo, Tiziano; Lombardo, Michael V; Campbell, Kathleen; Barnes, Cynthia Carter; Marinero, Steven; Solso, Stephanie; Young, Julia; Mayo, Maisi; Dale, Anders; Ahrens-Barbeau, Clelia; Murray, Sarah S; Lopez, Linda; Lewis, Nathan; Pierce, Karen; Courchesne, Eric

    2015-12-14

    Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi-tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment.

  2. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models.

    Science.gov (United States)

    Hirose, Osamu; Yoshida, Ryo; Imoto, Seiya; Yamaguchi, Rui; Higuchi, Tomoyuki; Charnock-Jones, D Stephen; Print, Cristin; Miyano, Satoru

    2008-04-01

    Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway. In this article, we present a novel approach based on the state space model to identify the transcriptional modules and module-based gene networks simultaneously. The state space model has the potential to infer large-scale gene networks, e.g. of order 10(3), from time-course gene expression profiles. Particularly, we succeeded in the identification of a cell cycle system by using the gene expression profiles of Saccharomyces cerevisiae in which the length of the time-course and number of genes were 24 and 4382, respectively. However, when analysing shorter time-course data, e.g. of length 10 or less, the parameter estimations of the state space model often fail due to overfitting. To extend the applicability of the state space model, we provide an approach to use the technical replicates of gene expression profiles, which are often measured in duplicate or triplicate. The use of technical replicates is important for achieving highly-efficient inferences of gene networks with short time-course data. The potential of the proposed method has been demonstrated through the time-course analysis of the gene expression profiles of human umbilical vein endothelial cells (HUVECs) undergoing growth factor deprivation-induced apoptosis. Supplementary Information and the software (TRANS-MNET) are available at http://daweb.ism.ac.jp/~yoshidar/software/ssm/.

  3. Variance of gene expression identifies altered network constraints in neurological disease.

    Directory of Open Access Journals (Sweden)

    Jessica C Mar

    2011-08-01

    Full Text Available Gene expression analysis has become a ubiquitous tool for studying a wide range of human diseases. In a typical analysis we compare distinct phenotypic groups and attempt to identify genes that are, on average, significantly different between them. Here we describe an innovative approach to the analysis of gene expression data, one that identifies differences in expression variance between groups as an informative metric of the group phenotype. We find that genes with different expression variance profiles are not randomly distributed across cell signaling networks. Genes with low-expression variance, or higher constraint, are significantly more connected to other network members and tend to function as core members of signal transduction pathways. Genes with higher expression variance have fewer network connections and also tend to sit on the periphery of the cell. Using neural stem cells derived from patients suffering from Schizophrenia (SZ, Parkinson's disease (PD, and a healthy control group, we find marked differences in expression variance in cell signaling pathways that shed new light on potential mechanisms associated with these diverse neurological disorders. In particular, we find that expression variance of core networks in the SZ patient group was considerably constrained, while in contrast the PD patient group demonstrated much greater variance than expected. One hypothesis is that diminished variance in SZ patients corresponds to an increased degree of constraint in these pathways and a corresponding reduction in robustness of the stem cell networks. These results underscore the role that variation plays in biological systems and suggest that analysis of expression variance is far more important in disease than previously recognized. Furthermore, modeling patterns of variability in gene expression could fundamentally alter the way in which we think about how cellular networks are affected by disease processes.

  4. Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

    Science.gov (United States)

    Kibinge, Nelson; Ono, Naoaki; Horie, Masafumi; Sato, Tetsuo; Sugiura, Tadao; Altaf-Ul-Amin, Md; Saito, Akira; Kanaya, Shigehiko

    2016-06-01

    Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Gene expression, signal transduction pathways and functional networks associated with growth of sporadic vestibular schwannomas

    DEFF Research Database (Denmark)

    Sass, Hjalte Christian Reeberg; Borup, Rehannah; Alanin, Mikkel

    2017-01-01

    The objective of this study was to determine global gene expression in relation to Vestibular schwannomas (VS) growth rate and to identify signal transduction pathways and functional molecular networks associated with growth. Repeated magnetic resonance imaging (MRI) prior to surgery determined...... of signal transduction pathways and functional molecular networks associated with tumor growth. In total 109 genes were deregulated in relation to tumor growth rate. Genes associated with apoptosis, growth and cell proliferation were deregulated. Gene ontology included regulation of the cell cycle, cell...... differentiation and proliferation, among other functions. Fourteen pathways were associated with tumor growth. Five functional molecular networks were generated. This first study on global gene expression in relation to vestibular schwannoma growth rate identified several genes, signal transduction pathways...

  6. Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors

    Directory of Open Access Journals (Sweden)

    Frank Emmert-Streib

    2013-02-01

    Full Text Available The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I observational gene expression data: normal environmental condition, (II interventional gene expression data: growth in rich media, (III interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

  7. Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty.

    Science.gov (United States)

    Grieb, Melanie; Burkovski, Andre; Sträng, J Eric; Kraus, Johann M; Groß, Alexander; Palm, Günther; Kühl, Michael; Kestler, Hans A

    2015-01-01

    Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene's state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

  8. Targeting c-Myc-activated genes with a correlation method: Detection of global changes in large gene expression network dynamics

    Science.gov (United States)

    Remondini, D.; O'Connell, B.; Intrator, N.; Sedivy, J. M.; Neretti, N.; Castellani, G. C.; Cooper, L. N.

    2005-01-01

    This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a linear Markov model. In particular, the network properties strongly depend on the exact time sequence relationships between genes and are destroyed by random temporal data shuffling. We discuss in detail the problem of finding targets of the c-myc protooncogene, which encodes a transcriptional regulator whose inappropriate expression has been correlated with a wide array of malignancies. The data used for network construction are a time series of gene expression, collected by microarray analysis of a rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein. We show that the correlation-based model can establish a clear relationship between network structure and the cascade of c-myc-activated genes. PMID:15867157

  9. Network statistics of genetically-driven gene co-expression modules in mouse crosses

    Directory of Open Access Journals (Sweden)

    Marie-Pier eScott-Boyer

    2013-12-01

    Full Text Available In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS. For six out of the 7 networks, we found that linkage to module QTLs (mQTLs could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as genetically-driven had network statistic properties (density, centralization and heterogeneity that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

  10. Gene co-expression network analysis in Rhodobacter capsulatus and application to comparative expression analysis of Rhodobacter sphaeroides

    Energy Technology Data Exchange (ETDEWEB)

    Pena-Castillo, Lourdes; Mercer, Ryan; Gurinovich, Anastasia; Callister, Stephen J.; Wright, Aaron T.; Westbye, Alexander; Beatty, J. T.; Lang, Andrew S.

    2014-08-28

    The genus Rhodobacter contains purple nonsulfur bacteria found mostly in freshwater environments. Representative strains of two Rhodobacter species, R. capsulatus and R. sphaeroides, have had their genomes fully sequenced and both have been the subject of transcriptional profiling studies. Gene co-expression networks can be used to identify modules of genes with similar expression profiles. Functional analysis of gene modules can then associate co-expressed genes with biological pathways, and network statistics can determine the degree of module preservation in related networks. In this paper, we constructed an R. capsulatus gene co-expression network, performed functional analysis of identified gene modules, and investigated preservation of these modules in R. capsulatus proteomics data and in R. sphaeroides transcriptomics data. Results: The analysis identified 40 gene co-expression modules in R. capsulatus. Investigation of the module gene contents and expression profiles revealed patterns that were validated based on previous studies supporting the biological relevance of these modules. We identified two R. capsulatus gene modules preserved in the protein abundance data. We also identified several gene modules preserved between both Rhodobacter species, which indicate that these cellular processes are conserved between the species and are candidates for functional information transfer between species. Many gene modules were non-preserved, providing insight into processes that differentiate the two species. In addition, using Local Network Similarity (LNS), a recently proposed metric for expression divergence, we assessed the expression conservation of between-species pairs of orthologs, and within-species gene-protein expression profiles. Conclusions: Our analyses provide new sources of information for functional annotation in R. capsulatus because uncharacterized genes in modules are now connected with groups of genes that constitute a joint functional

  11. Intracompartmental and intercompartmental transcriptional networks coordinate the expression of genes for organellar functions.

    Science.gov (United States)

    Leister, Dario; Wang, Xi; Haberer, Georg; Mayer, Klaus F X; Kleine, Tatjana

    2011-09-01

    Genes for mitochondrial and chloroplast proteins are distributed between the nuclear and organellar genomes. Organelle biogenesis and metabolism, therefore, require appropriate coordination of gene expression in the different compartments to ensure efficient synthesis of essential multiprotein complexes of mixed genetic origin. Whereas organelle-to-nucleus signaling influences nuclear gene expression at the transcriptional level, organellar gene expression (OGE) is thought to be primarily regulated posttranscriptionally. Here, we show that intracompartmental and intercompartmental transcriptional networks coordinate the expression of genes for organellar functions. Nearly 1,300 ATH1 microarray-based transcriptional profiles of nuclear and organellar genes for mitochondrial and chloroplast proteins in the model plant Arabidopsis (Arabidopsis thaliana) were analyzed. The activity of genes involved in organellar energy production (OEP) or OGE in each of the organelles and in the nucleus is highly coordinated. Intracompartmental networks that link the OEP and OGE gene sets serve to synchronize the expression of nucleus- and organelle-encoded proteins. At a higher regulatory level, coexpression of organellar and nuclear OEP/OGE genes typically modulates chloroplast functions but affects mitochondria only when chloroplast functions are perturbed. Under conditions that induce energy shortage, the intercompartmental coregulation of photosynthesis genes can even override intracompartmental networks. We conclude that dynamic intracompartmental and intercompartmental transcriptional networks for OEP and OGE genes adjust the activity of organelles in response to the cellular energy state and environmental stresses, and we identify candidate cis-elements involved in the transcriptional coregulation of nuclear genes. Regarding the transcriptional regulation of chloroplast genes, novel tentative target genes of σ factors are identified.

  12. Elucidating gene function and function evolution through comparison of co-expression networks in plants

    Directory of Open Access Journals (Sweden)

    Marek eMutwil

    2014-08-01

    Full Text Available The analysis of gene expression data has shown that transcriptionally coordinated (co-expressed genes are often functionally related, enabling scientists to use expression data in gene function prediction. This Focused Review discusses our original paper (Large-scale co-expression approach to dissect secondary cell wall formation across plant species, Frontiers in Plant Science 2:23. In this paper we applied cross-species analysis to co-expression networks of genes involved in cellulose biosynthesis. We show that the co-expression networks from different species are highly similar, indicating that whole biological pathways are conserved across species. This finding has two important implications. First, the analysis can transfer gene function annotation from well-studied plants, such as Arabidopsis, to other, uncharacterized plant species. As the analysis finds genes that have similar sequence and similar expression pattern across different organisms, functionally equivalent genes can be identified. Second, since co-expression analyses are often noisy, a comparative analysis should have higher performance, as parts of co-expression networks that are conserved are more likely to be functionally relevant. In this Focused Review, we outline the comparative analysis done in the original paper and comment on the recent advances and approaches that allow comparative analyses of co-function networks. We hypothesize that, in comparison to simple co-expression analysis, comparative analysis would yield more accurate gene function predictions. Finally, by combining comparative analysis with genomic information of green plants, we propose a possible composition of cellulose biosynthesis machinery during earlier stages of plant evolution.

  13. PINTA: a web server for network-based gene prioritization from expression data

    DEFF Research Database (Denmark)

    Nitsch, Daniela; Tranchevent, Léon-Charles; Goncalves, Joana P.

    2011-01-01

    network. Our strategy is meant for biological and medical researchers aiming at identifying novel disease genes using disease specific expression data. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization...... and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed...

  14. Statistical inference and reverse engineering of gene regulatory networks from observational expression data.

    Science.gov (United States)

    Emmert-Streib, Frank; Glazko, Galina V; Altay, Gökmen; de Matos Simoes, Ricardo

    2012-01-01

    In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms.

  15. The Relationship between Gene Network Structure and Expression Variation among Individuals and Species.

    Directory of Open Access Journals (Sweden)

    Karen E Sears

    2015-08-01

    Full Text Available Variation among individuals is a prerequisite of evolution by natural selection. As such, identifying the origins of variation is a fundamental goal of biology. We investigated the link between gene interactions and variation in gene expression among individuals and species using the mammalian limb as a model system. We first built interaction networks for key genes regulating early (outgrowth; E9.5-11 and late (expansion and elongation; E11-13 limb development in mouse. This resulted in an Early (ESN and Late (LSN Stage Network. Computational perturbations of these networks suggest that the ESN is more robust. We then quantified levels of the same key genes among mouse individuals and found that they vary less at earlier limb stages and that variation in gene expression is heritable. Finally, we quantified variation in gene expression levels among four mammals with divergent limbs (bat, opossum, mouse and pig and found that levels vary less among species at earlier limb stages. We also found that variation in gene expression levels among individuals and species are correlated for earlier and later limb development. In conclusion, results are consistent with the robustness of the ESN buffering among-individual variation in gene expression levels early in mammalian limb development, and constraining the evolution of early limb development among mammalian species.

  16. Using evolutionary conserved modules in gene networks as a strategy to leverage high throughput gene expression queries.

    Directory of Open Access Journals (Sweden)

    Jeanne M Serb

    Full Text Available BACKGROUND: Large-scale gene expression studies have not yielded the expected insight into genetic networks that control complex processes. These anticipated discoveries have been limited not by technology, but by a lack of effective strategies to investigate the data in a manageable and meaningful way. Previous work suggests that using a pre-determined seed-network of gene relationships to query large-scale expression datasets is an effective way to generate candidate genes for further study and network expansion or enrichment. Based on the evolutionary conservation of gene relationships, we test the hypothesis that a seed network derived from studies of retinal cell determination in the fly, Drosophila melanogaster, will be an effective way to identify novel candidate genes for their role in mouse retinal development. METHODOLOGY/PRINCIPAL FINDINGS: Our results demonstrate that a number of gene relationships regulating retinal cell differentiation in the fly are identifiable as pairwise correlations between genes from developing mouse retina. In addition, we demonstrate that our extracted seed-network of correlated mouse genes is an effective tool for querying datasets and provides a context to generate hypotheses. Our query identified 46 genes correlated with our extracted seed-network members. Approximately 54% of these candidates had been previously linked to the developing brain and 33% had been previously linked to the developing retina. Five of six candidate genes investigated further were validated by experiments examining spatial and temporal protein expression in the developing retina. CONCLUSIONS/SIGNIFICANCE: We present an effective strategy for pursuing a systems biology approach that utilizes an evolutionary comparative framework between two model organisms, fly and mouse. Future implementation of this strategy will be useful to determine the extent of network conservation, not just gene conservation, between species and will

  17. Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

    Energy Technology Data Exchange (ETDEWEB)

    Song, Mingzhou (Joe) [New Mexico State University, Las Cruces; Lewis, Chris K. [New Mexico State University, Las Cruces; Lance, Eric [New Mexico State University, Las Cruces; Chesler, Elissa J [ORNL; Kirova, Roumyana [Bristol-Myers Squibb Pharmaceutical Research & Development, NJ; Langston, Michael A [University of Tennessee, Knoxville (UTK); Bergeson, Susan [Texas Tech University, Lubbock

    2009-01-01

    The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptomic data is addressed. A network reconstruction algorithm was developed that uses the statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. Using temporal gene expression data collected from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol, this algorithm identified genes from a major neuronal pathway as putative components of the alcohol response mechanism. Three of these genes have known associations with alcohol in the literature. Several other potentially relevant genes, highlighted and agreeing with independent results from literature mining, may play a role in the response to alcohol. Additional, previously-unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

  18. A fast and efficient gene-network reconstruction method from multiple over-expression experiments

    Directory of Open Access Journals (Sweden)

    Thurner Stefan

    2009-08-01

    Full Text Available Abstract Background Reverse engineering of gene regulatory networks presents one of the big challenges in systems biology. Gene regulatory networks are usually inferred from a set of single-gene over-expressions and/or knockout experiments. Functional relationships between genes are retrieved either from the steady state gene expressions or from respective time series. Results We present a novel algorithm for gene network reconstruction on the basis of steady-state gene-chip data from over-expression experiments. The algorithm is based on a straight forward solution of a linear gene-dynamics equation, where experimental data is fed in as a first predictor for the solution. We compare the algorithm's performance with the NIR algorithm, both on the well known E. coli experimental data and on in-silico experiments. Conclusion We show superiority of the proposed algorithm in the number of correctly reconstructed links and discuss computational time and robustness. The proposed algorithm is not limited by combinatorial explosion problems and can be used in principle for large networks.

  19. Candidate gene prioritization by network analysis of differential expression using machine learning approaches

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    Nitsch Daniela

    2010-09-01

    Full Text Available Abstract Background Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. Results We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking. Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. Conclusion In this study we

  20. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data.

    Science.gov (United States)

    Xia, Jianguo; Gill, Erin E; Hancock, Robert E W

    2015-06-01

    Meta-analysis of gene expression data sets is increasingly performed to help identify robust molecular signatures and to gain insights into underlying biological processes. The complicated nature of such analyses requires both advanced statistics and innovative visualization strategies to support efficient data comparison, interpretation and hypothesis generation. NetworkAnalyst (http://www.networkanalyst.ca) is a comprehensive web-based tool designed to allow bench researchers to perform various common and complex meta-analyses of gene expression data via an intuitive web interface. By coupling well-established statistical procedures with state-of-the-art data visualization techniques, NetworkAnalyst allows researchers to easily navigate large complex gene expression data sets to determine important features, patterns, functions and connections, thus leading to the generation of new biological hypotheses. This protocol provides a step-wise description of how to effectively use NetworkAnalyst to perform network analysis and visualization from gene lists; to perform meta-analysis on gene expression data while taking into account multiple metadata parameters; and, finally, to perform a meta-analysis of multiple gene expression data sets. NetworkAnalyst is designed to be accessible to biologists rather than to specialist bioinformaticians. The complete protocol can be executed in ∼1.5 h. Compared with other similar web-based tools, NetworkAnalyst offers a unique visual analytics experience that enables data analysis within the context of protein-protein interaction networks, heatmaps or chord diagrams. All of these analysis methods provide the user with supporting statistical and functional evidence.

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

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    Gao Shouguo

    2011-08-01

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

  2. FastGCN: a GPU accelerated tool for fast gene co-expression networks.

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    Meimei Liang

    Full Text Available Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

  3. FastGCN: a GPU accelerated tool for fast gene co-expression networks.

    Science.gov (United States)

    Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

    2015-01-01

    Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

  4. Network analysis of differential expression for the identification of disease-causing genes.

    Directory of Open Access Journals (Sweden)

    Daniela Nitsch

    Full Text Available Genetic studies (in particular linkage and association studies identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved. We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes.

  5. Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics

    Science.gov (United States)

    Ronen, Michal; Rosenberg, Revital; Shraiman, Boris I.; Alon, Uri

    2002-08-01

    A basic challenge in systems biology is to understand the dynamical behavior of gene regulation networks. Current approaches aim at determining the network structure based on genomic-scale data. However, the network connectivity alone is not sufficient to define its dynamics; one needs to also specify the kinetic parameters for the regulation reactions. Here, we ask whether effective kinetic parameters can be assigned to a transcriptional network based on expression data. We present a combined experimental and theoretical approach based on accurate high temporal-resolution measurement of promoter activities from living cells by using green fluorescent protein (GFP) reporter plasmids. We present algorithms that use these data to assign effective kinetic parameters within a mathematical model of the network. To demonstrate this, we employ a well defined network, the SOS DNA repair system of Escherichia coli. We find a strikingly detailed temporal program of expression that correlates with the functional role of the SOS genes and is driven by a hierarchy of effective kinetic parameter strengths for the various promoters. The calculated parameters can be used to determine the kinetics of all SOS genes given the expression profile of just one representative, allowing a significant reduction in complexity. The concentration profile of the master SOS transcriptional repressor can be calculated, demonstrating that relative protein levels may be determined from purely transcriptional data. This finding opens the possibility of assigning kinetic parameters to transcriptional networks on a genomic scale.

  6. Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

    Science.gov (United States)

    Faria, José P.; Overbeek, Ross; Taylor, Ronald C.; Conrad, Neal; Vonstein, Veronika; Goelzer, Anne; Fromion, Vincent; Rocha, Miguel; Rocha, Isabel; Henry, Christopher S.

    2016-01-01

    We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of Bacillus subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs, and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches, and small regulatory RNAs. Overall, regulatory information is included in the model for ∼2500 of the ∼4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how ARs for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how ARs can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental

  7. Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

    Energy Technology Data Exchange (ETDEWEB)

    Faria, José P.; Overbeek, Ross; Taylor, Ronald C.; Conrad, Neal; Vonstein, Veronika; Goelzer, Anne; Fromion, Vincent; Rocha, Miguel; Rocha, Isabel; Henry, Christopher S.

    2016-03-18

    We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata

  8. Gene Expression Networks in the Murine Pulmonary Myocardium Provide Insight into the Pathobiology of Atrial Fibrillation

    Directory of Open Access Journals (Sweden)

    Jordan K. Boutilier

    2017-09-01

    Full Text Available The pulmonary myocardium is a muscular coat surrounding the pulmonary and caval veins. Although its definitive physiological function is unknown, it may have a pathological role as the source of ectopic beats initiating atrial fibrillation. How the pulmonary myocardium gains pacemaker function is not clearly defined, although recent evidence indicates that changed transcriptional gene expression networks are at fault. The gene expression profile of this distinct cell type in situ was examined to investigate underlying molecular events that might contribute to atrial fibrillation. Via systems genetics, a whole-lung transcriptome data set from the BXD recombinant inbred mouse resource was analyzed, uncovering a pulmonary cardiomyocyte gene network of 24 transcripts, coordinately regulated by chromosome 1 and 2 loci. Promoter enrichment analysis and interrogation of publicly available ChIP-seq data suggested that transcription of this gene network may be regulated by the concerted activity of NKX2-5, serum response factor, myocyte enhancer factor 2, and also, at a post-transcriptional level, by RNA binding protein motif 20. Gene ontology terms indicate that this gene network overlaps with molecular markers of the stressed heart. Therefore, we propose that perturbed regulation of this gene network might lead to altered calcium handling, myocyte growth, and contractile force contributing to the aberrant electrophysiological properties observed in atrial fibrillation. We reveal novel molecular interactions and pathways representing possible therapeutic targets for atrial fibrillation. In addition, we highlight the utility of recombinant inbred mouse resources in detecting and characterizing gene expression networks of relatively small populations of cells that have a pathological significance.

  9. Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

    Institute of Scientific and Technical Information of China (English)

    Kai; Shi; Zhi-Tong; Bing; Gui-Qun; Cao; Ling; Guo; Ya-Na; Cao; Hai-Ou; Jiang; Mei-Xia; Zhang

    2015-01-01

    AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study.METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes.The function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma.CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

  10. Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

    Directory of Open Access Journals (Sweden)

    Kai Shi

    2015-04-01

    Full Text Available AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis (WGCNA is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study. METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus (GEO database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes. The function of the genes were annotated by gene ontology (GO. RESULTS: In this study, we identified four co-expression modules significantly correlated with clinic traits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location (sclera and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter (LTD. Additionally, we identified the hug gene (top connectivity with other genes in each module. The hub gene RPS15A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma. CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

  11. Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome

    Directory of Open Access Journals (Sweden)

    Rajeevan Mangalathu S

    2008-11-01

    Full Text Available Abstract Background Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS data set. Results We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1 construct a co-expression network, 2 identify trait-related modules within the network, 3 use a trait-related genetic marker to prioritize genes within the module, 4 apply an integrated gene screening strategy to identify candidate genes and 5 carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways. Conclusion We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.

  12. Discovering missing reactions of metabolic networks by using gene co-expression data

    Science.gov (United States)

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2017-02-01

    Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.

  13. Regulatory Network Construction in Arabidopsis using genome-wide gene expression QTLs

    NARCIS (Netherlands)

    Keurentjes, J.J.B.; Fu, J.J.; Terpstra, I.R.; Garcia, J.M.; van den Ackerveken, G.; Snoek, L.B.; Peeters, A.J.M.; Vreugdenhil, D.; Koornreef, M.; Jansen, R.C.

    2007-01-01

    Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci.Keurentjes JJ, Fu J, Terpstra IR, Garcia JM, van den Ackerveken G, Snoek LB, Peeters AJ, Vreugdenhil D, Koornneef M, Jansen RC. Laboratory of Genetics, Wageningen University, Arboretumlaan 4,

  14. Stochastic gene expression in single cells: exploring the importance of noise in genetic networks

    Science.gov (United States)

    van Oudenaarden, Alexander

    2003-03-01

    Cells are intrinsically noisy biochemical reactors. This leads to random cell to cell variation (noise) in gene expression levels. First, I will address the source of this noise at the level of transcription and translation of a single gene. Our experimental results demonstrate that the intrinsic noise of a single gene is predominantly controlled at the translational level, and that increased translational efficiency leads to increased noise strength. This observation is consistent with a theoretical model in which proteins are randomly produced in sharp bursts followed by periods of slow decay. Second, I will explore the importance of genetic noise for a naturally occuring network: the lac operon. The classic lactose utilization network of E. coli has been under investigation for several decades and, in its simplest form the network may be modeled as a single positive feedback module. However, this simplicity is deceptive, as even this basic network is capable of complex metabolic behavior, including adaptation, amplification, and graded-to-binary response conversion. I will present single cell measurements on the expression of key genes in lactose uptake network and explore the importance of genetic noise on the regulation of these genes.

  15. How Molecular Competition Influences Fluxes in Gene Expression Networks

    NARCIS (Netherlands)

    De Vos, Dirk; Bruggeman, Frank J.; Westerhoff, Hans V.; Bakker, Barbara M.

    2011-01-01

    Often, in living cells different molecular species compete for binding to the same molecular target. Typical examples are the competition of genes for the transcription machinery or the competition of mRNAs for the translation machinery. Here we show that such systems have specific regulatory featur

  16. How molecular competition influences fluxes in gene expression networks

    NARCIS (Netherlands)

    Vos, D. de; Bruggeman, F.J.; Westerhoff, H.V.; Bakker, B.M.

    2011-01-01

    Often, in living cells different molecular species compete for binding to the same molecular target. Typical examples are the competition of genes for the transcription machinery or the competition of mRNAs for the translation machinery. Here we show that such systems have specific regulatory featur

  17. How Molecular Competition Influences Fluxes in Gene Expression Networks

    NARCIS (Netherlands)

    Vos, D. de; Bruggeman, F.J.; Westerhoff, H.V.; Bakker, B.M.

    2011-01-01

    Often, in living cells different molecular species compete for binding to the same molecular target. Typical examples are the competition of genes for the transcription machinery or the competition of mRNAs for the translation machinery. Here we show that such systems have specific regulatory featur

  18. A recursive network approach can identify constitutive regulatory circuits in gene expression data

    Science.gov (United States)

    Blasi, Monica Francesca; Casorelli, Ida; Colosimo, Alfredo; Blasi, Francesco Simone; Bignami, Margherita; Giuliani, Alessandro

    2005-03-01

    The activity of the cell is often coordinated by the organisation of proteins into regulatory circuits that share a common function. Genome-wide expression profiles might contain important information on these circuits. Current approaches for the analysis of gene expression data include clustering the individual expression measurements and relating them to biological functions as well as modelling and simulation of gene regulation processes by additional computer tools. The identification of the regulative programmes from microarray experiments is limited, however, by the intrinsic difficulty of linear methods to detect low-variance signals and by the sensitivity of the different approaches. Here we face the problem of recognising invariant patterns of correlations among gene expression reminiscent of regulation circuits. We demonstrate that a recursive neural network approach can identify genetic regulation circuits from expression data for ribosomal and genome stability genes. The proposed method, by greatly enhancing the sensitivity of microarray studies, allows the identification of important aspects of genetic regulation networks and might be useful for the discrimination of the different players involved in regulation circuits. Our results suggest that the constitutive regulatory networks involved in the generic organisation of the cell display a high degree of clustering depending on a modular architecture.

  19. Diversification in the genetic architecture of gene expression and transcriptional networks in organ differentiation of Populus.

    Science.gov (United States)

    Drost, Derek R; Benedict, Catherine I; Berg, Arthur; Novaes, Evandro; Novaes, Carolina R D B; Yu, Qibin; Dervinis, Christopher; Maia, Jessica M; Yap, John; Miles, Brianna; Kirst, Matias

    2010-05-04

    A fundamental goal of systems biology is to identify genetic elements that contribute to complex phenotypes and to understand how they interact in networks predictive of system response to genetic variation. Few studies in plants have developed such networks, and none have examined their conservation among functionally specialized organs. Here we used genetical genomics in an interspecific hybrid population of the model hardwood plant Populus to uncover transcriptional networks in xylem, leaves, and roots. Pleiotropic eQTL hotspots were detected and used to construct coexpression networks a posteriori, for which regulators were predicted based on cis-acting expression regulation. Networks were shown to be enriched for groups of genes that function in biologically coherent processes and for cis-acting promoter motifs with known roles in regulating common groups of genes. When contrasted among xylem, leaves, and roots, transcriptional networks were frequently conserved in composition, but almost invariably regulated by different loci. Similarly, the genetic architecture of gene expression regulation is highly diversified among plant organs, with less than one-third of genes with eQTL detected in two organs being regulated by the same locus. However, colocalization in eQTL position increases to 50% when they are detected in all three organs, suggesting conservation in the genetic regulation is a function of ubiquitous expression. Genes conserved in their genetic regulation among all organs are primarily cis regulated (approximately 92%), whereas genes with eQTL in only one organ are largely trans regulated. Trans-acting regulation may therefore be the primary driver of differentiation in function between plant organs.

  20. A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data.

    Science.gov (United States)

    Gui, Shupeng; Rice, Andrew P; Chen, Rui; Wu, Liang; Liu, Ji; Miao, Hongyu

    2017-01-31

    Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher. Here we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN) structures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM challenge benchmark data. The highlight of our method is that its superior performance does not degenerate even for a network size on the order of 10(4), and is thus readily applicable to large-scale complex networks. Such a breakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e., sparsity and hub gene structure) of complex networks in the regularized formulation. We also validate and illustrate the application of our algorithm in practice using the time-course gene expression data from a study on human respiratory epithelial cells in response to influenza A virus (IAV) infection, as well as the CHIP-seq data from ENCODE on transcription factor (TF) and target gene interactions. An interesting finding, owing to the proposed algorithm, is that the biggest hub structures (e.g., top ten) in the GRN all center at some transcription factors in the context of epithelial cell infection by IAV. The proposed algorithm is the first scalable method for large complex network structure identification. The GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose which gene functions to investigate in a biological event. The algorithm described in this article is implemented in MATLAB (Ⓡ) , and the source code is

  1. Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia.

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    Tejera, Eduardo; Bernardes, João; Rebelo, Irene

    2013-11-12

    In this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively. After co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods. Significant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia. Weighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied.Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally.

  2. Global analysis of phase locking in gene expression during cell cycle: the potential in network modeling

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    Hessner Martin J

    2010-12-01

    Full Text Available Abstract Background In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation. Results In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation. Conclusions Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.

  3. Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analyses

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    Lionikas Arimantas

    2012-11-01

    Full Text Available Abstract Background We have recently identified a number of Quantitative Trait Loci (QTL contributing to the 2-fold muscle weight difference between the LG/J and SM/J mouse strains and refined their confidence intervals. To facilitate nomination of the candidate genes responsible for these differences we examined the transcriptome of the tibialis anterior (TA muscle of each strain by RNA-Seq. Results 13,726 genes were expressed in mouse skeletal muscle. Intersection of a set of 1061 differentially expressed transcripts with a mouse muscle Bayesian Network identified a coherent set of differentially expressed genes that we term the LG/J and SM/J Regulatory Network (LSRN. The integration of the QTL, transcriptome and the network analyses identified eight key drivers of the LSRN (Kdr, Plbd1, Mgp, Fah, Prss23, 2310014F06Rik, Grtp1, Stk10 residing within five QTL regions, which were either polymorphic or differentially expressed between the two strains and are strong candidates for quantitative trait genes (QTGs underlying muscle mass. The insight gained from network analysis including the ability to make testable predictions is illustrated by annotating the LSRN with knowledge-based signatures and showing that the SM/J state of the network corresponds to a more oxidative state. We validated this prediction by NADH tetrazolium reductase staining in the TA muscle revealing higher oxidative potential of the SM/J compared to the LG/J strain (p Conclusion Thus, integration of fine resolution QTL mapping, RNA-Seq transcriptome information and mouse muscle Bayesian Network analysis provides a novel and unbiased strategy for nomination of muscle QTGs.

  4. Differences in human cortical gene expression match the temporal properties of large-scale functional networks.

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    Claudia Cioli

    Full Text Available We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas. Previous work suggests that functional cortical networks (resting state and task based are organized as two large networks (differentiated by their preferred information processing mode shaped like two rings. The first ring--Visual-Sensorimotor-Auditory (VSA--comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring--Parieto-Temporo-Frontal (PTF--comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found--with correspondence analysis--that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins--coded by genes that most differentiate the rings--were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1. Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium

  5. Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

    Science.gov (United States)

    Sayyed-Ahmad, Abdallah; Tuncay, Kagan; Ortoleva, Peter J

    2007-01-01

    Background Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion Multiplex time series data can be used for the construction of the network of

  6. Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

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    Tuncay Kagan

    2007-01-01

    Full Text Available Abstract Background Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion Multiplex time series data can be used for the

  7. Targeting single neuronal networks for gene expression and cell labeling in vivo.

    Science.gov (United States)

    Marshel, James H; Mori, Takuma; Nielsen, Kristina J; Callaway, Edward M

    2010-08-26

    To understand fine-scale structure and function of single mammalian neuronal networks, we developed and validated a strategy to genetically target and trace monosynaptic inputs to a single neuron in vitro and in vivo. The strategy independently targets a neuron and its presynaptic network for specific gene expression and fine-scale labeling, using single-cell electroporation of DNA to target infection and monosynaptic retrograde spread of a genetically modifiable rabies virus. The technique is highly reliable, with transsynaptic labeling occurring in every electroporated neuron infected by the virus. Targeting single neocortical neuronal networks in vivo, we found clusters of both spiny and aspiny neurons surrounding the electroporated neuron in each case, in addition to intricately labeled distal cortical and subcortical inputs. This technique, broadly applicable for probing and manipulating single neuronal networks with single-cell resolution in vivo, may help shed new light on fundamental mechanisms underlying circuit development and information processing by neuronal networks throughout the brain.

  8. Module network inference from a cancer gene expression data set identifies microRNA regulated modules.

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    Eric Bonnet

    Full Text Available BACKGROUND: MicroRNAs (miRNAs are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. METHODOLOGY/PRINCIPAL FINDINGS: During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. CONCLUSIONS/SIGNIFICANCE: Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be

  9. Regulation of a transcription factor network by Cdk1 coordinates late cell cycle gene expression.

    Science.gov (United States)

    Landry, Benjamin D; Mapa, Claudine E; Arsenault, Heather E; Poti, Kristin E; Benanti, Jennifer A

    2014-05-02

    To maintain genome stability, regulators of chromosome segregation must be expressed in coordination with mitotic events. Expression of these late cell cycle genes is regulated by cyclin-dependent kinase (Cdk1), which phosphorylates a network of conserved transcription factors (TFs). However, the effects of Cdk1 phosphorylation on many key TFs are not known. We find that elimination of Cdk1-mediated phosphorylation of four S-phase TFs decreases expression of many late cell cycle genes, delays mitotic progression, and reduces fitness in budding yeast. Blocking phosphorylation impairs degradation of all four TFs. Consequently, phosphorylation-deficient mutants of the repressors Yox1 and Yhp1 exhibit increased promoter occupancy and decreased expression of their target genes. Interestingly, although phosphorylation of the transcriptional activator Hcm1 on its N-terminus promotes its degradation, phosphorylation on its C-terminus is required for its activity, indicating that Cdk1 both activates and inhibits a single TF. We conclude that Cdk1 promotes gene expression by both activating transcriptional activators and inactivating transcriptional repressors. Furthermore, our data suggest that coordinated regulation of the TF network by Cdk1 is necessary for faithful cell division.

  10. Human population-specific gene expression and transcriptional network modification with polymorphic transposable elements.

    Science.gov (United States)

    Wang, Lu; Rishishwar, Lavanya; Mariño-Ramírez, Leonardo; Jordan, I King

    2016-12-19

    Transposable element (TE) derived sequences are known to contribute to the regulation of the human genome. The majority of known TE-derived regulatory sequences correspond to relatively ancient insertions, which are fixed across human populations. The extent to which human genetic variation caused by recent TE activity leads to regulatory polymorphisms among populations has yet to be thoroughly explored. In this study, we searched for associations between polymorphic TE (polyTE) loci and human gene expression levels using an expression quantitative trait loci (eQTL) approach. We compared locus-specific polyTE insertion genotypes to B cell gene expression levels among 445 individuals from 5 human populations. Numerous human polyTE loci correspond to both cis and trans eQTL, and their regulatory effects are directly related to cell type-specific function in the immune system. PolyTE loci are associated with differences in expression between European and African population groups, and a single polyTE loci is indirectly associated with the expression of numerous genes via the regulation of the B cell-specific transcription factor PAX5 The polyTE-gene expression associations we found indicate that human TE genetic variation can have important phenotypic consequences. Our results reveal that TE-eQTL are involved in population-specific gene regulation as well as transcriptional network modification.

  11. Human population-specific gene expression and transcriptional network modification with polymorphic transposable elements

    Science.gov (United States)

    Wang, Lu; Mariño-Ramírez, Leonardo

    2017-01-01

    Abstract Transposable element (TE) derived sequences are known to contribute to the regulation of the human genome. The majority of known TE-derived regulatory sequences correspond to relatively ancient insertions, which are fixed across human populations. The extent to which human genetic variation caused by recent TE activity leads to regulatory polymorphisms among populations has yet to be thoroughly explored. In this study, we searched for associations between polymorphic TE (polyTE) loci and human gene expression levels using an expression quantitative trait loci (eQTL) approach. We compared locus-specific polyTE insertion genotypes to B cell gene expression levels among 445 individuals from 5 human populations. Numerous human polyTE loci correspond to both cis and trans eQTL, and their regulatory effects are directly related to cell type-specific function in the immune system. PolyTE loci are associated with differences in expression between European and African population groups, and a single polyTE loci is indirectly associated with the expression of numerous genes via the regulation of the B cell-specific transcription factor PAX5. The polyTE-gene expression associations we found indicate that human TE genetic variation can have important phenotypic consequences. Our results reveal that TE-eQTL are involved in population-specific gene regulation as well as transcriptional network modification. PMID:27998931

  12. Mouse Social Network Dynamics and Community Structure are Associated with Plasticity-Related Brain Gene Expression.

    Science.gov (United States)

    Williamson, Cait M; Franks, Becca; Curley, James P

    2016-01-01

    Laboratory studies of social behavior have typically focused on dyadic interactions occurring within a limited spatiotemporal context. However, this strategy prevents analyses of the dynamics of group social behavior and constrains identification of the biological pathways mediating individual differences in behavior. In the current study, we aimed to identify the spatiotemporal dynamics and hierarchical organization of a large social network of male mice. We also sought to determine if standard assays of social and exploratory behavior are predictive of social behavior in this social network and whether individual network position was associated with the mRNA expression of two plasticity-related genes, DNA methyltransferase 1 and 3a. Mice were observed to form a hierarchically organized social network and self-organized into two separate social network communities. Members of both communities exhibited distinct patterns of socio-spatial organization within the vivaria that was not limited to only agonistic interactions. We further established that exploratory and social behaviors in standard behavioral assays conducted prior to placing the mice into the large group was predictive of initial network position and behavior but were not associated with final social network position. Finally, we determined that social network position is associated with variation in mRNA levels of two neural plasticity genes, DNMT1 and DNMT3a, in the hippocampus but not the mPOA. This work demonstrates the importance of understanding the role of social context and complex social dynamics in determining the relationship between individual differences in social behavior and brain gene expression.

  13. A contribution to the study of plant development evolution based on gene co-expression networks

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    Francisco J. Romero-Campero

    2013-08-01

    Full Text Available Phototrophic eukaryotes are among the most successful organisms on Earth due to their unparalleled efficiency at capturing light energy and fixing carbon dioxide to produce organic molecules. A conserved and efficient network of light-dependent regulatory modules could be at the bases of this success. This regulatory system conferred early advantages to phototrophic eukaryotes that allowed for specialization, complex developmental processes and modern plant characteristics. We have studied light-dependent gene regulatory modules from algae to plants employing integrative-omics approaches based on gene co-expression networks. Our study reveals some remarkably conserved ways in which eukaryotic phototrophs deal with day length and light signaling. Here we describe how a family of Arabidopsis transcription factors involved in photoperiod response has evolved from a single algal gene according to the innovation, amplification and divergence theory of gene evolution by duplication. These modifications of the gene co-expression networks from the ancient unicellular green algae Chlamydomonas reinhardtii to the modern brassica Arabidopsis thaliana may hint on the evolution and specialization of plants and other organisms.

  14. Dissecting Oct3/4-regulated gene networks in embryonic stem cells by expression profiling.

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    Ryo Matoba

    Full Text Available POU transcription factor Pou5f1 (Oct3/4 is required to maintain ES cells in an undifferentiated state. Here we show that global expression profiling of Oct3/4-manipulated ES cells delineates the downstream target genes of Oct3/4. Combined with data from genome-wide chromatin-immunoprecipitation (ChIP assays, this analysis identifies not only primary downstream targets of Oct3/4, but also secondary or tertiary targets. Furthermore, the analysis also reveals that downstream target genes are regulated either positively or negatively by Oct3/4. Identification of a group of genes that show both activation and repression depending on Oct3/4 expression levels provides a possible mechanism for the requirement of appropriate Oct3/4 expression to maintain undifferentiated ES cells. As a proof-of-principle study, one of the downstream genes, Tcl1, has been analyzed in detail. We show that Oct3/4 binds to the promoter region of Tcl1 and activates its transcription. We also show that Tcl1 is involved in the regulation of proliferation, but not differentiation, in ES cells. These findings suggest that the global expression profiling of gene-manipulated ES cells can help to delineate the structure and dynamics of gene regulatory networks.

  15. Dissecting Oct3/4-Regulated Gene Networks in Embryonic Stem Cells by Expression Profiling

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    Matoba, Ryo; Niwa, Hitoshi; Masui, Shinji; Ohtsuka, Satoshi; Carter, Mark G.; Sharov, Alexei A.; Ko, Minoru S.H.

    2006-01-01

    POU transcription factor Pou5f1 (Oct3/4) is required to maintain ES cells in an undifferentiated state. Here we show that global expression profiling of Oct3/4-manipulated ES cells delineates the downstream target genes of Oct3/4. Combined with data from genome-wide chromatin-immunoprecipitation (ChIP) assays, this analysis identifies not only primary downstream targets of Oct3/4, but also secondary or tertiary targets. Furthermore, the analysis also reveals that downstream target genes are regulated either positively or negatively by Oct3/4. Identification of a group of genes that show both activation and repression depending on Oct3/4 expression levels provides a possible mechanism for the requirement of appropriate Oct3/4 expression to maintain undifferentiated ES cells. As a proof-of-principle study, one of the downstream genes, Tcl1, has been analyzed in detail. We show that Oct3/4 binds to the promoter region of Tcl1 and activates its transcription. We also show that Tcl1 is involved in the regulation of proliferation, but not differentiation, in ES cells. These findings suggest that the global expression profiling of gene-manipulated ES cells can help to delineate the structure and dynamics of gene regulatory networks. PMID:17183653

  16. Dynamic changes in protein functional linkage networks revealed by integration with gene expression data.

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    Shubhada R Hegde

    2008-11-01

    Full Text Available Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein:protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein:protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.

  17. Identification of hub genes of pneumocyte senescence induced by thoracic irradiation using weighted gene co-expression network analysis

    Science.gov (United States)

    XING, YONGHUA; ZHANG, JUNLING; LU, LU; LI, DEGUAN; WANG, YUEYING; HUANG, SONG; LI, CHENGCHENG; ZHANG, ZHUBO; LI, JIANGUO; MENG, AIMIN

    2016-01-01

    Irradiation commonly causes pneumocyte senescence, which may lead to severe fatal lung injury characterized by pulmonary dysfunction and respiratory failure. However, the molecular mechanism underlying the induction of pneumocyte senescence by irradiation remains to be elucidated. In the present study, weighted gene co-expression network analysis (WGCNA) was used to screen for differentially expressed genes, and to identify the hub genes and gene modules, which may be critical for senescence. A total of 2,916 differentially expressed genes were identified between the senescence and non-senescence groups following thoracic irradiation. In total, 10 gene modules associated with cell senescence were detected, and six hub genes were identified, including B-cell scaffold protein with ankyrin repeats 1, translocase of outer mitochondrial membrane 70 homolog A, actin filament-associated protein 1, Cd84, Nuf2 and nuclear factor erythroid 2. These genes were markedly associated with cell proliferation, cell division and cell cycle arrest. The results of the present study demonstrated that WGCNA of microarray data may provide further insight into the molecular mechanism underlying pneumocyte senescence. PMID:26572216

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

    Science.gov (United States)

    de Jong, Simone; Boks, Marco P M; Fuller, Tova F; Strengman, Eric; Janson, Esther; de Kovel, Carolien G F; Ori, Anil P S; Vi, Nancy; Mulder, Flip; Blom, Jan Dirk; Glenthøj, Birte; Schubart, Chris D; Cahn, Wiepke; Kahn, René S; Horvath, Steve; Ophoff, Roel A

    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-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.

  19. A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks.

    Science.gov (United States)

    Lancashire, L J; Powe, D G; Reis-Filho, J S; Rakha, E; Lemetre, C; Weigelt, B; Abdel-Fatah, T M; Green, A R; Mukta, R; Blamey, R; Paish, E C; Rees, R C; Ellis, I O; Ball, G R

    2010-02-01

    Gene expression microarrays allow for the high throughput analysis of huge numbers of gene transcripts and this technology has been widely applied to the molecular and biological classification of cancer patients and in predicting clinical outcome. A potential handicap of such data intensive molecular technologies is the translation to clinical application in routine practice. In using an artificial neural network bioinformatic approach, we have reduced a 70 gene signature to just 9 genes capable of accurately predicting distant metastases in the original dataset. Upon validation in a follow-up cohort, this signature was an independent predictor of metastases free and overall survival in the presence of the 70 gene signature and other factors. Interestingly, the ANN signature and CA9 expression also split the groups defined by the 70 gene signature into prognostically distinct groups. Subsequently, the presence of protein for the principal prognosticator gene was categorically assessed in breast cancer tissue of an experimental and independent validation patient cohort, using immunohistochemistry. Importantly our principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis.

  20. Comprehensive gene expression profiling reveals synergistic functional networks in cerebral vessels after hypertension or hypercholesterolemia.

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    Wei-Yi Ong

    Full Text Available Atherosclerotic stenosis of cerebral arteries or intracranial large artery disease (ICLAD is a major cause of stroke especially in Asians, Hispanics and Africans, but relatively little is known about gene expression changes in vessels at risk. This study compares comprehensive gene expression profiles in the middle cerebral artery (MCA of New Zealand White rabbits exposed to two stroke risk factors i.e. hypertension and/or hypercholesterolemia, by the 2-Kidney-1-Clip method, or dietary supplementation with cholesterol. Microarray and Ingenuity Pathway Analyses of the MCA of the hypertensive rabbits showed up-regulated genes in networks containing the node molecules: UBC (ubiquitin, P38 MAPK, ERK, NFkB, SERPINB2, MMP1 and APP (amyloid precursor protein; and down-regulated genes related to MAPK, ERK 1/2, Akt, 26 s proteasome, histone H3 and UBC. The MCA of hypercholesterolemic rabbits showed differentially expressed genes that are surprisingly, linked to almost the same node molecules as the hypertensive rabbits, despite a relatively low percentage of 'common genes' (21 and 7% between the two conditions. Up-regulated common genes were related to: UBC, SERPINB2, TNF, HNF4A (hepatocyte nuclear factor 4A and APP, and down-regulated genes, related to UBC. Increased HNF4A message and protein were verified in the aorta. Together, these findings reveal similar nodal molecules and gene pathways in cerebral vessels affected by hypertension or hypercholesterolemia, which could be a basis for synergistic action of risk factors in the pathogenesis of ICLAD.

  1. Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data.

    Science.gov (United States)

    de Matos Simoes, Ricardo; Dalleau, Sabine; Williamson, Kate E; Emmert-Streib, Frank

    2015-05-14

    Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis. In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer. We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential

  2. Mining the tissue-tissue gene co-expression network for tumor microenvironment study and biomarker prediction

    OpenAIRE

    2013-01-01

    Background Recent discovery in tumor development indicates that the tumor microenvironment (mostly stroma cells) plays an important role in cancer development. To understand how the tumor microenvironment (TME) interacts with the tumor, we explore the correlation of the gene expressions between tumor and stroma. The tumor and stroma gene expression data are modeled as a weighted bipartite network (tumor-stroma coexpression network) where the weight of an edge indicates the correlation between...

  3. Global prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Maria D Chikina

    2009-06-01

    Full Text Available Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data.

  4. Genome-wide expression analysis of rice aquaporin genes and development of a functional gene network mediated by aquaporin expression in roots.

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    Nguyen, Minh Xuan; Moon, Sunok; Jung, Ki-Hong

    2013-10-01

    The world population continually faces challenges of water scarcity for agriculture. A common strategy called water-balance control has evolved to adapt plant growth to these challenges. Aquaporins are a family of integral membrane proteins that play a central role in water-balance control. In this study, we identified 34 members of the rice aquaporin gene family, adding a novel member to the previous list. A combination of phylogenetic tree and anatomical meta-expression profiling data consisting of 983 Affymetrix arrays and 209 Agilent 44 K arrays was used to identify tissue-preferred aquaporin genes and evaluate functional redundancy among aquaporin family members. Eight aquaporins showed root-preferred expression in the vegetative growth stage, while 4 showed leaf/shoot-preferred expression. Integrating stress-induced expression patterns into phylogenetic tree and semi-quantitative reverse transcriptase polymerase chain reaction (RT-PCR) analyses revealed that 3 rice aquaporin genes were markedly downregulated and 4 were upregulated by water deficiency in the root, suggesting that these candidate genes are key regulators of water uptake from the soil. Finally, we constructed a functional network of genes mediated by water stress and refined the network by confirming the differential expression using RT-PCR and real-time PCR. Our data will be useful to elucidate the molecular mechanism of water-balance control in rice root.

  5. Comprehensive Gene Expression Profiling Reveals Synergistic Functional Networks in Cerebral Vessels after Hypertension or Hypercholesterolemia

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    Ong, Wei-Yi; Ng, Mary Pei-Ern; Loke, Sau-Yeen; Jin, Shalai; Wu, Ya-Jun; Tanaka, Kazuhiro; Wong, Peter Tsun-Hon

    2013-01-01

    Atherosclerotic stenosis of cerebral arteries or intracranial large artery disease (ICLAD) is a major cause of stroke especially in Asians, Hispanics and Africans, but relatively little is known about gene expression changes in vessels at risk. This study compares comprehensive gene expression profiles in the middle cerebral artery (MCA) of New Zealand White rabbits exposed to two stroke risk factors i.e. hypertension and/or hypercholesterolemia, by the 2-Kidney-1-Clip method, or dietary supplementation with cholesterol. Microarray and Ingenuity Pathway Analyses of the MCA of the hypertensive rabbits showed up-regulated genes in networks containing the node molecules: UBC (ubiquitin), P38 MAPK, ERK, NFkB, SERPINB2, MMP1 and APP (amyloid precursor protein); and down-regulated genes related to MAPK, ERK 1/2, Akt, 26 s proteasome, histone H3 and UBC. The MCA of hypercholesterolemic rabbits showed differentially expressed genes that are surprisingly, linked to almost the same node molecules as the hypertensive rabbits, despite a relatively low percentage of ‘common genes’ (21 and 7%) between the two conditions. Up-regulated common genes were related to: UBC, SERPINB2, TNF, HNF4A (hepatocyte nuclear factor 4A) and APP, and down-regulated genes, related to UBC. Increased HNF4A message and protein were verified in the aorta. Together, these findings reveal similar nodal molecules and gene pathways in cerebral vessels affected by hypertension or hypercholesterolemia, which could be a basis for synergistic action of risk factors in the pathogenesis of ICLAD. PMID:23874591

  6. Gene expression correlations in human cancer cell lines define molecular interaction networks for epithelial phenotype.

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    Kurt W Kohn

    Full Text Available Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1; interactions at adherens junctions (CDH1, ADAP1, CAMSAP3; interactions at desmosomes (PPL, PKP3, JUP; transcription regulation of cell-cell junction complexes (GRHL1 and 2; epithelial RNA splicing regulators (ESRP1 and 2; epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B; epithelial Ca(+2 signaling (ATP2C2, S100A14, BSPRY; terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2; maintenance of apico-basal polarity (RAB25, LLGL2, EPN3. The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets.

  7. A novel mutual information-based Boolean network inference method from time-series gene expression data

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    Barman, Shohag; Kwon, Yung-Keun

    2017-01-01

    Background Inferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately. Results In this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI) method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods. Conclusions Taken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network. PMID:28178334

  8. An expression atlas of human primary cells: inference of gene function from coexpression networks.

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    Mabbott, Neil A; Baillie, J Kenneth; Brown, Helen; Freeman, Tom C; Hume, David A

    2013-09-20

    The specialisation of mammalian cells in time and space requires genes associated with specific pathways and functions to be co-ordinately expressed. Here we have combined a large number of publically available microarray datasets derived from human primary cells and analysed large correlation graphs of these data. Using the network analysis tool BioLayout Express3D we identify robust co-associations of genes expressed in a wide variety of cell lineages. We discuss the biological significance of a number of these associations, in particular the coexpression of key transcription factors with the genes that they are likely to control. We consider the regulation of genes in human primary cells and specifically in the human mononuclear phagocyte system. Of particular note is the fact that these data do not support the identity of putative markers of antigen-presenting dendritic cells, nor classification of M1 and M2 activation states, a current subject of debate within immunological field. We have provided this data resource on the BioGPS web site (http://biogps.org/dataset/2429/primary-cell-atlas/) and on macrophages.com (http://www.macrophages.com/hu-cell-atlas).

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

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

    2016-06-01

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

  10. Modeling and analyzing gene co-expression in hepatocellular carcinoma using actor-semiotic networks and centrality signatures.

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    Fung, David C Y

    2008-01-01

    Primary hepatocellular carcinoma (HCC) is currently the fifth most common malignancy and the third most common cause of cancer mortality worldwide. Because of its high prevalence in developing nations, there have been numerous efforts made in the molecular characterization of primary HCC. However, a better understanding into the pathology of HCC required software-assisted network modeling and analysis. In this paper, the author presented his first attempt in exploring the biological implication of gene co-expression in HCC using actor-semiotic network modeling and analysis. The network was first constructed by integrating inter-actor relationships, e.g. gene co-expression, microRNA-to-gene, and protein interactions, with semiotic relationships, e.g. gene-to-Gene Ontology Process. Topological features that are highly discriminative of the HCC phenotype were identified by visual inspection. Finally, the author devised a graph signature-based analysis method to supplement the network exploration.

  11. Modeling and Analyzing Gene Co-Expression in Hepatocellular Carcinoma Using Actor-Semiotic Networks and Centrality Signatures

    Directory of Open Access Journals (Sweden)

    David C.Y. Fung

    2008-01-01

    Full Text Available Primary hepatocellular carcinoma (HCC is currently the fifth most common malignancy and the third most common cause of cancer mortality worldwide. Because of its high prevalence in developing nations, there have been numerous efforts made in the molecular characterization of primary HCC. However, a better understanding into the pathology of HCC required software-assisted network modeling and analysis. In this paper, the author presented his first attempt in exploring the biological implication of gene co-expression in HCC using actor-semiotic network modeling and analysis. The network was first constructed by integrating inter-actor relationships, e.g. gene co-expression, microRNA-to-gene, and protein interactions, with semiotic relationships, e.g. gene-to-Gene Ontology Process. Topological features that are highly discriminative of the HCC phenotype were identified by visual inspection. Finally, the author devised a graph signature- based analysis method to supplement the network exploration.

  12. Commentary: BRAIN NETWORKS. Correlated Gene Expression Supports Synchronous Activity in Brain Networks. Science 348, 1241–4

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    Spiro P. Pantazatos

    2017-07-01

    Full Text Available A recent report claims that functional brain networks defined with resting-state functional magnetic resonance imaging (fMRI can be recapitulated with correlated gene expression (i.e., high within-network tissue-tissue “strength fraction,” SF (Richiardi et al., 2015. However, the authors do not adequately control for spatial proximity. We replicated their main analysis, performed a more effective adjustment for spatial proximity, and tested whether “null networks” (i.e., clusters with center coordinates randomly placed throughout cortex also exhibit high SF. Removing proximal tissue-tissue correlations by Euclidean distance, as opposed to removing correlations within arbitrary tissue labels as in Richiardi et al. (2015, reduces within-network SF to no greater than null. Moreover, randomly placed clusters also have significantly high SF, indicating that high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks defined by resting-state fMRI. We discuss why additional validations in the original article are invalid and/or misleading and suggest future directions.

  13. Threshold-dominated regulation hides genetic variation in gene expression networks

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    Plahte Erik

    2007-12-01

    Full Text Available Abstract Background In dynamical models with feedback and sigmoidal response functions, some or all variables have thresholds around which they regulate themselves or other variables. A mathematical analysis has shown that when the dose-response functions approach binary or on/off responses, any variable with an equilibrium value close to one of its thresholds is very robust to parameter perturbations of a homeostatic state. We denote this threshold robustness. To check the empirical relevance of this phenomenon with response function steepnesses ranging from a near on/off response down to Michaelis-Menten conditions, we have performed a simulation study to investigate the degree of threshold robustness in models for a three-gene system with one downstream gene, using several logical input gates, but excluding models with positive feedback to avoid multistationarity. Varying parameter values representing functional genetic variation, we have analysed the coefficient of variation (CV of the gene product concentrations in the stable state for the regulating genes in absolute terms and compared to the CV for the unregulating downstream gene. The sigmoidal or binary dose-response functions in these models can be considered as phenomenological models of the aggregated effects on protein or mRNA expression rates of all cellular reactions involved in gene expression. Results For all the models, threshold robustness increases with increasing response steepness. The CVs of the regulating genes are significantly smaller than for the unregulating gene, in particular for steep responses. The effect becomes less prominent as steepnesses approach Michaelis-Menten conditions. If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost. Threshold robustness arises when a variable is an active regulator around its threshold, and this function is maintained by

  14. Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets.

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    Puniya, Bhanwar Lal; Kulshreshtha, Deepika; Verma, Srikant Prasad; Kumar, Sanjiv; Ramachandran, Srinivasan

    2013-11-01

    We have carried out weighted gene co-expression network analysis of Mycobacterium tuberculosis to gain insights into gene expression architecture during log phase growth. The differentially expressed genes between at least one pair of 11 different M. tuberculosis strains as source of biological variability were used for co-expression network analysis. This data included genes with highest coefficient of variation in expression. Five distinct modules were identified using topological overlap based clustering. All the modules together showed significant enrichment in biological processes: fatty acid biosynthesis, cell membrane, intracellular membrane bound organelle, DNA replication, Quinone biosynthesis, cell shape and peptidoglycan biosynthesis, ribosome and structural constituents of ribosome and transposition. We then extracted the co-expressed connections which were supported either by transcriptional regulatory network or STRING database or high edge weight of topological overlap. The genes trpC, nadC, pitA, Rv3404c, atpA, pknA, Rv0996, purB, Rv2106 and Rv0796 emerged as top hub genes. After overlaying this network on the iNJ661 metabolic network, the reactions catalyzed by 15 highly connected metabolic genes were knocked down in silico and evaluated by Flux Balance Analysis. The results showed that in 12 out of 15 cases, in 11 more than 50% of reactions catalyzed by genes connected through co-expressed connections also had altered fluxes. The modules 'Turquoise', 'Blue' and 'Red' also showed enrichment in essential genes. We could map 152 of the previously known or proposed drug targets in these modules and identified 15 new potential drug targets based on their high degree of co-expressed connections and strong correlation with module eigengenes.

  15. Dissecting the logical types of network control in gene expression profiles

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    Geertz Marcel

    2008-02-01

    Full Text Available Abstract Background In the bacterium Escherichia coli the transcriptional regulation of gene expression involves both dedicated regulators binding specific DNA sites with high affinity and also global regulators – abundant DNA architectural proteins of the bacterial nucleoid binding multiple sites with a wide range of affinities and thus modulating the superhelical density of DNA. The first form of transcriptional regulation is predominantly pairwise and specific, representing digitial control, while the second form is (in strength and distribution continuous, representing analog control. Results Here we look at the properties of effective networks derived from significant gene expression changes under variation of the two forms of control and find that upon limitations of one type of control (caused e.g. by mutation of a global DNA architectural factor the other type can compensate for compromised regulation. Mutations of global regulators significantly enhance the digital control, whereas in the presence of global DNA architectural proteins regulation is mostly of the analog type, coupling spatially neighboring genomic loci. Taken together our data suggest that two logically distinct – digital and analog – types of control are balancing each other. Conclusion By revealing two distinct logical types of control, our approach provides basic insights into both the organizational principles of transcriptional regulation and the mechanisms buffering genetic flexibility. We anticipate that the general concept of distinguishing logical types of control will apply to many complex biological networks.

  16. MicroRNA-gene expression network in murine liver during Schistosoma japonicum infection.

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    Pengfei Cai

    Full Text Available BACKGROUND: Schistosomiasis japonica remains a significant public health problem in China and Southeast Asian countries. The most typical and serious outcome of the chronic oriental schistosomiasis is the progressive granuloma and fibrosis in the host liver, which has been a major medical challenge. However, the molecular mechanism underling the hepatic pathogenesis is still not clear. METHODOLOGY AND PRINCIPAL FINDINGS: Using microarrays, we quantified the temporal gene expression profiles in the liver of Schistosoma japonicum-infected BALB/c mice at 15, 30, and 45 day post infection (dpi with that from uninfected mice as controls. Gene expression alternation associated with liver damage was observed in the initial phase of infection (dpi 15, which became more magnificent with the onset of egg-laying. Up-regulated genes were dominantly associated with inflammatory infiltration, whereas down-regulated genes primarily led to the hepatic functional disorders. Simultaneously, microRNA profiles from the same samples were decoded by Solexa sequencing. More than 130 miRNAs were differentially expressed in murine liver during S. japonicum infection. MiRNAs significantly dysregulated in the mid-phase of infection (dpi 30, such as mmu-miR-146b and mmu-miR-155, may relate to the regulation of hepatic inflammatory responses, whereas miRNAs exhibiting a peak expression in the late phase of infection (dpi 45, such as mmu-miR-223, mmu-miR-146a/b, mmu-miR-155, mmu-miR-34c, mmu-miR-199, and mmu-miR-134, may represent a molecular signature of the development of schistosomal hepatopathy. Further, a dynamic miRNA-gene co-expression network in the progression of infection was constructed. CONCLUSIONS AND SIGNIFICANCE: This study presents a global view of dynamic expression of both mRNA and miRNA transcripts in murine liver during S. japonicum infection, and highlights that miRNAs may play a variety of regulatory roles in balancing the immune responses during the

  17. Physiological Responses and Gene Co-Expression Network of Mycorrhizal Roots under K(+) Deprivation.

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    Garcia, Kevin; Chasman, Deborah; Roy, Sushmita; Ané, Jean-Michel

    2017-03-01

    Arbuscular mycorrhizal (AM) associations enhance the phosphorous and nitrogen nutrition of host plants, but little is known about their role in potassium (K(+)) nutrition. Medicago truncatula plants were cocultured with the AM fungus Rhizophagus irregularis under high and low K(+) regimes for 6 weeks. We determined how K(+) deprivation affects plant development and mineral acquisition and how these negative effects are tempered by the AM colonization. The transcriptional response of AM roots under K(+) deficiency was analyzed by whole-genome RNA sequencing. K(+) deprivation decreased root biomass and external K(+) uptake and modulated oxidative stress gene expression in M. truncatula roots. AM colonization induced specific transcriptional responses to K(+) deprivation that seem to temper these negative effects. A gene network analysis revealed putative key regulators of these responses. This study confirmed that AM associations provide some tolerance to K(+) deprivation to host plants, revealed that AM symbiosis modulates the expression of specific root genes to cope with this nutrient stress, and identified putative regulators participating in these tolerance mechanisms. © 2017 American Society of Plant Biologists. All Rights Reserved.

  18. CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data.

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    Zheng, Guangyong; Xu, Yaochen; Zhang, Xiujun; Liu, Zhi-Ping; Wang, Zhuo; Chen, Luonan; Zhu, Xin-Guang

    2016-12-23

    A gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale. Here, we present a software package for gene regulatory network reconstruction at a genomic level, in which gene interaction is measured by the conditional mutual information measurement using a parallel computing framework (so the package is named CMIP). The package is a greatly improved implementation of our previous PCA-CMI algorithm. In CMIP, we provide not only an automatic threshold determination method but also an effective parallel computing framework for network inference. Performance tests on benchmark datasets show that the accuracy of CMIP is comparable to most current network inference methods. Moreover, running tests on synthetic datasets demonstrate that CMIP can handle large datasets especially genome-wide datasets within an acceptable time period. In addition, successful application on a real genomic dataset confirms its practical applicability of the package. This new software package provides a powerful tool for genomic network reconstruction to biological community. The software can be accessed at http://www.picb.ac.cn/CMIP/ .

  19. Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.

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    Hindol Rakshit

    Full Text Available BACKGROUND: Parkinson's Disease (PD is one of the most prevailing neurodegenerative diseases. Improving diagnoses and treatments of this disease is essential, as currently there exists no cure for this disease. Microarray and proteomics data have revealed abnormal expression of several genes and proteins responsible for PD. Nevertheless, few studies have been reported involving PD-specific protein-protein interactions. RESULTS: Microarray based gene expression data and protein-protein interaction (PPI databases were combined to construct the PPI networks of differentially expressed (DE genes in post mortem brain tissue samples of patients with Parkinson's disease. Samples were collected from the substantia nigra and the frontal cerebral cortex. From the microarray data, two sets of DE genes were selected by 2-tailed t-tests and Significance Analysis of Microarrays (SAM, run separately to construct two Query-Query PPI (QQPPI networks. Several topological properties of these networks were studied. Nodes with High Connectivity (hubs and High Betweenness Low Connectivity (bottlenecks were identified to be the most significant nodes of the networks. Three and four-cliques were identified in the QQPPI networks. These cliques contain most of the topologically significant nodes of the networks which form core functional modules consisting of tightly knitted sub-networks. Hitherto unreported 37 PD disease markers were identified based on their topological significance in the networks. Of these 37 markers, eight were significantly involved in the core functional modules and showed significant change in co-expression levels. Four (ARRB2, STX1A, TFRC and MARCKS out of the 37 markers were found to be associated with several neurotransmitters including dopamine. CONCLUSION: This study represents a novel investigation of the PPI networks for PD, a complex disease. 37 proteins identified in our study can be considered as PD network biomarkers. These network

  20. A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis.

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    Akutekwe, Arinze; Seker, Huseyin

    2014-01-01

    Computational and machine learning techniques have been applied in identifying biomarkers and constructing predictive models for diagnosis of hypertension. Strategies such as improved classification rules based on decision trees have been proposed. Other techniques such as Fuzzy Expert Systems (FES) and Neuro-Fuzzy Systems (NFS) have recently been applied. However, these methods lack the ability to detect temporal relationships among biomarker genes that will aid better understanding of the mechanism of hypertension disease. In this paper we apply a proposed two-stage bio-network construction approach that combines the power and computational efficiency of classification methods with the well-established predictive ability of Dynamic Bayesian Network. We demonstrate our method using the analysis of male young-onset hypertension microarray dataset. Four key genes were identified by the Least Angle Shrinkage and Selection Operator (LASSO) and three Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. Results show that cell regulation FOXQ1 may inhibit the expression of focusyltransferase-6 (FUT6) and that ABCG1 ATP-binding cassette sub-family G may also play inhibitory role against NR2E3 nuclear receptor sub-family 2 and CGB2 Chromatin Gonadotrophin.

  1. Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis.

    Science.gov (United States)

    Ludovini, Vienna; Bianconi, Fortunato; Siggillino, Annamaria; Piobbico, Danilo; Vannucci, Jacopo; Metro, Giulio; Chiari, Rita; Bellezza, Guido; Puma, Francesco; Della Fazia, Maria Agnese; Servillo, Giuseppe; Crinò, Lucio

    2016-05-24

    Risk assessment and treatment choice remains a challenge in early non-small-cell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR.From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS).Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.

  2. POWERDRESS and diversified expression of the MIR172 gene family bolster the floral stem cell network.

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    Rae Eden Yumul

    Full Text Available Termination of the stem cells in the floral meristem (also known as floral determinacy is critical for the reproductive success of plants, and the molecular activities regulating floral determinacy are precisely orchestrated during the course of floral development. In Arabidopsis thaliana, regulators of floral determinacy include several transcription factor genes, such as APETALA2 (AP2, AGAMOUS (AG, SUPERMAN (SUP, and CRABSCLAW (CRC, as well as a microRNA (miRNA, miR172, which targets AP2. How the transcription factor and miRNA genes are coordinately regulated to achieve floral determinacy is unknown. A mutation in POWERDRESS (PWR, a previously uncharacterized gene encoding a SANT-domain-containing protein, was isolated in this study as an enhancer of the weakly indeterminate ag-10 allele. PWR was found to promote the transcription of CRC, MIR172a, b, and c and/or enhance Pol II occupancy at their promoters, without affecting MIR172d or e. A mutation in mature miR172d was additionally found to enhance the determinacy defects of ag-10 in an AP2-dependent manner, providing direct evidence that miR172d is functional in repressing AP2 and thereby contributes to floral determinacy. Thus, while PWR promotes floral determinacy by enhancing the expression of three of the five MIR172 members as well as CRC, MIR172d, whose expression is PWR-independent, also functions in floral stem cell termination. Taken together, these findings demonstrate how transcriptional diversification and functional redundancy of a miRNA family along with PWR-mediated co-regulation of miRNA and transcription factor genes contribute to the robustness of the floral determinacy network.

  3. Artificial Neural Networks and Gene Expression Programing based age estimation using facial features

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    Baddrud Z. Laskar

    2015-10-01

    Full Text Available This work is about estimating human age automatically through analysis of facial images. It has got a lot of real-world applications. Due to prompt advances in the fields of machine vision, facial image processing, and computer graphics, automatic age estimation via faces in computer is one of the dominant topics these days. This is due to widespread real-world applications, in areas of biometrics, security, surveillance, control, forensic art, entertainment, online customer management and support, along with cosmetology. As it is difficult to estimate the exact age, this system is to estimate a certain range of ages. Four sets of classifications have been used to differentiate a person’s data into one of the different age groups. The uniqueness about this study is the usage of two technologies i.e., Artificial Neural Networks (ANN and Gene Expression Programing (GEP to estimate the age and then compare the results. New methodologies like Gene Expression Programing (GEP have been explored here and significant results were found. The dataset has been developed to provide more efficient results by superior preprocessing methods. This proposed approach has been developed, tested and trained using both the methods. A public data set was used to test the system, FG-NET. The quality of the proposed system for age estimation using facial features is shown by broad experiments on the available database of FG-NET.

  4. Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls

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    Knecht, Carolin; Fretter, Christoph; Rosenstiel, Philip; Krawczak, Michael; Hütt, Marc-Thorsten

    2016-09-01

    Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of ‘coherence’, we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data.

  5. Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

    NARCIS (Netherlands)

    Waaijenborg, S.; Zwinderman, A.H.

    2009-01-01

    ABSTRACT: BACKGROUND: We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the can

  6. Gene expression profiling in C57BL/6J and A/J mouse inbred strains reveals gene networks specific for brain regions independent of genetic background

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    Horvath Steve

    2010-01-01

    Full Text Available Abstract Background We performed gene expression profiling of the amygdala and hippocampus taken from inbred mouse strains C57BL/6J and A/J. The selected brain areas are implicated in neurobehavioral traits while these mouse strains are known to differ widely in behavior. Consequently, we hypothesized that comparing gene expression profiles for specific brain regions in these strains might provide insight into the molecular mechanisms of human neuropsychiatric traits. We performed a whole-genome gene expression experiment and applied a systems biology approach using weighted gene co-expression network analysis. Results We were able to identify modules of co-expressed genes that distinguish a strain or brain region. Analysis of the networks that are most informative for hippocampus and amygdala revealed enrichment in neurologically, genetically and psychologically related pathways. Close examination of the strain-specific gene expression profiles, however, revealed no functional relevance but a significant enrichment of single nucleotide polymorphisms in the probe sequences used for array hybridization. This artifact was not observed for the modules of co-expressed genes that distinguish amygdala and hippocampus. Conclusions The brain-region specific modules were found to be independent of genetic background and are therefore likely to represent biologically relevant molecular networks that can be studied to complement our knowledge about pathways in neuropsychiatric disease.

  7. A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression

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    So, Nina; Franks, Becca; Lim, Sean; Curley, James P.

    2015-01-01

    Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David’s Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg’s Hub Centrality and Bonacich’s Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing and intensive

  8. Gene expression profiles of the NCI-60 human tumor cell lines define molecular interaction networks governing cell migration processes.

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    Kurt W Kohn

    Full Text Available Although there is extensive information on gene expression and molecular interactions in various cell types, integrating those data in a functionally coherent manner remains challenging. This study explores the premise that genes whose expression at the mRNA level is correlated over diverse cell lines are likely to function together in a network of molecular interactions. We previously derived expression-correlated gene clusters from the database of the NCI-60 human tumor cell lines and associated each cluster with function categories of the Gene Ontology (GO database. From a cluster rich in genes associated with GO categories related to cell migration, we extracted 15 genes that were highly cross-correlated; prominent among them were RRAS, AXL, ADAM9, FN14, and integrin-beta1. We then used those 15 genes as bait to identify other correlated genes in the NCI-60 database. A survey of current literature disclosed, not only that many of the expression-correlated genes engaged in molecular interactions related to migration, invasion, and metastasis, but that highly cross-correlated subsets of those genes engaged in specific cell migration processes. We assembled this information in molecular interaction maps (MIMs that depict networks governing 3 cell migration processes: degradation of extracellular matrix, production of transient focal complexes at the leading edge of the cell, and retraction of the rear part of the cell. Also depicted are interactions controlling the release and effects of calcium ions, which may regulate migration in a spaciotemporal manner in the cell. The MIMs and associated text comprise a detailed and integrated summary of what is currently known or surmised about the role of the expression cross-correlated genes in molecular networks governing those processes.

  9. Transcriptional interference networks coordinate the expression of functionally-related genes clustered in the same genomic loci

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    Zsolt eBoldogkoi

    2012-07-01

    Full Text Available The regulation of gene expression is essential for normal functioning of biological systems in every form of life. Gene expression is primarily controlled at the level of transcription, especially at the phase of initiation. Non-coding RNAs are one of the major players at every level of genetic regulation, including the control of chromatin organisation, transcription, various post-transcriptional processes and translation. In this study, the Transcriptional Interference Network (TIN hypothesis was put forward in an attempt to explain the global expression of antisense RNAs and the overall occurrence of tandem gene clusters in the genomes of various biological systems ranging from viruses to mammalian cells. The TIN hypothesis suggests the existence of a novel layer of genetic regulation, based on the interactions between the transcriptional machineries of neighbouring genes at their overlapping regions, which are assumed to play a fundamental role in coordinating gene expression within a cluster of functionally-linked genes. It is claimed that the transcriptional overlaps between adjacent genes are much more widespread in genomes than is thought today. The Waterfall model of the TIN hypothesis postulates a unidirectional effect of upstream genes on the transcription of downstream genes within a cluster of tandemly-arrayed genes, while the Seesaw model proposes a mutual interdependence of gene expression between the oppositely-oriented genes. The TIN represents an auto-regulatory system with an exquisitely timed and highly synchronised cascade of gene expression in functionally-linked genes located in close physical proximity to each other. In this study, we focused on herpesviruses. The reason for this lies in the compressed nature of viral genes, which allows a tight regulation and an easier investigation of the transcriptional interactions between genes. However, I believe that the same or similar principles can be applied to cellular

  10. Expression profiling of Crambe abyssinica under arsenate stress identifies genes and gene networks involved in arsenic metabolism and detoxification

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    Kandasamy Suganthi

    2010-06-01

    Full Text Available Abstract Background Arsenic contamination is widespread throughout the world and this toxic metalloid is known to cause cancers of organs such as liver, kidney, skin, and lung in human. In spite of a recent surge in arsenic related studies, we are still far from a comprehensive understanding of arsenic uptake, detoxification, and sequestration in plants. Crambe abyssinica, commonly known as 'abyssinian mustard', is a non-food, high biomass oil seed crop that is naturally tolerant to heavy metals. Moreover, it accumulates significantly higher levels of arsenic as compared to other species of the Brassicaceae family. Thus, C. abyssinica has great potential to be utilized as an ideal inedible crop for phytoremediation of heavy metals and metalloids. However, the mechanism of arsenic metabolism in higher plants, including C. abyssinica, remains elusive. Results To identify the differentially expressed transcripts and the pathways involved in arsenic metabolism and detoxification, C. abyssinica plants were subjected to arsenate stress and a PCR-Select Suppression Subtraction Hybridization (SSH approach was employed. A total of 105 differentially expressed subtracted cDNAs were sequenced which were found to represent 38 genes. Those genes encode proteins functioning as antioxidants, metal transporters, reductases, enzymes involved in the protein degradation pathway, and several novel uncharacterized proteins. The transcripts corresponding to the subtracted cDNAs showed strong upregulation by arsenate stress as confirmed by the semi-quantitative RT-PCR. Conclusions Our study revealed novel insights into the plant defense mechanisms and the regulation of genes and gene networks in response to arsenate toxicity. The differential expression of transcripts encoding glutathione-S-transferases, antioxidants, sulfur metabolism, heat-shock proteins, metal transporters, and enzymes in the ubiquitination pathway of protein degradation as well as several unknown

  11. Modelling formulations using gene expression programming--a comparative analysis with artificial neural networks.

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    Colbourn, E A; Roskilly, S J; Rowe, R C; York, P

    2011-10-09

    This study has investigated the utility and potential advantages of gene expression programming (GEP)--a new development in evolutionary computing for modelling data and automatically generating equations that describe the cause-and-effect relationships in a system--to four types of pharmaceutical formulation and compared the models with those generated by neural networks, a technique now widely used in the formulation development. Both methods were capable of discovering subtle and non-linear relationships within the data, with no requirement from the user to specify the functional forms that should be used. Although the neural networks rapidly developed models with higher values for the ANOVA R(2) these were black box and provided little insight into the key relationships. However, GEP, although significantly slower at developing models, generated relatively simple equations describing the relationships that could be interpreted directly. The results indicate that GEP can be considered an effective and efficient modelling technique for formulation data. Copyright © 2011 Elsevier B.V. All rights reserved.

  12. A microarray gene expression data classification using hybrid back propagation neural network

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    Vimaladevi M.

    2014-01-01

    Full Text Available Classification of cancer establishes appropriate treatment and helps to decide the diagnosis. Cancer expands progressively from an alteration in a cell's genetic structure. This change (mutation results in cells with uncontrolled growth patterns. In cancer classification, the approach, Back propagation is sufficient and also it is a universal technique of training artificial neural networks. It is also called supervised learning method. It needs many dataset for input and output for making up the training set. The back propagation method may execute the function of collaborate multiple parties. In existing method, collaborative learning is limited and it considers only two parties. The proposed collaborative function can perform well and problems can be solved by utilizing the power of cloud computing. This technical note applies hybrid models of Back Propagation Neural networks (BPN and fast Genetic Algorithms (GA to estimate the feature selection in gene expression data. The proposed research work examines many feature selection algorithms which are “fragile”; that is, the superiority of their results varies broadly over data sets. By this research, it is suggested that this is due to higherorder interactions between features causing restricted minima in search space in which the algorithm becomes attentive. GAs may escape from such minima by chance, because works are highly stochastic. A neural net classifier with a genetic algorithm, using the GA to select features for classification by the neural net and incorporating the net as part of the objective function of the GA.

  13. The physics of protein-DNA interaction networks in the control of gene expression

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    Saiz, Leonor

    2012-05-01

    Protein-DNA interaction networks play a central role in many fundamental cellular processes. In gene regulation, physical interactions and reactions among the molecular components together with the physical properties of DNA control how genes are turned on and off. A key player in all these processes is the inherent flexibility of DNA, which provides an avenue for long-range interactions between distal DNA elements through DNA looping. Such versatility enables multiple interactions and results in additional complexity that is remarkably difficult to address with traditional approaches. This topical review considers recent advances in statistical physics methods to study the assembly of protein-DNA complexes with loops, their effects in the control of gene expression, and their explicit application to the prototypical lac operon genetic system of the E. coli bacterium. In the last decade, it has been shown that the underlying physical properties of DNA looping can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including the balance between robustness and sensitivity of the induction process. These physical properties are largely dependent on the free energy of DNA looping, which accounts for DNA bending and twisting effects. These new physical methods have also been used in reverse to uncover the actual in vivo free energy of looping double-stranded DNA in living cells, which was not possible with existing experimental techniques. The results obtained for DNA looping by the lac repressor inside the E. coli bacterium showed a more malleable DNA than expected as a result of the interplay of the simultaneous presence of two distinct conformations of looped DNA.

  14. A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development

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    Stigler Brandilyn

    2012-06-01

    Full Text Available Abstract Background Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments. Results To facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle and ectodermal (skin cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes

  15. A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development.

    Science.gov (United States)

    Stigler, Brandilyn; Chamberlin, Helen M

    2012-06-26

    Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments. To facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9. This work provides a strong

  16. Ganoderma lucidum polysaccharides in human monocytic leukemia cells: from gene expression to network construction

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    Ou Chern-Han

    2007-11-01

    Full Text Available Abstract Background Ganoderma lucidum has been widely used as a herbal medicine for promoting health and longevity in China and other Asian countries. Polysaccharide extracts from Ganoderma lucidum have been reported to exhibit immuno-modulating and anti-tumor activities. In previous studies, F3, the active component of the polysaccharide extract, was found to activate various cytokines such as IL-1, IL-6, IL-12, and TNF-α. This gave rise to our investigation on how F3 stimulates immuno-modulating or anti-tumor effects in human leukemia THP-1 cells. Results Here, we integrated time-course DNA microarray analysis, quantitative PCR assays, and bioinformatics methods to study the F3-induced effects in THP-1 cells. Significantly disturbed pathways induced by F3 were identified with statistical analysis on microarray data. The apoptosis induction through the DR3 and DR4/5 death receptors was found to be one of the most significant pathways and play a key role in THP-1 cells after F3 treatment. Based on time-course gene expression measurements of the identified pathway, we reconstructed a plausible regulatory network of the involved genes using reverse-engineering computational approach. Conclusion Our results showed that F3 may induce death receptor ligands to initiate signaling via receptor oligomerization, recruitment of specialized adaptor proteins and activation of caspase cascades.

  17. The Fanconi anemia/BRCA gene network in zebrafish: Embryonic expression and comparative genomics

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    Titus, Tom A.; Yan Yilin; Wilson, Catherine; Starks, Amber M.; Frohnmayer, Jonathan D.; Bremiller, Ruth A.; Canestro, Cristian; Rodriguez-Mari, Adriana; He Xinjun [Institute of Neuroscience, University of Oregon, 1425 E. 13th Avenue, Eugene, OR 97403 (United States); Postlethwait, John H., E-mail: jpostle@uoneuro.uoregon.edu [Institute of Neuroscience, University of Oregon, 1425 E. 13th Avenue, Eugene, OR 97403 (United States)

    2009-07-31

    Fanconi anemia (FA) is a genetic disease resulting in bone marrow failure, high cancer risks, and infertility, and developmental anomalies including microphthalmia, microcephaly, hypoplastic radius and thumb. Here we present cDNA sequences, genetic mapping, and genomic analyses for the four previously undescribed zebrafish FA genes (fanci, fancj, fancm, and fancn), and show that they reverted to single copy after the teleost genome duplication. We tested the hypothesis that FA genes are expressed during embryonic development in tissues that are disrupted in human patients by investigating fanc gene expression patterns. We found fanc gene maternal message, which can provide Fanc proteins to repair DNA damage encountered in rapid cleavage divisions. Zygotic expression was broad but especially strong in eyes, central nervous system and hematopoietic tissues. In the pectoral fin bud at hatching, fanc genes were expressed specifically in the apical ectodermal ridge, a signaling center for fin/limb development that may be relevant to the radius/thumb anomaly of FA patients. Hatching embryos expressed fanc genes strongly in the oral epithelium, a site of squamous cell carcinomas in FA patients. Larval and adult zebrafish expressed fanc genes in proliferative regions of the brain, which may be related to microcephaly in FA. Mature ovaries and testes expressed fanc genes in specific stages of oocyte and spermatocyte development, which may be related to DNA repair during homologous recombination in meiosis and to infertility in human patients. The intestine strongly expressed some fanc genes specifically in proliferative zones. Our results show that zebrafish has a complete complement of fanc genes in single copy and that these genes are expressed in zebrafish embryos and adults in proliferative tissues that are often affected in FA patients. These results support the notion that zebrafish offers an attractive experimental system to help unravel mechanisms relevant not only

  18. Weighted gene co-expression network analysis in identification of metastasis-related genes of lung squamous cell carcinoma based on the Cancer Genome Atlas database

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    Tian, Feng; Zhao, Jinlong; Kang, Zhenxing

    2017-01-01

    Background Lung squamous cell carcinoma (lung SCC) is a common type of malignancy. Its pathogenesis mechanism of tumor development is unclear. The aim of this study was to identify key genes for diagnosis biomarkers in lung SCC metastasis. Methods We searched and downloaded mRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify differences in mRNA expression of primary tumor tissues from lung SCC with and without metastasis. Gene co-expression network analysis, protein-protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and quantitative real-time polymerase chain reactions (qRT-PCR) were used to explore the biological functions of the identified dysregulated genes. Results Four hundred and eighty-two differentially expressed genes (DEGs) were identified between lung SCC with and without metastasis. Nineteen modules were identified in lung SCC through weighted gene co-expression network analysis (WGCNA). Twenty-three DEGs and 26 DEGs were significantly enriched in the respective pink and black module. KEGG pathway analysis displayed that 26 DEGs in the black module were significantly enriched in bile secretion pathway. Forty-nine DEGs in the two gene co-expression module were used to construct PPI network. CFTR in the black module was the hub protein, had the connectivity with 182 genes. The results of qRT-PCR displayed that FIGF, SFTPD, DYNLRB2 were significantly down-regulated in the tumor samples of lung SCC with metastasis and CFTR, SCGB3A2, SSTR1, SCTR, ROPN1L had the down-regulation tendency in lung SCC with metastasis compared to lung SCC without metastasis. Conclusions The dysregulated genes including CFTR, SCTR and FIGF might be involved in the pathology of lung SCC metastasis and could be used as potential diagnosis biomarkers or therapeutic targets for lung SCC.

  19. Weighted gene co-expression network analysis in identification of metastasis-related genes of lung squamous cell carcinoma based on the Cancer Genome Atlas database.

    Science.gov (United States)

    Tian, Feng; Zhao, Jinlong; Fan, Xinlei; Kang, Zhenxing

    2017-01-01

    Lung squamous cell carcinoma (lung SCC) is a common type of malignancy. Its pathogenesis mechanism of tumor development is unclear. The aim of this study was to identify key genes for diagnosis biomarkers in lung SCC metastasis. We searched and downloaded mRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify differences in mRNA expression of primary tumor tissues from lung SCC with and without metastasis. Gene co-expression network analysis, protein-protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and quantitative real-time polymerase chain reactions (qRT-PCR) were used to explore the biological functions of the identified dysregulated genes. Four hundred and eighty-two differentially expressed genes (DEGs) were identified between lung SCC with and without metastasis. Nineteen modules were identified in lung SCC through weighted gene co-expression network analysis (WGCNA). Twenty-three DEGs and 26 DEGs were significantly enriched in the respective pink and black module. KEGG pathway analysis displayed that 26 DEGs in the black module were significantly enriched in bile secretion pathway. Forty-nine DEGs in the two gene co-expression module were used to construct PPI network. CFTR in the black module was the hub protein, had the connectivity with 182 genes. The results of qRT-PCR displayed that FIGF, SFTPD, DYNLRB2 were significantly down-regulated in the tumor samples of lung SCC with metastasis and CFTR, SCGB3A2, SSTR1, SCTR, ROPN1L had the down-regulation tendency in lung SCC with metastasis compared to lung SCC without metastasis. The dysregulated genes including CFTR, SCTR and FIGF might be involved in the pathology of lung SCC metastasis and could be used as potential diagnosis biomarkers or therapeutic targets for lung SCC.

  20. Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

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    Zwinderman Aeilko H

    2009-09-01

    Full Text Available Abstract Background We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes. Results We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes. Conclusion We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.

  1. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks | Center for Cancer Research

    Science.gov (United States)

    The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification.

  2. Analysis of global gene expression in Brachypodium distachyon reveals extensive network plasticity in response to abiotic stress.

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    Henry D Priest

    Full Text Available Brachypodium distachyon is a close relative of many important cereal crops. Abiotic stress tolerance has a significant impact on productivity of agriculturally important food and feedstock crops. Analysis of the transcriptome of Brachypodium after chilling, high-salinity, drought, and heat stresses revealed diverse differential expression of many transcripts. Weighted Gene Co-Expression Network Analysis revealed 22 distinct gene modules with specific profiles of expression under each stress. Promoter analysis implicated short DNA sequences directly upstream of module members in the regulation of 21 of 22 modules. Functional analysis of module members revealed enrichment in functional terms for 10 of 22 network modules. Analysis of condition-specific correlations between differentially expressed gene pairs revealed extensive plasticity in the expression relationships of gene pairs. Photosynthesis, cell cycle, and cell wall expression modules were down-regulated by all abiotic stresses. Modules which were up-regulated by each abiotic stress fell into diverse and unique gene ontology GO categories. This study provides genomics resources and improves our understanding of abiotic stress responses of Brachypodium.

  3. Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing

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    Hideko Sone

    2011-12-01

    Full Text Available The establishment of more efficient approaches for developmental neurotoxicity testing (DNT has been an emerging issue for children’s environmental health. Here we describe a systematic approach for DNT using the neuronal differentiation of mouse embryonic stem cells (mESCs as a model of fetal programming. During embryoid body (EB formation, mESCs were exposed to 12 chemicals for 24 h and then global gene expression profiling was performed using whole genome microarray analysis. Gene expression signatures for seven kinds of gene sets related to neuronal development and neuronal diseases were selected for further analysis. At the later stages of neuronal cell differentiation from EBs, neuronal phenotypic parameters were determined using a high-content image analyzer. Bayesian network analysis was then performed based on global gene expression and neuronal phenotypic data to generate comprehensive networks with a linkage between early events and later effects. Furthermore, the probability distribution values for the strength of the linkage between parameters in each network was calculated and then used in principal component analysis. The characterization of chemicals according to their neurotoxic potential reveals that the multi-parametric analysis based on phenotype and gene expression profiling during neuronal differentiation of mESCs can provide a useful tool to monitor fetal programming and to predict developmentally neurotoxic compounds.

  4. Multi-parametric profiling network based on gene expression and phenotype data: a novel approach to developmental neurotoxicity testing.

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    Nagano, Reiko; Akanuma, Hiromi; Qin, Xian-Yang; Imanishi, Satoshi; Toyoshiba, Hiroyoshi; Yoshinaga, Jun; Ohsako, Seiichiroh; Sone, Hideko

    2012-01-01

    The establishment of more efficient approaches for developmental neurotoxicity testing (DNT) has been an emerging issue for children's environmental health. Here we describe a systematic approach for DNT using the neuronal differentiation of mouse embryonic stem cells (mESCs) as a model of fetal programming. During embryoid body (EB) formation, mESCs were exposed to 12 chemicals for 24 h and then global gene expression profiling was performed using whole genome microarray analysis. Gene expression signatures for seven kinds of gene sets related to neuronal development and neuronal diseases were selected for further analysis. At the later stages of neuronal cell differentiation from EBs, neuronal phenotypic parameters were determined using a high-content image analyzer. Bayesian network analysis was then performed based on global gene expression and neuronal phenotypic data to generate comprehensive networks with a linkage between early events and later effects. Furthermore, the probability distribution values for the strength of the linkage between parameters in each network was calculated and then used in principal component analysis. The characterization of chemicals according to their neurotoxic potential reveals that the multi-parametric analysis based on phenotype and gene expression profiling during neuronal differentiation of mESCs can provide a useful tool to monitor fetal programming and to predict developmentally neurotoxic compounds.

  5. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

    DEFF Research Database (Denmark)

    Kogelman, Lisette; Cirera Salicio, Susanna; Zhernakova, Daria V.

    2014-01-01

    interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model...... in a porcine model. Methods We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes...... in humans and rodents, e.g. CSF1R and MARC2. Conclusions To our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory...

  6. Insights into the Function of Long Noncoding RNAs in Sepsis Revealed by Gene Co-Expression Network Analysis

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    Diogo Vieira da Silva Pellegrina

    2017-01-01

    Full Text Available Sepsis is a major cause of death and its incidence and mortality increase exponentially with age. Most gene expression studies in sepsis have focused in protein-coding genes and the expression patterns, and potential roles of long noncoding RNAs (lncRNAs have not been investigated yet. In this study, we performed co-expression network analysis of protein-coding and lncRNAs measured in neutrophil granulocytes from adult and elderly septic patients, along with age-matched healthy controls. We found that the genes displaying highest network similarity are predominantly differently expressed in sepsis and are enriched in loci encoding proteins with structural or regulatory functions related to protein translation and mitochondrial energetic metabolism. A number of lncRNAs are strongly connected to genes from these pathways and may take part in regulatory loops that are perturbed in sepsis. Among those, the ribosomal pseudogenes RP11-302F12.1 and RPL13AP7 are differentially expressed and appear to have a regulatory role on protein translation in both the elderly and adults, and lncRNAs MALAT1, LINC00355, MYCNOS, and AC010970.2 display variable connection strength and inverted expression patterns between adult and elderly networks, suggesting that they are the best candidates to be further studied to understand the mechanisms by which the immune response is impaired by age. In summary, we report the expression of lncRNAs that are deregulated in patients with sepsis, including subsets that display hub properties in molecular pathways relevant to the disease pathogenesis and that may participate in gene expression regulatory circuits related to the poorer disease outcome observed in elderly subjects.

  7. A combinational feature selection and ensemble neural network method for classification of gene expression data

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    Jiang Tianzi

    2004-09-01

    Full Text Available Abstract Background Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. To date, this problem has received most attention in the context of cancer research, especially in tumor classification. Various feature selection methods and classifier design strategies also have been generally used and compared. However, most published articles on tumor classification have applied a certain technique to a certain dataset, and recently several researchers compared these techniques based on several public datasets. But, it has been verified that differently selected features reflect different aspects of the dataset and some selected features can obtain better solutions on some certain problems. At the same time, faced with a large amount of microarray data with little knowledge, it is difficult to find the intrinsic characteristics using traditional methods. In this paper, we attempt to introduce a combinational feature selection method in conjunction with ensemble neural networks to generally improve the accuracy and robustness of sample classification. Results We validate our new method on several recent publicly available datasets both with predictive accuracy of testing samples and through cross validation. Compared with the best performance of other current methods, remarkably improved results can be obtained using our new strategy on a wide range of different datasets. Conclusions Thus, we conclude that our methods can obtain more information in microarray data to get more accurate classification and also can help to extract the latent marker genes of the diseases for better diagnosis and treatment.

  8. Network State of Mind : Gene expression profiling of neuropsychiatric traits in human and mouse

    NARCIS (Netherlands)

    de Jong, S.

    2011-01-01

    Neuropsychiatric disorders like schizophrenia are likely caused by a large number of genes with a small effect that are difficult to identify using traditional genetic association studies. Therefore, we have applied system biology approaches in order to examine patterns of gene expression in healthy

  9. Network State of Mind : Gene expression profiling of neuropsychiatric traits in human and mouse

    NARCIS (Netherlands)

    de Jong, S.

    2011-01-01

    Neuropsychiatric disorders like schizophrenia are likely caused by a large number of genes with a small effect that are difficult to identify using traditional genetic association studies. Therefore, we have applied system biology approaches in order to examine patterns of gene expression in healthy

  10. Contribution of Network Connectivity in Determining the Relationship between Gene Expression and Metabolite Concentration Changes

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej; Sheridan, Steven; Patil, Kiran Raosaheb

    2014-01-01

    One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First, at the ...

  11. Identifying Tmem59 related gene regulatory network of mouse neural stem cell from a compendium of expression profiles

    Directory of Open Access Journals (Sweden)

    Guo Xiuyun

    2011-09-01

    Full Text Available Abstract Background Neural stem cells offer potential treatment for neurodegenerative disorders, such like Alzheimer's disease (AD. While much progress has been made in understanding neural stem cell function, a precise description of the molecular mechanisms regulating neural stem cells is not yet established. This lack of knowledge is a major barrier holding back the discovery of therapeutic uses of neural stem cells. In this paper, the regulatory mechanism of mouse neural stem cell (NSC differentiation by tmem59 is explored on the genome-level. Results We identified regulators of tmem59 during the differentiation of mouse NSCs from a compendium of expression profiles. Based on the microarray experiment, we developed the parallelized SWNI algorithm to reconstruct gene regulatory networks of mouse neural stem cells. From the inferred tmem59 related gene network including 36 genes, pou6f1 was identified to regulate tmem59 significantly and might play an important role in the differentiation of NSCs in mouse brain. There are four pathways shown in the gene network, indicating that tmem59 locates in the downstream of the signalling pathway. The real-time RT-PCR results shown that the over-expression of pou6f1 could significantly up-regulate tmem59 expression in C17.2 NSC line. 16 out of 36 predicted genes in our constructed network have been reported to be AD-related, including Ace, aqp1, arrdc3, cd14, cd59a, cds1, cldn1, cox8b, defb11, folr1, gdi2, mmp3, mgp, myrip, Ripk4, rnd3, and sncg. The localization of tmem59 related genes and functional-related gene groups based on the Gene Ontology (GO annotation was also identified. Conclusions Our findings suggest that the expression of tmem59 is an important factor contributing to AD. The parallelized SWNI algorithm increased the efficiency of network reconstruction significantly. This study enables us to highlight novel genes that may be involved in NSC differentiation and provides a shortcut to

  12. Modulation of Gene Expression Networks underlying Realgar-Induced Differentiation of Acute Promyelocytic Leukemia Cells

    Institute of Scientific and Technical Information of China (English)

    王怀宇; 刘陕西

    2002-01-01

    Objective: To elucidate the molecular mechanism of the differentiation of acute promyelocytic leukemia (APL) cell line NB4 induced by realgar. Methods: The response of NB4 cell to realgar was explored with a cDNA microarray representing 1003 different human genes. Results: The analysis of gene expression profiles indicated that 8 genes were up-regulated and 33 genes were down-regulated 48 hrs after realgar treatment. Among the 8 up-regulated genes, 2 genes were involved in ubiquitin proteasome degradation pathway. Some genes related to RNA processing, protein synthesis and signal transduction were down-regulated. Conclusion: The ubiquitin-proteasome degradation pathway may play an important role in the degradation of PML/RAR α fusion protein and the differentiation of NB4 cells.

  13. Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach.

    Science.gov (United States)

    Pham, Lisa M; Carvalho, Luis; Schaus, Scott; Kolaczyk, Eric D

    Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases.

  14. Plant Omics Data Center: an integrated web repository for interspecies gene expression networks with NLP-based curation.

    Science.gov (United States)

    Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro

    2015-01-01

    Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources.

  15. Plant Omics Data Center: An Integrated Web Repository for Interspecies Gene Expression Networks with NLP-Based Curation

    Science.gov (United States)

    Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro

    2015-01-01

    Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. PMID:25505034

  16. Network analysis reveals the relationship among wood properties, gene expression levels and genotypes of natural Populus trichocarpa accessions.

    Science.gov (United States)

    Porth, Ilga; Klápště, Jaroslav; Skyba, Oleksandr; Friedmann, Michael C; Hannemann, Jan; Ehlting, Juergen; El-Kassaby, Yousry A; Mansfield, Shawn D; Douglas, Carl J

    2013-11-01

    High-throughput approaches have been widely applied to elucidate the genetic underpinnings of industrially important wood properties. Wood traits are polygenic in nature, but gene hierarchies can be assessed to identify the most important gene variants controlling specific traits within complex networks defining the overall wood phenotype. We tested a large set of genetic, genomic, and phenotypic information in an integrative approach to predict wood properties in Populus trichocarpa. Nine-yr-old natural P. trichocarpa trees including accessions with high contrasts in six traits related to wood chemistry and ultrastructure were profiled for gene expression on 49k Nimblegen (Roche NimbleGen Inc., Madison, WI, USA) array elements and for 28,831 polymorphic single nucleotide polymorphisms (SNPs). Pre-selected transcripts and SNPs with high statistical dependence on phenotypic traits were used in Bayesian network learning procedures with a stepwise K2 algorithm to infer phenotype-centric networks. Transcripts were pre-selected at a much lower logarithm of Bayes factor (logBF) threshold than SNPs and were not accommodated in the networks. Using persistent variables, we constructed cross-validated networks for variability in wood attributes, which contained four to six variables with 94-100% predictive accuracy. Accommodated gene variants revealed the hierarchy in the genetic architecture that underpins substantial phenotypic variability, and represent new tools to support the maximization of response to selection.

  17. Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

    Science.gov (United States)

    Kaushik, Abhinav; Ali, Shakir; Gupta, Dinesh

    2017-01-01

    Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA. PMID:28084397

  18. Genome-wide profiling of 24 hr diel rhythmicity in the water flea, Daphnia pulex: network analysis reveals rhythmic gene expression and enhances functional gene annotation.

    Science.gov (United States)

    Rund, Samuel S C; Yoo, Boyoung; Alam, Camille; Green, Taryn; Stephens, Melissa T; Zeng, Erliang; George, Gary F; Sheppard, Aaron D; Duffield, Giles E; Milenković, Tijana; Pfrender, Michael E

    2016-08-18

    Marine and freshwater zooplankton exhibit daily rhythmic patterns of behavior and physiology which may be regulated directly by the light:dark (LD) cycle and/or a molecular circadian clock. One of the best-studied zooplankton taxa, the freshwater crustacean Daphnia, has a 24 h diel vertical migration (DVM) behavior whereby the organism travels up and down through the water column daily. DVM plays a critical role in resource tracking and the behavioral avoidance of predators and damaging ultraviolet radiation. However, there is little information at the transcriptional level linking the expression patterns of genes to the rhythmic physiology/behavior of Daphnia. Here we analyzed genome-wide temporal transcriptional patterns from Daphnia pulex collected over a 44 h time period under a 12:12 LD cycle (diel) conditions using a cosine-fitting algorithm. We used a comprehensive network modeling and analysis approach to identify novel co-regulated rhythmic genes that have similar network topological properties and functional annotations as rhythmic genes identified by the cosine-fitting analyses. Furthermore, we used the network approach to predict with high accuracy novel gene-function associations, thus enhancing current functional annotations available for genes in this ecologically relevant model species. Our results reveal that genes in many functional groupings exhibit 24 h rhythms in their expression patterns under diel conditions. We highlight the rhythmic expression of immunity, oxidative detoxification, and sensory process genes. We discuss differences in the chronobiology of D. pulex from other well-characterized terrestrial arthropods. This research adds to a growing body of literature suggesting the genetic mechanisms governing rhythmicity in crustaceans may be divergent from other arthropod lineages including insects. Lastly, these results highlight the power of using a network analysis approach to identify differential gene expression and provide novel

  19. CluGene: A Bioinformatics Framework for the Identification of Co-Localized, Co-Expressed and Co-Regulated Genes Aimed at the Investigation of Transcriptional Regulatory Networks from High-Throughput Expression Data.

    Directory of Open Access Journals (Sweden)

    Tania Dottorini

    Full Text Available The full understanding of the mechanisms underlying transcriptional regulatory networks requires unravelling of complex causal relationships. Genome high-throughput technologies produce a huge amount of information pertaining gene expression and regulation; however, the complexity of the available data is often overwhelming and tools are needed to extract and organize the relevant information. This work starts from the assumption that the observation of co-occurrent events (in particular co-localization, co-expression and co-regulation may provide a powerful starting point to begin unravelling transcriptional regulatory networks. Co-expressed genes often imply shared functional pathways; co-expressed and functionally related genes are often co-localized, too; moreover, co-expressed and co-localized genes are also potential targets for co-regulation; finally, co-regulation seems more frequent for genes mapped to proximal chromosome regions. Despite the recognized importance of analysing co-occurrent events, no bioinformatics solution allowing the simultaneous analysis of co-expression, co-localization and co-regulation is currently available. Our work resulted in developing and valuating CluGene, a software providing tools to analyze multiple types of co-occurrences within a single interactive environment allowing the interactive investigation of combined co-expression, co-localization and co-regulation of genes. The use of CluGene will enhance the power of testing hypothesis and experimental approaches aimed at unravelling transcriptional regulatory networks. The software is freely available at http://bioinfolab.unipg.it/.

  20. Identification of driving network of cellular differentiation from single sample time course gene expression data

    Science.gov (United States)

    Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

    Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

  1. Identifying modularity structure of a genetic network in gene expression profile data

    Directory of Open Access Journals (Sweden)

    Luigi Augugliaro

    2013-05-01

    Full Text Available Aim of this paper is to define a new statistical framework to identify central modules in Gaussian Graphical Models (GGMs estimated by gene expression data measured on a sample of patients with negative molecular response to Imatinib. Imatinib is a drug used to treat certain types of cancer that inmany medical studies has been reported to have a significant clinic effect on chronic myeloid leukemia (CML in chronic phase as well as in blast crisis. For centralmodule in a GGM we intend a module containing genes that are defined differentially expressed.

  2. Network-guided analysis of genes with altered somatic copy number and gene expression reveals pathways commonly perturbed in metastatic melanoma.

    Directory of Open Access Journals (Sweden)

    Armand Valsesia

    Full Text Available Cancer genomes frequently contain somatic copy number alterations (SCNA that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression ('SCNA-genes' in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer.

  3. Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways.

    Science.gov (United States)

    Pey, Jon; Valgepea, Kaspar; Rubio, Angel; Beasley, John E; Planes, Francisco J

    2013-12-08

    The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

  4. Fitness Effects of Network Non-Linearity Induced by Gene Expression Noise

    Science.gov (United States)

    Ray, Christian; Cooper, Tim; Balazsi, Gabor

    2012-02-01

    In the non-equilibrium dynamics of growing microbial cells, metabolic enzymes can create non-linearities in metabolite concentration because of non-linear degradation (utilization): an enzyme can saturate in the process of metabolite utilization. Increasing metabolite production past the saturation point then results in an ultrasensitive metabolite response. If the production rate of a metabolite depends on a second enzyme or other protein-mediated process, uncorrelated gene expression noise can thus cause transient metabolite concentration bursts. Such bursts are physiologically unnecessary and may represent a source of selection against the ultrasensitive switch, especially if the fluctuating metabolic intermediate is toxic. Selection may therefore favor correlated gene expression fluctuations for enzymes in the same pathway, such as by same-operon membership in bacteria. Using a modified experimental lac operon system, we are undertaking a combined theoretical-experimental approach to demonstrate that (i) the lac operon has an implicit ultrasensitive switch that we predict is avoided by gene expression correlations induced by same-operon membership; (ii) bacterial growth rates are sensitive to crossing the ultrasensitive threshold. Our results suggest that correlations in intrinsic gene expression noise are exploited by evolution to ameliorate the detrimental effects of nonlinearities in metabolite concentrations.

  5. Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci

    NARCIS (Netherlands)

    Keurentjes, Joost J.B.; Fu, Jingyuan; Terpstra, Inez R.; Garcia, Juan M.; Ackerveken, Guido van den; Snoek, L. Basten; Peeters, Anton J.M.; Vreugdenhil, Dick; Koornneef, Maarten; Jansen, Ritsert C.

    2007-01-01

    Accessions of a plant species can show considerable genetic differences that are analyzed effectively by using recombinant inbred line (RIL) populations. Here we describe the results of genome-wide expression variation analysis in an RIL population of Arabidopsis thaliana. For many genes, variation

  6. Network analysis of mitonuclear GWAS reveals functional networks and tissue expression profiles of disease-associated genes.

    Science.gov (United States)

    Johnson, Simon C; Gonzalez, Brenda; Zhang, Quanwei; Milholland, Brandon; Zhang, Zhengdong; Suh, Yousin

    2017-01-01

    While mitochondria have been linked to many human diseases through genetic association and functional studies, the precise role of mitochondria in specific pathologies, such as cardiovascular, neurodegenerative, and metabolic diseases, is often unclear. Here, we take advantage of the catalog of human genome-wide associations, whole-genome tissue expression and expression quantitative trait loci datasets, and annotated mitochondrial proteome databases to examine the role of common genetic variation in mitonuclear genes in human disease. Through pathway-based analysis we identified distinct functional pathways and tissue expression profiles associated with each of the major human diseases. Among our most striking findings, we observe that mitonuclear genes associated with cancer are broadly expressed among human tissues and largely represent one functional process, intrinsic apoptosis, while mitonuclear genes associated with other diseases, such as neurodegenerative and metabolic diseases, show tissue-specific expression profiles and are associated with unique functional pathways. These results provide new insight into human diseases using unbiased genome-wide approaches.

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

    DEFF Research Database (Denmark)

    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...... 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...

  8. Investigation of Gene Regulatory Networks Associated with Autism Spectrum Disorder Based on MiRNA Expression in China.

    Directory of Open Access Journals (Sweden)

    Fengzhen Huang

    Full Text Available Autism spectrum disorder (ASD comprise a group of neurodevelopmental disorders characterized by deficits in social and communication capacities and repetitive behaviors. Increasing neuroscientific evidence indicates that the neuropathology of ASD is widespread and involves epigenetic regulation in the brain. Differentially expressed miRNAs in the peripheral blood from autism patients were identified by high-throughput miRNA microarray analyses. Five of these miRNAs were confirmed through quantitative reverse transcription-polymerase chain reaction (qRT-PCR analysis. A search for candidate target genes of the five confirmed miRNAs was performed through a Kyoto encyclopedia of genes and genomes (KEGG biological pathways and Gene Ontology enrichment analysis of gene function to identify gene regulatory networks. To the best of our knowledge, this study provides the first global miRNA expression profile of ASD in China. The differentially expressed miR-34b may potentially explain the higher percentage of male ASD patients, and the aberrantly expressed miR-103a-3p may contribute to the abnormal ubiquitin-mediated proteolysis observed in ASD.

  9. Ascidian gene-expression profiles

    OpenAIRE

    Jeffery, William R.

    2002-01-01

    With the advent of gene-expression profiling, a large number of genes can now be investigated simultaneously during critical stages of development. This approach will be particularly informative in studies of ascidians, basal chordates whose genomes and embryology are uniquely suited for mapping developmental gene networks.

  10. Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli

    Directory of Open Access Journals (Sweden)

    Eils Roland

    2007-05-01

    Full Text Available Abstract Background Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks. Results Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium E. coli to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of E. coli against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture. Conclusion Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.

  11. Gene expression profiling in the stress control brain region hypothalamic paraventricular nucleus reveals a novel gene network including Amyloid beta Precursor Protein

    Directory of Open Access Journals (Sweden)

    Deussing Jan M

    2010-10-01

    Full Text Available Abstract Background The pivotal role of stress in the precipitation of psychiatric diseases such as depression is generally accepted. This study aims at the identification of genes that are directly or indirectly responding to stress. Inbred mouse strains that had been evidenced to differ in their stress response as well as in their response to antidepressant treatment were chosen for RNA profiling after stress exposure. Gene expression and regulation was determined by microarray analyses and further evaluated by bioinformatics tools including pathway and cluster analyses. Results Forced swimming as acute stressor was applied to C57BL/6J and DBA/2J mice and resulted in sets of regulated genes in the paraventricular nucleus of the hypothalamus (PVN, 4 h or 8 h after stress. Although the expression changes between the mouse strains were quite different, they unfolded in phases over time in both strains. Our search for connections between the regulated genes resulted in potential novel signalling pathways in stress. In particular, Guanine nucleotide binding protein, alpha inhibiting 2 (GNAi2 and Amyloid β (A4 precursor protein (APP were detected as stress-regulated genes, and together with other genes, seem to be integrated into stress-responsive pathways and gene networks in the PVN. Conclusions This search for stress-regulated genes in the PVN revealed its impact on interesting genes (GNAi2 and APP and a novel gene network. In particular the expression of APP in the PVN that is governing stress hormone balance, is of great interest. The reported neuroprotective role of this molecule in the CNS supports the idea that a short acute stress can elicit positive adaptational effects in the brain.

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

    Science.gov (United States)

    Wang, Daifeng; He, Fei; Maslov, Sergei; Gerstein, Mark

    2016-10-01

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

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

    Science.gov (United States)

    Gerstein, Mark

    2016-01-01

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

  14. Differences between Mice and Humans in Regulation and the Molecular Network of Collagen, Type III, Alpha-1 at the Gene Expression Level: Obstacles that Translational Research Must Overcome

    Directory of Open Access Journals (Sweden)

    Lishi Wang

    2015-07-01

    Full Text Available Collagen, type III, alpha-1 (COL3A1 is essential for normal collagen I fibrillogenesis in many organs. There are differences in phenotypes of mutations in the COL3A1 gene in humans and mutations in mice. In order to investigate whether the regulation and gene network of COL3A1 is the same in healthy populations of mice and humans, we compared the quantitative trait loci (QTL that regulate the expression level of COL3A1 and the gene network of COL3A1 pathways between humans and mice using whole genome expression profiles. Our results showed that, for the regulation of expression of Col3a1 in mice, an eQTL on chromosome (Chr 12 regulates the expression of Col3a1. However, expression of genes in the syntenic region on human Chr 7 has no association with the expression level of COL3A1. For the gene network comparison, we identified 44 top genes whose expression levels are strongly associated with that of Col3a1 in mice. We next identified 41 genes strongly associated with the expression level of COL3A1 in humans. There are a few but significant differences in the COL3A1 gene network between humans and mice. Several genes showed opposite association with expression of COL3A1. These genes are known to play important roles in development and function of the extracellular matrix of the lung. Difference in the molecular pathway of key genes in the COL3A1 gene network in humans and mice suggest caution should be used in extrapolating results from models of human lung diseases in mice to clinical lung diseases in humans. These differences may influence the efficacy of drugs in humans whose development employed mouse models.

  15. Pathway Detection from Protein Interaction Networks and Gene Expression Data Using Color-Coding Methods and A* Search Algorithms

    Directory of Open Access Journals (Sweden)

    Cheng-Yu Yeh

    2012-01-01

    Full Text Available With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73GHz and 1GB main memory running under windows operating system.

  16. Evolution‐development congruence in pattern formation dynamics: Bifurcations in gene expression and regulation of networks structures

    Science.gov (United States)

    Kohsokabe, Takahiro

    2016-01-01

    ABSTRACT Search for possible relationships between phylogeny and ontogeny is important in evolutionary‐developmental biology. Here we uncover such relationships by numerical evolution and unveil their origin in terms of dynamical systems theory. By representing developmental dynamics of spatially located cells with gene expression dynamics with cell‐to‐cell interaction under external morphogen gradient, gene regulation networks are evolved under mutation and selection with the fitness to approach a prescribed spatial pattern of expressed genes. For most numerical evolution experiments, evolution of pattern over generations and development of pattern by an evolved network exhibit remarkable congruence. Both in the evolution and development pattern changes consist of several epochs where stripes are formed in a short time, while for other temporal regimes, pattern hardly changes. In evolution, these quasi‐stationary regimes are generations needed to hit relevant mutations, while in development, they are due to some gene expression that varies slowly and controls the pattern change. The morphogenesis is regulated by combinations of feedback or feedforward regulations, where the upstream feedforward network reads the external morphogen gradient, and generates a pattern used as a boundary condition for the later patterns. The ordering from up to downstream is common in evolution and development, while the successive epochal changes in development and evolution are represented as common bifurcations in dynamical‐systems theory, which lead to the evolution‐development congruence. Mechanism of exceptional violation of the congruence is also unveiled. Our results provide a new look on developmental stages, punctuated equilibrium, developmental bottlenecks, and evolutionary acquisition of novelty in morphogenesis. J. Exp. Zool. (Mol. Dev. Evol.) 326B:61–84, 2016. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution

  17. Evolution-development congruence in pattern formation dynamics: Bifurcations in gene expression and regulation of networks structures.

    Science.gov (United States)

    Kohsokabe, Takahiro; Kaneko, Kunihiko

    2016-01-01

    Search for possible relationships between phylogeny and ontogeny is important in evolutionary-developmental biology. Here we uncover such relationships by numerical evolution and unveil their origin in terms of dynamical systems theory. By representing developmental dynamics of spatially located cells with gene expression dynamics with cell-to-cell interaction under external morphogen gradient, gene regulation networks are evolved under mutation and selection with the fitness to approach a prescribed spatial pattern of expressed genes. For most numerical evolution experiments, evolution of pattern over generations and development of pattern by an evolved network exhibit remarkable congruence. Both in the evolution and development pattern changes consist of several epochs where stripes are formed in a short time, while for other temporal regimes, pattern hardly changes. In evolution, these quasi-stationary regimes are generations needed to hit relevant mutations, while in development, they are due to some gene expression that varies slowly and controls the pattern change. The morphogenesis is regulated by combinations of feedback or feedforward regulations, where the upstream feedforward network reads the external morphogen gradient, and generates a pattern used as a boundary condition for the later patterns. The ordering from up to downstream is common in evolution and development, while the successive epochal changes in development and evolution are represented as common bifurcations in dynamical-systems theory, which lead to the evolution-development congruence. Mechanism of exceptional violation of the congruence is also unveiled. Our results provide a new look on developmental stages, punctuated equilibrium, developmental bottlenecks, and evolutionary acquisition of novelty in morphogenesis.

  18. Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork

    Directory of Open Access Journals (Sweden)

    Druka Arnis

    2008-11-01

    Full Text Available Abstract Background A typical genetical genomics experiment results in four separate data sets; genotype, gene expression, higher-order phenotypic data and metadata that describe the protocols, processing and the array platform. Used in concert, these data sets provide the opportunity to perform genetic analysis at a systems level. Their predictive power is largely determined by the gene expression dataset where tens of millions of data points can be generated using currently available mRNA profiling technologies. Such large, multidimensional data sets often have value beyond that extracted during their initial analysis and interpretation, particularly if conducted on widely distributed reference genetic materials. Besides quality and scale, access to the data is of primary importance as accessibility potentially allows the extraction of considerable added value from the same primary dataset by the wider research community. Although the number of genetical genomics experiments in different plant species is rapidly increasing, none to date has been presented in a form that allows quick and efficient on-line testing for possible associations between genes, loci and traits of interest by an entire research community. Description Using a reference population of 150 recombinant doubled haploid barley lines we generated novel phenotypic, mRNA abundance and SNP-based genotyping data sets, added them to a considerable volume of legacy trait data and entered them into the GeneNetwork http://www.genenetwork.org. GeneNetwork is a unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance, may interact to condition more complex biological phenotypes (higher-order traits. Here we describe these barley data sets and demonstrate some of the functionalities GeneNetwork provides as an easily accessible and integrated analytical environment for exploring them. Conclusion By

  19. Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.

    Science.gov (United States)

    Godsey, Brian

    2013-01-01

    Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined--there are more parameters than data points--and therefore data or parameter set reduction is often necessary. Correlation between variables in the model also contributes to confound network coefficient inference. In this paper, we present an algorithm that uses integrated, probabilistic clustering to ease the problems of under-determination and correlated variables within a fully Bayesian framework. Specifically, ours is a dynamic Bayesian network with integrated Gaussian mixture clustering, which we fit using variational Bayesian methods. We show, using public, simulated time-course data sets from the DREAM4 Challenge, that our algorithm outperforms non-clustering methods in many cases (7 out of 25) with fewer samples, rarely underperforming (1 out of 25), and often selects a non-clustering model if it better describes the data. Source code (GNU Octave) for BAyesian Clustering Over Networks (BACON) and sample data are available at: http://code.google.com/p/bacon-for-genetic-networks.

  20. In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma.

    Science.gov (United States)

    Zinati, Zahra; Shamloo-Dashtpagerdi, Roohollah; Behpouri, Ali

    2016-12-01

    As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites.

  1. In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L. stigma

    Directory of Open Access Journals (Sweden)

    Zahra Zinati

    2016-12-01

    Full Text Available As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L. owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag library from mature saffron stigmas. Then, a gene co-expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p along with the corresponding stem-looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites.

  2. In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma

    Science.gov (United States)

    Zinati, Zahra; Shamloo-Dashtpagerdi, Roohollah; Behpouri, Ali

    2016-01-01

    As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites. PMID:28261627

  3. A Gene Co-Expression Network in Whole Blood of Schizophrenia Patients Is Independent of Antipsychotic-Use and Enriched for Brain-Expressed Genes

    NARCIS (Netherlands)

    de Jong, Simone; Boks, Marco P. M.; Fuller, Tova F.; Strengman, Eric; Janson, Esther; de Kovel, Carolien G. F.; Ori, Anil P. S.; Vi, Nancy; Mulder, Flip; Blom, Jan Dirk; Glenthoj, Birte; Schubart, Chris D.; Cahn, Wiepke; Kahn, Rene S.; Horvath, Steve; Ophoff, Roel A.

    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

  4. Practical Utilization of OryzaExpress and Plant Omics Data Center Databases to Explore Gene Expression Networks in Oryza Sativa and Other Plant Species.

    Science.gov (United States)

    Kudo, Toru; Terashima, Shin; Takaki, Yuno; Nakamura, Yukino; Kobayashi, Masaaki; Yano, Kentaro

    2017-01-01

    Analysis of a gene expression network (GEN), which is constructed based on similarity of gene expression profiles, is a widely used approach to gain clues for new biological insights. The recent abundant availability of transcriptome data in public databases is enabling GEN analysis under various experimental conditions, and even comparative GEN analysis across species. To provide a platform to gain biological insights from public transcriptome data, valuable databases have been created and maintained. This chapter introduces the web database OryzaExpress, providing omics information on Oryza sativa (rice). The integrated database Plant Omics Data Center, supporting a wide variety of plant species, is also described to compare omics information among multiple plant species.

  5. Gene co-expression network analysis identifies porcine genes associated with variation in metabolizing fenbendazole and flunixin meglumine in the liver.

    Science.gov (United States)

    Howard, Jeremy T; Ashwell, Melissa S; Baynes, Ronald E; Brooks, James D; Yeatts, James L; Maltecca, Christian

    2017-05-02

    Identifying individual genetic variation in drug metabolism pathways is of importance not only in livestock, but also in humans in order to provide the ultimate goal of giving the right drug at the right dose at the right time. Our objective was to identify individual genes and gene networks involved in metabolizing fenbendazole (FBZ) and flunixin meglumine (FLU) in swine liver. The population consisted of female and castrated male pigs that were sired by boars represented by 4 breeds. Progeny were randomly placed into groups: no drug (UNT), FLU or FBZ administered. Liver transcriptome profiles from 60 animals with extreme (i.e. fast or slow drug metabolism) pharmacokinetic (PK) profiles were generated from RNA sequencing. Multiple cytochrome P450 (CYP1A1, CYP2A19 and CYP2C36) genes displayed different transcript levels across treated versus UNT. Weighted gene co-expression network analysis identified 5 and 3 modules of genes correlated with PK parameters and a portion of these were enriched for biological processes relevant to drug metabolism for FBZ and FLU, respectively. Genes within identified modules were shown to have a higher transcript level relationship (i.e. connectivity) in treated versus UNT animals. Investigation into the identified genes would allow for greater insight into FBZ and FLU metabolism.

  6. Stage-specific differential gene expression profiling and functional network analysis during morphogenesis of diphyodont dentition in miniature pigs, Sus Scrofa

    Science.gov (United States)

    2014-01-01

    Background Our current knowledge of tooth development derives mainly from studies in mice, which have only one set of non-replaced teeth, compared with the diphyodont dentition in humans. The miniature pig is also diphyodont, making it a valuable alternative model for understanding human tooth development and replacement. However, little is known about gene expression and function during swine odontogenesis. The goal of this study is to undertake the survey of differential gene expression profiling and functional network analysis during morphogenesis of diphyodont dentition in miniature pigs. The identification of genes related to diphyodont development should lead to a better understanding of morphogenetic patterns and the mechanisms of diphyodont replacement in large animal models and humans. Results The temporal gene expression profiles during early diphyodont development in miniature pigs were detected with the Affymetrix Porcine GeneChip. The gene expression data were further evaluated by ANOVA as well as pathway and STC analyses. A total of 2,053 genes were detected with differential expression. Several signal pathways and 151 genes were then identified through the construction of pathway and signal networks. Conclusions The gene expression profiles indicated that spatio-temporal down-regulation patterns of gene expression were predominant; while, both dynamic activation and inhibition of pathways occurred during the morphogenesis of diphyodont dentition. Our study offers a mechanistic framework for understanding dynamic gene regulation of early diphyodont development and provides a molecular basis for studying teeth development, replacement, and regeneration in miniature pigs. PMID:24498892

  7. Gene regulatory networks reused to build novel traits: co-option of an eye-related gene regulatory network in eye-like organs and red wing patches on insect wings is suggested by optix expression.

    Science.gov (United States)

    Monteiro, Antónia

    2012-03-01

    Co-option of the eye developmental gene regulatory network may have led to the appearance of novel functional traits on the wings of flies and butterflies. The first trait is a recently described wing organ in a species of extinct midge resembling the outer layers of the midge's own compound eye. The second trait is red pigment patches on Heliconius butterfly wings connected to the expression of an eye selector gene, optix. These examples, as well as others, are discussed regarding the type of empirical evidence and burden of proof that have been used to infer gene network co-option underlying the origin of novel traits. A conceptual framework describing increasing confidence in inference of network co-option is proposed. Novel research directions to facilitate inference of network co-option are also highlighted, especially in cases where the pre-existent and novel traits do not resemble each other.

  8. Integrating mitosis, toxicity, and transgene expression in a telecommunications packet-switched network model of lipoplex-mediated gene delivery.

    Science.gov (United States)

    Martin, Timothy M; Wysocki, Beata J; Beyersdorf, Jared P; Wysocki, Tadeusz A; Pannier, Angela K

    2014-08-01

    Gene delivery systems transport exogenous genetic information to cells or biological systems with the potential to directly alter endogenous gene expression and behavior with applications in functional genomics, tissue engineering, medical devices, and gene therapy. Nonviral systems offer advantages over viral systems because of their low immunogenicity, inexpensive synthesis, and easy modification but suffer from lower transfection levels. The representation of gene transfer using models offers perspective and interpretation of complex cellular mechanisms,including nonviral gene delivery where exact mechanisms are unknown. Here, we introduce a novel telecommunications model of the nonviral gene delivery process in which the delivery of the gene to a cell is synonymous with delivery of a packet of information to a destination computer within a packet-switched computer network. Such a model uses nodes and layers to simplify the complexity of modeling the transfection process and to overcome several challenges of existing models. These challenges include a limited scope and limited time frame, which often does not incorporate biological effects known to affect transfection. The telecommunication model was constructed in MATLAB to model lipoplex delivery of the gene encoding the green fluorescent protein to HeLa cells. Mitosis and toxicity events were included in the model resulting in simulation outputs of nuclear internalization and transfection efficiency that correlated with experimental data. A priori predictions based on model sensitivity analysis suggest that increasing endosomal escape and decreasing lysosomal degradation, protein degradation, and GFP-induced toxicity can improve transfection efficiency by three-fold. Application of the telecommunications model to nonviral gene delivery offers insight into the development of new gene delivery systems with therapeutically relevant transfection levels.

  9. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

    Science.gov (United States)

    2014-01-01

    Background Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. Methods We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. Results WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P < 0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and

  10. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model.

    Science.gov (United States)

    Kogelman, Lisette J A; Cirera, Susanna; Zhernakova, Daria V; Fredholm, Merete; Franke, Lude; Kadarmideen, Haja N

    2014-09-30

    Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be

  11. Computational analysis of HIV-1 resistance based on gene expression profiles and the virus-host interaction network.

    Directory of Open Access Journals (Sweden)

    Tao Huang

    Full Text Available A very small proportion of people remain negative for HIV infection after repeated HIV-1 viral exposure, which is called HIV-1 resistance. Understanding the mechanism of HIV-1 resistance is important for the development of HIV-1 vaccines and Acquired Immune Deficiency Syndrome (AIDS therapies. In this study, we analyzed the gene expression profiles of CD4+ T cells from HIV-1-resistant individuals and HIV-susceptible individuals. One hundred eighty-five discriminative HIV-1 resistance genes were identified using the Minimum Redundancy-Maximum Relevance (mRMR and Incremental Feature Selection (IFS methods. The virus protein target enrichment analysis of the 185 HIV-1 resistance genes suggested that the HIV-1 protein nef might play an important role in HIV-1 infection. Moreover, we identified 29 infection information exchanger genes from the 185 HIV-1 resistance genes based on a virus-host interaction network analysis. The infection information exchanger genes are located on the shortest paths between virus-targeted proteins and are important for the coordination of virus infection. These proteins may be useful targets for AIDS prevention or therapy, as intervention in these pathways could disrupt communication with virus-targeted proteins and HIV-1 infection.

  12. ALS disrupts spinal motor neuron maturation and aging pathways within gene co-expression networks

    Science.gov (United States)

    Ho, Ritchie; Sances, Samuel; Gowing, Genevieve; Amoroso, Mackenzie Weygandt; O'Rourke, Jacqueline G.; Sahabian, Anais; Wichterle, Hynek; Baloh, Robert H.; Sareen, Dhruv

    2016-01-01

    Modeling Amyotrophic Lateral Sclerosis (ALS) with human induced pluripotent stem cells (iPSCs) aims to reenact embryogenesis, maturation, and aging of spinal motor neurons (spMNs) in vitro. As the maturity of spMNs grown in vitro compared to spMNs in vivo remains largely unaddressed, it is unclear to what extent this in vitro system captures critical aspects of spMN development and molecular signatures associated with ALS. Here, we compared transcriptomes among iPSC-derived spMNs, fetal, and adult spinal tissues. This approach produced a maturation scale revealing that iPSC-derived spMNs were more similar to fetal spinal tissue than to adult spMNs. Additionally, we resolved gene networks and pathways associated with spMN maturation and aging. These networks enriched for pathogenic familial ALS genetic variants and were disrupted in sporadic ALS spMNs. Altogether, our findings suggest that developing strategies to further mature and age iPSC-derived spMNs will provide more effective iPSC models of ALS pathology. PMID:27428653

  13. Meta-analysis of expression signatures of muscle atrophy: gene interaction networks in early and late stages

    Directory of Open Access Journals (Sweden)

    Lanfranchi Gerolamo

    2008-12-01

    Full Text Available Abstract Background Skeletal muscle mass can be markedly reduced through a process called atrophy, as a consequence of many diseases or critical physiological and environmental situations. Atrophy is characterised by loss of contractile proteins and reduction of fiber volume. Although in the last decade the molecular aspects underlying muscle atrophy have received increased attention, the fine mechanisms controlling muscle degeneration are still incomplete. In this study we applied meta-analysis on gene expression signatures pertaining to different types of muscle atrophy for the identification of novel key regulatory signals implicated in these degenerative processes. Results We found a general down-regulation of genes involved in energy production and carbohydrate metabolism and up-regulation of genes for protein degradation and catabolism. Six functional pathways occupy central positions in the molecular network obtained by the integration of atrophy transcriptome and molecular interaction data. They are TGF-β pathway, apoptosis, membrane trafficking/cytoskeleton organization, NFKB pathways, inflammation and reorganization of the extracellular matrix. Protein degradation pathway is evident only in the network specific for muscle short-term response to atrophy. TGF-β pathway plays a central role with proteins SMAD3/4, MYC, MAX and CDKN1A in the general network, and JUN, MYC, GNB2L1/RACK1 in the short-term muscle response network. Conclusion Our study offers a general overview of the molecular pathways and cellular processes regulating the establishment and maintenance of atrophic state in skeletal muscle, showing also how the different pathways are interconnected. This analysis identifies novel key factors that could be further investigated as potential targets for the development of therapeutic treatments. We suggest that the transcription factors SMAD3/4, GNB2L1/RACK1, MYC, MAX and JUN, whose functions have been extensively studied in

  14. A gene co-expression network predicts functional genes controlling the re-establishment of desiccation tolerance in germinated Arabidopsis thaliana seeds.

    Science.gov (United States)

    Costa, Maria Cecília D; Righetti, Karima; Nijveen, Harm; Yazdanpanah, Farzaneh; Ligterink, Wilco; Buitink, Julia; Hilhorst, Henk W M

    2015-08-01

    During re-establishment of desiccation tolerance (DT), early events promote initial protection and growth arrest, while late events promote stress adaptation and contribute to survival in the dry state. Mature seeds of Arabidopsis thaliana are desiccation tolerant, but they lose desiccation tolerance (DT) while progressing to germination. Yet, there is a small developmental window during which DT can be rescued by treatment with abscisic acid (ABA). To gain temporal resolution and identify relevant genes in this process, data from a time series of microarrays were used to build a gene co-expression network. The network has two regions, namely early response (ER) and late response (LR). Genes in the ER region are related to biological processes, such as dormancy, acquisition of DT and drought, amplification of signals, growth arrest and induction of protection mechanisms (such as LEA proteins). Genes in the LR region lead to inhibition of photosynthesis and primary metabolism, promote adaptation to stress conditions and contribute to seed longevity. Phenotyping of 12 hubs in relation to re-establishment of DT with T-DNA insertion lines indicated a significant increase in the ability to re-establish DT compared with the wild-type in the lines cbsx4, at3g53040 and at4g25580, suggesting the operation of redundant and compensatory mechanisms. Moreover, we show that re-establishment of DT by polyethylene glycol and ABA occurs through partially overlapping mechanisms. Our data confirm that co-expression network analysis is a valid approach to examine data from time series of transcriptome analysis, as it provides promising insights into biologically relevant relations that help to generate new information about the roles of certain genes for DT.

  15. Gene co-expression networks and profiles reveal potential biomarkers of boar taint in pigs

    DEFF Research Database (Denmark)

    Drag, Markus; Skinkyté-Juskiené, Rúta; Do, Duy Ngoc;

    potential BT biomarkers for optimized breeding. Male pigs (n=48) with low, medium and high genetic merit of BT were selected and tissues from liver and testis were subjected to transcriptomic profiling by RNA-Seq. The reads were mapped to the Sus scrofa reference genome (Ensembl, ver. 79) which resulted...... synthesis. In testis, >80 DE genes were functionally classified by the PANTHER tool to “Gonadotropin releasing hormone receptor” and “Wnt signaling” pathways which play a role in reproductive maturation and proliferation of spermatogonia, respectively. WGCNA was used to build co-expression modules...... and enrichment analysis and semantic filtering revealed the GO terms “catalytic activity” and “transferase activity” to be overrepresented (p hormones. Extraction of hub...

  16. Gene co-expression networks and profiles reveal potential biomarkers of boar taint in pigs

    DEFF Research Database (Denmark)

    Drag, M.; Skinkyté-Juskiené, R.; Do, D. N.

    Boar taint (BT) is an offensive odour or taste of porcine meat which may occur in entire male pigs due to skatole and androstenone accumulation. To avoid BT, castration of young piglets is performed but this strategy is under debate due to animal welfare concerns. The study aimed to reveal...... synthesis. In testis, >80 DE genes were functionally classified by the PANTHER tool to “Gonadotropin releasing hormone receptor” and “Wnt signaling” pathways which play a role in reproductive maturation and proliferation of spermatogonia, respectively. WGCNA was used to build co-expression modules...... and enrichment analysis and semantic filtering revealed the GO terms “catalytic activity” and “transferase activity” to be overrepresented (p hormones. Extraction of hub...

  17. Gene co-expression analyses differentiate networks associated with diverse cancers harbouring TP53 missense or null mutations

    Directory of Open Access Journals (Sweden)

    Kathleen Oros Klein

    2016-08-01

    Full Text Available In a variety of solid cancers, missense mutations in the well-established TP53 tumour suppressor gene may lead to presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumour biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of co-expression of genes in tumours grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2 consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene’s strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumour biology.

  18. Application of Artificial Neural Networks in Cancer Classification and Diagnosis Prediction of a Subtype of Lymphoma Based on Gene Expression Profile

    Directory of Open Access Journals (Sweden)

    L Ziaei

    2006-01-01

    Full Text Available Background: Diffuse Large B-cell Lymphoma (DLBCL is the most common subtype of non-Hodgkin’s Lymphoma. DLBCL patients have different survivals after diagnosis. 40% of patients respond well to current therapy and have prolonged survival, whereas the remainders survive less than 5 years. In this study, we have applied artificial neural network to classify patients with DLBCL on the basis of their gene expression profiles. Finally, we have attempted to extract a number of genes that their differential expression were significant in DLBCL subtypes. Methods: We studied 40 patients and 4026 genes. In this study, genes were ranked based on their signal to noise (S/N ratios. After selecting a suitable threshold, some of them whose ratios were less than the threshold were removed. Then we used PCA for more reducing and Perceptron neural network for classification of these patients. We extracted some appropriate genes based on their prediction ability. Results: We considered various targets for patients classifying. Thus patients were classified based on their 5 years survival with accuracy of 93%, in regard to Alizadeh et al study results with accuracy of 100%, and regarding with their International Prognosis Index (IPI with accuracy of 89%. Conclusion: Combination of PCA and S/N ratio is an effective method for the reduction of the dimension and neural network is a robust tool for classification of patients according to their gene expression profile. Keywords: classification, gene expression, DLBCL, neural network, Perceptron

  19. Zipf's Law in Gene Expression

    CERN Document Server

    Furusawa, C; Furusawa, Chikara; Kaneko, Kunihiko

    2002-01-01

    Using data from gene expression databases on various organisms and tissues, including yeast, nematodes, human normal and cancer tissues, and embryonic stem cells, we found that the abundances of expressed genes exhibit a power-law distribution with an exponent close to -1, i.e., they obey Zipf's law. Furthermore, by simulations of a simple model with an intra-cellular reaction network, we found that Zipf's law of chemical abundance is a universal feature of cells where such a network optimizes the efficiency and faithfulness of self-reproduction. These findings provide novel insights into the nature of the organization of reaction dynamics in living cells.

  20. Zipf's Law in Gene Expression

    Science.gov (United States)

    Furusawa, Chikara; Kaneko, Kunihiko

    2003-02-01

    Using data from gene expression databases on various organisms and tissues, including yeast, nematodes, human normal and cancer tissues, and embryonic stem cells, we found that the abundances of expressed genes exhibit a power-law distribution with an exponent close to -1; i.e., they obey Zipf’s law. Furthermore, by simulations of a simple model with an intracellular reaction network, we found that Zipf’s law of chemical abundance is a universal feature of cells where such a network optimizes the efficiency and faithfulness of self-reproduction. These findings provide novel insights into the nature of the organization of reaction dynamics in living cells.

  1. Gene networks in skeletal muscle following endurance exercise are co-expressed in blood neutrophils and linked with blood inflammation markers.

    Science.gov (United States)

    Broadbent, James A; Sampson, Dayle; Sabapathy, Surendran; Haseler, Luke J; Wagner, Karl-Heinz; Bulmer, Andrew Cameron; Peake, Jonathan M; Neubauer, Oliver

    2017-01-19

    It remains incompletely understood whether there is an association between the transcriptome profiles of skeletal muscle and blood leukocytes in response to exercise or other physiological stressors. We have previously analyzed the changes in the muscle and blood neutrophil transcriptome in eight trained men before and 3 h, 48 h and 96 h after 2 h cycling and running. Because we collected muscle and blood in the same individuals and under the same conditions, we were able to directly compare gene expression between the muscle and blood neutrophils. Applying weighted gene co-expression network analysis (WGCNA) as an advanced network-driven method to these original datasets enabled us to compare the muscle and neutrophil transcriptomes in a rigorous and systematic manner. Two gene networks were identified that were preserved between skeletal muscle and blood neutrophils, functionally related to mitochondria and post-translational processes. Strong preservation measures (Zsummary > 10) for both muscle-neutrophil gene networks were evident within the post-exercise recovery period. Muscle and neutrophil gene co-expression was strongly correlated in the mitochondria-related network (r = 0.97; p = 3.17E-2). We also identified multiple correlations between muscular gene sub-networks and exercise-induced changes in blood leukocyte counts, inflammation and muscle damage markers. These data reveal previously unidentified gene co-expression between skeletal muscle and blood neutrophils following exercise, showing the value of WGCNA to understand exercise physiology. Furthermore, these findings provide preliminary evidence in support of the notion that blood neutrophil gene networks may potentially help us to track physiological and pathophysiological changes in the muscle.

  2. Profiling gene expression in human placentae of different gestational ages: an OPRU Network and UW SCOR Study.

    Science.gov (United States)

    Mikheev, Andrei M; Nabekura, Tomohiro; Kaddoumi, Amal; Bammler, Theo K; Govindarajan, Rajgopal; Hebert, Mary F; Unadkat, Jashvant D

    2008-11-01

    We used the whole-genome approach to identify major functional categories of genes whose expression depends on gestational age. Using microarray analysis, we compared gene expression profiles in the villous tissues of first (45-59 days) and second trimester (109-115 days) placentae with C-section term placentae. We found that in first trimester placentae, genes related to cell cycle, DNA, amino acids, and carbohydrate metabolism were significantly overrepresented, while genes related to signal transduction were underrepresented. Among genes involved in organism defense, we identified genes involved in chemical response, metabolism, and transport. Analysis of signal transduction pathways suggested, and subsequently confirmed independently, that the Wnt pathway was changed with gestational age leading to inhibition of beta-catenin protein expression. Our study will serve as a reference database to gain insight into the regulation of gene expression in the developing placentae and to compare with gene expression in placentae from complicated pregnancies.

  3. Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients.

    Science.gov (United States)

    Kusy, Maciej; Obrzut, Bogdan; Kluska, Jacek

    2013-12-01

    The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. One-hundred and seven patients treated by radical hysterectomy were analyzed. Each record representing a single patient consisted of 10 parameters. The occurrence and lack of perioperative complications imposed a two-class classification problem. In the simulations, GEP algorithm was compared to a multilayer perceptron (MLP), a radial basis function network neural, and a probabilistic neural network. The generalization ability of the models was assessed on the basis of their accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve (AUROC). The GEP classifier provided best results in the prediction of the adverse events with the accuracy of 71.96 %. Comparable but slightly worse outcomes were obtained using MLP, i.e., 71.87 %. For each of measured indices: accuracy, sensitivity, specificity, and the AUROC, the standard deviation was the smallest for the models generated by GEP classifier.

  4. Diversification in the genetic architecture of gene expression and transcriptional networks in organ differentiation of Populus

    OpenAIRE

    Drost, Derek R.; Benedict, Catherine I.; Berg, Arthur; Novaes, Evandro; Novaes, Carolina R. D. B.; Yu, Qibin; Dervinis, Christopher; Jessica M Maia; Yap, John; Miles, Brianna; Kirst, Matias

    2010-01-01

    A fundamental goal of systems biology is to identify genetic elements that contribute to complex phenotypes and to understand how they interact in networks predictive of system response to genetic variation. Few studies in plants have developed such networks, and none have examined their conservation among functionally specialized organs. Here we used genetical genomics in an interspecific hybrid population of the model hardwood plant Populus to uncover transcriptional networks in xylem, leav...

  5. Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk

    DEFF Research Database (Denmark)

    Kar, Siddhartha P; Tyrer, Jonathan P; Li, Qiyuan

    2015-01-01

    BACKGROUND: Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified...... identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P ... (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. CONCLUSION: We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. IMPACT: Network analysis...

  6. Vitamin D Impacts the Expression of Runx2 Target Genes and Modulates Inflammation, Oxidative Stress and Membrane Vesicle Biogenesis Gene Networks in 143B Osteosarcoma Cells

    Directory of Open Access Journals (Sweden)

    Rama Garimella

    2017-03-01

    Full Text Available Osteosarcoma (OS is an aggressive malignancy of bone affecting children, adolescents and young adults. Understanding vitamin D metabolism and vitamin D regulated genes in OS is an important aspect of vitamin D/cancer paradigm, and in evaluating vitamin D as adjuvant therapy for human OS. Vitamin D treatment of 143B OS cells induced significant and novel changes in the expression of genes that regulate: (a inflammation and immunity; (b formation of reactive oxygen species, metabolism of cyclic nucleotides, sterols, vitamins and mineral (calcium, quantity of gap junctions and skeletogenesis; (c bone mineral density; and (d cell viability of skeletal cells, aggregation of bone cancer cells and exocytosis of secretory vesicles. Ingenuity pathway analysis revealed significant reduction in Runx2 target genes such as fibroblast growth factor -1, -12 (FGF1 and FGF12, bone morphogenetic factor-1 (BMP1, SWI/SNF related, matrix associated actin dependent regulator of chromatin subfamily a, member 4 (SMARCA4, Matrix extracellular phosphoglycoprotein (MEPE, Integrin, β4 (ITGBP4, Matrix Metalloproteinase -1, -28 (MMP1 and MMP28, and signal transducer and activator of transcription-4 (STAT4 in vitamin D treated 143B OS cells. These genes interact with the inflammation, oxidative stress and membrane vesicle biogenesis gene networks. Vitamin D not only inhibited the expression of Runx2 target genes MMP1, MMP28 and kallikrein related peptidase-7 (KLK7, but also migration and invasion of 143B OS cells. Vitamin D regulated Runx2 target genes or their products represent potential therapeutic targets and laboratory biomarkers for applications in translational oncology.

  7. Vitamin D Impacts the Expression of Runx2 Target Genes and Modulates Inflammation, Oxidative Stress and Membrane Vesicle Biogenesis Gene Networks in 143B Osteosarcoma Cells.

    Science.gov (United States)

    Garimella, Rama; Tadikonda, Priyanka; Tawfik, Ossama; Gunewardena, Sumedha; Rowe, Peter; Van Veldhuizen, Peter

    2017-03-16

    Osteosarcoma (OS) is an aggressive malignancy of bone affecting children, adolescents and young adults. Understanding vitamin D metabolism and vitamin D regulated genes in OS is an important aspect of vitamin D/cancer paradigm, and in evaluating vitamin D as adjuvant therapy for human OS. Vitamin D treatment of 143B OS cells induced significant and novel changes in the expression of genes that regulate: (a) inflammation and immunity; (b) formation of reactive oxygen species, metabolism of cyclic nucleotides, sterols, vitamins and mineral (calcium), quantity of gap junctions and skeletogenesis; (c) bone mineral density; and (d) cell viability of skeletal cells, aggregation of bone cancer cells and exocytosis of secretory vesicles. Ingenuity pathway analysis revealed significant reduction in Runx2 target genes such as fibroblast growth factor -1, -12 (FGF1 and FGF12), bone morphogenetic factor-1 (BMP1), SWI/SNF related, matrix associated actin dependent regulator of chromatin subfamily a, member 4 (SMARCA4), Matrix extracellular phosphoglycoprotein (MEPE), Integrin, β4 (ITGBP4), Matrix Metalloproteinase -1, -28 (MMP1 and MMP28), and signal transducer and activator of transcription-4 (STAT4) in vitamin D treated 143B OS cells. These genes interact with the inflammation, oxidative stress and membrane vesicle biogenesis gene networks. Vitamin D not only inhibited the expression of Runx2 target genes MMP1, MMP28 and kallikrein related peptidase-7 (KLK7), but also migration and invasion of 143B OS cells. Vitamin D regulated Runx2 target genes or their products represent potential therapeutic targets and laboratory biomarkers for applications in translational oncology.

  8. Functional differentiation and spatial-temporal co-expression networks of the NBS-encoding gene family in Jilin ginseng, Panax ginseng C.A. Meyer.

    Science.gov (United States)

    Yin, Rui; Zhao, Mingzhu; Wang, Kangyu; Lin, Yanping; Wang, Yanfang; Sun, Chunyu; Wang, Yi; Zhang, Meiping

    2017-01-01

    Ginseng, Panax ginseng C.A. Meyer, is one of the most important medicinal plants for human health and medicine. It has been documented that over 80% of genes conferring resistance to bacteria, viruses, fungi and nematodes are contributed by the nucleotide binding site (NBS)-encoding gene family. Therefore, identification and characterization of NBS genes expressed in ginseng are paramount to its genetic improvement and breeding. However, little is known about the NBS-encoding genes in ginseng. Here we report genome-wide identification and systems analysis of the NBS genes actively expressed in ginseng (PgNBS genes). Four hundred twelve PgNBS gene transcripts, derived from 284 gene models, were identified from the transcriptomes of 14 ginseng tissues. These genes were classified into eight types, including TNL, TN, CNL, CN, NL, N, RPW8-NL and RPW8-N. Seven conserved motifs were identified in both the Toll/interleukine-1 receptor (TIR) and coiled-coil (CC) typed genes whereas six were identified in the RPW8 typed genes. Phylogenetic analysis showed that the PgNBS gene family is an ancient family, with a vast majority of its genes originated before ginseng originated. In spite of their belonging to a family, the PgNBS genes have functionally dramatically differentiated and been categorized into numerous functional categories. The expressions of the across tissues, different aged roots and the roots of different genotypes. However, they are coordinating in expression, forming a single co-expression network. These results provide a deeper understanding of the origin, evolution and functional differentiation and expression dynamics of the NBS-encoding gene family in plants in general and in ginseng particularly, and a NBS gene toolkit useful for isolation and characterization of disease resistance genes and for enhanced disease resistance breeding in ginseng and related species.

  9. Research of Gene Regulatory Network with Multi-Time Delay Based on Bayesian Network

    Institute of Scientific and Technical Information of China (English)

    LIU Bei; MENG Fanjiang; LI Yong; LIU Liyan

    2008-01-01

    The gene regulatory network was reconstructed according to time-series microarray data getting from hybridization at different time between gene chips to analyze coordination and restriction between genes. An algorithm for controlling the gene expression regulatory network of the whole cell was designed using Bayesian network which provides an effective aided analysis for gene regulatory network.

  10. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  11. Introduction: Cancer Gene Networks.

    Science.gov (United States)

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  12. Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis

    Directory of Open Access Journals (Sweden)

    Wang Shan

    2012-08-01

    Full Text Available Abstract Background Identification of the novel genes relevant to plant cell-wall (PCW synthesis represents a highly important and challenging problem. Although substantial efforts have been invested into studying this problem, the vast majority of the PCW related genes remain unknown. Results Here we present a computational study focused on identification of the novel PCW genes in Arabidopsis based on the co-expression analyses of transcriptomic data collected under 351 conditions, using a bi-clustering technique. Our analysis identified 217 highly co-expressed gene clusters (modules under some experimental conditions, each containing at least one gene annotated as PCW related according to the Purdue Cell Wall Gene Families database. These co-expression modules cover 349 known/annotated PCW genes and 2,438 new candidates. For each candidate gene, we annotated the specific PCW synthesis stages in which it is involved and predicted the detailed function. In addition, for the co-expressed genes in each module, we predicted and analyzed their cis regulatory motifs in the promoters using our motif discovery pipeline, providing strong evidence that the genes in each co-expression module are transcriptionally co-regulated. From the all co-expression modules, we infer that 108 modules are related to four major PCW synthesis components, using three complementary methods. Conclusions We believe our approach and data presented here will be useful for further identification and characterization of PCW genes. All the predicted PCW genes, co-expression modules, motifs and their annotations are available at a web-based database: http://csbl.bmb.uga.edu/publications/materials/shanwang/CWRPdb/index.html.

  13. A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways.

    Science.gov (United States)

    Musungu, Bryan M; Bhatnagar, Deepak; Brown, Robert L; Payne, Gary A; OBrian, Greg; Fakhoury, Ahmad M; Geisler, Matt

    2016-01-01

    A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus, a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays, and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays, there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus. Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus.

  14. Non-essential genes form the hubs of genome scale protein function and environmental gene expression networks in Salmonella enterica serovar Typhimurium

    DEFF Research Database (Denmark)

    Rosenkrantz, Jesper T.; Aarts, Henk; Abee, Tjakko

    2013-01-01

    Background: Salmonella Typhimurium is an important pathogen of human and animals. It shows a broad growth range and survives in harsh conditions. The aim of this study was to analyze transcriptional responses to a number of growth and stress conditions as well as the relationship of metabolic...... pathways and/or cell functions at the genome-scale-level by network analysis, and further to explore whether highly connected genes ( hubs) in these networks were essential for growth, stress adaptation and virulence. Results: De novo generated as well as published transcriptional data for 425 selected...... genes under a number of growth and stress conditions were used to construct a bipartite network connecting culture conditions and significantly regulated genes (transcriptional network). Also, a genome scale network was constructed for strain LT2. The latter connected genes with metabolic pathways...

  15. Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures

    Directory of Open Access Journals (Sweden)

    Williams Robert W

    2011-05-01

    Full Text Available Abstract Background The lung is critical in surveillance and initial defense against pathogens. In humans, as in mice, individual genetic differences strongly modulate pulmonary responses to infectious agents, severity of lung disease, and potential allergic reactions. In a first step towards understanding genetic predisposition and pulmonary molecular networks that underlie individual differences in disease vulnerability, we performed a global analysis of normative lung gene expression levels in inbred mouse strains and a large family of BXD strains that are widely used for systems genetics. Our goal is to provide a key community resource on the genetics of the normative lung transcriptome that can serve as a foundation for experimental analysis and allow predicting genetic predisposition and response to pathogens, allergens, and xenobiotics. Methods Steady-state polyA+ mRNA levels were assayed across a diverse and fully genotyped panel of 57 isogenic strains using the Affymetrix M430 2.0 array. Correlations of expression levels between genes were determined. Global expression QTL (eQTL analysis and network covariance analysis was performed using tools and resources in GeneNetwork http://www.genenetwork.org. Results Expression values were highly variable across strains and in many cases exhibited a high heri-tability factor. Several genes which showed a restricted expression to lung tissue were identified. Using correlations between gene expression values across all strains, we defined and extended memberships of several important molecular networks in the lung. Furthermore, we were able to extract signatures of immune cell subpopulations and characterize co-variation and shared genetic modulation. Known QTL regions for respiratory infection susceptibility were investigated and several cis-eQTL genes were identified. Numerous cis- and trans-regulated transcripts and chromosomal intervals with strong regulatory activity were mapped. The Cyp1a1 P

  16. Evolution of evolvability in gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Anton Crombach

    Full Text Available Gene regulatory networks are perhaps the most important organizational level in the cell where signals from the cell state and the outside environment are integrated in terms of activation and inhibition of genes. For the last decade, the study of such networks has been fueled by large-scale experiments and renewed attention from the theoretical field. Different models have been proposed to, for instance, investigate expression dynamics, explain the network topology we observe in bacteria and yeast, and for the analysis of evolvability and robustness of such networks. Yet how these gene regulatory networks evolve and become evolvable remains an open question. An individual-oriented evolutionary model is used to shed light on this matter. Each individual has a genome from which its gene regulatory network is derived. Mutations, such as gene duplications and deletions, alter the genome, while the resulting network determines the gene expression pattern and hence fitness. With this protocol we let a population of individuals evolve under Darwinian selection in an environment that changes through time. Our work demonstrates that long-term evolution of complex gene regulatory networks in a changing environment can lead to a striking increase in the efficiency of generating beneficial mutations. We show that the population evolves towards genotype-phenotype mappings that allow for an orchestrated network-wide change in the gene expression pattern, requiring only a few specific gene indels. The genes involved are hubs of the networks, or directly influencing the hubs. Moreover, throughout the evolutionary trajectory the networks maintain their mutational robustness. In other words, evolution in an alternating environment leads to a network that is sensitive to a small class of beneficial mutations, while the majority of mutations remain neutral: an example of evolution of evolvability.

  17. Identification of Potential Anticancer Activities of Novel Ganoderma lucidum Extracts Using Gene Expression and Pathway Network Analysis.

    Science.gov (United States)

    Kao, Chi H J; Bishop, Karen S; Xu, Yuanye; Han, Dug Yeo; Murray, Pamela M; Marlow, Gareth J; Ferguson, Lynnette R

    2016-01-01

    Ganoderma lucidum (lingzhi) has been used for the general promotion of health in Asia for many centuries. The common method of consumption is to boil lingzhi in water and then drink the liquid. In this study, we examined the potential anticancer activities of G. lucidum submerged in two commonly consumed forms of alcohol in East Asia: malt whiskey and rice wine. The anticancer effect of G. lucidum, using whiskey and rice wine-based extraction methods, has not been previously reported. The growth inhibition of G. lucidum whiskey and rice wine extracts on the prostate cancer cell lines, PC3 and DU145, was determined. Using Affymetrix gene expression assays, several biologically active pathways associated with the anticancer activities of G. lucidum extracts were identified. Using gene expression analysis (real-time polymerase chain reaction [RT-PCR]) and protein analysis (Western blotting), we confirmed the expression of key genes and their associated proteins that were initially identified with Affymetrix gene expression analysis.

  18. Infiltration-RNAseq: transcriptome profiling of Agrobacterium-mediated infiltration of transcription factors to discover gene function and expression networks in plants.

    Science.gov (United States)

    Bond, Donna M; Albert, Nick W; Lee, Robyn H; Gillard, Gareth B; Brown, Chris M; Hellens, Roger P; Macknight, Richard C

    2016-01-01

    Transcription factors (TFs) coordinate precise gene expression patterns that give rise to distinct phenotypic outputs. The identification of genes and transcriptional networks regulated by a TF often requires stable transformation and expression changes in plant cells. However, the production of stable transformants can be slow and laborious with no guarantee of success. Furthermore, transgenic plants overexpressing a TF of interest can present pleiotropic phenotypes and/or result in a high number of indirect gene expression changes. Therefore, fast, efficient, high-throughput methods for assaying TF function are needed. Agroinfiltration is a simple plant biology method that allows transient gene expression. It is a rapid and powerful tool for the functional characterisation of TF genes in planta. High throughput RNA sequencing is now a widely used method for analysing gene expression profiles (transcriptomes). By coupling TF agroinfiltration with RNA sequencing (named here as Infiltration-RNAseq), gene expression networks and gene function can be identified within a few weeks rather than many months. As a proof of concept, we agroinfiltrated Medicago truncatula leaves with M. truncatula LEGUME ANTHOCYANIN PRODUCITION 1 (MtLAP1), a MYB transcription factor involved in the regulation of the anthocyanin pathway, and assessed the resulting transcriptome. Leaves infiltrated with MtLAP1 turned red indicating the production of anthocyanin pigment. Consistent with this, genes encoding enzymes in the anthocyanin biosynthetic pathway, and known transcriptional activators and repressors of the anthocyanin biosynthetic pathway, were upregulated. A novel observation was the induction of a R3-MYB transcriptional repressor that likely provides transcriptional feedback inhibition to prevent the deleterious effects of excess anthocyanins on photosynthesis. Infiltration-RNAseq is a fast and convenient method for profiling TF-mediated gene expression changes. We utilised this method

  19. Constructing Bayesian networks by integrating gene expression and copy number data identifies NLGN4Y as a novel regulator of prostate cancer progression.

    Science.gov (United States)

    Gong, Yixuan; Wang, Li; Chippada-Venkata, Uma; Dai, Xudong; Oh, William K; Zhu, Jun

    2016-10-18

    To understand the heterogeneity of prostate cancer (PCa) and identify novel underlying drivers, we constructed integrative molecular Bayesian networks (IMBNs) for PCa by integrating gene expression and copy number alteration data from published datasets. After demonstrating such IMBNs with superior network accuracy, we identified multiple sub-networks within IMBNs related to biochemical recurrence (BCR) of PCa and inferred the corresponding key drivers. The key drivers regulated a set of common effectors including genes preferentially expressed in neuronal cells. NLGN4Y-a protein involved in synaptic adhesion in neurons-was ranked as the top gene closely linked to key drivers of myogenesis subnetworks. Lower expression of NLGN4Y was associated with higher grade PCa and an increased risk of BCR. We show that restoration of the protein expression of NLGN4Y in PC-3 cells leads to decreased cell proliferation, migration and inflammatory cytokine expression. Our results suggest that NLGN4Y is an important negative regulator in prostate cancer progression. More importantly, it highlights the value of IMBNs in generating biologically and clinically relevant hypotheses about prostate cancer that can be validated by independent studies.

  20. Gene Expression Omnibus (GEO)

    Data.gov (United States)

    U.S. Department of Health & Human Services — Gene Expression Omnibus is a public functional genomics data repository supporting MIAME-compliant submissions of array- and sequence-based data. Tools are provided...

  1. Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models.

    Science.gov (United States)

    Trümbach, Dietrich; Graf, Cornelia; Pütz, Benno; Kühne, Claudia; Panhuysen, Marcus; Weber, Peter; Holsboer, Florian; Wurst, Wolfgang; Welzl, Gerhard; Deussing, Jan M

    2010-11-19

    Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH) and its receptor type 1 (CRHR1) are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established in vitro model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms. We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD) discriminant function within GALGO, an R package based on a genetic algorithm (GA), was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size) in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic algorithm. With two unsupervised algorithms

  2. Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models

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    Holsboer Florian

    2010-11-01

    Full Text Available Abstract Background Dysregulation of the hypothalamic-pituitary-adrenal (HPA axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH and its receptor type 1 (CRHR1 are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established in vitro model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms. Results We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD discriminant function within GALGO, an R package based on a genetic algorithm (GA, was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic

  3. Expression atlas and comparative coexpression network analyses reveal important genes involved in the formation of lignified cell wall in Brachypodium distachyon.

    Science.gov (United States)

    Sibout, Richard; Proost, Sebastian; Hansen, Bjoern Oest; Vaid, Neha; Giorgi, Federico M; Ho-Yue-Kuang, Severine; Legée, Frédéric; Cézart, Laurent; Bouchabké-Coussa, Oumaya; Soulhat, Camille; Provart, Nicholas; Pasha, Asher; Le Bris, Philippe; Roujol, David; Hofte, Herman; Jamet, Elisabeth; Lapierre, Catherine; Persson, Staffan; Mutwil, Marek

    2017-08-01

    While Brachypodium distachyon (Brachypodium) is an emerging model for grasses, no expression atlas or gene coexpression network is available. Such tools are of high importance to provide insights into the function of Brachypodium genes. We present a detailed Brachypodium expression atlas, capturing gene expression in its major organs at different developmental stages. The data were integrated into a large-scale coexpression database ( www.gene2function.de), enabling identification of duplicated pathways and conserved processes across 10 plant species, thus allowing genome-wide inference of gene function. We highlight the importance of the atlas and the platform through the identification of duplicated cell wall modules, and show that a lignin biosynthesis module is conserved across angiosperms. We identified and functionally characterised a putative ferulate 5-hydroxylase gene through overexpression of it in Brachypodium, which resulted in an increase in lignin syringyl units and reduced lignin content of mature stems, and led to improved saccharification of the stem biomass. Our Brachypodium expression atlas thus provides a powerful resource to reveal functionally related genes, which may advance our understanding of important biological processes in grasses. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

  4. Differential expression analysis and regulatory network reconstruction for genes associated with muscle growth and adipose deposition in obese and lean pigs

    Institute of Scientific and Technical Information of China (English)

    Mingzhou Li; Xuewei Li; Li Zhu; Xiaokun Teng; Huasheng Xiao; Surong Shuai; Lei Chen; Qiang Li; Yujiao Guo

    2008-01-01

    During the growth and development of skeletal muscle cells and adipose cells, the regulatory mechanism of micro-effect polygenes determines porcine meat quality, carcass characteristics and other relative quantitative traits. Obese and lean type pig breeds show obvious differences in muscle growth and adipose deposition; however, the molecular mechanism underlying this phenotypic variation remains unknown. We used pathway-focused oligo microarray studies to examine the expression changes of 140 genes associated with muscle growth and adipose deposition in longissimus dorsi muscle at six growth stages (birth, 1, 2, 3, 4 and 5 months) of Landrace (a leaner, Western breed) and Taihu pigs (a fatty, indigenous, Chinese breed). Variance analysis (ANOVA) revealed that differences in the expression of 18 genes in Landrace pigs and three genes in Taihu pigs were very significant (FDR-adjusted permutation, P<0.01) and differences for 22 genes in Landrace pigs and seven genes in Taihu pigs were significant (FDR-adjusted permutation, P<0.05) among six growth stages. Clustering analysis revealed a high level of significance (FDR-adjusted, P<0.01) for four gene expression patterns, in which genes that strongly up-regulated were mainly associated with the positive regulation of myofiber formation and fatty acid biogenesis and genes that strongly down-regulated were mainly associated with the inhibition of cell proliferation and positive regulation of fatty acid P-oxidation. Based on a dynamic Bayesian network (DBN) model, gene regulatory networks (GRNs) were reconstructed from time-series data for each pig breed. These two GRNs initially revealed the distinct differences in physiological and biochemical aspects of muscle growth and adipose deposition between the two pig breeds; from these results, some potential key genes could be identified. Quantitative real-time RT-PCR (QRT-PCR) was used to verify the microarray data for five modulated genes, and a good correlation between the

  5. Network analysis of gene expression in mice provides new evidence of involvement of the mTOR pathway in antipsychotic-induced extrapyramidal symptoms.

    Science.gov (United States)

    Mas, S; Gassó, P; Boloc, D; Rodriguez, N; Mármol, F; Sánchez, J; Bernardo, M; Lafuente, A

    2016-06-01

    To identify potential candidate genes for future pharmacogenetic studies of antipsychotic (AP)-induced extrapyramidal symptoms (EPS), we used gene expression arrays to analyze changes induced by risperidone in mice strains with different susceptibility to EPS. We proposed a systems biology analytical approach that combined the identification of gene co-expression modules related to AP treatment, the construction of protein-protein interaction networks with genes included in identified modules and finally, gene set enrichment analysis of constructed networks. In response to risperidone, mice strain with susceptibility to develop EPS showed downregulation of genes involved in the mammalian target of rapamycin (mTOR) pathway and biological processes related to this pathway. Moreover, we also showed differences in the phosphorylation pattern of the ribosomal protein S6 (rpS6), which is a major downstream effector of mTOR. The present study provides new evidence of the involvement of the mTOR pathway in AP-induced EPS and offers new and valuable markers for pharmacogenetic studies.

  6. Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy.

    Science.gov (United States)

    Rittman, Timothy; Rubinov, Mikail; Vértes, Petra E; Patel, Ameera X; Ginestet, Cedric E; Ghosh, Boyd C P; Barker, Roger A; Spillantini, Maria Grazia; Bullmore, Edward T; Rowe, James B

    2016-12-01

    Abnormalities of tau protein are central to the pathogenesis of progressive supranuclear palsy, whereas haplotype variation of the tau gene MAPT influences the risk of Parkinson disease and Parkinson's disease dementia. We assessed whether regional MAPT expression might be associated with selective vulnerability of global brain networks to neurodegenerative pathology. Using task-free functional magnetic resonance imaging in progressive supranuclear palsy, Parkinson disease, and healthy subjects (n = 128), we examined functional brain networks and measured the connection strength between 471 gray matter regions. We obtained MAPT and SNCA microarray expression data in healthy subjects from the Allen brain atlas. Regional connectivity varied according to the normal expression of MAPT. The regional expression of MAPT correlated with the proportionate loss of regional connectivity in Parkinson's disease. Executive cognition was impaired in proportion to the loss of hub connectivity. These effects were not seen with SNCA, suggesting that alpha-synuclein pathology is not mediated through global network properties. The results establish a link between regional MAPT expression and selective vulnerability of functional brain networks to neurodegeneration.

  7. Yin Yang 1 and Adipogenic Gene Network Expression in Longissimus Muscle of Beef Cattle in Response to Nutritional Management

    Science.gov (United States)

    Moisá, Sonia J.; Shike, Daniel W.; Meteer, William T.; Keisler, Duane; Faulkner, Dan B.; Loor, Juan J.

    2013-01-01

    Among 36 differentially-expressed genes during growth in longissimus muscle (LM) of Angus steers, Yin Yang 1 (YY1) had the most relationships with other genes including some associated with adipocyte differentiation. The objective of this study was to examine the effect of nutritional management on mRNA expression of YY1 along with its targets genes PPARG, GTF2B, KAT2B, IGFBP5 and STAT5B. Longissimus from Angus and Angus × Simmental steers (7 total/treatment) on early weaning plus high-starch (EWS), normal weaning plus starch creep feeding (NWS), or normal weaning without starch creep feeding (NWN) was biopsied at 0, 96, and 240 days on treatments. Results suggest that YY1 does not exert control of adipogenesis in LM, and its expression is not sensitive to weaning age. Among the YY1-related genes, EWS led to greater IGFBP5 during growing and finishing phases. Pro-adipogenic transcriptional regulation was detected in EWS due to greater PPARG and VDR at 96 and 240 d vs. 0 d. GTF2B and KAT2B expression was lower in response to NWS and EWS than NWN, and was most pronounced at 240 d. The increase in PPARG and GTF2B expression between 96 and 240 d underscored the existence of a molecular programming mechanism that was sensitive to age and dietary starch. Such response partly explains the greater carcass fat deposition observed in response to NWS. PMID:23700364

  8. Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis.

    Science.gov (United States)

    Moignard, Victoria; Macaulay, Iain C; Swiers, Gemma; Buettner, Florian; Schütte, Judith; Calero-Nieto, Fernando J; Kinston, Sarah; Joshi, Anagha; Hannah, Rebecca; Theis, Fabian J; Jacobsen, Sten Eirik; de Bruijn, Marella F; Göttgens, Berthold

    2013-04-01

    Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterized by distinctive transcription factor expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated transcription factor pairings, including previously unrecognized relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single-cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.

  9. Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks

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    Zhang Weixiong

    2010-10-01

    Full Text Available Abstract Background Alzheimer's disease (AD is a progressive neurodegenerative disorder involving variations in the transcriptome of many genes. AD does not affect all brain regions simultaneously. Identifying the differences among the affected regions may shed more light onto the disease progression. We developed a novel method involving the differential topology of gene coexpression networks to understand the association among affected regions and disease severity. Methods We analysed microarray data of four regions - entorhinal cortex (EC, hippocampus (HIP, posterior cingulate cortex (PCC and middle temporal gyrus (MTG from AD affected and normal subjects. A coexpression network was built for each region and the topological overlap between them was examined. Genes with zero topological overlap between two region-specific networks were used to characterise the differences between the two regions. Results and conclusion Results indicate that MTG shows early AD pathology compared to the other regions. We postulate that if the MTG gets affected later in the disease, post-mortem analyses of individuals with end-stage AD will show signs of early AD in the MTG, while the EC, HIP and PCC will have severe pathology. Such knowledge is useful for data collection in clinical studies where sample selection is a limiting factor as well as highlighting the underlying biology of disease progression.

  10. Comparative RNA-Seq Analysis Reveals That Regulatory Network of Maize Root Development Controls the Expression of Genes in Response to N Stress.

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    Xiujing He

    Full Text Available Nitrogen (N is an essential nutrient for plants, and it directly affects grain yield and protein content in cereal crops. Plant root systems are not only critical for anchorage in the soil, but also for N acquisition. Therefore, genes controlling root development might also affect N uptake by plants. In this study, the responses of nitrogen on root architecture of mutant rtcs and wild-type of maize were investigated by morphological and physiological analysis. Subsequently, we performed a comparative RNA-Seq analysis to compare gene expression profiles between mutant rtcs roots and wild-type roots under different N conditions. We identified 786 co-modulated differentially expressed genes (DEGs related to root development. These genes participated in various metabolic processes. A co-expression cluster analysis and a cis-regulatory motifs analysis revealed the importance of the AP2-EREBP transcription factor family in the rtcs-dependent regulatory network. Some genotype-specific DEGs contained at least one LBD motif in their promoter region. Further analyses of the differences in gene transcript levels between rtcs and wild-type under different N conditions revealed 403 co-modulated DEGs with distinct functions. A comparative analysis revealed that the regulatory network controlling root development also controlled gene expression in response to N-deficiency. Several AP2-EREBP family members involved in multiple hormone signaling pathways were among the DEGs. These transcription factors might play important roles in the rtcs-dependent regulatory network related to root development and the N-deficiency response. Genes encoding the nitrate transporters NRT2-1, NAR2.1, NAR2.2, and NAR2.3 showed much higher transcript levels in rtcs than in wild-type under normal-N conditions. This result indicated that the LBD gene family mainly functions as transcriptional repressors, as noted in other studies. In summary, using a comparative RNA-Seq-based approach

  11. Comparative RNA-Seq Analysis Reveals That Regulatory Network of Maize Root Development Controls the Expression of Genes in Response to N Stress.

    Science.gov (United States)

    He, Xiujing; Ma, Haixia; Zhao, Xiongwei; Nie, Shujun; Li, Yuhua; Zhang, Zhiming; Shen, Yaou; Chen, Qi; Lu, Yanli; Lan, Hai; Zhou, Shufeng; Gao, Shibin; Pan, Guangtang; Lin, Haijian

    2016-01-01

    Nitrogen (N) is an essential nutrient for plants, and it directly affects grain yield and protein content in cereal crops. Plant root systems are not only critical for anchorage in the soil, but also for N acquisition. Therefore, genes controlling root development might also affect N uptake by plants. In this study, the responses of nitrogen on root architecture of mutant rtcs and wild-type of maize were investigated by morphological and physiological analysis. Subsequently, we performed a comparative RNA-Seq analysis to compare gene expression profiles between mutant rtcs roots and wild-type roots under different N conditions. We identified 786 co-modulated differentially expressed genes (DEGs) related to root development. These genes participated in various metabolic processes. A co-expression cluster analysis and a cis-regulatory motifs analysis revealed the importance of the AP2-EREBP transcription factor family in the rtcs-dependent regulatory network. Some genotype-specific DEGs contained at least one LBD motif in their promoter region. Further analyses of the differences in gene transcript levels between rtcs and wild-type under different N conditions revealed 403 co-modulated DEGs with distinct functions. A comparative analysis revealed that the regulatory network controlling root development also controlled gene expression in response to N-deficiency. Several AP2-EREBP family members involved in multiple hormone signaling pathways were among the DEGs. These transcription factors might play important roles in the rtcs-dependent regulatory network related to root development and the N-deficiency response. Genes encoding the nitrate transporters NRT2-1, NAR2.1, NAR2.2, and NAR2.3 showed much higher transcript levels in rtcs than in wild-type under normal-N conditions. This result indicated that the LBD gene family mainly functions as transcriptional repressors, as noted in other studies. In summary, using a comparative RNA-Seq-based approach, we identified

  12. Integrating mRNA and miRNA Weighted Gene Co-Expression Networks with eQTLs in the Nucleus Accumbens of Subjects with Alcohol Dependence.

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    Mohammed Mamdani

    Full Text Available Alcohol consumption is known to lead to gene expression changes in the brain. After performing weighted gene co-expression network analyses (WGCNA on genome-wide mRNA and microRNA (miRNA expression in Nucleus Accumbens (NAc of subjects with alcohol dependence (AD; N = 18 and of matched controls (N = 18, six mRNA and three miRNA modules significantly correlated with AD were identified (Bonferoni-adj. p≤ 0.05. Cell-type-specific transcriptome analyses revealed two of the mRNA modules to be enriched for neuronal specific marker genes and downregulated in AD, whereas the remaining four mRNA modules were enriched for astrocyte and microglial specific marker genes and upregulated in AD. Gene set enrichment analysis demonstrated that neuronal specific modules were enriched for genes involved in oxidative phosphorylation, mitochondrial dysfunction and MAPK signaling. Glial-specific modules were predominantly enriched for genes involved in processes related to immune functions, i.e. cytokine signaling (all adj. p≤ 0.05. In mRNA and miRNA modules, 461 and 25 candidate hub genes were identified, respectively. In contrast to the expected biological functions of miRNAs, correlation analyses between mRNA and miRNA hub genes revealed a higher number of positive than negative correlations (χ2 test p≤ 0.0001. Integration of hub gene expression with genome-wide genotypic data resulted in 591 mRNA cis-eQTLs and 62 miRNA cis-eQTLs. mRNA cis-eQTLs were significantly enriched for AD diagnosis and AD symptom counts (adj. p = 0.014 and p = 0.024, respectively in AD GWAS signals in a large, independent genetic sample from the Collaborative Study on Genetics of Alcohol (COGA. In conclusion, our study identified putative gene network hubs coordinating mRNA and miRNA co-expression changes in the NAc of AD subjects, and our genetic (cis-eQTL analysis provides novel insights into the etiological mechanisms of AD.

  13. Global gene expression profiling in infants with acute respiratory syncytial virus broncholitis demonstrates systemic activation of interferon signaling networks

    Science.gov (United States)

    Respiratory syncytial virus (RSV) is a leading cause of pediatric lower respiratory tract infections and has a high impact on pediatric emergency department utilization. Variation in host response may influence the pathogenesis and disease severity. We evaluated global gene expression profiles to be...

  14. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    Science.gov (United States)

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  15. Filtering Genes for Cluster and Network Analysis

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    Parkhomenko Elena

    2009-06-01

    Full Text Available Abstract Background Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias. Results This paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks. Conclusion The methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.

  16. Analysis of the Salmonella regulatory network suggests involvement of SsrB and H-NS in σ(E)-regulated SPI-2 gene expression.

    Science.gov (United States)

    Li, Jie; Overall, Christopher C; Nakayasu, Ernesto S; Kidwai, Afshan S; Jones, Marcus B; Johnson, Rudd C; Nguyen, Nhu T; McDermott, Jason E; Ansong, Charles; Heffron, Fred; Cambronne, Eric D; Adkins, Joshua N

    2015-01-01

    The extracytoplasmic functioning sigma factor σ(E) is known to play an essential role for Salmonella enterica serovar Typhimurium to survive and proliferate in macrophages and mice. However, its regulatory network is not well-characterized, especially during infection. Here we used microarray to identify genes regulated by σ(E) in Salmonella grown in three conditions: a nutrient-rich condition and two others that mimic early and late intracellular infection. We found that in each condition σ(E) regulated different sets of genes, and notably, several global regulators. When comparing nutrient-rich and infection-like conditions, large changes were observed in the expression of genes involved in Salmonella pathogenesis island (SPI)-1 type-three secretion system (TTSS), SPI-2 TTSS, protein synthesis, and stress responses. In total, the expression of 58% of Salmonella genes was affected by σ(E) in at least one of the three conditions. An important finding is that σ(E) up-regulates SPI-2 genes, which are essential for Salmonella intracellular survival, by up-regulating SPI-2 activator ssrB expression at the early stage of infection and down-regulating SPI-2 repressor hns expression at a later stage. Moreover, σ(E) is capable of countering the silencing of H-NS, releasing the expression of SPI-2 genes. This connection between σ(E) and SPI-2 genes, combined with the global regulatory effect of σ(E), may account for the lethality of rpoE-deficient Salmonella in murine infection.

  17. Gene Expression Profiling of Gastric Cancer

    Science.gov (United States)

    Marimuthu, Arivusudar; Jacob, Harrys K.C.; Jakharia, Aniruddha; Subbannayya, Yashwanth; Keerthikumar, Shivakumar; Kashyap, Manoj Kumar; Goel, Renu; Balakrishnan, Lavanya; Dwivedi, Sutopa; Pathare, Swapnali; Dikshit, Jyoti Bajpai; Maharudraiah, Jagadeesha; Singh, Sujay; Sameer Kumar, Ghantasala S; Vijayakumar, M.; Veerendra Kumar, Kariyanakatte Veeraiah; Premalatha, Chennagiri Shrinivasamurthy; Tata, Pramila; Hariharan, Ramesh; Roa, Juan Carlos; Prasad, T.S.K; Chaerkady, Raghothama; Kumar, Rekha Vijay; Pandey, Akhilesh

    2015-01-01

    Gastric cancer is the second leading cause of cancer death worldwide, both in men and women. A genomewide gene expression analysis was carried out to identify differentially expressed genes in gastric adenocarcinoma tissues as compared to adjacent normal tissues. We used Agilent’s whole human genome oligonucleotide microarray platform representing ~41,000 genes to carry out gene expression analysis. Two-color microarray analysis was employed to directly compare the expression of genes between tumor and normal tissues. Through this approach, we identified several previously known candidate genes along with a number of novel candidate genes in gastric cancer. Testican-1 (SPOCK1) was one of the novel molecules that was 10-fold upregulated in tumors. Using tissue microarrays, we validated the expression of testican-1 by immunohistochemical staining. It was overexpressed in 56% (160/282) of the cases tested. Pathway analysis led to the identification of several networks in which SPOCK1 was among the topmost networks of interacting genes. By gene enrichment analysis, we identified several genes involved in cell adhesion and cell proliferation to be significantly upregulated while those corresponding to metabolic pathways were significantly downregulated. The differentially expressed genes identified in this study are candidate biomarkers for gastric adenoacarcinoma. PMID:27030788

  18. Modeling of hysteresis in gene regulatory networks.

    Science.gov (United States)

    Hu, J; Qin, K R; Xiang, C; Lee, T H

    2012-08-01

    Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function.

  19. Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

    Science.gov (United States)

    Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

    2012-04-01

    The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the

  20. Vascular Gene Expression: A Hypothesis

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    Angélica Concepción eMartínez-Navarro

    2013-07-01

    Full Text Available The phloem is the conduit through which photoassimilates are distributed from autotrophic to heterotrophic tissues and is involved in the distribution of signaling molecules that coordinate plant growth and responses to the environment. Phloem function depends on the coordinate expression of a large array of genes. We have previously identified conserved motifs in upstream regions of the Arabidopsis genes, encoding the homologs of pumpkin phloem sap mRNAs, displaying expression in vascular tissues. This tissue-specific expression in Arabidopsis is predicted by the overrepresentation of GA/CT-rich motifs in gene promoters. In this work we have searched for common motifs in upstream regions of the homologous genes from plants considered to possess a primitive vascular tissue (a lycophyte, as well as from others that lack a true vascular tissue (a bryophyte, and finally from chlorophytes. Both lycophyte and bryophyte display motifs similar to those found in Arabidopsis with a significantly low E-value, while the chlorophytes showed either a different conserved motif or no conserved motif at all. These results suggest that these same genes are expressed coordinately in non- vascular plants; this coordinate expression may have been one of the prerequisites for the development of conducting tissues in plants. We have also analyzed the phylogeny of conserved proteins that may be involved in phloem function and development. The presence of CmPP16, APL, FT and YDA in chlorophytes suggests the recruitment of ancient regulatory networks for the development of the vascular tissue during evolution while OPS is a novel protein specific to vascular plants.

  1. Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

    Directory of Open Access Journals (Sweden)

    Sharma Animesh

    2007-01-01

    Full Text Available Abstract Background The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. Results Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable

  2. Murine hyperglycemic vasculopathy and cardiomyopathy: whole-genome gene expression analysis predicts cellular targets and regulatory networks influenced by mannose binding lectin

    Directory of Open Access Journals (Sweden)

    Chenhui eZou

    2012-02-01

    Full Text Available Hyperglycemia, in the absence of type 1 or 2 diabetes, is an independent risk factor for cardiovascular disease. We have previously demonstrated a central role for mannose binding lectin (MBL-mediated cardiac dysfunction in acute hyperglycemic mice. In this study, we applied whole genome microarray data analysis to investigate MBL’s role in systematic gene expression changes. The data predict possible intracellular events taking place in multiple cellular compartments such as enhanced insulin signaling pathway sensitivity, promoted mitochondrial respiratory function, improved cellular energy expenditure and protein quality control, improved cytoskeleton structure and facilitated intracellular trafficking, all of which may contribute to the organismal health of MBL null mice against acute hyperglycemia. Our data show a tight association between gene expression profile and tissue function which might be a very useful tool in predicting cellular targets and regulatory networks connected with in vivo observations, providing clues for further mechanistic studies.

  3. Computational modeling with forward and reverse engineering links signaling network and genomic regulatory responses: NF-κB signaling-induced gene expression responses in inflammation

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    Peng Chien

    2010-06-01

    Full Text Available Abstract Background Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Diverse cellular stimuli create a wide variety of transcription factor activities through signal transduction pathways, resulting in different gene expression patterns. Understanding the relationship between external stimuli and the corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to identify the physiological consequences of environmental stimuli. Results We proposed a systematic approach that combines forward and reverse engineering to link the signal transduction cascade with the gene responses. To demonstrate the feasibility of our strategy, we focused on linking the NF-κB signaling pathway with the inflammatory gene regulatory responses because NF-κB has long been recognized to play a crucial role in inflammation. We first utilized forward engineering (Hybrid Functional Petri Nets to construct the NF-κB signaling pathway and reverse engineering (Network Components Analysis to build a gene regulatory network (GRN. Then, we demonstrated that the corresponding IKK profiles can be identified in the GRN and are consistent with the experimental validation of the IKK kinase assay. We found that the time-lapse gene expression of several cytokines and chemokines (TNF-α, IL-1, IL-6, CXCL1, CXCL2 and CCL3 is concordant with the NF-κB activity profile, and these genes have stronger influence strength within the GRN. Such regulatory effects have highlighted the crucial roles of NF-κB signaling in the acute inflammatory response and enhance our understanding of the systemic inflammatory response syndrome. Conclusion We successfully identified and distinguished the corresponding signaling profiles among three microarray

  4. Gene expression profile of the cartilage tissue spontaneously regenerated in vivo by using a novel double-network gel: Comparisons with the normal articular cartilage

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    Kurokawa Takayuki

    2011-09-01

    Full Text Available Abstract Background We have recently found a phenomenon that spontaneous regeneration of a hyaline cartilage-like tissue can be induced in a large osteochondral defect by implanting a double-network (DN hydrogel plug, which was composed of poly-(2-Acrylamido-2-methylpropanesulfonic acid and poly-(N, N'-Dimetyl acrylamide, at the bottom of the defect. The purpose of this study was to clarify gene expression profile of the regenerated tissue in comparison with that of the normal articular cartilage. Methods We created a cylindrical osteochondral defect in the rabbit femoral grooves. Then, we implanted the DN gel plug at the bottom of the defect. At 2 and 4 weeks after surgery, the regenerated tissue was analyzed using DNA microarray and immunohistochemical examinations. Results The gene expression profiles of the regenerated tissues were macroscopically similar to the normal cartilage, but showed some minor differences. The expression degree of COL2A1, COL1A2, COL10A1, DCN, FMOD, SPARC, FLOD2, CHAD, CTGF, and COMP genes was greater in the regenerated tissue than in the normal cartilage. The top 30 genes that expressed 5 times or more in the regenerated tissue as compared with the normal cartilage included type-2 collagen, type-10 collagen, FN, vimentin, COMP, EF1alpha, TFCP2, and GAPDH genes. Conclusions The tissue regenerated by using the DN gel was genetically similar but not completely identical to articular cartilage. The genetic data shown in this study are useful for future studies to identify specific genes involved in spontaneous cartilage regeneration.

  5. (Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

    Science.gov (United States)

    He, Fei; Fromion, Vincent; Westerhoff, Hans V

    2013-11-21

    Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems

  6. Gene coexpression network analysis as a source of functional annotation for rice genes.

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    Kevin L Childs

    Full Text Available With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional

  7. Network analysis of gene expression in peripheral blood identifies mTOR and NF-κB pathways involved in antipsychotic-induced extrapyramidal symptoms.

    Science.gov (United States)

    Mas, S; Gassó, P; Parellada, E; Bernardo, M; Lafuente, A

    2015-10-01

    To identify the candidate genes for pharmacogenetic studies of antipsychotic (AP)-induced extrapyramidal symptoms (EPS), we propose a systems biology analytical approach, based on protein-protein interaction network construction and functional annotation analysis, of changes in gene expression (Human Genome U219 Array Plate) induced by treatment with risperidone or paliperidone in peripheral blood. 12 AP-naïve patients with first-episode psychosis participated in the present study. Our analysis revealed that, in response to AP treatment, constructed networks were enriched for different biological processes in patients without EPS (ubiquitination, protein folding and adenosine triphosphate (ATP) metabolism) compared with those presenting EPS (insulin receptor signaling, lipid modification, regulation of autophagy and immune response). Moreover, the observed differences also involved specific pathways, such as anaphase promoting complex /cdc20, prefoldin/CCT/triC and ATP synthesis in no-EPS patients, and mammalian target of rapamycin and NF-κB kinases in patients with EPS. Our results showing different patterns of gene expression in EPS patients, offer new and valuable markers for pharmacogenetic studies.

  8. Gene expression profiling in C57BL/6J and A/J mouse inbred strains reveals gene networks specific for brain regions independent of genetic background

    NARCIS (Netherlands)

    de Jong, Simone; Fuller, Tova F; Janson, Esther; Strengman, Eric; Horvath, Steve; Kas, Martien J H; Ophoff, Roel A

    2010-01-01

    BACKGROUND: We performed gene expression profiling of the amygdala and hippocampus taken from inbred mouse strains C57BL/6J and A/J. The selected brain areas are implicated in neurobehavioral traits while these mouse strains are known to differ widely in behavior. Consequently, we hypothesized that

  9. Gene regulatory networks governing pancreas development.

    Science.gov (United States)

    Arda, H Efsun; Benitez, Cecil M; Kim, Seung K

    2013-04-15

    Elucidation of cellular and gene regulatory networks (GRNs) governing organ development will accelerate progress toward tissue replacement. Here, we have compiled reference GRNs underlying pancreas development from data mining that integrates multiple approaches, including mutant analysis, lineage tracing, cell purification, gene expression and enhancer analysis, and biochemical studies of gene regulation. Using established computational tools, we integrated and represented these networks in frameworks that should enhance understanding of the surging output of genomic-scale genetic and epigenetic studies of pancreas development and diseases such as diabetes and pancreatic cancer. We envision similar approaches would be useful for understanding the development of other organs.

  10. Gene expression profiling of cultured human NF1 heterozygous (NF1+/-) melanocytes reveals downregulation of a transcriptional cis-regulatory network mediating activation of the melanocyte-specific dopachrome tautomerase (DCT) gene.

    Science.gov (United States)

    Boucneau, Joachim; De Schepper, Sofie; Vuylsteke, Marnik; Van Hummelen, Paul; Naeyaert, Jean-Marie; Lambert, Jo

    2005-08-01

    One of the major primary features of the neurocutaneous genetic disorder Neurofibromatosis type 1 are the hyperpigmentary café-au-lait macules where disregulation of melanocyte biology is supposed to play a key etiopathogenic role. To gain better insight into the possible role of the tumor suppressor gene NF1, a transcriptomic microarray analysis was performed on human NF1 heterozygous (NF1+/-) melanocytes of a Neurofibromatosis type 1 patient and NF1 wild type (NF1+/+) melanocytes of a healthy control patient, both cultured from normally pigmented skin and hyperpigmented lesional café-au-lait skin. From the magnitude of gene effects, we found that gene expression was affected most strongly by genotype and less so by lesional type. A total of 137 genes had a significant twofold or more up- (72) or downregulated (65) expression in NF1+/- melanocytes compared with NF1+/+ melanocytes. Melanocytes cultured from hyperpigmented café-au-lait skin showed 37 upregulated genes whereas only 14 were downregulated compared with normal skin melanocytes. In addition, significant genotype xlesional type interactions were observed for 465 genes. Differentially expressed genes were mainly involved in regulating cell proliferation and cell adhesion. A high number of transcription factor genes, among which a specific subset important in melanocyte lineage development, were downregulated in the cis-regulatory network governing the activation of the melanocyte-specific dopachrome tautomerase (DCT) gene. Although the results presented have been obtained with a restricted number of patients (one NF1 patient and one control) and using cDNA microarrays that may limit their interpretation, the data nevertheless addresses for the first time the effect of a heterozygous NF1 gene on the expression of the human melanocyte transcriptome and has generated several interesting candidate genes helpful in elucidating the etiopathology of café-au-lait macules in NF1 patients.

  11. Noise in eukaryotic gene expression

    Science.gov (United States)

    Blake, William J.; KÆrn, Mads; Cantor, Charles R.; Collins, J. J.

    2003-04-01

    Transcription in eukaryotic cells has been described as quantal, with pulses of messenger RNA produced in a probabilistic manner. This description reflects the inherently stochastic nature of gene expression, known to be a major factor in the heterogeneous response of individual cells within a clonal population to an inducing stimulus. Here we show in Saccharomyces cerevisiae that stochasticity (noise) arising from transcription contributes significantly to the level of heterogeneity within a eukaryotic clonal population, in contrast to observations in prokaryotes, and that such noise can be modulated at the translational level. We use a stochastic model of transcription initiation specific to eukaryotes to show that pulsatile mRNA production, through reinitiation, is crucial for the dependence of noise on transcriptional efficiency, highlighting a key difference between eukaryotic and prokaryotic sources of noise. Furthermore, we explore the propagation of noise in a gene cascade network and demonstrate experimentally that increased noise in the transcription of a regulatory protein leads to increased cell-cell variability in the target gene output, resulting in prolonged bistable expression states. This result has implications for the role of noise in phenotypic variation and cellular differentiation.

  12. Tumor-specific gene expression patterns with gene expression profiles

    Institute of Scientific and Technical Information of China (English)

    RUAN Xiaogang; LI Yingxin; LI Jiangeng; GONG Daoxiong; WANG Jinlian

    2006-01-01

    Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.

  13. Comprehensive meta-analysis, co-expression, and miRNA nested network analysis identifies gene candidates in citrus against Huanglongbing disease.

    Science.gov (United States)

    Rawat, Nidhi; Kiran, Sandhya P; Du, Dongliang; Gmitter, Fred G; Deng, Zhanao

    2015-07-28

    Huanglongbing (HLB), the most devastating disease of citrus, is associated with infection by Candidatus Liberibacter asiaticus (CaLas) and is vectored by the Asian citrus psyllid (ACP). Recently, the molecular basis of citrus-HLB interactions has been examined using transcriptome analyses, and these analyses have identified many probe sets and pathways modulated by CaLas infection among different citrus cultivars. However, lack of consistency among reported findings indicates that an integrative approach is needed. This study was designed to identify the candidate probe sets in citrus-HLB interactions using meta-analysis and gene co-expression network modelling. Twenty-two publically available transcriptome studies on citrus-HLB interactions, comprising 18 susceptible (S) datasets and four resistant (R) datasets, were investigated using Limma and RankProd methods of meta-analysis. A combined list of 7,412 differentially expressed probe sets was generated using a Teradata in-house Structured Query Language (SQL) script. We identified the 65 most common probe sets modulated in HLB disease among different tissues from the S and R datasets. Gene ontology analysis of these probe sets suggested that carbohydrate metabolism, nutrient transport, and biotic stress were the core pathways that were modulated in citrus by CaLas infection and HLB development. We also identified R-specific probe sets, which encoded leucine-rich repeat proteins, chitinase, constitutive disease resistance (CDR), miraculins, and lectins. Weighted gene co-expression network analysis (WGCNA) was conducted on 3,499 probe sets, and 21 modules with major hub probe sets were identified. Further, a miRNA nested network was created to examine gene regulation of the 3,499 target probe sets. Results suggest that csi-miR167 and csi-miR396 could affect ion transporters and defence response pathways, respectively. Most of the potential candidate hub probe sets were co-expressed with gibberellin pathway (GA

  14. Glucocorticoid receptor-dependent gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Phillip Phuc Le

    2005-08-01

    Full Text Available While the molecular mechanisms of glucocorticoid regulation of transcription have been studied in detail, the global networks regulated by the glucocorticoid receptor (GR remain unknown. To address this question, we performed an orthogonal analysis to identify direct targets of the GR. First, we analyzed the expression profile of mouse livers in the presence or absence of exogenous glucocorticoid, resulting in over 1,300 differentially expressed genes. We then executed genome-wide location analysis on chromatin from the same livers, identifying more than 300 promoters that are bound by the GR. Intersecting the two lists yielded 53 genes whose expression is functionally dependent upon the ligand-bound GR. Further network and sequence analysis of the functional targets enabled us to suggest interactions between the GR and other transcription factors at specific target genes. Together, our results further our understanding of the GR and its targets, and provide the basis for more targeted glucocorticoid therapies.

  15. Integrative analysis of miRNA and gene expression reveals regulatory networks in tamoxifen-resistant breast cancer

    DEFF Research Database (Denmark)

    Joshi, Tejal; Elias, Daniel; Stenvang, Jan

    2016-01-01

    Tamoxifen is an effective anti-estrogen treatment for patients with estrogen receptor-positive (ER+) breast cancer, however, tamoxifen resistance is frequently observed. To elucidate the underlying molecular mechanisms of tamoxifen resistance, we performed a systematic analysis of mi...... and siRNAmediated gene knockdown, we showed that both SNAI2 and FYN significantly affect the growth of TamR cell lines. Finally, we show that a combination of 2 miRNAs (miR-190b and miR-516a-5p) exhibiting altered expression in TamR cell lines were predictive of treatment outcome in a cohort of ER......+ breast cancer patients receiving adjuvant tamoxifen mono-therapy. Our results provide new insight into the molecular mechanisms of tamoxifen resistance and may form the basis for future medical intervention for the large number of women with tamoxifen-resistant ER+ breast cancer....

  16. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

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    Fei Liu

    2016-08-01

    Full Text Available The inference of gene regulatory networks (GRNs from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN, to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only

  17. Genes2FANs: connecting genes through functional association networks

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    Dannenfelser Ruth

    2012-07-01

    Full Text Available Abstract 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

  18. Random matrix analysis for gene interaction networks in cancer cells

    CERN Document Server

    Kikkawa, Ayumi

    2016-01-01

    Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO. The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells. Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the net...

  19. Inferring gene regression networks with model trees

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    Aguilar-Ruiz Jesus S

    2010-10-01

    Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear

  20. The functional landscape of mouse gene expression

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    Zhang Wen

    2004-12-01

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

  1. Screening of differentially expressed genes in pathological scar tissues using expression microarray.

    Science.gov (United States)

    Huang, L P; Mao, Z; Zhang, L; Liu, X X; Huang, C; Jia, Z S

    2015-09-09

    Pathological scar tissues and normal skin tissues were differentiated by screening for differentially expressed genes in pathologic scar tissues via gene expression microarray. The differentially expressed gene data was analyzed by gene ontology and pathway analyses. There were 5001 up- or down-regulated genes in 2-fold differentially expressed genes, 956 up- or down-regulated genes in 5-fold differentially expressed genes, and 114 up- or down-regulated genes in 20-fold differentially expressed genes. Therefore, significant differences were observed in the gene expression in pathological scar tissues and normal foreskin tissues. The development of pathological scar tissues has been correlated to changes in multiple genes and pathways, which are believed to form a dynamic network connection.

  2. X chromosome regulation of autosomal gene expression in bovine blastocysts

    Science.gov (United States)

    Itoh, Yuichiro; Arnold, Arthur P.

    2014-01-01

    Although X chromosome inactivation in female mammals evolved to balance the expression of X chromosome and autosomal genes in the two sexes, female embryos pass through developmental stages in which both X chromosomes are active in somatic cells. Bovine blastocysts show higher expression of many X genes in XX than XY embryos, suggesting that X inactivation is not complete. Here we reanalyzed bovine blastocyst microarray expression data from a network perspective with a focus on interactions between X chromosome and autosomal genes. Whereas male to female ratios of expression of autosomal genes were distributed around a mean of 1, X chromosome genes were clearly shifted towards higher expression in females. We generated gene coexpression networks and identified a major module of genes with correlated gene expression that includes female-biased X genes and sexually dimorphic autosomal genes for which the sexual dimorphism is likely driven by the X genes. In this module, expression of X chromosome genes correlates with autosome genes, more than the expression of autosomal genes with each other. Our study identifies correlated patterns of autosomal and X-linked genes that are likely influenced by the sexual imbalance of X gene expression when X inactivation is inefficient. PMID:24817096

  3. Model transcriptional networks with continuously varying expression levels

    Directory of Open Access Journals (Sweden)

    Carneiro Mauricio O

    2011-12-01

    Full Text Available Abstract Background At a time when genomes are being sequenced by the hundreds, much attention has shifted from identifying genes and phenotypes to understanding the networks of interactions among genes. We developed a gene network developmental model expanding on previous models of transcription regulatory networks. In our model, each network is described by a matrix representing the interactions between transcription factors, and a vector of continuous values representing the transcription factor expression in an individual. Results In this work we used the gene network model to look at the impact of mating as well as insertions and deletions of genes in the evolution of complexity of these networks. We found that the natural process of diploid mating increases the likelihood of maintaining complexity, especially in higher order networks (more than 10 genes. We also show that gene insertion is a very efficient way to add more genes to a network as it provides a much higher chance of developmental stability. Conclusions The continuous model affords a more complete view of the evolution of interacting genes. The notion of a continuous output vector also incorporates the reality of gene networks and graded concentrations of gene products.

  4. The flow of gene expression.

    Science.gov (United States)

    Misteli, Tom

    2004-03-01

    Gene expression is a highly interconnected multistep process. A recent meeting in Iguazu Falls, Argentina, highlighted the need to uncover both the molecular details of each single step as well as the mechanisms of coordination among processes in order to fully understand the expression of genes.

  5. Digital gene expression signatures for maize development.

    Science.gov (United States)

    Eveland, Andrea L; Satoh-Nagasawa, Namiko; Goldshmidt, Alexander; Meyer, Sandra; Beatty, Mary; Sakai, Hajime; Ware, Doreen; Jackson, David

    2010-11-01

    Genome-wide expression signatures detect specific perturbations in developmental programs and contribute to functional resolution of key regulatory networks. In maize (Zea mays) inflorescences, mutations in the RAMOSA (RA) genes affect the determinacy of axillary meristems and thus alter branching patterns, an important agronomic trait. In this work, we developed and tested a framework for analysis of tag-based, digital gene expression profiles using Illumina's high-throughput sequencing technology and the newly assembled B73 maize reference genome. We also used a mutation in the RA3 gene to identify putative expression signatures specific to stem cell fate in axillary meristem determinacy. The RA3 gene encodes a trehalose-6-phosphate phosphatase and may act at the interface between developmental and metabolic processes. Deep sequencing of digital gene expression libraries, representing three biological replicate ear samples from wild-type and ra3 plants, generated 27 million 20- to 21-nucleotide reads with frequencies spanning 4 orders of magnitude. Unique sequence tags were anchored to 3'-ends of individual transcripts by DpnII and NlaIII digests, which were multiplexed during sequencing. We mapped 86% of nonredundant signature tags to the maize genome, which associated with 37,117 gene models and unannotated regions of expression. In total, 66% of genes were detected by at least nine reads in immature maize ears. We used comparative genomics to leverage existing information from Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) in functional analyses of differentially expressed maize genes. Results from this study provide a basis for the analysis of short-read expression data in maize and resolved specific expression signatures that will help define mechanisms of action for the RA3 gene.

  6. Studying the Complex Expression Dependences between Sets of Coexpressed Genes

    Directory of Open Access Journals (Sweden)

    Mario Huerta

    2014-01-01

    Full Text Available Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. The use of clustering methods to obtain sets of coexpressed genes from expression arrays is very common; nevertheless there are no appropriate tools to study the expression networks among these sets of coexpressed genes. The aim of the developed tools is to allow studying the complex expression dependences that exist between sets of coexpressed genes. For this purpose, we start detecting the nonlinear expression relationships between pairs of genes, plus the coexpressed genes. Next, we form networks among sets of coexpressed genes that maintain nonlinear expression dependences between all of them. The expression relationship between the sets of coexpressed genes is defined by the expression relationship between the skeletons of these sets, where this skeleton represents the coexpressed genes with a well-defined nonlinear expression relationship with the skeleton of the other sets. As a result, we can study the nonlinear expression relationships between a target gene and other sets of coexpressed genes, or start the study from the skeleton of the sets, to study the complex relationships of activation and deactivation between the sets of coexpressed genes that carry out the different cellular processes present in the expression experiments.

  7. Shared Pathways Among Autism Candidate Genes Determined by Co-expression Network Analysis of the Developing Human Brain Transcriptome

    NARCIS (Netherlands)

    Mahfouz, A.; Ziats, M.N.; Rennert, O.M.; Lelieveldt, B.P.F.; Reinders, M.J.T.

    2015-01-01

    Autism spectrum disorder (ASD) is a neurodevelopmental syndrome known to have a significant but complex genetic etiology. Hundreds of diverse genes have been implicated in ASD; yet understanding how many genes, each with disparate function, can all be linked to a single clinical phenotype remains un

  8. The TRANSFAC system on gene expression regulation.

    Science.gov (United States)

    Wingender, E; Chen, X; Fricke, E; Geffers, R; Hehl, R; Liebich, I; Krull, M; Matys, V; Michael, H; Ohnhäuser, R; Prüss, M; Schacherer, F; Thiele, S; Urbach, S

    2001-01-01

    The TRANSFAC database on transcription factors and their DNA-binding sites and profiles (http://www.gene-regulation.de/) has been quantitatively extended and supplemented by a number of modules. These modules give information about pathologically relevant mutations in regulatory regions and transcription factor genes (PathoDB), scaffold/matrix attached regions (S/MARt DB), signal transduction (TRANSPATH) and gene expression sources (CYTOMER). Altogether, these distinct database modules constitute the TRANSFAC system. They are accompanied by a number of program routines for identifying potential transcription factor binding sites or for localizing individual components in the regulatory network of a cell.

  9. Modulation of imprinted gene expression following superovulation.

    Science.gov (United States)

    Fortier, Amanda L; McGraw, Serge; Lopes, Flavia L; Niles, Kirsten M; Landry, Mylène; Trasler, Jacquetta M

    2014-05-05

    Although assisted reproductive technologies increase the risk of low birth weight and genomic imprinting disorders, the precise underlying causes remain unclear. Using a mouse model, we previously showed that superovulation alters the expression of imprinted genes in the placenta at 9.5days (E9.5) of gestation. Here, we investigate whether effects of superovulation on genomic imprinting persisted at later stages of development and assess the surviving fetuses for growth and morphological abnormalities. Superovulation, followed by embryo transfer at E3.5, as compared to spontaneous ovulation (controls), resulted in embryos of normal size and weight at 14.5 and 18.5days of gestation. The normal monoallelic expression of the imprinted genes H19, Snrpn and Kcnq1ot1 was unaffected in either the placentae or the embryos from the superovulated females at E14.5 or E18.5. However, for the paternally expressed imprinted gene Igf2, superovulation generated placentae with reduced production of the mature protein at E9.5 and significantly more variable mRNA levels at E14.5. We propose that superovulation results in the ovulation of abnormal oocytes with altered expression of imprinted genes, but that the coregulated genes of the imprinted gene network result in modulated expression. Copyright © 2014. Published by Elsevier Ireland Ltd.

  10. Gene expression regulation in roots under drought.

    Science.gov (United States)

    Janiak, Agnieszka; Kwaśniewski, Mirosław; Szarejko, Iwona

    2016-02-01

    Stress signalling and regulatory networks controlling expression of target genes are the basis of plant response to drought. Roots are the first organs exposed to water deficiency in the soil and are the place of drought sensing. Signalling cascades transfer chemical signals toward the shoot and initiate molecular responses that lead to the biochemical and morphological changes that allow plants to be protected against water loss and to tolerate stress conditions. Here, we present an overview of signalling network and gene expression regulation pathways that are actively induced in roots under drought stress. In particular, the role of several transcription factor (TF) families, including DREB, AP2/ERF, NAC, bZIP, MYC, CAMTA, Alfin-like and Q-type ZFP, in the regulation of root response to drought are highlighted. The information provided includes available data on mutual interactions between these TFs together with their regulation by plant hormones and other signalling molecules. The most significant downstream target genes and molecular processes that are controlled by the regulatory factors are given. These data are also coupled with information about the influence of the described regulatory networks on root traits and root development which may translate to enhanced drought tolerance. This is the first literature survey demonstrating the gene expression regulatory machinery that is induced by drought stress, presented from the perspective of roots.

  11. Cell cycle-dependent gene networks relevant to cancer

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The analysis of sophisticated interplays between cell cycle-dependent genes in a disease condition is one of the largely unexplored areas in modern tumor biology research. Many cell cycle-dependent genes are either oncogenes or suppressor genes, or are closely asso- ciated with the transition of a cell cycle. However, it is unclear how the complicated relationships between these cell cycle-dependent genes are, especially in cancers. Here, we sought to identify significant expression relationships between cell cycle-dependent genes by analyzing a HeLa microarray dataset using a local alignment algorithm and constructed a gene transcriptional network specific to the cancer by assembling these newly identified gene-gene relationships. We further characterized this global network by partitioning the whole network into several cell cycle phase-specific sub-networks. All generated networks exhibited the power-law node-degree dis- tribution, and the average clustering coefficients of these networks were remarkably higher than those of pure scale-free networks, indi- cating a property of hierarchical modularity. Based on the known protein-protein interactions and Gene Ontology annotation data, the proteins encoded by cell cycle-dependent interacting genes tended to share the same biological functions or to be involved in the same biological processes, rather than interacting by physical means. Finally, we identified the hub genes related to cancer based on the topo- logical importance that maintain the basic structure of cell cycle-dependent gene networks.

  12. Human Lacrimal Gland Gene Expression

    Science.gov (United States)

    Aakalu, Vinay Kumar; Parameswaran, Sowmya; Maienschein-Cline, Mark; Bahroos, Neil; Shah, Dhara; Ali, Marwan; Krishnakumar, Subramanian

    2017-01-01

    Background The study of human lacrimal gland biology and development is limited. Lacrimal gland tissue is damaged or poorly functional in a number of disease states including dry eye disease. Development of cell based therapies for lacrimal gland diseases requires a better understanding of the gene expression and signaling pathways in lacrimal gland. Differential gene expression analysis between lacrimal gland and other embryologically similar tissues may be helpful in furthering our understanding of lacrimal gland development. Methods We performed global gene expression analysis of human lacrimal gland tissue using Affymetrix ® gene expression arrays. Primary data from our laboratory was compared with datasets available in the NLM GEO database for other surface ectodermal tissues including salivary gland, skin, conjunctiva and corneal epithelium. Results The analysis revealed statistically significant difference in the gene expression of lacrimal gland tissue compared to other ectodermal tissues. The lacrimal gland specific, cell surface secretory protein encoding genes and critical signaling pathways which distinguish lacrimal gland from other ectodermal tissues are described. Conclusions Differential gene expression in human lacrimal gland compared with other ectodermal tissue types revealed interesting patterns which may serve as the basis for future studies in directed differentiation among other areas. PMID:28081151

  13. Gene expression profiles in irradiated cancer cells

    Science.gov (United States)

    Minafra, L.; Bravatà, V.; Russo, G.; Ripamonti, M.; Gilardi, M. C.

    2013-07-01

    Knowledge of the molecular and genetic mechanisms underlying cellular response to radiation may provide new avenues to develop innovative predictive tests of radiosensitivity of tumours and normal tissues and to improve individual therapy. Nowadays very few studies describe molecular changes induced by hadrontherapy treatments, therefore this field has to be explored and clarified. High-throughput methodologies, such as DNA microarray, allow us to analyse mRNA expression of thousands of genes simultaneously in order to discover new genes and pathways as targets of response to hadrontherapy. Our aim is to elucidate the molecular networks involved in the sensitivity/resistance of cancer cell lines subjected to hadrontherapy treatments with a genomewide approach by using cDNA microarray technology to identify gene expression profiles and candidate genes responsible of differential cellular responses.

  14. Gene expression profiles in irradiated cancer cells

    Energy Technology Data Exchange (ETDEWEB)

    Minafra, L.; Bravatà, V.; Russo, G.; Ripamonti, M.; Gilardi, M. C. [IBFM CNR - LATO, Cefalù, Segrate (Italy)

    2013-07-26

    Knowledge of the molecular and genetic mechanisms underlying cellular response to radiation may provide new avenues to develop innovative predictive tests of radiosensitivity of tumours and normal tissues and to improve individual therapy. Nowadays very few studies describe molecular changes induced by hadrontherapy treatments, therefore this field has to be explored and clarified. High-throughput methodologies, such as DNA microarray, allow us to analyse mRNA expression of thousands of genes simultaneously in order to discover new genes and pathways as targets of response to hadrontherapy. Our aim is to elucidate the molecular networks involved in the sensitivity/resistance of cancer cell lines subjected to hadrontherapy treatments with a genomewide approach by using cDNA microarray technology to identify gene expression profiles and candidate genes responsible of differential cellular responses.

  15. Design and Implementation of Visual Dynamic Display Software of Gene Expression Based on GTK

    Institute of Scientific and Technical Information of China (English)

    JIANG Wei; MENG Fanjiang; LI Yong; YU Xiao

    2009-01-01

    The paper presented an implement method for a dynamic gene expression display software based on the GTK. This method established the dynamic presentation system of gene expression which according to gene expression data from gene chip hybridize at different time, adopted a linearity combination model and Pearson correlation coefficient algorithm. The system described the gene expression changes in graphic form, the gene expression changes with time and the changes in characteristics of the gene expression, also the changes in relations of the gene expression and regulation relationships among genes. The system also provided an integrated platform for analysis on gene chips data, especially for the research on the network of gene regulation.

  16. Regulatory Networks:. Inferring Functional Relationships Through Co-Expression

    Science.gov (United States)

    Wanke, Dierk; Hahn, Achim; Kilian, Joachim; Harter, Klaus; Berendzen, Kenneth W.

    2010-01-01

    Gene expression data not only provide us insights into discrete transcript abundance of specific genes, but contain cryptic information that can not readily be assessed without interpretation. We again used data of the plant Arabidopsis thaliana as our reference organism, yet the analysis presented herein can be performed with any organism with various data sources. Within the cell, information is transduced via different signaling cascades and results in differential gene expression responses. The incoming signals are perceived from upstream signaling components and handed to downstream messengers that further deliver the signals to effector proteins which can directly influence gene expression. In most cases, we can assume that proteins, which are connected to other signaling components within such a regulatory network, exhibit similar expression trajectories. Thus, we extracted a known functional network from literature and demonstrated that it is possible to superimpose microarray expression data onto the pathways. Thereby, we could follow the information flow through time reflected by gene expression changes. This allowed us to predict, whether the upstream signal was transmitted from known elements contained in the network or relayed from outside components. We next conducted the vice versa approach and used large scale microarray expression data to build a co-expression matrix for all genes present on the array. From this, we computed a regulatory network, which allowed us to deduce known and novel signaling pathways.

  17. Modeling of gap gene expression in Drosophila Kruppel mutants.

    Directory of Open Access Journals (Sweden)

    Konstantin Kozlov

    Full Text Available The segmentation gene network in Drosophila embryo solves the fundamental problem of embryonic patterning: how to establish a periodic pattern of gene expression, which determines both the positions and the identities of body segments. The gap gene network constitutes the first zygotic regulatory tier in this process. Here we have applied the systems-level approach to investigate the regulatory effect of gap gene Kruppel (Kr on segmentation gene expression. We acquired a large dataset on the expression of gap genes in Kr null mutants and demonstrated that the expression levels of these genes are significantly reduced in the second half of cycle 14A. To explain this novel biological result we applied the gene circuit method which extracts regulatory information from spatial gene expression data. Previous attempts to use this formalism to correctly and quantitatively reproduce gap gene expression in mutants for a trunk gap gene failed, therefore here we constructed a revised model and showed that it correctly reproduces the expression patterns of gap genes in Kr null mutants. We found that the remarkable alteration of gap gene expression patterns in Kr mutants can be explained by the dynamic decrease of activating effect of Cad on a target gene and exclusion of Kr gene from the complex network of gap gene interactions, that makes it possible for other interactions, in particular, between hb and gt, to come into effect. The successful modeling of the quantitative aspects of gap gene expression in mutant for the trunk gap gene Kr is a significant achievement of this work. This result also clearly indicates that the oversimplified representation of transcriptional regulation in the previous models is one of the reasons for unsuccessful attempts of mutant simulations.

  18. The effect of negative autoregulation on eukaryotic gene expression

    Science.gov (United States)

    Nevozhay, Dmitry; Adams, Rhys; Murphy, Kevin; Josic, Kresimir; Balázsi, G. Ábor

    2009-03-01

    Negative autoregulation is a frequent motif in gene regulatory networks, which has been studied extensively in prokaryotes. Nevertheless, some effects of negative feedback on gene expression in eukaryotic transcriptional networks remain unknown. We studied how the strength of negative feedback regulation affects the characteristics of gene expression in yeast cells carrying synthetic transcriptional cascades. We observed a drastic reduction of gene expression noise and a change in the shape of the dose-response curve. We explained these experimentally observed effects by stochastic simulations and a simple set of algebraic equations.

  19. Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling

    Science.gov (United States)

    Tegnér, Jesper; Yeung, M. K. Stephen; Hasty, Jeff; Collins, James J.

    2003-01-01

    While the fundamental building blocks of biology are being tabulated by the various genome projects, microarray technology is setting the stage for the task of deducing the connectivity of large-scale gene networks. We show how the perturbation of carefully chosen genes in a microarray experiment can be used in conjunction with a reverse engineering algorithm to reveal the architecture of an underlying gene regulatory network. Our iterative scheme identifies the network topology by analyzing the steady-state changes in gene expression resulting from the systematic perturbation of a particular node in the network. We highlight the validity of our reverse engineering approach through the successful deduction of the topology of a linear in numero gene network and a recently reported model for the segmentation polarity network in Drosophila melanogaster. Our method may prove useful in identifying and validating specific drug targets and in deconvolving the effects of chemical compounds. PMID:12730377

  20. Effects of a silenced gene in Boolean network models

    Directory of Open Access Journals (Sweden)

    Emir Haliki

    2017-03-01

    Full Text Available Gene regulation and their regulatory networks are one of the most challenging research problems of computational biology and complexity sciences. Gene regulation is formed by indirect interaction between DNA segments which are protein coding genes to configure the expression level of one another. Prevention of expression of any genes in gene regulation at the levels of transcription or translation indicates the gene silencing event. The present study examined what types of results in gene silencing would bring about in the dynamics of Boolean genetic regulatory mechanisms. The analytical study was performed in gene expression variations of Boolean dynamics first, then the related numerical analysis was simulated in real networks in the literature.

  1. Gene regulatory networks elucidating huanglongbing disease mechanisms.

    Science.gov (United States)

    Martinelli, Federico; Reagan, Russell L; Uratsu, Sandra L; Phu, My L; Albrecht, Ute; Zhao, Weixiang; Davis, Cristina E; Bowman, Kim D; Dandekar, Abhaya M

    2013-01-01

    Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas), especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes) would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation), sucrose metabolism (upregulation), and starch biosynthesis (upregulation). In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70) was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur.

  2. Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk

    NARCIS (Netherlands)

    Kar, S.P.; Tyrer, J.P.; Li, Q.; Lawrenson, K.; Aben, K.K.H.; Anton-Culver, H.; Antonenkova, N.; Chenevix-Trench, G.; Baker, H.; Bandera, E.V.; Bean, Y.T.; Beckmann, M.W.; Berchuck, A.; Bisogna, M.; Bjorge, L.; Bogdanova, N.; Brinton, L.; Brooks-Wilson, A.; Butzow, R.; Campbell, I.; Carty, K.; Chang-Claude, J.; Chen, Y.A.; Chen, Z.; Cook, L.S.; Cramer, D.; Cunningham, J.M.; Cybulski, C.; Dansonka-Mieszkowska, A.; Dennis, J.; Dicks, E.; Doherty, J.A.; Dork, T.; Bois, A. du; Durst, M.; Eccles, D.; Easton, D.F.; Edwards, R.P.; Ekici, A.B.; Fasching, P.A.; Fridley, B.L.; Gao, Y.T.; Gentry-Maharaj, A.; Giles, G.G.; Glasspool, R.; Goode, E.L.; Goodman, M.T.; Grownwald, J.; Harrington, P.; Harter, P.; Hein, A.; Heitz, F.; Hildebrandt, M.A.T.; Hillemanns, P.; Hogdall, E.; Hogdall, C.K.; Hosono, S.; Iversen, E.S.; Jakubowska, A.; Paul, J.; Jensen, A.; Ji, B.T.; Karlan, B.Y.; Kjaer, S.K.; Kelemen, L.E.; Kellar, M.; Kelley, J.; Kiemeney, L.A.L.M.; Krakstad, C.; Kupryjanczyk, J.; Lambrechts, D.; Lambrechts, S.; Le, N.D.; Lee, A.W.; Lele, S.; Leminen, A.; Lester, J.; Levine, D.A.; Liang, D.; Lissowska, J.; Lu, K.; Lubinski, J.; Lundvall, L.; Massuger, L.F.; Matsuo, K.; McGuire, V.; McLaughlin, J.R.; McNeish, I.A.; Menon, U.; Modugno, F.; Moysich, K.B.; Narod, S.A.; Nedergaard, L.; Ness, R.B.; Nevanlinna, H.; Odunsi, K.; Olson, S.H.; Orlow, I.; Orsulic, S.; Weber, R.P.

    2015-01-01

    BACKGROUND: Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by co

  3. Gene regulatory network inference using out of equilibrium statistical mechanics.

    Science.gov (United States)

    Benecke, Arndt

    2008-08-01

    Spatiotemporal control of gene expression is fundamental to multicellular life. Despite prodigious efforts, the encoding of gene expression regulation in eukaryotes is not understood. Gene expression analyses nourish the hope to reverse engineer effector-target gene networks using inference techniques. Inference from noisy and circumstantial data relies on using robust models with few parameters for the underlying mechanisms. However, a systematic path to gene regulatory network reverse engineering from functional genomics data is still impeded by fundamental problems. Recently, Johannes Berg from the Theoretical Physics Institute of Cologne University has made two remarkable contributions that significantly advance the gene regulatory network inference problem. Berg, who uses gene expression data from yeast, has demonstrated a nonequilibrium regime for mRNA concentration dynamics and was able to map the gene regulatory process upon simple stochastic systems driven out of equilibrium. The impact of his demonstration is twofold, affecting both the understanding of the operational constraints under which transcription occurs and the capacity to extract relevant information from highly time-resolved expression data. Berg has used his observation to predict target genes of selected transcription factors, and thereby, in principle, demonstrated applicability of his out of equilibrium statistical mechanics approach to the gene network inference problem.

  4. TP53 mutations, expression and interaction networks in human cancers.

    Science.gov (United States)

    Wang, Xiaosheng; Sun, Qingrong

    2017-01-03

    Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

  5. Genetic architecture of gene expression in the chicken

    Directory of Open Access Journals (Sweden)

    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.

  6. Inference of gene pathways using mixture Bayesian networks

    Directory of Open Access Journals (Sweden)

    Ko Younhee

    2009-05-01

    Full Text Available Abstract Background Inference of gene networks typically relies on measurements across a wide range of conditions or treatments. Although one network structure is predicted, the relationship between genes could vary across conditions. A comprehensive approach to infer general and condition-dependent gene networks was evaluated. This approach integrated Bayesian network and Gaussian mixture models to describe continuous microarray gene expression measurements, and three gene networks were predicted. Results The first reconstructions of a circadian rhythm pathway in honey bees and an adherens junction pathway in mouse embryos were obtained. In addition, general and condition-specific gene relationships, some unexpected, were detected in these two pathways and in a yeast cell-cycle pathway. The mixture Bayesian network approach identified all (honey bee circadian rhythm and mouse adherens junction pathways or the vast majority (yeast cell-cycle pathway of the gene relationships reported in empirical studies. Findings across the three pathways and data sets indicate that the mixture Bayesian network approach is well-suited to infer gene pathways based on microarray data. Furthermore, the interpretation of model estimates provided a broader understanding of the relationships between genes. The mixture models offered a comprehensive description of the relationships among genes in complex biological processes or across a wide range of conditions. The mixture parameter estimates and corresponding odds that the gene network inferred for a sample pertained to each mixture component allowed the uncovering of both general and condition-dependent gene relationships and patterns of expression. Conclusion This study demonstrated the two main benefits of learning gene pathways using mixture Bayesian networks. First, the identification of the optimal number of mixture components supported by the data offered a robust approach to infer gene relationships and

  7. Hierarchy of gene expression data is predictive of future breast cancer outcome

    Science.gov (United States)

    Chen, Man; Deem, Michael W.

    2013-10-01

    We calculate measures of hierarchy in gene and tissue networks of breast cancer patients. We find that the likelihood of metastasis in the future is correlated with increased values of network hierarchy for expression networks of cancer-associated genes, due to the correlated expression of cancer-specific pathways. Conversely, future metastasis and quick relapse times are negatively correlated with the values of network hierarchy in the expression network of all genes, due to the dedifferentiation of gene pathways and circuits. These results suggest that the hierarchy of gene expression may be useful as an additional biomarker for breast cancer prognosis.

  8. Reverse-engineering transcriptional modules from gene expression data

    OpenAIRE

    Michoel, Tom; De Smet, Riet; Joshi, Anagha; Marchal, Kathleen; de Peer, Yves Van

    2009-01-01

    "Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the data set used to learn the models.

  9. Evolvability and hierarchy in rewired bacterial gene networks

    Science.gov (United States)

    Isalan, Mark; Lemerle, Caroline; Michalodimitrakis, Konstantinos; Beltrao, Pedro; Horn, Carsten; Raineri, Emanuele; Garriga-Canut, Mireia; Serrano, Luis

    2009-01-01

    Sequencing DNA from several organisms has revealed that duplication and drift of existing genes have primarily molded the contents of a given genome. Though the effect of knocking out or over-expressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network evolvability, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or σ-factor genes in Escherichia coli, added over a wild-type genetic background. Here we show that ~95% of new networks are tolerated by the bacteria, that very few alter growth, and that expression level correlates with factor position in the wild-type network hierarchy. Most importantly, we find that certain networks consistently survive over the wild-type under various selection pressures. Therefore new links in the network are rarely a barrier for evolution and can even confer a fitness advantage. PMID:18421347

  10. Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)

    Science.gov (United States)

    Wang, Sheng; Fu, Zhi-yong; Chen, Hong-song; Nie, Yun-peng; Wang, Ke-lin

    2016-11-01

    Nonlinear complexity is a characteristic of hydrologic processes. Using fewer model parameters is recommended to reduce error. This study investigates, and compares, the ability of gene expression programming (GEP) and artificial neural network (ANN) techniques in modeling ET0 by using fewer meteorological parameters in the karst area of northwest Guangxi province, China. Over a 5-year period (2008-2012), meteorological data consisting of maximum and minimum air temperature, relative humidity, wind speed, and sunshine duration were collected from four weather stations: BaiSe, DuAn, HeChi, and RongAn. The ET0 calculated by the FAO-56 PM equation was used as a reference to evaluate results for GEP, ANN, and Hargreaves models. The coefficient of determination ( R 2) and the root mean square error (RMSE) were used as statistical indicators. Evaluations revealed that GEP, and ANN, can be used to successfully model ET0. In most cases, when using the same input variables, ANN models were superior to GEP. We then established ET0 equations with fewer parameters under various conditions. GEP can produce simple explicit mathematical formulations which are easier to use than the ANN models.

  11. Gene expression in colorectal cancer

    DEFF Research Database (Denmark)

    Birkenkamp-Demtroder, Karin; Christensen, Lise Lotte; Olesen, Sanne Harder

    2002-01-01

    Understanding molecular alterations in colorectal cancer (CRC) is needed to define new biomarkers and treatment targets. We used oligonucleotide microarrays to monitor gene expression of about 6,800 known genes and 35,000 expressed sequence tags (ESTs) on five pools (four to six samples in each p...... with a high frequency of loss of heterozygosity. The genes and ESTs presented in this study encode new potential tumor markers as well as potential novel therapeutic targets for prevention or therapy of CRC.......Understanding molecular alterations in colorectal cancer (CRC) is needed to define new biomarkers and treatment targets. We used oligonucleotide microarrays to monitor gene expression of about 6,800 known genes and 35,000 expressed sequence tags (ESTs) on five pools (four to six samples in each...... pool) of total RNA from left-sided sporadic colorectal carcinomas. We compared normal tissue to carcinoma tissue from Dukes' stages A-D (noninvasive to distant metastasis) and identified 908 known genes and 4,155 ESTs that changed remarkably from normal to tumor tissue. Based on intensive filtering 226...

  12. Using effective subnetworks to predict selected properties of gene networks.

    Directory of Open Access Journals (Sweden)

    Gemunu H Gunaratne

    Full Text Available BACKGROUND: Difficulties associated with implementing gene therapy are caused by the complexity of the underlying regulatory networks. The forms of interactions between the hundreds of genes, proteins, and metabolites in these networks are not known very accurately. An alternative approach is to limit consideration to genes on the network. Steady state measurements of these influence networks can be obtained from DNA microarray experiments. However, since they contain a large number of nodes, the computation of influence networks requires a prohibitively large set of microarray experiments. Furthermore, error estimates of the network make verifiable predictions impossible. METHODOLOGY/PRINCIPAL FINDINGS: Here, we propose an alternative approach. Rather than attempting to derive an accurate model of the network, we ask what questions can be addressed using lower dimensional, highly simplified models. More importantly, is it possible to use such robust features in applications? We first identify a small group of genes that can be used to affect changes in other nodes of the network. The reduced effective empirical subnetwork (EES can be computed using steady state measurements on a small number of genetically perturbed systems. We show that the EES can be used to make predictions on expression profiles of other mutants, and to compute how to implement pre-specified changes in the steady state of the underlying biological process. These assertions are verified in a synthetic influence network. We also use previously published experimental data to compute the EES associated with an oxygen deprivation network of E.coli, and use it to predict gene expression levels on a double mutant. The predictions are significantly different from the experimental results for less than of genes. CONCLUSIONS/SIGNIFICANCE: The constraints imposed by gene expression levels of mutants can be used to address a selected set of questions about a gene network.

  13. Gene Expression Profiling of Colorectal Tumors and Normal Mucosa by Microarrays Meta-Analysis Using Prediction Analysis of Microarray, Artificial Neural Network, Classification, and Regression Trees

    Directory of Open Access Journals (Sweden)

    Chi-Ming Chu

    2014-01-01

    Full Text Available Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0. Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances. Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes, CA7, SPIB, GUCA2B, AQP8, IL6R and CWH43; oncogenes, SPP1 and TCN1. Genes of higher significances showed lower variation in rank ordering by different methods. Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%. This method can be applied to future studies. Among the top eight genes, CA7, TCN1, and CWH43 have not been reported to be related to CRC.

  14. Phytochrome-regulated Gene Expression

    Institute of Scientific and Technical Information of China (English)

    Peter H. Quail

    2007-01-01

    Identification of all genes involved in the phytochrome (phy)-mediated responses of plants to their light environment is an important goal in providing an overall understanding of light-regulated growth and development. This article highlights and integrates the central findings of two recent comprehensive studies in Arabidopsis that have identified the genome-wide set of phy-regulated genes that respond rapidly to red-light signals upon first exposure of dark-grown seedlings, and have tested the functional relevance to normal seedling photomorphogenesis of an initial subset of these genes. The data: (a) reveal considerable complexity in the channeling of the light signals through the different phy-family members (phyA to phyE) to responsive genes; (b) identify a diversity of transcription-factor-encoding genes as major early, if not primary, targets of phy signaling, and, therefore, as potentially important regulators in the transcriptional-network hierarchy; and (c) identify auxin-related genes as the dominant class among rapidly-regulated, hormone-related genes. However, reverse-genetic functional profiling of a selected subset of these genes reveals that only a limited fraction are necessary for optimal phy-induced seedling deetiolation.

  15. Correction of gene expression data

    DEFF Research Database (Denmark)

    Darbani Shirvanehdeh, Behrooz; Stewart, C. Neal, Jr.; Noeparvar, Shahin;

    2014-01-01

    This report investigates for the first time the potential inter-treatment bias source of cell number for gene expression studies. Cell-number bias can affect gene expression analysis when comparing samples with unequal total cellular RNA content or with different RNA extraction efficiencies...... an analytical approach to examine the suitability of correction methods by considering the inter-treatment bias as well as the inter-replicate variance, which allows use of the best correction method with minimum residual bias. Analyses of RNA sequencing and microarray data showed that the efficiencies...

  16. Distribution of population-averaged observables in stochastic gene expression

    Science.gov (United States)

    Bhattacharyya, Bhaswati; Kalay, Ziya

    2014-01-01

    Observation of phenotypic diversity in a population of genetically identical cells is often linked to the stochastic nature of chemical reactions involved in gene regulatory networks. We investigate the distribution of population-averaged gene expression levels as a function of population, or sample, size for several stochastic gene expression models to find out to what extent population-averaged quantities reflect the underlying mechanism of gene expression. We consider three basic gene regulation networks corresponding to transcription with and without gene state switching and translation. Using analytical expressions for the probability generating function of observables and large deviation theory, we calculate the distribution and first two moments of the population-averaged mRNA and protein levels as a function of model parameters, population size, and number of measurements contained in a data set. We validate our results using stochastic simulations also report exact results on the asymptotic properties of population averages which show qualitative differences among different models.

  17. Identifying gene regulatory network rewiring using latent differential graphical models.

    Science.gov (United States)

    Tian, Dechao; Gu, Quanquan; Ma, Jian

    2016-09-30

    Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions.

  18. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

    DEFF Research Database (Denmark)

    Kogelman, Lisette; Cirera Salicio, Susanna; Zhernakova, Daria V.;

    2014-01-01

    (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. Results WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P ... the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using...

  19. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    Science.gov (United States)

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  20. Gene expression profile analysis of human intervertebral disc degeneration

    OpenAIRE

    Kai Chen; Dajiang Wu; Xiaodong Zhu; Haijian Ni; Xianzhao Wei; Ningfang Mao; Yang Xie; Yunfei Niu; Ming Li

    2013-01-01

    In this study, we used microarray analysis to investigate the biogenesis and progression of intervertebral disc degeneration. The gene expression profiles of 37 disc tissue samples obtained from patients with herniated discs and degenerative disc disease collected by the National Cancer Institute Cooperative Tissue Network were analyzed. Differentially expressed genes between more and less degenerated discs were identified by significant analysis of microarray. A total of 555 genes were signi...

  1. X chromosome regulation of autosomal gene expression in bovine blastocysts

    OpenAIRE

    Itoh, Yuichiro; Arnold, Arthur P.

    2014-01-01

    Although X chromosome inactivation in female mammals evolved to balance the expression of X chromosome and autosomal genes in the two sexes, female embryos pass through developmental stages in which both X chromosomes are active in somatic cells. Bovine blastocysts show higher expression of many X genes in XX than XY embryos, suggesting that X inactivation is not complete. Here we reanalyzed bovine blastocyst microarray expression data from a network perspective with a focus on interactions b...

  2. X chromosome regulation of autosomal gene expression in bovine blastocysts

    OpenAIRE

    Itoh, Yuichiro; Arnold, Arthur P.

    2014-01-01

    Although X chromosome inactivation in female mammals evolved to balance the expression of X chromosome and autosomal genes in the two sexes, female embryos pass through developmental stages in which both X chromosomes are active in somatic cells. Bovine blastocysts show higher expression of many X genes in XX than XY embryos, suggesting that X inactivation is not complete. Here we reanalyzed bovine blastocyst microarray expression data from a network perspective with a focus on interactions b...

  3. Homeobox gene expression in Brachiopoda

    DEFF Research Database (Denmark)

    Altenburger, Andreas; Martinez, Pedro; Wanninger, Andreas

    2011-01-01

    The molecular control that underlies brachiopod ontogeny is largely unknown. In order to contribute to this issue we analyzed the expression pattern of two homeobox containing genes, Not and Cdx, during development of the rhynchonelliform (i.e., articulate) brachiopod Terebratalia transversa. Not...

  4. Extracting expression modules from perturbational gene expression compendia

    Directory of Open Access Journals (Sweden)

    Van Dijck Patrick

    2008-04-01

    Saccharomyces cerevisiae and we analyze one pheromone response-related module in more detail, demonstrating the potential of ENIGMA to generate detailed predictions. Conclusion It is increasingly recognized that perturbational expression compendia are essential to identify the gene networks underlying cellular function, and efforts to build these for different organisms are currently underway. We show that ENIGMA constitutes a valuable addition to the repertoire of methods to analyze such data.

  5. A network view on Schizophrenia related genes

    Directory of Open Access Journals (Sweden)

    Sreedevi Chandrasekaran

    2012-03-01

    Full Text Available This study is a part of a project investigating the molecular determinants of neurological diseases. To account for the systemic nature of these diseases we proceeded from a well established list of 38 schizophrenia-related genes (Allen et al., 2008; Ross et al., 2006 and investigated their closest network environment. The created networks were compared to recently proposed list of 173 schizophrenia related genes (Sun et al., 2009. 115 genes were predicted as potentially related to schizophrenia and subjected to GSEA. The enriched groups of proteins included neuromodulators, neurotransmitters and lipid transport. Over 100 signaling pathways were found significantly involved, signal transduction emerging as the most highly significant biological process. Next, we analyzed two microarray expression datasets derived from olfactory mucosa biopsies of schizophrenic patients and postmortem brain tissue samples from SMRIDB. The systems biology analysis resulted in a number of other genes predicted to be potentially related to schizophrenia, as well as in additional information of interest for elucidating molecular mechanisms of schizophrenia.

  6. Pathways of Lipid Metabolism in Marine Algae, Co-Expression Network, Bottlenecks and Candidate Genes for Enhanced Production of EPA and DHA in Species of Chromista

    Directory of Open Access Journals (Sweden)

    Alice Mühlroth

    2013-11-01

    Full Text Available The importance of n-3 long chain polyunsaturated fatty acids (LC-PUFAs for human health has received more focus the last decades, and the global consumption of n-3 LC-PUFA has increased. Seafood, the natural n-3 LC-PUFA source, is harvested beyond a sustainable capacity, and it is therefore imperative to develop alternative n-3 LC-PUFA sources for both eicosapentaenoic acid (EPA, 20:5n-3 and docosahexaenoic acid (DHA, 22:6n-3. Genera of algae such as Nannochloropsis, Schizochytrium, Isochrysis and Phaedactylum within the kingdom Chromista have received attention due to their ability to produce n-3 LC-PUFAs. Knowledge of LC-PUFA synthesis and its regulation in algae at the molecular level is fragmentary and represents a bottleneck for attempts to enhance the n-3 LC-PUFA levels for industrial production. In the present review, Phaeodactylum tricornutum has been used to exemplify the synthesis and compartmentalization of n-3 LC-PUFAs. Based on recent transcriptome data a co-expression network of 106 genes involved in lipid metabolism has been created. Together with recent molecular biological and metabolic studies, a model pathway for n-3 LC-PUFA synthesis in P. tricornutum has been proposed, and is compared to industrialized species of Chromista. Limitations of the n-3 LC-PUFA synthesis by enzymes such as thioesterases, elongases, acyl-CoA synthetases and acyltransferases are discussed and metabolic bottlenecks are hypothesized such as the supply of the acetyl-CoA and NADPH. A future industrialization will depend on optimization of chemical compositions and increased biomass production, which can be achieved by exploitation of the physiological potential, by selective breeding and by genetic engineering.

  7. Neighboring Genes Show Correlated Evolution in Gene Expression

    Science.gov (United States)

    Ghanbarian, Avazeh T.; Hurst, Laurence D.

    2015-01-01

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

  8. Transcriptomic analysis of gene expression profiles of stomach carcinoma reveal abnormal expression of mitotic components.

    Science.gov (United States)

    Tong, Hongfei; Wang, Jisheng; Chen, Hui; Wang, Zhaohong; Fan, Henwei; Ni, Zhonglin

    2017-02-01

    In order to explore the etiology of gastric cancer on global gene expression level, we developed advanced bioinformatic analysis to investigate the variations of global gene expression and the interactions among them. We downloaded the dataset GSE63288 from Gene Expression Omnibus (GEO) database which included 22 human gastric cancer and 22 healthy control samples. We identified the differential expression genes, and explored the Gene ontology (GO) and pathways of the differentially expressed genes. Furthermore, integrative interaction network and co-expression network were employed to identify the key genes which may contribute to gastric cancer progression. The results indicated that 5 kinases including BUB1, TTK protein kinase, Citron Rho-interacting kinase (CIT), ZAK and NEK2 were upregulated in gastric cancer. Interestingly, BUB1, TTK, CIT and NEK2 have shown high expression similarities and bound with each other, and participated in multiple phases of mitosis. Moreover, a subnet of co-expression genes e.g. KIF14, PRC1, CENPF and CENPI was also involved in mitosis which was functionally coupled with the kinases above. By validation assays, the results indicated that CIT, PRC1, TTK and KIF14 were significantly upregulated in gastric cancer. These evidences have suggested that aberrant expression of these genes may drive gastric cancer including progression, invasion and metastasis. Although the causal relationships between gastric cancer and the genes are still lacking, it was reasonable to take them as biomarkers for diagnosis of gastric cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Genome Wide Expression Profiling of Cancer Cell Lines Cultured in Microgravity Reveals Significant Dysregulation of Cell Cycle and MicroRNA Gene Networks.

    Directory of Open Access Journals (Sweden)

    Prasanna Vidyasekar

    Full Text Available Zero gravity causes several changes in metabolic and functional aspects of the human body and experiments in space flight have demonstrated alterations in cancer growth and progression. This study reports the genome wide expression profiling of a colorectal cancer cell line-DLD-1, and a lymphoblast leukemic cell line-MOLT-4, under simulated microgravity in an effort to understand central processes and cellular functions that are dysregulated among both cell lines. Altered cell morphology, reduced cell viability and an aberrant cell cycle profile in comparison to their static controls were observed in both cell lines under microgravity. The process of cell cycle in DLD-1 cells was markedly affected with reduced viability, reduced colony forming ability, an apoptotic population and dysregulation of cell cycle genes, oncogenes, and cancer progression and prognostic markers. DNA microarray analysis revealed 1801 (upregulated and 2542 (downregulated genes (>2 fold in DLD-1 cultures under microgravity while MOLT-4 cultures differentially expressed 349 (upregulated and 444 (downregulated genes (>2 fold under microgravity. The loss in cell proliferative capacity was corroborated with the downregulation of the cell cycle process as demonstrated by functional clustering of DNA microarray data using gene ontology terms. The genome wide expression profile also showed significant dysregulation of post transcriptional gene silencing machinery and multiple microRNA host genes that are potential tumor suppressors and proto-oncogenes including MIR22HG, MIR17HG and MIR21HG. The MIR22HG, a tumor-suppressor gene was one of the highest upregulated genes in the microarray data showing a 4.4 log fold upregulation under microgravity. Real time PCR validated the dysregulation in the host gene by demonstrating a 4.18 log fold upregulation of the miR-22 microRNA. Microarray data also showed dysregulation of direct targets of miR-22, SP1, CDK6 and CCNA2.

  10. The Effects of Hallucinogens on Gene Expression.

    Science.gov (United States)

    Martin, David A; Nichols, Charles D

    2017-07-05

    The classic serotonergic hallucinogens, or psychedelics, have the ability to profoundly alter perception and behavior. These can include visual distortions, hallucinations, detachment from reality, and mystical experiences. Some psychedelics, like LSD, are able to produce these effects with remarkably low doses of drug. Others, like psilocybin, have recently been demonstrated to have significant clinical efficacy in the treatment of depression, anxiety, and addiction that persist for at least several months after only a single therapeutic session. How does this occur? Much work has recently been published from imaging studies showing that psychedelics alter brain network connectivity. They facilitate a disintegration of the default mode network, producing a hyperconnectivity between brain regions that allow centers that do not normally communicate with each other to do so. The immediate and acute effects on both behaviors and network connectivity are likely mediated by effector pathways downstream of serotonin 5-HT2A receptor activation. These acute molecular processes also influence gene expression changes, which likely influence synaptic plasticity and facilitate more long-term changes in brain neurochemistry ultimately underlying the therapeutic efficacy of a single administration to achieve long-lasting effects. In this review, we summarize what is currently known about the molecular genetic responses to psychedelics within the brain and discuss how gene expression changes may contribute to altered cellular physiology and behaviors.

  11. Alterations in the expression of a neurodevelopmental gene exert long-lasting effects on cognitive-emotional phenotypes and functional brain networks: translational evidence from the stress-resilient Ahi1 knockout mouse.

    Science.gov (United States)

    Lotan, A; Lifschytz, T; Mernick, B; Lory, O; Levi, E; Ben-Shimol, E; Goelman, G; Lerer, B

    2017-06-01

    Many psychiatric disorders are highly heritable and may represent the clinical outcome of early aberrations in the formation of neural networks. The placement of brain connectivity as an 'intermediate phenotype' renders it an attractive target for exploring its interaction with genomics and behavior. Given the complexity of genetic make up and phenotypic heterogeneity in humans, translational studies are indicated. Recently, we demonstrated that a mouse model with heterozygous knockout of the key neurodevelopmental gene Ahi1 displays a consistent stress-resilient phenotype. Extending these data, the current research describes our multi-faceted effort to link early variations in Ahi1 expression with long-term consequences for functional brain networks and cognitive-emotional phenotypes. By combining behavioral paradigms with graph-based analysis of whole-brain functional networks, and then cross-validating the data with robust neuroinformatic data sets, our research suggests that physiological variation in gene expression during neurodevelopment is eventually translated into a continuum of global network metrics that serve as intermediate phenotypes. Within this framework, we suggest that organization of functional brain networks may result, in part, from an adaptive trade-off between efficiency and resilience, ultimately culminating in a phenotypic diversity that encompasses dimensions such as emotional regulation and cognitive function.

  12. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

    Directory of Open Access Journals (Sweden)

    Yun-Xia Zhang

    2016-01-01

    Full Text Available Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes and differentially expressed genes (DEGs between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.

  13. Predicting gene expression from sequence: a reexamination.

    Directory of Open Access Journals (Sweden)

    Yuan Yuan

    2007-11-01

    Full Text Available Although much of the information regarding genes' expressions is encoded in the genome, deciphering such information has been very challenging. We reexamined Beer and Tavazoie's (BT approach to predict mRNA expression patterns of 2,587 genes in Saccharomyces cerevisiae from the information in their respective promoter sequences. Instead of fitting complex Bayesian network models, we trained naïve Bayes classifiers using only the sequence-motif matching scores provided by BT. Our simple models correctly predict expression patterns for 79% of the genes, based on the same criterion and the same cross-validation (CV procedure as BT, which compares favorably to the 73% accuracy of BT. The fact that our approach did not use position and orientation information of the predicted binding sites but achieved a higher prediction accuracy, motivated us to investigate a few biological predictions made by BT. We found that some of their predictions, especially those related to motif orientations and positions, are at best circumstantial. For example, the combinatorial rules suggested by BT for the PAC and RRPE motifs are not unique to the cluster of genes from which the predictive model was inferred, and there are simpler rules that are statistically more significant than BT's ones. We also show that CV procedure used by BT to estimate their method's prediction accuracy is inappropriate and may have overestimated the prediction accuracy by about 10%.

  14. Gene Expression in Trypanosomatid Parasites

    Directory of Open Access Journals (Sweden)

    Santiago Martínez-Calvillo

    2010-01-01

    Full Text Available The parasites Leishmania spp., Trypanosoma brucei, and Trypanosoma cruzi are the trypanosomatid protozoa that cause the deadly human diseases leishmaniasis, African sleeping sickness, and Chagas disease, respectively. These organisms possess unique mechanisms for gene expression such as constitutive polycistronic transcription of protein-coding genes and trans-splicing. Little is known about either the DNA sequences or the proteins that are involved in the initiation and termination of transcription in trypanosomatids. In silico analyses of the genome databases of these parasites led to the identification of a small number of proteins involved in gene expression. However, functional studies have revealed that trypanosomatids have more general transcription factors than originally estimated. Many posttranslational histone modifications, histone variants, and chromatin modifying enzymes have been identified in trypanosomatids, and recent genome-wide studies showed that epigenetic regulation might play a very important role in gene expression in this group of parasites. Here, we review and comment on the most recent findings related to transcription initiation and termination in trypanosomatid protozoa.

  15. Noise-based switches and amplifiers for gene expression

    CERN Document Server

    Hasty, J; Dolnik, M; Collins, J J; Hasty, Jeff; Pradines, Joel; Dolnik, Milos

    2000-01-01

    The regulation of cellular function is often controlled at the level of gene transcription. Such genetic regulation usually consists of interacting networks, whereby gene products from a single network can act to control their own expression or the production of protein in another network. Engineered control of cellular function through the design and manipulation of such networks lies within the constraints of current technology. Here we develop a model describing the regulation of gene expression, and elucidate the effects of noise on the formulation. We consider a single network derived from bacteriophage $\\lambda$, and construct a two-parameter deterministic model describing the temporal evolution of the concentration of $\\lambda$ repressor protein. Bistability in the steady-state protein concentration arises naturally, and we show how the bistable regime is enhanced with the addition of the first operator site in the promotor region. We then show how additive and multiplicative external noise can be used...

  16. Synthetic promoter libraries- tuning of gene expression

    DEFF Research Database (Denmark)

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

    2006-01-01

    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 ...

  17. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    Science.gov (United States)

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  18. Synthetic gene networks in plant systems.

    Science.gov (United States)

    Junker, Astrid; Junker, Björn H

    2012-01-01

    Synthetic biology methods are routinely applied in the plant field as in other eukaryotic model systems. Several synthetic components have been developed in plants and an increasing number of studies report on the assembly into functional synthetic genetic circuits. This chapter gives an overview of the existing plant genetic networks and describes in detail the application of two systems for inducible gene expression. The ethanol-inducible system relies on the ethanol-responsive interaction of the AlcA transcriptional activator and the AlcR receptor resulting in the transcription of the gene of interest (GOI). In comparison, the translational fusion of GOI and the glucocorticoid receptor (GR) domain leads to the dexamethasone-dependent nuclear translocation of the GOI::GR protein. This chapter contains detailed protocols for the application of both systems in the model plants potato and Arabidopsis, respectively.

  19. Analysis of gene expression pattern and neuroanatomical correlates for SLC20A2 (PiT-2) shows a molecular network with potential impact in idiopathic basal ganglia calcification ("Fahr's disease").

    Science.gov (United States)

    da Silva, R J Galdino; Pereira, I C L; Oliveira, J R M

    2013-06-01

    Familial idiopathic basal ganglia calcification (FIBGC), also known as "Fahr's disease," is a neuropsychiatric disorder with motor and cognitive symptoms. It is characterized pathologically by bilateral calcification most commonly in the basal ganglia and also in other brain regions such as the thalamus and cerebellum. A recent report by Wang et al. (2012) discovered multiple families with FIBGC carrying mutations in the SLC20A2 gene, encoding the inorganic phosphate transporter PiT-2, which segregated in an autosomal dominant pattern. To understand further the role of SLC20A2 in FIBGC brain pathology, here we described the gene expression pattern across the whole brain for SLC20A2, using the Allen Institute Human Brain Atlas database. Microarray analysis provided evidence that the neuroanatomical pattern of expression for SLC20A2 is highest in the regions most commonly affected in FIBGC. Neuroanatomical regions that demonstrated high correlation or anti-correlation with SLC20A2 expression also showed a molecular network with potential to explain the limited neuroanatomical distribution of calcifications in IBGC. Lastly, these co-expression networks suggest additional further candidate genes for FIBGC.

  20. Classification with binary gene expressions

    OpenAIRE

    Tuna, Salih; Niranjan, Mahesan

    2009-01-01

    Microarray gene expression measurements are reported, used and archived usually to high numerical precision. However, properties of mRNA molecules, such as their low stability and availability in small copy numbers, and the fact that measurements correspond to a population of cells, rather than a single cell, makes high precision meaningless. Recent work shows that reducing measurement precision leads to very little loss of information, right down to binary levels. In this paper we show how p...

  1. The Gene Expression Omnibus database

    Science.gov (United States)

    Clough, Emily; Barrett, Tanya

    2016-01-01

    The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome–protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/. PMID:27008011

  2. Gene expression throughout a vertebrate's embryogenesis

    Directory of Open Access Journals (Sweden)

    Hinton David E

    2011-02-01

    Full Text Available Abstract Background Describing the patterns of gene expression during embryonic development has broadened our understanding of the processes and patterns that define morphogenesis. Yet gene expression patterns have not been described throughout vertebrate embryogenesis. This study presents statistical analyses of gene expression during all 40 developmental stages in the teleost Fundulus heteroclitus using four biological replicates per stage. Results Patterns of gene expression for 7,000 genes appear to be important as they recapitulate developmental timing. Among the 45% of genes with significant expression differences between pairs of temporally adjacent stages, significant differences in gene expression vary from as few as five to more than 660. Five adjacent stages have disproportionately more significant changes in gene expression (> 200 genes relative to other stages: four to eight and eight to sixteen cell stages, onset of circulation, pre and post-hatch, and during complete yolk absorption. The fewest differences among adjacent stages occur during gastrulation. Yet, at stage 16, (pre-mid-gastrulation the largest number of genes has peak expression. This stage has an over representation of genes in oxidative respiration and protein expression (ribosomes, translational genes and proteases. Unexpectedly, among all ribosomal genes, both strong positive and negative correlations occur. Similar correlated patterns of expression occur among all significant genes. Conclusions These data provide statistical support for the temporal dynamics of developmental gene expression during all stages of vertebrate development.

  3. Genome-wide patterns of Arabidopsis gene expression in nature.

    Directory of Open Access Journals (Sweden)

    Christina L Richards

    Full Text Available Organisms in the wild are subject to multiple, fluctuating environmental factors, and it is in complex natural environments that genetic regulatory networks actually function and evolve. We assessed genome-wide gene expression patterns in the wild in two natural accessions of the model plant Arabidopsis thaliana and examined the nature of transcriptional variation throughout its life cycle and gene expression correlations with natural environmental fluctuations. We grew plants in a natural field environment and measured genome-wide time-series gene expression from the plant shoot every three days, spanning the seedling to reproductive stages. We find that 15,352 genes were expressed in the A. thaliana shoot in the field, and accession and flowering status (vegetative versus flowering were strong components of transcriptional variation in this plant. We identified between ∼110 and 190 time-varying gene expression clusters in the field, many of which were significantly overrepresented by genes regulated by abiotic and biotic environmental stresses. The two main principal components of vegetative shoot gene expression (PC(veg correlate to temperature and precipitation occurrence in the field. The largest PC(veg axes included thermoregulatory genes while the second major PC(veg was associated with precipitation and contained drought-responsive genes. By exposing A. thaliana to natural environments in an open field, we provide a framework for further understanding the genetic networks that are deployed in natural environments, and we connect plant molecular genetics in the laboratory to plant organismal ecology in the wild.

  4. Antisense expression increases gene expression variability and locus interdependency

    OpenAIRE

    Xu, Zhenyu; Wei, Wu; Gagneur, Julien; Clauder-Münster, Sandra; Smolik, Miłosz; Huber, Wolfgang; Steinmetz, Lars M.

    2011-01-01

    Genome-wide transcription profiling has revealed extensive expression of non-coding RNAs antisense to genes, yet their functions, if any, remain to be understood. In this study, we perform a systematic analysis of sense–antisense expression in response to genetic and environmental changes in yeast. We find that antisense expression is associated with genes of larger expression variability. This is characterized by more ‘switching off' at low levels of expression for genes with antisense compa...

  5. Proteomic and gene expression patterns of keratoconus

    Directory of Open Access Journals (Sweden)

    Arkasubhra Ghosh

    2013-01-01

    Full Text Available Keratoconus is a progressive corneal thinning disease associated with significant tissue remodeling activities and activation of a variety of signaling networks. However, it is not understood how differential gene and protein expression direct function in keratoconus corneas to drive the underlying pathology, ectasia. Research in the field has focused on discovering differentially expressed genes and proteins and quantifying their levels and activities in keratoconus patient samples. In this study, both microarray analysis of total ribonucleic acid (RNA and whole proteome analyses are carried out using corneal epithelium and tears from keratoconus patients and compared to healthy controls. A number of structural proteins, signaling molecules, cytokines, proteases, and enzymes have been found to be deregulated in keratoconus corneas. Together, the data provide clues to the complex process of corneal degradation which suggest novel ways to clinically diagnose and manage the disease. This review will focus on discussing these recent advances in the knowledge of keratoconus biology from a gene expression and function point-of-view.

  6. Visually Relating Gene Expression and in vivo DNA Binding Data

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Min-Yu; Mackey, Lester; Ker?,; nen, Soile V. E.; Weber, Gunther H.; Jordan, Michael I.; Knowles, David W.; Biggin, Mark D.; Hamann, Bernd

    2011-09-20

    Gene expression and in vivo DNA binding data provide important information for understanding gene regulatory networks: in vivo DNA binding data indicate genomic regions where transcription factors are bound, and expression data show the output resulting from this binding. Thus, there must be functional relationships between these two types of data. While visualization and data analysis tools exist for each data type alone, there is a lack of tools that can easily explore the relationship between them. We propose an approach that uses the average expression driven by multiple of ciscontrol regions to visually relate gene expression and in vivo DNA binding data. We demonstrate the utility of this tool with examples from the network controlling early Drosophila development. The results obtained support the idea that the level of occupancy of a transcription factor on DNA strongly determines the degree to which the factor regulates a target gene, and in some cases also controls whether the regulation is positive or negative.

  7. Inferring latent gene regulatory network kinetics

    NARCIS (Netherlands)

    González, Javier; Vujačić, Ivan; Wit, Ernst

    2013-01-01

    Regulatory networks consist of genes encoding transcription factors (TFs) and the genes they activate or repress. Various types of systems of ordinary differential equations (ODE) have been proposed to model these networks, ranging from linear to Michaelis-Menten approaches. In practice, a serious d

  8. On-Chip Integration of Cell-Free Gene Expression

    Science.gov (United States)

    Buxboim, Amnon; Morpurgo, Margherita; Bar-Dagan, Maya; Frydman, Veronica; Zbaida, David; Bar-Ziv, Roy

    2006-03-01

    We present a synthetic approach for the study of gene networks in vitro which is complementary to traditional in vivo methodologies. We have developed a technology for submicron integration of functional genes and on-chip protein synthesis using a cell-free transcription/translation system. The interaction between genes is facilitated by diffusion of on-chip gene expression products from `source' genes towards `acceptor' genes. Our technology is simple and inexpensive and can serve as an improved platform for a wide variety of protein and DNA biochip applications.

  9. Indeterminacy of reverse engineering of Gene Regulatory Networks: the curse of gene elasticity.

    Directory of Open Access Journals (Sweden)

    Arun Krishnan

    Full Text Available BACKGROUND: Gene Regulatory Networks (GRNs have become a major focus of interest in recent years. A number of reverse engineering approaches have been developed to help uncover the regulatory networks giving rise to the observed gene expression profiles. However, this is an overspecified problem due to the fact that more than one genotype (network wiring can give rise to the same phenotype. We refer to this phenomenon as "gene elasticity." In this work, we study the effect of this particular problem on the pure, data-driven inference of gene regulatory networks. METHODOLOGY: We simulated a four-gene network in order to produce "data" (protein levels that we use in lieu of real experimental data. We then optimized the network connections between the four genes with a view to obtain the original network that gave rise to the data. We did this for two different cases: one in which only the network connections were optimized and the other in which both the network connections as well as the kinetic parameters (given as reaction probabilities in our case were estimated. We observed that multiple genotypes gave rise to very similar protein levels. Statistical experimentation indicates that it is impossible to differentiate between the different networks on the basis of both equilibrium as well as dynamic data. CONCLUSIONS: We show explicitly that reverse engineering of GRNs from pure expression data is an indeterminate problem. Our results suggest the unsuitability of an inferential, purely data-driven approach for the reverse engineering transcriptional networks in the case of gene regulatory networks displaying a certain level of complexity.

  10. Decoupling Linear and Nonlinear Associations of Gene Expression

    KAUST Repository

    Itakura, Alan

    2013-05-01

    The FANTOM consortium has generated a large gene expression dataset of different cell lines and tissue cultures using the single-molecule sequencing technology of HeliscopeCAGE. This provides a unique opportunity to investigate novel associations between gene expression over time and different cell types. Here, we create a MatLab wrapper for a powerful and computationally intensive set of statistics known as Maximal Information Coefficient, and then calculate this statistic for a large, comprehensive dataset containing gene expression of a variety of differentiating tissues. We then distinguish between linear and nonlinear associations, and then create gene association networks. Following this analysis, we are then able to identify clusters of linear gene associations that then associate nonlinearly with other clusters of linearity, providing insight to much more complex connections between gene expression patterns than previously anticipated.

  11. Gene expression analysis identifies global gene dosage sensitivity in cancer

    DEFF Research Database (Denmark)

    Fehrmann, Rudolf S. N.; Karjalainen, Juha M.; Krajewska, Malgorzata;

    2015-01-01

    expression. We reanalyzed 77,840 expression profiles and observed a limited set of 'transcriptional components' that describe well-known biology, explain the vast majority of variation in gene expression and enable us to predict the biological function of genes. On correcting expression profiles...... for these components, we observed that the residual expression levels (in 'functional genomic mRNA' profiling) correlated strongly with copy number. DNA copy number correlated positively with expression levels for 99% of all abundantly expressed human genes, indicating global gene dosage sensitivity. By applying...

  12. Identification of four soybean reference genes for gene expression normalization

    Science.gov (United States)

    Gene expression analysis requires the use of reference genes stably expressed independently of specific tissues or environmental conditions. Housekeeping genes (e.g., actin, tubulin, ribosomal, polyubiquitin and elongation factor 1-alpha) are commonly used as reference genes with the assumption tha...

  13. Motif Participation by Genes in E. coli Transcriptional Networks

    Directory of Open Access Journals (Sweden)

    Michael eMayo

    2012-09-01

    Full Text Available Motifs are patterns of recurring connections among the genes of genetic networks that occur more frequently than would be expected from randomized networks with the same degree sequence. Although the abundance of certain three-node motifs, such as the feed-forward loop, is positively correlated with a networks’ ability to tolerate moderate disruptions to gene expression, little is known regarding the connectivity of individual genes participating in multiple motifs. Using the transcriptional network of the bacterium Escherichia coli, we investigate this feature by reconstructing the distribution of genes participating in feed-forward loop motifs from its largest connected network component. We contrast these motif participation distributions with those obtained from model networks built using the preferential attachment mechanism employed by many biological and man-made networks. We report that, although some of these model networks support a motif participation distribution that appears qualitatively similar to that obtained from the bacterium Escherichia coli, the probability for a node to support a feed-forward loop motif may instead be strongly influenced by only a few master transcriptional regulators within the network. From these analyses we conclude that such master regulators may be a crucial ingredient to describe coupling among feed-forward loop motifs in transcriptional regulatory networks.

  14. Stable Gene Regulatory Network Modeling From Steady-State Data

    Directory of Open Access Journals (Sweden)

    Joy Edward Larvie

    2016-04-01

    Full Text Available Gene regulatory networks represent an abstract mapping of gene regulations in living cells. They aim to capture dependencies among molecular entities such as transcription factors, proteins and metabolites. In most applications, the regulatory network structure is unknown, and has to be reverse engineered from experimental data consisting of expression levels of the genes usually measured as messenger RNA concentrations in microarray experiments. Steady-state gene expression data are obtained from measurements of the variations in expression activity following the application of small perturbations to equilibrium states in genetic perturbation experiments. In this paper, the least absolute shrinkage and selection operator-vector autoregressive (LASSO-VAR originally proposed for the analysis of economic time series data is adapted to include a stability constraint for the recovery of a sparse and stable regulatory network that describes data obtained from noisy perturbation experiments. The approach is applied to real experimental data obtained for the SOS pathway in Escherichia coli and the cell cycle pathway for yeast Saccharomyces cerevisiae. Significant features of this method are the ability to recover networks without inputting prior knowledge of the network topology, and the ability to be efficiently applied to large scale networks due to the convex nature of the method.

  15. A complex network analysis of hypertension-related genes

    Science.gov (United States)

    Wang, Huan; Xu, Chuan-Yun; Hu, Jing-Bo; Cao, Ke-Fei

    2014-01-01

    In this paper, a network of hypertension-related genes is constructed by analyzing the correlations of gene expression data among the Dahl salt-sensitive rat and two consomic rat strains. The numerical calculations show that this sparse and assortative network has small-world and scale-free properties. Further, 16 key hub genes (Col4a1, Lcn2, Cdk4, etc.) are determined by introducing an integrated centrality and have been confirmed by biological/medical research to play important roles in hypertension.

  16. Dextran sulfate sodium-induced colitis alters stress-associated behaviour and neuropeptide gene expression in the amygdala-hippocampus network of mice.

    Science.gov (United States)

    Reichmann, Florian; Hassan, Ahmed Mostafa; Farzi, Aitak; Jain, Piyush; Schuligoi, Rufina; Holzer, Peter

    2015-06-12

    Psychological stress causes disease exacerbation and relapses in inflammatory bowel disease (IBD) patients. Since studies on stress processing during visceral inflammation are lacking, we investigated the effects of experimental colitis as well as psychological stress on neurochemical and neuroendocrine changes as well as behaviour in mice. Dextran sulfate sodium (DSS)-induced colitis and water avoidance stress (WAS) were used as mouse models of colitis and mild psychological stress, respectively. We measured WAS-associated behaviour, gene expression and proinflammatory cytokine levels within the amygdala, hippocampus and hypothalamus as well as plasma levels of cytokines and corticosterone in male C57BL/6N mice. Animals with DSS-induced colitis presented with prolonged immobility during the WAS session, which was associated with brain region-dependent alterations of neuropeptide Y (NPY), NPY receptor Y1, corticotropin-releasing hormone (CRH), CRH receptor 1, brain-derived neurotrophic factor and glucocorticoid receptor gene expression. Furthermore, the combination of DSS and WAS increased interleukin-6 and growth regulated oncogene-α levels in the brain. Altered gut-brain signalling in the course of DSS-induced colitis is thought to cause the observed distinct gene expression changes in the limbic system and the aberrant molecular and behavioural stress responses. These findings provide new insights into the effects of stress during IBD.

  17. Quantitative modeling of a gene's expression from its intergenic sequence.

    Directory of Open Access Journals (Sweden)

    Md Abul Hassan Samee

    2014-03-01

    Full Text Available Modeling a gene's expression from its intergenic locus and trans-regulatory context is a fundamental goal in computational biology. Owing to the distributed nature of cis-regulatory information and the poorly understood mechanisms that integrate such information, gene locus modeling is a more challenging task than modeling individual enhancers. Here we report the first quantitative model of a gene's expression pattern as a function of its locus. We model the expression readout of a locus in two tiers: 1 combinatorial regulation by transcription factors bound to each enhancer is predicted by a thermodynamics-based model and 2 independent contributions from multiple enhancers are linearly combined to fit the gene expression pattern. The model does not require any prior knowledge about enhancers contributing toward a gene's expression. We demonstrate that the model captures the complex multi-domain expression patterns of anterior-posterior patterning genes in the early Drosophila embryo. Altogether, we model the expression patterns of 27 genes; these include several gap genes, pair-rule genes, and anterior, posterior, trunk, and terminal genes. We find that the model-selected enhancers for each gene overlap strongly with its experimentally characterized enhancers. Our findings also suggest the presence of sequence-segments in the locus that would contribute ectopic expression patterns and hence were "shut down" by the model. We applied our model to identify the transcription factors responsible for forming the stripe boundaries of the studied genes. The resulting network of regulatory interactions exhibits a high level of agreement with known regulatory influences on the target genes. Finally, we analyzed whether and why our assumption of enhancer independence was necessary for the genes we studied. We found a deterioration of expression when binding sites in one enhancer were allowed to influence the readout of another enhancer. Thus, interference

  18. Postpartal immunometabolic gene network expression and function in blood neutrophils are altered in response to prepartal energy intake and postpartal intramammary inflammatory challenge.

    Science.gov (United States)

    Moyes, K M; Graugnard, D E; Khan, M J; Mukesh, M; Loor, J J

    2014-01-01

    The effect of over-feeding energy prepartum on blood polymorphonuclear neutrophil (PMN) response remains unclear. Cows fed controlled (CON; 1.34Mcal/kg of dry matter) or excess energy (OVE; 1.62Mcal/kg dry matter) during the dry period (~45d before expected calving date) received an intramammary (IM) challenge with Escherichia coli lipopolysaccharide (LPS) during the postpartal period to determine the effects of IM LPS and prepartal diet on the expression of key genes associated with immunometabolic response in blood PMN. Feed intake and daily milk yield were recorded throughout the study period. At 7d in milk (DIM), all cows received LPS (200µg) into 1 rear mammary quarter. Blood PMN were isolated at 7, 14, and 30 DIM, as well as before (0h) and after (12h) IM LPS challenge for gene expression analysis using quantitative real time PCR. Phagocytosis capabilities in vitro were assessed at 7, 14, and 30 DIM. Data were analyzed using the MIXED procedure of SAS with repeated measures. No differences in feed intake and milk yield were observed between OVE- and CON-fed cows. As expected, IM LPS challenge altered the expression of genes associated with the immune response (e.g., 1.9- and 1.8-fold for SELL and TLR2, respectively), metabolism (e.g., 1.8- and -1.8-fold for LDHA and SLC2A1, respectively), and transcription (e.g., 1.1- and 1.7-fold for NCOR1 and PPARD, respectively). At 12h postchallenge, an upregulation of TLR2 (1.8-fold), HIF1A (1.9-fold), and NFKB1 (1.5-fold) was observed for OVE rather than CON. At 7 DIM, S100A9 tended (2.2-fold) to be upregulated for OVE rather than CON. At 14 DIM, OVE resulted in lower PMN phagocytosis and an upregulation of NCOR2 (1.6-fold) and RXRA (1.9-fold) compared with CON-fed cows. At 30 DIM, an upregulation of MPO (3.5-fold) and PLA2G4A (1.5-fold) and a tendency for RXRA (1.7-fold) was observed for OVE- rather than CON-fed cows. Our results suggest that IM LPS challenge altered gene expression associated with metabolism in PMN

  19. MRI of Transgene Expression: Correlation to Therapeutic Gene Expression

    Directory of Open Access Journals (Sweden)

    Tomotsugu Ichikawa

    2002-01-01

    Full Text Available Magnetic resonance imaging (MRI can provide highresolution 3D maps of structural and functional information, yet its use of mapping in vivo gene expression has only recently been explored. A potential application for this technology is to noninvasively image transgene expression. The current study explores the latter using a nonregulatable internalizing engineered transferrin receptor (ETR whose expression can be probed for with a superparamagnetic Tf-CLIO probe. Using an HSV-based amplicon vector system for transgene delivery, we demonstrate that: 1 ETR is a sensitive MR marker gene; 2 several transgenes can be efficiently expressed from a single amplicon; 3 expression of each transgene results in functional gene product; and 4 ETR gene expression correlates with expression of therapeutic genes when the latter are contained within the same amplicon. These data, taken together, suggest that MRI of ETR expression can serve as a surrogate for measuring therapeutic transgene expression.

  20. Multi-edge gene set networks reveal novel insights into global relationships between biological themes.

    Directory of Open Access Journals (Sweden)

    Jignesh R Parikh

    Full Text Available Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at http://blaispathways.dfci.harvard.edu/metanet/.

  1. Multi-edge gene set networks reveal novel insights into global relationships between biological themes.

    Science.gov (United States)

    Parikh, Jignesh R; Xia, Yu; Marto, Jarrod A

    2012-01-01

    Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at http://blaispathways.dfci.harvard.edu/metanet/.

  2. Graphlet Based Metrics for the Comparison of Gene Regulatory Networks

    Science.gov (United States)

    Martin, Alberto J. M.; Dominguez, Calixto; Contreras-Riquelme, Sebastián; Holmes, David S.; Perez-Acle, Tomas

    2016-01-01

    Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto). PMID:27695050

  3. Correlating Expression Data with Gene Function Using Gene Ontology

    Institute of Scientific and Technical Information of China (English)

    LIU,Qi; DENG,Yong; WANG,Chuan; SHI,Tie-Liu; LI,Yi-Xue

    2006-01-01

    Clustering is perhaps one of the most widely used tools for microarray data analysis. Proposed roles for genes of unknown function are inferred from clusters of genes similarity expressed across many biological conditions.However, whether function annotation by similarity metrics is reliable or not and to what extent the similarity in gene expression patterns is useful for annotation of gene functions, has not been evaluated. This paper made a comprehensive research on the correlation between the similarity of expression data and of gene functions using Gene Ontology. It has been found that although the similarity in expression patterns and the similarity in gene functions are significantly dependent on each other, this association is rather weak. In addition, among the three categories of Gene Ontology, the similarity of expression data is more useful for cellular component annotation than for biological process and molecular function. The results presented are interesting for the gene functions prediction research area.

  4. Gene expression profiling of mouse embryos with microarrays

    Science.gov (United States)

    Sharov, Alexei A.; Piao, Yulan; Ko, Minoru S. H.

    2011-01-01

    Global expression profiling by DNA microarrays provides a snapshot of cell and tissue status and becomes an essential tool in biological and medical sciences. Typical questions that can be addressed by microarray analysis in developmental biology include: (1) to find a set of genes expressed in a specific cell type; (2) to identify genes expressed commonly in multiple cell types; (3) to follow the time-course changes of gene expression patterns; (4) to demonstrate cell’s identity by showing similarities or differences among two or multiple cell types; (5) to find regulatory pathways and/or networks affected by gene manipulations, such as overexpression or repression of gene expression; (6) to find downstream target genes of transcription factors; (7) to find downstream target genes of cell signaling; (8) to examine the effects of environmental manipulation of cells on gene expression patterns; and (9) to find the effects of genetic manipulation in embryos and adults. Here we describe strategies for executing these experiments and monitoring changes of cell state with gene expression microarrays in application to mouse embryology. Both statistical assessment and interpretation of data are discussed. We also present a protocol for performing microarray analysis on a small amount of embryonic materials. PMID:20699157

  5. How difficult is inference of mammalian causal gene regulatory networks?

    Directory of Open Access Journals (Sweden)

    Djordje Djordjevic

    Full Text Available Gene regulatory networks (GRNs play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect, which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference

  6. How difficult is inference of mammalian causal gene regulatory networks?

    Science.gov (United States)

    Djordjevic, Djordje; Yang, Andrian; Zadoorian, Armella; Rungrugeecharoen, Kevin; Ho, Joshua W K

    2014-01-01

    Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for

  7. Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

    Institute of Scientific and Technical Information of China (English)

    Gui-xia Liu; Wei Feng; Han Wang; Lei Liu; Chun-guang Zhou

    2009-01-01

    In the post-genomic biology era, the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system, and it has been a challenging task in bioinformatics. The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages, but how to determine the network structure and parameters is still important to be explored. This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network .The new algorithm is evaluated with the use of both simulated and yeast cell cycle data. The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

  8. Gene Regulatory Network Reconstruction Using Conditional Mutual Information

    Directory of Open Access Journals (Sweden)

    Xiaodong Wang

    2008-06-01

    Full Text Available The inference of gene regulatory network from expression data is an important area of research that provides insight to the inner workings of a biological system. The relevance-network-based approaches provide a simple and easily-scalable solution to the understanding of interaction between genes. Up until now, most works based on relevance network focus on the discovery of direct regulation using correlation coefficient or mutual information. However, some of the more complicated interactions such as interactive regulation and coregulation are not easily detected. In this work, we propose a relevance network model for gene regulatory network inference which employs both mutual information and conditional mutual information to determine the interactions between genes. For this purpose, we propose a conditional mutual information estimator based on adaptive partitioning which allows us to condition on both discrete and continuous random variables. We provide experimental results that demonstrate that the proposed regulatory network inference algorithm can provide better performance when the target network contains coregulated and interactively regulated genes.

  9. Adaptive Evolution of Gene Expression in Drosophila

    Directory of Open Access Journals (Sweden)

    Armita Nourmohammad

    2017-08-01

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

  10. Sexual Dimorphism and Aging in the Human Hyppocampus: Identification, Validation, and Impact of Differentially Expressed Genes by Factorial Microarray and Network Analysis

    Science.gov (United States)

    Guebel, Daniel V.; Torres, Néstor V.

    2016-01-01

    Motivation: In the brain of elderly-healthy individuals, the effects of sexual dimorphism and those due to normal aging appear overlapped. Discrimination of these two dimensions would powerfully contribute to a better understanding of the etiology of some neurodegenerative diseases, such as “sporadic” Alzheimer. Methods: Following a system biology approach, top-down and bottom-up strategies were combined. First, public transcriptome data corresponding to the transition from adulthood to the aging stage in normal, human hippocampus were analyzed through an optimized microarray post-processing (Q-GDEMAR method) together with a proper experimental design (full factorial analysis). Second, the identified genes were placed in context by building compatible networks. The subsequent ontology analyses carried out on these networks clarify the main functionalities involved. Results: Noticeably we could identify large sets of genes according to three groups: those that exclusively depend on the sex, those that exclusively depend on the age, and those that depend on the particular combinations of sex and age (interaction). The genes identified were validated against three independent sources (a proteomic study of aging, a senescence database, and a mitochondrial genetic database). We arrived to several new inferences about the biological functions compromised during aging in two ways: by taking into account the sex-independent effects of aging, and considering the interaction between age and sex where pertinent. In particular, we discuss the impact of our findings on the functions of mitochondria, autophagy, mitophagia, and microRNAs. Conclusions: The evidence obtained herein supports the occurrence of significant neurobiological differences in the hippocampus, not only between adult and elderly individuals, but between old-healthy women and old-healthy men. Hence, to obtain realistic results in further analysis of the transition from the normal aging to incipient

  11. Modular reorganization of the global network of gene regulatory interactions during perinatal human brain development

    OpenAIRE

    Monzón-Sandoval, Jimena; Castillo-Morales, Atahualpa; Urrutia, Araxi O.; Gutierrez, Humberto

    2016-01-01

    Background During early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether...

  12. Gene-based and semantic structure of the Gene Ontology as a complex network

    Science.gov (United States)

    Coronnello, Claudia; Tumminello, Michele; Miccichè, Salvatore

    2016-09-01

    The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The Gene Ontology (GO) is constantly evolving over time. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. Here we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium. Moreover, the GO is a natural example of bipartite network of terms and genes. Here we are interested in studying the properties of the projected network of terms, i.e. a gene-based weighted network of GO terms, in which a link between any two terms is set if at least one gene is annotated in both terms. One aim of the present paper is to compare the structural properties of the semantic and the gene-based network. The relative importance of terms is very similar in the two networks, but the community structure changes. We show that in some cases GO terms that appear to be distinct from a semantic point of view are instead connected, and appear in the same community when considering their gene content. The identification of such gene-based communities of terms might therefore be the basis of a simple protocol aiming at improving the semantic structure of GO. Information about terms that share large gene content might also be important from a biomedical point of view, as it might reveal how genes over-expressed in a certain term also affect other biological processes, molecular functions and cellular components not directly linked according to GO semantics.

  13. GeneNetwork: framework for web-based genetics

    NARCIS (Netherlands)

    Sloan, Zachary; Arends, Danny; Broman, Karl W.; Centeno, Arthur; Furlotte, Nicholas; Nijveen, H.; Yan, Lei; Zhou, Xiang; Williams, Robert W.; Prins, Pjotr

    2016-01-01

    GeneNetwork (GN) is a free and open source (FOSS) framework for web-based genetics that can be deployed anywhere. GN allows biologists to upload high-throughput experimental data, such as expression data from microarrays and RNA-seq, and also `classic' phenotypes, such as disease phenotypes. These p

  14. Gravity-Induced Gene Expression in Plants.

    Science.gov (United States)

    Sederoff, Heike; Heber, Steffen; Howard, Brian; Myburg-Nichols, Henrietta; Hammond, Rebecca; Salinas-Mondragon, Raul; Brown, Christopher S.

    Plants sense changes in their orientation towards the vector of gravity and respond with directional growth. Several metabolites in the signal transduction cascade have been identified. However, very little is known about the interaction between these sensing and signal transduction events and even less is known about their role in the differential growth response. Gravity induced changes in transcript abundance have been identified in Arabidopsis whole seedlings and root apices (Moseyko et al. 2002; Kimbrough et al. 2004). Gravity induced transcript abundance changes can be observed within less than 1 min after stimulation (Salinas-Mondragon et al. 2005). Gene expression however requires not only transcription but also translation of the mRNA. Translation can only occur when mRNA is associated with ribosomes, even though not all mRNA associated with ribosomes is actively translated. To approximate translational capacity we quantified whole genome transcript abundances in corn stem pulvini during the first hour after gravity stimulation in total and poly-ribosomal fractions. As in Arabidopsis root apices, transcript abundances of several clusters of genes responded to gravity stimulation. The vast majority of these transcripts were also found to associate with polyribosomes in the same temporal and quantitative pattern. These genes are transcriptionally regulated by gravity stimulation, but do not exhibit translational regulation. However, a small group of genes showed increased transcriptional regulation after gravity stimulation, but no association with polysomes. These transcripts likely are translationally repressed. The mechanism of translational repression for these transcripts is unknown. Based on the hypothesis that the genes essential for gravitropic responses should be expressed in most or all species, we compared the temporal gravity induced expression pattern of all orthologs identified between maize and Arabidopsis. A small group of genes showed high

  15. Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

    Science.gov (United States)

    Wolen, Aaron R; Miles, Michael F

    2012-01-01

    For complex disorders such as alcoholism, identifying the genes linked to these diseases and their specific roles is difficult. Traditional genetic approaches, such as genetic association studies (including genome-wide association studies) and analyses of quantitative trait loci (QTLs) in both humans and laboratory animals already have helped identify some candidate genes. However, because of technical obstacles, such as the small impact of any individual gene, these approaches only have limited effectiveness in identifying specific genes that contribute to complex diseases. The emerging field of systems biology, which allows for analyses of entire gene networks, may help researchers better elucidate the genetic basis of alcoholism, both in humans and in animal models. Such networks can be identified using approaches such as high-throughput molecular profiling (e.g., through microarray-based gene expression analyses) or strategies referred to as genetical genomics, such as the mapping of expression QTLs (eQTLs). Characterization of gene networks can shed light on the biological pathways underlying complex traits and provide the functional context for identifying those genes that contribute to disease development.

  16. Simultaneous clustering of multiple gene expression and physical interaction datasets.

    Directory of Open Access Journals (Sweden)

    Manikandan Narayanan

    2010-04-01

    Full Text Available Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes.

  17. Clustering Algorithms: Their Application to Gene Expression Data

    Science.gov (United States)

    Oyelade, Jelili; Isewon, Itunuoluwa; Oladipupo, Funke; Aromolaran, Olufemi; Uwoghiren, Efosa; Ameh, Faridah; Achas, Moses; Adebiyi, Ezekiel

    2016-01-01

    Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure. PMID:27932867

  18. Internal signal stochastic resonance of a synthetic gene network

    Institute of Scientific and Technical Information of China (English)

    WANG; Zhiwei; HOU; Zhonghuai; XIN; Houwen

    2005-01-01

    The dynamics behavior of a synthetic gene network controlled by random noise is investigated using a model proposed recently. The phenomena of noise induced oscillation (NIO) of the protein concentrations and internal signal stochastic resonance (SR) are studied by computer simulation. We also find that there exists an optimal noise intensity that can most favor the occurrence of effective oscillation (EO). Finally we discuss the potential constructive roles of SR on gene expression systems.

  19. Network of tRNA Gene Sequences

    Institute of Scientific and Technical Information of China (English)

    WEI Fang-ping; LI Sheng; MA Hong-ru

    2008-01-01

    A network of 3719 tRNA gene sequences was constructed using simplest alignment. Its topology, degree distribution and clustering coefficient were studied. The behaviors of the network shift from fluctuated distribution to scale-free distribution when the similarity degree of the tRNA gene sequences increases. The tRNA gene sequences with the same anticodon identity are more self-organized than those with different anticodon identities and form local clusters in the network. Some vertices of the local cluster have a high connection with other local clusters, and the probable reason was given. Moreover, a network constructed by the same number of random tRNA sequences was used to make comparisons. The relationships between the properties of the tRNA similarity network and the characters of tRNA evolutionary history were discussed.

  20. Distribution of population averaged observables in stochastic gene expression

    Science.gov (United States)

    Bhattacharyya, Bhaswati; Kalay, Ziya

    2014-03-01

    Observation of phenotypic diversity in a population of genetically identical cells is often linked to the stochastic nature of chemical reactions involved in gene regulatory networks. We investigate the distribution of population averaged gene expression levels as a function of population, or sample size for several stochastic gene expression models to find out to what extent population averaged quantities reflect the underlying mechanism of gene expression. We consider three basic gene regulation networks corresponding to transcription with and without gene state switching and translation. Using analytical expressions for the probability generating function (pgf) of observables and Large Deviation Theory, we calculate the distribution of population averaged mRNA and protein levels as a function of model parameters and population size. We validate our results using stochastic simulations also report exact results on the asymptotic properties of population averages which show qualitative differences for different models. We calculate the skewness and coefficient of variance for pgfs to estimate the sample size required for population average that contains information about gene expression models. This is relevant to experiments where a large number of data points are unavailable.

  1. Transcriptional control in the segmentation gene network of Drosophila.

    Directory of Open Access Journals (Sweden)

    Mark D Schroeder

    2004-09-01

    Full Text Available The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross- regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a

  2. Analysis of Stage-Specific Gene Expression Profiles in the Uterine Endometrium during Pregnancy in Pigs.

    Science.gov (United States)

    Kim, Mingoo; Seo, Heewon; Choi, Yohan; Yoo, Inkyu; Seo, Minseok; Lee, Chang-Kyu; Kim, Heebal; Ka, Hakhyun

    2015-01-01

    The uterine endometrium plays a critical role in regulating the estrous cycle and the establishment and maintenance of pregnancy in mammalian species. Many studies have investigated the expression and function of genes in the uterine endometrium, but the global expression pattern of genes and relationships among genes differentially expressed in the uterine endometrium during gestation in pigs remain unclear. Thus, this study investigated global gene expression profiles using microarray in pigs. Diverse transcriptome analyses including clustering, network, and differentially expressed gene (DEG) analyses were performed to detect endometrial gene expression changes during the different gestation stages. In total, 6,991 genes were found to be differentially expressed by comparing genes expressed on day (D) 12 of pregnancy with those on D15, D30, D60, D90 and D114 of pregnancy, and clustering analysis of detected DEGs distinguished 8 clusters. Furthermore, several pregnancy-related hub genes such as ALPPL2, RANBP17, NF1B, SPP1, and CST6 were discovered through network analysis. Finally, detected hub genes were technically validated by quantitative RT-PCR. These results suggest the complex network characteristics involved in uterine endometrial gene expression during pregnancy and indicate that diverse patterns of stage-specific gene expression and network connections may play a critical role in endometrial remodeling and in placental and fetal development to establish and maintenance of pregnancy in pigs.

  3. Analysis of Stage-Specific Gene Expression Profiles in the Uterine Endometrium during Pregnancy in Pigs.

    Directory of Open Access Journals (Sweden)

    Mingoo Kim

    Full Text Available The uterine endometrium plays a critical role in regulating the estrous cycle and the establishment and maintenance of pregnancy in mammalian species. Many studies have investigated the expression and function of genes in the uterine endometrium, but the global expression pattern of genes and relationships among genes differentially expressed in the uterine endometrium during gestation in pigs remain unclear. Thus, this study investigated global gene expression profiles using microarray in pigs. Diverse transcriptome analyses including clustering, network, and differentially expressed gene (DEG analyses were performed to detect endometrial gene expression changes during the different gestation stages. In total, 6,991 genes were found to be differentially expressed by comparing genes expressed on day (D 12 of pregnancy with those on D15, D30, D60, D90 and D114 of pregnancy, and clustering analysis of detected DEGs distinguished 8 clusters. Furthermore, several pregnancy-related hub genes such as ALPPL2, RANBP17, NF1B, SPP1, and CST6 were discovered through network analysis. Finally, detected hub genes were technically validated by quantitative RT-PCR. These results suggest the complex network characteristics involved in uterine endometrial gene expression during pregnancy and indicate that diverse patterns of stage-specific gene expression and network connections may play a critical role in endometrial remodeling and in placental and fetal development to establish and maintenance of pregnancy in pigs.

  4. Resistance Genes in Global Crop Breeding Networks.

    Science.gov (United States)

    Garrett, K A; Andersen, K F; Asche, F; Bowden, R L; Forbes, G A; Kulakow, P A; Zhou, B

    2017-08-31

    Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

  5. Digital Cushion Fatty Acid Composition and Lipid Metabolism Gene Network Expression in Holstein Dairy Cows Fed a High-Energy Diet.

    Directory of Open Access Journals (Sweden)

    Zeeshan Muhammad Iqbal

    Full Text Available The hoof digital cushion is a complex structure composed of adipose tissue beneath the distal phalanx, i.e. axial, middle and abaxial fat pad. The major role of these fat depots is dampening compression of the corium underneath the cushion. The study aimed to determine expression of target genes and fatty acid profiles in the hoof of non-pregnant dry Holstein cows fed low (CON or high-energy (OVE diets. The middle fat pad of the hoof digital cushion was collected soon after slaughter. Despite the lack of effect on expression of the transcription regulators SREBF1 and PPARG, the expression of the lipogenic enzymes ACACA, FASN, SCD, and DGAT2 was upregulated with OVE. Along with the upregulation of G6PD and IDH1, important for NADPH synthesis during lipogenesis, and the basal glucose transporter SLC2A1, these data indicated a pro-lipogenic response in the digital cushion with OVE. The expression of the lipid droplet-associated protein PLIN2 was upregulated while expression of lipolytic enzymes (ATGL, ABDH5, and LIPE only tended to be upregulated with OVE. Therefore, OVE induced lipogenesis, lipid droplet formation, and lipolysis, albeit to different extents. Although concentration of monounsaturated fatty acids (MUFA did not differ, among the polyunsaturated fatty acids (PUFA, the concentration of 20:5n3 was lower with OVE. Among the saturated fatty acids, 20:0 concentration was greater with OVE. Although data indicated that the hoof digital cushion metabolic transcriptome is responsive to higher-energy diets, this did not translate into marked differences in the fatty acid composition. The decrease in concentration of PUFA, which could contribute to synthesis of inflammatory molecules, in OVE-fed cows indicated that feeding higher-energy diets might be detrimental for the mediation of inflammation in digital cushion. This effect could be further exacerbated by physiologic and endocrine changes during the peripartal period that favor inflammation.

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

    DEFF Research Database (Denmark)

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

    2008-01-01

    The gene co-expression analysis toolbox (GeneCAT) introduces several novel microarray data analyzing tools. First, the multigene co-expression analysis, combined with co-expressed gene networks, provides a more powerful data mining technique than standard, single-gene co-expression analysis. Second......, the high-throughput Map-O-Matic tool matches co-expression pattern of multiple query genes to genes present in user-defined subdatabases, and can therefore be used for gene mapping in forward genetic screens. Third, Rosetta combines co-expression analysis with BLAST and can be used to find 'true' gene...... orthologs in the plant model organisms Arabidopsis thaliana and Hordeum vulgare (Barley). GeneCAT is equipped with expression data for the model plant A. thaliana, and first to introduce co-expression mining tools for the monocot Barley. GeneCAT is available at http://genecat.mpg.de....

  7. Methods for monitoring multiple gene expression

    Energy Technology Data Exchange (ETDEWEB)

    Berka, Randy (Davis, CA); Bachkirova, Elena (Davis, CA); Rey, Michael (Davis, CA)

    2012-05-01

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

  8. Methods for monitoring multiple gene expression

    Energy Technology Data Exchange (ETDEWEB)

    Berka, Randy; Bachkirova, Elena; Rey, Michael

    2013-10-01

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

  9. Methods for monitoring multiple gene expression

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-05-01

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

  10. Methods for monitoring multiple gene expression

    Energy Technology Data Exchange (ETDEWEB)

    Berka, Randy; Bachkirova, Elena; Rey, Michael

    2013-10-01

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

  11. Noise and Stochasticity in Gene Expression : A Pathogenic Fate Determinant

    NARCIS (Netherlands)

    Jørgensen, Mikkel Girke; Raaphorst, Renske van; Veening, Jan-Willem; Harwood, Colin; Wipat, Anil

    2013-01-01

    Not all cells in a bacterial population exhibit exactly the same phenotype, even though they grow in the same environment and are genetically identical. This phenomenon is known as phenotypic variation. The major source of phenotypic variation is noise or stochasticity in gene expression networks,

  12. Listening to the noise: random fluctuations reveal gene network parameters

    Energy Technology Data Exchange (ETDEWEB)

    Munsky, Brian [Los Alamos National Laboratory; Khammash, Mustafa [UCSB

    2009-01-01

    The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations of cells exhibit cell-to-cell variability that frequently manifests as significant phenotypic differences within the cellular population. The stochastic fluctuations in cellular constituents induced by noise can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.

  13. Amplification of kinetic oscillations in gene expression

    Science.gov (United States)

    Zhdanov, V. P.

    2008-10-01

    Because of the feedbacks between the DNA transcription and mRNA translation, the gene expression in cells may exhibit bistability and oscillations. The deterministic and stochastic calculations presented illustrate how the bistable kinetics of expression of one gene in a cell can be influenced by the kinetic oscillations in the expression of another gene. Due to stability of the states of the bistable kinetics of gene 1 and the relatively small difference between the maximum and minimum protein amounts during the oscillations of gene 2, the induced oscillations of gene 1 are found to typically be related either to the low-or high-reactive state of this gene. The quality of the induced oscillations may be appreciably better than that of the inducing oscillations. This means that gene 1 can serve as an amplifier of the kinetic oscillations of gene 2.

  14. cis sequence effects on gene expression

    Directory of Open Access Journals (Sweden)

    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.

  15. Modeling gene regulatory networks: A network simplification algorithm

    Science.gov (United States)

    Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

    2016-12-01

    Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

  16. Crowdsourcing the nodulation gene network discovery environment.

    Science.gov (United States)

    Li, Yupeng; Jackson, Scott A

    2016-05-26

    The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created. The platform implements the jQuery and vis.js JavaScript libraries, so that users are able to interactively visualize and edit the gene network, and easily access the information about the network, e.g. gene lists, gene interactions and gene functional annotations. In addition, all the gene information is written on MediaWiki pages, enabling users to edit and contribute to the network curation. Utilizing the continuously updated, collaboratively written, and community-reviewed Wikipedia model, the platform could, in a short time, become a comprehensive knowledge base of nodulation-related pathways. The platform could also be used for other biological processes, and thus has great potential for integrating and advancing our understanding of the functional genomics and systems biology of any process for any species. The platform is available at http://crowd.bioops.info/ , and the source code can be openly accessed at https://github.com/bioops/crowdnodnet under MIT License.

  17. The cytokine-mediated crosstalk between primary human acute myeloid cells and mesenchymal stem cells alters the local cytokine network and the global gene expression profile of the mesenchymal cells

    Directory of Open Access Journals (Sweden)

    Håkon Reikvam

    2015-11-01

    Full Text Available Interactions between acute myeloid leukemia (AML blasts and neighboring stromal cells are important for disease development and chemosensitivity. However, the molecular mechanisms involved in the cytokine-mediated crosstalk between mesenchymal stem cells (MSCs and AML cells are largely unknown. Leukemic cells derived from 18 unselected AML patients were cultured with bone marrow MSCs derived from healthy donors; the populations then being separated by a semipermeable membrane. Coculture had only minor effects on MSC proliferation. The unique cytokine network in cocultures was determined by high constitutive MSC release of certain cytokines (especially IL-6 and vascular endothelial growth factor and constitutive release of a wide range of soluble mediators by primary AML cells. However, the AML cell release varied considerably between patients, and these differences between patients were also reflected in the coculture levels even though supra-additive effects were seen for many mediators. These effects on the local cytokine network were dependent on a functional crosstalk between the two cell subsets. The crosstalk altered the global gene expression profile of the MSCs, especially expression of genes encoding proteins involved in downstream signaling from Toll like receptors, NFκB signaling and CCL/CXCL chemokine release. Thus, primary AML cells alter the functional phenotype of normal MSCs.

  18. Evaluation of global differential gene and protein expression in primary Pterygium: S100A8 and S100A9 as possible drivers of a signaling network.

    Directory of Open Access Journals (Sweden)

    Aihua Hou

    Full Text Available PURPOSE: Pterygium is a wing shaped fibrovascular growth on the ocular surface, characterized by fibrosis, angiogenesis, extracellular matrix remodeling, and inflammatory infiltrates. Epidemiologic studies have linked pterygium formation to various chronic inflammatory conditions, such as ultraviolet radiation, sawdust exposure, and dry eye disease. The purpose of this study is to identify proteins that are differentially expressed in primary pterygium by using a combination of gene microarray and proteomic platforms. METHODS: Paired pterygium and uninvolved conjunctiva tissues of four patients were evaluated for differences in global gene transcript levels using a genechip microarray. Proteins extracted from another four pairs of tissues were quantified by iTRAQ approach. Western blot and immunofluorescent staining on additional patients were used to validate dysregulated protein expression obtained from microarray and proteomics data. In addition, primary conjunctival fibroblasts were treated with recombinant S100A8, S100A9 or both. Transcript level changes of a panel of potential target genes were evaluated by real time-PCR. RESULTS: The following were up-regulated at both protein and transcript levels S100 A8 and A9, aldehyde dehydrogenase 3 family, member1 (ALDH3A1 and vimentin (VIM. Conversely, serpin peptidase inhibitor clade A member 1 (SERPINA1 and transferrin (TF were down-regulated. Upon adding S100A8, S100A9 or both, the inflammatory chemokine CXCL1, matrix proteins vimentin, biglycan, and gelsolin, as well as annexin-A2, thymosin-β4, chymase (CMA1, member of Ras oncogene family RAB10 and SERPINA1 were found to be up-regulated. CONCLUSIONS: We identified 3 up-regulated and 2 down-regulated proteins by using a stringent approach comparing microarray and proteomic data. On stimulating cells with S100A8/9, a repertoire of key genes found to be up-regulated in pterygium tissue, were induced in these cells. S100A8/9 may be an upstream

  19. Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network

    Directory of Open Access Journals (Sweden)

    Xuan Nguyen

    2012-06-01

    Full Text Available Abstract Background Dynamic Bayesian network (DBN is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN. Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT. GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.

  20. Identifying disease feature genes based on cellular localized gene functional modules and regulation networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Min; ZHU Jing; GUO Zheng; LI Xia; YANG Da; WANG Lei; RAO Shaoqi

    2006-01-01

    Identifying disease-relevant genes and functional modules, based on gene expression profiles and gene functional knowledge, is of high importance for studying disease mechanisms and subtyping disease phenotypes. Using gene categories of biological process and cellular component in Gene Ontology, we propose an approach to selecting functional modules enriched with differentially expressed genes, and identifying the feature functional modules of high disease discriminating abilities. Using the differentially expressed genes in each feature module as the feature genes, we reveal the relevance of the modules to the studied diseases. Using three datasets for prostate cancer, gastric cancer, and leukemia, we have demonstrated that the proposed modular approach is of high power in identifying functionally integrated feature gene subsets that are highly relevant to the disease mechanisms. Our analysis has also shown that the critical disease-relevant genes might be better recognized from the gene regulation network, which is constructed using the characterized functional modules, giving important clues to the concerted mechanisms of the modules responding to complex disease states. In addition, the proposed approach to selecting the disease-relevant genes by jointly considering the gene functional knowledge suggests a new way for precisely classifying disease samples with clear biological interpretations, which is critical for the clinical diagnosis and the elucidation of the pathogenic basis of complex diseases.

  1. Deriving Trading Rules Using Gene Expression Programming

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    Adrian VISOIU

    2011-01-01

    Full Text Available This paper presents how buy and sell trading rules are generated using gene expression programming with special setup. Market concepts are presented and market analysis is discussed with emphasis on technical analysis and quantitative methods. The use of genetic algorithms in deriving trading rules is presented. Gene expression programming is applied in a form where multiple types of operators and operands are used. This gives birth to multiple gene contexts and references between genes in order to keep the linear structure of the gene expression programming chromosome. The setup of multiple gene contexts is presented. The case study shows how to use the proposed gene setup to derive trading rules encoded by Boolean expressions, using a dataset with the reference exchange rates between the Euro and the Romanian leu. The conclusions highlight the positive results obtained in deriving useful trading rules.

  2. A gene regulatory network armature for T-lymphocyte specification

    Energy Technology Data Exchange (ETDEWEB)

    Fung, Elizabeth-sharon [Los Alamos National Laboratory

    2008-01-01

    Choice of a T-lymphoid fate by hematopoietic progenitor cells depends on sustained Notch-Delta signaling combined with tightly-regulated activities of multiple transcription factors. To dissect the regulatory network connections that mediate this process, we have used high-resolution analysis of regulatory gene expression trajectories from the beginning to the end of specification; tests of the short-term Notchdependence of these gene expression changes; and perturbation analyses of the effects of overexpression of two essential transcription factors, namely PU.l and GATA-3. Quantitative expression measurements of >50 transcription factor and marker genes have been used to derive the principal components of regulatory change through which T-cell precursors progress from primitive multipotency to T-lineage commitment. Distinct parts of the path reveal separate contributions of Notch signaling, GATA-3 activity, and downregulation of PU.l. Using BioTapestry, the results have been assembled into a draft gene regulatory network for the specification of T-cell precursors and the choice of T as opposed to myeloid dendritic or mast-cell fates. This network also accommodates effects of E proteins and mutual repression circuits of Gfil against Egr-2 and of TCF-l against PU.l as proposed elsewhere, but requires additional functions that remain unidentified. Distinctive features of this network structure include the intense dose-dependence of GATA-3 effects; the gene-specific modulation of PU.l activity based on Notch activity; the lack of direct opposition between PU.l and GATA-3; and the need for a distinct, late-acting repressive function or functions to extinguish stem and progenitor-derived regulatory gene expression.

  3. Identifying glioblastoma gene networks based on hypergeometric test analysis.

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    Vasileios Stathias

    Full Text Available Patient specific therapy is emerging as an important possibility for many cancer patients. However, to identify such therapies it is essential to determine the genomic and transcriptional alterations present in one tumor relative to control samples. This presents a challenge since use of a single sample precludes many standard statistical analysis techniques. We reasoned that one means of addressing this issue is by comparing transcriptional changes in one tumor with those observed in a large cohort of patients analyzed by The Cancer Genome Atlas (TCGA. To test this directly, we devised a bioinformatics pipeline to identify differentially expressed genes in tumors resected from patients suffering from the most common malignant adult brain tumor, glioblastoma (GBM. We performed RNA sequencing on tumors from individual GBM patients and filtered the results through the TCGA database in order to identify possible gene networks that are overrepresented in GBM samples relative to controls. Importantly, we demonstrate that hypergeometric-based analysis of gene pairs identifies gene networks that validate experimentally. These studies identify a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM and other cancers.

  4. 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...... beta-glucuronidase, resulting in an operon structure in which both genes are transcribed from a common promoter. We show that there is a linear correlation between the expressions of the two genes, which facilitates screening for mutants with suitable enzyme activities. In a second example, we show......, overexpression was achieved by introducing an additional gene copy into a phage attachment site on the chromosome. This resulted in a series of strains with phosphofructokinase activities from 1.4 to 11 times the wild-type activity level. In this example, the pfk gene was cloned upstream of a gusA gene encoding...

  5. Associating genes and protein complexes with disease via network propagation.

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    Oron Vanunu

    2010-01-01

    Full Text Available A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.

  6. Transposable element influences on gene expression in plants.

    Science.gov (United States)

    Hirsch, Cory D; Springer, Nathan M

    2017-01-01

    Transposable elements (TEs) comprise a major portion of many plant genomes and bursts of TE movements cause novel genomic variation within species. In order to maintain proper gene function, plant genomes have evolved a variety of mechanisms to tolerate the presence of TEs within or near genes. Here, we review our understanding of the interactions between TEs and gene expression in plants by assessing three ways that transposons can influence gene expression. First, there is growing evidence that TE insertions within introns or untranslated regions of genes are often tolerated and have minimal impact on expression level or splicing. However, there are examples in which TE insertions within genes can result in aberrant or novel transcripts. Second, TEs can provide novel alternative promoters, which can lead to new expression patterns or original coding potential of an alternate transcript. Third, TE insertions near genes can influence regulation of gene expression through a variety of mechanisms. For example, TEs may provide novel cis-acting regulatory sites behaving as enhancers or insert within existing enhancers to influence transcript production. Alternatively, TEs may change chromatin modifications in regions near genes, which in turn can influence gene expression levels. Together, the interactions of genes and TEs provide abundant evidence for the role of TEs in changing basic functions within plant genomes beyond acting as latent genomic elements or as simple insertional mutagens. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Characterization of differentially expressed genes involved in pathways associated with gastric cancer.

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    Hao Li

    Full Text Available To explore the patterns of gene expression in gastric cancer, a total of 26 paired gastric cancer and noncancerous tissues from patients were enrolled for gene expression microarray analyses. Limma methods were applied to analyze the data, and genes were considered to be significantly differentially expressed if the False Discovery Rate (FDR value was 2. Subsequently, Gene Ontology (GO categories were used to analyze the main functions of the differentially expressed genes. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG database, we found pathways significantly associated with the differential genes. Gene-Act network and co-expression network were built respectively based on the relationships among the genes, proteins and compounds in the database. 2371 mRNAs and 350 lncRNAs considered as significantly differentially expressed genes were selected for the further analysis. The GO categories, pathway analyses and the Gene-Act network showed a consistent result that up-regulated genes were responsible for tumorigenesis, migration, angiogenesis and microenvironment formation, while down-regulated genes were involved in metabolism. These results of this study provide some novel findings on coding RNAs, lncRNAs, pathways and the co-expression network in gastric cancer which will be useful to guide further investigation and target therapy for this disease.

  8. Boolean networks using the chi-square test for inferring large-scale gene regulatory networks

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    Lee Jae K

    2007-02-01

    Full Text Available Abstract Background Boolean network (BN modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that its computation time is very high or often impractical to construct large-scale gene networks. We propose a variable selection method that are not only reduces BN computation times significantly but also obtains optimal network constructions by using chi-square statistics for testing the independence in contingency tables. Results Both the computation time and accuracy of the network structures estimated by the proposed method are compared with those of the original BN methods on simulated and real yeast cell cycle microarray gene expression data sets. Our results reveal that the proposed chi-square testing (CST-based BN method significantly improves the computation time, while its ability to identify all the true network mechanisms was effectively the same as that of full-search BN methods. The proposed BN algorithm is approximately 70.8 and 7.6 times faster than the original BN algorithm when the error sizes of the Best-Fit Extension problem are 0 and 1, respectively. Further, the false positive error rate of the proposed CST-based BN algorithm tends to be less than that of the original BN. Conclusion The CST-based BN method dramatically improves the computation time of the original BN algorithm. Therefore, it can efficiently infer large-scale gene regulatory network mechanisms.

  9. Gene expression profiling during murine tooth development

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    Maria A dos Santos silva Landin

    2012-07-01

    Full Text Available The aim of this study was to describe the expression of genes, including ameloblastin (Ambn, amelogenin X chromosome (Amelx and enamelin (Enam during early (pre-secretory tooth development. The expression of these genes has predominantly been studied at post-secretory stages. Deoxyoligonucleotide microarrays were used to study gene expression during development of the murine first molar tooth germ at 24h intervals, starting at the eleventh embryonic day (E11.5 and up to the seventh day after birth (P7. The profile search function of Spotfire software was used to select genes with similar expression profile as the enamel genes (Ambn, Amelx and Enam. Microarray results where validated using real-time Reverse Transcription-Polymerase Chain Reaction (real-time RT-PCR, and translated proteins identified by Western blotting. In situ localisation of the Ambn, Amelx and Enam mRNAs were monitored from E12.5 to E17.5 using deoxyoligonucleotide probes. Bioinformatics analysis was used to associate biological functions with differentially (p ≤0.05 expressed (DE genes.Microarray results showed a total of 4362 genes including Ambn, Amelx and Enam to be significant differentially expressed throughout the time-course. The expression of the three enamel genes was low at pre-natal stages (E11.5-P0 increasing after birth (P1-P7. Profile search lead to isolation of 87 genes with significantly similar expression to the three enamel proteins. The mRNAs expressed in dental epithelium and epithelium derived cells. Although expression of Ambn, Amelx and Enam were lower during early tooth development compared to secretory stages enamel proteins were detectable by Western blotting. Bioinformatic analysis associated the 87 genes with multiple biological functions. Around thirty-five genes were associated with fifteen transcription factors.

  10. Gene Expression Patterns in Ovarian Carcinomas

    Science.gov (United States)

    Schaner, Marci E.; Ross, Douglas T.; Ciaravino, Giuseppe; Sørlie, Therese; Troyanskaya, Olga; Diehn, Maximilian; Wang, Yan C.; Duran, George E.; Sikic, Thomas L.; Caldeira, Sandra; Skomedal, Hanne; Tu, I-Ping; Hernandez-Boussard, Tina; Johnson, Steven W.; O'Dwyer, Peter J.; Fero, Michael J.; Kristensen, Gunnar B.; Børresen-Dale, Anne-Lise; Hastie, Trevor; Tibshirani, Robert; van de Rijn, Matt; Teng, Nelson N.; Longacre, Teri A.; Botstein, David; Brown, Patrick O.; Sikic, Branimir I.

    2003-01-01

    We used DNA microarrays to characterize the global gene expression patterns in surface epithelial cancers of the ovary. We identified groups of genes that distinguished the clear cell subtype from other ovarian carcinomas, grade I and II from grade III serous papillary carcinomas, and ovarian from breast carcinomas. Six clear cell carcinomas were distinguished from 36 other ovarian carcinomas (predominantly serous papillary) based on their gene expression patterns. The differences may yield insights into the worse prognosis and therapeutic resistance associated with clear cell carcinomas. A comparison of the gene expression patterns in the ovarian cancers to published data of gene expression in breast cancers revealed a large number of differentially expressed genes. We identified a group of 62 genes that correctly classified all 125 breast and ovarian cancer specimens. Among the best discriminators more highly expressed in the ovarian carcinomas were PAX8 (paired box gene 8), mesothelin, and ephrin-B1 (EFNB1). Although estrogen receptor was expressed in both the ovarian and breast cancers, genes that are coregulated with the estrogen receptor in breast cancers, including GATA-3, LIV-1, and X-box binding protein 1, did not show a similar pattern of coexpression in the ovarian cancers. PMID:12960427

  11. Microanalysis of gene expression in cultured cells

    NARCIS (Netherlands)

    E. van der Veer (Eveliene)

    1982-01-01

    textabstractIn this thesis two aspects of gene expression in cultured cells have been studied: the heterogeneity in gene expression in relation with the development and application of microchemical techniques for the prenatal diagnosis of inborn errors of metabolism and the possibility of inducing g

  12. Arabidopsis gene expression patterns during spaceflight

    Science.gov (United States)

    Paul, A.-L.; Ferl, R. J.

    The exposure of Arabidopsis thaliana (Arabidopsis) plants to spaceflight environments resulted in the differential expression of hundreds of genes. A 5 day mission on orbiter Columbia in 1999 (STS-93) carried transgenic Arabidopsis plants engineered with a transgene composed of the alcohol dehydrogenase (Adh) gene promoter linked to the β -Glucuronidase (GUS) reporter gene. The plants were used to evaluate the effects of spaceflight on two fronts. First, expression patterns visualized with the Adh/GUS transgene were used to address specifically the possibility that spaceflight induces a hypoxic stress response, and to assess whether any spaceflight response was similar to control terrestrial hypoxia-induced gene expression patterns. (Paul et al., Plant Physiol. 2001, 126:613). Second, genome-wide patterns of native gene expression were evaluated utilizing the Affymetrix ATH1 GeneChip? array of 8,000 Arabidopsis genes. As a control for the veracity of the array analyses, a selection of genes identified with the arrays was further characterized with quantitative Real-Time RT PCR (ABI - TaqmanTM). Comparison of the patterns of expression for arrays of hybridized with RNA isolated from plants exposed to spaceflight compared to the control arrays revealed hundreds of genes that were differentially expressed in response to spaceflight, yet most genes that are hallmarks of hypoxic stress were unaffected. These results will be discussed in light of current models for plant responses to the spaceflight environment, and with regard to potential future flight opportunities.

  13. Robustness under functional constraint: the genetic network for temporal expression in Drosophila neurogenesis.

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    Akihiko Nakajima

    2010-04-01

    Full Text Available Precise temporal coordination of gene expression is crucial for many developmental processes. One central question in developmental biology is how such coordinated expression patterns are robustly controlled. During embryonic development of the Drosophila central nervous system, neural stem cells called neuroblasts express a group of genes in a definite order, which leads to the diversity of cell types. We produced all possible regulatory networks of these genes and examined their expression dynamics numerically. From the analysis, we identified requisite regulations and predicted an unknown factor to reproduce known expression profiles caused by loss-of-function or overexpression of the genes in vivo, as well as in the wild type. Following this, we evaluated the stability of the actual Drosophila network for sequential expression. This network shows the highest robustness against parameter variations and gene expression fluctuations among the possible networks that reproduce the expression profiles. We propose a regulatory module composed of three types of regulations that is responsible for precise sequential expression. This study suggests that the Drosophila network for sequential expression has evolved to generate the robust temporal expression for neuronal specification.

  14. 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.

  15. Expression of Sox genes in tooth development.

    Science.gov (United States)

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

    2015-01-01

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

  16. Expression of Sox genes in tooth development

    Science.gov (United States)

    KAWASAKI, KATSUSHIGE; KAWASAKI, MAIKO; WATANABE, MOMOKO; IDRUS, ERIK; NAGAI, TAKAHIRO; OOMMEN, SHELLY; MAEDA, TAKEYASU; HAGIWARA, NOBUKO; QUE, JIANWEN; SHARPE, PAUL T.; OHAZAMA, ATSUSHI

    2017-01-01

    Members of the Sox gene family play roles in many biological processes including organogenesis. We carried out comparative in situ hybridization analysis of seventeen sox genes (Sox1-14, 17, 18, 21) during murine odontogenesis from the epithelial thickening to the cytodifferentiation stages. Localized expression of five Sox genes (Sox6, 9, 13, 14 and 21) was observed in tooth bud epithelium. Sox13 showed restricted expression in the primary enamel knots. At the early bell stage, three Sox genes (Sox8, 11, 17 and 21) were expressed in pre-ameloblasts, whereas two others (Sox5 and 18) showed expression in odontoblasts. Sox genes thus showed a dynamic spatio-temporal expression during tooth development. PMID:26864488

  17. Gene set analysis for longitudinal gene expression data

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    Piepho Hans-Peter

    2011-07-01

    Full Text Available Abstract Background Gene set analysis (GSA has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for independent microarray samples at the level of gene sets rather than individual genes. Nowadays, an increasing number of microarray studies are conducted to explore the dynamic changes of gene expression in a variety of species and biological scenarios. In these longitudinal studies, gene expression is repeatedly measured over time such that a GSA needs to take into account the within-gene correlations in addition to possible between-gene correlations. Results We provide a robust nonparametric approach to compare the expressions of longitudinally measured sets of genes under multiple treatments or experimental conditions. The limiting distributions of our statistics are derived when the number of genes goes to infinity while the number of replications can be small. When the number of genes in a gene set is small, we recommend permutation tests based on our nonparametric test statistics to achieve reliable type I error and better power while incorporating unknown correlations between and within-genes. Simulation results demonstrate that the proposed method has a greater power than other methods for various data distributions and heteroscedastic correlation structures. This method was used for an IL-2 stimulation study and significantly altered gene sets were identified. Conclusions The simulation study and the real data application showed that the proposed gene set analysis provides a promising tool for longitudinal microarray analysis. R scripts for simulating longitudinal data and calculating the nonparametric statistics are posted on the North Dakota INBRE website http://ndinbre.org/programs/bioinformatics.php. Raw microarray data is available in Gene Expression Omnibus (National Center for Biotechnology Information with

  18. Evolution of the mammalian embryonic pluripotency gene regulatory network

    Science.gov (United States)

    Fernandez-Tresguerres, Beatriz; Cañon, Susana; Rayon, Teresa; Pernaute, Barbara; Crespo, Miguel; Torroja, Carlos; Manzanares, Miguel

    2010-01-01

    Embryonic pluripotency in the mouse is established and maintained by a gene-regulatory network under the control of a core set of transcription factors that include octamer-binding protein 4 (Oct4; official name POU domain, class 5, transcription factor 1, Pou5f1), sex-determining region Y (SRY)-box containing gene 2 (Sox2), and homeobox protein Nanog. Although this network is largely conserved in eutherian mammals, very little information is available regarding its evolutionary conservation in other vertebrates. We have compared the embryonic pluripotency networks in mouse and chick by means of expression analysis in the pregastrulation chicken embryo, genomic comparisons, and functional assays of pluripotency-related regulatory elements in ES cells and blastocysts. We find that multiple components of the network are either novel to mammals or have acquired novel expression domains in early developmental stages of the mouse. We also find that the downstream action of the mouse core pluripotency factors is mediated largely by genomic sequence elements nonconserved with chick. In the case of Sox2 and Fgf4, we find that elements driving expression in embryonic pluripotent cells have evolved by a small number of nucleotide changes that create novel binding sites for core factors. Our results show that the network in charge of embryonic pluripotency is an evolutionary novelty of mammals that is related to the comparatively extended period during which mammalian embryonic cells need to be maintained in an undetermined state before engaging in early differentiation events. PMID:21048080

  19. Mutational robustness of gene regulatory networks.

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    Aalt D J van Dijk

    Full Text Available Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive. In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

  20. Murine heart gene expression during acute Chagasic myocarditis

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    Andrés F. Henao-Martínez

    2015-06-01

    Full Text Available Chagas disease is transmitted by the parasite, Trypanosoma cruzi. Acute infection is characterized by acute myocarditis, although it is largely asymptomatic. Initial cardiac insult could be a determinant to the posterior development of chronic Chagasic cardiomyopathy, usually after 10 years in only approximately 30% of chronically infected patients. Herein, we characterized the acute gene expression profiling in heart tissue of two strains of mice infected with T. cruzi (tulahuen strain at 4 weeks and their respective controls. Gene sequence data are available at NCBI under GEO accession number: GSE63847. The output of the genes expression suggests differences in involvement of protein kinase B (AKT, NCAM1, HLA-DRA, and ubiquitin C genes networks. These gene activation differences may correlate with myocardial contractility during the acute infection.

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

    DEFF Research Database (Denmark)

    Manijak, Mieszko P.; Nielsen, Henrik Bjørn

    2011-01-01

    BACKGROUND: Although, systematic analysis of gene annotation is a powerful tool for interpreting gene expression data, it sometimes is blurred by incomplete gene annotation, missing expression response of key genes and secondary gene expression responses. These shortcomings may be partially...... circumvented by instead matching gene expression signatures to signatures of other experiments. FINDINGS: To facilitate this we present the Functional Association Response by Overlap (FARO) server, that match input signatures to a compendium of 242 gene expression signatures, extracted from more than 1700...

  2. Evolution and Expression Patterns of TCP Genes in Asparagales

    Science.gov (United States)

    Madrigal, Yesenia; Alzate, Juan F.; Pabón-Mora, Natalia

    2017-01-01

    CYCLOIDEA-like genes are involved in the symmetry gene network, limiting cell proliferation in the dorsal regions of bilateral flowers in core eudicots. CYC-like and closely related TCP genes (acronym for TEOSINTE BRANCHED1, CYCLOIDEA, and PROLIFERATION CELL FACTOR) have been poorly studied in Asparagales, the largest order of monocots that includes both bilateral flowers in Orchidaceae (ca. 25.000 spp) and radially symmetrical flowers in Hypoxidaceae (ca. 200 spp). With the aim of assessing TCP gene evolution in the Asparagales, we isolated TCP-like genes from publicly available databases and our own transcriptomes of Cattleya trianae (Orchidaceae) and Hypoxis decumbens (Hypoxidaceae). Our matrix contains 452 sequences representing the three major clades of TCP genes. Besides the previously identified CYC specific core eudicot duplications, our ML phylogenetic analyses recovered an early CIN-like duplication predating all angiosperms, two CIN-like Asparagales-specific duplications and a duplication prior to the diversification of Orchidoideae and Epidendroideae. In addition, we provide evidence of at least three duplications of PCF-like genes in Asparagales. While CIN-like and PCF-like genes have multiplied in Asparagales, likely enhancing the genetic network for cell proliferation, CYC-like genes remain as single, shorter copies with low expression. Homogeneous expression of CYC-like genes in the labellum as well as the lateral petals suggests little contribution to the bilateral perianth in C. trianae. CIN-like and PCF-like gene expression suggests conserved roles in cell proliferation in leaves, sepals and petals, carpels, ovules and fruits in Asparagales by comparison with previously reported functions in core eudicots and monocots. This is the first large scale analysis of TCP-like genes in Asparagales that will serve as a platform for in-depth functional studies in emerging model monocots. PMID:28144250

  3. Evolution and Expression Patterns of TCP Genes in Asparagales.

    Science.gov (United States)

    Madrigal, Yesenia; Alzate, Juan F; Pabón-Mora, Natalia

    2017-01-01

    CYCLOIDEA-like genes are involved in the symmetry gene network, limiting cell proliferation in the dorsal regions of bilateral flowers in core eudicots. CYC-like and closely related TCP genes (acronym for TEOSINTE BRANCHED1, CYCLOIDEA, and PROLIFERATION CELL FACTOR) have been poorly studied in Asparagales, the largest order of monocots that includes both bilateral flowers in Orchidaceae (ca. 25.000 spp) and radially symmetrical flowers in Hypoxidaceae (ca. 200 spp). With the aim of assessing TCP gene evolution in the Asparagales, we isolated TCP-like genes from publicly available databases and our own transcriptomes of Cattleya trianae (Orchidaceae) and Hypoxis decumbens (Hypoxidaceae). Our matrix contains 452 sequences representing the three major clades of TCP genes. Besides the previously identified CYC specific core eudicot duplications, our ML phylogenetic analyses recovered an early CIN-like duplication predating all angiosperms, two CIN-like Asparagales-specific duplications and a duplication prior to the diversification of Orchidoideae and Epidendroideae. In addition, we provide evidence of at least three duplications of PCF-like genes in Asparagales. While CIN-like and PCF-like genes have multiplied in Asparagales, likely enhancing the genetic network for cell proliferation, CYC-like genes remain as single, shorter copies with low expression. Homogeneous expression of CYC-like genes in the labellum as well as the lateral petals suggests little contribution to the bilateral perianth in C. trianae. CIN-like and PCF-like gene expression suggests conserved roles in cell proliferation in leaves, sepals and petals, carpels, ovules and fruits in Asparagales by comparison with previously reported functions in core eudicots and monocots. This is the first large scale analysis of TCP-like genes in Asparagales that will serve as a platform for in-depth functional studies in emerging model monocots.

  4. Estradiol-induced gene expression in largemouth bass (Micropterus salmoides)

    Science.gov (United States)

    Bowman, C.J.; Kroll, K.J.; Gross, T.G.; Denslow, N.D.

    2002-01-01

    Vitellogenin (Vtg) and estrogen receptor (ER) gene expression levels were measured in largemouth bass to evaluate the activation of the ER-mediated pathway by estradiol (E2). Single injections of E2 ranging from 0.0005 to 5 mg/kg up-regulated plasma Vtg in a dose-dependent manner. Vtg and ER mRNAs were measured using partial cDNA sequences corresponding to the C-terminal domain for Vtg and the ligand-binding domain of ER?? sequences. After acute E2-exposures (2 mg/kg), Vtg and ER mRNAs and plasma Vtg levels peaked after 2 days. The rate of ER mRNA accumulation peaked 36-42 h earlier than Vtg mRNA. The expression window for ER defines the primary response to E2 in largemouth bass and that for Vtg a delayed primary response. The specific effect of E2 on other estrogen-regulated genes was tested during these same time windows using differential display RT-PCR. Specific up-regulated genes that are expressed in the same time window as Vtg were ERp72 (a membrane-bound disulfide isomerase) and a gene with homology to an expressed gene identified in zebrafish. Genes that were expressed in a pattern that mimics the ER include the gene for zona radiata protein ZP2, and a gene with homology to an expressed gene found in winter flounder. One gene for fibrinogen ?? was down-regulated and an unidentified gene was transiently up-regulated after 12 h of exposure and returned to basal levels by 48 h. Taken together these studies indicate that the acute molecular response to E2 involves a complex network of responses over time. ?? 2002 Elsevier Science Ireland Ltd. All rights reserved.

  5. 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

  6. A Network Approach to Predict Pathogenic Genes for Fusarium graminearum

    Science.gov (United States)

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

    2010-01-01

    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

  7. Inferring Phylogenetic Networks from Gene Order Data

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    Alexey Anatolievich Morozov

    2013-01-01

    Full Text Available Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary, sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm. Binary encoding can also be useful, but only when the methods mentioned above cannot be used.

  8. Lists2Networks: Integrated analysis of gene/protein lists

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    Ma'ayan Avi

    2010-02-01

    Full Text Available Abstract Background Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses. Results We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system. Conclusions Lists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org.

  9. Paternal irradiation perturbs the expression of circadian genes in offspring

    Energy Technology Data Exchange (ETDEWEB)

    Gomes, Andre M.G.F.; Barber, Ruth C.; Dubrova, Yuri E., E-mail: yed2@le.ac.uk

    2015-05-15

    Highlights: • We have analysed gene expression in the offspring of irradiated male mice. • CBA/Ca and BALB/c male mice were used in our study. • The pattern of gene expression was established in four tissues. • Expression of genes in involved in rhythmic process/circadian rhythm is compromised. • Our data may explain the phenomenon of transgenerational genomic instability. - Abstract: The circadian system represents a complex network which influences the timing of many biological processes. Recent studies have established that circadian alterations play an important role in the susceptibility to many human diseases, including cancer. Here we report that paternal irradiation in mice significantly affects the expression of genes involved in rhythmic processes in their first-generation offspring. Using microarrays, the patterns of gene expression were established for brain, kidney, liver and spleen samples from the non-exposed offspring of irradiated CBA/Ca and BALB/c male mice. The most over-represented categories among the genes differentially expressed in the offspring of control and irradiated males were those involved in rhythmic process, circadian rhythm and DNA-dependent regulation of transcription. The results of our study therefore provide a plausible explanation for the transgenerational effects of paternal irradiation, including increased transgenerational carcinogenesis described in other studies.

  10. Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation

    Science.gov (United States)

    Goode, Debbie K.; Obier, Nadine; Vijayabaskar, M.S.; Lie-A-Ling, Michael; Lilly, Andrew J.; Hannah, Rebecca; Lichtinger, Monika; Batta, Kiran; Florkowska, Magdalena; Patel, Rahima; Challinor, Mairi; Wallace, Kirstie; Gilmour, Jane; Assi, Salam A.; Cauchy, Pierre; Hoogenkamp, Maarten; Westhead, David R.; Lacaud, Georges; Kouskoff, Valerie; Göttgens, Berthold; Bonifer, Constanze

    2016-01-01

    Summary Metazoan development involves the successive activation and silencing of specific gene expression programs and is driven by tissue-specific transcription factors programming the chromatin landscape. To understand how this process executes an entire developmental pathway, we generated global gene expression, chromatin accessibility, histone modification, and transcription factor binding data from purified embryonic stem cell-derived cells representing six sequential stages of hematopoietic specification and differentiation. Our data reveal the nature of regulatory elements driving differential gene expression and inform how transcription factor binding impacts on promoter activity. We present a dynamic core regulatory network model for hematopoietic specification and demonstrate its utility for the design of reprogramming experiments. Functional studies motivated by our genome-wide data uncovered a stage-specific role for TEAD/YAP factors in mammalian hematopoietic specification. Our study presents a powerful resource for studying hematopoiesis and demonstrates how such data advance our understanding of mammalian development. PMID:26923725

  11. Clinicopathologic and gene expression parameters predict liver cancer prognosis

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    Hao Ke

    2011-11-01

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

  12. Evolution of a core gene network for skeletogenesis in chordates.

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    Jochen Hecht

    2008-03-01

    Full Text Available The skeleton is one of the most important features for the reconstruction of vertebrate phylogeny but few data are available to understand its molecular origin. In mammals the Runt genes are central regulators of skeletogenesis. Runx2 was shown to be essential for osteoblast differentiation, tooth development, and bone formation. Both Runx2 and Runx3 are essential for chondrocyte maturation. Furthermore, Runx2 directly regulates Indian hedgehog expression, a master coordinator of skeletal development. To clarify the correlation of Runt gene evolution and the emergence of cartilage and bone in vertebrates, we cloned the Runt genes from hagfish as representative of jawless fish (MgRunxA, MgRunxB and from dogfish as representative of jawed cartilaginous fish (ScRunx1-3. According to our phylogenetic reconstruction the stem species of chordates harboured a single Runt gene and thereafter Runt locus duplications occurred during early vertebrate evolution. All newly isolated Runt genes were expressed in cartilage according to quantitative PCR. In situ hybridisation confirmed high MgRunxA expression in hard cartilage of hagfish. In dogfish ScRunx2 and ScRunx3 were expressed in embryonal cartilage whereas all three Runt genes were detected in teeth and placoid scales. In cephalochordates (lancelets Runt, Hedgehog and SoxE were strongly expressed in the gill bars and expression of Runt and Hedgehog was found in endo- as well as ectodermal cells. Furthermore we demonstrate that the lancelet Runt protein binds to Runt binding sites in the lancelet Hedgehog promoter and regulates its activity. Together, these results suggest that Runt and Hedgehog were part of a core gene network for cartilage formation, which was already active in the gill bars of the common ancestor of cephalochordates and vertebrates and diversified after Runt duplications had occurred during vertebrate evolution. The similarities in expression patterns of Runt genes support the view

  13. Genes responsive to elevated CO2 concentrations in triploid white poplar and integrated gene network analysis.

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    Juanjuan Liu

    Full Text Available BACKGROUND: The atmospheric CO2 concentration increases every year. While the effects of elevated CO2 on plant growth, physiology and metabolism have been studied, there is now a pressing need to understand the molecular mechanisms of how plants will respond to future increases in CO2 concentration using genomic techniques. PRINCIPAL FINDINGS: Gene expression in triploid white poplar ((Populus tomentosa ×P. bolleana ×P. tomentosa leaves was investigated using the Affymetrix poplar genome gene chip, after three months of growth in controlled environment chambers under three CO2 concentrations. Our physiological findings showed the growth, assessed as stem diameter, was significantly increased, and the net photosynthetic rate was decreased in elevated CO2 concentrations. The concentrations of four major endogenous hormones appeared to actively promote plant development. Leaf tissues under elevated CO2 concentrations had 5,127 genes with different expression patterns in comparison to leaves under the ambient CO2 concentration. Among these, 8 genes were finally selected for further investigation by using randomized variance model corrective ANOVA analysis, dynamic gene expression profiling, gene network construction, and quantitative real-time PCR validation. Among the 8 genes in the network, aldehyde dehydrogenase and pyruvate kinase were situated in the core and had interconnections with other genes. CONCLUSIONS: Under elevated CO2 concentrations, 8 significantly changed key genes involved in metabolism and responding to stimulus of external environment were identified. These genes play crucial roles in the signal transduction network and show strong correlations with elevated CO2 exposure. This study provides several target genes, further investigation of which could provide an initial step for better understanding the molecular mechanisms of plant acclimation and evolution in future rising CO2 concentrations.

  14. Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

    Science.gov (United States)

    Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

    2017-01-01

    The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

  15. Medusa structure of the gene regulatory network: dominance of transcription factors in cancer subtype classification.

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    Guo, Yuchun; Feng, Ying; Trivedi, Niraj S; Huang, Sui

    2011-05-01

    Gene expression profiles consisting of ten thousands of transcripts are used for clustering of tissue, such as tumors, into subtypes, often without considering the underlying reason that the distinct patterns of expression arise because of constraints in the realization of gene expression profiles imposed by the gene regulatory network. The topology of this network has been suggested to consist of a regulatory core of genes represented most prominently by transcription factors (TFs) and microRNAs, that influence the expression of other genes, and of a periphery of 'enslaved' effector genes that are regulated but not regulating. This 'medusa' architecture implies that the core genes are much stronger determinants of the realized gene expression profiles. To test this hypothesis, we examined the clustering of gene expression profiles into known tumor types to quantitatively demonstrate that TFs, and even more pronounced, microRNAs, are much stronger discriminators of tumor type specific gene expression patterns than a same number of randomly selected or metabolic genes. These findings lend support to the hypothesis of a medusa architecture and of the canalizing nature of regulation by microRNAs. They also reveal the degree of freedom for the expression of peripheral genes that are less stringently associated with a tissue type specific global gene expression profile.