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  1. Discovering Study-Specific Gene Regulatory Networks

    OpenAIRE

    2014-01-01

    This article has been made available through the Brunel Open Access Publishing Fund. This article has been made available through the Brunel Open Access Publishing Fund. Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus appro...

  2. Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation

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    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

  3. 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.

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

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    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

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

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    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.

  6. A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo

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    Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar; Otim, Ochan; Brown, C. Titus; Livi, Carolina B.; Lee, Pei Yun; Revilla, Roger; Schilstra, Maria J.; Clarke, Peter J C.; Rust, Alistair G.; Pan, Zhengjun; Arnone, Maria I.; Rowen, Lee; Cameron, R. Andrew; McClay, David R.; Hood, Leroy; Bolouri, Hamid

    2002-01-01

    We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. The model of the network is in a continuous process of revision and growth as new genes are added and new experimental results become available; see http://www.its.caltech.edu/mirsky/endomeso.htm (End-mes Gene Network Update) for the latest version. The network contains over 40 genes at present, many newly uncovered in the course of this work, and most encoding DNA-binding transcriptional regulatory factors. The architecture of the network was approached initially by construction of a logic model that integrated the extensive experimental evidence now available on endomesoderm specification. The internal linkages between genes in the network have been determined functionally, by measurement of the effects of regulatory perturbations on the expression of all relevant genes in the network. Five kinds of perturbation have been applied: (1) use of morpholino antisense oligonucleotides targeted to many of the key regulatory genes in the network; (2) transformation of other regulatory factors into dominant repressors by construction of Engrailed repressor domain fusions; (3) ectopic expression of given regulatory factors, from genetic expression constructs and from injected mRNAs; (4) blockade of the beta-catenin/Tcf pathway by introduction of mRNA encoding the intracellular domain of cadherin; and (5) blockade of the Notch signaling pathway by introduction of mRNA encoding the extracellular domain of the Notch receptor. The network model predicts the cis-regulatory inputs that link each gene into the network. Therefore, its architecture is testable by cis-regulatory analysis. Strongylocentrotus purpuratus and Lytechinus variegatus genomic BAC recombinants that include a large number of the genes in the network have been sequenced and annotated. Tests of the cis-regulatory predictions of

  7. Large-scale modeling of condition-specific gene regulatory networks by information integration and inference.

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    Ellwanger, Daniel Christian; Leonhardt, Jörn Florian; Mewes, Hans-Werner

    2014-12-01

    Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η(2) (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.

  8. Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

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    Xiaosheng Wang

    2010-03-01

    Full Text Available Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

  9. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks.

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    Oded Magger

    Full Text Available The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.

  10. NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

    Science.gov (United States)

    da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

    2016-06-02

    The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

  11. Identifying disease-specific genes based on their topological significance in protein networks

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    Cherba David

    2009-03-01

    Full Text Available Abstract Background The identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest. Results In this paper we describe novel computational methodology capable of predicting key regulatory genes and proteins in disease- and condition-specific biological networks. The algorithm builds shortest path network connecting condition-specific genes (e.g. differentially expressed genes using global database of protein interactions from MetaCore. We evaluate the number of all paths traversing each node in the shortest path network in relation to the total number of paths going via the same node in the global network. Using these numbers and the relative size of the initial data set, we determine the statistical significance of the network connectivity provided through each node. We applied this method to gene expression data from psoriasis patients and identified many confirmed biological targets of psoriasis and suggested several new targets. Using predicted regulatory nodes we were able to reconstruct disease pathways that are in excellent agreement with the current knowledge on the pathogenesis of psoriasis. Conclusion The systematic and automated approach described in this paper is readily applicable to uncovering high-quality therapeutic targets, and holds great promise for developing network-based combinational treatment strategies for a wide range of diseases.

  12. A Hh-driven gene network controls specification, pattern and size of the Drosophila simple eyes.

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    Aguilar-Hidalgo, Daniel; Domínguez-Cejudo, María A; Amore, Gabriele; Brockmann, Anette; Lemos, María C; Córdoba, Antonio; Casares, Fernando

    2013-01-01

    During development, extracellular signaling molecules interact with intracellular gene networks to control the specification, pattern and size of organs. One such signaling molecule is Hedgehog (Hh). Hh is known to act as a morphogen, instructing different fates depending on the distance to its source. However, how Hh, when signaling across a cell field, impacts organ-specific transcriptional networks is still poorly understood. Here, we investigate this issue during the development of the Drosophila ocellar complex. The development of this sensory structure, which is composed of three simple eyes (or ocelli) located at the vertices of a triangular patch of cuticle on the dorsal head, depends on Hh signaling and on the definition of three domains: two areas of eya and so expression--the prospective anterior and posterior ocelli--and the intervening interocellar domain. Our results highlight the role of the homeodomain transcription factor engrailed (en) both as a target and as a transcriptional repressor of hh signaling in the prospective interocellar region. Furthermore, we identify a requirement for the Notch pathway in the establishment of en maintenance in a Hh-independent manner. Therefore, hh signals transiently during the specification of the interocellar domain, with en being required here for hh signaling attenuation. Computational analysis further suggests that this network design confers robustness to signaling noise and constrains phenotypic variation. In summary, using genetics and modeling we have expanded the ocellar gene network to explain how the interaction between the Hh gradient and this gene network results in the generation of stable mutually exclusive gene expression domains. In addition, we discuss some general implications our model may have in some Hh-driven gene networks.

  13. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors.

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    Kemmeren, Patrick; Sameith, Katrin; van de Pasch, Loes A L; Benschop, Joris J; Lenstra, Tineke L; Margaritis, Thanasis; O'Duibhir, Eoghan; Apweiler, Eva; van Wageningen, Sake; Ko, Cheuk W; van Heesch, Sebastiaan; Kashani, Mehdi M; Ampatziadis-Michailidis, Giannis; Brok, Mariel O; Brabers, Nathalie A C H; Miles, Anthony J; Bouwmeester, Diane; van Hooff, Sander R; van Bakel, Harm; Sluiters, Erik; Bakker, Linda V; Snel, Berend; Lijnzaad, Philip; van Leenen, Dik; Groot Koerkamp, Marian J A; Holstege, Frank C P

    2014-04-24

    To understand regulatory systems, it would be useful to uniformly determine how different components contribute to the expression of all other genes. We therefore monitored mRNA expression genome-wide, for individual deletions of one-quarter of yeast genes, focusing on (putative) regulators. The resulting genetic perturbation signatures reflect many different properties. These include the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. The data are assembled into a genetic perturbation network that shows different connectivities for different classes of regulators. Four feed-forward loop (FFL) types are overrepresented, including incoherent type 2 FFLs that likely represent feedback. Systematic transcription factor classification shows a surprisingly high abundance of gene-specific repressors, suggesting that yeast chromatin is not as generally restrictive to transcription as is often assumed. The data set is useful for studying individual genes and for discovering properties of an entire regulatory system.

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

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    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.

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

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    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

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

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    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.

  17. Gene networks activated by specific patterns of action potentials in dorsal root ganglia neurons

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    Lee, Philip R.; Cohen, Jonathan E.; Iacobas, Dumitru A.; Iacobas, Sanda; Fields, R. Douglas

    2017-01-01

    Gene regulatory networks underlie the long-term changes in cell specification, growth of synaptic connections, and adaptation that occur throughout neonatal and postnatal life. Here we show that the transcriptional response in neurons is exquisitely sensitive to the temporal nature of action potential firing patterns. Neurons were electrically stimulated with the same number of action potentials, but with different inter-burst intervals. We found that these subtle alterations in the timing of action potential firing differentially regulates hundreds of genes, across many functional categories, through the activation or repression of distinct transcriptional networks. Our results demonstrate that the transcriptional response in neurons to environmental stimuli, coded in the pattern of action potential firing, can be very sensitive to the temporal nature of action potential delivery rather than the intensity of stimulation or the total number of action potentials delivered. These data identify temporal kinetics of action potential firing as critical components regulating intracellular signalling pathways and gene expression in neurons to extracellular cues during early development and throughout life. PMID:28256583

  18. Roles of the Wnt effector POP-1/TCF in the C. elegans endomesoderm specification gene network.

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    Owraghi, Melissa; Broitman-Maduro, Gina; Luu, Thomas; Roberson, Heather; Maduro, Morris F

    2010-04-15

    In C. elegans the 4-cell stage blastomere EMS is an endomesodermal precursor. Its anterior daughter, MS, makes primarily mesodermal cells, while its posterior daughter E generates the entire intestine. The gene regulatory network underlying specification of MS and E has been the subject of study for more than 15 years. A key component of the specification of the two cells is the involvement of the Wnt/beta-catenin asymmetry pathway, which through its nuclear effector POP-1, specifies MS and E as different from each other. Loss of pop-1 function results in the mis-specification of MS as an E-like cell, because POP-1 directly represses the end-1 and end-3 genes in MS, which would otherwise promote an endoderm fate. A long-standing question has been whether POP-1 plays a role in specifying MS fate beyond repression of endoderm fate. This question has been difficult to ask because the only chromosomal lesions that remove both end-1 and end-3 are large deletions removing hundreds of genes. Here, we report the construction of bona fide end-1 end-3 double mutants. In embryos lacking activity of end-1, end-3 and pop-1 together, we find that MS fate is partially restored, while E expresses early markers of MS fate and adopts characteristics of both MS and C. Our results suggest that POP-1 is not critical for MS specification beyond repression of endoderm specification, and reveal that Wnt-modified POP-1 and END-1/3 further reinforce E specification by repressing MS fate in E. By comparison, a previous work suggested that in the related nematode C. briggsae, Cb-POP-1 is not required to repress endoderm specification in MS, in direct contrast with Ce-POP-1, but is critical for repression of MS fate in E. The findings reported here shed new light on the flexibility of combinatorial control mechanisms in endomesoderm specification in Caenorhabditis. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  19. Gene Regulatory Network Analysis Reveals Differences in Site-specific Cell Fate Determination in Mammalian Brain

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    Gokhan eErtaylan

    2014-12-01

    Full Text Available Neurogenesis - the generation of new neurons - is an ongoing process that persists in the adult mammalian brain of several species, including humans. In this work we analyze two discrete brain regions: the subventricular zone (SVZ lining the walls of the lateral ventricles; and the subgranular zone (SGZ of the dentate gyrus of the hippocampus in mice and shed light on the SVZ and SGZ specific neurogenesis. We propose a computational model that relies on the construction and analysis of region specific gene regulatory networks from the publicly available data on these two regions. Using this model a number of putative factors involved in neuronal stem cell (NSC identity and maintenance were identified. We also demonstrate potential gender and niche-derived differences based on cell surface and nuclear receptors via Ar, Hif1a and Nr3c1.We have also conducted cell fate determinant analysis for SVZ NSC populations to Olfactory Bulb interneurons and SGZ NSC populations to the granule cells of the Granular Cell Layer. We report thirty-one candidate cell fate determinant gene pairs, ready to be validated. We focus on Ar - Pax6 in SVZ and Sox2 - Ncor1 in SGZ. Both pairs are expressed and localized in the suggested anatomical structures as shown by in situ hybridization and found to physically interact.Finally, we conclude that there are fundamental differences between SGZ and SVZ neurogenesis. We argue that these regulatory mechanisms are linked to the observed differential neurogenic potential of these regions. The presence of nuclear and cell surface receptors in the region specific regulatory circuits indicate the significance of niche derived extracellular factors, hormones and region specific factors such as the oxygen sensitivity, dictating SGZ and SVZ specific neurogenesis.

  20. Assessment of FBA Based Gene Essentiality Analysis in Cancer with a Fast Context-Specific Network Reconstruction Method.

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    Luis Tobalina

    Full Text Available Gene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date.In this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE, evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the in-silico results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.

  1. A compendium of human genes regulating feeding behavior and body weight, its functional characterization and identification of GWAS genes involved in brain-specific PPI network.

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    Ignatieva, Elena V; Afonnikov, Dmitry A; Saik, Olga V; Rogaev, Evgeny I; Kolchanov, Nikolay A

    2016-12-22

    Obesity is heritable. It predisposes to many diseases. The objectives of this study were to create a compendium of genes relevant to feeding behavior (FB) and/or body weight (BW) regulation; to construct and to analyze networks formed by associations between genes/proteins; and to identify the most significant genes, biological processes/pathways, and tissues/organs involved in BW regulation. The compendium of genes controlling FB or BW includes 578 human genes. Candidate genes were identified from various sources, including previously published original research and review articles, GWAS meta-analyses, and OMIM (Online Mendelian Inheritance in Man). All genes were ranked according to knowledge about their biological role in body weight regulation and classified according to expression patterns or functional characteristics. Substantial and overrepresented numbers of genes from the compendium encoded cell surface receptors, signaling molecules (hormones, neuropeptides, cytokines), transcription factors, signal transduction proteins, cilium and BBSome components, and lipid binding proteins or were present in the brain-specific list of tissue-enriched genes identified with TSEA tool. We identified 27 pathways from KEGG, REACTOME and BIOCARTA whose genes were overrepresented in the compendium. Networks formed by physical interactions or homological relationships between proteins or interactions between proteins involved in biochemical/signaling pathways were reconstructed and analyzed. Subnetworks and clusters identified by the MCODE tool included genes/proteins associated with cilium morphogenesis, signal transduction proteins (particularly, G protein-coupled receptors, kinases or proteins involved in response to insulin stimulus) and transcription regulation (particularly nuclear receptors). We ranked GWAS genes according to the number of neighbors in three networks and revealed 22 GWAS genes involved in the brain-specific PPI network. On the base of the most

  2. 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.

  3. Temporal network based analysis of cell specific vein graft transcriptome defines key pathways and hub genes in implantation injury.

    Directory of Open Access Journals (Sweden)

    Manoj Bhasin

    Full Text Available Vein graft failure occurs between 1 and 6 months after implantation due to obstructive intimal hyperplasia, related in part to implantation injury. The cell-specific and temporal response of the transcriptome to vein graft implantation injury was determined by transcriptional profiling of laser capture microdissected endothelial cells (EC and medial smooth muscle cells (SMC from canine vein grafts, 2 hours (H to 30 days (D following surgery. Our results demonstrate a robust genomic response beginning at 2 H, peaking at 12-24 H, declining by 7 D, and resolving by 30 D. Gene ontology and pathway analyses of differentially expressed genes indicated that implantation injury affects inflammatory and immune responses, apoptosis, mitosis, and extracellular matrix reorganization in both cell types. Through backpropagation an integrated network was built, starting with genes differentially expressed at 30 D, followed by adding upstream interactive genes from each prior time-point. This identified significant enrichment of IL-6, IL-8, NF-κB, dendritic cell maturation, glucocorticoid receptor, and Triggering Receptor Expressed on Myeloid Cells (TREM-1 signaling, as well as PPARα activation pathways in graft EC and SMC. Interactive network-based analyses identified IL-6, IL-8, IL-1α, and Insulin Receptor (INSR as focus hub genes within these pathways. Real-time PCR was used for the validation of two of these genes: IL-6 and IL-8, in addition to Collagen 11A1 (COL11A1, a cornerstone of the backpropagation. In conclusion, these results establish causality relationships clarifying the pathogenesis of vein graft implantation injury, and identifying novel targets for its prevention.

  4. Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

    Science.gov (United States)

    Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

    2009-03-11

    Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene

  5. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors

    NARCIS (Netherlands)

    Kemmeren, P.P.C.W.; Sameith, K.; van de Pasch, L.A.L.; Benschop, J.J.; Lenstra, T.L.; Margaritis, A.; O'Duibhir, E.; Apweiler, E.; van Wageningen, S.; Ko, C.W.; van Heesch, S.A.A.C.; Kashani, M.M.; Ampatziadis-Michailidis, G.; Brok, M.O.; Brabers, N.A.C.H.; Miles, A.J.; Bouwmeester, D.; van Hooff, S.R.; van Bakel, H.H.M.J.; Sluiters, E.C.; Bakker, L.V.; Snel, B.; Lijnzaad, P.; van Leenen, D.; Groot Koerkamp, M.J.A.; Holstege, F.C.P.

    2014-01-01

    To understand regulatory systems, it would be useful to uniformly determine how different components contribute to the expression of all other genes. We therefore monitored mRNA expression genomewide, for individual deletions of one-quarter of yeast genes, focusing on (putative) regulators. The resu

  6. 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

  7. 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

  8. 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.

  9. Filtering Genes for Cluster and Network Analysis

    Directory of Open Access Journals (Sweden)

    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.

  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. 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

  12. A sperm-specific proteome-scale metabolic network model identifies non-glycolytic genes for energy deficiency in asthenozoospermia.

    Science.gov (United States)

    Asghari, Arvand; Marashi, Sayed-Amir; Ansari-Pour, Naser

    2017-04-01

    About 15% of couples experience difficulty in conceiving a child, of which half of the cases are thought to be male-related. Asthenozoospermia, or low sperm motility, is one of the frequent types of male infertility. Although energy metabolism is suggested to be central to the etiology of asthenozoospermia, very few attempts have been made to identify its underlying metabolic pathways. Here, we reconstructed SpermNet, the first proteome-scale model of the sperm cell by using whole-proteome data and the mCADRE algorithm. The reconstructed model was then analyzed using the COBRA toolbox. Genes were knocked-out in the model to investigate their effect on ATP production. A total of 78 genes elevated ATP production rate considerably of which most encode components of oxidative phosphorylation, fatty acid oxidation, the Krebs cycle, and members of the solute carrier 25 family. Among them, we identified 11 novel genes which have previously not been associated with sperm cell energy metabolism and may thus be implicated in asthenozoospermia. We further examined the reconstructed model by in silico knock out of currently known asthenozoospermia implicated-genes that were not predicted by our model. The pathways affected by knocking out these genes were also related to energy metabolism, confirming previous findings. Therefore, our model not only predicts the known pathways, it also identifies several non-glycolytic genes for deficient energy metabolism in asthenozoospermia. Finally, this model supports the notion that metabolic pathways besides glycolysis such as oxidative phosphorylation and fatty acid oxidation are essential for sperm energy metabolism and if validated, may form a basis for fertility recovery.

  13. 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.

  14. 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

  15. Computational network design from functional specifications

    KAUST Repository

    Peng, Chi Han

    2016-07-11

    Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications.

  16. 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

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

    Directory of Open Access Journals (Sweden)

    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

  18. 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.

  19. Human-specific transcriptional networks in the brain.

    Science.gov (United States)

    Konopka, Genevieve; Friedrich, Tara; Davis-Turak, Jeremy; Winden, Kellen; Oldham, Michael C; Gao, Fuying; Chen, Leslie; Wang, Guang-Zhong; Luo, Rui; Preuss, Todd M; Geschwind, Daniel H

    2012-08-23

    Understanding human-specific patterns of brain gene expression and regulation can provide key insights into human brain evolution and speciation. Here, we use next-generation sequencing, and Illumina and Affymetrix microarray platforms, to compare the transcriptome of human, chimpanzee, and macaque telencephalon. Our analysis reveals a predominance of genes differentially expressed within human frontal lobe and a striking increase in transcriptional complexity specific to the human lineage in the frontal lobe. In contrast, caudate nucleus gene expression is highly conserved. We also identify gene coexpression signatures related to either neuronal processes or neuropsychiatric diseases, including a human-specific module with CLOCK as its hub gene and another module enriched for neuronal morphological processes and genes coexpressed with FOXP2, a gene important for language evolution. These data demonstrate that transcriptional networks have undergone evolutionary remodeling even within a given brain region, providing a window through which to view the foundation of uniquely human cognitive capacities.

  20. 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.

  1. Compressed Adjacency Matrices: Untangling Gene Regulatory Networks.

    Science.gov (United States)

    Dinkla, K; Westenberg, M A; van Wijk, J J

    2012-12-01

    We present a novel technique-Compressed Adjacency Matrices-for visualizing gene regulatory networks. These directed networks have strong structural characteristics: out-degrees with a scale-free distribution, in-degrees bound by a low maximum, and few and small cycles. Standard visualization techniques, such as node-link diagrams and adjacency matrices, are impeded by these network characteristics. The scale-free distribution of out-degrees causes a high number of intersecting edges in node-link diagrams. Adjacency matrices become space-inefficient due to the low in-degrees and the resulting sparse network. Compressed adjacency matrices, however, exploit these structural characteristics. By cutting open and rearranging an adjacency matrix, we achieve a compact and neatly-arranged visualization. Compressed adjacency matrices allow for easy detection of subnetworks with a specific structure, so-called motifs, which provide important knowledge about gene regulatory networks to domain experts. We summarize motifs commonly referred to in the literature, and relate them to network analysis tasks common to the visualization domain. We show that a user can easily find the important motifs in compressed adjacency matrices, and that this is hard in standard adjacency matrix and node-link diagrams. We also demonstrate that interaction techniques for standard adjacency matrices can be used for our compressed variant. These techniques include rearrangement clustering, highlighting, and filtering.

  2. Identification of transcriptional regulatory networks specific to pilocytic astrocytoma

    Directory of Open Access Journals (Sweden)

    Gutmann David H

    2011-07-01

    Full Text Available Abstract Background Pilocytic Astrocytomas (PAs are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified. In an effort to better understand the molecular biology underlying the pathogenesis of these pediatric brain tumors, we performed higher-order transcriptional network analysis of a large gene expression dataset to identify gene regulatory pathways that are specific to this tumor type, relative to other, more aggressive glial or histologically distinct brain tumours. Methods RNA derived from frozen human PA tumours was subjected to microarray-based gene expression profiling, using Affymetrix U133Plus2 GeneChip microarrays. This data set was compared to similar data sets previously generated from non-malignant human brain tissue and other brain tumour types, after appropriate normalization. Results In this study, we examined gene expression in 66 PA tumors compared to 15 non-malignant cortical brain tissues, and identified 792 genes that demonstrated consistent differential expression between independent sets of PA and non-malignant specimens. From this entire 792 gene set, we used the previously described PAP tool to assemble a core transcriptional regulatory network composed of 6 transcription factor genes (TFs and 24 target genes, for a total of 55 interactions. A similar analysis of oligodendroglioma and glioblastoma multiforme (GBM gene expression data sets identified distinct, but overlapping, networks. Most importantly, comparison of each of the brain tumor type-specific networks revealed a network unique to PA that included repressed expression of ONECUT2, a gene frequently methylated in other tumor types, and 13 other uniquely predicted TF-gene interactions. Conclusions These results suggest specific transcriptional pathways that may operate to create the unique molecular phenotype of PA and thus opportunities for corresponding targeted therapeutic

  3. A Dynamic Algebraic Specification for Social Networks

    CERN Document Server

    Ksystra, Katerina; Triantafyllou, Nikolaos; Stefaneas, Petros

    2011-01-01

    With the help of the Internet, social networks have grown rapidly. This has increased security requirements. We present a formalization of social networks as composite behavioral objects, defined using the Observational Transition System (OTS) approach. Our definition is then translated to the OTS/CafeOBJ algebraic specification methodology. This translation allows the formal verification of safety properties for social networks via the Proof Score method. Finally, using this methodology we formally verify some security properties.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. Organ and Tissue-specific Sucrose Transporters. Important Hubs in Gene and Metabolite Networks Regulating Carbon Use in Wood-forming Tissues of Populus

    Energy Technology Data Exchange (ETDEWEB)

    Harding, Scott A. [Univ. of Georgia, Athens, GA (United States); Tsai, Chung-Jui [Univ. of Georgia, Athens, GA (United States)

    2016-01-04

    The overall project objective was to probe the relationship between sucrose transporters and plant productivity in the biomass for biofuels woody perennial, Populus. At the time the proposal was written, sucrose transporters had already been investigated in many plant model systems, primarily with respect to the export of photosynthate sucrose from source leaves, and the uptake of sucrose in storage organs and seeds. Preliminary findings by the PI found that in Populus, sucrose transporter genes (SUTs) were well expressed in wood-forming tissues that comprise the feedstock for biofuels production. Because sucrose comprises by far the predominant form in which photosynthate is delivered from source organs to sink organs like roots and wood-forming tissues, SUTs control a gate that nominally at least could impact the allocation or partitioning of sucrose for potentially competing end uses like growth (stem biomass) and storage. In addition, water use might be conditioned by the way in which sucrose is distributed throughout the plant, and/or by the way in which sucrose is partitioned intracellularly. Several dozen transgenic lines were produced in year 1 of the project to perturb the expression ratio of multiple plasma membrane (PM) SUTs (intercellular trafficking), versus the single tonoplast membrane (TM) sucrose transporter that effectively regulates intracellular trafficking of sucrose. It was possible to obtain transgenic lines with dual SUT gene knockdown using the 35S promoter, but not the wood-specific TUA1 promoter. By the end of project year 2, a decision was made to work with the 35S plants while archiving the TUA1 plants. The PhD candidate charged with producing the transgenic lines abandoned the project during its second year, substantially contributing to the decision to operate with just the 35S lines. That student’s interests ranged more toward evolutionary topics, and a report on SUT gene evolution was published (Peng et al 2014).

  10. 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.

  11. 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

  12. Paper-based Synthetic Gene Networks

    Science.gov (United States)

    Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

    2014-01-01

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

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

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    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

  14. Sharing and Specificity of Co-expression Networks across 35 Human Tissues.

    Science.gov (United States)

    Pierson, Emma; Koller, Daphne; Battle, Alexis; Mostafavi, Sara; Ardlie, Kristin G; Getz, Gad; Wright, Fred A; Kellis, Manolis; Volpi, Simona; Dermitzakis, Emmanouil T

    2015-05-01

    To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.

  15. Sharing and Specificity of Co-expression Networks across 35 Human Tissues.

    Directory of Open Access Journals (Sweden)

    Emma Pierson

    2015-05-01

    Full Text Available To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.

  16. Modality-specificity of selective attention networks

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    Hannah Jamieson Stewart

    2015-11-01

    Full Text Available Objective: To establish the modality specificity and generality of selective attention networks. Method: Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT, the Test of Everyday Attention (TEA, and the Test of Attention in Listening (TAiL. These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. Results: The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled ‘general attention’. The third component was labeled ‘auditory attention’, as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as ‘spatial orienting’ and ‘spatial conflict’, respectively – they were comprised of orienting and conflict resolution measures from the vANT, aANT and TAiL attend-location task – all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location. Conclusions: These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific.

  17. 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.

  18. Modular composition of gene transcription networks.

    Directory of Open Access Journals (Sweden)

    Andras Gyorgy

    2014-03-01

    Full Text Available Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.

  19. Modular composition of gene transcription networks.

    Science.gov (United States)

    Gyorgy, Andras; Del Vecchio, Domitilla

    2014-03-01

    Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.

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

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    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. 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.

  3. Optimal finite horizon control in gene regulatory networks

    Science.gov (United States)

    Liu, Qiuli

    2013-06-01

    As a paradigm for modeling gene regulatory networks, probabilistic Boolean networks (PBNs) form a subclass of Markov genetic regulatory networks. To date, many different stochastic optimal control approaches have been developed to find therapeutic intervention strategies for PBNs. A PBN is essentially a collection of constituent Boolean networks via a probability structure. Most of the existing works assume that the probability structure for Boolean networks selection is known. Such an assumption cannot be satisfied in practice since the presence of noise prevents the probability structure from being accurately determined. In this paper, we treat a case in which we lack the governing probability structure for Boolean network selection. Specifically, in the framework of PBNs, the theory of finite horizon Markov decision process is employed to find optimal constituent Boolean networks with respect to the defined objective functions. In order to illustrate the validity of our proposed approach, an example is also displayed.

  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. Genes2FANs: connecting genes through functional association networks

    Directory of Open Access Journals (Sweden)

    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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees

    Directory of Open Access Journals (Sweden)

    Chen Xiaoyu

    2007-12-01

    Full Text Available Abstract Background In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression. Results We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure. Conclusion Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

  14. Validation of Gene Regulatory Network Inference Based on Controllability

    Directory of Open Access Journals (Sweden)

    Edward eDougherty

    2013-12-01

    Full Text Available There are two distinct issues regarding network validation: (1 Does an inferred network provide good predictions relative to experimental data? (2 Does a network inference algorithm applied within a certain network model framework yield networks that are accurate relative to some criterion of goodness? The first issue concerns scientific validation and the second concerns algorithm validation. In this paper we consider inferential validation relative to controllability; that is, if an inference procedure is applied to synthetic data generated from a gene regulatory network and an intervention procedure is designed on the inferred network, how well does it perform on the true network? The reasoning behind such a criterion is that, if our purpose is to use gene regulatory networks to design therapeutic intervention strategies, then we are not concerned with network fidelity, per se, but only with our ability to design effective interventions based on the inferred network. We will consider the problem from the perspectives of stationary control, which involves designing a control policy to be applied over time based on the current state of the network, with the decision procedure itself being time independent. {The objective of a control policy is to optimally reduce the total steady-state probability mass of the undesirable states (phenotypes, which is equivalent to optimally increasing the total steady-state mass of the desirable states. Based on this criterion we compare several proposed network inference procedures. We will see that inference procedure psi may perform poorer than inference procedure xi relative to inferring the full network structure but perform better than xi relative to controllability. Hence, when one is aiming at a specific application, it may be wise to use an objective-based measure of inference validity.

  15. The underlying molecular and network level mechanisms in the evolution of robustness in gene regulatory networks.

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    Mario Pujato

    Full Text Available Gene regulatory networks show robustness to perturbations. Previous works identified robustness as an emergent property of gene network evolution but the underlying molecular mechanisms are poorly understood. We used a multi-tier modeling approach that integrates molecular sequence and structure information with network architecture and population dynamics. Structural models of transcription factor-DNA complexes are used to estimate relative binding specificities. In this model, mutations in the DNA cause changes on two levels: (a at the sequence level in individual binding sites (modulating binding specificity, and (b at the network level (creating and destroying binding sites. We used this model to dissect the underlying mechanisms responsible for the evolution of robustness in gene regulatory networks. Results suggest that in sparse architectures (represented by short promoters, a mixture of local-sequence and network-architecture level changes are exploited. At the local-sequence level, robustness evolves by decreasing the probabilities of both the destruction of existent and generation of new binding sites. Meanwhile, in highly interconnected architectures (represented by long promoters, robustness evolves almost entirely via network level changes, deleting and creating binding sites that modify the network architecture.

  16. 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

  17. Stability Depends on Positive Autoregulation in Boolean Gene Regulatory Networks

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    Pinho, Ricardo; Garcia, Victor; Irimia, Manuel; Feldman, Marcus W.

    2014-01-01

    Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs). The most basic motif, autoregulation, has been associated with bistability (when positive) and with homeostasis and robustness to noise (when negative), but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals. PMID:25375153

  18. Stability depends on positive autoregulation in Boolean gene regulatory networks.

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    Ricardo Pinho

    2014-11-01

    Full Text Available Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs. The most basic motif, autoregulation, has been associated with bistability (when positive and with homeostasis and robustness to noise (when negative, but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals.

  19. A method for developing regulatory gene set networks to characterize complex biological systems.

    Science.gov (United States)

    Suphavilai, Chayaporn; Zhu, Liugen; Chen, Jake Y

    2015-01-01

    Traditional approaches to studying molecular networks are based on linking genes or proteins. Higher-level networks linking gene sets or pathways have been proposed recently. Several types of gene set networks have been used to study complex molecular networks such as co-membership gene set networks (M-GSNs) and co-enrichment gene set networks (E-GSNs). Gene set networks are useful for studying biological mechanism of diseases and drug perturbations. In this study, we proposed a new approach for constructing directed, regulatory gene set networks (R-GSNs) to reveal novel relationships among gene sets or pathways. We collected several gene set collections and high-quality gene regulation data in order to construct R-GSNs in a comparative study with co-membership gene set networks (M-GSNs). We described a method for constructing both global and disease-specific R-GSNs and determining their significance. To demonstrate the potential applications to disease biology studies, we constructed and analysed an R-GSN specifically built for Alzheimer's disease. R-GSNs can provide new biological insights complementary to those derived at the protein regulatory network level or M-GSNs. When integrated properly to functional genomics data, R-GSNs can help enable future research on systems biology and translational bioinformatics.

  20. 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.

  1. 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.

  2. Context-specific metabolic networks are consistent with experiments.

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    Scott A Becker

    2008-05-01

    Full Text Available Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

  3. Polyandry and sex-specific gene expression.

    Science.gov (United States)

    Mank, Judith E; Wedell, Nina; Hosken, David J

    2013-03-05

    Polyandry is widespread in nature, and has important evolutionary consequences for the evolution of sexual dimorphism and sexual conflict. Although many of the phenotypic consequences of polyandry have been elucidated, our understanding of the impacts of polyandry and mating systems on the genome is in its infancy. Polyandry can intensify selection on sexual characters and generate more intense sexual conflict. This has consequences for sequence evolution, but also for sex-biased gene expression, which acts as a link between mating systems, sex-specific selection and the evolution of sexual dimorphism. We discuss this and the remarkable confluence of sexual-conflict theory and patterns of gene expression, while also making predictions about transcription patterns, mating systems and sexual conflict. Gene expression is a key link in the genotype-phenotype chain, and although in its early stages, understanding the sexual selection-transcription relationship will provide significant insights into this critical association.

  4. Transcriptional delay stabilizes bistable gene networks

    Science.gov (United States)

    Gupta, Chinmaya; López, José Manuel; Ott, William; Josić, Krešimir; Bennett, Matthew R.

    2014-01-01

    Transcriptional delay can significantly impact the dynamics of gene networks. Here we examine how such delay affects bistable systems. We investigate several stochastic models of bistable gene networks and find that increasing delay dramatically increases the mean residence times near stable states. To explain this, we introduce a non-Markovian, analytically tractable reduced model. The model shows that stabilization is the consequence of an increased number of failed transitions between stable states. Each of the bistable systems that we simulate behaves in this manner. PMID:23952450

  5. 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.

  6. [Advances in lineage-specific genes].

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    Huanping, Zhang; Tongming, Yin

    2015-06-01

    Lineage-specific genes (LSGs) are defined as genes found in one particular taxonomic group but have no significant sequence similarity with genes from other lineages, which compose about 10%?20% of the total genes in the genome of a focal organism. LSGs were first uncovered in the yeast genome in 1996. The development of the whole genome sequencing leads to the emergence of studies on LSGs as a hot topic in comparative genomics. LSGs have been extensively studied on microbial species, lower marine organisms, plant (such as Arabidopsis thaliana, Oryza sativa, Populus), insects, primate, etc; the biological functions of LSGs are important to clarify the evolution and adaptability of a species. In this review, we summarize the progress of LSGs studies, including LSGs identification, gene characterization, origin and evolution of LSGs, biological function, and expression analysis of LSGs. In addition, we discuss the existing problems and future directions for studies in this area. Our purpose is to provide some unique insights into the researches of LSGs.

  7. WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.

    Science.gov (United States)

    Amgalan, Bayarbaatar; Lee, Hyunju

    2014-01-01

    Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC) method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function) over the standard simplex (sparsity constraints). We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.

  8. Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining.

    Science.gov (United States)

    Hur, Junguk; Ozgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

    2012-12-20

    Fever is one of the most common adverse events of vaccines. The detailed mechanisms of fever and vaccine-associated gene interaction networks are not fully understood. In the present study, we employed a genome-wide, Centrality and Ontology-based Network Discovery using Literature data (CONDL) approach to analyse the genes and gene interaction networks associated with fever or vaccine-related fever responses. Over 170,000 fever-related articles from PubMed abstracts and titles were retrieved and analysed at the sentence level using natural language processing techniques to identify genes and vaccines (including 186 Vaccine Ontology terms) as well as their interactions. This resulted in a generic fever network consisting of 403 genes and 577 gene interactions. A vaccine-specific fever sub-network consisting of 29 genes and 28 gene interactions was extracted from articles that are related to both fever and vaccines. In addition, gene-vaccine interactions were identified. Vaccines (including 4 specific vaccine names) were found to directly interact with 26 genes. Gene set enrichment analysis was performed using the genes in the generated interaction networks. Moreover, the genes in these networks were prioritized using network centrality metrics. Making scientific discoveries and generating new hypotheses were possible by using network centrality and gene set enrichment analyses. For example, our study found that the genes in the generic fever network were more enriched in cell death and responses to wounding, and the vaccine sub-network had more gene enrichment in leukocyte activation and phosphorylation regulation. The most central genes in the vaccine-specific fever network are predicted to be highly relevant to vaccine-induced fever, whereas genes that are central only in the generic fever network are likely to be highly relevant to generic fever responses. Interestingly, no Toll-like receptors (TLRs) were found in the gene-vaccine interaction network. Since

  9. 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

  10. Tuplix Calculus Specifications of Financial Transfer Networks

    CERN Document Server

    Bergstra, J A; van der Zwaag, M B

    2008-01-01

    We study the application of Tuplix Calculus in modular financial budget design. We formalize organizational structure using financial transfer networks. We consider the notion of flux of money over a network, and a way to enforce the matching of influx and outflux for parts of a network. We exploit so-called signed attribute notation to make internal streams visible through encapsulations. Finally, we propose a Tuplix Calculus construct for the definition of data functions.

  11. Measuring semantic similarities by combining gene ontology annotations and gene co-function networks.

    Science.gov (United States)

    Peng, Jiajie; Uygun, Sahra; Kim, Taehyong; Wang, Yadong; Rhee, Seung Y; Chen, Jin

    2015-02-14

    Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited. Supplementary information and software are available at http://www.msu.edu/~jinchen/NETSIM .

  12. Transcriptional control in the segmentation gene network of Drosophila.

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    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

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

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    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

  14. 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

  15. 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...

  16. Gene-network analysis identifies susceptibility genes related to glycobiology in autism.

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    Bert van der Zwaag

    Full Text Available The recent identification of copy-number variation in the human genome has opened up new avenues for the discovery of positional candidate genes underlying complex genetic disorders, especially in the field of psychiatric disease. One major challenge that remains is pinpointing the susceptibility genes in the multitude of disease-associated loci. This challenge may be tackled by reconstruction of functional gene-networks from the genes residing in these loci. We applied this approach to autism spectrum disorder (ASD, and identified the copy-number changes in the DNA of 105 ASD patients and 267 healthy individuals with Illumina Humanhap300 Beadchips. Subsequently, we used a human reconstructed gene-network, Prioritizer, to rank candidate genes in the segmental gains and losses in our autism cohort. This analysis highlighted several candidate genes already known to be mutated in cognitive and neuropsychiatric disorders, including RAI1, BRD1, and LARGE. In addition, the LARGE gene was part of a sub-network of seven genes functioning in glycobiology, present in seven copy-number changes specifically identified in autism patients with limited co-morbidity. Three of these seven copy-number changes were de novo in the patients. In autism patients with a complex phenotype and healthy controls no such sub-network was identified. An independent systematic analysis of 13 published autism susceptibility loci supports the involvement of genes related to glycobiology as we also identified the same or similar genes from those loci. Our findings suggest that the occurrence of genomic gains and losses of genes associated with glycobiology are important contributors to the development of ASD.

  17. Linear control theory for gene network modeling.

    Science.gov (United States)

    Shin, Yong-Jun; Bleris, Leonidas

    2010-09-16

    Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  18. Mutated Genes in Schizophrenia Map to Brain Networks

    Science.gov (United States)

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the ... in People with Serious Mental Illness Clues for Schizophrenia in Rare Gene Glitch Recognizing Schizophrenia: Seeking Clues to a Difficult ...

  19. Multiple lineage specific expansions within the guanylyl cyclase gene family

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    O'Halloran Damien M

    2006-03-01

    , which have occurred within the GC gene family during metazoan evolution. Our phylogenetic analyses reveal that the rGC and sGC multi-domain proteins evolved early in eumetazoan evolution. Subsequent gene duplications, tissue specific expression patterns and lineage specific expansions resulted in the evolution of new networks of interaction and new biological functions associated with the maintenance of organismal complexity and homeostasis.

  20. SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

    Science.gov (United States)

    Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

    2013-04-30

    The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

  1. 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.

  2. Literature-Based Discovery of IFN-γ and Vaccine-Mediated Gene Interaction Networks

    Directory of Open Access Journals (Sweden)

    Arzucan Özgür

    2010-01-01

    Full Text Available Interferon-gamma (IFN-γ regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-γ-related gene interaction network from a large amount of IFN-γ research reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP and network centrality analysis. The interaction network of human IFN-γ (Gene symbol: IFNG and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-γ relatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.

  3. Gene therapy on demand: site specific regulation of gene therapy.

    Science.gov (United States)

    Jazwa, Agnieszka; Florczyk, Urszula; Jozkowicz, Alicja; Dulak, Jozef

    2013-08-10

    Since 1990 when the first clinical gene therapy trial was conducted, much attention and considerable promise have been given to this form of treatment. Gene therapy has been used with success in patients suffering from severe combined immunodeficiency syndromes (X-SCID and ADA-deficiency), Leber's congenital amaurosis, hemophilia, β-thalassemia and adrenoleukodystrophy. Last year, the first therapeutic vector (Glybera) for treatment of lipoprotein lipase deficiency has been registered in the European Union. Nevertheless, there are still several numerous issues that need to be improved to make this technique more safe, effective and easily accessible for patients. Introduction of the therapeutic gene to the given cells should provide the level of expression which will restore the production of therapeutic protein to normal values or will provide therapeutic efficacy despite not fully physiological expression. However, in numerous diseases the expression of therapeutic genes has to be kept at certain level for some time, and then might be required to be switched off to be activated again when worsening of the symptoms may aggravate the risk of disease relapse. In such cases the promoters which are regulated by local conditions may be more required. In this article the special emphasis is to discuss the strategies of regulation of gene expression by endogenous stimuli. Particularly, the hypoxia- or miRNA-regulated vectors offer the possibilities of tight but, at the same time, condition-dependent and cell-specific expression. Such means have been already tested in certain pathophysiological conditions. This creates the chance for the translational approaches required for development of effective treatments of so far incurable diseases.

  4. Engineering stability in gene networks by autoregulation

    Science.gov (United States)

    Becskei, Attila; Serrano, Luis

    2000-06-01

    The genetic and biochemical networks which underlie such things as homeostasis in metabolism and the developmental programs of living cells, must withstand considerable variations and random perturbations of biochemical parameters. These occur as transient changes in, for example, transcription, translation, and RNA and protein degradation. The intensity and duration of these perturbations differ between cells in a population. The unique state of cells, and thus the diversity in a population, is owing to the different environmental stimuli the individual cells experience and the inherent stochastic nature of biochemical processes (for example, refs 5 and 6). It has been proposed, but not demonstrated, that autoregulatory, negative feedback loops in gene circuits provide stability, thereby limiting the range over which the concentrations of network components fluctuate. Here we have designed and constructed simple gene circuits consisting of a regulator and transcriptional repressor modules in Escherichia coli and we show the gain of stability produced by negative feedback.

  5. 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

  6. Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.

    Science.gov (United States)

    Pérez-Montarelo, Dafne; Hudson, Nicholas J; Fernández, Ana I; Ramayo-Caldas, Yuliaxis; Dalrymple, Brian P; Reverter, Antonio

    2012-01-01

    The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF) metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species.

  7. Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.

    Directory of Open Access Journals (Sweden)

    Dafne Pérez-Montarelo

    Full Text Available The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species.

  8. An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis.

    Science.gov (United States)

    Vashisht, Shikha; Bagler, Ganesh

    2012-01-01

    Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a 'Cancer Genes Network', a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.

  9. An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis.

    Directory of Open Access Journals (Sweden)

    Shikha Vashisht

    Full Text Available Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a 'Cancer Genes Network', a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.

  10. Disease Gene Prioritization Using Network and Feature

    Science.gov (United States)

    Agam, Gady; Balasubramanian, Sandhya; Xu, Jinbo; Gilliam, T. Conrad; Maltsev, Natalia; Börnigen, Daniela

    2015-01-01

    Abstract Identifying high-confidence candidate genes that are causative for disease phenotypes, from the large lists of variations produced by high-throughput genomics, can be both time-consuming and costly. The development of novel computational approaches, utilizing existing biological knowledge for the prioritization of such candidate genes, can improve the efficiency and accuracy of the biomedical data analysis. It can also reduce the cost of such studies by avoiding experimental validations of irrelevant candidates. In this study, we address this challenge by proposing a novel gene prioritization approach that ranks promising candidate genes that are likely to be involved in a disease or phenotype under study. This algorithm is based on the modified conditional random field (CRF) model that simultaneously makes use of both gene annotations and gene interactions, while preserving their original representation. We validated our approach on two independent disease benchmark studies by ranking candidate genes using network and feature information. Our results showed both high area under the curve (AUC) value (0.86), and more importantly high partial AUC (pAUC) value (0.1296), and revealed higher accuracy and precision at the top predictions as compared with other well-performed gene prioritization tools, such as Endeavour (AUC-0.82, pAUC-0.083) and PINTA (AUC-0.76, pAUC-0.066). We were able to detect more target genes (9/18/19/27) on top positions (1/5/10/20) compared to Endeavour (3/11/14/23) and PINTA (6/10/13/18). To demonstrate its usability, we applied our method to a case study for the prediction of molecular mechanisms contributing to intellectual disability and autism. Our approach was able to correctly recover genes related to both disorders and provide suggestions for possible additional candidates based on their rankings and functional annotations. PMID:25844670

  11. Improving gene regulatory network inference using network topology information.

    Science.gov (United States)

    Nair, Ajay; Chetty, Madhu; Wangikar, Pramod P

    2015-09-01

    Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

  12. Floral morphogenesis: stochastic explorations of a gene network epigenetic landscape.

    Directory of Open Access Journals (Sweden)

    Elena R Alvarez-Buylla

    Full Text Available In contrast to the classical view of development as a preprogrammed and deterministic process, recent studies have demonstrated that stochastic perturbations of highly non-linear systems may underlie the emergence and stability of biological patterns. Herein, we address the question of whether noise contributes to the generation of the stereotypical temporal pattern in gene expression during flower development. We modeled the regulatory network of organ identity genes in the Arabidopsis thaliana flower as a stochastic system. This network has previously been shown to converge to ten fixed-point attractors, each with gene expression arrays that characterize inflorescence cells and primordial cells of sepals, petals, stamens, and carpels. The network used is binary, and the logical rules that govern its dynamics are grounded in experimental evidence. We introduced different levels of uncertainty in the updating rules of the network. Interestingly, for a level of noise of around 0.5-10%, the system exhibited a sequence of transitions among attractors that mimics the sequence of gene activation configurations observed in real flowers. We also implemented the gene regulatory network as a continuous system using the Glass model of differential equations, that can be considered as a first approximation of kinetic-reaction equations, but which are not necessarily equivalent to the Boolean model. Interestingly, the Glass dynamics recover a temporal sequence of attractors, that is qualitatively similar, although not identical, to that obtained using the Boolean model. Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model. Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification. It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of

  13. 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.

  14. Gene network analysis in a pediatric cohort identifies novel lung function genes.

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    Bruce A Ong

    Full Text Available Lung function is a heritable trait and serves as an important clinical predictor of morbidity and mortality for pulmonary conditions in adults, however, despite its importance, no studies have focused on uncovering pediatric-specific loci influencing lung function. To identify novel genetic determinants of pediatric lung function, we conducted a genome-wide association study (GWAS of four pulmonary function traits, including FVC, FEV1, FEV1/FVC and FEF25-75% in 1556 children. Further, we carried out gene network analyses for each trait including all SNPs with a P-value of <1.0 × 10(-3 from the individual GWAS. The GWAS identified SNPs with notable trends towards association with the pulmonary function measures, including the previously described INTS12 locus association with FEV1 (pmeta=1.41 × 10(-7. The gene network analyses identified 34 networks of genes associated with pulmonary function variables in Caucasians. Of those, the glycoprotein gene network reached genome-wide significance for all four variables. P-value range pmeta=6.29 × 10(-4 - 2.80 × 10(-8 on meta-analysis. In this study, we report on specific pathways that are significantly associated with pediatric lung function at genome-wide significance. In addition, we report the first loci associated with lung function in both pediatric Caucasian and African American populations.

  15. 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.

  16. 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.

  17. 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...

  18. 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

  19. Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network.

    Science.gov (United States)

    Karadeniz, İlknur; Hur, Junguk; He, Yongqun; Özgür, Arzucan

    2015-01-01

    Brucella is an intracellular bacterium that causes chronic brucellosis in humans and various mammals. The identification of host-Brucella interaction is crucial to understand host immunity against Brucella infection and Brucella pathogenesis against host immune responses. Most of the information about the inter-species interactions between host and Brucella genes is only available in the text of the scientific publications. Many text-mining systems for extracting gene and protein interactions have been proposed. However, only a few of them have been designed by considering the peculiarities of host-pathogen interactions. In this paper, we used a text mining approach for extracting host-Brucella gene-gene interactions from the abstracts of articles in PubMed. The gene-gene interactions here represent the interactions between genes and/or gene products (e.g., proteins). The SciMiner tool, originally designed for detecting mammalian gene/protein names in text, was extended to identify host and Brucella gene/protein names in the abstracts. Next, sentence-level and abstract-level co-occurrence based approaches, as well as sentence-level machine learning based methods, originally designed for extracting intra-species gene interactions, were utilized to extract the interactions among the identified host and Brucella genes. The extracted interactions were manually evaluated. A total of 46 host-Brucella gene interactions were identified and represented as an interaction network. Twenty four of these interactions were identified from sentence-level processing. Twenty two additional interactions were identified when abstract-level processing was performed. The Interaction Network Ontology (INO) was used to represent the identified interaction types at a hierarchical ontology structure. Ontological modeling of specific gene-gene interactions demonstrates that host-pathogen gene-gene interactions occur at experimental conditions which can be ontologically represented. Our

  20. A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks.

    Science.gov (United States)

    Xiang, Zuoshuang; Qin, Tingting; Qin, Zhaohui S; He, Yongqun

    2013-10-16

    The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining

  1. 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.

  2. Do Specific Types of Networking Predict Specific Mobility Outcomes? A Two-Year Prospective Study

    Science.gov (United States)

    Wolff, Hans-Georg; Moser, Klaus

    2010-01-01

    Previous research has established a general relation between networking and career outcomes, as postulated by theories on protean careers and career self management. We suggest that specific facets of networking behavior differentially affect specific career mobility outcomes over time. In a 2-year prospective study, we examined the impact of six…

  3. Do Specific Types of Networking Predict Specific Mobility Outcomes? A Two-Year Prospective Study

    Science.gov (United States)

    Wolff, Hans-Georg; Moser, Klaus

    2010-01-01

    Previous research has established a general relation between networking and career outcomes, as postulated by theories on protean careers and career self management. We suggest that specific facets of networking behavior differentially affect specific career mobility outcomes over time. In a 2-year prospective study, we examined the impact of six…

  4. Chaotic motifs in gene regulatory networks.

    Science.gov (United States)

    Zhang, Zhaoyang; Ye, Weiming; Qian, Yu; Zheng, Zhigang; Huang, Xuhui; Hu, Gang

    2012-01-01

    Chaos should occur often in gene regulatory networks (GRNs) which have been widely described by nonlinear coupled ordinary differential equations, if their dimensions are no less than 3. It is therefore puzzling that chaos has never been reported in GRNs in nature and is also extremely rare in models of GRNs. On the other hand, the topic of motifs has attracted great attention in studying biological networks, and network motifs are suggested to be elementary building blocks that carry out some key functions in the network. In this paper, chaotic motifs (subnetworks with chaos) in GRNs are systematically investigated. The conclusion is that: (i) chaos can only appear through competitions between different oscillatory modes with rivaling intensities. Conditions required for chaotic GRNs are found to be very strict, which make chaotic GRNs extremely rare. (ii) Chaotic motifs are explored as the simplest few-node structures capable of producing chaos, and serve as the intrinsic source of chaos of random few-node GRNs. Several optimal motifs causing chaos with atypically high probability are figured out. (iii) Moreover, we discovered that a number of special oscillators can never produce chaos. These structures bring some advantages on rhythmic functions and may help us understand the robustness of diverse biological rhythms. (iv) The methods of dominant phase-advanced driving (DPAD) and DPAD time fraction are proposed to quantitatively identify chaotic motifs and to explain the origin of chaotic behaviors in GRNs.

  5. VLSI synthesis of digital application specific neural networks

    Science.gov (United States)

    Beagles, Grant; Winters, Kel

    1991-01-01

    Neural networks tend to fall into two general categories: (1) software simulations, or (2) custom hardware that must be trained. The scope of this project is the merger of these two classifications into a system whereby a software model of a network is trained to perform a specific task and the results used to synthesize a standard cell realization of the network using automated tools.

  6. 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...

  7. 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.

  8. Inferring slowly-changing dynamic gene-regulatory networks

    NARCIS (Netherlands)

    Wit, Ernst C.; Abbruzzo, Antonino

    2015-01-01

    Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a cla

  9. Specification Mining for Intrusion Detection in Networked Control Systems

    NARCIS (Netherlands)

    Caselli, Marco; Zambon, Emmanuele; Amann, Johanna; Sommer, Robin; Kargl, Frank

    2016-01-01

    This paper discusses a novel approach to specification-based intrusion detection in the field of networked control systems. Our approach reduces the substantial human effort required to deploy a specification-based intrusion detection system by automating the development of its specification rules.

  10. 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.

  11. Utility communication networks and services specification, deployment and operation

    CERN Document Server

    2017-01-01

    This CIGRE green book begins by addressing the specification and provision of communication services in the context of operational applications for electrical power utilities, before subsequently providing guidelines on the deployment or transformation of networks to deliver these specific communication services. Lastly, it demonstrates how these networks and their services can be monitored, operated, and maintained to ensure that the requisite high level of service quality is consistently achieved.

  12. 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

  13. Structures and Boolean Dynamics in Gene Regulatory Networks

    Science.gov (United States)

    Szedlak, Anthony

    This dissertation discusses the topological and dynamical properties of GRNs in cancer, and is divided into four main chapters. First, the basic tools of modern complex network theory are introduced. These traditional tools as well as those developed by myself (set efficiency, interset efficiency, and nested communities) are crucial for understanding the intricate topological properties of GRNs, and later chapters recall these concepts. Second, the biology of gene regulation is discussed, and a method for disease-specific GRN reconstruction developed by our collaboration is presented. This complements the traditional exhaustive experimental approach of building GRNs edge-by-edge by quickly inferring the existence of as of yet undiscovered edges using correlations across sets of gene expression data. This method also provides insight into the distribution of common mutations across GRNs. Third, I demonstrate that the structures present in these reconstructed networks are strongly related to the evolutionary histories of their constituent genes. Investigation of how the forces of evolution shaped the topology of GRNs in multicellular organisms by growing outward from a core of ancient, conserved genes can shed light upon the ''reverse evolution'' of normal cells into unicellular-like cancer states. Next, I simulate the dynamics of the GRNs of cancer cells using the Hopfield model, an infinite range spin-glass model designed with the ability to encode Boolean data as attractor states. This attractor-driven approach facilitates the integration of gene expression data into predictive mathematical models. Perturbations representing therapeutic interventions are applied to sets of genes, and the resulting deviations from their attractor states are recorded, suggesting new potential drug targets for experimentation. Finally, I extend the Hopfield model to modular networks, cyclic attractors, and complex attractors, and apply these concepts to simulations of the cell cycle

  14. Phase transitions in the evolution of gene regulatory networks

    Science.gov (United States)

    Skanata, Antun; Kussell, Edo

    The role of gene regulatory networks is to respond to environmental conditions and optimize growth of the cell. A typical example is found in bacteria, where metabolic genes are activated in response to nutrient availability, and are subsequently turned off to conserve energy when their specific substrates are depleted. However, in fluctuating environmental conditions, regulatory networks could experience strong evolutionary pressures not only to turn the right genes on and off, but also to respond optimally under a wide spectrum of fluctuation timescales. The outcome of evolution is predicted by the long-term growth rate, which differentiates between optimal strategies. Here we present an analytic computation of the long-term growth rate in randomly fluctuating environments, by using mean-field and higher order expansion in the environmental history. We find that optimal strategies correspond to distinct regions in the phase space of fluctuations, separated by first and second order phase transitions. The statistics of environmental randomness are shown to dictate the possible evolutionary modes, which either change the structure of the regulatory network abruptly, or gradually modify and tune the interactions between its components.

  15. Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome.

    Directory of Open Access Journals (Sweden)

    Marco Galardini

    2015-09-01

    Full Text Available Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF. Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in

  16. Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome.

    Science.gov (United States)

    Galardini, Marco; Brilli, Matteo; Spini, Giulia; Rossi, Matteo; Roncaglia, Bianca; Bani, Alessia; Chiancianesi, Manuela; Moretto, Marco; Engelen, Kristof; Bacci, Giovanni; Pini, Francesco; Biondi, Emanuele G; Bazzicalupo, Marco; Mengoni, Alessio

    2015-09-01

    Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF). Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in bacterial

  17. Navigating cancer network attractors for tumor-specific therapy

    DEFF Research Database (Denmark)

    Creixell, Pau; Schoof, Erwin; Erler, Janine Terra

    2012-01-01

    Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach...... these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper...... understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies....

  18. Gene network interconnectedness and the generalized topological overlap measure

    Directory of Open Access Journals (Sweden)

    Horvath Steve

    2007-01-01

    Full Text Available Abstract Background Network methods are increasingly used to represent the interactions of genes and/or proteins. Genes or proteins that are directly linked may have a similar biological function or may be part of the same biological pathway. Since the information on the connection (adjacency between 2 nodes may be noisy or incomplete, it can be desirable to consider alternative measures of pairwise interconnectedness. Here we study a class of measures that are proportional to the number of neighbors that a pair of nodes share in common. For example, the topological overlap measure by Ravasz et al. 1 can be interpreted as a measure of agreement between the m = 1 step neighborhoods of 2 nodes. Several studies have shown that two proteins having a higher topological overlap are more likely to belong to the same functional class than proteins having a lower topological overlap. Here we address the question whether a measure of topological overlap based on higher-order neighborhoods could give rise to a more robust and sensitive measure of interconnectedness. Results We generalize the topological overlap measure from m = 1 step neighborhoods to m ≥ 2 step neighborhoods. This allows us to define the m-th order generalized topological overlap measure (GTOM by (i counting the number of m-step neighbors that a pair of nodes share and (ii normalizing it to take a value between 0 and 1. Using theoretical arguments, a yeast co-expression network application, and a fly protein network application, we illustrate the usefulness of the proposed measure for module detection and gene neighborhood analysis. Conclusion Topological overlap can serve as an important filter to counter the effects of spurious or missing connections between network nodes. The m-th order topological overlap measure allows one to trade-off sensitivity versus specificity when it comes to defining pairwise interconnectedness and network modules.

  19. A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

    Science.gov (United States)

    Kerkhofs, Johan; Geris, Liesbet

    2015-01-01

    Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour. PMID:26067297

  20. Gene Networks Underlying Chronic Sleep Deprivation in Drosophila

    Science.gov (United States)

    2014-06-15

    SECURITY CLASSIFICATION OF: Studies of the gene network affected by sleep deprivation and stress in the fruit fly Drosophila have revealed the...15-Apr-2009 14-Apr-2013 Approved for Public Release; Distribution Unlimited Gene Networks Underlying Chronic Sleep Deprivation in Drosophila The...Chronic Sleep Deprivation in Drosophila Report Title Studies of the gene network affected by sleep deprivation and stress in the fruit fly Drosophila have

  1. Overview of methods of reverse engineering of gene regulatory networks: Boolean and Bayesian networks

    OpenAIRE

    Frolova A. O.

    2012-01-01

    Reverse engineering of gene regulatory networks is an intensively studied topic in Systems Biology as it reconstructs regulatory interactions between all genes in the genome in the most complete form. The extreme computational complexity of this problem and lack of thorough reviews on reconstruction methods of gene regulatory network is a significant obstacle to further development of this area. In this article the two most common methods for modeling gene regulatory networks are surveyed: Bo...

  2. 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.

  3. A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

    Science.gov (United States)

    Kentzoglanakis, Kyriakos; Poole, Matthew

    2012-01-01

    In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

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

    Directory of Open Access Journals (Sweden)

    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

  5. Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

    Directory of Open Access Journals (Sweden)

    Bhavnani Suresh K

    2010-11-01

    Full Text Available Abstract Background In a recent study, two-dimensional (2D network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method revealed that genes implicated in many diseases (non-specific genes tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.

  6. Identifying Gene Regulatory Networks in Arabidopsis by In Silico Prediction, Yeast-1-Hybrid, and Inducible Gene Profiling Assays.

    Science.gov (United States)

    Sparks, Erin E; Benfey, Philip N

    2016-01-01

    A system-wide understanding of gene regulation will provide deep insights into plant development and physiology. In this chapter we describe a threefold approach to identify the gene regulatory networks in Arabidopsis thaliana that function in a specific tissue or biological process. Since no single method is sufficient to establish comprehensive and high-confidence gene regulatory networks, we focus on the integration of three approaches. First, we describe an in silico prediction method of transcription factor-DNA binding, then an in vivo assay of transcription factor-DNA binding by yeast-1-hybrid and lastly the identification of co-expression clusters by transcription factor induction in planta. Each of these methods provides a unique tool to advance our understanding of gene regulation, and together provide a robust model for the generation of gene regulatory networks.

  7. Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    2015-06-01

    Full Text Available In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational

  8. Processing of Feature Selectivity in Cortical Networks with Specific Connectivity.

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    Full Text Available Although non-specific at the onset of eye opening, networks in rodent visual cortex attain a non-random structure after eye opening, with a specific bias for connections between neurons of similar preferred orientations. As orientation selectivity is already present at eye opening, it remains unclear how this specificity in network wiring contributes to feature selectivity. Using large-scale inhibition-dominated spiking networks as a model, we show that feature-specific connectivity leads to a linear amplification of feedforward tuning, consistent with recent electrophysiological single-neuron recordings in rodent neocortex. Our results show that optimal amplification is achieved at an intermediate regime of specific connectivity. In this configuration a moderate increase of pairwise correlations is observed, consistent with recent experimental findings. Furthermore, we observed that feature-specific connectivity leads to the emergence of orientation-selective reverberating activity, and entails pattern completion in network responses. Our theoretical analysis provides a mechanistic understanding of subnetworks' responses to visual stimuli, and casts light on the regime of operation of sensory cortices in the presence of specific connectivity.

  9. Netter: re-ranking gene network inference predictions using structural network properties.

    Science.gov (United States)

    Ruyssinck, Joeri; Demeester, Piet; Dhaene, Tom; Saeys, Yvan

    2016-02-09

    Many algorithms have been developed to infer the topology of gene regulatory networks from gene expression data. These methods typically produce a ranking of links between genes with associated confidence scores, after which a certain threshold is chosen to produce the inferred topology. However, the structural properties of the predicted network do not resemble those typical for a gene regulatory network, as most algorithms only take into account connections found in the data and do not include known graph properties in their inference process. This lowers the prediction accuracy of these methods, limiting their usability in practice. We propose a post-processing algorithm which is applicable to any confidence ranking of regulatory interactions obtained from a network inference method which can use, inter alia, graphlets and several graph-invariant properties to re-rank the links into a more accurate prediction. To demonstrate the potential of our approach, we re-rank predictions of six different state-of-the-art algorithms using three simple network properties as optimization criteria and show that Netter can improve the predictions made on both artificially generated data as well as the DREAM4 and DREAM5 benchmarks. Additionally, the DREAM5 E.coli. community prediction inferred from real expression data is further improved. Furthermore, Netter compares favorably to other post-processing algorithms and is not restricted to correlation-like predictions. Lastly, we demonstrate that the performance increase is robust for a wide range of parameter settings. Netter is available at http://bioinformatics.intec.ugent.be. Network inference from high-throughput data is a long-standing challenge. In this work, we present Netter, which can further refine network predictions based on a set of user-defined graph properties. Netter is a flexible system which can be applied in unison with any method producing a ranking from omics data. It can be tailored to specific prior

  10. A new method for discovering disease-specific MiRNA-target regulatory networks.

    Directory of Open Access Journals (Sweden)

    Miriam Baglioni

    Full Text Available Genes and their expression regulation are among the key factors in the comprehension of the genesis and development of complex diseases. In this context, microRNAs (miRNAs are post-transcriptional regulators that play an important role in gene expression since they are frequently deregulated in pathologies like cardiovascular disease and cancer. In vitro validation of miRNA--targets regulation is often too expensive and time consuming to be carried out for every possible alternative. As a result, a tool able to provide some criteria to prioritize trials is becoming a pressing need. Moreover, before planning in vitro experiments, the scientist needs to evaluate the miRNA-target genes interaction network. In this paper we describe the miRable method whose purpose is to identify new potentially relevant genes and their interaction networks associate to a specific pathology. To achieve this goal miRable follows a system biology approach integrating together general-purpose medical knowledge (literature, Protein-Protein Interaction networks, prediction tools and pathology specific data (gene expression data. A case study on Prostate Cancer has shown that miRable is able to: 1 find new potential miRNA-targets pairs, 2 highlight novel genes potentially involved in a disease but never or little studied before, 3 reconstruct all possible regulatory subnetworks starting from the literature to expand the knowledge on the regulation of miRNA regulatory mechanisms.

  11. A new method for discovering disease-specific MiRNA-target regulatory networks.

    Science.gov (United States)

    Baglioni, Miriam; Russo, Francesco; Geraci, Filippo; Rizzo, Milena; Rainaldi, Giuseppe; Pellegrini, Marco

    2015-01-01

    Genes and their expression regulation are among the key factors in the comprehension of the genesis and development of complex diseases. In this context, microRNAs (miRNAs) are post-transcriptional regulators that play an important role in gene expression since they are frequently deregulated in pathologies like cardiovascular disease and cancer. In vitro validation of miRNA--targets regulation is often too expensive and time consuming to be carried out for every possible alternative. As a result, a tool able to provide some criteria to prioritize trials is becoming a pressing need. Moreover, before planning in vitro experiments, the scientist needs to evaluate the miRNA-target genes interaction network. In this paper we describe the miRable method whose purpose is to identify new potentially relevant genes and their interaction networks associate to a specific pathology. To achieve this goal miRable follows a system biology approach integrating together general-purpose medical knowledge (literature, Protein-Protein Interaction networks, prediction tools) and pathology specific data (gene expression data). A case study on Prostate Cancer has shown that miRable is able to: 1) find new potential miRNA-targets pairs, 2) highlight novel genes potentially involved in a disease but never or little studied before, 3) reconstruct all possible regulatory subnetworks starting from the literature to expand the knowledge on the regulation of miRNA regulatory mechanisms.

  12. In silico network topology-based prediction of gene essentiality

    CERN Document Server

    da Silva, Joao Paulo Muller; Mombach, Jose Carlos Merino; Vieira, Renata; da Silva, Jose Guliherme Camargo; Lemke, Ney; Sinigaglia, Marialva

    2007-01-01

    The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision tree-based machine learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes...

  13. 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.

  14. 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.

  15. 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.

  16. Cerebellar network plasticity: from genes to fast oscillation.

    Science.gov (United States)

    Cheron, G; Servais, L; Dan, B

    2008-04-22

    The role of the cerebellum has been increasingly recognized not only in motor control but in sensory, cognitive and emotional learning and regulation. Purkinje cells, being the sole output from the cerebellar cortex, occupy an integrative position in this network. Plasticity at this level is known to critically involve calcium signaling. In the last few years, electrophysiological study of genetically engineered mice has demonstrated the topical role of several genes encoding calcium-binding proteins (calretinin, calbindin, parvalbumin). Specific inactivation of these genes results in the emergence of a fast network oscillation (ca. 160 Hz) throughout the cerebellar cortex in alert animals, associated with ataxia. This oscillation is produced by synchronization of Purkinje cells along the parallel fiber beam. It behaves as an electrophysiological arrest rhythm, being blocked by sensorimotor stimulation. Pharmacological manipulations showed that the oscillation is blocked by GABA(A) and NMDA antagonists as well as gap junction blockers. This cerebellar network oscillation has also been documented in mouse models of human conditions with complex developmental cerebellar dysfunction, such as Angelman syndrome and fetal alcohol syndrome. Recent evidence suggests a relationship between fast oscillation and cerebellar long term depression (LTD). This may have major implications for future therapeutic targeting.

  17. 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...

  18. Relative stability of network states in Boolean network models of gene regulation in development.

    Science.gov (United States)

    Zhou, Joseph Xu; Samal, Areejit; d'Hérouël, Aymeric Fouquier; Price, Nathan D; Huang, Sui

    2016-01-01

    Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.

  19. EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks.

    Science.gov (United States)

    Sambaturu, Narmada; Mishra, Madhulika; Chandra, Nagasuma

    2016-08-18

    In biological systems, diseases are caused by small perturbations in a complex network of interactions between proteins. Perturbations typically affect only a small number of proteins, which go on to disturb a larger part of the network. To counteract this, a stress-response is launched, resulting in a complex pattern of variations in the cell. Identifying the key players involved in either spreading the perturbation or responding to it can give us important insights. We develop an algorithm, EpiTracer, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out. We propose a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network by identifying highest activity paths specific to the condition of interest, obtained by mapping gene expression profiles to the PPI network. We demonstrate the algorithm using an overexpression study and a knockdown study. In the overexpression study, the gene that was overexpressed (PARK2) was highlighted as the most important epicenter specific to the perturbation. The other top-ranked epicenters were involved in either supporting the activity of PARK2, or counteracting it. Also, 5 of the identified epicenters showed no significant differential expression, showing that our method can find information which simple differential expression analysis cannot. In the second dataset (SP1 knockdown), alternative regulators of SP1 targets were highlighted as epicenters. Also, the gene that was knocked down (SP1) was picked up as an epicenter specific to the control condition. Sensitivity analysis showed that the genes identified as epicenters remain largely unaffected by small changes. We develop an algorithm, EpiTracer, to find epicenters in condition-specific biological networks, given the PPI network and gene expression levels. EpiTracer includes programs which can extract the

  20. Coordinations between gene modules control the operation of plant amino acid metabolic networks

    Directory of Open Access Journals (Sweden)

    Galili Gad

    2009-01-01

    Full Text Available Abstract Background Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results. Results Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes. Conclusion Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.

  1. Analysis of regulatory networks constructed based on gene coexpression in pituitary adenoma.

    Science.gov (United States)

    Gong, Jie; Diao, Bo; Yao, Guo Jie; Liu, Ying; Xu, Guo Zheng

    2013-12-01

    Gene coexpression patterns can reveal gene collections with functional consistency. This study systematically constructs regulatory networks for pituitary tumours by integrating gene coexpression, transcriptional and posttranscriptional regulation. Through network analysis, we elaborate the incidence mechanism of pituitary adenoma. The Pearson's correlation coefficient was utilized to calculate the level of gene coexpression. By comparing pituitary adenoma samples with normal samples, pituitary adenoma-specific gene coexpression patterns were identified. For pituitary adenoma-specific coexpressed genes, we integrated transcription factor (TF) and microRNA (miRNA) regulation to construct a complex regulatory network from the transcriptional and posttranscriptional perspectives. Network module analysis identified the synergistic regulation of genes by miRNAs and TFs in pituitary adenoma. We identified 142 pituitary adenoma-specific active genes, including 43 TFs and 99 target genes of TFs. Functional enrichment of these 142 genes revealed that the occurrence of pituitary adenoma induced abnormalities in intracellular metabolism and angiogenesis process. These 142 genes were also significantly enriched in adenoma pathway. Module analysis of the systematic regulatory network found that three modules contained elements that were closely related to pituitary adenoma, such as FGF2 and SP1, as well as transcription factors and miRNAs involved in the tumourigenesis. These results show that in the occurrence of pituitary adenoma, miRNA, TF and genes interact with each other. Based on gene expression, the proposed method integrates interaction information from different levels and systematically explains the occurrence of pituitary tumours. It facilitates the tracing of the origin of the disease and can provide basis for early diagnosis of complex diseases or cancer without obvious symptoms.

  2. Analysis of regulatory networks constructed based on gene coexpression in pituitary adenoma

    Indian Academy of Sciences (India)

    Jie Gong; Bo Diao; Guo Jie Yao; Ying Liu; Guo Zheng Xu

    2013-12-01

    Gene coexpression patterns can reveal gene collections with functional consistency. This study systematically constructs regulatory networks for pituitary tumours by integrating gene coexpression, transcriptional and posttranscriptional regulation. Through network analysis, we elaborate the incidence mechanism of pituitary adenoma. The Pearson’s correlation coefficient was utilized to calculate the level of gene coexpression. By comparing pituitary adenoma samples with normal samples, pituitary adenoma-specific gene coexpression patterns were identified. For pituitary adenoma-specific coexpressed genes, we integrated transcription factor (TF) and microRNA (miRNA) regulation to construct a complex regulatory network from the transcriptional and posttranscriptional perspectives. Network module analysis identified the synergistic regulation of genes by miRNAs and TFs in pituitary adenoma. We identified 142 pituitary adenoma-specific active genes, including 43 TFs and 99 target genes of TFs. Functional enrichment of these 142 genes revealed that the occurrence of pituitary adenoma induced abnormalities in intracellular metabolism and angiogenesis process. These 142 genes were also significantly enriched in adenoma pathway. Module analysis of the systematic regulatory network found that three modules contained elements that were closely related to pituitary adenoma, such as FGF2 and SP1, as well as transcription factors and miRNAs involved in the tumourigenesis. These results show that in the occurrence of pituitary adenoma, miRNA, TF and genes interact with each other. Based on gene expression, the proposed method integrates interaction information from different levels and systematically explains the occurrence of pituitary tumours. It facilitates the tracing of the origin of the disease and can provide basis for early diagnosis of complex diseases or cancer without obvious symptoms.

  3. Dissecting specific and global transcriptional regulation of bacterial gene expression

    NARCIS (Netherlands)

    Gerosa, Luca; Kochanowski, Karl; Heinemann, Matthias; Sauer, Uwe

    2013-01-01

    Gene expression is regulated by specific transcriptional circuits but also by the global expression machinery as a function of growth. Simultaneous specific and global regulation thus constitutes an additional-but often neglected-layer of complexity in gene expression. Here, we develop an experiment

  4. Exhaustive Search for Fuzzy Gene Networks from Microarray Data

    Energy Technology Data Exchange (ETDEWEB)

    Sokhansanj, B A; Fitch, J P; Quong, J N; Quong, A A

    2003-07-07

    Recent technological advances in high-throughput data collection allow for the study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are required to interpret large and complex data sets. Rationally designed system perturbations (e.g. gene knock-outs, metabolite removal, etc) can be used to iteratively refine hypothetical models, leading to a modeling-experiment cycle for high-throughput biological system analysis. We use fuzzy logic gene network models because they have greater resolution than Boolean logic models and do not require the precise parameter measurement needed for chemical kinetics-based modeling. The fuzzy gene network approach is tested by exhaustive search for network models describing cyclin gene interactions in yeast cell cycle microarray data, with preliminary success in recovering interactions predicted by previous biological knowledge and other analysis techniques. Our goal is to further develop this method in combination with experiments we are performing on bacterial regulatory networks.

  5. NAC genes: Time-specific regulators of hormonal signaling in Arabidopsis

    DEFF Research Database (Denmark)

    Jensen, Michael Krogh; Kjærsgaard, Trine; Petersen, Klaus

    2010-01-01

    Environmental stresses on both animals and plants impose massive transcriptional perturbations. Successful adaptations to such stresses are being orchestrated by both activating and repressing effects of transcription factors on specific target genes. We have recently published a systematic chara...... genes upon stimuli with seven phytohormones. Our analysis could be a first indication of NAC-centered transcriptional networks, which coordinate timely hormonal signaling in plants....

  6. NAC genes: Time-specific regulators of hormonal signaling in Arabidopsis

    DEFF Research Database (Denmark)

    Jensen, Michael Krogh; Kjærsgaard, Trine; Petersen, Klaus

    2010-01-01

    Environmental stresses on both animals and plants impose massive transcriptional perturbations. Successful adaptations to such stresses are being orchestrated by both activating and repressing effects of transcription factors on specific target genes. We have recently published a systematic chara...... genes upon stimuli with seven phytohormones. Our analysis could be a first indication of NAC-centered transcriptional networks, which coordinate timely hormonal signaling in plants....

  7. Fast reconstruction of compact context-specific metabolic network models.

    Directory of Open Access Journals (Sweden)

    Nikos Vlassis

    2014-01-01

    Full Text Available Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue, and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.

  8. Human brain networks function in connectome-specific harmonic waves.

    Science.gov (United States)

    Atasoy, Selen; Donnelly, Isaac; Pearson, Joel

    2016-01-21

    A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call 'connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory-inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation-inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.

  9. Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity.

    Science.gov (United States)

    De, Rishika; Hu, Ting; Moore, Jason H; Gilbert-Diamond, Diane

    2015-01-01

    Recent findings have reemphasized the importance of epistasis, or gene-gene interactions, as a contributing factor to the unexplained heritability of obesity. Network-based methods such as statistical epistasis networks (SEN), present an intuitive framework to address the computational challenge of studying pairwise interactions between thousands of genetic variants. In this study, we aimed to analyze pairwise interactions that are associated with Body Mass Index (BMI) between SNPs from twelve genes robustly associated with obesity (BDNF, ETV5, FAIM2, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, and TMEM18). We used information gain measures to identify all SNP-SNP interactions among and between these genes that were related to obesity (BMI > 30 kg/m(2)) within the Framingham Heart Study Cohort; interactions exceeding a certain threshold were used to build an SEN. We also quantified whether interactions tend to occur more between SNPs from the same gene (dyadicity) or between SNPs from different genes (heterophilicity). We identified a highly connected SEN of 709 SNPs and 1241 SNP-SNP interactions. Combining the SEN framework with dyadicity and heterophilicity analyses, we found 1 dyadic gene (TMEM18, P-value = 0.047) and 3 heterophilic genes (KCTD15, P-value = 0.045; SH2B1, P-value = 0.003; and TMEM18, P-value = 0.001). We also identified a lncRNA SNP (rs4358154) as a key node within the SEN using multiple network measures. This study presents an analytical framework to characterize the global landscape of genetic interactions from genome-wide arrays and also to discover nodes of potential biological significance within the identified network.

  10. Noise reduction facilitated by dosage compensation in gene networks

    Science.gov (United States)

    Peng, Weilin; Song, Ruijie; Acar, Murat

    2016-01-01

    Genetic noise together with genome duplication and volume changes during cell cycle are significant contributors to cell-to-cell heterogeneity. How can cells buffer the effects of these unavoidable epigenetic and genetic variations on phenotypes that are sensitive to such variations? Here we show that a simple network motif that is essential for network-dosage compensation can reduce the effects of extrinsic noise on the network output. Using natural and synthetic gene networks with and without the network motif, we measure gene network activity in single yeast cells and find that the activity of the compensated network is significantly lower in noise compared with the non-compensated network. A mathematical analysis provides intuitive insights into these results and a novel stochastic model tracking cell-volume and cell-cycle predicts the experimental results. Our work implies that noise is a selectable trait tunable by evolution. PMID:27694830

  11. Formal Specification Based Automatic Test Generation for Embedded Network Systems

    Directory of Open Access Journals (Sweden)

    Eun Hye Choi

    2014-01-01

    Full Text Available Embedded systems have become increasingly connected and communicate with each other, forming large-scaled and complicated network systems. To make their design and testing more reliable and robust, this paper proposes a formal specification language called SENS and a SENS-based automatic test generation tool called TGSENS. Our approach is summarized as follows: (1 A user describes requirements of target embedded network systems by logical property-based constraints using SENS. (2 Given SENS specifications, test cases are automatically generated using a SAT-based solver. Filtering mechanisms to select efficient test cases are also available in our tool. (3 In addition, given a testing goal by the user, test sequences are automatically extracted from exhaustive test cases. We’ve implemented our approach and conducted several experiments on practical case studies. Through the experiments, we confirmed the efficiency of our approach in design and test generation of real embedded air-conditioning network systems.

  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. Internet factories: Creating application-specific networks on-demand

    NARCIS (Netherlands)

    Strijkers, R.J.; Makkes, M.X.; Laat, C. de; Meijer, R.J.

    2014-01-01

    We introduce the concept of Internet factories. Internet factories structure the task of creating and managing application-specific overlay networks using infrastructure-as-a-service clouds. We describe the Internet factory architecture and report on a proof of concept with three examples that progr

  14. 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.

  15. Cell type-specific properties and environment shape tissue specificity of cancer genes.

    Science.gov (United States)

    Schaefer, Martin H; Serrano, Luis

    2016-02-09

    One of the biggest mysteries in cancer research remains why mutations in certain genes cause cancer only at specific sites in the human body. The poor correlation between the expression level of a cancer gene and the tissues in which it causes malignant transformations raises the question of which factors determine the tissue-specific effects of a mutation. Here, we explore why some cancer genes are associated only with few different cancer types (i.e., are specific), while others are found mutated in a large number of different types of cancer (i.e., are general). We do so by contrasting cellular functions of specific-cancer genes with those of general ones to identify properties that determine where in the body a gene mutation is causing malignant transformations. We identified different groups of cancer genes that did not behave as expected (i.e., DNA repair genes being tissue specific, immune response genes showing a bimodal specificity function or strong association of generally expressed genes to particular cancers). Analysis of these three groups demonstrates the importance of environmental impact for understanding why certain cancer genes are only involved in the development of some cancer types but are rarely found mutated in other types of cancer.

  16. Reconstruction of large-scale gene regulatory networks using Bayesian model averaging.

    Science.gov (United States)

    Kim, Haseong; Gelenbe, Erol

    2012-09-01

    Gene regulatory networks provide the systematic view of molecular interactions in a complex living system. However, constructing large-scale gene regulatory networks is one of the most challenging problems in systems biology. Also large burst sets of biological data require a proper integration technique for reliable gene regulatory network construction. Here we present a new reverse engineering approach based on Bayesian model averaging which attempts to combine all the appropriate models describing interactions among genes. This Bayesian approach with a prior based on the Gibbs distribution provides an efficient means to integrate multiple sources of biological data. In a simulation study with maximum of 2000 genes, our method shows better sensitivity than previous elastic-net and Gaussian graphical models, with a fixed specificity of 0.99. The study also shows that the proposed method outperforms the other standard methods for a DREAM dataset generated by nonlinear stochastic models. In brain tumor data analysis, three large-scale networks consisting of 4422 genes were built using the gene expression of non-tumor, low and high grade tumor mRNA expression samples, along with DNA-protein binding affinity information. We found that genes having a large variation of degree distribution among the three tumor networks are the ones that see most involved in regulatory and developmental processes, which possibly gives a novel insight concerning conventional differentially expressed gene analysis.

  17. Two different network topologies yield bistability in models of mesoderm and anterior mesendoderm specification in amphibians.

    Science.gov (United States)

    Brown, L E; King, J R; Loose, M

    2014-07-21

    Understanding the Gene Regulatory Networks (GRNs) that underlie development is a major question for systems biology. The establishment of the germ layers is amongst the earliest events of development and has been characterised in numerous model systems. The establishment of the mesoderm is best characterised in the frog Xenopus laevis and has been well studied both experimentally and mathematically. However, the Xenopus network has significant differences from that in mouse and humans, including the presence of multiple copies of two key genes in the network, Mix and Nodal. The axolotl, a urodele amphibian, provides a model with all the benefits of amphibian embryology but crucially only a single Mix and Nodal gene required for the specification of the mesoderm. Remarkably, the number of genes within the network is not the only difference. The interaction between Mix and Brachyury, two transcription factors involved in the establishment of the endoderm and mesoderm respectively, is not conserved. While Mix represses Brachyury in Xenopus, it activates Brachyury in axolotl. Thus, whilst the topology of the networks in the two species differs, both are able to form mesoderm and endoderm in vivo. Based on current knowledge of the structure of the mesendoderm GRN we develop deterministic models that describe the time evolution of transcription factors in a single axolotl cell and compare numerical simulations with previous results from Xenopus. The models are shown to have stable steady states corresponding to mesoderm and anterior mesendoderm, with the in vitro model showing how the concentration of Activin can determine cell fate, while the in vivo model shows that β-catenin concentration can determine cell fate. Moreover, our analysis suggests that additional components must be important in the axolotl network in the specification of the full range of tissues.

  18. 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.

  19. Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; He, Yongqun

    2017-03-14

    these gene interaction networks identified top ranked E. coli genes and 6 INO interaction types (e.g., regulation and gene expression). Vaccine-related E. coli gene-gene interaction network was constructed using ontology-based literature mining strategy, which identified important E. coli vaccine genes and their interactions with other genes through specific interaction types.

  20. 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

  1. Inferring slowly-changing dynamic gene-regulatory networks.

    Science.gov (United States)

    Wit, Ernst C; Abbruzzo, Antonino

    2015-01-01

    Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experiments are designed in order to tease out temporal changes in the underlying network. It is typically reasonable to assume that changes in genomic networks are few, because biological systems tend to be stable. We introduce a new model for estimating slow changes in dynamic gene-regulatory networks, which is suitable for high-dimensional data, e.g. time-course microarray data. Our aim is to estimate a dynamically changing genomic network based on temporal activity measurements of the genes in the network. Our method is based on the penalized likelihood with l1-norm, that penalizes conditional dependencies between genes as well as differences between conditional independence elements across time points. We also present a heuristic search strategy to find optimal tuning parameters. We re-write the penalized maximum likelihood problem into a standard convex optimization problem subject to linear equality constraints. We show that our method performs well in simulation studies. Finally, we apply the proposed model to a time-course T-cell dataset.

  2. Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

    KAUST Repository

    Chowdhary, Rajesh

    2012-04-06

    Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

  3. Medusa structure of the gene regulatory network: dominance of transcription factors in cancer subtype classification.

    Science.gov (United States)

    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.

  4. Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

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    Ai-Di Zhang

    2013-01-01

    Full Text Available With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.

  5. Genes2GO: A web application for querying gene sets for specific GO terms.

    Science.gov (United States)

    Chawla, Konika; Kuiper, Martin

    2016-01-01

    Gene ontology annotations have become an essential resource for biological interpretations of experimental findings. The process of gathering basic annotation information in tables that link gene sets with specific gene ontology terms can be cumbersome, in particular if it requires above average computer skills or bioinformatics expertise. We have therefore developed Genes2GO, an intuitive R-based web application. Genes2GO uses the biomaRt package of Bioconductor in order to retrieve custom sets of gene ontology annotations for any list of genes from organisms covered by the Ensembl database. Genes2GO produces a binary matrix file, indicating for each gene the presence or absence of specific annotations for a gene. It should be noted that other GO tools do not offer this user-friendly access to annotations. Genes2GO is freely available and listed under http://www.semantic-systems-biology.org/tools/externaltools/.

  6. Overview of methods of reverse engineering of gene regulatory networks: Boolean and Bayesian networks

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    Frolova A. O.

    2012-06-01

    Full Text Available Reverse engineering of gene regulatory networks is an intensively studied topic in Systems Biology as it reconstructs regulatory interactions between all genes in the genome in the most complete form. The extreme computational complexity of this problem and lack of thorough reviews on reconstruction methods of gene regulatory network is a significant obstacle to further development of this area. In this article the two most common methods for modeling gene regulatory networks are surveyed: Boolean and Bayesian networks. The mathematical description of each method is given, as well as several algorithmic approaches to modeling gene networks using these methods; the complexity of algorithms and the problems that arise during its implementation are also noted.

  7. Effects of a silenced gene in Boolean network models

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    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.

  8. Gene specific actions of thyroid hormone receptor subtypes.

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    Jean Z Lin

    Full Text Available There are two homologous thyroid hormone (TH receptors (TRs α and β, which are members of the nuclear hormone receptor (NR family. While TRs regulate different processes in vivo and other highly related NRs regulate distinct gene sets, initial studies of TR action revealed near complete overlaps in their actions at the level of individual genes. Here, we assessed the extent that TRα and TRβ differ in target gene regulation by comparing effects of equal levels of stably expressed exogenous TRs +/- T(3 in two cell backgrounds (HepG2 and HeLa. We find that hundreds of genes respond to T(3 or to unliganded TRs in both cell types, but were not able to detect verifiable examples of completely TR subtype-specific gene regulation. TR actions are, however, far from identical and we detect TR subtype-specific effects on global T(3 response kinetics in HepG2 cells and many examples of TR subtype specificity at the level of individual genes, including effects on magnitude of response to TR +/- T(3, TR regulation patterns and T(3 dose response. Cycloheximide (CHX treatment confirms that at least some differential effects involve verifiable direct TR target genes. TR subtype/gene-specific effects emerge in the context of widespread variation in target gene response and we suggest that gene-selective effects on mechanism of TR action highlight differences in TR subtype function that emerge in the environment of specific genes. We propose that differential TR actions could influence physiologic and pharmacologic responses to THs and selective TR modulators (STRMs.

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

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    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.

  10. PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space.

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    Ma, Chihua; Luciani, Timothy; Terebus, Anna; Liang, Jie; Marai, G Elisabeta

    2017-02-15

    Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes. We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.

  11. Systematic design of mouse Vh gene family-specific oligonucleotides

    NARCIS (Netherlands)

    Seijen, AM; Seijen, HG; Bos, NA

    2001-01-01

    Kabat's database has often been used to design mouse Vh gene-specific 5 ' primers. The emphasis was mostly on constructing a universal (degenerate) 5 ' primer or 5 ' primer set, which would be able to match every mouse Vh gene. We were interested in finding oligonucleotides that could be used as pri

  12. Systematic design of mouse Vh gene family-specific oligonucleotides

    NARCIS (Netherlands)

    Seijen, AM; Seijen, HG; Bos, NA

    2001-01-01

    Kabat's database has often been used to design mouse Vh gene-specific 5 ' primers. The emphasis was mostly on constructing a universal (degenerate) 5 ' primer or 5 ' primer set, which would be able to match every mouse Vh gene. We were interested in finding oligonucleotides that could be used as pri

  13. Gene duplication models for directed networks with limits on growth

    Science.gov (United States)

    Enemark, Jakob; Sneppen, Kim

    2007-11-01

    Background: Duplication of genes is important for evolution of molecular networks. Many authors have therefore considered gene duplication as a driving force in shaping the topology of molecular networks. In particular it has been noted that growth via duplication would act as an implicit means of preferential attachment, and thereby provide the observed broad degree distributions of molecular networks. Results: We extend current models of gene duplication and rewiring by including directions and the fact that molecular networks are not a result of unidirectional growth. We introduce upstream sites and downstream shapes to quantify potential links during duplication and rewiring. We find that this in itself generates the observed scaling of transcription factors for genome sites in prokaryotes. The dynamical model can generate a scale-free degree distribution, p(k)\\propto 1/k^{\\gamma } , with exponent γ = 1 in the non-growing case, and with γ>1 when the network is growing. Conclusions: We find that duplication of genes followed by substantial recombination of upstream regions could generate features of genetic regulatory networks. Our steady state degree distribution is however too broad to be consistent with data, thereby suggesting that selective pruning acts as a main additional constraint on duplicated genes. Our analysis shows that gene duplication can only be a main cause for the observed broad degree distributions if there are also substantial recombinations between upstream regions of genes.

  14. Media-specific rate allocation in heterogeneous wireless networks

    Institute of Scientific and Technical Information of China (English)

    JURCA Dan; FROSSARD Pascal

    2006-01-01

    We address the problem of joint path selection and rate allocation in multipath wireless streaming, in order to optimize a media specific quality of service. We leverage on the existence of multiple parallel wireless services, in order to enhance the received video quality at a wireless client. An optimization problem is proposed, aimed at minimizing a video distortion metric based on sequence-dependent parameters, and transmission channel characteristics, for a given wireless network infrastructure.Even if joint optimal path selection and rate allocation is in general an NP complete problem, an in-depth analysis of the media distortion evolution allows defining a low complexity optimal streaming strategy, under reasonable network assumptions. In particular, we show that a greedy allocation of rates along paths with increasing error probability leads to an optimal solution. We argue that a network path should not be chosen for transmission, unless all other available paths with lower error probability have been chosen. Moreover, the chosen paths should be used at their maximum end-to-end bandwidth. These results are demonstrated for both independent network paths, and non-disjoint channel segments, in generic network topologies. Simulation results showed that the optimal rate allocation carefully trades off total encoding/transmission rate, with the end-to-end transmission error probability and the number of chosen paths. In many cases, the optimal rate allocation provides more than 20% improvement in received video quality, compared to heuristic-based algorithms.

  15. 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.

  16. An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction.

    Science.gov (United States)

    Piro, Rosario Michael; Ala, Ugo; Molineris, Ivan; Grassi, Elena; Bracco, Chiara; Perego, Gian Paolo; Provero, Paolo; Di Cunto, Ferdinando

    2011-11-01

    Gene coexpression relationships that are phylogenetically conserved between human and mouse have been shown to provide important clues about gene function that can be efficiently used to identify promising candidate genes for human hereditary disorders. In the past, such approaches have considered mostly generic gene expression profiles that cover multiple tissues and organs. The individual genes of multicellular organisms, however, can participate in different transcriptional programs, operating at scales as different as single-cell types, tissues, organs, body regions or the entire organism. Therefore, systematic analysis of tissue-specific coexpression could be, in principle, a very powerful strategy to dissect those functional relationships among genes that emerge only in particular tissues or organs. In this report, we show that, in fact, conserved coexpression as determined from tissue-specific and condition-specific data sets can predict many functional relationships that are not detected by analyzing heterogeneous microarray data sets. More importantly, we find that, when combined with disease networks, the simultaneous use of both generic (multi-tissue) and tissue-specific conserved coexpression allows a more efficient prediction of human disease genes than the use of generic conserved coexpression alone. Using this strategy, we were able to identify high-probability candidates for 238 orphan disease loci. We provide proof of concept that this combined use of generic and tissue-specific conserved coexpression can be very useful to prioritize the mutational candidates obtained from deep-sequencing projects, even in the case of genetic disorders as heterogeneous as XLMR.

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

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    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.

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

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    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.

  19. NetDiff - Bayesian model selection for differential gene regulatory network inference.

    Science.gov (United States)

    Thorne, Thomas

    2016-12-16

    Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation.

  20. Discovering cancer genes by integrating network and functional properties

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    Davis David P

    2009-09-01

    Full Text Available Abstract Background Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO annotations, to facilitate the identification of cancer genes. Methods Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1. Results Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1. Conclusion Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.

  1. Housekeeping and tissue-specific genes in mouse tissues

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    St-Amand Jonny

    2007-05-01

    Full Text Available Abstract Background This study aims to characterize the housekeeping and tissue-specific genes in 15 mouse tissues by using the serial analysis of gene expression (SAGE strategy which indicates the relative level of expression for each transcript matched to the tag. Results Here, we identified constantly expressed housekeeping genes, such as eukaryotic translation elongation factor 2, which is expressed in all tissues without significant difference in expression levels. Moreover, most of these genes were not regulated by experimental conditions such as steroid hormones, adrenalectomy and gonadectomy. In addition, we report previously postulated housekeeping genes such as peptidyl-prolyl cis-trans isomerase A, glyceraldehyde-3-phosphate dehydrogenase and beta-actin, which are expressed in all the tissues, but with significant difference in their expression levels. We have also identified genes uniquely detected in each of the 15 tissues and other tissues from public databases. Conclusion These identified housekeeping genes could represent appropriate controls for RT-PCR and northern blot when comparing the expression levels of genes in several tissues. The results reveal several tissue-specific genes highly expressed in testis and pituitary gland. Furthermore, the main function of tissue-specific genes expressed in liver, lung and bone is the cell defence, whereas several keratins involved in cell structure function are exclusively detected in skin and vagina. The results from this study can be used for example to target a tissue for agent delivering by using the promoter of tissue-specific genes. Moreover, this study could be used as basis for further researches on physiology and pathology of these tissues.

  2. Effective Boolean dynamics analysis to identify functionally important genes in large-scale signaling networks.

    Science.gov (United States)

    Trinh, Hung-Cuong; Kwon, Yung-Keun

    2015-11-01

    Efficiently identifying functionally important genes in order to understand the minimal requirements of normal cellular development is challenging. To this end, a variety of structural measures have been proposed and their effectiveness has been investigated in recent literature; however, few studies have shown the effectiveness of dynamics-based measures. This led us to investigate a dynamic measure to identify functionally important genes, and the effectiveness of which was verified through application on two large-scale human signaling networks. We specifically consider Boolean sensitivity-based dynamics against an update-rule perturbation (BSU) as a dynamic measure. Through investigations on two large-scale human signaling networks, we found that genes with relatively high BSU values show slower evolutionary rate and higher proportions of essential genes and drug targets than other genes. Gene-ontology analysis showed clear differences between the former and latter groups of genes. Furthermore, we compare the identification accuracies of essential genes and drug targets via BSU and five well-known structural measures. Although BSU did not always show the best performance, it effectively identified the putative set of genes, which is significantly different from the results obtained via the structural measures. Most interestingly, BSU showed the highest synergy effect in identifying the functionally important genes in conjunction with other measures. Our results imply that Boolean-sensitive dynamics can be used as a measure to effectively identify functionally important genes in signaling networks.

  3. fabp4 is central to eight obesity associated genes: a functional gene network-based polymorphic study.

    Science.gov (United States)

    Bag, Susmita; Ramaiah, Sudha; Anbarasu, Anand

    2015-01-07

    Network study on genes and proteins offers functional basics of the complexity of gene and protein, and its interacting partners. The gene fatty acid-binding protein 4 (fabp4) is found to be highly expressed in adipose tissue, and is one of the most abundant proteins in mature adipocytes. Our investigations on functional modules of fabp4 provide useful information on the functional genes interacting with fabp4, their biochemical properties and their regulatory functions. The present study shows that there are eight set of candidate genes: acp1, ext2, insr, lipe, ostf1, sncg, usp15, and vim that are strongly and functionally linked up with fabp4. Gene ontological analysis of network modules of fabp4 provides an explicit idea on the functional aspect of fabp4 and its interacting nodes. The hierarchal mapping on gene ontology indicates gene specific processes and functions as well as their compartmentalization in tissues. The fabp4 along with its interacting genes are involved in lipid metabolic activity and are integrated in multi-cellular processes of tissues and organs. They also have important protein/enzyme binding activity. Our study elucidated disease-associated nsSNP prediction for fabp4 and it is interesting to note that there are four rsID׳s (rs1051231, rs3204631, rs140925685 and rs141169989) with disease allelic variation (T104P, T126P, G27D and G90V respectively). On the whole, our gene network analysis presents a clear insight about the interactions and functions associated with fabp4 gene network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Identification of the two rotavirus genes determining neutralization specificities

    Energy Technology Data Exchange (ETDEWEB)

    Offit, P.A.; Blavat, G.

    1986-01-01

    Bovine rotavirus NCDV and simian rotavirus SA-11 represent two distinct rotavirus serotypes. A genetic approach was used to determine which viral gene segments segregated with serotype-specific viral neutralization. There were 16 reassortant rotarviruses derived by coinfection of MA-104 cells in vitro with the SA-11 and NCDV strains. The parental origin of reassortant rotavirus double-stranded RNA segments was determined by gene segment mobility in polyacrylamide gels and by hybridization with radioactively labeled parental viral transcripts. The authors found that two rotavirus gene segments found previously to code for outer capsid proteins vp3 and vp7 cosegreated with virus neutralization specificities.

  5. Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.

    Science.gov (United States)

    Pacheco, Maria Pires; John, Elisabeth; Kaoma, Tony; Heinäniemi, Merja; Nicot, Nathalie; Vallar, Laurent; Bueb, Jean-Luc; Sinkkonen, Lasse; Sauter, Thomas

    2015-10-19

    The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic

  6. The cell specificity of gene expression in the response to heat stress in corals.

    Science.gov (United States)

    Traylor-Knowles, N; Rose, N H; Palumbi, S R

    2017-03-02

    Previous transcriptional studies in heat stressed corals have shown that many genes are responsive to generalized heat stress whereas the expression patterns of specific gene networks after heat stress show strong correlations with variation in bleaching outcomes. However, where these specific genes are expressed is unknown. Here we employed in situ hybridization to identify patterns of spatial gene expression of genes previously predicted to be involved in general stress response and bleaching. We found that Tumor Necrosis Factor Receptors (TNFRs), known to be strong responders to heat stress, were not expressed in gastrodermal symbiont-containing cells but were widely expressed in specific cells of the epidermal layer. The transcription factors AP-1 and FosB implicated as early signals of heat stress and were widely expressed throughout the oral gastrodermis and epidermis. By contrast, a G-protein coupled receptor gene (GPCR), and a fructose bisphosphate aldolase C gene (Aldolase), previously implicated in bleaching, was expressed in symbiont containing gastrodermal cells, and in epidermal tissue. Finally, Chordin-like/Kielin (Chordin-like) a gene highly correlated to bleaching was expressed solely in the oral gastrodermis. From this study we confirm that heat responsive genes occur widely in coral tissues outside of symbiont containing cells, and that gene expression in response to heat stress that causes bleaching does not signal by itself that a gene is expressed in the symbiotic cells where bleaching occurs. Joint information about expression patterns in response to heat and cell specificity will allow greater dissection of the regulatory pathways and specific cell reactions that lead to coral bleaching.

  7. Gene Regulatory Network Reconstruction Using Conditional Mutual Information

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    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.

  8. Cancer specificity of promoters of the genes controlling cell proliferation.

    Science.gov (United States)

    Kashkin, Kirill; Chernov, Igor; Stukacheva, Elena; Monastyrskaya, Galina; Uspenskaya, Natalya; Kopantzev, Eugene; Sverdlov, Eugene

    2015-02-01

    Violation of proliferation control is a common feature of cancer cells. We put forward the hypothesis that promoters of genes involved in the control of cell proliferation should possess intrinsic cancer specific activity. We cloned promoter regions of CDC6, POLD1, CKS1B, MCM2, and PLK1 genes into pGL3 reporter vector and studied their ability to drive heterologous gene expression in transfected cancer cells of different origin and in normal human fibroblasts. Each promoter was cloned in short (335-800 bp) and long (up to 2.3 kb) variants to cover probable location of core and whole promoter regulatory elements. Cloned promoters were significantly more active in cancer cells than in normal fibroblasts that may indicate their cancer specificity. Both versions of CDC6 promoters were shown to be most active while the activities of others were close to that of BIRC5 gene (survivin) gene promoter. Long and short variants of each cloned promoter demonstrated very similar cancer specificity with the exception of PLK1-long promoter that was substantially more specific than its short variant and other promoters under study. The data indicate that most of the important cis-regulatory transcription elements responsible for intrinsic cancer specificity are located in short variants of the promoters under study. CDC6 short promoter may serve as a promising candidate for transcription targeted cancer gene therapy.

  9. Nucleus- and cell-specific gene expression in monkey thalamus.

    Science.gov (United States)

    Murray, Karl D; Choudary, Prabhakara V; Jones, Edward G

    2007-02-06

    Nuclei of the mammalian thalamus are aggregations of neurons with unique architectures and input-output connections, yet the molecular determinants of their organizational specificity remain unknown. By comparing expression profiles of thalamus and cerebral cortex in adult rhesus monkeys, we identified transcripts that are unique to dorsal thalamus or to individual nuclei within it. Real-time quantitative PCR and in situ hybridization analyses confirmed the findings. Expression profiling of individual nuclei microdissected from the dorsal thalamus revealed additional subsets of nucleus-specific genes. Functional annotation using Gene Ontology (GO) vocabulary and Ingenuity Pathways Analysis revealed overrepresentation of GO categories related to development, morphogenesis, cell-cell interactions, and extracellular matrix within the thalamus- and nucleus-specific genes, many involved in the Wnt signaling pathway. Examples included the transcription factor TCF7L2, localized exclusively to excitatory neurons; a calmodulin-binding protein PCP4; the bone extracellular matrix molecules SPP1 and SPARC; and other genes involved in axon outgrowth and cell matrix interactions. Other nucleus-specific genes such as CBLN1 are involved in synaptogenesis. The genes identified likely underlie nuclear specification, cell phenotype, and connectivity during development and their maintenance in the adult thalamus.

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

    Directory of Open Access Journals (Sweden)

    Eddy Edward M

    2007-07-01

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

  11. Gene regulatory network interactions in sea urchin endomesoderm induction.

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    Aditya J Sethi

    2009-02-01

    Full Text Available A major goal of contemporary studies of embryonic development is to understand large sets of regulatory changes that accompany the phenomenon of embryonic induction. The highly resolved sea urchin pregastrular endomesoderm-gene regulatory network (EM-GRN provides a unique framework to study the global regulatory interactions underlying endomesoderm induction. Vegetal micromeres of the sea urchin embryo constitute a classic endomesoderm signaling center, whose potential to induce archenteron formation from presumptive ectoderm was demonstrated almost a century ago. In this work, we ectopically activate the primary mesenchyme cell-GRN (PMC-GRN that operates in micromere progeny by misexpressing the micromere determinant Pmar1 and identify the responding EM-GRN that is induced in animal blastomeres. Using localized loss-of -function analyses in conjunction with expression of endo16, the molecular definition of micromere-dependent endomesoderm specification, we show that the TGFbeta cytokine, ActivinB, is an essential component of this induction in blastomeres that emit this signal, as well as in cells that respond to it. We report that normal pregastrular endomesoderm specification requires activation of the Pmar1-inducible subset of the EM-GRN by the same cytokine, strongly suggesting that early micromere-mediated endomesoderm specification, which regulates timely gastrulation in the sea urchin embryo, is also ActivinB dependent. This study unexpectedly uncovers the existence of an additional uncharacterized micromere signal to endomesoderm progenitors, significantly revising existing models. In one of the first network-level characterizations of an intercellular inductive phenomenon, we describe an important in vivo model of the requirement of ActivinB signaling in the earliest steps of embryonic endomesoderm progenitor specification.

  12. Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

    Science.gov (United States)

    Martin, O. C.; Krzywicki, A.; Zagorski, M.

    2016-07-01

    Living cells can maintain their internal states, react to changing environments, grow, differentiate, divide, etc. All these processes are tightly controlled by what can be called a regulatory program. The logic of the underlying control can sometimes be guessed at by examining the network of influences amongst genetic components. Some associated gene regulatory networks have been studied in prokaryotes and eukaryotes, unveiling various structural features ranging from broad distributions of out-degrees to recurrent "motifs", that is small subgraphs having a specific pattern of interactions. To understand what factors may be driving such structuring, a number of groups have introduced frameworks to model the dynamics of gene regulatory networks. In that context, we review here such in silico approaches and show how selection for phenotypes, i.e., network function, can shape network structure.

  13. Frequency-specific network topologies in the resting human brain

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    Shuntaro eSasai

    2014-12-01

    Full Text Available A community is a set of nodes with dense inter-connections, while there are sparse connections between different communities. A hub is a highly connected node with high centrality. It has been shown that both communities and hubs exist simultaneously in the brain’s functional connectivity network, as estimated by correlations among low-frequency spontaneous fluctuations in functional magnetic resonance imaging (fMRI signal changes (0.01–0.10 Hz. This indicates that the brain has a spatial organization that promotes both segregation and integration of information. Here, we demonstrate that frequency-specific network topologies that characterize segregation and integration also exist within this frequency range. In investigating the coherence spectrum among 87 brain regions, we found that two frequency bands, 0.01–0.03 Hz (very low frequency [VLF] band and 0.07–0.09 Hz (low frequency [LF] band, mainly contributed to functional connectivity. Comparing graph theoretical indices for the VLF and LF bands revealed that the network in the former had a higher capacity for information segregation between identified communities than the latter. Hubs in the VLF band were mainly located within the anterior cingulate cortices, whereas those in the LF band were located in the posterior cingulate cortices and thalamus. Thus, depending on the timescale of brain activity, at least two distinct network topologies contributed to information segregation and integration. This suggests that the brain intrinsically has timescale-dependent functional organizations.

  14. Molecular basis for gene-specific transactivation by nuclear receptors

    DEFF Research Database (Denmark)

    Jørgensen, Mads Aagaard; Siersbæk, Rasmus; Mandrup, Susanne

    2010-01-01

    most likely be accounted for by mechanisms involving receptor-specific interactions with DNA as well as receptor-specific interactions with protein complexes binding to adjacent and distant DNA sequences. Here, we review key molecular aspects of transactivation by NRs with special emphasis......Nuclear receptors (NRs) are key transcriptional regulators of metazoan physiology and metabolism. Different NRs bind to similar or even identical core response elements; however, they regulate transcription in a highly receptor- and gene-specific manner. These differences in gene activation can...... on the recent advances in the molecular mechanisms responsible for receptor- and gene-specific transcriptional activation. This article is part of a Special Issue entitled: Translating nuclear receptors from health to disease....

  15. Saliva microbiota carry caries-specific functional gene signatures.

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    Fang Yang

    Full Text Available Human saliva microbiota is phylogenetically divergent among host individuals yet their roles in health and disease are poorly appreciated. We employed a microbial functional gene microarray, HuMiChip 1.0, to reconstruct the global functional profiles of human saliva microbiota from ten healthy and ten caries-active adults. Saliva microbiota in the pilot population featured a vast diversity of functional genes. No significant distinction in gene number or diversity indices was observed between healthy and caries-active microbiota. However, co-presence network analysis of functional genes revealed that caries-active microbiota was more divergent in non-core genes than healthy microbiota, despite both groups exhibited a similar degree of conservation at their respective core genes. Furthermore, functional gene structure of saliva microbiota could potentially distinguish caries-active patients from healthy hosts. Microbial functions such as Diaminopimelate epimerase, Prephenate dehydrogenase, Pyruvate-formate lyase and N-acetylmuramoyl-L-alanine amidase were significantly linked to caries. Therefore, saliva microbiota carried disease-associated functional signatures, which could be potentially exploited for caries diagnosis.

  16. Gene regulation: hacking the network on a sugar high.

    Science.gov (United States)

    Ellis, Tom; Wang, Xiao; Collins, James J

    2008-04-11

    In a recent issue of Molecular Cell, Kaplan et al. (2008) determine the input functions for 19 E. coli sugar-utilization genes by using a two-dimensional high-throughput approach. The resulting input-function map reveals that gene network regulation follows non-Boolean, and often nonmonotonic, logic.

  17. General approach for in vivo recovery of cell type-specific effector gene sets.

    Science.gov (United States)

    Barsi, Julius C; Tu, Qiang; Davidson, Eric H

    2014-05-01

    Differentially expressed, cell type-specific effector gene sets hold the key to multiple important problems in biology, from theoretical aspects of developmental gene regulatory networks (GRNs) to various practical applications. Although individual cell types of interest have been recovered by various methods and analyzed, systematic recovery of multiple cell type-specific gene sets from whole developing organisms has remained problematic. Here we describe a general methodology using the sea urchin embryo, a material of choice because of the large-scale GRNs already solved for this model system. This method utilizes the regulatory states expressed by given cells of the embryo to define cell type and includes a fluorescence activated cell sorting (FACS) procedure that results in no perturbation of transcript representation. We have extensively validated the method by spatial and qualitative analyses of the transcriptome expressed in isolated embryonic skeletogenic cells and as a consequence, generated a prototypical cell type-specific transcriptome database.

  18. Mutation of senataxin alters disease-specific transcriptional networks in patients with ataxia with oculomotor apraxia type 2.

    Science.gov (United States)

    Fogel, Brent L; Cho, Ellen; Wahnich, Amanda; Gao, Fuying; Becherel, Olivier J; Wang, Xizhe; Fike, Francesca; Chen, Leslie; Criscuolo, Chiara; De Michele, Giuseppe; Filla, Alessandro; Collins, Abigail; Hahn, Angelika F; Gatti, Richard A; Konopka, Genevieve; Perlman, Susan; Lavin, Martin F; Geschwind, Daniel H; Coppola, Giovanni

    2014-09-15

    Senataxin, encoded by the SETX gene, contributes to multiple aspects of gene expression, including transcription and RNA processing. Mutations in SETX cause the recessive disorder ataxia with oculomotor apraxia type 2 (AOA2) and a dominant juvenile form of amyotrophic lateral sclerosis (ALS4). To assess the functional role of senataxin in disease, we examined differential gene expression in AOA2 patient fibroblasts, identifying a core set of genes showing altered expression by microarray and RNA-sequencing. To determine whether AOA2 and ALS4 mutations differentially affect gene expression, we overexpressed disease-specific SETX mutations in senataxin-haploinsufficient fibroblasts and observed changes in distinct sets of genes. This implicates mutation-specific alterations of senataxin function in disease pathogenesis and provides a novel example of allelic neurogenetic disorders with differing gene expression profiles. Weighted gene co-expression network analysis (WGCNA) demonstrated these senataxin-associated genes to be involved in both mutation-specific and shared functional gene networks. To assess this in vivo, we performed gene expression analysis on peripheral blood from members of 12 different AOA2 families and identified an AOA2-specific transcriptional signature. WGCNA identified two gene modules highly enriched for this transcriptional signature in the peripheral blood of all AOA2 patients studied. These modules were disease-specific and preserved in patient fibroblasts and in the cerebellum of Setx knockout mice demonstrating conservation across species and cell types, including neurons. These results identify novel genes and cellular pathways related to senataxin function in normal and disease states, and implicate alterations in gene expression as underlying the phenotypic differences between AOA2 and ALS4.

  19. 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.

  20. Lineage-specific expansion of IFIT gene family: an insight into coevolution with IFN gene family.

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    Ying Liu

    Full Text Available In mammals, IFIT (Interferon [IFN]-induced proteins with Tetratricopeptide Repeat [TPR] motifs family genes are involved in many cellular and viral processes, which are tightly related to mammalian IFN response. However, little is known about non-mammalian IFIT genes. In the present study, IFIT genes are identified in the genome databases from the jawed vertebrates including the cartilaginous elephant shark but not from non-vertebrates such as lancelet, sea squirt and acorn worm, suggesting that IFIT gene family originates from a vertebrate ancestor about 450 million years ago. IFIT family genes show conserved gene structure and gene arrangements. Phylogenetic analyses reveal that this gene family has expanded through lineage-specific and species-specific gene duplication. Interestingly, IFN gene family seem to share a common ancestor and a similar evolutionary mechanism; the function link of IFIT genes to IFN response is present early since the origin of both gene families, as evidenced by the finding that zebrafish IFIT genes are upregulated by fish IFNs, poly(I:C and two transcription factors IRF3/IRF7, likely via the IFN-stimulated response elements (ISRE within the promoters of vertebrate IFIT family genes. These coevolution features creates functional association of both family genes to fulfill a common biological process, which is likely selected by viral infection during evolution of vertebrates. Our results are helpful for understanding of evolution of vertebrate IFN system.

  1. 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.

  2. SPECIFICITY OF COMMUNICATION DOCTOR – PATIENT, ONLINE, THROUGH SOCIAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Florin-Alexandru LUCA

    2014-12-01

    Full Text Available A survey in 2013 of Division Market Research Group Rentrop Straton Romanian-German - "state services and private clinics. Comparative Analysis ", highlights the fact that into urban population in Romania, the internet is the most popular source of information on health services. According to the survey, 81.9% of respondents use the Internet to find out about the best doctors available to them. Preferences of respondents to the online environment as the main source of information translates into ease of access and the possibility of interaction, but also trust recommendations based on personal experiences, recommendations can be found through discussion forums, mostly or comments left on pages on social networking sites. In the present study we try to emphasize the specificity of communication physician - patient online through social networks.

  3. Fast algorithm for the reconciliation of gene trees and LGT networks.

    Science.gov (United States)

    Scornavacca, Celine; Mayol, Joan Carles Pons; Cardona, Gabriel

    2017-04-07

    In phylogenomics, reconciliations aim at explaining the discrepancies between the evolutionary histories of genes and species. Several reconciliation models are available when the evolution of the species of interest is modelled via phylogenetic trees; the most commonly used are the DL model, accounting for duplications and losses in gene evolution and yielding polynomially-solvable problems, and the DTL model, which also accounts for gene transfers and implies NP-hard problems. However, when dealing with non-tree-like evolutionary events such as hybridisations, phylogenetic networks - and not phylogenetic trees - should be used to model species evolution. Reconciliation models involving phylogenetic networks are still at their early days. In this paper, we propose a new reconciliation model in which the evolution of species is modelled by a special kind of phylogenetic networks - the LGT networks. Our model considers duplications, losses and transfers of genes, but restricts transfers to happen through some specific arcs of the network, called secondary arcs. Moreover, we provide a polynomial algorithm to compute the most parsimonious reconciliation between a gene tree and an LGT network under this model. Our method, when combined with quartet decomposition methods to detect putative "highways" of transfers, permits to refine their analyses by allowing to examine the two possible directions of a highway and even consider combinations of highways.

  4. NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

    Science.gov (United States)

    Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

    2014-01-01

    One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

  5. NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

    Directory of Open Access Journals (Sweden)

    Joeri Ruyssinck

    Full Text Available One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made

  6. 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.

  7. Lab-specific gene expression signatures in pluripotent stem cells.

    Science.gov (United States)

    Newman, Aaron M; Cooper, James B

    2010-08-06

    Pluripotent stem cells derived from both embryonic and reprogrammed somatic cells have significant potential for human regenerative medicine. Despite similarities in developmental potential, however, several groups have found fundamental differences between embryonic stem cell (ESC) and induced-pluripotent stem cell (iPSC) lines that may have important implications for iPSC-based medical therapies. Using an unsupervised clustering algorithm, we further studied the genetic homogeneity of iPSC and ESC lines by reanalyzing microarray gene expression data from seven different laboratories. Unexpectedly, this analysis revealed a strong correlation between gene expression signatures and specific laboratories in both ESC and iPSC lines. Nearly one-third of the genes with lab-specific expression signatures are also differentially expressed between ESCs and iPSCs. These data are consistent with the hypothesis that in vitro microenvironmental context differentially impacts the gene expression signatures of both iPSCs and ESCs.

  8. The incorporation of epigenetics in artificial gene regulatory networks.

    Science.gov (United States)

    Turner, Alexander P; Lones, Michael A; Fuente, Luis A; Stepney, Susan; Caves, Leo S D; Tyrrell, Andy M

    2013-05-01

    Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.

  9. The synthetic genetic interaction network reveals small molecules that target specific pathways in Sacchromyces cerevisiae.

    Science.gov (United States)

    Tamble, Craig M; St Onge, Robert P; Giaever, Guri; Nislow, Corey; Williams, Alexander G; Stuart, Joshua M; Lokey, R Scott

    2011-06-01

    High-throughput elucidation of synthetic genetic interactions (SGIs) has contributed to a systems-level understanding of genetic robustness and fault-tolerance encoded in the genome. Pathway targets of various compounds have been predicted by comparing chemical-genetic synthetic interactions to a network of SGIs. We demonstrate that the SGI network can also be used in a powerful reverse pathway-to-drug approach for identifying compounds that target specific pathways of interest. Using the SGI network, the method identifies an indicator gene that may serve as a good candidate for screening a library of compounds. The indicator gene is selected so that compounds found to produce sensitivity in mutants deleted for the indicator gene are likely to abrogate the target pathway. We tested the utility of the SGI network for pathway-to-drug discovery using the DNA damage checkpoint as the target pathway. An analysis of the compendium of synthetic lethal interactions in yeast showed that superoxide dismutase 1 (SOD1) has significant SGI connectivity with a large subset of DNA damage checkpoint and repair (DDCR) genes in Saccharomyces cerevisiae, and minimal SGIs with non-DDCR genes. We screened a sod1Δ strain against three National Cancer Institute (NCI) compound libraries using a soft agar high-throughput halo assay. Fifteen compounds out of ∼3100 screened showed selective toxicity toward sod1Δ relative to the isogenic wild type (wt) strain. One of these, 1A08, caused a transient increase in growth in the presence of sublethal doses of DNA damaging agents, suggesting that 1A08 inhibits DDCR signaling in yeast. Genome-wide screening of 1A08 against the library of viable homozygous deletion mutants further supported DDCR as the relevant targeted pathway of 1A08. When assayed in human HCT-116 colorectal cancer cells, 1A08 caused DNA-damage resistant DNA synthesis and blocked the DNA-damage checkpoint selectively in S-phase.

  10. Quality assurance of the gene ontology using abstraction networks.

    Science.gov (United States)

    Ochs, Christopher; Perl, Yehoshua; Halper, Michael; Geller, James; Lomax, Jane

    2016-06-01

    The gene ontology (GO) is used extensively in the field of genomics. Like other large and complex ontologies, quality assurance (QA) efforts for GO's content can be laborious and time consuming. Abstraction networks (AbNs) are summarization networks that reveal and highlight high-level structural and hierarchical aggregation patterns in an ontology. They have been shown to successfully support QA work in the context of various ontologies. Two kinds of AbNs, called the area taxonomy and the partial-area taxonomy, are developed for GO hierarchies and derived specifically for the biological process (BP) hierarchy. Within this framework, several QA heuristics, based on the identification of groups of anomalous terms which exhibit certain taxonomy-defined characteristics, are introduced. Such groups are expected to have higher error rates when compared to other terms. Thus, by focusing QA efforts on anomalous terms one would expect to find relatively more erroneous content. By automatically identifying these potential problem areas within an ontology, time and effort will be saved during manual reviews of GO's content. BP is used as a testbed, with samples of three kinds of anomalous BP terms chosen for a taxonomy-based QA review. Additional heuristics for QA are demonstrated. From the results of this QA effort, it is observed that different kinds of inconsistencies in the modeling of GO can be exposed with the use of the proposed heuristics. For comparison, the results of QA work on a sample of terms chosen from GO's general population are presented.

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

    Science.gov (United States)

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

    2014-01-01

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

  12. Injury, inflammation and the emergence of human specific genes

    Science.gov (United States)

    2016-07-12

    indistinguishable.6 Interestingly, just as we noted the expression of human -specific genes in human immune cells (Table 1), Long and colleagues noted the wide...nervous system, it presumably alters a7AChR activities on human cognition and memory . In other examples, the human antimicrobial defensins are highly...genes in circulating and resident human immune cells can be studied in mice after the transplantation and engraft- ment of human hemato-lymphoid immune

  13. A gene regulatory network for root epidermis cell differentiation in Arabidopsis.

    Directory of Open Access Journals (Sweden)

    Angela Bruex

    2012-01-01

    Full Text Available The root epidermis of Arabidopsis provides an exceptional model for studying the molecular basis of cell fate and differentiation. To obtain a systems-level view of root epidermal cell differentiation, we used a genome-wide transcriptome approach to define and organize a large set of genes into a transcriptional regulatory network. Using cell fate mutants that produce only one of the two epidermal cell types, together with fluorescence-activated cell-sorting to preferentially analyze the root epidermis transcriptome, we identified 1,582 genes differentially expressed in the root-hair or non-hair cell types, including a set of 208 "core" root epidermal genes. The organization of the core genes into a network was accomplished by using 17 distinct root epidermis mutants and 2 hormone treatments to perturb the system and assess the effects on each gene's transcript accumulation. In addition, temporal gene expression information from a developmental time series dataset and predicted gene associations derived from a Bayesian modeling approach were used to aid the positioning of genes within the network. Further, a detailed functional analysis of likely bHLH regulatory genes within the network, including MYC1, bHLH54, bHLH66, and bHLH82, showed that three distinct subfamilies of bHLH proteins participate in root epidermis development in a stage-specific manner. The integration of genetic, genomic, and computational analyses provides a new view of the composition, architecture, and logic of the root epidermal transcriptional network, and it demonstrates the utility of a comprehensive systems approach for dissecting a complex regulatory network.

  14. Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

    Institute of Scientific and Technical Information of China (English)

    GAO Lei; LI Xia; GUO Zheng; ZHU MingZhu; LI YanHui; RAO ShaoQi

    2007-01-01

    GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to "biology process" by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

  15. Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

  16. 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.

  17. Cell-specific DNA methylation patterns of retina-specific genes.

    Directory of Open Access Journals (Sweden)

    Shannath L Merbs

    Full Text Available Many studies have demonstrated that epigenetic mechanisms are important in the regulation of gene expression during embryogenesis, gametogenesis, and other forms of tissue-specific gene regulation. We sought to explore the possible role of epigenetics, specifically DNA methylation, in the establishment and maintenance of cell type-restricted gene expression in the retina. To assess the relationship between DNA methylation status and expression level of retinal genes, bisulfite sequence analysis of the 1000 bp region around the transcription start sites (TSS of representative rod and cone photoreceptor-specific genes and gene expression analysis were performed in the WERI and Y79 human retinoblastoma cell lines. Next, the homologous genes in mouse were bisulfite sequenced in the retina and in non-expressing tissues. Finally, bisulfite sequencing was performed on isolated photoreceptor and non-photoreceptor retinal cells isolated by laser capture microdissection. Differential methylation of rhodopsin (RHO, retinal binding protein 3 (RBP3, IRBP cone opsin, short-wave-sensitive (OPN1SW, cone opsin, middle-wave-sensitive (OPN1MW, and cone opsin, long-wave-sensitive (OPN1LW was found in the retinoblastoma cell lines that inversely correlated with gene expression levels. Similarly, we found tissue-specific hypomethylation of the promoter region of Rho and Rbp3 in mouse retina as compared to non-expressing tissues, and also observed hypomethylation of retinal-expressed microRNAs. The Rho and Rbp3 promoter regions were unmethylated in expressing photoreceptor cells and methylated in non-expressing, non-photoreceptor cells from the inner nuclear layer. A third regional hypomethylation pattern of photoreceptor-specific genes was seen in a subpopulation of non-expressing photoreceptors (Rho in cones from the Nrl -/- mouse and Opn1sw in rods. These results demonstrate that a number of photoreceptor-specific genes have cell-specific differential DNA

  18. A meta-analysis based method for prioritizing candidate genes involved in a pre-specific function

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    Jingjing Zhai

    2016-12-01

    Full Text Available The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed for Arabidopsis thaliana and many non-model plant species. Nevertheless, these network-based gene prioritization algorithms have encountered several problems; one in particular is that of unsatisfactory prediction accuracy due to limited network coverage, varying link quality, and/or uncertain network connectivity. Thus a model that integrates complementary biological data may be expected to increase the prediction accuracy of gene prioritization. Towards this goal, we developed a novel gene prioritization method named RafSee, to rank candidate genes using a random forest algorithm that integrates sequence, evolutionary, and epigenetic features of plants. Subsequently, we proposed an integrative approach named RAP (Rank Aggregation-based data fusion for gene Prioritization, in which an order statistics-based meta-analysis was used to aggregate the rank of the network-based gene prioritization method and RafSee, for accurately prioritizing candidate genes involved in a pre-specific biological function. Finally, we showcased the utility of RAP by prioritizing 380 flowering-time genes in Arabidopsis. The ‘leave-one-out’ cross-validation experiment showed that RafSee could work as a complement to a current state-of-art network-based gene prioritization system (AraNet v2. Moreover, RAP ranked 53.68% (204/380 flowering-time genes higher than AraNet v2, resulting in an 39.46% improvement in term of the first quartile rank. Further evaluations also showed that RAP was effective in prioritizing genes-related to different abiotic stresses. To enhance the usability of RAP for Arabidopsis and non-model plant species, an R package implementing the method is freely available at http://bioinfo.nwafu.edu.cn/software.

  19. Heritable and lineage-specific gene knockdown in zebrafish embryo.

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    Mei Dong

    Full Text Available BACKGROUND: Reduced expression of developmentally important genes and tumor suppressors due to haploinsufficiency or epigenetic suppression has been shown to contribute to the pathogenesis of various malignancies. However, methodology that allows spatio-temporally knockdown of gene expression in various model organisms such as zebrafish has not been well established, which largely limits the potential of zebrafish as a vertebrate model of human malignant disorders. PRINCIPAL FINDING: Here, we report that multiple copies of small hairpin RNA (shRNA are expressed from a single transcript that mimics the natural microRNA-30e precursor (mir-shRNA. The mir-shRNA, when microinjected into zebrafish embryos, induced an efficient knockdown of two developmentally essential genes chordin and alpha-catenin in a dose-controllable fashion. Furthermore, we designed a novel cassette vector to simultaneously express an intronic mir-shRNA and a chimeric red fluorescent protein driven by lineage-specific promoter, which efficiently reduced the expression of a chromosomally integrated reporter gene and an endogenously expressed gata-1 gene in the developing erythroid progenitors and hemangioblasts, respectively. SIGNIFICANCE: This methodology provides an invaluable tool to knockdown developmental important genes in a tissue-specific manner or to establish animal models, in which the gene dosage is critically important in the pathogenesis of human disorders. The strategy should be also applicable to other model organisms.

  20. Global Identification of Genes Specific for Rice Meiosis.

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    Zhang, Bingwei; Xu, Meng; Bian, Shiquan; Hou, Lili; Tang, Ding; Li, Yafei; Gu, Minghong; Cheng, Zhukuan; Yu, Hengxiu

    2015-01-01

    The leptotene-zygotene transition is a major step in meiotic progression during which pairing between homologous chromosomes is initiated and double strand breaks occur. OsAM1, a homologue of maize AM1 and Arabidopsis SWI1, encodes a protein with a coiled-coil domain in its central region that is required for the leptotene-zygotene transition during rice meiosis. To gain more insight into the role of OsAM1 in rice meiosis and identify additional meiosis-specific genes, we characterized the transcriptomes of young panicles of Osam1 mutant and wild-type rice plants using RNA-Seq combined with bioinformatic and statistical analyses. As a result, a total of 25,750 and 28,455 genes were expressed in young panicles of wild-type and Osam1 mutant plants, respectively, and 4,400 differentially expressed genes (DEGs; log2 Ratio ≥ 1, FDR ≤ 0.05) were identified. Of these DEGs, four known rice meiosis-specific genes were detected, and 22 new putative meiosis-related genes were found by mapping these DEGs to reference biological pathways in the KEGG database. We identified eight additional well-conserved OsAM1-responsive rice meiotic genes by comparing our RNA-Seq data with known meiotic genes in Arabidopsis and fission yeast.

  1. Solution of the quasispecies model for an arbitrary gene network

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    Tannenbaum, Emmanuel; Shakhnovich, Eugene I.

    2004-08-01

    In this paper, we study the equilibrium behavior of Eigen’s quasispecies equations for an arbitrary gene network. We consider a genome consisting of N genes, so that the full genome sequence σ may be written as σ=σ1σ2⋯σN , where σi are sequences of individual genes. We assume a single fitness peak model for each gene, so that gene i has some “master” sequence σi,0 for which it is functioning. The fitness landscape is then determined by which genes in the genome are functioning and which are not. The equilibrium behavior of this model may be solved in the limit of infinite sequence length. The central result is that, instead of a single error catastrophe, the model exhibits a series of localization to delocalization transitions, which we term an “error cascade.” As the mutation rate is increased, the selective advantage for maintaining functional copies of certain genes in the network disappears, and the population distribution delocalizes over the corresponding sequence spaces. The network goes through a series of such transitions, as more and more genes become inactivated, until eventually delocalization occurs over the entire genome space, resulting in a final error catastrophe. This model provides a criterion for determining the conditions under which certain genes in a genome will lose functionality due to genetic drift. It also provides insight into the response of gene networks to mutagens. In particular, it suggests an approach for determining the relative importance of various genes to the fitness of an organism, in a more accurate manner than the standard “deletion set” method. The results in this paper also have implications for mutational robustness and what C.O. Wilke termed “survival of the flattest.”

  2. Compensatory evolution for a gene deletion is not limited to its immediate functional network

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    Bull JJ

    2009-05-01

    Full Text Available Abstract Background Genetic disruption of an important phenotype should favor compensatory mutations that restore the phenotype. If the genetic basis of the phenotype is modular, with a network of interacting genes whose functions are specific to that phenotype, compensatory mutations are expected among the genes of the affected network. This perspective was tested in the bacteriophage T3 using a genome deleted of its DNA ligase gene, disrupting DNA metabolism. Results In two replicate, long-term adaptations, phage compensatory evolution accommodated the low ligase level provided by the host without reinventing its own ligase. In both lines, fitness increased substantially but remained well below that of the intact genome. Each line accumulated over a dozen compensating mutations during long-term adaptation, and as expected, many of the compensatory changes were within the DNA metabolism network. However, several compensatory changes were outside the network and defy any role in DNA metabolism or biochemical connection to the disruption. In one line, these extra-network changes were essential to the recovery. The genes experiencing compensatory changes were moderately conserved between T3 and its relative T7 (25% diverged, but the involvement of extra-network changes was greater in T3. Conclusion Compensatory evolution was only partly limited to the known functionally interacting partners of the deleted gene. Thus gene interactions contributing to fitness were more extensive than suggested by the functional properties currently ascribed to the genes. Compensatory evolution offers an easy method of discovering genome interactions among specific elements that does not rest on an a priori knowledge of those elements or their interactions.

  3. 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.

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

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    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.

  5. Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context.

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    Azree Nazri

    Full Text Available The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT and Fisher's inverse combined probability test (FICPT; and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR, Maximum Relevance Minimum Redundancy (MRNET, Relevance Network (RN and Bayesian Network (BN. We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.

  6. Disease-aging network reveals significant roles of aging genes in connecting genetic diseases.

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    Wang, Jiguang; Zhang, Shihua; Wang, Yong; Chen, Luonan; Zhang, Xiang-Sun

    2009-09-01

    One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system-based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases.

  7. Artificial neural networks modeling gene-environment interaction

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    Günther Frauke

    2012-05-01

    Full Text Available Abstract Background Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. Results In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. Conclusion Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.

  8. iSLIM: a comprehensive approach to mapping and characterizing gene regulatory networks.

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    Rockel, Sylvie; Geertz, Marcel; Hens, Korneel; Deplancke, Bart; Maerkl, Sebastian J

    2013-02-01

    Mapping gene regulatory networks is a significant challenge in systems biology, yet only a few methods are currently capable of systems-level identification of transcription factors (TFs) that bind a specific regulatory element. We developed a microfluidic method for integrated systems-level interaction mapping of TF-DNA interactions, generating and interrogating an array of 423 full-length Drosophila TFs. With integrated systems-level interaction mapping, it is now possible to rapidly and quantitatively map gene regulatory networks of higher eukaryotes.

  9. Convolutional Neural Networks for patient-specific ECG classification.

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    Kiranyaz, Serkan; Ince, Turker; Hamila, Ridha; Gabbouj, Moncef

    2015-01-01

    We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).

  10. A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.

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    Warren D Anderson

    2017-07-01

    Full Text Available Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension. We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

  11. Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes

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    Liu, Xiao-Lin; Ming, Ya-Nan; Zhang, Jing-Yi; Chen, Xiao-Yu; Zeng, Min-De; Mao, Yi-Min

    2017-01-01

    We sought to identify common key regulators and build a gene-metabolite network in different nonalcoholic fatty liver disease (NAFLD) phenotypes. We used a high-fat diet (HFD), a methionine-choline-deficient diet (MCDD) and streptozocin (STZ) to establish nonalcoholic fatty liver (NAFL), nonalcoholic steatohepatitis (NASH) and NAFL+type 2 diabetes mellitus (T2DM) in rat models, respectively. Transcriptomics and metabolomics analyses were performed in rat livers and serum. A functional network-based regulation model was constructed using Cytoscape with information derived from transcriptomics and metabolomics. The results revealed that 96 genes, 17 liver metabolites and 4 serum metabolites consistently changed in different NAFLD phenotypes (>2-fold, PGene-metabolite network analysis identified ccl2 and jun as hubs with the largest connections to other genes, which were mainly involved in tumor necrosis factor, P53, nuclear factor-kappa B, chemokine, peroxisome proliferator activated receptor and Toll-like receptor signaling pathways. The specifically regulated genes and metabolites in different NAFLD phenotypes constructed their own networks, which were mainly involved in the lipid and fatty acid metabolism in HFD models, the inflammatory and immune response in MCDD models, and the AMPK signaling pathway and response to insulin in HFD+STZ models. Our study identified networks showing the general and specific characteristics in different NAFLD phenotypes, complementing the genetic and metabolic features in NAFLD with hepatic and extra-hepatic manifestations. PMID:28082742

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

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

    2015-06-01

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

  13. Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

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    Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

    2014-10-01

    To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  14. Global and gene specific DNA methylation changes during zebrafish development

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    DNA methylation is dynamic through the life of an organism. In this study, we measured the global and gene specific DNA methylation changes in zebrafish at different developmental stages. We found that the methylation percentage of cytosines was 11.75 ± 0.96% in 3.3 hour post fertilization (hpf) zeb...

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

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    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.

  16. Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.

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    Han, Hyun Wook; Ohn, Jung Hun; Moon, Jisook; Kim, Ju Han

    2013-11-01

    Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.

  17. Contribution of distinct homeodomain DNA binding specificities to Drosophila embryonic mesodermal cell-specific gene expression programs.

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    Brian W Busser

    Full Text Available Homeodomain (HD proteins are a large family of evolutionarily conserved transcription factors (TFs having diverse developmental functions, often acting within the same cell types, yet many members of this family paradoxically recognize similar DNA sequences. Thus, with multiple family members having the potential to recognize the same DNA sequences in cis-regulatory elements, it is difficult to ascertain the role of an individual HD or a subclass of HDs in mediating a particular developmental function. To investigate this problem, we focused our studies on the Drosophila embryonic mesoderm where HD TFs are required to establish not only segmental identities (such as the Hox TFs, but also tissue and cell fate specification and differentiation (such as the NK-2 HDs, Six HDs and identity HDs (I-HDs. Here we utilized the complete spectrum of DNA binding specificities determined by protein binding microarrays (PBMs for a diverse collection of HDs to modify the nucleotide sequences of numerous mesodermal enhancers to be recognized by either no or a single subclass of HDs, and subsequently assayed the consequences of these changes on enhancer function in transgenic reporter assays. These studies show that individual mesodermal enhancers receive separate transcriptional input from both I-HD and Hox subclasses of HDs. In addition, we demonstrate that enhancers regulating upstream components of the mesodermal regulatory network are targeted by the Six class of HDs. Finally, we establish the necessity of NK-2 HD binding sequences to activate gene expression in multiple mesodermal tissues, supporting a potential role for the NK-2 HD TF Tinman (Tin as a pioneer factor that cooperates with other factors to regulate cell-specific gene expression programs. Collectively, these results underscore the critical role played by HDs of multiple subclasses in inducing the unique genetic programs of individual mesodermal cells, and in coordinating the gene regulatory

  18. Contribution of distinct homeodomain DNA binding specificities to Drosophila embryonic mesodermal cell-specific gene expression programs.

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    Busser, Brian W; Gisselbrecht, Stephen S; Shokri, Leila; Tansey, Terese R; Gamble, Caitlin E; Bulyk, Martha L; Michelson, Alan M

    2013-01-01

    Homeodomain (HD) proteins are a large family of evolutionarily conserved transcription factors (TFs) having diverse developmental functions, often acting within the same cell types, yet many members of this family paradoxically recognize similar DNA sequences. Thus, with multiple family members having the potential to recognize the same DNA sequences in cis-regulatory elements, it is difficult to ascertain the role of an individual HD or a subclass of HDs in mediating a particular developmental function. To investigate this problem, we focused our studies on the Drosophila embryonic mesoderm where HD TFs are required to establish not only segmental identities (such as the Hox TFs), but also tissue and cell fate specification and differentiation (such as the NK-2 HDs, Six HDs and identity HDs (I-HDs)). Here we utilized the complete spectrum of DNA binding specificities determined by protein binding microarrays (PBMs) for a diverse collection of HDs to modify the nucleotide sequences of numerous mesodermal enhancers to be recognized by either no or a single subclass of HDs, and subsequently assayed the consequences of these changes on enhancer function in transgenic reporter assays. These studies show that individual mesodermal enhancers receive separate transcriptional input from both I-HD and Hox subclasses of HDs. In addition, we demonstrate that enhancers regulating upstream components of the mesodermal regulatory network are targeted by the Six class of HDs. Finally, we establish the necessity of NK-2 HD binding sequences to activate gene expression in multiple mesodermal tissues, supporting a potential role for the NK-2 HD TF Tinman (Tin) as a pioneer factor that cooperates with other factors to regulate cell-specific gene expression programs. Collectively, these results underscore the critical role played by HDs of multiple subclasses in inducing the unique genetic programs of individual mesodermal cells, and in coordinating the gene regulatory networks

  19. Genes expressed in specific areas of the human fetal cerebral cortex display distinct patterns of evolution.

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    Nelle Lambert

    Full Text Available The developmental mechanisms through which the cerebral cortex increased in size and complexity during primate evolution are essentially unknown. To uncover genetic networks active in the developing cerebral cortex, we combined three-dimensional reconstruction of human fetal brains at midgestation and whole genome expression profiling. This novel approach enabled transcriptional characterization of neurons from accurately defined cortical regions containing presumptive Broca and Wernicke language areas, as well as surrounding associative areas. We identified hundreds of genes displaying differential expression between the two regions, but no significant difference in gene expression between left and right hemispheres. Validation by qRTPCR and in situ hybridization confirmed the robustness of our approach and revealed novel patterns of area- and layer-specific expression throughout the developing cortex. Genes differentially expressed between cortical areas were significantly associated with fast-evolving non-coding sequences harboring human-specific substitutions that could lead to divergence in their repertoires of transcription factor binding sites. Strikingly, while some of these sequences were accelerated in the human lineage only, many others were accelerated in chimpanzee and/or mouse lineages, indicating that genes important for cortical development may be particularly prone to changes in transcriptional regulation across mammals. Genes differentially expressed between cortical regions were also enriched for transcriptional targets of FoxP2, a key gene for the acquisition of language abilities in humans. Our findings point to a subset of genes with a unique combination of cortical areal expression and evolutionary patterns, suggesting that they play important roles in the transcriptional network underlying human-specific neural traits.

  20. Hypertension and genetic variation in endothelial-specific genes.

    Directory of Open Access Journals (Sweden)

    Erik Larsson

    Full Text Available Genome-wide association (GWA studies usually detect common genetic variants with low-to-medium effect sizes. Many contributing variants are not revealed, since they fail to reach significance after strong correction for multiple comparisons. The WTCCC study for hypertension, for example, failed to identify genome-wide significant associations. We hypothesized that genetic variation in genes expressed specifically in the endothelium may be important for hypertension development. Results from the WTCCC study were combined with previously published gene expression data from mice to specifically investigate SNPs located within endothelial-specific genes, bypassing the requirement for genome-wide significance. Six SNPs from the WTCCC study were selected for independent replication in 5205 hypertensive patients and 5320 population-based controls, and successively in a cohort of 16,537 individuals. A common variant (rs10860812 in the DRAM (damage-regulated autophagy modulator locus showed association with hypertension (P = 0.008 in the replication study. The minor allele (A had a protective effect (OR = 0.93; 95% CI 0.88-0.98 per A-allele, which replicates the association in the WTCCC GWA study. However, a second follow-up, in the larger cohort, failed to reveal an association with blood pressure. We further tested the endothelial-specific genes for co-localization with a panel of newly discovered SNPs from large meta-GWAS on hypertension or blood pressure. There was no significant overlap between those genes and hypertension or blood pressure loci. The result does not support the hypothesis that genetic variation in genes expressed in endothelium plays an important role for hypertension development. Moreover, the discordant association of rs10860812 with blood pressure in the case control study versus the larger Malmö Preventive Project-study highlights the importance of rigorous replication in multiple large independent studies.

  1. Functional annotation of novel lineage-specific genes using co-expression and promoter analysis

    Directory of Open Access Journals (Sweden)

    Loor Juan J

    2010-03-01

    Full Text Available Abstract Background The diversity of placental architectures within and among mammalian orders is believed to be the result of adaptive evolution. Although, the genetic basis for these differences is unknown, some may arise from rapidly diverging and lineage-specific genes. Previously, we identified 91 novel lineage-specific transcripts (LSTs from a cow term-placenta cDNA library, which are excellent candidates for adaptive placental functions acquired by the ruminant lineage. The aim of the present study was to infer functions of previously uncharacterized lineage-specific genes (LSGs using co-expression, promoter, pathway and network analysis. Results Clusters of co-expressed genes preferentially expressed in liver, placenta and thymus were found using 49 previously uncharacterized LSTs as seeds. Over-represented composite transcription factor binding sites (TFBS in promoters of clustered LSGs and known genes were then identified computationally. Functions were inferred for nine previously uncharacterized LSGs using co-expression analysis and pathway analysis tools. Our results predict that these LSGs may function in cell signaling, glycerophospholipid/fatty acid metabolism, protein trafficking, regulatory processes in the nucleus, and processes that initiate parturition and immune system development. Conclusions The placenta is a rich source of lineage-specific genes that function in the adaptive evolution of placental architecture and functions. We have shown that co-expression, promoter, and gene network analyses are useful methods to infer functions of LSGs with heretofore unknown functions. Our results indicate that many LSGs are involved in cellular recognition and developmental processes. Furthermore, they provide guidance for experimental approaches to validate the functions of LSGs and to study their evolution.

  2. Functional annotation of novel lineage-specific genes using co-expression and promoter analysis.

    Science.gov (United States)

    Kumar, Charu G; Everts, Robin E; Loor, Juan J; Lewin, Harris A

    2010-03-09

    The diversity of placental architectures within and among mammalian orders is believed to be the result of adaptive evolution. Although, the genetic basis for these differences is unknown, some may arise from rapidly diverging and lineage-specific genes. Previously, we identified 91 novel lineage-specific transcripts (LSTs) from a cow term-placenta cDNA library, which are excellent candidates for adaptive placental functions acquired by the ruminant lineage. The aim of the present study was to infer functions of previously uncharacterized lineage-specific genes (LSGs) using co-expression, promoter, pathway and network analysis. Clusters of co-expressed genes preferentially expressed in liver, placenta and thymus were found using 49 previously uncharacterized LSTs as seeds. Over-represented composite transcription factor binding sites (TFBS) in promoters of clustered LSGs and known genes were then identified computationally. Functions were inferred for nine previously uncharacterized LSGs using co-expression analysis and pathway analysis tools. Our results predict that these LSGs may function in cell signaling, glycerophospholipid/fatty acid metabolism, protein trafficking, regulatory processes in the nucleus, and processes that initiate parturition and immune system development. The placenta is a rich source of lineage-specific genes that function in the adaptive evolution of placental architecture and functions. We have shown that co-expression, promoter, and gene network analyses are useful methods to infer functions of LSGs with heretofore unknown functions. Our results indicate that many LSGs are involved in cellular recognition and developmental processes. Furthermore, they provide guidance for experimental approaches to validate the functions of LSGs and to study their evolution.

  3. Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates.

    Directory of Open Access Journals (Sweden)

    Daniel Boloc

    Full Text Available Retinitis pigmentosa (RP is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA. The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies.We have built an RP-specific network (RPGeNet by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space.In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates.

  4. Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates

    Science.gov (United States)

    Boloc, Daniel; Castillo-Lara, Sergio; Marfany, Gemma; Gonzàlez-Duarte, Roser; Abril, Josep F.

    2015-01-01

    Background Retinitis pigmentosa (RP) is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA). The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies. Methodology We have built an RP-specific network (RPGeNet) by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space. Conclusions In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates. PMID:26267445

  5. A maize-specifically expressed gene cluster in Ustilago maydis.

    Science.gov (United States)

    Basse, Christoph W; Kolb, Sebastian; Kahmann, Regine

    2002-01-01

    The corn pathogen Ustilago maydis requires its host plant maize for development and completion of its sexual cycle. We have identified the fungal mig2-1 gene as being specifically expressed during this biotrophic stage. Intriguingly, mig2-1 is part of a gene cluster comprising five highly homologous and similarly regulated genes designated mig2-1 to mig2-5. Deletion analysis of the mig2-1 promoter provides evidence for negative and positive regulation. The predicted polypeptides of all five genes lack significant homologies to known genes but have characteristic N-terminal secretion sequences. The secretion signals of mig2-1 and mig2-5 were shown to be functional, and secretion of a full length Mig2-1-eGFP fusion protein to the extracellular space was demonstrated. The central domains of the Mig2 proteins are highly variable whereas the C-termini are strongly conserved and share a characteristic pattern of eight cysteine residues. The mig2 gene cluster was conserved in a wide collection of U. maydis strains. Interestingly, some U. maydis isolates from South America had lost the mig2-4 gene as a result of a homologous recombination event. Furthermore, the related Ustilago scitaminea strain, which is pathogenic on sugar cane, appears to lack the mig2 cluster. We describe a model of how the mig2 cluster might have evolved and discuss its possible role in governing host interaction.

  6. Visualizing Gene - Interactions within the Rice and Maize Network

    Science.gov (United States)

    Sampong, A.; Feltus, A.; Smith, M.

    2014-12-01

    The purpose of this research was to design a simpler visualization tool for comparing or viewing gene interaction graphs in systems biology. This visualization tool makes it possible and easier for a researcher to visualize the biological metadata of a plant and interact with the graph on a webpage. Currently available visualization software like Cytoscape and Walrus are difficult to interact with and do not scale effectively for large data sets, limiting the ability to visualize interactions within a biological system. The visualization tool developed is useful for viewing and interpreting the dataset of a gene interaction network. The graph layout drawn by this visualization tool is an improvement from the previous method of comparing lines of genes in two separate data files to, now having the ability to visually see the layout of the gene networks and how the two systems are related. The graph layout presented by the visualization tool draws a graph of the sample rice and maize gene networks, linking the common genes found in both plants and highlighting the functions served by common genes from each plant. The success of this visualization tool will enable Dr. Feltus to continue his investigations and draw conclusions on the biological evolution of the sorghum plant as well. REU Funded by NSF ACI Award 1359223 Vetria L. Byrd, PI

  7. Boolean networks using the chi-square test for inferring large-scale gene regulatory networks

    Directory of Open Access Journals (Sweden)

    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.

  8. 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.

  9. Kidney-specific transposon-mediated gene transfer in vivo

    Science.gov (United States)

    Woodard, Lauren E.; Cheng, Jizhong; Welch, Richard C.; Williams, Felisha M.; Luo, Wentian; Gewin, Leslie S.; Wilson, Matthew H.

    2017-01-01

    Methods enabling kidney-specific gene transfer in adult mice are needed to develop new therapies for kidney disease. We attempted kidney-specific gene transfer following hydrodynamic tail vein injection using the kidney-specific podocin and gamma-glutamyl transferase promoters, but found expression primarily in the liver. In order to achieve kidney-specific transgene expression, we tested direct hydrodynamic injection of a DNA solution into the renal pelvis and found that luciferase expression was strong in the kidney and absent from extra-renal tissues. We observed heterogeneous, low-level transfection of the collecting duct, proximal tubule, distal tubule, interstitial cells, and rarely glomerular cells following injection. To assess renal injury, we performed the renal pelvis injections on uninephrectomised mice and found that their blood urea nitrogen was elevated at two days post-transfer but resolved within two weeks. Although luciferase expression quickly decreased following renal pelvis injection, the use of the piggyBac transposon system improved long-term expression. Immunosuppression with cyclophosphamide stabilised luciferase expression, suggesting immune clearance of the transfected cells occurs in immunocompetent animals. Injection of a transposon expressing erythropoietin raised the haematocrit, indicating that the developed injection technique can elicit a biologic effect in vivo. Hydrodynamic renal pelvis injection enables transposon mediated-kidney specific gene transfer in adult mice. PMID:28317878

  10. 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.

  11. 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.

  12. Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections.

    Directory of Open Access Journals (Sweden)

    Ettie M Lipner

    Full Text Available Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects.

  13. Transcriptomic network analysis of micronuclei-related genes: a case study

    DEFF Research Database (Denmark)

    van Leeuwen, D. M.; Pedersen, Marie; Knudsen, Lisbeth E.

    2011-01-01

    on the network. Six genes from the network, i.e. BAX, DMNT1, PCNA, HIC1, p21 and CDC20, were retrieved. Based on these six genes and in combination with p53 and IL-6, a dedicated network was created. This dedicated network is possibly suited for the development of a reporter gene assay that could be used...

  14. Ground rules of the pluripotency gene regulatory network.

    KAUST Repository

    Li, Mo

    2017-01-03

    Pluripotency is a state that exists transiently in the early embryo and, remarkably, can be recapitulated in vitro by deriving embryonic stem cells or by reprogramming somatic cells to become induced pluripotent stem cells. The state of pluripotency, which is stabilized by an interconnected network of pluripotency-associated genes, integrates external signals and exerts control over the decision between self-renewal and differentiation at the transcriptional, post-transcriptional and epigenetic levels. Recent evidence of alternative pluripotency states indicates the regulatory flexibility of this network. Insights into the underlying principles of the pluripotency network may provide unprecedented opportunities for studying development and for regenerative medicine.

  15. Random matrix analysis of localization properties of gene coexpression network.

    Science.gov (United States)

    Jalan, Sarika; Solymosi, Norbert; Vattay, Gábor; Li, Baowen

    2010-04-01

    We analyze gene coexpression network under the random matrix theory framework. The nearest-neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range and deviates afterwards. Eigenvector analysis of the network using inverse participation ratio suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets: (a) The nondegenerate part that follows RMT. (b) The nondegenerate part, at both ends and at intermediate eigenvalues, which deviates from RMT and expected to contain information about important nodes in the network. (c) The degenerate part with zero eigenvalue, which fluctuates around RMT-predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties.

  16. Propagation of genetic variation in gene regulatory networks.

    Science.gov (United States)

    Plahte, Erik; Gjuvsland, Arne B; Omholt, Stig W

    2013-08-01

    A future quantitative genetics theory should link genetic variation to phenotypic variation in a causally cohesive way based on how genes actually work and interact. We provide a theoretical framework for predicting and understanding the manifestation of genetic variation in haploid and diploid regulatory networks with arbitrary feedback structures and intra-locus and inter-locus functional dependencies. Using results from network and graph theory, we define propagation functions describing how genetic variation in a locus is propagated through the network, and show how their derivatives are related to the network's feedback structure. Similarly, feedback functions describe the effect of genotypic variation of a locus on itself, either directly or mediated by the network. A simple sign rule relates the sign of the derivative of the feedback function of any locus to the feedback loops involving that particular locus. We show that the sign of the phenotypically manifested interaction between alleles at a diploid locus is equal to the sign of the dominant feedback loop involving that particular locus, in accordance with recent results for a single locus system. Our results provide tools by which one can use observable equilibrium concentrations of gene products to disclose structural properties of the network architecture. Our work is a step towards a theory capable of explaining the pleiotropy and epistasis features of genetic variation in complex regulatory networks as functions of regulatory anatomy and functional location of the genetic variation.

  17. Identification of the key regulating genes of diminished ovarian reserve (DOR) by network and gene ontology analysis.

    Science.gov (United States)

    Pashaiasl, Maryam; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2016-09-01

    Diminished ovarian reserve (DOR) is one of the reasons for infertility that not only affects both older and young women. Ovarian reserve assessment can be used as a new prognostic tool for infertility treatment decision making. Here, up- and down-regulated gene expression profiles of granulosa cells were analysed to generate a putative interaction map of the involved genes. In addition, gene ontology (GO) analysis was used to get insight intol the biological processes and molecular functions of involved proteins in DOR. Eleven up-regulated genes and nine down-regulated genes were identified and assessed by constructing interaction networks based on their biological processes. PTGS2, CTGF, LHCGR, CITED, SOCS2, STAR and FSTL3 were the key nodes in the up-regulated networks, while the IGF2, AMH, GREM, and FOXC1 proteins were key in the down-regulated networks. MIRN101-1, MIRN153-1 and MIRN194-1 inhibited the expression of SOCS2, while CSH1 and BMP2 positively regulated IGF1 and IGF2. Ossification, ovarian follicle development, vasculogenesis, sequence-specific DNA binding transcription factor activity, and golgi apparatus are the major differential groups between up-regulated and down-regulated genes in DOR. Meta-analysis of publicly available transcriptomic data highlighted the high coexpression of CTGF, connective tissue growth factor, with the other key regulators of DOR. CTGF is involved in organ senescence and focal adhesion pathway according to GO analysis. These findings provide a comprehensive system biology based insight into the aetiology of DOR through network and gene ontology analyses.

  18. Study on the Specific Gene Expression during Spermatogenesis of Rat

    Institute of Scientific and Technical Information of China (English)

    吴燕婉; 王春梅; 黄海燕; 石心泉; 邢志军; 刘德瑜

    2001-01-01

    Objective To explore specific gene expression for regulating meiosis of germ cells during spermatogenesis of rat testis Materials & Methods Male SD rats, aged 1, 3 and 8 weeks, were observed in this study. The methods of morphological observation on testicular tissues embedded by resin and mRNA differential display (DDRT-PCR) were combined to obtain specific mRNA expression gene fragments during the testicular development. Reverse dot blot hybridization was operated to further screen the positive differential DNA fragments.The positive DNA segments were sub-cloned in pGEM-T-Easy vector and transformed into the competent E-coli 109 straint. Northern blot analysis and in situ hybridization were also carried out for identifying tissue-specific expression as well as cell-specific expression DNA fragments. To screen λ-ZAP-II rat testicular gene library was searched for the original gene.Results Eighty two differential cDNA fragments were obtained through primary DDRT-PCR, among which 40 differential cDNA fragments were selected for further screening with reverse dot blot hybridization. After the reverse dot blot hybridization,12 primary differential DNA fragments were obtained. The size of DNA fragments ranged from 250 to 500 bp. The in situ hybridization of the testicular tissue showed that a specific DNA fragment derived from 8-week old rat testis, named CG14, was hybridized in adult rat testicular section, in which the positive nucleic acid signals were distributed specifically in the primary spermatocytes. Another DNA fragment derived from 1-week old rat testis, named AA 1 1, was hybridized specifically in Sertoli cell of 1-week old rat testis. Northern blot hybridization with [α-32P] dCTP labeled CG 1 4 probe, including cardiac, liver, kidney, brain, testis, and epididymis tissue mRNAs of rat, showed that an mRNA specific hybridization band, size of 1. 258 kb, was found in testis tissue and size of 1. 531 kb of another hybridization band present in epididymis

  19. 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.

  20. 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.

  1. 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.

  2. Gene recovery microdissection (GRM) a process for producing chromosome region-specific libraries of expressed genes

    Energy Technology Data Exchange (ETDEWEB)

    Christian, A T; Coleman, M A; Tucker, J D

    2001-02-08

    Gene Recovery Microdissection (GRM) is a unique and cost-effective process for producing chromosome region-specific libraries of expressed genes. It accelerates the pace, reduces the cost, and extends the capabilities of functional genomic research, the means by which scientists will put to life-saving, life-enhancing use their knowledge of any plant or animal genome.

  3. An algebra-based method for inferring gene regulatory networks.

    Science.gov (United States)

    Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

    2014-03-26

    The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

  4. Network Analysis Identifies Disease-Specific Pathways for Parkinson's Disease.

    Science.gov (United States)

    Monti, Chiara; Colugnat, Ilaria; Lopiano, Leonardo; Chiò, Adriano; Alberio, Tiziana

    2016-12-21

    Neurodegenerative diseases are characterized by the progressive loss of specific neurons in selected regions of the central nervous system. The main clinical manifestation (movement disorders, cognitive impairment, and/or psychiatric disturbances) depends on the neuron population being primarily affected. Parkinson's disease is a common movement disorder, whose etiology remains mostly unknown. Progressive loss of dopaminergic neurons in the substantia nigra causes an impairment of the motor control. Some of the pathogenetic mechanisms causing the progressive deterioration of these neurons are not specific for Parkinson's disease but are shared by other neurodegenerative diseases, like Alzheimer's disease and amyotrophic lateral sclerosis. Here, we performed a meta-analysis of the literature of all the quantitative proteomic investigations of neuronal alterations in different models of Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis to distinguish between general and Parkinson's disease-specific pattern of neurodegeneration. Then, we merged proteomics data with genetics information from the DisGeNET database. The comparison of gene and protein information allowed us to identify 25 proteins involved uniquely in Parkinson's disease and we verified the alteration of one of them, i.e., transaldolase 1 (TALDO1), in the substantia nigra of 5 patients. By using open-source bioinformatics tools, we identified the biological processes specifically affected in Parkinson's disease, i.e., proteolysis, mitochondrion organization, and mitophagy. Eventually, we highlighted four cellular component complexes mostly involved in the pathogenesis: the proteasome complex, the protein phosphatase 2A, the chaperonins CCT complex, and the complex III of the respiratory chain.

  5. MiRTargetLink--miRNAs, Genes and Interaction Networks.

    Science.gov (United States)

    Hamberg, Maarten; Backes, Christina; Fehlmann, Tobias; Hart, Martin; Meder, Benjamin; Meese, Eckart; Keller, Andreas

    2016-04-14

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink.

  6. Network discovery pipeline elucidates conserved time-of-day-specific cis-regulatory modules.

    Directory of Open Access Journals (Sweden)

    Todd P Michael

    2008-02-01

    Full Text Available Correct daily phasing of transcription confers an adaptive advantage to almost all organisms, including higher plants. In this study, we describe a hypothesis-driven network discovery pipeline that identifies biologically relevant patterns in genome-scale data. To demonstrate its utility, we analyzed a comprehensive matrix of time courses interrogating the nuclear transcriptome of Arabidopsis thaliana plants grown under different thermocycles, photocycles, and circadian conditions. We show that 89% of Arabidopsis transcripts cycle in at least one condition and that most genes have peak expression at a particular time of day, which shifts depending on the environment. Thermocycles alone can drive at least half of all transcripts critical for synchronizing internal processes such as cell cycle and protein synthesis. We identified at least three distinct transcription modules controlling phase-specific expression, including a new midnight specific module, PBX/TBX/SBX. We validated the network discovery pipeline, as well as the midnight specific module, by demonstrating that the PBX element was sufficient to drive diurnal and circadian condition-dependent expression. Moreover, we show that the three transcription modules are conserved across Arabidopsis, poplar, and rice. These results confirm the complex interplay between thermocycles, photocycles, and the circadian clock on the daily transcription program, and provide a comprehensive view of the conserved genomic targets for a transcriptional network key to successful adaptation.

  7. Laminin Mediates Tissue-specific Gene Expression in Mammary Epithelia

    Energy Technology Data Exchange (ETDEWEB)

    Streuli, Charles H; Schmidhauser, Christian; Bailey, Nina; Yurchenco, Peter; Skubitz, Amy P. N.; Roskelley, Calvin; Bissell, Mina J

    1995-04-01

    Tissue-specific gene expression in mammary epithelium is dependent on the extracellular matrix as well as hormones. There is good evidence that the basement membrane provides signals for regulating beta-casein expression, and that integrins are involved in this process. Here, we demonstrate that in the presence of lactogenic hormones, laminin can direct expression of the beta-casein gene. Mouse mammary epithelial cells plated on gels of native laminin or laminin-entactin undergo functional differentiation. On tissue culture plastic, mammary cells respond to soluble basement membrane or purified laminin, but not other extracellular matrix components, by synthesizing beta-casein. In mammary cells transfected with chloramphenicol acetyl transferase reporter constructs, laminin activates transcription from the beta-casein promoter through a specific enhancer element. The inductive effect of laminin on casein expression was specifically blocked by the E3 fragment of the carboxy terminal region of the alpha 1 chain of laminin, by antisera raised against the E3 fragment, and by a peptide corresponding to a sequence within this region. Our results demonstrate that laminin can direct tissue-specific gene expression in epithelial cells through its globular domain.

  8. Human DJ-1-specific Transcriptional Activation of Tyrosine Hydroxylase Gene*

    Science.gov (United States)

    Ishikawa, Shizuma; Taira, Takahiro; Takahashi-Niki, Kazuko; Niki, Takeshi; Ariga, Hiroyoshi; Iguchi-Ariga, Sanae M. M.

    2010-01-01

    Loss-of-function mutation in the DJ-1 gene causes a subset of familial Parkinson disease. The mechanism underlying DJ-1-related selective vulnerability in the dopaminergic pathway is, however, not known. DJ-1 has multiple functions, including transcriptional regulation, and one of transcriptional target genes for DJ-1 is the tyrosine hydroxylase (TH) gene, the product of which is a key enzyme for dopamine biosynthesis. It has been reported that DJ-1 is a neuroprotective transcriptional co-activator that sequesters a transcriptional co-repressor polypyrimidine tract-binding protein-associated splicing factor (PSF) from the TH gene promoter. In this study, we found that knockdown of human DJ-1 by small interference RNA in human dopaminergic cell lines attenuated TH gene expression and 4-dihydroxy-l-phenylalanine production but that knockdown or knock-out of mouse DJ-1 in mouse cell lines or in mice did not affect such expression and TH activity. In reporter assays using the human TH gene promoter linked to the luciferase gene, stimulation of TH promoter activity was observed in human cells, but not mouse cells, that had been transfected with DJ-1. Although human DJ-1 and mouse DJ-1 were associated either with human or with mouse PSF, TH promoter activity inhibited by PSF was restored by human DJ-1 but not by mouse DJ-1. Chromatin immunoprecipitation assays revealed that the complex of PSF with DJ-1 bound to the human but not the mouse TH gene promoter. These results suggest a novel species-specific transcriptional regulation of the TH promoter by DJ-1 and one of the mechanisms for no reduction of TH in DJ-1-knock-out mice. PMID:20938049

  9. 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

  10. 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

  11. Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

    Institute of Scientific and Technical Information of China (English)

    Guixia Liu; Lei Liu; Chunyu Liu; Ming Zheng; Lanying Su; Chunguang Zhou

    2011-01-01

    Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly, in this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.

  12. Universal and specific quantitative detection of botulinum neurotoxin genes

    Directory of Open Access Journals (Sweden)

    Arnon Stephen S

    2010-10-01

    Full Text Available Abstract Background Clostridium botulinum, an obligate anaerobic spore-forming bacterium, produces seven antigenic variants of botulinum toxin that are distinguished serologically and termed "serotypes". Botulinum toxin blocks the release of acetylcholine at neuromuscular junctions resulting in flaccid paralysis. The potential lethality of the disease warrants a fast and accurate means of diagnosing suspected instances of food contamination or human intoxication. Currently, the Food and Drug Administration (FDA-accepted assay to detect and type botulinum neurotoxins (BoNTs is the mouse protection bioassay. While specific and sensitive, this assay requires the use of laboratory animals, may take up to four days to achieve a diagnosis, and is unsuitable for high-throughput analysis. We report here a two-step PCR assay that identifies all toxin types, that achieves the specificity of the mouse bioassay while surpassing it in equivalent sensitivity, that has capability for high-throughput analysis, and that provides quantitative results within hours. The first step of our assay consists of a conventional PCR that detects the presence of C. botulinum regardless of the neurotoxin type. The second step uses quantitative PCR (qPCR technology to determine the specific serotype of the neurotoxin. Results We assayed purified C. botulinum DNA and crude toxin preparations, as well as food and stool from healthy individuals spiked with purified BoNT DNA, and one stool sample from a case of infant botulism for the presence of the NTNH gene, which is part of the BoNT gene cluster, and for the presence of serotype-specific BoNT genes. The PCR surpassed the mouse bioassay both in specificity and sensitivity, detecting positive signals in BoNT preparations containing well below the 1 LD50 required for detection via the mouse bioassay. These results were type-specific and we were reliably able to quantify as few as 10 genomic copies. Conclusions While other studies

  13. Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network

    Directory of Open Access Journals (Sweden)

    Delyon Bernard

    2010-11-01

    Full Text Available Abstract Background A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. Results We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation. Conclusions The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific.

  14. Chronic ethanol exposure produces time- and brain region-dependent changes in gene coexpression networks.

    Directory of Open Access Journals (Sweden)

    Elizabeth A Osterndorff-Kahanek

    Full Text Available Repeated ethanol exposure and withdrawal in mice increases voluntary drinking and represents an animal model of physical dependence. We examined time- and brain region-dependent changes in gene coexpression networks in amygdala (AMY, nucleus accumbens (NAC, prefrontal cortex (PFC, and liver after four weekly cycles of chronic intermittent ethanol (CIE vapor exposure in C57BL/6J mice. Microarrays were used to compare gene expression profiles at 0-, 8-, and 120-hours following the last ethanol exposure. Each brain region exhibited a large number of differentially expressed genes (2,000-3,000 at the 0- and 8-hour time points, but fewer changes were detected at the 120-hour time point (400-600. Within each region, there was little gene overlap across time (~20%. All brain regions were significantly enriched with differentially expressed immune-related genes at the 8-hour time point. Weighted gene correlation network analysis identified modules that were highly enriched with differentially expressed genes at the 0- and 8-hour time points with virtually no enrichment at 120 hours. Modules enriched for both ethanol-responsive and cell-specific genes were identified in each brain region. These results indicate that chronic alcohol exposure causes global 'rewiring' of coexpression systems involving glial and immune signaling as well as neuronal genes.

  15. 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.

  16. An improved, bias-reduced probabilistic functional gene network of baker's yeast, Saccharomyces cerevisiae.

    Directory of Open Access Journals (Sweden)

    Insuk Lee

    Full Text Available BACKGROUND: Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. METHODOLOGY/PRINCIPAL FINDINGS: We report a significantly improved version (v. 2 of a probabilistic functional gene network of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. CONCLUSIONS/SIGNIFICANCE: YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome. YeastNet is available from http://www.yeastnet.org.

  17. Network analysis of genes and their association with diseases.

    Science.gov (United States)

    Kontou, Panagiota I; Pavlopoulou, Athanasia; Dimou, Niki L; Pavlopoulos, Georgios A; Bagos, Pantelis G

    2016-09-15

    A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms.

  18. 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

  19. A gene network engineering platform for lactic acid bacteria.

    Science.gov (United States)

    Kong, Wentao; Kapuganti, Venkata S; Lu, Ting

    2016-02-29

    Recent developments in synthetic biology have positioned lactic acid bacteria (LAB) as a major class of cellular chassis for applications. To achieve the full potential of LAB, one fundamental prerequisite is the capacity for rapid engineering of complex gene networks, such as natural biosynthetic pathways and multicomponent synthetic circuits, into which cellular functions are encoded. Here, we present a synthetic biology platform for rapid construction and optimization of large-scale gene networks in LAB. The platform involves a copy-controlled shuttle for hosting target networks and two associated strategies that enable efficient genetic editing and phenotypic validation. By using a nisin biosynthesis pathway and its variants as examples, we demonstrated multiplex, continuous editing of small DNA parts, such as ribosome-binding sites, as well as efficient manipulation of large building blocks such as genes and operons. To showcase the platform, we applied it to expand the phenotypic diversity of the nisin pathway by quickly generating a library of 63 pathway variants. We further demonstrated its utility by altering the regulatory topology of the nisin pathway for constitutive bacteriocin biosynthesis. This work demonstrates the feasibility of rapid and advanced engineering of gene networks in LAB, fostering their applications in biomedicine and other areas.

  20. Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

    Science.gov (United States)

    Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

    2015-01-01

    Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

  1. Repressor-mediated tissue-specific gene expression in plants

    Science.gov (United States)

    Meagher, Richard B.; Balish, Rebecca S.; Tehryung, Kim; McKinney, Elizabeth C.

    2009-02-17

    Plant tissue specific gene expression by way of repressor-operator complexes, has enabled outcomes including, without limitation, male sterility and engineered plants having root-specific gene expression of relevant proteins to clean environmental pollutants from soil and water. A mercury hyperaccumulation strategy requires that mercuric ion reductase coding sequence is strongly expressed. The actin promoter vector, A2pot, engineered to contain bacterial lac operator sequences, directed strong expression in all plant vegetative organs and tissues. In contrast, the expression from the A2pot construct was restricted primarily to root tissues when a modified bacterial repressor (LacIn) was coexpressed from the light-regulated rubisco small subunit promoter in above-ground tissues. Also provided are analogous repressor operator complexes for selective expression in other plant tissues, for example, to produce male sterile plants.

  2. Plant gravitropic signal transduction: A network analysis leads to gene discovery

    Science.gov (United States)

    Wyatt, Sarah

    Gravity plays a fundamental role in plant growth and development. Although a significant body of research has helped define the events of gravity perception, the role of the plant growth regulator auxin, and the mechanisms resulting in the gravity response, the events of signal transduction, those that link the biophysical action of perception to a biochemical signal that results in auxin redistribution, those that regulate the gravitropic effects on plant growth, remain, for the most part, a “black box.” Using a cold affect, dubbed the gravity persistent signal (GPS) response, we developed a mutant screen to specifically identify components of the signal transduction pathway. Cloning of the GPS genes have identified new proteins involved in gravitropic signaling. We have further exploited the GPS response using a multi-faceted approach including gene expression microarrays, proteomics analysis, and bioinformatics analysis and continued mutant analysis to identified additional genes, physiological and biochemical processes. Gene expression data provided the foundation of a regulatory network for gravitropic signaling. Based on these gene expression data and related data sets/information from the literature/repositories, we constructed a gravitropic signaling network for Arabidopsis inflorescence stems. To generate the network, both a dynamic Bayesian network approach and a time-lagged correlation coefficient approach were used. The dynamic Bayesian network added existing information of protein-protein interaction while the time-lagged correlation coefficient allowed incorporation of temporal regulation and thus could incorporate the time-course metric from the data set. Thus the methods complemented each other and provided us with a more comprehensive evaluation of connections. Each method generated a list of possible interactions associated with a statistical significance value. The two networks were then overlaid to generate a more rigorous, intersected

  3. 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...

  4. 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...

  5. Important aspects of placental-specific gene transfer.

    Science.gov (United States)

    Kaufman, Melissa R; Albers, Renee E; Keoni, Chanel; Kulkarni-Datar, Kashmira; Natale, David R; Brown, Thomas L

    2014-10-15

    The placenta is a unique and highly complex organ that develops only during pregnancy and is essential for growth and survival of the developing fetus. The placenta provides the vital exchange of gases and wastes, the necessary nutrients for fetal development, acts as immune barrier that protects against maternal rejection, and produces numerous hormones and growth factors that promote fetal maturity to regulate pregnancy until parturition. Abnormal placental development is a major underlying cause of pregnancy-associated disorders that often result in preterm birth. Defects in placental stem cell propagation, growth, and differentiation are the major factors that affect embryonic and fetal well-being and dramatically increase the risk of pregnancy complications. Understanding the processes that regulate placentation is important in determining the underlying factors behind abnormal placental development. The ability to manipulate genes in a placenta-specific manner provides a unique tool to analyze development and eliminates potentially confounding results that can occur with traditional gene knockouts. Trophoblast stem cells and mouse embryos are not overly amenable to traditional gene transfer techniques. Most viral vectors, however, have a low infection rate and often lead to mosaic transgenesis. Although the traditional method of embryo transfer is intrauterine surgical implantation, the methodology reported here, combining lentiviral blastocyst infection and nonsurgical embryo transfer, leads to highly efficient and placental-specific gene transfer. Numerous advantages of our optimized procedures include increased investigator safety, a reduction in animal stress, rapid and noninvasive embryo transfer, and higher a rate of pregnancy and live birth.

  6. MS/MS networking guided analysis of molecule and gene cluster families.

    Science.gov (United States)

    Nguyen, Don Duy; Wu, Cheng-Hsuan; Moree, Wilna J; Lamsa, Anne; Medema, Marnix H; Zhao, Xiling; Gavilan, Ronnie G; Aparicio, Marystella; Atencio, Librada; Jackson, Chanaye; Ballesteros, Javier; Sanchez, Joel; Watrous, Jeramie D; Phelan, Vanessa V; van de Wiel, Corine; Kersten, Roland D; Mehnaz, Samina; De Mot, René; Shank, Elizabeth A; Charusanti, Pep; Nagarajan, Harish; Duggan, Brendan M; Moore, Bradley S; Bandeira, Nuno; Palsson, Bernhard Ø; Pogliano, Kit; Gutiérrez, Marcelino; Dorrestein, Pieter C

    2013-07-09

    The ability to correlate the production of specialized metabolites to the genetic capacity of the organism that produces such molecules has become an invaluable tool in aiding the discovery of biotechnologically applicable molecules. Here, we accomplish this task by matching molecular families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one molecule to one organism at a time, such as how it is traditionally done. We can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. We matched the molecular families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular molecules with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a molecular family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779(T). The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at molecular family-gene cluster families of hundreds or more diverse organisms in one single MS/MS network.

  7. MS/MS networking guided analysis of molecule and gene cluster families

    Science.gov (United States)

    Nguyen, Don Duy; Wu, Cheng-Hsuan; Moree, Wilna J.; Lamsa, Anne; Medema, Marnix H.; Zhao, Xiling; Gavilan, Ronnie G.; Aparicio, Marystella; Atencio, Librada; Jackson, Chanaye; Ballesteros, Javier; Sanchez, Joel; Watrous, Jeramie D.; Phelan, Vanessa V.; van de Wiel, Corine; Kersten, Roland D.; Mehnaz, Samina; De Mot, René; Shank, Elizabeth A.; Charusanti, Pep; Nagarajan, Harish; Duggan, Brendan M.; Moore, Bradley S.; Bandeira, Nuno; Palsson, Bernhard Ø.; Pogliano, Kit; Gutiérrez, Marcelino; Dorrestein, Pieter C.

    2013-01-01

    The ability to correlate the production of specialized metabolites to the genetic capacity of the organism that produces such molecules has become an invaluable tool in aiding the discovery of biotechnologically applicable molecules. Here, we accomplish this task by matching molecular families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one molecule to one organism at a time, such as how it is traditionally done. We can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. We matched the molecular families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular molecules with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a molecular family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779T. The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at molecular family–gene cluster families of hundreds or more diverse organisms in one single MS/MS network. PMID:23798442

  8. Regulatory gene mutation: a driving force behind group a Streptococcus strain- and serotype-specific variation.

    Science.gov (United States)

    Sarkar, Poulomee; Sumby, Paul

    2017-02-01

    Data from multiple bacterial pathogens are consistent with regulator-encoding genes having higher mutation frequencies than the genome average. Such mutations drive both strain- and type- (e.g., serotype, haplotype) specific phenotypic heterogeneity, and may challenge public health due to the potential of variants to circumvent established treatment and/or preventative regimes. Here, using the human bacterial pathogen the group A Streptococcus (GAS; S. pyogenes) as a model organism, we review the types and regulatory-, phenotypic-, and disease-specific consequences of naturally occurring regulatory gene mutations. Strain-specific regulator mutations that will be discussed include examples that transform isolates into hyper-invasive forms by enhancing expression of immunomodulatory virulence factors, and examples that promote asymptomatic carriage of the organism. The discussion of serotype-specific regulator mutations focuses on serotype M3 GAS isolates, and how the identified rewiring of regulatory networks in this serotype may be contributing to a decades old epidemiological association of M3 isolates with particularly severe invasive infections. We conclude that mutation plays an outsized role in GAS pathogenesis and has clinical relevance. Given the phenotypic variability associated with regulatory gene mutations, the rapid examination of these genes in infecting isolates may inform with respect to potential patient complications and treatment options.

  9. 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.

  10. Construction of coffee transcriptome networks based on gene annotation semantics.

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    Castillo, Luis F; Galeano, Narmer; Isaza, Gustavo A; Gaitán, Alvaro

    2012-07-24

    Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST) and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF) using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.

  11. Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

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    Chen Jiun-Ching

    2007-05-01

    Full Text Available Abstract Background Genome-wide identification of specific oligonucleotides (oligos is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. Results We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes. Conclusion The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through

  12. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.

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    Daniel S Himmelstein

    2015-07-01

    Full Text Available The first decade of Genome Wide Association Studies (GWAS has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79 from a withheld multiple sclerosis (MS GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3 validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io. Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach

  13. Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii.

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    Anitha, P; Anbarasu, Anand; Ramaiah, Sudha

    2014-05-01

    Multi Drug Resistance (MDR) in Acinetobacter baumannii is one of the major threats for emerging nosocomial infections in hospital environment. Multidrug-resistance in A. baumannii may be due to the implementation of multi-combination resistance mechanisms such as β-lactamase synthesis, Penicillin-Binding Proteins (PBPs) changes, alteration in porin proteins and in efflux pumps against various existing classes of antibiotics. Multiple antibiotic resistance genes are involved in MDR. These resistance genes are transferred through plasmids, which are responsible for the dissemination of antibiotic resistance among Acinetobacter spp. In addition, these resistance genes may also have a tendency to interact with each other or with their gene products. Therefore, it becomes necessary to understand the impact of these interactions in antibiotic resistance mechanism. Hence, our study focuses on protein and gene network analysis on various resistance genes, to elucidate the role of the interacting proteins and to study their functional contribution towards antibiotic resistance. From the search tool for the retrieval of interacting gene/protein (STRING), a total of 168 functional partners for 15 resistance genes were extracted based on the confidence scoring system. The network study was then followed up with functional clustering of associated partners using molecular complex detection (MCODE). Later, we selected eight efficient clusters based on score. Interestingly, the associated protein we identified from the network possessed greater functional similarity with known resistance genes. This network-based approach on resistance genes of A. baumannii could help in identifying new genes/proteins and provide clues on their association in antibiotic resistance.

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

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    Liang Jinghang

    2012-08-01

    Full Text Available Abstract Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs. As a logical model, probabilistic Boolean networks (PBNs consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n or O(nN2n for a sparse matrix. Results This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN. An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n, where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a

  15. Gene Networks in the Wild: Identifying Transcriptional Modules that Mediate Coral Resistance to Experimental Heat Stress.

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    Rose, Noah H; Seneca, Francois O; Palumbi, Stephen R

    2015-12-28

    Organisms respond to environmental variation partly through changes in gene expression, which underlie both homeostatic and acclimatory responses to environmental stress. In some cases, so many genes change in expression in response to different influences that understanding expression patterns for all these individual genes becomes difficult. To reduce this problem, we use a systems genetics approach to show that variation in the expression of thousands of genes of reef-building corals can be explained as variation in the expression of a small number of coexpressed "modules." Modules were often enriched for specific cellular functions and varied predictably among individuals, experimental treatments, and physiological state. We describe two transcriptional modules for which expression levels immediately after heat stress predict bleaching a day later. One of these early "bleaching modules" is enriched for sequence-specific DNA-binding proteins, particularly E26 transformation-specific (ETS)-family transcription factors. The other module is enriched for extracellular matrix proteins. These classes of bleaching response genes are clear in the modular gene expression analysis we conduct but are much more difficult to discern in single gene analyses. Furthermore, the ETS-family module shows repeated differences in expression among coral colonies grown in the same common garden environment, suggesting a heritable genetic or epigenetic basis for these expression polymorphisms. This finding suggests that these corals harbor high levels of gene-network variation, which could facilitate rapid evolution in the face of environmental change.

  16. Learning a Markov Logic network for supervised gene regulatory network inference.

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    Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

    2013-09-12

    Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

  17. Gene expression profiles of hepatic cell-type specific marker genes in progression of liver fibrosis

    Institute of Scientific and Technical Information of China (English)

    Yoshiyuki Takahara; Mitsuo Takahashi; Hiroki Wagatsuma; Fumihiko Yokoya; Qing-Wei Zhang; Mutsuyo Yamaguchi; Hiroyuki Aburatani; Norifumi Kawada

    2006-01-01

    AIM: To determine the gene expression profile data for the whole liver during development of dimethylnitrosamine (DMN)-induced hepatic fibrosis.METHODS: Marker genes were identified for different types of hepatic cells, including hepatic stellate cells (HSCs), Kupffer cells (including other inflammatory cells),and hepatocytes, using independent temporal DNA microarray data obtained from isolated hepatic cells.RESULTS: The cell-type analysis of gene expression gave several key results and led to formation of three hypotheses: (1) changes in the expression of HSCspecific marker genes during fibrosis were similar to gene expression data in in vitro cultured HSCs, suggesting a major role of the self-activating characteristics of HSCs in formation of fibrosis; (2) expression of mast cell-specific marker genes reached a peak during liver fibrosis,suggesting a possible role of mast cells in formation of fibrosis; and (3) abnormal expression of hepatocytespecific marker genes was found across several metabolic pathways during fibrosis, including sulfur-containing amino acid metabolism, fatty acid metabolism, and drug metabolism, suggesting a mechanistic relationship between these abnormalities and symptoms of liver fibrosis.CONCLUSION: Analysis of marker genes for specific hepatic cell types can identify the key aspects of fibrogenesis. Sequential activation of inflammatory cells and the self-supporting properties of HSCs play an important role in development of fibrosis.

  18. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

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    Vipin Narang

    Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

  19. 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.

  20. Vitamin D and gene networks in human osteoblasts

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    Jeroen evan de Peppel

    2014-04-01

    Full Text Available Bone formation is indirectly influenced by 1,25-dihydroxyvitamin D3 (1,25D3 through the stimulation of calcium uptake in the intestine and re-absorption in the kidneys. Direct effects on osteoblasts and bone formation have also been established. The vitamin D receptor (VDR is expressed in osteoblasts and 1,25D3 modifies gene expression of various osteoblast differentiation and mineralization-related genes, such as alkaline phosphatase (ALPL, osteocalcin (BGLAP and osteopontin (SPP1. 1,25D3 is known to stimulate mineralization of human osteoblasts in vitro, and recently it was shown that 1,25D3 induces mineralization via effects in the period preceding mineralization during the pre-mineralization period. For a full understanding of the action of 1,25D3 in osteoblasts it is important to get an integrated network view of the 1,25D3-regulated genes during osteoblast differentiation and mineralization. The current data will be presented and discussed alluding to future studies to fully delineate the 1,25D3 action in osteoblast. Describing and understanding the vitamin D regulatory networks and identifying the dominant players in these networks may help develop novel (personalized vitamin D-based treatments. The following topics will be discussed in this overview: 1 Bone metabolism and osteoblasts, 2 Vitamin D, bone metabolism and osteoblast function, 3 Vitamin D induced transcriptional networks in the context of osteoblast differentiation and bone formation.

  1. Translational cross talk in gene networks.

    Science.gov (United States)

    Mather, William H; Hasty, Jeff; Tsimring, Lev S; Williams, Ruth J

    2013-06-04

    It has been shown experimentally that competition for limited translational resources by upstream mRNAs can lead to an anticorrelation between protein counts. Here, we investigate a stochastic model for this phenomenon, in which gene transcripts of different types compete for a finite pool of ribosomes. Throughout, we utilize concepts from the theory of multiclass queues to describe a qualitative shift in protein count statistics as the system transitions from being underloaded (ribosomes exceed transcripts in number) to being overloaded (transcripts exceed ribosomes in number). The exact analytical solution of a simplified stochastic model, in which the numbers of competing mRNAs and ribosomes are fixed, exhibits weak positive correlations between steady-state protein counts when total transcript count slightly exceeds ribosome count, whereas the solution can exhibit strong negative correlations when total transcript count significantly exceeds ribosome count. Extending this analysis, we find approximate but reasonably accurate solutions for a more realistic model, in which abundances of mRNAs and ribosomes are allowed to fluctuate randomly. Here, ribosomal fluctuations contribute positively and mRNA fluctuations contribute negatively to correlations, and when mRNA fluctuations dominate ribosomal fluctuations, a strong anticorrelation extremum reliably occurs near the transition from the underloaded to the overloaded regime. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  2. Toward an orofacial gene regulatory network.

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    Kousa, Youssef A; Schutte, Brian C

    2016-03-01

    Orofacial clefting is a common birth defect with significant morbidity. A panoply of candidate genes have been discovered through synergy of animal models and human genetics. Among these, variants in interferon regulatory factor 6 (IRF6) cause syndromic orofacial clefting and contribute risk toward isolated cleft lip and palate (1/700 live births). Rare variants in IRF6 can lead to Van der Woude syndrome (1/35,000 live births) and popliteal pterygium syndrome (1/300,000 live births). Furthermore, IRF6 regulates GRHL3 and rare variants in this downstream target can also lead to Van der Woude syndrome. In addition, a common variant (rs642961) in the IRF6 locus is found in 30% of the world's population and contributes risk for isolated orofacial clefting. Biochemical studies revealed that rs642961 abrogates one of four AP-2alpha binding sites. Like IRF6 and GRHL3, rare variants in TFAP2A can also lead to syndromic orofacial clefting with lip pits (branchio-oculo-facial syndrome). The literature suggests that AP-2alpha, IRF6 and GRHL3 are part of a pathway that is essential for lip and palate development. In addition to updating the pathways, players and pursuits, this review will highlight some of the current questions in the study of orofacial clefting.

  3. Snapshot of iron response in Shewanella oneidensis by gene network reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Yunfeng; Harris, Daniel P.; Luo, Feng; Xiong, Wenlu; Joachimiak, Marcin; Wu, Liyou; Dehal, Paramvir; Jacobsen, Janet; Yang, Zamin; Palumbo, Anthony V.; Arkin, Adam P.; Zhou, Jizhong

    2008-10-09

    Background: Iron homeostasis of Shewanella oneidensis, a gamma-proteobacterium possessing high iron content, is regulated by a global transcription factor Fur. However, knowledge is incomplete about other biological pathways that respond to changes in iron concentration, as well as details of the responses. In this work, we integrate physiological, transcriptomics and genetic approaches to delineate the iron response of S. oneidensis. Results: We show that the iron response in S. oneidensis is a rapid process. Temporal gene expression profiles were examined for iron depletion and repletion, and a gene co-expression network was reconstructed. Modules of iron acquisition systems, anaerobic energy metabolism and protein degradation were the most noteworthy in the gene network. Bioinformatics analyses suggested that genes in each of the modules might be regulated by DNA-binding proteins Fur, CRP and RpoH, respectively. Closer inspection of these modules revealed a transcriptional regulator (SO2426) involved in iron acquisition and ten transcriptional factors involved in anaerobic energy metabolism. Selected genes in the network were analyzed by genetic studies. Disruption of genes encoding a putative alcaligin biosynthesis protein (SO3032) and a gene previously implicated in protein degradation (SO2017) led to severe growth deficiency under iron depletion conditions. Disruption of a novel transcriptional factor (SO1415) caused deficiency in both anaerobic iron reduction and growth with thiosulfate or TMAO as an electronic acceptor, suggesting that SO1415 is required for specific branches of anaerobic energy metabolism pathways. Conclusions: Using a reconstructed gene network, we identified major biological pathways that were differentially expressed during iron depletion and repletion. Genetic studies not only demonstrated the importance of iron acquisition and protein degradation for iron depletion, but also characterized a novel transcriptional factor (SO1415) with a

  4. Conservation and diversification of an ancestral chordate gene regulatory network for dorsoventral patterning.

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    Iryna Kozmikova

    Full Text Available Formation of a dorsoventral axis is a key event in the early development of most animal embryos. It is well established that bone morphogenetic proteins (Bmps and Wnts are key mediators of dorsoventral patterning in vertebrates. In the cephalochordate amphioxus, genes encoding Bmps and transcription factors downstream of Bmp signaling such as Vent are expressed in patterns reminiscent of those of their vertebrate orthologues. However, the key question is whether the conservation of expression patterns of network constituents implies conservation of functional network interactions, and if so, how an increased functional complexity can evolve. Using heterologous systems, namely by reporter gene assays in mammalian cell lines and by transgenesis in medaka fish, we have compared the gene regulatory network implicated in dorsoventral patterning of the basal chordate amphioxus and vertebrates. We found that Bmp but not canonical Wnt signaling regulates promoters of genes encoding homeodomain proteins AmphiVent1 and AmphiVent2. Furthermore, AmphiVent1 and AmphiVent2 promoters appear to be correctly regulated in the context of a vertebrate embryo. Finally, we show that AmphiVent1 is able to directly repress promoters of AmphiGoosecoid and AmphiChordin genes. Repression of genes encoding dorsal-specific signaling molecule Chordin and transcription factor Goosecoid by Xenopus and zebrafish Vent genes represents a key regulatory interaction during vertebrate axis formation. Our data indicate high evolutionary conservation of a core Bmp-triggered gene regulatory network for dorsoventral patterning in chordates and suggest that co-option of the canonical Wnt signaling pathway for dorsoventral patterning in vertebrates represents one of the innovations through which an increased morphological complexity of vertebrate embryo is achieved.

  5. Site-specific gene expression patterns in oral cancer.

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    Frohwitter, Gesche; Buerger, Horst; Korsching, Eberhard; van Diest, Paul J; Kleinheinz, Johannes; Fillies, Thomas

    2017-05-10

    Squamous cell carcinomas (SCCs) are the most prevalent malignant tumours within the head and neck. Evidence exists that distinct genes are differentially regulated in SCCs of the oral cavity compared to other head and neck regions. Given this background, the aim of this study was to investigate whether such tumour site-specific gene expression can also be observed in different localizations within the oral cavity. Using tissue microarrays (TMAs), we investigated 76 SCCs of the floor of the mouth, 49 SCCs of the tongue and 68 SCCs of other anatomic regions within the oral cavity. The expression of 17 genes involved in cell cycle and growth control (p16, p21, p27, p53, cyclin D1, EGFR, c-kit, bcl-6), cell adhesion (alpha-, beta-, and gamma-catenin), and apoptosis/stress response genes (Hif-1-alpha, Glut 1, CA IX, caspase, hsp70, XIAP) were investigated by means of immunohistochemistry. The data were subjected to chi(2), interdependency and Kaplan-Meier analysis. Our study suggests a remote difference in the site-specific gene expression patterns of oral cancer. X-linked inhibitor of apoptosis (XIAP) showed a significantly higher expression (p oral cavity. The increased XIAP expression was further associated with significantly decreased overall survival in all cases of SCCs of the oral cavity (p Expression levels of p53, CA IX, beta-catenin, Hif-1-alpha, and c-kit were also observed to be inversely related between SCCs of the floor of the mouth and those of the tongue respectively, although these differences did not reach statistical significance. Overall and event-free survival did not differ in patients with T1/T2/N0 SCCs according to tumour localization. In summary, the protein expression patterns of SCCs of the oral cavity suggest the existence of a molecular and morphological spectrum of SCCs in the oral cavity. In particular the expression pattern of XIAP indicates distinct gene expression patterns between carcinomas of the floor of the mouth and oral tongue

  6. Variations in CCL3L gene cluster sequence and non-specific gene copy numbers

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    Edberg Jeffrey C

    2010-03-01

    Full Text Available Abstract Background Copy number variations (CNVs of the gene CC chemokine ligand 3-like1 (CCL3L1 have been implicated in HIV-1 susceptibility, but the association has been inconsistent. CCL3L1 shares homology with a cluster of genes localized to chromosome 17q12, namely CCL3, CCL3L2, and, CCL3L3. These genes are involved in host defense and inflammatory processes. Several CNV assays have been developed for the CCL3L1 gene. Findings Through pairwise and multiple alignments of these genes, we have shown that the homology between these genes ranges from 50% to 99% in complete gene sequences and from 70-100% in the exonic regions, with CCL3L1 and CCL3L3 being identical. By use of MEGA 4 and BioEdit, we aligned sense primers, anti-sense primers, and probes used in several previously described assays against pre-multiple alignments of all four chemokine genes. Each set of probes and primers aligned and matched with overlapping sequences in at least two of the four genes, indicating that previously utilized RT-PCR based CNV assays are not specific for only CCL3L1. The four available assays measured median copies of 2 and 3-4 in European and African American, respectively. The concordance between the assays ranged from 0.44-0.83 suggesting individual discordant calls and inconsistencies with the assays from the expected gene coverage from the known sequence. Conclusions This indicates that some of the inconsistencies in the association studies could be due to assays that provide heterogenous results. Sequence information to determine CNV of the three genes separately would allow to test whether their association with the pathogenesis of a human disease or phenotype is affected by an individual gene or by a combination of these genes.

  7. Phylogenetics of Lophotrochozoan bHLH Genes and the Evolution of Lineage-Specific Gene Duplicates

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    Bao, Yongbo

    2017-01-01

    The gain and loss of genes encoding transcription factors is of importance to understanding the evolution of gene regulatory complexity. The basic helix–loop–helix (bHLH) genes encode a large superfamily of transcription factors. We systematically classify the bHLH genes from five mollusc, two annelid and one brachiopod genomes, tracing the pattern of bHLH gene evolution across these poorly studied Phyla. In total, 56–88 bHLH genes were identified in each genome, with most identifiable as members of previously described bilaterian families, or of new families we define. Of such families only one, Mesp, appears lost by all these species. Additional duplications have also played a role in the evolution of the bHLH gene repertoire, with many new lophotrochozoan-, mollusc-, bivalve-, or gastropod-specific genes defined. Using a combination of transcriptome mining, RT-PCR, and in situ hybridization we compared the expression of several of these novel genes in tissues and embryos of the molluscs Crassostrea gigas and Patella vulgata, finding both conserved expression and evidence for neofunctionalization. We also map the positions of the genes across these genomes, identifying numerous gene linkages. Some reflect recent paralog divergence by tandem duplication, others are remnants of ancient tandem duplications dating to the lophotrochozoan or bilaterian common ancestors. These data are built into a model of the evolution of bHLH genes in molluscs, showing formidable evolutionary stasis at the family level but considerable within-family diversification by tandem gene duplication. PMID:28338988

  8. Analysis of genetic interaction networks shows that alternatively spliced genes are highly versatile.

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    Talavera, David; Sheoran, Ritika; Lovell, Simon C

    2013-01-01

    Alternative splicing has the potential to increase the diversity of the transcriptome and proteome. Where more than one transcript arises from a gene they are often so different that they are quite unlikely to have the same function. However, it remains unclear if alternative splicing generally leads to a gene being involved in multiple biological processes or whether it alters the function within a single process. Knowing that genetic interactions occur between functionally related genes, we have used them as a proxy for functional versatility, and have analysed the sets of genes of two well-characterised model organisms: Caenorhabditis elegans and Drosophila melanogaster. Using network analyses we find that few genes are functionally homogenous (only involved in a few functionally-related biological processes). Moreover, there are differences between alternatively spliced genes and genes with a single transcript; specifically, genes with alternatively splicing are, on average, involved in more biological processes. Finally, we suggest that factors other than specific functional classes determine whether a gene is alternatively spliced.

  9. Evolution of the mammalian embryonic pluripotency gene regulatory network

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    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

  10. A highly sensitive and specific system for large-scale gene expression profiling

    Directory of Open Access Journals (Sweden)

    Wang Hui-Yun

    2008-01-01

    Full Text Available Abstract Background Rapid progress in the field of gene expression-based molecular network integration has generated strong demand on enhancing the sensitivity and data accuracy of experimental systems. To meet the need, a high-throughput gene profiling system of high specificity and sensitivity has been developed. Results By using specially designed primers, the new system amplifies sequences in neighboring exons separated by big introns so that mRNA sequences may be effectively discriminated from other highly related sequences including their genes, unprocessed transcripts, pseudogenes and pseudogene transcripts. Probes used for microarray detection consist of sequences in the two neighboring exons amplified by the primers. In conjunction with a newly developed high-throughput multiplex amplification system and highly simplified experimental procedures, the system can be used to analyze >1,000 mRNA species in a single assay. It may also be used for gene expression profiling of very few (n = 100 or single cells. Highly reproducible results were obtained from duplicate samples with the same number of cells, and from those with a small number (100 and a large number (10,000 of cells. The specificity of the system was demonstrated by comparing results from a breast cancer cell line, MCF-7, and an ovarian cancer cell line, NCI/ADR-RES, and by using genomic DNA as starting material. Conclusion Our approach may greatly facilitate the analysis of combinatorial expression of known genes in many important applications, especially when the amount of RNA is limited.

  11. Cell type-specific genes show striking and distinct patterns of spatial expression in the mouse brain.

    Science.gov (United States)

    Ko, Younhee; Ament, Seth A; Eddy, James A; Caballero, Juan; Earls, John C; Hood, Leroy; Price, Nathan D

    2013-02-19

    To characterize gene expression patterns in the regional subdivisions of the mammalian brain, we integrated spatial gene expression patterns from the Allen Brain Atlas for the adult mouse with panels of cell type-specific genes for neurons, astrocytes, and oligodendrocytes from previously published transcriptome profiling experiments. We found that the combined spatial expression patterns of 170 neuron-specific transcripts revealed strikingly clear and symmetrical signatures for most of the brain's major subdivisions. Moreover, the brain expression spatial signatures correspond to anatomical structures and may even reflect developmental ontogeny. Spatial expression profiles of astrocyte- and oligodendrocyte-specific genes also revealed regional differences; these defined fewer regions and were less distinct but still symmetrical in the coronal plane. Follow-up analysis suggested that region-based clustering of neuron-specific genes was related to (i) a combination of individual genes with restricted expression patterns, (ii) region-specific differences in the relative expression of functional groups of genes, and (iii) regional differences in neuronal density. Products from some of these neuron-specific genes are present in peripheral blood, raising the possibility that they could reflect the activities of disease- or injury-perturbed networks and collectively function as biomarkers for clinical disease diagnostics.

  12. Origins of Specificity and Promiscuity in Metabolic Networks

    Science.gov (United States)

    Carbonell, Pablo; Lecointre, Guillaume; Faulon, Jean-Loup

    2011-01-01

    How enzymes have evolved to their present form is linked to the question of how pathways emerged and evolved into extant metabolic networks. To investigate this mechanism, we have explored the chemical diversity present in a largely unbiased data set of catalytic reactions processed by modern enzymes across the tree of life. In order to get a quantitative estimate of enzyme chemical diversity, we measure enzyme multispecificity or promiscuity using the reaction molecular signatures. Our main finding is that reactions that are catalyzed by a highly specific enzyme are shared by poorly divergent species, suggesting a later emergence of this function during evolution. In contrast, reactions that are catalyzed by highly promiscuous enzymes are more likely to appear uniformly distributed across species in the tree of life. From a functional point of view, promiscuous enzymes are mainly involved in amino acid and lipid metabolisms, which might be associated with the earliest form of biochemical reactions. In this way, results presented in this paper might assist us with the identification of primeval promiscuous catalytic functions contributing to life's minimal metabolism. PMID:22052908

  13. Covert brand recognition engages emotion-specific brain networks.

    Science.gov (United States)

    Casarotto, Silvia; Ricciardi, Emiliano; Romani, Simona; Dalli, Daniele; Pietrini, Pietro

    2012-12-01

    Consumer goods' brands have become a major driver of consumers' choice: they have got symbolic, relational and even social properties that add substantial cultural and affective value to goods and services. Therefore, measuring the role of brands in consumers' cognitive and affective processes would be very helpful to better understand economic decision making. This work aimed at finding the neural correlates of automatic, spontaneous emotional response to brands, showing how deeply integrated are consumption symbols within the cognitive and affective processes of individuals. Functional magnetic resonance imaging (fMRI) was measured during a visual oddball paradigm consisting in the presentation of scrambled pictures as frequent stimuli, colored squares as targets, and brands and emotional pictures (selected from the International Affective Picture System [IAPS]) as emotionally-salient distractors. Affective rating of brands was assessed individually after scanning by a validated questionnaire. Results showed that, similarly to IAPS pictures, brands activated a well-defined emotional network, including amygdala and dorsolateral prefrontal cortex, highly specific of affective valence. In conclusion, this work identified the neural correlates of brands within cognitive and affective processes of consumers.

  14. Construction and analysis of regulatory genetic networks in cervical cancer based on involved microRNAs, target genes, transcription factors and host genes.

    Science.gov (United States)

    Wang, Ning; Xu, Zhiwen; Wang, Kunhao; Zhu, Minghui; Li, Yang

    2014-04-01

    Over recent years, genes and microRNA (miRNA/miR) have been considered as key biological factors in human carcinogenesis. During cancer development, genes may act as multiple identities, including target genes of miRNA, transcription factors and host genes. The present study concentrated on the regulatory networks consisting of the biological factors involved in cervical cancer in order to investigate their features and affect on this specific pathology. Numerous raw data was collected and organized into purposeful structures, and adaptive procedures were defined for application to the prepared data. The networks were therefore built with the factors as basic components according to their interacting associations. The networks were constructed at three levels of interdependency, including a differentially-expressed network, a related network and a global network. Comparisons and analyses were made at a systematic level rather than from an isolated gene or miRNA. Critical hubs were extracted in the core networks and notable features were discussed, including self-adaption feedback regulation. The present study expounds the pathogenesis from a novel point of view and is proposed to provide inspiration for further investigation and therapy.

  15. Prioritization of Susceptibility Genes for Ectopic Pregnancy by Gene Network Analysis.

    Science.gov (United States)

    Liu, Ji-Long; Zhao, Miao

    2016-02-01

    Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%-2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk-based computational method named TM-rank to prioritize ectopic pregnancy-related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes.

  16. 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.

  17. Optimal Control of Gene Regulatory Networks with Effectiveness of Multiple Drugs: A Boolean Network Approach

    Science.gov (United States)

    Kobayashi, Koichi; Hiraishi, Kunihiko

    2013-01-01

    Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of gene regulatory networks, and gene expression is expressed by a binary value (0 or 1). In the control problem, we assume that the concentration level of a part of genes is arbitrarily determined as the control input. However, there are cases that no gene satisfying this assumption exists, and it is important to consider structural control via external stimuli. Furthermore, these controls are realized by multiple drugs, and it is also important to consider multiple effects such as duration of effect and side effects. In this paper, we propose a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. First, a BN model and duration of drug effectiveness are discussed. Next, the optimal control problem is formulated and is reduced to an integer linear programming problem. Finally, numerical simulations are shown. PMID:24058904

  18. Regulation of flowering in rice: two florigen genes, a complex gene network, and natural variation.

    Science.gov (United States)

    Tsuji, Hiroyuki; Taoka, Ken-ichiro; Shimamoto, Ko

    2011-02-01

    Photoperiodic control of flowering time consists of a complicated network that converges into the generation of a mobile flowering signal called florigen. Recent advances identifying the protein FT/Hd3a as the molecular nature responsible for florigen activity have focused current research on florigen genes as the important output of this complex signaling network. Rice is a model system for short-day plants and recent progress in elucidating the flowering network from rice and Arabidopsis, a long-day plant, provides an evolutionarily comparative view of the photoperiodic flowering pathway. This review summarizes photoperiodic flowering control in rice, including the interaction of complex layers of gene networks contributed from evolutionarily unique factors and the regulatory adaptation of conserved factors.

  19. A highly specific pathogen-responsive promoter element from the immediate-early activated CMPG1 gene in Petroselinum crispum.

    Science.gov (United States)

    Kirsch, C; Logemann, E; Lippok, B; Schmelzer, E; Hahlbrock, K

    2001-04-01

    Within the complex signalling network from pathogen-derived elicitor perception to defense-related gene activation, some immediate-early responding genes may have pivotal roles in downstream transcriptional regulation. We have identified the parsley (Petroselinum crispum) ELI17 gene as a particularly fast-responding gene possessing a new type of W box-containing, elicitor-responsive promoter element, E17. Highly selective E17-mediated reporter gene expression at pathogen infection sites in transgenic Arabidopsis thaliana plants demonstrated the potential of this promoter element for designing new strategies in resistance breeding as well as for further analysis of the early components of defense-related gene activation mechanisms. The protein encoded by the ELI17 gene exhibits various structural characteristics of established transcription factors and is designated as a CMPG protein according to the first four strictly conserved amino acids defining a newly emerging class of plant-specific proteins.

  20. Modifier genes and the plasticity of genetic networks in mice.

    Directory of Open Access Journals (Sweden)

    Bruce A Hamilton

    Full Text Available Modifier genes are an integral part of the genetic landscape in both humans and experimental organisms, but have been less well explored in mammals than other systems. A growing number of modifier genes in mouse models of disease nonetheless illustrate the potential for novel findings, while new technical advances promise many more to come. Modifier genes in mouse models include induced mutations and spontaneous or wild-derived variations captured in inbred strains. Identification of modifiers among wild-derived variants in particular should detect disease modifiers that have been shaped by selection and might therefore be compatible with high fitness and function. Here we review selected examples and argue that modifier genes derived from natural variation may provide a bias for nodes in genetic networks that have greater intrinsic plasticity and whose therapeutic manipulation may therefore be more resilient to side effects than conventional targets.

  1. Finding recurrence networks' threshold adaptively for a specific time series

    Science.gov (United States)

    Eroglu, D.; Marwan, N.; Prasad, S.; Kurths, J.

    2014-11-01

    Recurrence-plot-based recurrence networks are an approach used to analyze time series using a complex networks theory. In both approaches - recurrence plots and recurrence networks -, a threshold to identify recurrent states is required. The selection of the threshold is important in order to avoid bias of the recurrence network results. In this paper, we propose a novel method to choose a recurrence threshold adaptively. We show a comparison between the constant threshold and adaptive threshold cases to study period-chaos and even period-period transitions in the dynamics of a prototypical model system. This novel method is then used to identify climate transitions from a lake sediment record.

  2. The use of gene interaction networks to improve the identification of cancer driver genes

    Directory of Open Access Journals (Sweden)

    Emilie Ramsahai

    2017-01-01

    Full Text Available Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

  3. Genes responsive to elevated CO2 concentrations in triploid white poplar and integrated gene network analysis.

    Directory of Open Access Journals (Sweden)

    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.

  4. Methods for Characterizing the Epigenetic Attractors Landscape Associated with Boolean Gene Regulatory Networks

    OpenAIRE

    Davila-Velderrain, Jose; Juarez-Ramiro, Luis; Martinez-Garcia, Juan C.; Alvarez-Buylla, Elena R

    2015-01-01

    Gene regulatory network (GRN) modeling is a well-established theoretical framework for the study of cell-fate specification during developmental processes. Recently, dynamical models of GRNs have been taken as a basis for formalizing the metaphorical model of Waddington's epigenetic landscape, providing a natural extension for the general protocol of GRN modeling. In this contribution we present in a coherent framework a novel implementation of two previously proposed general frameworks for m...

  5. The effects of a DTNBP1 gene variant on attention networks: an fMRI study

    Directory of Open Access Journals (Sweden)

    Thimm Markus

    2010-09-01

    Full Text Available Abstract Background Attention deficits belong to the main cognitive symptoms of schizophrenia and come along with altered neural activity in previously described cerebral networks. Given the high heritability of schizophrenia the question arises if impaired function of these networks is modulated by susceptibility genes and detectable in healthy risk allele carriers. Methods The present event-related fMRI study investigated the effect of the single nucleotide polymorphism (SNP rs1018381 of the DTNBP1 (dystrobrevin-binding protein 1 gene on brain activity in 80 subjects while performing the attention network test (ANT. In this reaction time task three domains of attention are probed simultaneously: alerting, orienting and executive control of attention. Results Risk allele carriers showed impaired performance in the executive control condition associated with reduced neural activity in the left superior frontal gyrus [Brodmann area (BA 9]. Risk allele carriers did not show alterations in the alerting and orienting networks. Conclusions BA 9 is a key region of schizophrenia pathology and belongs to a network that has been shown previously to be involved in impaired executive control mechanisms in schizophrenia. Our results identified the impact of DTNBP1 on the development of a specific attention deficit via modulation of a left prefrontal network.

  6. Layer-specific chromatin accessibility landscapes reveal regulatory networks in adult mouse visual cortex

    Science.gov (United States)

    Gray, Lucas T; Yao, Zizhen; Nguyen, Thuc Nghi; Kim, Tae Kyung; Zeng, Hongkui; Tasic, Bosiljka

    2017-01-01

    Mammalian cortex is a laminar structure, with each layer composed of a characteristic set of cell types with different morphological, electrophysiological, and connectional properties. Here, we define chromatin accessibility landscapes of major, layer-specific excitatory classes of neurons, and compare them to each other and to inhibitory cortical neurons using the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq). We identify a large number of layer-specific accessible sites, and significant association with genes that are expressed in specific cortical layers. Integration of these data with layer-specific transcriptomic profiles and transcription factor binding motifs enabled us to construct a regulatory network revealing potential key layer-specific regulators, including Cux1/2, Foxp2, Nfia, Pou3f2, and Rorb. This dataset is a valuable resource for identifying candidate layer-specific cis-regulatory elements in adult mouse cortex. DOI: http://dx.doi.org/10.7554/eLife.21883.001 PMID:28112643

  7. Ethanol modulation of gene networks: implications for alcoholism.

    Science.gov (United States)

    Farris, Sean P; Miles, Michael F

    2012-01-01

    Alcoholism is a complex disease caused by a confluence of environmental and genetic factors influencing multiple brain pathways to produce a variety of behavioral sequelae, including addiction. Genetic factors contribute to over 50% of the risk for alcoholism and recent evidence points to a large number of genes with small effect sizes as the likely molecular basis for this disease. Recent progress in genomics (microarrays or RNA-Seq) and genetics has led to the identification of a large number of potential candidate genes influencing ethanol behaviors or alcoholism itself. To organize this complex information, investigators have begun to focus on the contribution of gene networks, rather than individual genes, for various ethanol-induced behaviors in animal models or behavioral endophenotypes comprising alcoholism. This chapter reviews some of the methods used for constructing gene networks from genomic data and some of the recent progress made in applying such approaches to the study of the neurobiology of ethanol. We show that rapid technology development in gathering genomic data, together with sophisticated experimental design and a growing collection of analysis tools are producing novel insights for understanding the molecular basis of alcoholism and that such approaches promise new opportunities for therapeutic development.

  8. Comparison of evolutionary algorithms in gene regulatory network model inference.

    LENUS (Irish Health Repository)

    2010-01-01

    ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

  9. 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

  10. Optimal Constrained Stationary Intervention in Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Golnaz Vahedi

    2008-05-01

    Full Text Available A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the reward function and then applies this formulation to bound the expected number of treatments. A mutated mammalian cell cycle is considered as a case study.

  11. Optimal Constrained Stationary Intervention in Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Faryabi Babak

    2008-01-01

    Full Text Available A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the reward function and then applies this formulation to bound the expected number of treatments. A mutated mammalian cell cycle is considered as a case study.

  12. Noise Control in Gene Regulatory Networks with Negative Feedback.

    Science.gov (United States)

    Hinczewski, Michael; Thirumalai, D

    2016-07-01

    Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.

  13. Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms

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    Tauch Andreas

    2009-01-01

    Full Text Available Abstract Background Transcriptional regulation of gene activity is essential for any living organism. Transcription factors therefore recognize specific binding sites within the DNA to regulate the expression of particular target genes. The genome-scale reconstruction of the emerging regulatory networks is important for biotechnology and human medicine but cost-intensive, time-consuming, and impossible to perform for any species separately. By using bioinformatics methods one can partially transfer networks from well-studied model organisms to closely related species. However, the prediction quality is limited by the low level of evolutionary conservation of the transcription factor binding sites, even within organisms of the same genus. Results Here we present an integrated bioinformatics workflow that assures the reliability of transferred gene regulatory networks. Our approach combines three methods that can be applied on a large-scale: re-assessment of annotated binding sites, subsequent binding site prediction, and homology detection. A gene regulatory interaction is considered to be conserved if (1 the transcription factor, (2 the adjusted binding site, and (3 the target gene are conserved. The power of the approach is demonstrated by transferring gene regulations from the model organism Corynebacterium glutamicum to the human pathogens C. diphtheriae, C. jeikeium, and the biotechnologically relevant C. efficiens. For these three organisms we identified reliable transcriptional regulations for ~40% of the common transcription factors, compared to ~5% for which knowledge was available before. Conclusion Our results suggest that trustworthy genome-scale transfer of gene regulatory networks between organisms is feasible in general but still limited by the level of evolutionary conservation.

  14. "Every Gene Is Everywhere but the Environment Selects": Global Geolocalization of Gene Sharing in Environmental Samples through Network Analysis.

    Science.gov (United States)

    Fondi, Marco; Karkman, Antti; Tamminen, Manu V; Bosi, Emanuele; Virta, Marko; Fani, Renato; Alm, Eric; McInerney, James O

    2016-05-13

    The spatial distribution of microbes on our planet is famously formulated in the Baas Becking hypothesis as "everything is everywhere but the environment selects." While this hypothesis does not strictly rule out patterns caused by geographical effects on ecology and historical founder effects, it does propose that the remarkable dispersal potential of microbes leads to distributions generally shaped by environmental factors rather than geographical distance. By constructing sequence similarity networks from uncultured environmental samples, we show that microbial gene pool distributions are not influenced nearly as much by geography as ecology, thus extending the Bass Becking hypothesis from whole organisms to microbial genes. We find that gene pools are shaped by their broad ecological niche (such as sea water, fresh water, host, and airborne). We find that freshwater habitats act as a gene exchange bridge between otherwise disconnected habitats. Finally, certain antibiotic resistance genes deviate from the general trend of habitat specificity by exhibiting a high degree of cross-habitat mobility. The strong cross-habitat mobility of antibiotic resistance genes is a cause for concern and provides a paradigmatic example of the rate by which genes colonize new habitats when new selective forces emerge.

  15. Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

    Directory of Open Access Journals (Sweden)

    Nolan Priedigkeit

    2015-02-01

    Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

  16. Topological effects of data incompleteness of gene regulatory networks

    CERN Document Server

    Sanz, J; Borge-Holthoefer, J; Moreno, Y

    2012-01-01

    The topological analysis of biological networks has been a prolific topic in network science during the last decade. A persistent problem with this approach is the inherent uncertainty and noisy nature of the data. One of the cases in which this situation is more marked is that of transcriptional regulatory networks (TRNs) in bacteria. The datasets are incomplete because regulatory pathways associated to a relevant fraction of bacterial genes remain unknown. Furthermore, direction, strengths and signs of the links are sometimes unknown or simply overlooked. Finally, the experimental approaches to infer the regulations are highly heterogeneous, in a way that induces the appearance of systematic experimental-topological correlations. And yet, the quality of the available data increases constantly. In this work we capitalize on these advances to point out the influence of data (in)completeness and quality on some classical results on topological analysis of TRNs, specially regarding modularity at different level...

  17. Dose response relationship in anti-stress gene regulatory networks.

    OpenAIRE

    Qiang Zhang; Andersen, Melvin E.

    2007-01-01

    To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misf...

  18. Specific regulatory motifs predict glucocorticoid responsiveness of hippocampal gene expression.

    Science.gov (United States)

    Datson, N A; Polman, J A E; de Jonge, R T; van Boheemen, P T M; van Maanen, E M T; Welten, J; McEwen, B S; Meiland, H C; Meijer, O C

    2011-10-01

    The glucocorticoid receptor (GR) is an ubiquitously expressed ligand-activated transcription factor that mediates effects of cortisol in relation to adaptation to stress. In the brain, GR affects the hippocampus to modulate memory processes through direct binding to glucocorticoid response elements (GREs) in the DNA. However, its effects are to a high degree cell specific, and its target genes in different cell types as well as the mechanisms conferring this specificity are largely unknown. To gain insight in hippocampal GR signaling, we characterized to which GRE GR binds in the rat hippocampus. Using a position-specific scoring matrix, we identified evolutionary-conserved putative GREs from a microarray based set of hippocampal target genes. Using chromatin immunoprecipitation, we were able to confirm GR binding to 15 out of a selection of 32 predicted sites (47%). The majority of these 15 GREs are previously undescribed and thus represent novel GREs that bind GR and therefore may be functional in the rat hippocampus. GRE nucleotide composition was not predictive for binding of GR to a GRE. A search for conserved flanking sequences that may predict GR-GRE interaction resulted in the identification of GC-box associated motifs, such as Myc-associated zinc finger protein 1, within 2 kb of GREs with GR binding in the hippocampus. This enrichment was not present around nonbinding GRE sequences nor around proven GR-binding sites from a mesenchymal stem-like cell dataset that we analyzed. GC-binding transcription factors therefore may be unique partners for DNA-bound GR and may in part explain cell-specific transcriptional regulation by glucocorticoids in the context of the hippocampus.

  19. Prioritisation and network analysis of Crohn's disease susceptibility genes.

    Directory of Open Access Journals (Sweden)

    Daniele Muraro

    Full Text Available Recent Genome-Wide Association Studies (GWAS have revealed numerous Crohn's disease susceptibility genes and a key challenge now is in understanding how risk polymorphisms in associated genes might contribute to development of this disease. For a gene to contribute to disease phenotype, its risk variant will likely adversely communicate with a variety of other gene products to result in dysregulation of common signaling pathways. A vital challenge is to elucidate pathways of potentially greatest influence on pathological behaviour, in a manner recognizing how multiple relevant genes may yield integrative effect. In this work we apply mathematical analysis of networks involving the list of recently described Crohn's susceptibility genes, to prioritise pathways in relation to their potential development of this disease. Prioritisation was performed by applying a text mining and a diffusion based method (GRAIL, GPEC. Prospective biological significance of the resulting prioritised list of proteins is highlighted by changes in their gene expression levels in Crohn's patients intestinal tissue in comparison with healthy donors.

  20. Specific expression of bioluminescence reporter gene in cardiomyocyte regulated by tissue specific promoter

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Vu Hong; Tae, Seong Ho; Le, Nguyen Uyen Chi; Min, Jung Joon [Chonnam National University Medical School, Gwangju (Korea, Republic of)

    2007-07-01

    As the human heart is not capable of regenerating the great numbers of cardiac cells that are lost after myocardial infarction, impaired cardiac function is the inevitable result of ischemic disease. Recently, human embryonic stem cells (hESCs) have gained popularity as a potentially ideal cell candidate for tissue regeneration. In particular, hESCs are capable of cardiac lineage-specific differentiation and confer improvement of cardiac function following transplantation into animal models. Although such data are encouraging, the specific strategy for in vivo and non-invasive detection of differentiated cardiac lineage is still limited. Therefore, in the present study, we established the gene construction in which the optical reporter gene Firefly luciferase was controlled by Myosin Heavy Chain promoter for specific expressing in heart cells. The vector consisting of - MHC promoter and a firefly luciferase coding sequence flanked by full-length bovine growth hormone (BGH) 3'-polyadenylation sequence based on pcDNA3.1- vector backbone. To test the specific transcription of this promoter in g of MHC-Fluc or CMV-Flue (for control) plasmid DNA in myocardial tissue, 20 phosphate-buffered saline was directly injected into mouse myocardium through a midline sternotomy and liver. After 1 week of injection, MHC-Fluc expression was detected from heart region which was observed under cooled CCD camera of in vivo imaging system but not from liver. In control group injected with CMV-Flue, the bioluminescence was detected from all these organs. The expression of Flue under control of Myosin Heavy Chain promoter may become a suitable optical reporter gene for stem cell-derived cardiac lineage differentiation study.

  1. The immunoglobulin genes: structure and specificity in chronic lymphocytic leukemia.

    Science.gov (United States)

    Tobin, Gerard

    2007-06-01

    The rearrangement of the immunoglobulin genes (IG) provides a large diversity of B-cell receptors conformations and allows the immune system to respond differently to foreign antigens. In chronic lymphocytic leukemia (CLL), there are a restricted number of stereotyped B-cell receptors rearranged by the tumor B-cells between CLL patients. These subsets with stereotyped receptors appear to have clinical implications, for example cases that rearrange the IGHV3-21 gene display poor clinical prognosis. The number of subsets with stereotyped receptors has been reported at a frequency of over 20% of CLL cases; however, the specificities of these receptors are still not clearly defined. Reactivity to epitopes from bacterial antigen, cytoskeleton components such as vimentin, and antigens on viable and apoptotic T-cell have been proposed. The role of antigen in CLL development is currently being more clearly defined with identification of stereotyped receptors, and their antigen specificity and the continued role antigen stimulation plays in CLL disease will be an important question in the future.

  2. An animal model with a cardiomyocyte-specific deletion of estrogen receptor alpha: functional, metabolic, and differential network analysis.

    Directory of Open Access Journals (Sweden)

    Sriram Devanathan

    Full Text Available Estrogen exerts diverse biological effects in multiple tissues in both animals and humans. Much of the accumulated knowledge on the role of estrogen receptor (ER in the heart has been obtained from studies using ovariectomized mice, whole body ER gene knock-out animal models, ex vivo heart studies, or from isolated cardiac myocytes. In light of the wide systemic influence of ER signaling in regulating a host of biological functions in multiple tissues, it is difficult to infer the direct role of ER on the heart. Therefore, we developed a mouse model with a cardiomyocyte-specific deletion of the ERα allele (cs-ERα-/-. Male and female cs-ERα-/- mice with age/sex-matched wild type controls were examined for differences in cardiac structure and function by echocardiogram and differential gene expression microarray analysis. Our study revealed sex-differences in structural parameters in the hearts of cs-ERα-/- mice, with minimal functional differences. Analysis of microarray data revealed differential variations in the expression of 208 genes affecting multiple transcriptional networks. Furthermore, we report sex-specific differences in the expression of 56 genes. Overall, we developed a mouse model with cardiac-specific deletion of ERα to characterize the role of ERα in the heart independent of systemic effects. Our results suggest that ERα is involved in controlling the expression of diverse genes and networks in the cardiomyocyte in a sex-dependent manner.

  3. Fuzzy tandem repeats containing p53 response elements may define species-specific p53 target genes.

    Directory of Open Access Journals (Sweden)

    Iva Simeonova

    2012-06-01

    Full Text Available Evolutionary forces that shape regulatory networks remain poorly understood. In mammals, the Rb pathway is a classic example of species-specific gene regulation, as a germline mutation in one Rb allele promotes retinoblastoma in humans, but not in mice. Here we show that p53 transactivates the Retinoblastoma-like 2 (Rbl2 gene to produce p130 in murine, but not human, cells. We found intronic fuzzy tandem repeats containing perfect p53 response elements to be important for this regulation. We next identified two other murine genes regulated by p53 via fuzzy tandem repeats: Ncoa1 and Klhl26. The repeats are poorly conserved in evolution, and the p53-dependent regulation of the murine genes is lost in humans. Our results indicate a role for the rapid evolution of tandem repeats in shaping differences in p53 regulatory networks between mammalian species.

  4. Identification of cell cycle-regulated genes by convolutional neural network.

    Science.gov (United States)

    Liu, Chenglin; Cui, Peng; Huang, Tao

    2017-04-17

    The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype were analyzed. Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight of the cell cycle mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. A hierarchy of ECM-mediated signalling tissue-specific gene expression regulates tissue-specific gene expression

    Energy Technology Data Exchange (ETDEWEB)

    Roskelley, Calvin D; Srebrow, Anabella; Bissell, Mina J

    1995-10-07

    A dynamic and reciprocal flow of information between cells and the extracellular matrix contributes significantly to the regulation of form and function in developing systems. Signals generated by the extracellular matrix do not act in isolation. Instead, they are processed within the context of global signalling hierarchies whose constituent inputs and outputs are constantly modulated by all the factors present in the cell's surrounding microenvironment. This is particularly evident in the mammary gland, where the construction and subsequent destruction of such a hierarchy regulates changes in tissue-specific gene expression, morphogenesis and apoptosis during each developmental cycle of pregnancy, lactation and involution.

  6. Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning.

    Science.gov (United States)

    Luong, Ngoc Hoang; Poutré, Han La; Bosman, Peter A N

    2017-04-07

    This article tackles the Distribution Network Expansion Planning (DNEP) problemthat has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. Because of many real-world details involved, the structure of the problem is not exploited easily using mathematical programming techniques, for which reason we consider solving this problem with evolutionary algorithms (EAs). We compare three types of EAs for optimizing expansion plans: the classic genetic algorithm (GA), the estimation-of-distribution algorithm (EDA), and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). Not fully knowing the structure of the problem, we study the effect of linkage learning through the use of three linkage models: univariate, marginal product, and linkage tree. We furthermore experiment with the impact of incorporating different levels of problem-specific knowledge in the variation operators. Experiments show that the use of problem-specific variation operators is far more important for the classic GA to find high-quality solutions. In all EAs, the marginal product model and its linkage learning procedure have difficulty in capturing and exploiting the DNEP problemstructure. GOMEA, especiallywhen combined with the linkage tree structure, is found to have the most robust performance by far, even when an out-of-the-box variant is used that does not exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for power system expansion planning problems, EAs that have the ability to effectively model and efficiently exploit problem structures, such as GOMEA, should be given priority, especially in the case of black-box or grey-box optimization.

  7. Gene Network for Identifying the Entropy Changes of Different Modules in Pediatric Sepsis

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2016-12-01

    Full Text Available Background/Aims: Pediatric sepsis is a disease that threatens life of children. The incidence of pediatric sepsis is higher in developing countries due to various reasons, such as insufficient immunization and nutrition, water and air pollution, etc. Exploring the potential genes via different methods is of significance for the prevention and treatment of pediatric sepsis. This study aimed to identify potential genes associated with pediatric sepsis utilizing analysis of gene network and entropy. Methods: The mRNA expression in the blood samples collected from 20 septic children and 30 healthy controls was quantified by using Affymetrix HG-U133A microarray. Two condition-specific protein-protein interaction networks (PINs, one for the healthy control and the other one for the children with sepsis, were deduced by combining the fundamental human PINs with gene expression profiles in the two phenotypes. Subsequently, distinct modules from the two conditional networks were extracted by adopting a maximal clique-merging approach. Delta entropy (ΔS was calculated between sepsis and control modules. Results: Then, key genes displaying changes in gene composition were identified by matching the control and sepsis modules. Two objective modules were obtained, in which ribosomal protein RPL4 and RPL9 as well as TOP2A were probably considered as the key genes differentiating sepsis from healthy controls. Conclusion: According to previous reports and this work, TOP2A is the potential gene therapy target for pediatric sepsis. The relationship between pediatric sepsis and RPL4 and RPL9 needs further investigation.

  8. Syntenator: Multiple gene order alignments with a gene-specific scoring function

    Directory of Open Access Journals (Sweden)

    Dieterich Christoph

    2008-11-01

    Full Text Available Abstract Background Identification of homologous regions or conserved syntenies across genomes is one crucial step in comparative genomics. This task is usually performed by genome alignment softwares like WABA or blastz. In case of conserved syntenies, such regions are defined as conserved gene orders. On the gene order level, homologous regions can even be found between distantly related genomes, which do not align on the nucleotide sequence level. Results We present a novel approach to identify regions of conserved synteny across multiple genomes. Syntenator represents genomes and alignments thereof as partial order graphs (POGs. These POGs are aligned by a dynamic programming approach employing a gene-specific scoring function. The scoring function reflects the level of protein sequence similarity for each possible gene pair. Our method consistently defines larger homologous regions in pairwise gene order alignments than nucleotide-level comparisons. Our method is superior to methods that work on predefined homology gene sets (as implemented in Blockfinder. Syntenator successfully reproduces 80% of the EnsEMBL man-mouse conserved syntenic blocks. The full potential of our method becomes visible by comparing remotely related genomes and multiple genomes. Gene order alignments potentially resolve up to 75% of the EnsEMBL 1:many orthology relations and 27% of the many:many orthology relations. Conclusion We propose Syntenator as a software solution to reliably infer conserved syntenies among distantly related genomes. The software is available from http://www2.tuebingen.mpg.de/abt4/plone.

  9. Attention reorganizes connectivity across networks in a frequency specific manner

    DEFF Research Database (Denmark)

    Kwon, Soyoung; Watanabe, Masataka; Fischer, Elvira

    2017-01-01

    Attention allows our brain to focus its limited resources on a given task. It does so by selective modulation of neural activity and of functional connectivity (FC) across brain-wide networks. While there is extensive literature on activity changes, surprisingly few studies examined brain-wide FC......-segregated analyses. We found that FC derived from long blocks had a nearly two-fold higher gain compared to FC derived from traditional (short) block designs. Second, attention enhanced intrinsic (negative or positive) correlations across networks, such as between the default-mode network (DMN), the dorsal attention...... network (DAN), and the visual system (VIS). In contrast attention de-correlated the intrinsically correlated visual regions. Third, the de-correlation within VIS was driven primarily by high frequencies, whereas the increase in DAN-VIS predominantly by low frequencies. These results pinpoint two...

  10. Expression Patterns of Glucose Transporter-1 Gene and Thyroid Specific Genes in Human Papillary Thyroid Carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sungeun; Chung, Junekey; Min Haesook and others

    2014-06-15

    The expression of glucose transporter-1 (Glut-1) gene and those of major thyroid-specific genes were examined in papillary carcinoma tissues, and the expressions of these genes were compared with cancer differentiation grades. Twenty-four human papillary carcinoma tissues were included in this study. The expressions of Glut-1- and thyroid-specific genes [sodium/iodide symporter (NIS), thyroid peroxidase, thyroglobulin, TSH receptor and pendrin] were analyzed by RT-PCR. Expression levels were expressed as ratios versus the expression of beta-actin. Pathologic differentiation of papillary carcinoma was classified into a relatively well-differentiated group (n=13) and relatively less differentiated group (n=11). Glut-1 gene expression was significantly higher in the less differentiated group (0.66±0.04) than in the well-differentiated group (0.59±0.07). The expression levels of the NIS, PD and TG genes were significantly higher in the well-differentiated group (NIS: 0.67±0.20, PD: 0.65±0.21, TG: 0.74±0.16) than in the less differentiated group (NIS: 0.36±0.05, PD: 0.49±0.08, TG: 0.60±0.11), respectively. A significant negative correlation was found between Glut-1 and NIS expression, and positive correlations were found between NIS and TG, and between NIS and PD. The NIS, PD and TG genes were highly expressed in well-differentiated thyroid carcinomas, whereas the Glut-1 gene was highly expressed in less differentiated thyroid carcinomas. These findings provide a molecular rationale for the management of papillary carcinoma, especially in the selection of FDG PET or radioiodine whole-body scan and I-131-based therapy.

  11. Landslide susceptibility and risk assessment: specificities for road networks

    Science.gov (United States)

    Pellicani, Roberta; Argentiero, Ilenia; Parisi, Alessandro; Spilotro, Giuseppe

    2017-04-01

    A regional-scale assessment of landslide susceptibility and risk along the main road corridors crossing the provincial territory of Matera (Basilicata Region, Southern Italy) was carried out. The entire provincial road network extends for about 1,320 km through a territory, of which represents the main connection infrastructure among thirty-one municipalities due to the lack of an efficient integrated transportation system through the whole regional territory. For this reason, the strategic importance of these roads consists in their uniqueness in connecting every urban center with the socio-economic surrounding context. These roads and their vehicular traffic are continuously exposed to instability processes (about the 40% of the total length is disrupted by landslides), characterized both by high intensity and low frequency and by low intensity and high frequency. This last typology, consisting in small shallow landslides, is particularly hazardous for the roads since it is widespread along the road network, its occurrence is connected to rainfalls and determines high vulnerability conditions for the road in terms of interruption of vehicular traffic. A GIS-based heuristic-bivariate statistical predictive model was performed to assess and map the landslide susceptibility in the study area, by using a polynomial function of eight predisposing factors, weighted according to their influence on the landslide phenomena, recognized and collected in an inventory. Susceptibility associated to small shallow phenomena was assessed by using a polynomial function of specific factors, such as slope angle and aspect, lithological outcrops, rainfalls, etc. In absence of detailed input data, the spatial distribution of landslide risk along the road corridors was assessed and mapped using a qualitative hazard-consequence matrix approach, by which risk is obtained by combining hazard categories with consequence classes pairwise in a two-dimensional table or matrix. Landslide

  12. 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.

  13. State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

    Science.gov (United States)

    Al Qazlan, Tuqyah Abdullah; Kara-Mohamed, Chafia

    2015-01-01

    To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework. PMID:25879048

  14. State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

    Directory of Open Access Journals (Sweden)

    Tuqyah Abdullah Al Qazlan

    2015-01-01

    Full Text Available To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

  15. Application of Radial Basis Network Model for HIV/AIDs Regimen Specifications

    CERN Document Server

    Balasubramanie, P

    2009-01-01

    HIV/AIDs Regimen specification one of many problems for which bioinformaticians have implemented and trained machine learning methods such as neural networks. Predicting HIV resistance would be much easier, but unfortunately we rarely have enough structural information available to train a neural network. To network model designed to predict how long the HIV patient can prolong his/her life time with certain regimen specification. To learn this model 300 patient's details have taken as a training set to train the network and 100 patients medical history has taken to test this model. This network model is trained using MAT lab implementation.

  16. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

    Science.gov (United States)

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

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

  17. Evolutionary Conservation and Divergence of Gene Coexpression Networks in Gossypium (Cotton) Seeds.

    Science.gov (United States)

    Hu, Guanjing; Hovav, Ran; Grover, Corrinne E; Faigenboim-Doron, Adi; Kadmon, Noa; Page, Justin T; Udall, Joshua A; Wendel, Jonathan F

    2016-12-01

    The cotton genus (Gossypium) provides a superior system for the study of diversification, genome evolution, polyploidization, and human-mediated selection. To gain insight into phenotypic diversification in cotton seeds, we conducted coexpression network analysis of developing seeds from diploid and allopolyploid cotton species and explored network properties. Key network modules and functional associations were identified related to seed oil content and seed weight. We compared species-specific networks to reveal topological changes, including rewired edges and differentially coexpressed genes, associated with speciation, polyploidy, and cotton domestication. Network comparisons among species indicate that topologies are altered in addition to gene expression profiles, indicating that changes in transcriptomic coexpression relationships play a role in the developmental architecture of cotton seed development. The global network topology of allopolyploids, especially for domesticated G. hirsutum, resembles the network of the A-genome diploid more than that of the D-genome parent, despite its D-like phenotype in oil content. Expression modifications associated with allopolyploidy include coexpression level dominance and transgressive expression, suggesting that the transcriptomic architecture in polyploids is to some extent a modular combination of that of its progenitor genomes. Among allopolyploids, intermodular relationships are more preserved between two different wild allopolyploid species than they are between wild and domesticated forms of a cultivated cotton, and regulatory connections of oil synthesis-related pathways are denser and more closely clustered in domesticated vs. wild G. hirsutum. These results demonstrate substantial modification of genic coexpression under domestication. Our work demonstrates how network inference informs our understanding of the transcriptomic architecture of phenotypic variation associated with temporal scales ranging from

  18. The SLEEPER genes: a transposase-derived angiosperm-specific gene family

    Directory of Open Access Journals (Sweden)

    Knip Marijn

    2012-10-01

    Full Text Available Abstract Background DAYSLEEPER encodes a domesticated transposase from the hAT-superfamily, which is essential for development in Arabidopsis thaliana. Little is known about the presence of DAYSLEEPER orthologs in other species, or how and when it was domesticated. We studied the presence of DAYSLEEPER orthologs in plants and propose a model for the domestication of the ancestral DAYSLEEPER gene in angiosperms. Results Using specific BLAST searches in genomic and EST libraries, we found that DAYSLEEPER-like genes (hereafter called SLEEPER genes are unique to angiosperms. Basal angiosperms as well as grasses (Poaceae and dicotyledonous plants possess such putative orthologous genes, but SLEEPER-family genes were not found in gymnosperms, mosses and algae. Most species contain more than one SLEEPER gene. All SLEEPERs contain a C2H2 type BED-zinc finger domain and a hATC dimerization domain. We designated 3 motifs, partly overlapping the BED-zinc finger and dimerization domain, which are hallmark features in the SLEEPER family. Although SLEEPER genes are structurally conserved between species, constructs with SLEEPER genes from grapevine and rice did not complement the daysleeper phenotype in Arabidopsis, when expressed under control of the DAYSLEEPER promoter. However these constructs did cause a dominant phenotype when expressed in Arabidopsis. Rice plant lines with an insertion in the RICESLEEPER1 or 2 locus displayed phenotypic abnormalities, indicating that these genes are functional and important for normal development in rice. We suggest a model in which we hypothesize that an ancestral hAT transposase was retrocopied and stably integrated in the genome during early angiosperm evolution. Evidence is also presented for more recent retroposition events of SLEEPER genes, such as an event in the rice genome, which gave rise to the RICESLEEPER1 and 2 genes. Conclusions We propose the ancestral SLEEPER gene was formed after a process of retro

  19. Topology association analysis in weighted protein interaction network for gene prioritization

    Science.gov (United States)

    Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

    2016-11-01

    Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

  20. Information technology - Telecommunications and information exchange between systems - Private integrated services network - Specification, functional model and information flows - Call interception additional network feature

    CERN Document Server

    International Organization for Standardization. Geneva

    2003-01-01

    Information technology - Telecommunications and information exchange between systems - Private integrated services network - Specification, functional model and information flows - Call interception additional network feature

  1. NANOPARTICLE AS A NEW GENE TRANSFERRING VECTOR IN SPECIFIC EXPRESSION GENE

    Institute of Scientific and Technical Information of China (English)

    管珩; 李拥军; 郑曰宏; 刘昌伟; 杨菁; 宋存先; 王彭延; 赵三妹; 王宗立; 佘铭鹏

    2002-01-01

    Objective. To evaluate the possibility and efficiency of nanoparticle as a new vector in specific gene transference.Methods. Nanoparticle-DNA complex was prepared with Poly- dl-lactic-co-glycolic acid (PLGA) beating antisense monocyte chemotactic protein-1 (A-MCP-1), a specific expression gene, and the package efficiency, release progress in vitro, and the size of the complex were determined. The possibility of the new vector was evaluated with genomic DNA PCR by transferring gene into cultured smooth muscle cells (SMC), cationic lipids as a control. For study in vivo, jugular vein-to-artery bypass grafting procedures were performed on 20 New Zealand white rabbits, of which 6 grafts were transferred with nanoparticle-A-MCP-1 (200 μg), 6 with A - MCP - 1(200 μ g) by cationic liposome, 4 with LNCX plasmid, and 4 as control. Fourteen days after the grafts were harvested, the expression of A-MCP-1 and its effect on MCP-1 in vein grafts were detected by dot blot, and the morphologic evaluation of grafts was performed.Results. The package efficiency of the nanoparticle-DNA complex was 0. 9%, release progress in vitro lasted 2 weeks, and the size ranged from 150 to 300nm. SMC genomic DNA PCR showed that A-MCP-1 gene could be successfully transfected into cells by nanoparticle. The study in vivo indicated that A-MCP-1 mRNA was expressed in both local gene delivery groups, nanoparticle and liposome, meanwhile, MCP-1 expression in vein grafts was significantly inhibited and neointimal hyperplasia was notably reduced.Conclusion. Nanoparticle can act as a vector to transfect specific gene.

  2. Evolution of a core gene network for skeletogenesis in chordates.

    Directory of Open Access Journals (Sweden)

    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

  3. Specific Resting-state Brain Networks in Mesial Temporal Lobe Epilepsy

    Directory of Open Access Journals (Sweden)

    Mona eManeshi

    2014-07-01

    Full Text Available We studied with fMRI differences in resting-state networks between patients with mesial temporal lobe epilepsy (MTLE and healthy subjects. To avoid any a priori hypothesis, we use a data-driven analysis assessing differences between groups independently of structures involved. Shared and specific independent component analysis (SSICA is an exploratory method based on independent component analysis, which performs between-group network comparison. It extracts and classifies components (networks in those common between groups and those specific to one group. Resting fMRI data was collected from 10 healthy subjects and 10 MTLE patients. SSICA was applied multiple times with altered initializations and different numbers of specific components. This resulted in many components specific to patients and to controls. Spatial clustering identified the reliable resting-state networks among all specific components in each group. For each reliable specific network, power spectrum analysis was performed on reconstructed time series to estimate connectivity in each group and differences between groups. Two reliable networks, corresponding to statistically significant clusters robustly detected with clustering were labelled as specific to MTLE and one as specific to the control group. The most reliable MTLE network included hippocampus and amygdala bilaterally. The other MTLE network included the postcentral gyri and temporal poles. The control-specific network included bilateral precuneus, anterior cingulate, thalamus, parahippocampal gyrus. Results indicated that the two MTLE networks show increased connectivity in patients whereas the control-specific network shows decreased connectivity in patients. Our findings complement results from seed-based connectivity analysis (Pittau et al., 2012. The pattern of changes in connectivity between mesial temporal lobe structures and other areas may help us understand the cognitive impairments often reported in

  4. Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network.

    Science.gov (United States)

    Keith, Benjamin P; Robertson, David L; Hentges, Kathryn E

    2014-01-01

    Mutations in genes potentially lead to a number of genetic diseases with differing severity. These disease genes have been the focus of research in recent years showing that the disease gene population as a whole is not homogeneous, and can be categorized according to their interactions. Locus heterogeneity describes a single disorder caused by mutations in different genes each acting individually to cause the same disease. Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population. Through the manual curation of known causative genes of 100 diseases displaying locus heterogeneity and 397 single-gene Mendelian disorders, we use network parameters to show that our locus heterogeneity network displays distinct properties from the global disease network and a Mendelian network. Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity. Further topological analysis of this network revealed clustering of locus heterogeneity genes that cause identical disorders, indicating that these disease genes are involved in similar biological processes. We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity.

  5. [Epigenetics: gene and epigene networks in ontogeny and phylogeny].

    Science.gov (United States)

    Churaev, R N

    2006-09-01

    An attempt was made to systematize theoretical and experimental epigenetic data in the framework of genetics as a science on laws of preservation, coding, transfer, and transformation of heritable information in the living systems. The structure of the total hereditary memory is discussed in context of the theory of epigenes, hereditary units of the next to genes level of complexity. In epigenes as cells of functional hereditary memory, part of the hereditary information is stored, coded, and transmitted to the progeny irrespective of the primary structure of the genomic DNA molecules. The principles of the structure and the general laws of functioning of cellular governing gene networks are presented. The ontogenetic and phylogenetic role of epigene networks as the second level of the hereditary system is considered. Arguments for inheritance of somatic epimutations are presented, as well as the results of in silico and in vivo experiments showing the possibility of an epigenetic mechanism of primary biochemical divergent determination (autodetermination). A network hypothesis on material carriers of the common heterotary memory is formulated.

  6. Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

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    Chen Xin

    2012-10-01

    Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable

  7. Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development.

    Science.gov (United States)

    Mounet, Fabien; Moing, Annick; Garcia, Virginie; Petit, Johann; Maucourt, Michael; Deborde, Catherine; Bernillon, Stéphane; Le Gall, Gwénaëlle; Colquhoun, Ian; Defernez, Marianne; Giraudel, Jean-Luc; Rolin, Dominique; Rothan, Christophe; Lemaire-Chamley, Martine

    2009-03-01

    Variations in early fruit development and composition may have major impacts on the taste and the overall quality of ripe tomato (Solanum lycopersicum) fruit. To get insights into the networks involved in these coordinated processes and to identify key regulatory genes, we explored the transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the existence of clear structural and compositional differences between expanding mesocarp and locular tissue during fruit development (12-35 d postanthesis). Transcriptome and metabolome analyses were carried out with tomato microarrays and analytical methods including proton nuclear magnetic resonance and liquid chromatography-mass spectrometry, respectively. Pairwise comparisons of metabolite contents and gene expression profiles detected up to 37 direct gene-metabolite correlations involving regulatory genes (e.g. the correlations between glutamine, bZIP, and MYB transcription factors). Correlation network analyses revealed the existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a large regulatory network. This approach proved to be a valuable strategy for identifying specific subsets of genes implicated in key processes of fruit development and metabolism, which are therefore potential targets for genetic improvement of tomato fruit quality.

  8. Tissue-specific expression and regulatory networks of pig microRNAome.

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    Paolo Martini

    Full Text Available BACKGROUND: Despite the economic and medical importance of the pig, knowledge about its genome organization, gene expression regulation, and molecular mechanisms involved in physiological processes is far from that achieved for mouse and rat, the two most used model organisms in biomedical research. MicroRNAs (miRNAs are a wide class of molecules that exert a recognized role in gene expression modulation, but only 280 miRNAs in pig have been characterized to date. RESULTS: We applied a novel computational approach to predict species-specific and conserved miRNAs in the pig genome, which were then subjected to experimental validation. We experimentally identified candidate miRNAs sequences grouped in high-confidence (424 and medium-confidence (353 miRNAs according to RNA-seq results. A group of miRNAs was also validated by PCR experiments. We established the subtle variability in expression of isomiRs and miRNA-miRNA star couples supporting a biological function for these molecules. Finally, miRNA and mRNA expression profiles produced from the same sample of 20 different tissue of the animal were combined, using a correlation threshold to filter miRNA-target predictions, to identify tissue-specific regulatory networks. CONCLUSIONS: Our data represent a significant progress in the current understanding of miRNAome in pig. The identification of miRNAs, their target mRNAs, and the construction of regulatory circuits will provide new insights into the complex biological networks in several tissues of this important animal model.

  9. Gene Networks in Plant Ozone Stress Response and Tolerance

    Institute of Scientific and Technical Information of China (English)

    Agnieszka Ludwikow; Jan Sadowski

    2008-01-01

    For many plant species ozone stress has become much more severe in the last decade. The accumulating evidence for the significant effects of ozone pollutant on crop and forest yield situate ozone as one of the most important environmental stress factors that limits plant productivity woddwide. Today, transcdptomic approaches seem to give the best coverage of genome level responses. Therefore, microarray serves as an invaluable tool for global gene expression analyses, unravelling new information about gene pathways, in-species and crose-species gene expression comparison, and for the characterization of unknown relationships between genes. In this review we summadze the recent progress in the transcdptomics of ozone to demonstrate the benefits that can be harvested from the application of integrative and systematic analytical approaches to study ozone stress response. We focused our consideration on microarray analyses identifying gene networks responsible for response and tolerance to elevated ozone concentration. From these analyses it is now possible to notice how plant ozone defense responses depend on the interplay between many complex signaling pathways and metabolite signals.

  10. Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

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    Alina Sîrbu

    2015-05-01

    Full Text Available Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions. Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

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

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    Quan Li

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

  12. Autonomous Boolean modelling of developmental gene regulatory networks

    Science.gov (United States)

    Cheng, Xianrui; Sun, Mengyang; Socolar, Joshua E. S.

    2013-01-01

    During early embryonic development, a network of regulatory interactions among genes dynamically determines a pattern of differentiated tissues. We show that important timing information associated with the interactions can be faithfully represented in autonomous Boolean models in which binary variables representing expression levels are updated in continuous time, and that such models can provide a direct insight into features that are difficult to extract from ordinary differential equation (ODE) models. As an application, we model the experimentally well-studied network controlling fly body segmentation. The Boolean model successfully generates the patterns formed in normal and genetically perturbed fly embryos, permits the derivation of constraints on the time delay parameters, clarifies the logic associated with different ODE parameter sets and provides a platform for studying connectivity and robustness in parameter space. By elucidating the role of regulatory time delays in pattern formation, the results suggest new types of experimental measurements in early embryonic development. PMID:23034351

  13. Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development.

    Science.gov (United States)

    Thomas, Michael S C; Forrester, Neil A; Ronald, Angelica

    2016-01-01

    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description-four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene

  14. 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

  15. 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

  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. iFish: predicting the pathogenicity of human nonsynonymous variants using gene-specific/family-specific attributes and classifiers.

    Science.gov (United States)

    Wang, Meng; Wei, Liping

    2016-08-16

    Accurate prediction of the pathogenicity of genomic variants, especially nonsynonymous single nucleotide variants (nsSNVs), is essential in biomedical research and clinical genetics. Most current prediction methods build a generic classifier for all genes. However, different genes and gene families have different features. We investigated whether gene-specific and family-specific customized classifiers could improve prediction accuracy. Customized gene-specific and family-specific attributes were selected with AIC, BIC, and LASSO, and Support Vector Machine classifiers were generated for 254 genes and 152 gene families, covering a total of 5,985 genes. Our results showed that the customized attributes reflected key features of the genes and gene families, and the customized classifiers achieved higher prediction accuracy than the generic classifier. The customized classifiers and the generic classifier for other genes and families were integrated into a new tool named iFish (integrated Functional inference of SNVs in human, http://ifish.cbi.pku.edu.cn). iFish outperformed other methods on benchmark datasets as well as on prioritization of candidate causal variants from whole exome sequencing. iFish provides a user-friendly web-based interface and supports other functionalities such as integration of genetic evidence. iFish would facilitate high-throughput evaluation and prioritization of nsSNVs in human genetics research.

  18. Transcriptome analysis of neo-tetraploid rice reveals specific differential gene expressions associated with fertility and heterosis

    Science.gov (United States)

    Guo, Haibin; Mendrikahy, Jean Nestor; Xie, Lei; Deng, Junfeng; Lu, Zijun; Wu, Jinwen; Li, Xiang; Shahid, Muhammad Qasim; Liu, Xiangdong

    2017-01-01

    Polyploid rice hybrids have a powerful biological and yield potential that may become a new way for rice breeding; however, low fertility is major hindrance in commercial utilization. Here, we developed a neo-tetraploid rice that could overcome the sterility of autotetraploid rice and produce high heterosis. Transcriptome analysis of F1 hybrid developed by crossing neo-tetraploid with autotetraploid rice displayed 807, 663 and 866 differentially expressed genes that uniquely associated with F1 and specific to (DEGFu-sp) anther, ovary and leaf, respectively. Of the DEGFu-sp, 1224 genes displayed nonadditive expression; 44 and 10 genes were annotated as TFs and methyltransferase or hydroxymethyltransferase, respectively. Gene ontology enrichment and co-expression analysis revealed specific differential gene expressions in the DEGFu-sp to leaf, anther and ovary, such as genes related to photosynthesis, metabolic process and transport, and co-expression network including fertility, resistance and epigenetic elements. Of the DEGFu-sp to anther, 42 meiosis stage-specific genes, eight meiosis-related genes, such as RAD51 and SMC2, were identified. We identified 38 miRNAs from DEGFu-sp to anther, and their targets were associated with pollen fertility and retrotransposon protein. Our study provides new germplasm for polyploid rice breeding, and revealed complex regulatory mechanisms that might be associated with heterosis and fertility. PMID:28071676

  19. Transcriptome analysis of neo-tetraploid rice reveals specific differential gene expressions associated with fertility and heterosis.

    Science.gov (United States)

    Guo, Haibin; Mendrikahy, Jean Nestor; Xie, Lei; Deng, Junfeng; Lu, Zijun; Wu, Jinwen; Li, Xiang; Shahid, Muhammad Qasim; Liu, Xiangdong

    2017-01-10

    Polyploid rice hybrids have a powerful biological and yield potential that may become a new way for rice breeding; however, low fertility is major hindrance in commercial utilization. Here, we developed a neo-tetraploid rice that could overcome the sterility of autotetraploid rice and produce high heterosis. Transcriptome analysis of F1 hybrid developed by crossing neo-tetraploid with autotetraploid rice displayed 807, 663 and 866 differentially expressed genes that uniquely associated with F1 and specific to (DEGFu-sp) anther, ovary and leaf, respectively. Of the DEGFu-sp, 1224 genes displayed nonadditive expression; 44 and 10 genes were annotated as TFs and methyltransferase or hydroxymethyltransferase, respectively. Gene ontology enrichment and co-expression analysis revealed specific differential gene expressions in the DEGFu-sp to leaf, anther and ovary, such as genes related to photosynthesis, metabolic process and transport, and co-expression network including fertility, resistance and epigenetic elements. Of the DEGFu-sp to anther, 42 meiosis stage-specific genes, eight meiosis-related genes, such as RAD51 and SMC2, were identified. We identified 38 miRNAs from DEGFu-sp to anther, and their targets were associated with pollen fertility and retrotransposon protein. Our study provides new germplasm for polyploid rice breeding, and revealed complex regulatory mechanisms that might be associated with heterosis and fertility.

  20. Exploring the potential relevance of human-specific genes to complex disease

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    Cooper David N

    2011-01-01

    Full Text Available Abstract Although human disease genes generally tend to be evolutionarily more ancient than non-disease genes, complex disease genes appear to be represented more frequently than Mendelian disease genes among genes of more recent evolutionary origin. It is therefore proposed that the analysis of human-specific genes might provide new insights into the genetics of complex disease. Cross-comparison with the Human Gene Mutation Database (http://www.hgmd.org revealed a number of examples of disease-causing and disease-associated mutations in putatively human-specific genes. A sizeable proportion of these were missense polymorphisms associated with complex disease. Since both human-specific genes and genes associated with complex disease have often experienced particularly rapid rates of evolutionary change, either due to weaker purifying selection or positive selection, it is proposed that a significant number of human-specific genes may play a role in complex disease.

  1. A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data

    Institute of Scientific and Technical Information of China (English)

    Xiao-Gang Ruan; Jin-Lian Wang; Jian-Geng Li

    2006-01-01

    Computational analysis is essential for transforming the masses of microarray data into a mechanistic understanding of cancer. Here we present a method for finding gene functional modules of cancer from microarray data and have applied it to colon cancer. First, a colon cancer gene network and a normal colon tissue gene network were constructed using correlations between the genes. Then the modules that tended to have a homogeneous functional composition were identified by splitting up the network. Analysis of both networks revealed that they are scale-free.Comparison of the gene functional modules for colon cancer and normal tissues showed that the modules' functions changed with their structures.

  2. 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

  3. Computational studies of gene regulatory networks: in numero molecular biology.

    Science.gov (United States)

    Hasty, J; McMillen, D; Isaacs, F; Collins, J J

    2001-04-01

    Remarkable progress in genomic research is leading to a complete map of the building blocks of biology. Knowledge of this map is, in turn, setting the stage for a fundamental description of cellular function at the DNA level. Such a description will entail an understanding of gene regulation, in which proteins often regulate their own production or that of other proteins in a complex web of interactions. The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will probably involve the union of new experiments and computational modelling techniques.

  4. Majority Rules with Random Tie-Breaking in Boolean Gene Regulatory Networks

    Science.gov (United States)

    Chaouiya, Claudine; Ourrad, Ouerdia; Lima, Ricardo

    2013-01-01

    We consider threshold Boolean gene regulatory networks, where the update function of each gene is described as a majority rule evaluated among the regulators of that gene: it is turned ON when the sum of its regulator contributions is positive (activators contribute positively whereas repressors contribute negatively) and turned OFF when this sum is negative. In case of a tie (when contributions cancel each other out), it is often assumed that the gene keeps it current state. This framework has been successfully used to model cell cycle control in yeast. Moreover, several studies consider stochastic extensions to assess the robustness of such a model. Here, we introduce a novel, natural stochastic extension of the majority rule. It consists in randomly choosing the next value of a gene only in case of a tie. Hence, the resulting model includes deterministic and probabilistic updates. We present variants of the majority rule, including alternate treatments of the tie situation. Impact of these variants on the corresponding dynamical behaviours is discussed. After a thorough study of a class of two-node networks, we illustrate the interest of our stochastic extension using a published cell cycle model. In particular, we demonstrate that steady state analysis can be rigorously performed and can lead to effective predictions; these relate for example to the identification of interactions whose addition would ensure that a specific state is absorbing. PMID:23922761

  5. Majority rules with random tie-breaking in Boolean gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Claudine Chaouiya

    Full Text Available We consider threshold boolean gene regulatory networks, where the update function of each gene is described as a majority rule evaluated among the regulators of that gene: it is turned ON when the sum of its regulator contributions is positive (activators contribute positively whereas repressors contribute negatively and turned OFF when this sum is negative. In case of a tie (when contributions cancel each other out, it is often assumed that the gene keeps it current state. This framework has been successfully used to model cell cycle control in yeast. Moreover, several studies consider stochastic extensions to assess the robustness of such a model. Here, we introduce a novel, natural stochastic extension of the majority rule. It consists in randomly choosing the next value of a gene only in case of a tie. Hence, the resulting model includes deterministic and probabilistic updates. We present variants of the majority rule, including alternate treatments of the tie situation. Impact of these variants on the corresponding dynamical behaviours is discussed. After a thorough study of a class of two-node networks, we illustrate the interest of our stochastic extension using a published cell cycle model. In particular, we demonstrate that steady state analysis can be rigorously performed and can lead to effective predictions; these relate for example to the identification of interactions whose addition would ensure that a specific state is absorbing.

  6. A systematic molecular circuit design method for gene networks under biochemical time delays and molecular noises

    Directory of Open Access Journals (Sweden)

    Chang Yu-Te

    2008-11-01

    Full Text Available Abstract Background Gene networks in nanoscale are of nonlinear stochastic process. Time delays are common and substantial in these biochemical processes due to gene transcription, translation, posttranslation protein modification and diffusion. Molecular noises in gene networks come from intrinsic fluctuations, transmitted noise from upstream genes, and the global noise affecting all genes. Knowledge of molecular noise filtering and biochemical process delay compensation in gene networks is crucial to understand the signal processing in gene networks and the design of noise-tolerant and delay-robust gene circuits for synthetic biology. Results A nonlinear stochastic dynamic model with multiple time delays is proposed for describing a gene network under process delays, intrinsic molecular fluctuations, and extrinsic molecular noises. Then, the stochastic biochemical processing scheme of gene regulatory networks for attenuating these molecular noises and compensating process delays is investigated from the nonlinear signal processing perspective. In order to improve the robust stability for delay toleration and noise filtering, a robust gene circuit for nonlinear stochastic time-delay gene networks is engineered based on the nonlinear robust H∞ stochastic filtering scheme. Further, in order to avoid solving these complicated noise-tolerant and delay-robust design problems, based on Takagi-Sugeno (T-S fuzzy time-delay model and linear matrix inequalities (LMIs technique, a systematic gene circuit design method is proposed to simplify the design procedure. Conclusion The proposed gene circuit design method has much potential for application to systems biology, synthetic biology and drug design when a gene regulatory network has to be designed for improving its robust stability and filtering ability of disease-perturbed gene network or when a synthetic gene network needs to perform robustly under process delays and molecular noises.

  7. Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes.

    Science.gov (United States)

    Wuttke, Daniel; Connor, Richard; Vora, Chintan; Craig, Thomas; Li, Yang; Wood, Shona; Vasieva, Olga; Shmookler Reis, Robert; Tang, Fusheng; de Magalhães, João Pedro

    2012-01-01

    Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR-essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR-essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR-essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR-essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR-induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple organisms led

  8. Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes.

    Directory of Open Access Journals (Sweden)

    Daniel Wuttke

    Full Text Available Dietary restriction (DR, limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR-essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/. To dissect the interactions of DR-essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR-essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR-essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2 had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR-induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of

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

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    Guo Zheng

    2006-01-01

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

  10. Dissecting the Gene Network of Dietary Restriction to Identify Evolutionarily Conserved Pathways and New Functional Genes

    Science.gov (United States)

    Wuttke, Daniel; Connor, Richard; Vora, Chintan; Craig, Thomas; Li, Yang; Wood, Shona; Vasieva, Olga; Shmookler Reis, Robert; Tang, Fusheng; de Magalhães, João Pedro

    2012-01-01

    Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR–essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR–essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR–essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR–essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR–induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple

  11. Network of European studies of genes in growth (NESTEGG).

    Science.gov (United States)

    Johnston, Linda B; Ester, Wiestske; Caliebe, Janina; Molinas, Catherine; Wollmann, Hartmut; Fryklund, Linda; Clark, Adrian J; Ranke, Michael B; Tauber, Maithe; Hokken Koelega, Anita; Savage, Martin O

    2009-04-01

    The network of European studies of genes in growth (NESTEGG) is an international growth genomics project, focusing on the birth size phenotypes of small for gestational age (SGA) and idiopathic short stature. Seven hundred controls and 1,275 cases with their parents have been recruited. Detailed clinical histories and auxological measurements are recorded in a clinical database. Candidate gene studies are being undertaken with the study DNA samples. These genetic data will be used to explore associations with the clinical phenotypes of short stature and SGA birth size, and, in a subset, response to growth hormone (GH) therapy. This article describes the study methodology and reviews the association of the exon 3-deleted genotype of the GH receptor with GH responsiveness in GH-treated children born SGA. Copyright 2009 S. Karger AG, Basel.

  12. Gene expression analysis distinguishes tissue specific and gender related functions among adult Ascaris suum tissues

    Science.gov (United States)

    Wang, Zhengyuan; Gao, Xin; Martin, John; Yin, Yong; Abubucker, Sahar; Rash, Amy C.; Li, Ben-Wen; Nash, Bill; Hallsworth-Pepin, Kym; Jasmer, Douglas P.; Mitreva, Makedonka

    2013-01-01

    Over a billion people are infected by Ascaris spp. intestinal parasites. To clarify functional differences among tissues of adult A. suum, we compared gene expression by various tissues of these worms by expression microarray methods.. The A. suum genome was sequenced and assembled to allow generation of microarray elements. Expression of over 40,000 60-mer elements was investigated in a variety of tissues from both male and female adult worms. Nearly 50 percent of the elements for which signal was detected exhibited differential expression among different tissues. The unique profile of transcripts identified for each tissue clarified functional distinctions among tissues, such as chitin binding in the ovary and peptidase activity in the intestines. Interestingly, hundreds of gender-specific elements were characterized in multiple non-reproductive tissues of female or male worms, with most prominence of gender differences in intestinal tissue. A. suum genes from the same family were frequently expressed differently among tissues. Transcript abundance for genes specific to A. suum, by comparison to Caenorhabditis elegans, varied to a greater extent among tissues than for genes conserved between A. suum and C. elegans. Analysis using C. elegans protein interaction data identified functional modules conserved between these two nematodes, resulting in identification of functional predictions of essential subnetworks of protein interactions and how these networks may vary among nematode tissues. A notable finding was very high module similarity between adult reproductive tissues and intestine. Our results provide the most comprehensive assessment of gene expression among tissues of a parasitic nematode to date. PMID:23572074

  13. A clade-specific Arabidopsis gene connects primary metabolism and senescence

    Directory of Open Access Journals (Sweden)

    Dallas C. Jones

    2016-07-01

    Full Text Available Nearly immobile, plants have evolved new components to be able to respond to changing environments. One example is Qua Quine Starch (QQS, AT3G30720, an Arabidopsis thaliana-specific orphan gene that integrates primary metabolism with adaptation to environment changes. SAQR (Senescence-Associated and QQS-Related, AT1G64360, is unique to a clade within the family Brassicaceae; as such, the gene may have arisen about 20 million years ago. SAQR is up-regulated in QQS RNAi mutants and in the apx1 mutant under light-induced oxidative stress. SAQR plays a role in carbon allocation: overexpression lines of SAQR have significantly decreased starch content; conversely, in a SAQR T-DNA knockout line, starch accumulation is increased. Meta-analysis of public microarray data indicates that SAQR expression is correlated with expression of a subset of genes involved in senescence, defense, and stress responses. SAQR promoter::GUS expression analysis reveals that SAQR expression increases after leaf expansion and photosynthetic capacity have peaked, just prior to visible natural senescence. SAQR is expressed predominantly within leaf and cotyledon vasculature, increasing in intensity as natural senescence continues, and then decreasing prior to death. In contrast, under experimentally-induced senescence, SAQR expression increases in vasculature of cotyledons but not in true leaves. In SAQR knockout line, the transcript level of the dirigent-like disease resistance gene (AT1G22900 is increased, while that of the Early Light Induced Protein 1 gene (ELIP1, AT3G22840 is decreased. Taken together, these data indicate that SAQR may function in the QQS network, playing a role in integration of primary metabolism with adaptation to internal and environmental changes, specifically those that affect the process of senescence.

  14. Maternal diets trigger sex-specific divergent trajectories of gene expression and epigenetic systems in mouse placenta.

    Directory of Open Access Journals (Sweden)

    Anne Gabory

    Full Text Available Males and females responses to gestational overnutrition set the stage for subsequent sex-specific differences in adult onset non communicable diseases. Placenta, as a widely recognized programming agent, contibutes to the underlying processes. According to our previous findings, a high-fat diet during gestation triggers sex-specific epigenetic alterations within CpG and throughout the genome, together with the deregulation of clusters of imprinted genes. We further investigated the impact of diet and sex on placental histology, transcriptomic and epigenetic signatures in mice. Both basal gene expression and response to maternal high-fat diet were sexually dimorphic in whole placentas. Numerous genes showed sexually dimorphic expression, but only 11 genes regardless of the diet. In line with the key role of genes belonging to the sex chromosomes, 3 of these genes were Y-specific and 3 were X-specific. Amongst all the genes that were differentially expressed under a high-fat diet, only 16 genes were consistently affected in both males and females. The differences were not only quantitative but remarkably qualitative. The biological functions and networks of genes dysregulated differed markedly between the sexes. Seven genes of the epigenetic machinery were dysregulated, due to effects of diet, sex or both, including the Y- and X-linked histone demethylase paralogues Kdm5c and Kdm5d, which could mark differently male and female epigenomes. The DNA methyltransferase cofactor Dnmt3l gene expression was affected, reminiscent of our previous observation of changes in global DNA methylation. Overall, this striking sexual dimorphism of programming trajectories impose a considerable revision of the current dietary interventions protocols.

  15. The role of master regulators in gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Enrique Hernández Lemus

    2015-05-01

    Full Text Available Gene regulatory networks present a wide variety of dynamical responses to intrinsic and extrinsic perturbations. Arguably, one of the most important of such coordinated responses is the one of amplification cascades, in which activation of a few key-responsive transcription factors (termed master regulators, MRs lead to a large series of transcriptional activation events. This is so since master regulators are transcription factors controlling the expression of other transcription factor molecules and so on. MRs hold a central position related to transcriptional dynamics and control of gene regulatory networks and are often involved in complex feedback and feedforward loops inducing non-trivial dynamics. Recent studies have pointed out to the myocyte enhancing factor 2C (MEF2C, also known as MADS box transcription enhancer factor 2, polypeptide C as being one of such master regulators involved in the pathogenesis of primary breast cancer. In this work, we perform an integrative genomic analysis of the transcriptional regulation activity of MEF2C and its target genes to evaluate to what extent are these molecules inducing collective responses leading to gene expression deregulation and carcinogenesis. We also analyzed a number of induced dynamic responses, in particular those associated with transcriptional bursts, and nonlinear cascading to evaluate the influence they may have in malignant phenotypes and cancer. Received: 20 Novembre 2014, Accepted: 24 June 2015; Edited by: C. A. Condat, G. J. Sibona; DOI: http://dx.doi.org/10.4279/PIP.070011 Cite as: E Hernández-Lemus, K Baca-López, R Lemus, R García-Herrera, Papers in Physics 7, 070011 (2015

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

    Directory of Open Access Journals (Sweden)

    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

  17. De novo design of protein homo-oligomers with modular hydrogen bond network-mediated specificity

    Science.gov (United States)

    Boyken, Scott E.; Chen, Zibo; Groves, Benjamin; Langan, Robert A.; Oberdorfer, Gustav; Ford, Alex; Gilmore, Jason; Xu, Chunfu; DiMaio, Frank; Pereira, Jose Henrique; Sankaran, Banumathi; Seelig, Georg; Zwart, Peter H.; Baker, David

    2017-01-01

    In nature, structural specificity in DNA and proteins is encoded quite differently: in DNA, specificity arises from modular hydrogen bonds in the core of the double helix, whereas in proteins, specificity arises largely from buried hydrophobic packing complemented by irregular peripheral polar interactions. Here we describe a general approach for designing a wide range of protein homo-oligomers with specificity determined by modular arrays of central hydrogen bond networks. We use the approach to design dimers, trimers, and tetramers consisting of two concentric rings of helices, including previously not seen triangular, square, and supercoiled topologies. X-ray crystallography confirms that the structures overall, and the hydrogen bond networks in particular, are nearly identical to the design models, and the networks confer interaction specificity in vivo. The ability to design extensive hydrogen bond networks with atomic accuracy is a milestone for protein design and enables the programming of protein interaction specificity for a broad range of synthetic biology applications. PMID:27151862

  18. 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.

  19. The ubiquitous transcription factor CTCF promotes lineage-specific epigenomic remodeling and establishment of transcriptional networks driving cell differentiation.

    Science.gov (United States)

    Dubois-Chevalier, Julie; Staels, Bart; Lefebvre, Philippe; Eeckhoute, Jérôme

    2015-01-01

    Cell differentiation relies on tissue-specific transcription factors (TFs) that cooperate to establish unique transcriptomes and phenotypes. However, the role of ubiquitous TFs in these processes remains poorly defined. Recently, we have shown that the CCCTC-binding factor (CTCF) is required for adipocyte differentiation through epigenomic remodelling of adipose tissue-specific enhancers and transcriptional activation of Peroxisome proliferator-activated receptor gamma (PPARG), the main driver of the adipogenic program (PPARG), and its target genes. Here, we discuss how these findings, together with the recent literature, illuminate a functional role for ubiquitous TFs in lineage-determining transcriptional networks.

  20. CRISPR loci reveal networks of gene exchange in archaea

    Directory of Open Access Journals (Sweden)

    Brodt Avital

    2011-12-01

    Full Text Available Abstract Background CRISPR (Clustered, Regularly, Interspaced, Short, Palindromic Repeats loci provide prokaryotes with an adaptive immunity against viruses and other mobile genetic elements. CRISPR arrays can be transcribed and processed into small crRNA molecules, which are then used by the cell to target the foreign nucleic acid. Since spacers are accumulated by active CRISPR/Cas systems, the sequences of these spacers provide a record of the past "infection history" of the organism. Results Here we analyzed all currently known spacers present in archaeal genomes and identified their source by DNA similarity. While nearly 50% of archaeal spacers matched mobile genetic elements, such as plasmids or viruses, several others matched chromosomal genes of other organisms, primarily other archaea. Thus, networks of gene exchange between archaeal species were revealed by the spacer analysis, including many cases of inter-genus and inter-species gene transfer events. Spacers that recognize viral sequences tend to be located further away from the leader sequence, implying that there exists a selective pressure for their retention. Conclusions CRISPR spacers provide direct evidence for extensive gene exchange in archaea, especially within genera, and support the current dogma where the primary role of the CRISPR/Cas system is anti-viral and anti-plasmid defense. Open peer review This article was reviewed by: Profs. W. Ford Doolittle, John van der Oost, Christa Schleper (nominated by board member Prof. J Peter Gogarten

  1. Specification and Validation of an Edge Router Discovery Protocol for Mobile Ad Hoc Networks

    DEFF Research Database (Denmark)

    Kristensen, Lars Michael; Jensen, Kurt

    2004-01-01

    We present an industrial project at Ericsson Telebit A/S where Coloured Petri Nets (CP-nets or CPNs) have been used for the design and specification of an edge router discovery protocol for mobile ad-hoc networks. The Edge Router Discovery Protocol (ERDP) supports an edge router in a stationary...... core network in assigning network address prefixes to gateways in mobile ad-hoc networks. This paper focuses on how CP-nets and the CPN computer tools have been applied in the development of ERDP. A CPN model has been constructed that constitutes a formal executable specification of ERDP. Simulation...

  2. Formal Specification and Validation of Secure Connection Establishment in a Generic Access Network Scenario

    DEFF Research Database (Denmark)

    Fleischer, Paul; Kristensen, Lars Michael

    2008-01-01

    The Generic Access Network (GAN) architecture is defined by the 3rd Generation Partnership Project (3GPP), and allows telephone services, such as SMS and voice-calls, to be accessed via generic IP networks. The main usage of this is to allow mobile phones to use WiFi in addition to the usual GSM...... network. The GAN specification relies on the Internet Protocol Security layer (IPSec) and the Internet Key Exchange protocol (IKEv2) to provide encryption across IP networks, and thus avoid compromising the security of the telephone networks. The detailed usage of these two Internet protocols (IPSec...

  3. Network properties of complex human disease genes identified through genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Fredrik Barrenas

    Full Text Available BACKGROUND: Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs, thereby eliminating discovery bias. PRINCIPAL FINDINGS: We derived a network of complex diseases (n = 54 and complex disease genes (n = 349 to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process. CONCLUSIONS: This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

  4. Network properties of complex human disease genes identified through genome-wide association studies.

    Science.gov (United States)

    Barrenas, Fredrik; Chavali, Sreenivas; Holme, Petter; Mobini, Reza; Benson, Mikael

    2009-11-30

    Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias. We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process. This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

  5. Heritable and Lineage-Specific Gene Knockdown in Zebrafish Embryo

    NARCIS (Netherlands)

    Dong, Mei; Fu, Yan-Fang; Du, Ting-Ting; Jing, Chang-Bin; Fu, Chun-Tang; Chen, Yi; Jin, Yi; Deng, Min; Liu, Ting Xi

    2009-01-01

    Background: Reduced expression of developmentally important genes and tumor suppressors due to haploinsufficiency or epigenetic suppression has been shown to contribute to the pathogenesis of various malignancies. However, methodology that allows spatio-temporally knockdown of gene expression in var

  6. Arizona TeleMedicine Network: System Procurement Specifications, Phase 1.

    Science.gov (United States)

    Atlantic Research Corp., Alexandria, VA.

    Defining the system procurement specifications for a telecommunications system designed to provide health services to rurally isolated American Indians living on reservations in Arizona, this document presents detailed specifications for: (1) a complete communications facility; (2) a mobile health communications treatment and diagnosis unit; (3)…

  7. The vertebrate Hox gene regulatory network for hindbrain segmentation: Evolution and diversification: Coupling of a Hox gene regulatory network to hindbrain segmentation is an ancient trait originating at the base of vertebrates.

    Science.gov (United States)

    Parker, Hugo J; Bronner, Marianne E; Krumlauf, Robb

    2016-06-01

    Hindbrain development is orchestrated by a vertebrate gene regulatory network that generates segmental patterning along the anterior-posterior axis via Hox genes. Here, we review analyses of vertebrate and invertebrate chordate models that inform upon the evolutionary origin and diversification of this network. Evidence from the sea lamprey reveals that the hindbrain regulatory network generates rhombomeric compartments with segmental Hox expression and an underlying Hox code. We infer that this basal feature was present in ancestral vertebrates and, as an evolutionarily constrained developmental state, is fundamentally important for patterning of the vertebrate hindbrain across diverse lineages. Despite the common ground plan, vertebrates exhibit neuroanatomical diversity in lineage-specific patterns, with different vertebrates revealing variations of Hox expression in the hindbrain that could underlie this diversification. Invertebrate chordates lack hindbrain segmentation but exhibit some conserved aspects of this network, with retinoic acid signaling playing a role in establishing nested domains of Hox expression. © 2016 WILEY Periodicals, Inc.

  8. Automatic, context-specific generation of Gene Ontology slims

    Directory of Open Access Journals (Sweden)

    Sehgal Muhammad

    2010-10-01

    Full Text Available Abstract Background The use of ontologies to control vocabulary and structure annotation has added value to genome-scale data, and contributed to the capture and re-use of knowledge across research domains. Gene Ontology (GO is widely used to capture detailed expert knowledge in genomic-scale datasets and as a consequence has grown to contain many terms, making it unwieldy for many applications. To increase its ease of manipulation and efficiency of use, subsets called GO slims are often created by collapsing terms upward into more general, high-level terms relevant to a particular context. Creation of a GO slim currently requires manipulation and editing of GO by an expert (or community familiar with both the ontology and the biological context. Decisions about which terms to include are necessarily subjective, and the creation process itself and subsequent curation are time-consuming and largely manual. Results Here we present an objective framework for generating customised ontology slims for specific annotated datasets, exploiting information latent in the structure of the ontology graph and in the annotation data. This framework combines ontology engineering approaches, and a data-driven algorithm that draws on graph and information theory. We illustrate this method by application to GO, generating GO slims at different information threshol