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Sample records for ancient gene network

  1. Ancient Resistome.

    Science.gov (United States)

    Olaitan, Abiola Olumuyiwa; Rolain, Jean-Marc

    2016-08-01

    Antibiotic resistance is an ancient biological mechanism in bacteria, although its proliferation in our contemporary world has been amplified through antimicrobial therapy. Recent studies conducted on ancient environmental and human samples have uncovered numerous antibiotic-resistant bacteria and resistance genes. The resistance genes that have been reported from the analysis of ancient bacterial DNA include genes coding for several classes of antibiotics, such as glycopeptides, β-lactams, tetracyclines, and macrolides. The investigation of the resistome of ancient bacteria is a recent and emerging field of research, and technological advancements such as next-generation sequencing will further contribute to its growth. It is hoped that the knowledge gained from this research will help us to better understand the evolution of antibiotic resistance genes and will also be used in drug design as a proactive measure against antibiotic resistance.

  2. Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification.

    Science.gov (United States)

    Ziesemer, Kirsten A; Mann, Allison E; Sankaranarayanan, Krithivasan; Schroeder, Hannes; Ozga, Andrew T; Brandt, Bernd W; Zaura, Egija; Waters-Rist, Andrea; Hoogland, Menno; Salazar-García, Domingo C; Aldenderfer, Mark; Speller, Camilla; Hendy, Jessica; Weston, Darlene A; MacDonald, Sandy J; Thomas, Gavin H; Collins, Matthew J; Lewis, Cecil M; Hofman, Corinne; Warinner, Christina

    2015-11-13

    To date, characterization of ancient oral (dental calculus) and gut (coprolite) microbiota has been primarily accomplished through a metataxonomic approach involving targeted amplification of one or more variable regions in the 16S rRNA gene. Specifically, the V3 region (E. coli 341-534) of this gene has been suggested as an excellent candidate for ancient DNA amplification and microbial community reconstruction. However, in practice this metataxonomic approach often produces highly skewed taxonomic frequency data. In this study, we use non-targeted (shotgun metagenomics) sequencing methods to better understand skewed microbial profiles observed in four ancient dental calculus specimens previously analyzed by amplicon sequencing. Through comparisons of microbial taxonomic counts from paired amplicon (V3 U341F/534R) and shotgun sequencing datasets, we demonstrate that extensive length polymorphisms in the V3 region are a consistent and major cause of differential amplification leading to taxonomic bias in ancient microbiome reconstructions based on amplicon sequencing. We conclude that systematic amplification bias confounds attempts to accurately reconstruct microbiome taxonomic profiles from 16S rRNA V3 amplicon data generated using universal primers. Because in silico analysis indicates that alternative 16S rRNA hypervariable regions will present similar challenges, we advocate for the use of a shotgun metagenomics approach in ancient microbiome reconstructions.

  3. Establishing the validity of domestication genes using DNA from ancient chickens

    Science.gov (United States)

    Girdland Flink, Linus; Allen, Richard; Barnett, Ross; Malmström, Helena; Peters, Joris; Eriksson, Jonas; Andersson, Leif; Dobney, Keith

    2014-01-01

    Modern domestic plants and animals are subject to human-driven selection for desired phenotypic traits and behavior. Large-scale genetic studies of modern domestic populations and their wild relatives have revealed not only the genetic mechanisms underlying specific phenotypic traits, but also allowed for the identification of candidate domestication genes. Our understanding of the importance of these genes during the initial stages of the domestication process traditionally rests on the assumption that robust inferences about the past can be made on the basis of modern genetic datasets. A growing body of evidence from ancient DNA studies, however, has revealed that ancient and even historic populations often bear little resemblance to their modern counterparts. Here, we test the temporal context of selection on specific genetic loci known to differentiate modern domestic chickens from their extant wild ancestors. We extracted DNA from 80 ancient chickens excavated from 12 European archaeological sites, dated from ∼280 B.C. to the 18th century A.D. We targeted three unlinked genetic loci: the mitochondrial control region, a gene associated with yellow skin color (β-carotene dioxygenase 2), and a putative domestication gene thought to be linked to photoperiod and reproduction (thyroid-stimulating hormone receptor, TSHR). Our results reveal significant variability in both nuclear genes, suggesting that the commonality of yellow skin in Western breeds and the near fixation of TSHR in all modern chickens took place only in the past 500 y. In addition, mitochondrial variation has increased as a result of recent admixture with exotic breeds. We conclude by emphasizing the perils of inferring the past from modern genetic data alone. PMID:24753608

  4. Establishing the validity of domestication genes using DNA from ancient chickens.

    Science.gov (United States)

    Girdland Flink, Linus; Allen, Richard; Barnett, Ross; Malmström, Helena; Peters, Joris; Eriksson, Jonas; Andersson, Leif; Dobney, Keith; Larson, Greger

    2014-04-29

    Modern domestic plants and animals are subject to human-driven selection for desired phenotypic traits and behavior. Large-scale genetic studies of modern domestic populations and their wild relatives have revealed not only the genetic mechanisms underlying specific phenotypic traits, but also allowed for the identification of candidate domestication genes. Our understanding of the importance of these genes during the initial stages of the domestication process traditionally rests on the assumption that robust inferences about the past can be made on the basis of modern genetic datasets. A growing body of evidence from ancient DNA studies, however, has revealed that ancient and even historic populations often bear little resemblance to their modern counterparts. Here, we test the temporal context of selection on specific genetic loci known to differentiate modern domestic chickens from their extant wild ancestors. We extracted DNA from 80 ancient chickens excavated from 12 European archaeological sites, dated from ∼ 280 B.C. to the 18th century A.D. We targeted three unlinked genetic loci: the mitochondrial control region, a gene associated with yellow skin color (β-carotene dioxygenase 2), and a putative domestication gene thought to be linked to photoperiod and reproduction (thyroid-stimulating hormone receptor, TSHR). Our results reveal significant variability in both nuclear genes, suggesting that the commonality of yellow skin in Western breeds and the near fixation of TSHR in all modern chickens took place only in the past 500 y. In addition, mitochondrial variation has increased as a result of recent admixture with exotic breeds. We conclude by emphasizing the perils of inferring the past from modern genetic data alone.

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

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    Francisco J. Romero-Campero

    2013-08-01

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

  6. Characterization of an ancient lepidopteran lateral gene transfer.

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    David Wheeler

    Full Text Available Bacteria to eukaryote lateral gene transfers (LGT are an important potential source of material for the evolution of novel genetic traits. The explosion in the number of newly sequenced genomes provides opportunities to identify and characterize examples of these lateral gene transfer events, and to assess their role in the evolution of new genes. In this paper, we describe an ancient lepidopteran LGT of a glycosyl hydrolase family 31 gene (GH31 from an Enterococcus bacteria. PCR amplification between the LGT and a flanking insect gene confirmed that the GH31 was integrated into the Bombyx mori genome and was not a result of an assembly error. Database searches in combination with degenerate PCR on a panel of 7 lepidopteran families confirmed that the GH31 LGT event occurred deep within the Order approximately 65-145 million years ago. The most basal species in which the LGT was found is Plutella xylostella (superfamily: Yponomeutoidea. Array data from Bombyx mori shows that GH31 is expressed, and low dN/dS ratios indicates the LGT coding sequence is under strong stabilizing selection. These findings provide further support for the proposition that bacterial LGTs are relatively common in insects and likely to be an underappreciated source of adaptive genetic material.

  7. Ancient expansion of the hox cluster in lepidoptera generated four homeobox genes implicated in extra-embryonic tissue formation.

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    Laura Ferguson

    2014-10-01

    Full Text Available Gene duplications within the conserved Hox cluster are rare in animal evolution, but in Lepidoptera an array of divergent Hox-related genes (Shx genes has been reported between pb and zen. Here, we use genome sequencing of five lepidopteran species (Polygonia c-album, Pararge aegeria, Callimorpha dominula, Cameraria ohridella, Hepialus sylvina plus a caddisfly outgroup (Glyphotaelius pellucidus to trace the evolution of the lepidopteran Shx genes. We demonstrate that Shx genes originated by tandem duplication of zen early in the evolution of large clade Ditrysia; Shx are not found in a caddisfly and a member of the basally diverging Hepialidae (swift moths. Four distinct Shx genes were generated early in ditrysian evolution, and were stably retained in all descendent Lepidoptera except the silkmoth which has additional duplications. Despite extensive sequence divergence, molecular modelling indicates that all four Shx genes have the potential to encode stable homeodomains. The four Shx genes have distinct spatiotemporal expression patterns in early development of the Speckled Wood butterfly (Pararge aegeria, with ShxC demarcating the future sites of extraembryonic tissue formation via strikingly localised maternal RNA in the oocyte. All four genes are also expressed in presumptive serosal cells, prior to the onset of zen expression. Lepidopteran Shx genes represent an unusual example of Hox cluster expansion and integration of novel genes into ancient developmental regulatory networks.

  8. Dynamic expression of ancient and novel molluscan shell genes during ecological transitions

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    Wörheide Gert

    2007-09-01

    Full Text Available Abstract Background The Mollusca constitute one of the most morphologically and ecologically diverse metazoan phyla, occupying a wide range of marine, terrestrial and freshwater habitats. The evolutionary success of the molluscs can in part be attributed to the evolvability of the external shell. Typically, the shell first forms during embryonic and larval development, changing dramatically in shape, colour and mineralogical composition as development and maturation proceeds. Major developmental transitions in shell morphology often correlate with ecological transitions (e.g. from a planktonic to benthic existence at metamorphosis. While the genes involved in molluscan biomineralisation are beginning to be identified, there is little understanding of how these are developmentally regulated, or if the same genes are operational at different stages of the mollusc's life. Results Here we relate the developmental expression of nine genes in the tissue responsible for shell production – the mantle – to ecological transitions that occur during the lifetime of the tropical abalone Haliotis asinina (Vetigastropoda. Four of these genes encode evolutionarily ancient proteins, while four others encode secreted proteins with little or no identity to known proteins. Another gene has been previously described from the mantle of another haliotid vetigastropod. All nine genes display dynamic spatial and temporal expression profiles within the larval shell field and juvenile mantle. Conclusion These expression data reflect the regulatory complexity that underlies molluscan shell construction from larval stages to adulthood, and serves to highlight the different ecological demands placed on each stage. The use of both ancient and novel genes in all stages of shell construction also suggest that a core set of shell-making genes was provided by a shared metazoan ancestor, which has been elaborated upon to produce the range of molluscan shell types we see today.

  9. Two Ancient Gene Families Are Critical for Maintenance of the Mammalian Skin Barrier in Postnatal Life.

    Science.gov (United States)

    Cangkrama, Michael; Darido, Charbel; Georgy, Smitha R; Partridge, Darren; Auden, Alana; Srivastava, Seema; Wilanowski, Tomasz; Jane, Stephen M

    2016-07-01

    The skin barrier is critical for mammalian survival in the terrestrial environment, affording protection against fluid loss, microbes, toxins, and UV exposure. Many genes indispensable for barrier formation in the embryo have been identified, but loss of these genes in adult mice does not induce barrier regression. We describe a complex regulatory network centered on two ancient gene families, the grainyhead-like (Grhl) transcription factors and the protein cross-linking enzymes (tissue transglutaminases [Tgms]), which are essential for skin permeability barrier maintenance in adult mice. Embryonic deletion of Grhl3 induces loss of Tgm1 expression, which disrupts the cornified envelope, thus preventing permeability barrier formation leading to neonatal death. However, gene deletion of Grhl3 in adult mice does not disrupt the preformed barrier, with cornified envelope integrity maintained by Grhl1 and Tgm5, which are up-regulated in response to postnatal loss of Grhl3. Concomitant deletion of both Grhl factors in adult mice induced loss of Tgm1 and Tgm5 expression, perturbation of the cornified envelope, and complete permeability barrier regression that was incompatible with life. These findings define the molecular safeguards for barrier function that accompany the transition from intrauterine to terrestrial life. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  10. Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification

    NARCIS (Netherlands)

    Ziesemer, K.A.; Mann, A.E.; Sankaranarayanan, K.; Schroeder, H.; Ozga, A.T.; Brandt, B.W.; Zaura, E.; Waters-Rist, A.; Hoogland, M.; Salazar-García, D.C.; Aldenderfer, M.; Speller, C.; Hendy, J.; Weston, D.A.; MacDonald, S.J.; Thomas, G.H.; Collins, M.J.; Lewis, C.M.; Hofman, C.; Warinner, C.

    2015-01-01

    To date, characterization of ancient oral (dental calculus) and gut (coprolite) microbiota has been primarily accomplished through a metataxonomic approach involving targeted amplification of one or more variable regions in the 16S rRNA gene. Specifically, the V3 region (E. coli 341-534) of this

  11. Resurrecting ancestral genes in bacteria to interpret ancient biosignatures

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    Kacar, Betul; Guy, Lionel; Smith, Eric; Baross, John

    2017-11-01

    Two datasets, the geologic record and the genetic content of extant organisms, provide complementary insights into the history of how key molecular components have shaped or driven global environmental and macroevolutionary trends. Changes in global physico-chemical modes over time are thought to be a consistent feature of this relationship between Earth and life, as life is thought to have been optimizing protein functions for the entirety of its approximately 3.8 billion years of history on the Earth. Organismal survival depends on how well critical genetic and metabolic components can adapt to their environments, reflecting an ability to optimize efficiently to changing conditions. The geologic record provides an array of biologically independent indicators of macroscale atmospheric and oceanic composition, but provides little in the way of the exact behaviour of the molecular components that influenced the compositions of these reservoirs. By reconstructing sequences of proteins that might have been present in ancient organisms, we can downselect to a subset of possible sequences that may have been optimized to these ancient environmental conditions. How can one use modern life to reconstruct ancestral behaviours? Configurations of ancient sequences can be inferred from the diversity of extant sequences, and then resurrected in the laboratory to ascertain their biochemical attributes. One way to augment sequence-based, single-gene methods to obtain a richer and more reliable picture of the deep past, is to resurrect inferred ancestral protein sequences in living organisms, where their phenotypes can be exposed in a complex molecular-systems context, and then to link consequences of those phenotypes to biosignatures that were preserved in the independent historical repository of the geological record. As a first step beyond single-molecule reconstruction to the study of functional molecular systems, we present here the ancestral sequence reconstruction of the

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

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    Min Song

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

  13. Lateral Gene Transfer Dynamics in the Ancient Bacterial Genus Streptomyces

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    Bradon R. McDonald

    2017-06-01

    Full Text Available Lateral gene transfer (LGT profoundly shapes the evolution of bacterial lineages. LGT across disparate phylogenetic groups and genome content diversity between related organisms suggest a model of bacterial evolution that views LGT as rampant and promiscuous. It has even driven the argument that species concepts and tree-based phylogenetics cannot be applied to bacteria. Here, we show that acquisition and retention of genes through LGT are surprisingly rare in the ubiquitous and biomedically important bacterial genus Streptomyces. Using a molecular clock, we estimate that the Streptomyces bacteria are ~380 million years old, indicating that this bacterial genus is as ancient as land vertebrates. Calibrating LGT rate to this geologic time span, we find that on average only 10 genes per million years were acquired and subsequently maintained. Over that same time span, Streptomyces accumulated thousands of point mutations. By explicitly incorporating evolutionary timescale into our analyses, we provide a dramatically different view on the dynamics of LGT and its impact on bacterial evolution.

  14. Cancer tumors as Metazoa 1.0: tapping genes of ancient ancestors

    International Nuclear Information System (INIS)

    Davies, P C W; Lineweaver, C H

    2011-01-01

    The genes of cellular cooperation that evolved with multicellularity about a billion years ago are the same genes that malfunction to cause cancer. We hypothesize that cancer is an atavistic condition that occurs when genetic or epigenetic malfunction unlocks an ancient 'toolkit' of pre-existing adaptations, re-establishing the dominance of an earlier layer of genes that controlled loose-knit colonies of only partially differentiated cells, similar to tumors. The existence of such a toolkit implies that the progress of the neoplasm in the host organism differs distinctively from normal Darwinian evolution. Comparative genomics and the phylogeny of basal metazoans, opisthokonta and basal multicellular eukaryotes should help identify the relevant genes and yield the order in which they evolved. This order will be a rough guide to the reverse order in which cancer develops, as mutations disrupt the genes of cellular cooperation. Our proposal is consistent with current understanding of cancer and explains the paradoxical rapidity with which cancer acquires a suite of mutually-supportive complex abilities. Finally we make several predictions and suggest ways to test this model

  15. Distinguish of Famous Jun Porcelain in Ancient and Present Age by INAA and BP Neural Network

    International Nuclear Information System (INIS)

    Li Guoxia; Liang Xianhua; Zhao Weijuan; Sun Hongwei; Guo Min; Xie Jianzhong; Gao Zhengyao; Cui Pengfei; Yang Dawei; Li rongwu; Zhao Qingyun; Sun Xinmin; Zhao Wenjun; Feng Songlin

    2010-01-01

    Forty samples of Jun porcelain from an ancient Juntai kiln and 3 modern Jun kilns (Kongjia, Miaojia and Xinghang) were selected and analyzed for 25 elements by INAA.The data were trained and forecasted by BP neural network. The results indicate that the network can distinguish unknown body and glaze samples of the official Jun porcelain and the modern top-grade Jun porcelain after proper training. (authors)

  16. 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.

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    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.

  17. The Evolution of Gene Regulatory Networks that Define Arthropod Body Plans.

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    Auman, Tzach; Chipman, Ariel D

    2017-09-01

    Our understanding of the genetics of arthropod body plan development originally stems from work on Drosophila melanogaster from the late 1970s and onward. In Drosophila, there is a relatively detailed model for the network of gene interactions that proceeds in a sequential-hierarchical fashion to define the main features of the body plan. Over the years, we have a growing understanding of the networks involved in defining the body plan in an increasing number of arthropod species. It is now becoming possible to tease out the conserved aspects of these networks and to try to reconstruct their evolution. In this contribution, we focus on several key nodes of these networks, starting from early patterning in which the main axes are determined and the broad morphological domains of the embryo are defined, and on to later stage wherein the growth zone network is active in sequential addition of posterior segments. The pattern of conservation of networks is very patchy, with some key aspects being highly conserved in all arthropods and others being very labile. Many aspects of early axis patterning are highly conserved, as are some aspects of sequential segment generation. In contrast, regional patterning varies among different taxa, and some networks, such as the terminal patterning network, are only found in a limited range of taxa. The growth zone segmentation network is ancient and is probably plesiomorphic to all arthropods. In some insects, it has undergone significant modification to give rise to a more hardwired network that generates individual segments separately. In other insects and in most arthropods, the sequential segmentation network has undergone a significant amount of systems drift, wherein many of the genes have changed. However, it maintains a conserved underlying logic and function. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please

  18. Ancient horizontal gene transfer from bacteria enhances biosynthetic capabilities of fungi.

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    Imke Schmitt

    Full Text Available Polyketides are natural products with a wide range of biological functions and pharmaceutical applications. Discovery and utilization of polyketides can be facilitated by understanding the evolutionary processes that gave rise to the biosynthetic machinery and the natural product potential of extant organisms. Gene duplication and subfunctionalization, as well as horizontal gene transfer are proposed mechanisms in the evolution of biosynthetic gene clusters. To explain the amount of homology in some polyketide synthases in unrelated organisms such as bacteria and fungi, interkingdom horizontal gene transfer has been evoked as the most likely evolutionary scenario. However, the origin of the genes and the direction of the transfer remained elusive.We used comparative phylogenetics to infer the ancestor of a group of polyketide synthase genes involved in antibiotic and mycotoxin production. We aligned keto synthase domain sequences of all available fungal 6-methylsalicylic acid (6-MSA-type PKSs and their closest bacterial relatives. To assess the role of symbiotic fungi in the evolution of this gene we generated 24 6-MSA synthase sequence tags from lichen-forming fungi. Our results support an ancient horizontal gene transfer event from an actinobacterial source into ascomycete fungi, followed by gene duplication.Given that actinobacteria are unrivaled producers of biologically active compounds, such as antibiotics, it appears particularly promising to study biosynthetic genes of actinobacterial origin in fungi. The large number of 6-MSA-type PKS sequences found in lichen-forming fungi leads us hypothesize that the evolution of typical lichen compounds, such as orsellinic acid derivatives, was facilitated by the gain of this bacterial polyketide synthase.

  19. Tamil merchant in ancient Mesopotamia.

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    Malliya Gounder Palanichamy

    Full Text Available Recent analyses of ancient Mesopotamian mitochondrial genomes have suggested a genetic link between the Indian subcontinent and Mesopotamian civilization. There is no consensus on the origin of the ancient Mesopotamians. They may be descendants of migrants, who founded regional Mesopotamian groups like that of Terqa or they may be merchants who were involved in trans Mesopotamia trade. To identify the Indian source population showing linkage to the ancient Mesopotamians, we screened a total of 15,751 mitochondrial DNAs (11,432 from the literature and 4,319 from this study representing all major populations of India. Our results although suggest that south India (Tamil Nadu and northeast India served as the source of the ancient Mesopotamian mtDNA gene pool, mtDNA of these ancient Mesopotamians probably contributed by Tamil merchants who were involved in the Indo-Roman trade.

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

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

    2017-09-20

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

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

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    Kevin L Childs

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

  2. Spider Transcriptomes Identify Ancient Large-Scale Gene Duplication Event Potentially Important in Silk Gland Evolution.

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    Clarke, Thomas H; Garb, Jessica E; Hayashi, Cheryl Y; Arensburger, Peter; Ayoub, Nadia A

    2015-06-08

    The evolution of specialized tissues with novel functions, such as the silk synthesizing glands in spiders, is likely an influential driver of adaptive success. Large-scale gene duplication events and subsequent paralog divergence are thought to be required for generating evolutionary novelty. Such an event has been proposed for spiders, but not tested. We de novo assembled transcriptomes from three cobweb weaving spider species. Based on phylogenetic analyses of gene families with representatives from each of the three species, we found numerous duplication events indicative of a whole genome or segmental duplication. We estimated the age of the gene duplications relative to several speciation events within spiders and arachnids and found that the duplications likely occurred after the divergence of scorpions (order Scorpionida) and spiders (order Araneae), but before the divergence of the spider suborders Mygalomorphae and Araneomorphae, near the evolutionary origin of spider silk glands. Transcripts that are expressed exclusively or primarily within black widow silk glands are more likely to have a paralog descended from the ancient duplication event and have elevated amino acid replacement rates compared with other transcripts. Thus, an ancient large-scale gene duplication event within the spider lineage was likely an important source of molecular novelty during the evolution of silk gland-specific expression. This duplication event may have provided genetic material for subsequent silk gland diversification in the true spiders (Araneomorphae). © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  3. Interactive visualization of gene regulatory networks with associated gene expression time series data

    NARCIS (Netherlands)

    Westenberg, M.A.; Hijum, van S.A.F.T.; Lulko, A.T.; Kuipers, O.P.; Roerdink, J.B.T.M.; Linsen, L.; Hagen, H.; Hamann, B.

    2008-01-01

    We present GENeVis, an application to visualize gene expression time series data in a gene regulatory network context. This is a network of regulator proteins that regulate the expression of their respective target genes. The networks are represented as graphs, in which the nodes represent genes,

  4. 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.

  5. Current approaches to gene regulatory network modelling

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    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. Genes2FANs: connecting genes through functional association networks

    Science.gov (United States)

    2012-01-01

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

  7. Assessing the fidelity of ancient DNA sequences amplified from nuclear genes

    DEFF Research Database (Denmark)

    Binladen, Jonas; Wiuf, Carsten Henrik; Gilbert, M. Thomas P.

    2006-01-01

    To date, the field of ancient DNA has relied almost exclusively on mitochondrial DNA (mtDNA) sequences. However, a number of recent studies have reported the successful recovery of ancient nuclear DNA (nuDNA) sequences, thereby allowing the characterization of genetic loci directly involved...... in phenotypic traits of extinct taxa. It is well documented that postmortem damage in ancient mtDNA can lead to the generation of artifactual sequences. However, as yet no one has thoroughly investigated the damage spectrum in ancient nuDNA. By comparing clone sequences from 23 fossil specimens, recovered from...... adenine), respectively. Type 2 transitions are by far the most dominant and increase relative to those of type 1 with damage load. The results suggest that the deamination of cytosine (and 5-methyl cytosine) to uracil (and thymine) is the main cause of miscoding lesions in both ancient mtDNA and nu...

  8. Fatty acid-binding protein genes of the ancient, air-breathing, ray-finned fish, spotted gar (Lepisosteus oculatus).

    Science.gov (United States)

    Venkatachalam, Ananda B; Fontenot, Quenton; Farrara, Allyse; Wright, Jonathan M

    2018-03-01

    With the advent of high-throughput DNA sequencing technology, the genomic sequence of many disparate species has led to the relatively new discipline of genomics, the study of genome structure, function and evolution. Much work has been focused on the role of whole genome duplications (WGD) in the architecture of extant vertebrate genomes, particularly those of teleost fishes which underwent a WGD early in the teleost radiation >230 million years ago (mya). Our past work has focused on the fate of duplicated copies of a multigene family coding for the intracellular lipid-binding protein (iLBP) genes in the teleost fishes. To define the evolutionary processes that determined the fate of duplicated genes and generated the structure of extant fish genomes, however, requires comparative genomic analysis with a fish lineage that diverged before the teleost WGD, such as the spotted gar (Lepisosteus oculatus), an ancient, air-breathing, ray-finned fish. Here, we describe the genomic organization, chromosomal location and tissue-specific expression of a subfamily of the iLBP genes that code for fatty acid-binding proteins (Fabps) in spotted gar. Based on this work, we have defined the minimum suite of fabp genes prior to their duplication in the teleost lineages ~230-400 mya. Spotted gar, therefore, serves as an appropriate outgroup, or ancestral/ancient fish, that did not undergo the teleost-specific WGD. As such, analyses of the spatio-temporal regulation of spotted gar genes provides a foundation to determine whether the duplicated fabp genes have been retained in teleost genomes owing to either sub- or neofunctionalization. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. SL1 RNA gene recovery from Enterobius vermicularis ancient DNA in pre-Columbian human coprolites.

    Science.gov (United States)

    Iñiguez, Alena Mayo; Reinhard, Karl; Carvalho Gonçalves, Marcelo Luiz; Ferreira, Luiz Fernando; Araújo, Adauto; Paulo Vicente, Ana Carolina

    2006-11-01

    Enterobius vermicularis, pinworm, is one of the most common helminths worldwide, infecting nearly a billion people at all socio-economic levels. In prehistoric populations the paleoparasitological findings show a pinworm homogeneous distribution among hunter-gatherers in North America, intensified with the advent of agriculture. This same increase also occurred in the transition from nomad hunter-gatherers to sedentary farmers in South America, although E. vermicularis infection encompasses only the ancient Andean peoples, with no record among the pre-Colombian populations in the South American lowlands. However, the outline of pinworm paleoepidemiology has been supported by microscopic finding of eggs recovered from coprolites. Since molecular techniques are precise and sensitive in detecting pathogen ancient DNA (aDNA), and also could provide insights into the parasite evolutionary history, in this work we have performed a molecular paleoparasitological study of E. vermicularis. aDNA was recovered and pinworm 5S rRNA spacer sequences were determined from pre-Columbian coprolites (4110 BC-AD 900) from four different North and South American archaeological sites. The sequence analysis confirmed E. vermicularis identity and revealed a similarity among ancient and modern sequences. Moreover, polymorphisms were identified at the relative positions 160, 173 and 180, in independent coprolite samples from Tulán, San Pedro de Atacama, Chile (1080-950 BC). We also verified the presence of peculiarities (Splicing leader (SL1) RNA sequence, spliced donor site, the Sm antigen biding site, and RNA secondary structure) which characterise the SL1 RNA gene. The analysis shows that the SL1 RNA gene of contemporary pinworms was present in pre-Columbian E. vermicularis by 6110 years ago. We were successful in detecting E. vermicularis aDNA even in coprolites without direct microscopic evidence of the eggs, improving the diagnosis of helminth infections in the past and further

  10. Ancient and modern DNA reveal dynamics of domestication and cross-continental dispersal of the dromedary

    Science.gov (United States)

    Almathen, Faisal; Charruau, Pauline; Mohandesan, Elmira; Mwacharo, Joram M.; Orozco-terWengel, Pablo; Pitt, Daniel; Abdussamad, Abdussamad M.; Uerpmann, Margarethe; Uerpmann, Hans-Peter; De Cupere, Bea; Magee, Peter; Alnaqeeb, Majed A.; Salim, Bashir; Raziq, Abdul; Dessie, Tadelle; Abdelhadi, Omer M.; Banabazi, Mohammad H.; Al-Eknah, Marzook; Walzer, Chris; Faye, Bernard; Hofreiter, Michael; Peters, Joris; Hanotte, Olivier

    2016-01-01

    Dromedaries have been fundamental to the development of human societies in arid landscapes and for long-distance trade across hostile hot terrains for 3,000 y. Today they continue to be an important livestock resource in marginal agro-ecological zones. However, the history of dromedary domestication and the influence of ancient trading networks on their genetic structure have remained elusive. We combined ancient DNA sequences of wild and early-domesticated dromedary samples from arid regions with nuclear microsatellite and mitochondrial genotype information from 1,083 extant animals collected across the species’ range. We observe little phylogeographic signal in the modern population, indicative of extensive gene flow and virtually affecting all regions except East Africa, where dromedary populations have remained relatively isolated. In agreement with archaeological findings, we identify wild dromedaries from the southeast Arabian Peninsula among the founders of the domestic dromedary gene pool. Approximate Bayesian computations further support the “restocking from the wild” hypothesis, with an initial domestication followed by introgression from individuals from wild, now-extinct populations. Compared with other livestock, which show a long history of gene flow with their wild ancestors, we find a high initial diversity relative to the native distribution of the wild ancestor on the Arabian Peninsula and to the brief coexistence of early-domesticated and wild individuals. This study also demonstrates the potential to retrieve ancient DNA sequences from osseous remains excavated in hot and dry desert environments. PMID:27162355

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

    Science.gov (United States)

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

    2014-12-10

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

  12. Sparsity in Model Gene Regulatory Networks

    International Nuclear Information System (INIS)

    Zagorski, M.

    2011-01-01

    We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

  13. Phylogenetic analysis of ferlin genes reveals ancient eukaryotic origins

    Directory of Open Access Journals (Sweden)

    Lek Monkol

    2010-07-01

    reproduction-related divergence and specialization of species-specific functions within their genus. Conclusions Our phylogenetic studies provide evolutionary insight into the ferlin gene family. We highlight the existence of ferlin-like proteins throughout eukaryotic evolution, from unicellular phytoplankton and apicomplexan parasites, through to humans. We characterise the preservation of ferlin structural motifs, not only of C2 domains, but also the more poorly characterised ferlin-specific motifs representing the DysF, FerA and FerB domains. Our data suggest an ancient role of ferlin proteins, with lessons from vertebrate biology and human disease suggesting a role relating to vesicle fusion and plasma membrane specialization.

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

    Directory of Open Access Journals (Sweden)

    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. Lateral Gene Transfer Dynamics in the Ancient Bacterial Genus Streptomyces.

    Science.gov (United States)

    McDonald, Bradon R; Currie, Cameron R

    2017-06-06

    Lateral gene transfer (LGT) profoundly shapes the evolution of bacterial lineages. LGT across disparate phylogenetic groups and genome content diversity between related organisms suggest a model of bacterial evolution that views LGT as rampant and promiscuous. It has even driven the argument that species concepts and tree-based phylogenetics cannot be applied to bacteria. Here, we show that acquisition and retention of genes through LGT are surprisingly rare in the ubiquitous and biomedically important bacterial genus Streptomyces Using a molecular clock, we estimate that the Streptomyces bacteria are ~380 million years old, indicating that this bacterial genus is as ancient as land vertebrates. Calibrating LGT rate to this geologic time span, we find that on average only 10 genes per million years were acquired and subsequently maintained. Over that same time span, Streptomyces accumulated thousands of point mutations. By explicitly incorporating evolutionary timescale into our analyses, we provide a dramatically different view on the dynamics of LGT and its impact on bacterial evolution. IMPORTANCE Tree-based phylogenetics and the use of species as units of diversity lie at the foundation of modern biology. In bacteria, these pillars of evolutionary theory have been called into question due to the observation of thousands of lateral gene transfer (LGT) events within and between lineages. Here, we show that acquisition and retention of genes through LGT are exceedingly rare in the bacterial genus Streptomyces , with merely one gene acquired in Streptomyces lineages every 100,000 years. These findings stand in contrast to the current assumption of rampant genetic exchange, which has become the dominant hypothesis used to explain bacterial diversity. Our results support a more nuanced understanding of genetic exchange, with LGT impacting evolution over short timescales but playing a significant role over long timescales. Deeper understanding of LGT provides new

  16. Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.

    Science.gov (United States)

    Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

    2004-03-01

    By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.

  17. Experimental Evolution of Escherichia coli Harboring an Ancient Translation Protein.

    Science.gov (United States)

    Kacar, Betül; Ge, Xueliang; Sanyal, Suparna; Gaucher, Eric A

    2017-03-01

    The ability to design synthetic genes and engineer biological systems at the genome scale opens new means by which to characterize phenotypic states and the responses of biological systems to perturbations. One emerging method involves inserting artificial genes into bacterial genomes and examining how the genome and its new genes adapt to each other. Here we report the development and implementation of a modified approach to this method, in which phylogenetically inferred genes are inserted into a microbial genome, and laboratory evolution is then used to examine the adaptive potential of the resulting hybrid genome. Specifically, we engineered an approximately 700-million-year-old inferred ancestral variant of tufB, an essential gene encoding elongation factor Tu, and inserted it in a modern Escherichia coli genome in place of the native tufB gene. While the ancient homolog was not lethal to the cell, it did cause a twofold decrease in organismal fitness, mainly due to reduced protein dosage. We subsequently evolved replicate hybrid bacterial populations for 2000 generations in the laboratory and examined the adaptive response via fitness assays, whole genome sequencing, proteomics, and biochemical assays. Hybrid lineages exhibit a general adaptive strategy in which the fitness cost of the ancient gene was ameliorated in part by upregulation of protein production. Our results suggest that an ancient-modern recombinant method may pave the way for the synthesis of organisms that exhibit ancient phenotypes, and that laboratory evolution of these organisms may prove useful in elucidating insights into historical adaptive processes.

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

    Directory of Open Access Journals (Sweden)

    Ma'ayan Avi

    2007-10-01

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

  19. 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.

  20. Combinatorial explosion in model gene networks

    Science.gov (United States)

    Edwards, R.; Glass, L.

    2000-09-01

    The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

  1. Networks in biological systems: An investigation of the Gene Ontology as an evolving network

    International Nuclear Information System (INIS)

    Coronnello, C; Tumminello, M; Micciche, S; Mantegna, R.N.

    2009-01-01

    Many biological systems can be described as networks where different elements interact, in order to perform biological processes. We introduce a network associated with the Gene Ontology. Specifically, we construct a correlation-based network where the vertices are the terms of the Gene Ontology and the link between each two terms is weighted on the basis of the number of genes that they have in common. We analyze a filtered network obtained from the correlation-based network and we characterize its evolution over different releases of the Gene Ontology.

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

    KAUST Repository

    Fujii, Chisato; Kuwahara, Hiroyuki; Yu, Ge; Guo, Lili; Gao, Xin

    2016-01-01

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

  3. Learning gene regulatory networks 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.

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

    Science.gov (United States)

    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

  5. 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 ... have a high number of spontaneous mutations in genes that form a network in the front region ...

  6. Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes.

    Directory of Open Access Journals (Sweden)

    Fei Xiao

    Full Text Available Combining path consistency (PC algorithms with conditional mutual information (CMI are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference, to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.

  7. 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

    2017-10-01

    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 .

  8. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

    Science.gov (United States)

    Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

    2015-06-12

    During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

  9. Multiscale Embedded Gene Co-expression Network Analysis.

    Directory of Open Access Journals (Sweden)

    Won-Min Song

    2015-11-01

    Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

  10. Multiscale Embedded Gene Co-expression Network Analysis.

    Science.gov (United States)

    Song, Won-Min; Zhang, Bin

    2015-11-01

    Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

  11. Ancient genes establish stress-induced mutation as a hallmark of cancer.

    Science.gov (United States)

    Cisneros, Luis; Bussey, Kimberly J; Orr, Adam J; Miočević, Milica; Lineweaver, Charles H; Davies, Paul

    2017-01-01

    Cancer is sometimes depicted as a reversion to single cell behavior in cells adapted to live in a multicellular assembly. If this is the case, one would expect that mutation in cancer disrupts functional mechanisms that suppress cell-level traits detrimental to multicellularity. Such mechanisms should have evolved with or after the emergence of multicellularity. This leads to two related, but distinct hypotheses: 1) Somatic mutations in cancer will occur in genes that are younger than the emergence of multicellularity (1000 million years [MY]); and 2) genes that are frequently mutated in cancer and whose mutations are functionally important for the emergence of the cancer phenotype evolved within the past 1000 million years, and thus would exhibit an age distribution that is skewed to younger genes. In order to investigate these hypotheses we estimated the evolutionary ages of all human genes and then studied the probability of mutation and their biological function in relation to their age and genomic location for both normal germline and cancer contexts. We observed that under a model of uniform random mutation across the genome, controlled for gene size, genes less than 500 MY were more frequently mutated in both cases. Paradoxically, causal genes, defined in the COSMIC Cancer Gene Census, were depleted in this age group. When we used functional enrichment analysis to explain this unexpected result we discovered that COSMIC genes with recessive disease phenotypes were enriched for DNA repair and cell cycle control. The non-mutated genes in these pathways are orthologous to those underlying stress-induced mutation in bacteria, which results in the clustering of single nucleotide variations. COSMIC genes were less common in regions where the probability of observing mutational clusters is high, although they are approximately 2-fold more likely to harbor mutational clusters compared to other human genes. Our results suggest this ancient mutational response to

  12. 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.

  13. Functional Module Analysis for Gene Coexpression Networks with Network Integration.

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    Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

    2015-01-01

    Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

  14. 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.

  15. A decision support system for the reading of ancient documents

    DEFF Research Database (Denmark)

    Roued-Cunliffe, Henriette

    2011-01-01

    The research presented in this thesis is based in the Humanities discipline of Ancient History and begins by attempting to understand the interpretation process involved in reading ancient documents and how this process can be aided by computer systems such as Decision Support Systems (DSS...... this process in the five areas: remembering complex reasoning, searching huge datasets, international collaboration, publishing editions, and image enhancement. This research contains a large practical element involving the development of a DSS prototype. The prototype is used to illustrate how a DSS......, by remembering complex reasoning, can aid the process of interpretation that is reading ancient documents. It is based on the idea that the interpretation process goes through a network of interpretation. The network of interpretation illustrates a recursive process where scholars move between reading levels...

  16. Positive selection and ancient duplications in the evolution of class B floral homeotic genes of orchids and grasses

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    Koch Marcus A

    2009-04-01

    Full Text Available Abstract Background Positive selection is recognized as the prevalence of nonsynonymous over synonymous substitutions in a gene. Models of the functional evolution of duplicated genes consider neofunctionalization as key to the retention of paralogues. For instance, duplicate transcription factors are specifically retained in plant and animal genomes and both positive selection and transcriptional divergence appear to have played a role in their diversification. However, the relative impact of these two factors has not been systematically evaluated. Class B MADS-box genes, comprising DEF-like and GLO-like genes, encode developmental transcription factors essential for establishment of perianth and male organ identity in the flowers of angiosperms. Here, we contrast the role of positive selection and the known divergence in expression patterns of genes encoding class B-like MADS-box transcription factors from monocots, with emphasis on the family Orchidaceae and the order Poales. Although in the monocots these two groups are highly diverse and have a strongly canalized floral morphology, there is no information on the role of positive selection in the evolution of their distinctive flower morphologies. Published research shows that in Poales, class B-like genes are expressed in stamens and in lodicules, the perianth organs whose identity might also be specified by class B-like genes, like the identity of the inner tepals of their lily-like relatives. In orchids, however, the number and pattern of expression of class B-like genes have greatly diverged. Results The DEF-like genes from Orchidaceae form four well-supported, ancient clades of orthologues. In contrast, orchid GLO-like genes form a single clade of ancient orthologues and recent paralogues. DEF-like genes from orchid clade 2 (OMADS3-like genes are under less stringent purifying selection than the other orchid DEF-like and GLO-like genes. In comparison with orchids, purifying selection

  17. Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

    Science.gov (United States)

    Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  18. 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.

  19. Candidate Gene Identification of Flowering Time Genes in Cotton

    Directory of Open Access Journals (Sweden)

    Corrinne E. Grover

    2015-07-01

    Full Text Available Flowering time control is critically important to all sexually reproducing angiosperms in both natural ecological and agronomic settings. Accordingly, there is much interest in defining the genes involved in the complex flowering-time network and how these respond to natural and artificial selection, the latter often entailing transitions in day-length responses. Here we describe a candidate gene analysis in the cotton genus , which uses homologs from the well-described flowering network to bioinformatically and phylogenetically identify orthologs in the published genome sequence from Ulbr., one of the two model diploid progenitors of the commercially important allopolyploid cottons, L. and L. Presence and patterns of expression were evaluated from 13 aboveground tissues related to flowering for each of the candidate genes using allopolyploid as a model. Furthermore, we use a comparative context to determine copy number variability of each key gene family across 10 published angiosperm genomes. Data suggest a pattern of repeated loss of duplicates following ancient whole-genome doubling events in diverse lineages. The data presented here provide a foundation for understanding both the parallel evolution of day-length neutrality in domesticated cottons and the flowering-time network, in general, in this important crop plant.

  20. 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

  1. Mutational robustness of gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    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.

  2. Learning gene networks under SNP perturbations using eQTL datasets.

    Directory of Open Access Journals (Sweden)

    Lingxue Zhang

    2014-02-01

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

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

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    Satoru Hayasaka

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

  4. From Gene Trees to a Dated Allopolyploid Network: Insights from the Angiosperm Genus Viola (Violaceae)

    Science.gov (United States)

    Marcussen, Thomas; Heier, Lise; Brysting, Anne K.; Oxelman, Bengt; Jakobsen, Kjetill S.

    2015-01-01

    Allopolyploidization accounts for a significant fraction of speciation events in many eukaryotic lineages. However, existing phylogenetic and dating methods require tree-like topologies and are unable to handle the network-like phylogenetic relationships of lineages containing allopolyploids. No explicit framework has so far been established for evaluating competing network topologies, and few attempts have been made to date phylogenetic networks. We used a four-step approach to generate a dated polyploid species network for the cosmopolitan angiosperm genus Viola L. (Violaceae Batch.). The genus contains ca 600 species and both recent (neo-) and more ancient (meso-) polyploid lineages distributed over 16 sections. First, we obtained DNA sequences of three low-copy nuclear genes and one chloroplast region, from 42 species representing all 16 sections. Second, we obtained fossil-calibrated chronograms for each nuclear gene marker. Third, we determined the most parsimonious multilabeled genome tree and its corresponding network, resolved at the section (not the species) level. Reconstructing the “correct” network for a set of polyploids depends on recovering all homoeologs, i.e., all subgenomes, in these polyploids. Assuming the presence of Viola subgenome lineages that were not detected by the nuclear gene phylogenies (“ghost subgenome lineages”) significantly reduced the number of inferred polyploidization events. We identified the most parsimonious network topology from a set of five competing scenarios differing in the interpretation of homoeolog extinctions and lineage sorting, based on (i) fewest possible ghost subgenome lineages, (ii) fewest possible polyploidization events, and (iii) least possible deviation from expected ploidy as inferred from available chromosome counts of the involved polyploid taxa. Finally, we estimated the homoploid and polyploid speciation times of the most parsimonious network. Homoploid speciation times were estimated by

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

    Science.gov (United States)

    Gerstein, Mark

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Daifeng Wang

    2016-10-01

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

  7. Convergent evolution of gene networks by single-gene duplications in higher eukaryotes

    OpenAIRE

    Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

    2004-01-01

    By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix–loop–helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks e...

  8. Discovering disease-associated genes in weighted protein-protein interaction networks

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Stanley, H. Eugene

    2018-04-01

    Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

  9. Research on the tourism resource development from the perspective of network capability-Taking Wuxi Huishan Ancient Town as an example

    Science.gov (United States)

    Bao, Yanli; Hua, Hefeng

    2017-03-01

    Network capability is the enterprise's capability to set up, manage, maintain and use a variety of relations between enterprises, and to obtain resources for improving competitiveness. Tourism in China is in a transformation period from sightseeing to leisure and vacation. Scenic spots as well as tourist enterprises can learn from some other enterprises in the process of resource development, and build up its own network relations in order to get resources for their survival and development. Through the effective management of network relations, the performance of resource development will be improved. By analyzing literature on network capability and the case analysis of Wuxi Huishan Ancient Town, the role of network capacity in the tourism resource development is explored and resource development path is built from the perspective of network capability. Finally, the tourism resource development process model based on network capacity is proposed. This model mainly includes setting up network vision, resource identification, resource acquisition, resource utilization and tourism project development. In these steps, network construction, network management and improving network center status are key points.

  10. Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

    Science.gov (United States)

    Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

    2015-01-01

    Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

  11. The Reconstruction and Analysis of Gene Regulatory Networks.

    Science.gov (United States)

    Zheng, Guangyong; Huang, Tao

    2018-01-01

    In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

  12. MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

    Science.gov (United States)

    Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

    2017-06-16

    The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis

  13. Screening a novel Na+/H+ antiporter gene from a metagenomic library of halophiles colonizing in the Dagong Ancient Brine Well in China.

    Science.gov (United States)

    Xiang, Wenliang; Zhang, Jie; Li, Lin; Liang, Huazhong; Luo, Hai; Zhao, Jian; Yang, Zhirong; Sun, Qun

    2010-05-01

    Metagenomic DNA libraries constructed from the Dagong Ancient Brine Well were screened for genes with Na(+)/H(+) antiporter activity on the antiporter-deficient Escherichia coli KNabc strain. One clone with a stable Na(+)-resistant phenotype was obtained and its Na(+)/H(+) antiporter gene was sequenced and designated as m-nha. The deduced amino acid sequence of M-Nha protein consists of 523 residues with a calculated molecular weight of 58 147 Da and a pI of 5.50, which is homologous with NhaH from Halobacillus dabanensis D-8(T) (92%) and Halobacillus aidingensis AD-6(T) (86%), and with Nhe2 from Bacillus sp. NRRL B-14911 (64%). It had a hydropathy profile with 10 putative transmembrane domains and a long carboxyl terminal hydrophilic tail of 140 amino acid residues, similar to Nhap from Synechocystis sp. and Aphanothece halophytica, as well as NhaG from Bacillus subtilis. The m-nha gene in the antiporter-negative mutant E. coli KNabc conferred resistance to Na(+) and the ability to grow under alkaline conditions. The difference in amino acid sequence and the putative secondary structure suggested that the m-nha isolated from the Dagong Ancient Brine Well in this study was a novel Na(+)/H(+) antiporter gene.

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

    Directory of Open Access Journals (Sweden)

    Ettore Mosca

    2017-09-01

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

  15. Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

    Science.gov (United States)

    Uddin, Raihan; Singh, Shiva M

    2017-01-01

    As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

  16. Co-Option and De Novo Gene Evolution Underlie Molluscan Shell Diversity

    Science.gov (United States)

    Aguilera, Felipe; McDougall, Carmel

    2017-01-01

    Abstract Molluscs fabricate shells of incredible diversity and complexity by localized secretions from the dorsal epithelium of the mantle. Although distantly related molluscs express remarkably different secreted gene products, it remains unclear if the evolution of shell structure and pattern is underpinned by the differential co-option of conserved genes or the integration of lineage-specific genes into the mantle regulatory program. To address this, we compare the mantle transcriptomes of 11 bivalves and gastropods of varying relatedness. We find that each species, including four Pinctada (pearl oyster) species that diverged within the last 20 Ma, expresses a unique mantle secretome. Lineage- or species-specific genes comprise a large proportion of each species’ mantle secretome. A majority of these secreted proteins have unique domain architectures that include repetitive, low complexity domains (RLCDs), which evolve rapidly, and have a proclivity to expand, contract and rearrange in the genome. There are also a large number of secretome genes expressed in the mantle that arose before the origin of gastropods and bivalves. Each species expresses a unique set of these more ancient genes consistent with their independent co-option into these mantle gene regulatory networks. From this analysis, we infer lineage-specific secretomes underlie shell diversity, and include both rapidly evolving RLCD-containing proteins, and the continual recruitment and loss of both ancient and recently evolved genes into the periphery of the regulatory network controlling gene expression in the mantle epithelium. PMID:28053006

  17. Fused Regression for Multi-source Gene Regulatory Network Inference.

    Directory of Open Access Journals (Sweden)

    Kari Y Lam

    2016-12-01

    Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

  18. An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods

    Science.gov (United States)

    Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E.; Re, Matteo

    2014-01-01

    Objective In the context of “network medicine”, gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. Materials and methods We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. Results The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different “informativeness” embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Conclusions Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further

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

    Science.gov (United States)

    Zhou, Xionghui; Liu, Juan

    2014-01-01

    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 signature for

  20. Reveal genes functionally associated with ACADS by a network study.

    Science.gov (United States)

    Chen, Yulong; Su, Zhiguang

    2015-09-15

    Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Recurrent neural network based hybrid model for reconstructing gene regulatory network.

    Science.gov (United States)

    Raza, Khalid; Alam, Mansaf

    2016-10-01

    One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    DEFF Research Database (Denmark)

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

    2007-01-01

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

  3. Transcriptional delay stabilizes bistable gene networks.

    Science.gov (United States)

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

    2013-08-02

    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.

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

    Science.gov (United States)

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

    2015-06-01

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

  5. Gene regulation is governed by a core network in hepatocellular carcinoma.

    Science.gov (United States)

    Gu, Zuguang; Zhang, Chenyu; Wang, Jin

    2012-05-01

    Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to HCC and we believe further

  6. Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks.

    Science.gov (United States)

    Zhang, Yihua; Li, Wan; Feng, Yuyan; Guo, Shanshan; Zhao, Xilei; Wang, Yahui; He, Yuehan; He, Weiming; Chen, Lina

    2017-11-28

    Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.

  7. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

    Science.gov (United States)

    Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

    2012-01-01

    Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

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

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

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

  9. An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

    Science.gov (United States)

    Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E; Re, Matteo

    2014-06-01

    In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both

  10. High-resolution analysis of cytosine methylation in ancient DNA.

    Directory of Open Access Journals (Sweden)

    Bastien Llamas

    Full Text Available Epigenetic changes to gene expression can result in heritable phenotypic characteristics that are not encoded in the DNA itself, but rather by biochemical modifications to the DNA or associated chromatin proteins. Interposed between genes and environment, these epigenetic modifications can be influenced by environmental factors to affect phenotype for multiple generations. This raises the possibility that epigenetic states provide a substrate for natural selection, with the potential to participate in the rapid adaptation of species to changes in environment. Any direct test of this hypothesis would require the ability to measure epigenetic states over evolutionary timescales. Here we describe the first single-base resolution of cytosine methylation patterns in an ancient mammalian genome, by bisulphite allelic sequencing of loci from late Pleistocene Bison priscus remains. Retrotransposons and the differentially methylated regions of imprinted loci displayed methylation patterns identical to those derived from fresh bovine tissue, indicating that methylation patterns are preserved in the ancient DNA. Our findings establish the biochemical stability of methylated cytosines over extensive time frames, and provide the first direct evidence that cytosine methylation patterns are retained in DNA from ancient specimens. The ability to resolve cytosine methylation in ancient DNA provides a powerful means to study the role of epigenetics in evolution.

  11. Topography and the road network in the territory of ancient Naissus

    OpenAIRE

    Ljubomirović Irena V.

    2016-01-01

    Archaeological research revealed that ancient Naissus was located on the right bank of the Nišava, on the territory partially covered by the Niš Fortress. The ancient town developed on a wide and flat terrain, which offered good conditions for settlement, but also for raising fortifications. According to modern scholars, the urban settlement on the right bank of the Nišava was preceded by a small native village (vicus), which was important for the erection of the town fortifications. Numerous...

  12. The integration of weighted human gene association networks based on link prediction.

    Science.gov (United States)

    Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

    2017-01-31

    Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

  13. Ancient trade routes shaped the genetic structure of horses in eastern Eurasia.

    Science.gov (United States)

    Warmuth, Vera M; Campana, Michael G; Eriksson, Anders; Bower, Mim; Barker, Graeme; Manica, Andrea

    2013-11-01

    Animal exchange networks have been shown to play an important role in determining gene flow among domestic animal populations. The Silk Road is one of the oldest continuous exchange networks in human history, yet its effectiveness in facilitating animal exchange across large geographical distances and topographically challenging landscapes has never been explicitly studied. Horses are known to have been traded along the Silk Roads; however, extensive movement of horses in connection with other human activities may have obscured the genetic signature of the Silk Roads. To investigate the role of the Silk Roads in shaping the genetic structure of horses in eastern Eurasia, we analysed microsatellite genotyping data from 455 village horses sampled from 17 locations. Using least-cost path methods, we compared the performance of models containing the Silk Roads as corridors for gene flow with models containing single landscape features. We also determined whether the recent isolation of former Soviet Union countries from the rest of Eurasia has affected the genetic structure of our samples. The overall level of genetic differentiation was low, consistent with historically high levels of gene flow across the study region. The spatial genetic structure was characterized by a significant, albeit weak, pattern of isolation by distance across the continent with no evidence for the presence of distinct genetic clusters. Incorporating landscape features considerably improved the fit of the data; however, when we controlled for geographical distance, only the correlation between genetic differentiation and the Silk Roads remained significant, supporting the effectiveness of this ancient trade network in facilitating gene flow across large geographical distances in a topographically complex landscape. © 2013 John Wiley & Sons Ltd.

  14. 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

    2014-11-06

    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 an alternate, versatile 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.

  15. 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

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

    Science.gov (United States)

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

    2018-02-13

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

  17. Inferring time-varying network topologies from gene expression data.

    Science.gov (United States)

    Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

    2007-01-01

    Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

  18. Inference of cancer-specific gene regulatory networks using soft computing rules.

    Science.gov (United States)

    Wang, Xiaosheng; Gotoh, Osamu

    2010-03-24

    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.

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

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Wang

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

  20. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

  2. A flood-based information flow analysis and network minimization method for gene regulatory networks.

    Science.gov (United States)

    Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

    2013-04-24

    Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

  3. System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

    Science.gov (United States)

    Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

    2018-01-01

    Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

  4. Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

    Science.gov (United States)

    Wang, Yi Kan; Hurley, Daniel G; Schnell, Santiago; Print, Cristin G; Crampin, Edmund J

    2013-01-01

    We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

  5. A hybrid network-based method for the detection of disease-related genes

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

    2018-02-01

    Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

  6. Nuclear counterparts of the cytoplasmic mitochondrial 12S rRNA gene: a problem of ancient DNA and molecular phylogenies.

    Science.gov (United States)

    van der Kuyl, A C; Kuiken, C L; Dekker, J T; Perizonius, W R; Goudsmit, J

    1995-06-01

    Monkey mummy bones and teeth originating from the North Saqqara Baboon Galleries (Egypt), soft tissue from a mummified baboon in a museum collection, and nineteenth/twentieth-century skin fragments from mangabeys were used for DNA extraction and PCR amplification of part of the mitochondrial 12S rRNA gene. Sequences aligning with the 12S rRNA gene were recovered but were only distantly related to contemporary monkey mitochondrial 12S rRNA sequences. However, many of these sequences were identical or closely related to human nuclear DNA sequences resembling mitochondrial 12S rRNA (isolated from a cell line depleted in mitochondria) and therefore have to be considered contamination. Subsequently in a separate study we were able to recover genuine mitochondrial 12S rRNA sequences from many extant species of nonhuman Old World primates and sequences closely resembling the human nuclear integrations. Analysis of all sequences by the neighbor-joining (NJ) method indicated that mitochondrial DNA sequences and their nuclear counterparts can be divided into two distinct clusters. One cluster contained all temporary cytoplasmic mitochondrial DNA sequences and approximately half of the monkey nuclear mitochondriallike sequences. A second cluster contained most human nuclear sequences and the other half of monkey nuclear sequences with a separate branch leading to human and gorilla mitochondrial and nuclear sequences. Sequences recovered from ancient materials were equally divided between the two clusters. These results constitute a warning for when working with ancient DNA or performing phylogenetic analysis using mitochondrial DNA as a target sequence: Nuclear counterparts of mitochondrial genes may lead to faulty interpretation of results.

  7. Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon

    Directory of Open Access Journals (Sweden)

    Satoru Koda

    2017-11-01

    Full Text Available We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX model with a group smoothly clipped absolute deviation (SCAD method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon. To reveal the diurnal changes in the transcriptome in B. distachyon, we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon. On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon, aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

  8. The integration of weighted gene association networks based on information entropy.

    Science.gov (United States)

    Yang, Fan; Wu, Duzhi; Lin, Limei; Yang, Jian; Yang, Tinghong; Zhao, Jing

    2017-01-01

    Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.

  9. Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster

    Directory of Open Access Journals (Sweden)

    Kim Man-Sun

    2012-05-01

    Full Text Available Abstract Background Network motifs provided a “conceptual tool” for understanding the functional principles of biological networks, but such motifs have primarily been used to consider static network structures. Static networks, however, cannot be used to reveal time- and region-specific traits of biological systems. To overcome this limitation, we proposed the concept of a “spatiotemporal network motif,” a spatiotemporal sequence of network motifs of sub-networks which are active only at specific time points and body parts. Results On the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns. Conclusions Taken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks.

  10. Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

    Science.gov (United States)

    Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

    2017-08-01

    This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

  11. Inferring Phylogenetic Networks from Gene Order Data

    Directory of Open Access Journals (Sweden)

    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.

  12. An Organismal Model for Gene Regulatory Networks in the Gut-Associated Immune Response

    Directory of Open Access Journals (Sweden)

    Katherine M. Buckley

    2017-10-01

    Full Text Available The gut epithelium is an ancient site of complex communication between the animal immune system and the microbial world. While elements of self-non-self receptors and effector mechanisms differ greatly among animal phyla, some aspects of recognition, regulation, and response are broadly conserved. A gene regulatory network (GRN approach provides a means to investigate the nature of this conservation and divergence even as more peripheral functional details remain incompletely understood. The sea urchin embryo is an unparalleled experimental model for detangling the GRNs that govern embryonic development. By applying this theoretical framework to the free swimming, feeding larval stage of the purple sea urchin, it is possible to delineate the conserved regulatory circuitry that regulates the gut-associated immune response. This model provides a morphologically simple system in which to efficiently unravel regulatory connections that are phylogenetically relevant to immunity in vertebrates. Here, we review the organism-wide cellular and transcriptional immune response of the sea urchin larva. A large set of transcription factors and signal systems, including epithelial expression of interleukin 17 (IL17, are important mediators in the activation of the early gut-associated response. Many of these have homologs that are active in vertebrate immunity, while others are ancient in animals but absent in vertebrates or specific to echinoderms. This larval model provides a means to experimentally characterize immune function encoded in the sea urchin genome and the regulatory interconnections that control immune response and resolution across the tissues of the organism.

  13. Ancient connection between NKL genes and the mesoderm? Insights from Tlx expression in a ctenophore.

    Science.gov (United States)

    Derelle, Romain; Manuel, Michaël

    2007-04-01

    In recent years, evo-devo studies on non-bilaterian metazoans have improved our understanding of the early evolution of animal body plans. In particular, works on cnidarians suggested that contrary to classical views, the mesoderm originated far before the emergence of the Bilateria. In this context, a synthesis of genomic and functional data concerning the Antennapedia (Antp) superclass of homeobox genes suggested that early in animal evolution, each of the three germ layers was under the control of one cluster of Antp genes. In particular, the patterning and differentiation of the mesoderm was under the control of the NKL cluster. The ctenophores stand as a key taxon with respect to such issues because unlike other non-bilaterian phyla, their intermediate germ layer satisfies the strict embryological definition of a mesoderm. For that reason, we investigated the only known member of the NKL group in Ctenophora, a gene previously isolated from Pleurobrachia and attributed to the Tlx family. In our analysis of the NKL group, this ctenophore gene branches as the sister-group of bilaterian Tlx genes, but without statistical support. The expression pattern of this gene was revealed by in situ hybridisation in the adult ctenophore. The expression territories of PpiTlx are predominantly ectodermal, in two distinct types of ciliated epidermal cells and in one category of gland cells. We also identified a probable endodermal site of expression. Because we failed to detect any mesodermal expression, the results do not provide support to the hypothesis of an ancient functional association between the NKL group and the mesoderm.

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

    Directory of Open Access Journals (Sweden)

    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.

  15. Pathogens and host immunity in the ancient human oral cavity

    DEFF Research Database (Denmark)

    Warinner, Christina; Rodrigues, João F Matias; Vyas, Rounak

    2014-01-01

    Calcified dental plaque (dental calculus) preserves for millennia and entraps biomolecules from all domains of life and viruses. We report the first, to our knowledge, high-resolution taxonomic and protein functional characterization of the ancient oral microbiome and demonstrate that the oral...... cavity has long served as a reservoir for bacteria implicated in both local and systemic disease. We characterize (i) the ancient oral microbiome in a diseased state, (ii) 40 opportunistic pathogens, (iii) ancient human-associated putative antibiotic resistance genes, (iv) a genome reconstruction...... calculus permits the simultaneous investigation of pathogen activity, host immunity and diet, thereby extending direct investigation of common diseases into the human evolutionary past....

  16. Tissue-specific functional networks for prioritizing phenotype and disease genes.

    Directory of Open Access Journals (Sweden)

    Yuanfang Guan

    Full Text Available Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as "functionality" and "functional relationships" are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

  17. Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome.

    Science.gov (United States)

    Quevedo-Tumailli, Viviana F; Ortega-Tenezaca, Bernabé; González-Díaz, Humbert

    2018-03-02

    The spatial distribution of genes in chromosomes seems not to be random. For instance, only 10% of genes are transcribed from bidirectional promoters in humans, and many more are organized into larger clusters. This raises intriguing questions previously asked by different authors. We would like to add a few more questions in this context, related to gene orientation inversions. Does gene orientation (inversion) follow a random pattern? Is it relevant to biological activity somehow? We define a new kind of network coined as the gene orientation inversion network (GOIN). GOIN's complex network encodes short- and long-range patterns of inversion of the orientation of pairs of gene in the chromosome. We selected Plasmodium falciparum as a case of study due to the high relevance of this parasite to public health (causal agent of malaria). We constructed here for the first time all of the GOINs for the genome of this parasite. These networks have an average of 383 nodes (genes in one chromosome) and 1314 links (pairs of gene with inverse orientation). We calculated node centralities and other parameters of these networks. These numerical parameters were used to study different properties of gene inversion patterns, for example, distribution, local communities, similarity to Erdös-Rényi random networks, randomness, and so on. We find clues that seem to indicate that gene orientation inversion does not follow a random pattern. We noted that some gene communities in the GOINs tend to group genes encoding for RIFIN-related proteins in the proteome of the parasite. RIFIN-like proteins are a second family of clonally variant proteins expressed on the surface of red cells infected with Plasmodium falciparum. Consequently, we used these centralities as input of machine learning (ML) models to predict the RIFIN-like activity of 5365 proteins in the proteome of Plasmodium sp. The best linear ML model found discriminates RIFIN-like from other proteins with sensitivity and

  18. A pathway-based network analysis of hypertension-related genes

    Science.gov (United States)

    Wang, Huan; Hu, Jing-Bo; Xu, Chuan-Yun; Zhang, De-Hai; Yan, Qian; Xu, Ming; Cao, Ke-Fei; Zhang, Xu-Sheng

    2016-02-01

    Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.

  19. An ancient duplication of exon 5 in the Snap25 gene is required for complex neuronal development/function.

    Directory of Open Access Journals (Sweden)

    Jenny U Johansson

    2008-11-01

    Full Text Available Alternative splicing is an evolutionary innovation to create functionally diverse proteins from a limited number of genes. SNAP-25 plays a central role in neuroexocytosis by bridging synaptic vesicles to the plasma membrane during regulated exocytosis. The SNAP-25 polypeptide is encoded by a single copy gene, but in higher vertebrates a duplication of exon 5 has resulted in two mutually exclusive splice variants, SNAP-25a and SNAP-25b. To address a potential physiological difference between the two SNAP-25 proteins, we generated gene targeted SNAP-25b deficient mouse mutants by replacing the SNAP-25b specific exon with a second SNAP-25a equivalent. Elimination of SNAP-25b expression resulted in developmental defects, spontaneous seizures, and impaired short-term synaptic plasticity. In adult mutants, morphological changes in hippocampus and drastically altered neuropeptide expression were accompanied by severe impairment of spatial learning. We conclude that the ancient exon duplication in the Snap25 gene provides additional SNAP-25-function required for complex neuronal processes in higher eukaryotes.

  20. Uncovering co-expression gene network modules regulating fruit acidity in diverse apples.

    Science.gov (United States)

    Bai, Yang; Dougherty, Laura; Cheng, Lailiang; Zhong, Gan-Yuan; Xu, Kenong

    2015-08-16

    Acidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates. Guided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P acidity. Overall, this study provides important insight into the Ma1-mediated gene network controlling acidity in mature apple fruit of diverse genetic background.

  1. RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

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    Marco Grimaldi

    Full Text Available RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.

  2. Hybrid stochastic simplifications for multiscale gene networks

    Directory of Open Access Journals (Sweden)

    Debussche Arnaud

    2009-09-01

    Full Text Available Abstract Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion 123 which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

  3. Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

    Science.gov (United States)

    Reddy, Anupama; Huang, C Chris; Liu, Huiqing; Delisi, Charles; Nevalainen, Marja T; Szalma, Sandor; Bhanot, Gyan

    2010-01-01

    We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily

  4. Deconstructing the pluripotency gene regulatory network

    KAUST Repository

    Li, Mo

    2018-04-04

    Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

  5. Deconstructing the pluripotency gene regulatory network

    KAUST Repository

    Li, Mo; Belmonte, Juan Carlos Izpisua

    2018-01-01

    Pluripotent stem cells can be isolated from embryos or derived by reprogramming. Pluripotency is stabilized by an interconnected network of pluripotency genes that cooperatively regulate gene expression. Here we describe the molecular principles of pluripotency gene function and highlight post-transcriptional controls, particularly those induced by RNA-binding proteins and alternative splicing, as an important regulatory layer of pluripotency. We also discuss heterogeneity in pluripotency regulation, alternative pluripotency states and future directions of pluripotent stem cell research.

  6. 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

    checkpoint and aneuploidy. The MN-related gene network was tested against a transcriptomics case study associated with MN measurements. In this case study, transcriptomic data from children and adults differentially exposed to ambient air pollution in the Czech Republic were analysed and visualised......Mechanistically relevant information on responses of humans to xenobiotic exposure in relation to chemically induced biological effects, such as micronuclei (MN) formation can be obtained through large-scale transcriptomics studies. Network analysis may enhance the analysis and visualisation...... of such data. Therefore, this study aimed to develop a 'MN formation' network based on a priori knowledge, by using the pathway tool MetaCore. The gene network contained 27 genes and three gene complexes that are related to processes involved in MN formation, e.g. spindle assembly checkpoint, cell cycle...

  7. Synchronous versus asynchronous modeling of gene regulatory networks.

    Science.gov (United States)

    Garg, Abhishek; Di Cara, Alessandro; Xenarios, Ioannis; Mendoza, Luis; De Micheli, Giovanni

    2008-09-01

    In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

  8. Ancient mitogenomics

    DEFF Research Database (Denmark)

    Ho, Simon Y. W.; Gilbert, Tom

    2010-01-01

    the technical challenges that face researchers in the field. We catalogue the diverse sequencing methods and source materials used to obtain ancient mitogenomic sequences, summarise the associated genetic and phylogenetic studies that have been conducted, and evaluate the future prospects of the field.......The mitochondrial genome has been the traditional focus of most research into ancient DNA, owing to its high copy number and population-level variability. Despite this long-standing interest in mitochondrial DNA, it was only in 2001 that the first complete ancient mitogenomic sequences were...... obtained. As a result of various methodological developments, including the introduction of high-throughput sequencing techniques, the total number of ancient mitogenome sequences has increased rapidly over the past few years. In this review, we present a brief history of ancient mitogenomics and describe...

  9. The prevalence of gene duplications and their ancient origin in Rhodobacter sphaeroides 2.4.1

    Directory of Open Access Journals (Sweden)

    Cho Hyuk

    2010-12-01

    Full Text Available Abstract Background Rhodobacter sphaeroides 2.4.1 is a metabolically versatile organism that belongs to α-3 subdivision of Proteobacteria. The present study was to identify the extent, history, and role of gene duplications in R. sphaeroides 2.4.1, an organism that possesses two chromosomes. Results A protein similarity search (BLASTP identified 1247 orfs (~29.4% of the total protein coding orfs that are present in 2 or more copies, 37.5% (234 gene-pairs of which exist in duplicate copies. The distribution of the duplicate gene-pairs in all Clusters of Orthologous Groups (COGs differed significantly when compared to the COG distribution across the whole genome. Location plots revealed clusters of gene duplications that possessed the same COG classification. Phylogenetic analyses were performed to determine a tree topology predicting either a Type-A or Type-B phylogenetic relationship. A Type-A phylogenetic relationship shows that a copy of the protein-pair matches more with an ortholog from a species closely related to R. sphaeroides while a Type-B relationship predicts the highest match between both copies of the R. sphaeroides protein-pair. The results revealed that ~77% of the proteins exhibited a Type-A phylogenetic relationship demonstrating the ancient origin of these gene duplications. Additional analyses on three other strains of R. sphaeroides revealed varying levels of gene loss and retention in these strains. Also, analyses on common gene pairs among the four strains revealed that these genes experience similar functional constraints and undergo purifying selection. Conclusions Although the results suggest that the level of gene duplication in organisms with complex genome structuring (more than one chromosome seems to be not markedly different from that in organisms with only a single chromosome, these duplications may have aided in genome reorganization in this group of eubacteria prior to the formation of R. sphaeroides as gene

  10. SELANSI: a toolbox for simulation of stochastic gene regulatory networks.

    Science.gov (United States)

    Pájaro, Manuel; Otero-Muras, Irene; Vázquez, Carlos; Alonso, Antonio A

    2018-03-01

    Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi. antonio@iim.csic.es. © The Author(s) 2017. Published by Oxford University Press.

  11. Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

    Directory of Open Access Journals (Sweden)

    Sungyoung Lee

    2012-12-01

    Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  12. Network graph analysis of gene-gene interactions in genome-wide association study data.

    Science.gov (United States)

    Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

    2012-12-01

    Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

  13. A Regulatory Network Analysis of Orphan Genes in Arabidopsis Thaliana

    Science.gov (United States)

    Singh, Pramesh; Chen, Tianlong; Arendsee, Zebulun; Wurtele, Eve S.; Bassler, Kevin E.

    Orphan genes, which are genes unique to each particular species, have recently drawn significant attention for their potential usefulness for organismal robustness. Their origin and regulatory interaction patterns remain largely undiscovered. Recently, methods that use the context likelihood of relatedness to infer a network followed by modularity maximizing community detection algorithms on the inferred network to find the functional structure of regulatory networks were shown to be effective. We apply improved versions of these methods to gene expression data from Arabidopsis thaliana, identify groups (clusters) of interacting genes with related patterns of expression and analyze the structure within those groups. Focusing on clusters that contain orphan genes, we compare the identified clusters to gene ontology (GO) terms, regulons, and pathway designations and analyze their hierarchical structure. We predict new regulatory interactions and unravel the structure of the regulatory interaction patterns of orphan genes. Work supported by the NSF through Grants DMR-1507371 and IOS-1546858.

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

    Directory of Open Access Journals (Sweden)

    Marie-Pier eScott-Boyer

    2013-12-01

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

  15. Learning gene regulatory networks from only positive and unlabeled data

    Directory of Open Access Journals (Sweden)

    Elkan Charles

    2010-05-01

    Full Text Available Abstract Background Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact. Results A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement. Conclusions Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

  16. Inferring the gene network underlying the branching of tomato inflorescence.

    Directory of Open Access Journals (Sweden)

    Laura Astola

    Full Text Available The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior.

  17. 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

    Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

  18. On the dynamics of a gene regulatory network

    International Nuclear Information System (INIS)

    Grammaticos, B; Carstea, A S; Ramani, A

    2006-01-01

    We examine the dynamics of a network of genes focusing on a periodic chain of genes, of arbitrary length. We show that within a given class of sigmoids representing the equilibrium probability of the binding of the RNA polymerase to the core promoter, the system possesses a single stable fixed point. By slightly modifying the sigmoid, introducing 'stiffer' forms, we show that it is possible to find network configurations exhibiting bistable behaviour. Our results do not depend crucially on the length of the chain considered: calculations with finite chains lead to similar results. However, a realistic study of regulatory genetic networks would require the consideration of more complex topologies and interactions

  19. Ancient DNA

    DEFF Research Database (Denmark)

    Willerslev, Eske; Cooper, Alan

    2004-01-01

    ancient DNA, palaeontology, palaeoecology, archaeology, population genetics, DNA damage and repair......ancient DNA, palaeontology, palaeoecology, archaeology, population genetics, DNA damage and repair...

  20. Functional modules by relating protein interaction networks and gene expression.

    Science.gov (United States)

    Tornow, Sabine; Mewes, H W

    2003-11-01

    Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

  1. Genome-wide analysis of the MYB gene family in physic nut (Jatropha curcas L.).

    Science.gov (United States)

    Zhou, Changpin; Chen, Yanbo; Wu, Zhenying; Lu, Wenjia; Han, Jinli; Wu, Pingzhi; Chen, Yaping; Li, Meiru; Jiang, Huawu; Wu, Guojiang

    2015-11-01

    The MYB proteins comprise one of the largest transcription factor families in plants, and play key roles in regulatory networks controlling development, metabolism, and stress responses. A total of 125 MYB genes (JcMYB) have been identified in the physic nut (Jatropha curcas L.) genome, including 120 2R-type MYB, 4 3R-MYB, and 1 4R-MYB genes. Based on exon-intron arrangement of MYBs from both lower (Physcomitrella patens) and higher (physic nut, Arabidopsis, and rice) plants, we can classify plant MYB genes into ten groups (MI-X), except for MIX genes which are nonexistent in higher plants. We also observed that MVIII genes may be one of the most ancient MYB types which consist of both R2R3- and 3R-MYB genes. Most MYB genes (76.8% in physic nut) belong to the MI group which can be divided into 34 subgroups. The JcMYB genes were nonrandomly distributed on its 11 linkage groups (LGs). The expansion of MYB genes across several subgroups was observed and resulted from genome triplication of ancient dicotyledons and from both ancient and recent tandem duplication events in the physic nut genome. The expression patterns of several MYB duplicates in the physic nut showed differences in four tissues (root, stem, leaf, and seed), and 34 MYB genes responded to at least one abiotic stressor (drought, salinity, phosphate starvation, and nitrogen starvation) in leaves and/or roots based on the data analysis of digital gene expression tags. Overexpression of the JcMYB001 gene in Arabidopsis increased its sensitivity to drought and salinity stresses. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    DEFF Research Database (Denmark)

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

    2010-01-01

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

  3. Did the ancient Egyptians migrate to ancient Nigeria?

    Directory of Open Access Journals (Sweden)

    Jock M. Agai

    2014-01-01

    Full Text Available Literatures concerning the history of West African peoples published from 1900 to 1970 debate�the possible migrations of the Egyptians into West Africa. Writers like Samuel Johnson and�Lucas Olumide believe that the ancient Egyptians penetrated through ancient Nigeria but Leo�Frobenius and Geoffrey Parrinder frowned at this opinion. Using the works of these early�20th century writers of West African history together with a Yoruba legend which teaches�about the origin of their earliest ancestor(s, this researcher investigates the theories that the�ancient Egyptians had contact with the ancient Nigerians and particularly with the Yorubas.Intradisciplinary and/or interdisciplinary implications: There is an existing ideology�amongst the Yorubas and other writers of Yoruba history that the original ancestors of�the Yorubas originated in ancient Egypt hence there was migration between Egypt and�Yorubaland. This researcher contends that even if there was migration between Egypt and�Nigeria, such migration did not take place during the predynastic and dynastic period as�speculated by some scholars. The subject is open for further research.

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

    Directory of Open Access Journals (Sweden)

    Benjamin A Logsdon

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

  5. Local and global responses in complex gene regulation networks

    Science.gov (United States)

    Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

    2009-04-01

    An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.

  6. Lake Tanganyika--a 'melting pot' of ancient and young cichlid lineages (Teleostei: Cichlidae?

    Directory of Open Access Journals (Sweden)

    Juliane D Weiss

    Full Text Available A long history of research focused on the East Africa cichlid radiations (EAR revealed discrepancies between mtDNA and nuclear phylogenies, suggesting that interspecific hybridisation may have been significant during the radiation of these fishes. The approximately 250 cichlid species of Lake Tanganyika have their roots in a monophyletic African cichlid assemblage, but controversies remain about the precise phylogenetic origin and placement of different lineages and consequently about L. Tanganyika colonization scenarios. 3312 AFLP loci and the mitochondrial ND2 gene were genotyped for 91 species representing almost all major lacustrine and riverine haplotilapiine east African cichlid lineages with a focus on L. Tanganyika endemics. Explicitly testing for the possibility of ancient hybridisation events, a comprehensive phylogenetic network hypothesis is proposed for the origin and diversification of L. Tanganyika cichlids. Inference of discordant phylogenetic signal strongly suggests that the genomes of two endemic L. Tanganyika tribes, Eretmodini and Tropheini, are composed of an ancient mixture of riverine and lacustrine lineages. For the first time a strong monophyly signal of all non-haplochromine mouthbrooding species endemic to L. Tanganyika ("ancient mouthbrooders" was detected. Further, in the genomes of early diverging L. Tanganyika endemics Trematocarini, Bathybatini, Hemibatini and Boulengerochromis genetic components of other lineages belonging to the East African Radiation appear to be present. In combination with recent palaeo-geological results showing that tectonic activity in the L. Tanganyika region resulted in highly dynamic and heterogeneous landscape evolution over the Neogene and Pleistocene, the novel phylogenetic data render a single lacustrine basin as the geographical cradle of the endemic L. Tanganyika cichlid lineages unlikely. Instead a scenario of a pre-rift origin of several independent L. Tanganyika precursor

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

    Directory of Open Access Journals (Sweden)

    David A Garfield

    2013-10-01

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

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

    Science.gov (United States)

    Kar, Siddhartha P; Tyrer, Jonathan P; Li, Qiyuan; Lawrenson, Kate; Aben, Katja K H; Anton-Culver, Hoda; Antonenkova, Natalia; Chenevix-Trench, Georgia; Baker, Helen; Bandera, Elisa V; Bean, Yukie T; Beckmann, Matthias W; Berchuck, Andrew; Bisogna, Maria; Bjørge, Line; Bogdanova, Natalia; Brinton, Louise; Brooks-Wilson, Angela; Butzow, Ralf; Campbell, Ian; Carty, Karen; Chang-Claude, Jenny; Chen, Yian Ann; Chen, Zhihua; Cook, Linda S; Cramer, Daniel; Cunningham, Julie M; Cybulski, Cezary; Dansonka-Mieszkowska, Agnieszka; Dennis, Joe; Dicks, Ed; Doherty, Jennifer A; Dörk, Thilo; du Bois, Andreas; Dürst, Matthias; Eccles, Diana; Easton, Douglas F; Edwards, Robert P; Ekici, Arif B; Fasching, Peter A; Fridley, Brooke L; Gao, Yu-Tang; Gentry-Maharaj, Aleksandra; Giles, Graham G; Glasspool, Rosalind; Goode, Ellen L; Goodman, Marc T; Grownwald, Jacek; Harrington, Patricia; Harter, Philipp; Hein, Alexander; Heitz, Florian; Hildebrandt, Michelle A T; Hillemanns, Peter; Hogdall, Estrid; Hogdall, Claus K; Hosono, Satoyo; Iversen, Edwin S; Jakubowska, Anna; Paul, James; Jensen, Allan; Ji, Bu-Tian; Karlan, Beth Y; Kjaer, Susanne K; Kelemen, Linda E; Kellar, Melissa; Kelley, Joseph; Kiemeney, Lambertus A; Krakstad, Camilla; Kupryjanczyk, Jolanta; Lambrechts, Diether; Lambrechts, Sandrina; Le, Nhu D; Lee, Alice W; Lele, Shashi; Leminen, Arto; Lester, Jenny; Levine, Douglas A; Liang, Dong; Lissowska, Jolanta; Lu, Karen; Lubinski, Jan; Lundvall, Lene; Massuger, Leon; Matsuo, Keitaro; McGuire, Valerie; McLaughlin, John R; McNeish, Iain A; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B; Narod, Steven A; Nedergaard, Lotte; Ness, Roberta B; Nevanlinna, Heli; Odunsi, Kunle; Olson, Sara H; Orlow, Irene; Orsulic, Sandra; Weber, Rachel Palmieri; Pearce, Celeste Leigh; Pejovic, Tanja; Pelttari, Liisa M; Permuth-Wey, Jennifer; Phelan, Catherine M; Pike, Malcolm C; Poole, Elizabeth M; Ramus, Susan J; Risch, Harvey A; Rosen, Barry; Rossing, Mary Anne; Rothstein, Joseph H; Rudolph, Anja; Runnebaum, Ingo B; Rzepecka, Iwona K; Salvesen, Helga B; Schildkraut, Joellen M; Schwaab, Ira; Shu, Xiao-Ou; Shvetsov, Yurii B; Siddiqui, Nadeem; Sieh, Weiva; Song, Honglin; Southey, Melissa C; Sucheston-Campbell, Lara E; Tangen, Ingvild L; Teo, Soo-Hwang; Terry, Kathryn L; Thompson, Pamela J; Timorek, Agnieszka; Tsai, Ya-Yu; Tworoger, Shelley S; van Altena, Anne M; Van Nieuwenhuysen, Els; Vergote, Ignace; Vierkant, Robert A; Wang-Gohrke, Shan; Walsh, Christine; Wentzensen, Nicolas; Whittemore, Alice S; Wicklund, Kristine G; Wilkens, Lynne R; Woo, Yin-Ling; Wu, Xifeng; Wu, Anna; Yang, Hannah; Zheng, Wei; Ziogas, Argyrios; Sellers, Thomas A; Monteiro, Alvaro N A; Freedman, Matthew L; Gayther, Simon A; Pharoah, Paul D P

    2015-10-01

    Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations. We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. ©2015 American Association for Cancer Research.

  9. 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

  10. Singular Perturbation Analysis and Gene Regulatory Networks with Delay

    Science.gov (United States)

    Shlykova, Irina; Ponosov, Arcady

    2009-09-01

    There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

  11. 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.

  12. MINER: exploratory analysis of gene interaction networks by machine learning from expression data

    Directory of Open Access Journals (Sweden)

    Sivieng Jane

    2009-12-01

    Full Text Available Abstract Background The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. Results We have developed MINER (Microarray Interactive Network Exploration and Representation, an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Conclusion Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

  13. 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.

  14. 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

  15. Integrative analyses of leprosy susceptibility genes indicate a common autoimmune profile.

    Science.gov (United States)

    Zhang, Deng-Feng; Wang, Dong; Li, Yu-Ye; Yao, Yong-Gang

    2016-04-01

    Leprosy is an ancient chronic infection in the skin and peripheral nerves caused by Mycobacterium leprae. The development of leprosy depends on genetic background and the immune status of the host. However, there is no systematic view focusing on the biological pathways, interaction networks and overall expression pattern of leprosy-related immune and genetic factors. To identify the hub genes in the center of leprosy genetic network and to provide an insight into immune and genetic factors contributing to leprosy. We retrieved all reported leprosy-related genes and performed integrative analyses covering gene expression profiling, pathway analysis, protein-protein interaction network, and evolutionary analyses. A list of 123 differentially expressed leprosy related genes, which were enriched in activation and regulation of immune response, was obtained in our analyses. Cross-disorder analysis showed that the list of leprosy susceptibility genes was largely shared by typical autoimmune diseases such as lupus erythematosus and arthritis, suggesting that similar pathways might be affected in leprosy and autoimmune diseases. Protein-protein interaction (PPI) and positive selection analyses revealed a co-evolution network of leprosy risk genes. Our analyses showed that leprosy associated genes constituted a co-evolution network and might undergo positive selection driven by M. leprae. We suggested that leprosy may be a kind of autoimmune disease and the development of leprosy is a matter of defect or over-activation of body immunity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. The importance of studying inherited hematological disorders in ancient Anatolian populations

    Directory of Open Access Journals (Sweden)

    Yeşim Doğan Alakoç

    2011-12-01

    Full Text Available Before analysis of DNA from ancient remains was possible, anthropologists studied evolution and migration patterns using data obtained from population genetic studies on modern populations combined with data obtained from morphological evaluations of ancient remains. Currently, DNA analysis of ancient populations is making a valuable contribution to these efforts. Researchers that perform ancient DNA analysis prefer to study polymorphisms on the Y chromosome or mitochondrial DNA because the results are easier to statistically evaluate. To evaluate polymorphisms on diploid genomes, which are more informative, only mutations that have been extensively examined in modern populations should be chosen. The most extensively evaluated mutations are those related to prevalent inherited disorders. As such, beta-thalassemia, sickle cell anemia, FVL mutation of globin and the factor V genes are good candidates for DNA studies in ancient populations. These mutations are common in Anatolia, host to many civilizations since the Paleolithic period. This history makes Anatolia a good place for conducting research that could enhance our understanding of human evolution and migration patterns.

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

    Directory of Open Access Journals (Sweden)

    Mark D Schroeder

    2004-09-01

    Full Text Available The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross- regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a

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

    Directory of Open Access Journals (Sweden)

    Guo Zheng

    2006-01-01

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

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

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Watson-Haigh, Nathan S.

    2012-01-01

    Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four......) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT...... (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended....

  20. Feature recognition and detection for ancient architecture based on machine vision

    Science.gov (United States)

    Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

    2018-03-01

    Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

  1. 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.

  2. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  3. Global similarity and local divergence in human and mouse gene co-expression networks

    Directory of Open Access Journals (Sweden)

    Koonin Eugene V

    2006-09-01

    Full Text Available Abstract Background A genome-wide comparative analysis of human and mouse gene expression patterns was performed in order to evaluate the evolutionary divergence of mammalian gene expression. Tissue-specific expression profiles were analyzed for 9,105 human-mouse orthologous gene pairs across 28 tissues. Expression profiles were resolved into species-specific coexpression networks, and the topological properties of the networks were compared between species. Results At the global level, the topological properties of the human and mouse gene coexpression networks are, essentially, identical. For instance, both networks have topologies with small-world and scale-free properties as well as closely similar average node degrees, clustering coefficients, and path lengths. However, the human and mouse coexpression networks are highly divergent at the local level: only a small fraction ( Conclusion The dissonance between global versus local network divergence suggests that the interspecies similarity of the global network properties is of limited biological significance, at best, and that the biologically relevant aspects of the architectures of gene coexpression are specific and particular, rather than universal. Nevertheless, there is substantial evolutionary conservation of the local network structure which is compatible with the notion that gene coexpression networks are subject to purifying selection.

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

    Science.gov (United States)

    Lipner, Ettie M.; Garcia, Benjamin J.; Strong, Michael

    2016-01-01

    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. PMID:26751573

  5. Construction of functional linkage gene networks by data integration.

    Science.gov (United States)

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

    2013-01-01

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

  6. ICan: an integrated co-alteration network to identify ovarian cancer-related genes.

    Science.gov (United States)

    Zhou, Yuanshuai; Liu, Yongjing; Li, Kening; Zhang, Rui; Qiu, Fujun; Zhao, Ning; Xu, Yan

    2015-01-01

    Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183). In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.

  7. Exploiting a Reference Genome in Terms of Duplications: The Network of Paralogs and Single Copy Genes in Arabidopsis thaliana

    Directory of Open Access Journals (Sweden)

    Mara Sangiovanni

    2013-12-01

    Full Text Available Arabidopsis thaliana became the model organism for plant studies because of its small diploid genome, rapid lifecycle and short adult size. Its genome was the first among plants to be sequenced, becoming the reference in plant genomics. However, the Arabidopsis genome is characterized by an inherently complex organization, since it has undergone ancient whole genome duplications, followed by gene reduction, diploidization events and extended rearrangements, which relocated and split up the retained portions. These events, together with probable chromosome reductions, dramatically increased the genome complexity, limiting its role as a reference. The identification of paralogs and single copy genes within a highly duplicated genome is a prerequisite to understand its organization and evolution and to improve its exploitation in comparative genomics. This is still controversial, even in the widely studied Arabidopsis genome. This is also due to the lack of a reference bioinformatics pipeline that could exhaustively identify paralogs and singleton genes. We describe here a complete computational strategy to detect both duplicated and single copy genes in a genome, discussing all the methodological issues that may strongly affect the results, their quality and their reliability. This approach was used to analyze the organization of Arabidopsis nuclear protein coding genes, and besides classifying computationally defined paralogs into networks and single copy genes into different classes, it unraveled further intriguing aspects concerning the genome annotation and the gene relationships in this reference plant species. Since our results may be useful for comparative genomics and genome functional analyses, we organized a dedicated web interface to make them accessible to the scientific community.

  8. 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.

  9. Artificial neural network inference (ANNI: a study on gene-gene interaction for biomarkers in childhood sarcomas.

    Directory of Open Access Journals (Sweden)

    Dong Ling Tong

    Full Text Available OBJECTIVE: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI. METHOD: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. RESULTS: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS; FCGRT and OLFM1 in Ewing's sarcoma (EWS suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. CONCLUSIONS: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.

  10. Sequence-based model of gap gene regulatory network.

    Science.gov (United States)

    Kozlov, Konstantin; Gursky, Vitaly; Kulakovskiy, Ivan; Samsonova, Maria

    2014-01-01

    The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded. The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3

  11. Insights into Modern Human Prehistory Using Ancient Genomes.

    Science.gov (United States)

    Yang, Melinda A; Fu, Qiaomei

    2018-03-01

    The genetic relationship of past modern humans to today's populations and each other was largely unknown until recently, when advances in ancient DNA sequencing allowed for unprecedented analysis of the genomes of these early people. These ancient genomes reveal new insights into human prehistory not always observed studying present-day populations, including greater details on the genetic diversity, population structure, and gene flow that characterized past human populations, particularly in early Eurasia, as well as increased insight on the relationship between archaic and modern humans. Here, we review genetic studies on ∼45000- to 7500-year-old individuals associated with mainly preagricultural cultures found in Eurasia, the Americas, and Africa. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Mt-rps3 is an ancient gene which provides insight into the evolution of fungal mitochondrial genomes.

    Science.gov (United States)

    Korovesi, Artemis G; Ntertilis, Maria; Kouvelis, Vassili N

    2018-05-12

    The nuclear ribosomal protein S3 (Rps3) is implicated in the assembly of the ribosomal small subunit. Fungi and plants present a gene copy in their mitochondrial (mt) genomes. An analysis of 303 complete fungal mt genomes showed that, when rps3 is found, it is either a free-standing gene or an anchored gene within the omega intron of the rnl gene. Early divergent fungi, Basidiomycota and all yeasts but the CTG group belong to the first case, and Pezizomycotina to the second. Its position, size and genetic code employed are conserved within species of the same Order. Size variability is attributed to different number of repeats. These repeats consist of AT-rich sequences. MtRps3 proteins lack the KH domain, necessary for binding to rRNA, in their N-terminal region. Their C-terminal region is conserved in all Domains of life. Phylogenetic analysis showed that nuclear and mt Rps3 proteins are descendants of archaeal and a-proteobacterial homologues, respectively. Thus, fungal mt-rps3 gene is an ancient gene which evolved within the endosymbiotic model and presents different evolutionary routes: (a) coming from a-proteobacteria, it was relocated to another region of the mt genome, (b) via its insertion to the omega intron, it was transferred to the nucleus and/or got lost, and (c) it was re-routed to the mt genome again. Today, Basidiomycota and Saccharomycetales seem to follow the first evolutionary route and almost all Pezizomycotina support the second scenario with their exceptions being the result of the third scenario, i.e., the gene's re-entry to the mt genome. Copyright © 2018. Published by Elsevier Inc.

  13. The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks

    Directory of Open Access Journals (Sweden)

    Frank eEmmert-Streib

    2014-02-01

    Full Text Available In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of $351$ patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome $21$ is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

  14. Annotating gene sets by mining large literature collections with protein networks.

    Science.gov (United States)

    Wang, Sheng; Ma, Jianzhu; Yu, Michael Ku; Zheng, Fan; Huang, Edward W; Han, Jiawei; Peng, Jian; Ideker, Trey

    2018-01-01

    Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

  15. Inferring the conservative causal core of gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Emmert-Streib Frank

    2010-09-01

    Full Text Available Abstract Background Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. Results In this paper, we introduce a novel gene regulatory network inference (GRNI algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. Conclusions For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

  16. Inferring the conservative causal core of gene regulatory networks.

    Science.gov (United States)

    Altay, Gökmen; Emmert-Streib, Frank

    2010-09-28

    Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

  17. Inferring the palaeoenvironment of ancient bacteria on the basis of resurrected proteins

    Science.gov (United States)

    Gaucher, Eric A.; Thomson, J. Michael; Burgan, Michelle F.; Benner, Steven A.

    2003-01-01

    Features of the physical environment surrounding an ancestral organism can be inferred by reconstructing sequences of ancient proteins made by those organisms, resurrecting these proteins in the laboratory, and measuring their properties. Here, we resurrect candidate sequences for elongation factors of the Tu family (EF-Tu) found at ancient nodes in the bacterial evolutionary tree, and measure their activities as a function of temperature. The ancient EF-Tu proteins have temperature optima of 55-65 degrees C. This value seems to be robust with respect to uncertainties in the ancestral reconstruction. This suggests that the ancient bacteria that hosted these particular genes were thermophiles, and neither hyperthermophiles nor mesophiles. This conclusion can be compared and contrasted with inferences drawn from an analysis of the lengths of branches in trees joining proteins from contemporary bacteria, the distribution of thermophily in derived bacterial lineages, the inferred G + C content of ancient ribosomal RNA, and the geological record combined with assumptions concerning molecular clocks. The study illustrates the use of experimental palaeobiochemistry and assumptions about deep phylogenetic relationships between bacteria to explore the character of ancient life.

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

    Science.gov (United States)

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

    2017-08-01

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

  19. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

    Directory of Open Access Journals (Sweden)

    Xiaodong Cai

    Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

  20. Modeling stochasticity and robustness in gene regulatory networks.

    Science.gov (United States)

    Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; De Micheli, Giovanni; Xenarios, Ioannis

    2009-06-15

    Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

  1. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

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

    Indian Academy of Sciences (India)

    2015-09-28

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

  3. Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

    Science.gov (United States)

    Ferreira, Gustavo Rodrigues; Nakaya, Helder Imoto; Costa, Luciano da Fontoura

    2018-04-01

    The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

  4. Biological consequences of ancient gene acquisition and duplication in the large genome soil bacterium, ""solibacter usitatus"" strain Ellin6076

    Energy Technology Data Exchange (ETDEWEB)

    Challacombe, Jean F [Los Alamos National Laboratory; Eichorst, Stephanie A [Los Alamos National Laboratory; Xie, Gary [Los Alamos National Laboratory; Kuske, Cheryl R [Los Alamos National Laboratory; Hauser, Loren [ORNL; Land, Miriam [ORNL

    2009-01-01

    Bacterial genome sizes range from ca. 0.5 to 10Mb and are influenced by gene duplication, horizontal gene transfer, gene loss and other evolutionary processes. Sequenced genomes of strains in the phylum Acidobacteria revealed that 'Solibacter usistatus' strain Ellin6076 harbors a 9.9 Mb genome. This large genome appears to have arisen by horizontal gene transfer via ancient bacteriophage and plasmid-mediated transduction, as well as widespread small-scale gene duplications. This has resulted in an increased number of paralogs that are potentially ecologically important (ecoparalogs). Low amino acid sequence identities among functional group members and lack of conserved gene order and orientation in the regions containing similar groups of paralogs suggest that most of the paralogs were not the result of recent duplication events. The genome sizes of cultured subdivision 1 and 3 strains in the phylum Acidobacteria were estimated using pulsed-field gel electrophoresis to determine the prevalence of the large genome trait within the phylum. Members of subdivision 1 were estimated to have smaller genome sizes ranging from ca. 2.0 to 4.8 Mb, whereas members of subdivision 3 had slightly larger genomes, from ca. 5.8 to 9.9 Mb. It is hypothesized that the large genome of strain Ellin6076 encodes traits that provide a selective metabolic, defensive and regulatory advantage in the variable soil environment.

  5. 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.

  6. Evolution of Cis-Regulatory Elements and Regulatory Networks in Duplicated Genes of Arabidopsis.

    Science.gov (United States)

    Arsovski, Andrej A; Pradinuk, Julian; Guo, Xu Qiu; Wang, Sishuo; Adams, Keith L

    2015-12-01

    Plant genomes contain large numbers of duplicated genes that contribute to the evolution of new functions. Following duplication, genes can exhibit divergence in their coding sequence and their expression patterns. Changes in the cis-regulatory element landscape can result in changes in gene expression patterns. High-throughput methods developed recently can identify potential cis-regulatory elements on a genome-wide scale. Here, we use a recent comprehensive data set of DNase I sequencing-identified cis-regulatory binding sites (footprints) at single-base-pair resolution to compare binding sites and network connectivity in duplicated gene pairs in Arabidopsis (Arabidopsis thaliana). We found that duplicated gene pairs vary greatly in their cis-regulatory element architecture, resulting in changes in regulatory network connectivity. Whole-genome duplicates (WGDs) have approximately twice as many footprints in their promoters left by potential regulatory proteins than do tandem duplicates (TDs). The WGDs have a greater average number of footprint differences between paralogs than TDs. The footprints, in turn, result in more regulatory network connections between WGDs and other genes, forming denser, more complex regulatory networks than shown by TDs. When comparing regulatory connections between duplicates, WGDs had more pairs in which the two genes are either partially or fully diverged in their network connections, but fewer genes with no network connections than the TDs. There is evidence of younger TDs and WGDs having fewer unique connections compared with older duplicates. This study provides insights into cis-regulatory element evolution and network divergence in duplicated genes. © 2015 American Society of Plant Biologists. All Rights Reserved.

  7. 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.

  8. Exploring Ancient Skies A Survey of Ancient and Cultural Astronomy

    CERN Document Server

    Kelley, David H

    2011-01-01

    Exploring Ancient Skies brings together the methods of archaeology and the insights of modern astronomy to explore the science of astronomy as it was practiced in various cultures prior to the invention of the telescope. The book reviews an enormous and growing body of literature on the cultures of the ancient Mediterranean, the Far East, and the New World (particularly Mesoamerica), putting the ancient astronomical materials into their archaeological and cultural contexts. The authors begin with an overview of the field and proceed to essential aspects of naked-eye astronomy, followed by an examination of specific cultures. The book concludes by taking into account the purposes of ancient astronomy: astrology, navigation, calendar regulation, and (not least) the understanding of our place and role in the universe. Skies are recreated to display critical events as they would have appeared to ancient observers—events such as the supernova of 1054 A.D., the "lion horoscope," and the Star of Bethlehem. Explori...

  9. On the Interplay between Entropy and Robustness of Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Bor-Sen Chen

    2010-05-01

    Full Text Available The interplay between entropy and robustness of gene network is a core mechanism of systems biology. The entropy is a measure of randomness or disorder of a physical system due to random parameter fluctuation and environmental noises in gene regulatory networks. The robustness of a gene regulatory network, which can be measured as the ability to tolerate the random parameter fluctuation and to attenuate the effect of environmental noise, will be discussed from the robust H∞ stabilization and filtering perspective. In this review, we will also discuss their balancing roles in evolution and potential applications in systems and synthetic biology.

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

    Directory of Open Access Journals (Sweden)

    Shohag Barman

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

  11. Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining.

    Science.gov (United States)

    Kreula, Sanna M; Kaewphan, Suwisa; Ginter, Filip; Jones, Patrik R

    2018-01-01

    The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to ( i ) discover novel candidate associations between different genes or proteins in the network, and ( ii ) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.

  12. An integer optimization algorithm for robust identification of non-linear gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Chemmangattuvalappil Nishanth

    2012-09-01

    Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

  13. Utilization of ancient permafrost carbon in headwaters of Arctic fluvial networks

    NARCIS (Netherlands)

    Mann, Paul J.; Eglinton, Timothy I.; McIntyre, Cameron P.; Zimov, Nikita; Davydova, Anna; Vonk, Jorien E.; Holmes, Robert M.; Spencer, Robert G M

    2015-01-01

    Northern high-latitude rivers are major conduits of carbon from land to coastal seas and the Arctic Ocean. Arctic warming is promoting terrestrial permafrost thaw and shifting hydrologic flowpaths, leading to fluvial mobilization of ancient carbon stores. Here we describe 14 C and 13 C

  14. Gene regulatory networks elucidating huanglongbing disease mechanisms.

    Directory of Open Access Journals (Sweden)

    Federico Martinelli

    Full Text Available 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.

  15. A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

    Science.gov (United States)

    Barone, Antonio; Toti, Paolo; Giuca, Maria Rita; Derchi, Giacomo; Covani, Ugo

    2015-07-01

    In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

  16. FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

    Science.gov (United States)

    Ernst, Mathias; Du, Yang; Warsow, Gregor; Hamed, Mohamed; Endlich, Nicole; Endlich, Karlhans; Murua Escobar, Hugo; Sklarz, Lisa-Madeleine; Sender, Sina; Junghanß, Christian; Möller, Steffen; Fuellen, Georg; Struckmann, Stephan

    2017-02-16

    To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.

  17. Statistical indicators of collective behavior and functional clusters in gene networks of yeast

    Science.gov (United States)

    Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

    2006-03-01

    We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

  18. Identification and network-enabled characterization of auxin response factor genes in Medicago truncatula

    Directory of Open Access Journals (Sweden)

    David J. Burks

    2016-12-01

    Full Text Available The Auxin Response Factor (ARF family of transcription factors is an important regulator of environmental response and symbiotic nodulation in the legume Medicago truncatula. While previous studies have identified members of this family, a recent spurt in gene expression data coupled with genome update and reannotation calls for a reassessment of the prevalence of ARF genes and their interaction networks in M. truncatula. We performed a comprehensive analysis of the M. truncatula genome and transcriptome that entailed search for novel ARF genes and the co-expression networks. Our investigation revealed 8 novel M. truncatula ARF (MtARF genes, of the total 22 identified, and uncovered novel gene co-expression networks as well. Furthermore, the topological clustering and single enrichment analysis of several network models revealed the roles of individual members of the MtARF family in nitrogen regulation, nodule initiation, and post-embryonic development through a specialized protein packaging and secretory pathway. In summary, this study not just shines new light on an important gene family, but also provides a guideline for identification of new members of gene families and their functional characterization through network analyses.

  19. Unveiling network-based functional features through integration of gene expression into protein networks.

    Science.gov (United States)

    Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali

    2018-06-01

    Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.

  20. Digital Signal Processing and Control for the Study of Gene Networks

    Science.gov (United States)

    Shin, Yong-Jun

    2016-04-01

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

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

    Directory of Open Access Journals (Sweden)

    Thurner Stefan

    2009-08-01

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

  2. Gene co-expression networks shed light into diseases of brain iron accumulation.

    Science.gov (United States)

    Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M; Botía, Juan A; Collingwood, Joanna F; Hardy, John; Milward, Elizabeth A; Ryten, Mina; Houlden, Henry

    2016-03-01

    Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  3. Evolution of red algal plastid genomes: ancient architectures, introns, horizontal gene transfer, and taxonomic utility of plastid markers.

    Directory of Open Access Journals (Sweden)

    Jan Janouškovec

    Full Text Available Red algae have the most gene-rich plastid genomes known, but despite their evolutionary importance these genomes remain poorly sampled. Here we characterize three complete and one partial plastid genome from a diverse range of florideophytes. By unifying annotations across all available red algal plastid genomes we show they all share a highly compact and slowly-evolving architecture and uniquely rich gene complements. Both chromosome structure and gene content have changed very little during red algal diversification, and suggest that plastid-to nucleus gene transfers have been rare. Despite their ancient character, however, the red algal plastids also contain several unprecedented features, including a group II intron in a tRNA-Met gene that encodes the first example of red algal plastid intron maturase - a feature uniquely shared among florideophytes. We also identify a rare case of a horizontally-acquired proteobacterial operon, and propose this operon may have been recruited for plastid function and potentially replaced a nucleus-encoded plastid-targeted paralogue. Plastid genome phylogenies yield a fully resolved tree and suggest that plastid DNA is a useful tool for resolving red algal relationships. Lastly, we estimate the evolutionary rates among more than 200 plastid genes, and assess their usefulness for species and subspecies taxonomy by comparison to well-established barcoding markers such as cox1 and rbcL. Overall, these data demonstrates that red algal plastid genomes are easily obtainable using high-throughput sequencing of total genomic DNA, interesting from evolutionary perspectives, and promising in resolving red algal relationships at evolutionarily-deep and species/subspecies levels.

  4. Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis

    Science.gov (United States)

    Scott, RE; Ghule, PN; Stein, JL; Stein, GS

    2015-01-01

    The early stages of carcinogenesis are linked to defects in the cell cycle. A series of cell cycle checkpoints are involved in this process. The G1/S checkpoint that serves to integrate the control of cell proliferation and differentiation is linked to carcinogenesis and the mitotic spindle checkpoint with the development of chromosomal instability. This paper presents the outcome of systems biology studies designed to evaluate if networks of covariate cell cycle gene transcripts exist in proliferative mammalian tissues including mice, rats and humans. The GeneNetwork website that contains numerous gene expression datasets from different species, sexes and tissues represents the foundational resource for these studies (www.genenetwork.org). In addition, WebGestalt, a gene ontology tool, facilitated the identification of expression networks of genes that co-vary with key cell cycle targets, especially Cdc20 and Plk1 (www.bioinfo.vanderbilt.edu/webgestalt). Cell cycle expression networks of such covariate mRNAs exist in multiple proliferative tissues including liver, lung, pituitary, adipose and lymphoid tissues among others but not in brain or retina that have low proliferative potential. Sixty-three covariate cell cycle gene transcripts (mRNAs) compose the average cell cycle network with p = e−13 to e−36. Cell cycle expression networks show species, sex and tissue variability and they are enriched in mRNA transcripts associated with mitosis many of which are associated with chromosomal instability. PMID:25808367

  5. Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.

    Science.gov (United States)

    Jia, Bin; Wang, Xiaodong

    2013-12-17

    : The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF3), and the fifth-degree cubature Kalman filter (CKF5) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.

  6. 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

  7. Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease.

    Science.gov (United States)

    Johnson, Michael R; Shkura, Kirill; Langley, Sarah R; Delahaye-Duriez, Andree; Srivastava, Prashant; Hill, W David; Rackham, Owen J L; Davies, Gail; Harris, Sarah E; Moreno-Moral, Aida; Rotival, Maxime; Speed, Doug; Petrovski, Slavé; Katz, Anaïs; Hayward, Caroline; Porteous, David J; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Starr, John M; Liewald, David C; Visconti, Alessia; Falchi, Mario; Bottolo, Leonardo; Rossetti, Tiziana; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Grote, Alexander; Helmstaedter, Christoph; Becker, Albert J; Kaminski, Rafal M; Deary, Ian J; Petretto, Enrico

    2016-02-01

    Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease-associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.

  8. Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice

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    Shuchi eSmita

    2015-12-01

    Full Text Available MYB transcription factor (TF is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by top down and guide gene approaches. More than 50% of OsMYBs were strongly correlated under fifty experimental conditions with 51 hub genes via top down approach. Further, clusters were identified using Markov Clustering (MCL. To maximize the clustering performance, parameter evaluation of the MCL inflation score (I was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by guide gene approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought

  9. The capacity for multistability in small gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Grotewold Erich

    2009-09-01

    Full Text Available Abstract Background Recent years have seen a dramatic increase in the use of mathematical modeling to gain insight into gene regulatory network behavior across many different organisms. In particular, there has been considerable interest in using mathematical tools to understand how multistable regulatory networks may contribute to developmental processes such as cell fate determination. Indeed, such a network may subserve the formation of unicellular leaf hairs (trichomes in the model plant Arabidopsis thaliana. Results In order to investigate the capacity of small gene regulatory networks to generate multiple equilibria, we present a chemical reaction network (CRN-based modeling formalism and describe a number of methods for CRN analysis in a parameter-free context. These methods are compared and applied to a full set of one-component subnetworks, as well as a large random sample from 40,680 similarly constructed two-component subnetworks. We find that positive feedback and cooperativity mediated by transcription factor (TF dimerization is a requirement for one-component subnetwork bistability. For subnetworks with two components, the presence of these processes increases the probability that a randomly sampled subnetwork will exhibit multiple equilibria, although we find several examples of bistable two-component subnetworks that do not involve cooperative TF-promoter binding. In the specific case of epidermal differentiation in Arabidopsis, dimerization of the GL3-GL1 complex and cooperative sequential binding of GL3-GL1 to the CPC promoter are each independently sufficient for bistability. Conclusion Computational methods utilizing CRN-specific theorems to rule out bistability in small gene regulatory networks are far superior to techniques generally applicable to deterministic ODE systems. Using these methods to conduct an unbiased survey of parameter-free deterministic models of small networks, and the Arabidopsis epidermal cell

  10. Ancient genomes

    OpenAIRE

    Hoelzel, A Rus

    2005-01-01

    Ever since its invention, the polymerase chain reaction has been the method of choice for work with ancient DNA. In an application of modern genomic methods to material from the Pleistocene, a recent study has instead undertaken to clone and sequence a portion of the ancient genome of the cave bear.

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

    Directory of Open Access Journals (Sweden)

    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

  12. Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

    Science.gov (United States)

    Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

    2013-03-28

    Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. Copyright © 2013 Elsevier Inc. All rights reserved.

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

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    Meimei Liang

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

  14. Epigenetic Modulation of Brain Gene Networks for Cocaine and Alcohol Abuse

    Directory of Open Access Journals (Sweden)

    Sean P Farris

    2015-05-01

    Full Text Available Cocaine and alcohol are two substances of abuse that prominently affect the central nervous system (CNS. Repeated exposure to cocaine and alcohol leads to longstanding changes in gene expression, and subsequent functional CNS plasticity, throughout multiple brain regions. Epigenetic modifications of histones are one proposed mechanism guiding these enduring changes to the transcriptome. Characterizing the large number of available biological relationships as network models can reveal unexpected biochemical relationships. Clustering analysis of variation from whole-genome sequencing of gene expression (RNA-Seq and histone H3 lysine 4 trimethylation (H3K4me3 events (ChIP-Seq revealed the underlying structure of the transcriptional and epigenomic landscape within hippocampal postmortem brain tissue of drug abusers and control cases. Distinct sets of interrelated networks for cocaine and alcohol abuse were determined for each abusive substance. The network approach identified subsets of functionally related genes that are regulated in agreement with H3K4me3 changes, suggesting cause and effect relationships between this epigenetic mark and gene expression. Gene expression networks consisted of recognized substrates for addiction, such as the dopamine- and cAMP-regulated neuronal phosphoprotein PPP1R1B / DARPP-32 and the vesicular glutamate transporter SLC17A7 / VGLUT1 as well as potentially novel molecular targets for substance abuse. Through a systems biology based approach our results illustrate the utility of integrating epigenetic and transcript expression to establish relevant biological networks in the human brain for addiction. Future work with laboratory models may clarify the functional relevance of these gene networks for cocaine and alcohol, and provide a framework for the development of medications for the treatment of addiction.

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

    Science.gov (United States)

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

    2016-01-11

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

  16. Estimating immunoregulatory gene networks in human herpesvirus type 6-infected T cells

    International Nuclear Information System (INIS)

    Takaku, Tomoiku; Ohyashiki, Junko H.; Zhang, Yu; Ohyashiki, Kazuma

    2005-01-01

    The immune response to viral infection involves complex network of dynamic gene and protein interactions. We present here the dynamic gene network of the host immune response during human herpesvirus type 6 (HHV-6) infection in an adult T-cell leukemia cell line. Using a pathway-focused oligonucleotide DNA microarray, we found a possible association between chemokine genes regulating Th1/Th2 balance and genes regulating T-cell proliferation during HHV-6B infection. Gene network analysis using an integrated comprehensive workbench, VoyaGene, revealed that a gene encoding a TEC-family kinase, ITK, might be a putative modulator in the host immune response against HHV-6B infection. We conclude that Th2-dominated inflammatory reaction in host cells may play an important role in HHV-6B-infected T cells, thereby suggesting the possibility that ITK might be a therapeutic target in diseases related to dysregulation of Th1/Th2 balance. This study describes a novel approach to find genes related with the complex host-virus interaction using microarray data employing the Bayesian statistical framework

  17. 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.

  18. Inferring gene networks from discrete expression data

    KAUST Repository

    Zhang, L.; Mallick, B. K.

    2013-01-01

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

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

    Science.gov (United States)

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

    2018-05-14

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

  20. Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.

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    Wei, Jiangyong; Hu, Xiaohua; Zou, Xiufen; Tian, Tianhai

    2017-12-28

    Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate

  1. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

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    Sapna Kumari

    Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

  2. Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks.

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    Zhou, Xuezhong; Liu, Baoyan; Wu, Zhaohui; Feng, Yi

    2007-10-01

    The amount of biomedical data in different disciplines is growing at an exponential rate. Integrating these significant knowledge sources to generate novel hypotheses for systems biology research is difficult. Traditional Chinese medicine (TCM) is a completely different discipline, and is a complementary knowledge system to modern biomedical science. This paper uses a significant TCM bibliographic literature database in China, together with MEDLINE, to help discover novel gene functional knowledge. We present an integrative mining approach to uncover the functional gene relationships from MEDLINE and TCM bibliographic literature. This paper introduces TCM literature (about 50,000 records) as one knowledge source for constructing literature-based gene networks. We use the TCM diagnosis, TCM syndrome, to automatically congregate the related genes. The syndrome-gene relationships are discovered based on the syndrome-disease relationships extracted from TCM literature and the disease-gene relationships in MEDLINE. Based on the bubble-bootstrapping and relation weight computing methods, we have developed a prototype system called MeDisco/3S, which has name entity and relation extraction, and online analytical processing (OLAP) capabilities, to perform the integrative mining process. We have got about 200,000 syndrome-gene relations, which could help generate syndrome-based gene networks, and help analyze the functional knowledge of genes from syndrome perspective. We take the gene network of Kidney-Yang Deficiency syndrome (KYD syndrome) and the functional analysis of some genes, such as CRH (corticotropin releasing hormone), PTH (parathyroid hormone), PRL (prolactin), BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 2, early onset), to demonstrate the preliminary results. The underlying hypothesis is that the related genes of the same syndrome will have some biological functional relationships, and will constitute a functional network. This paper presents

  3. Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

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    Xiaobo Guo

    Full Text Available Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs. It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC curve and the precision-recall (PR curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

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

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

    2010-10-04

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

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

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    Xiaoping Liu

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

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

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

    2009-01-01

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

  7. Developing integrated crop knowledge networks to advance candidate gene discovery.

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    Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

    2016-12-01

    The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

  8. A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes

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

    2013-01-01

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

  9. A network approach to analyzing highly recombinant malaria parasite genes.

    Science.gov (United States)

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

    2013-01-01

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

  10. A network approach to analyzing highly recombinant malaria parasite genes.

    Directory of Open Access Journals (Sweden)

    Daniel B Larremore

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

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

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    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.

  12. Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

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    Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

    2017-01-01

    The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

  13. Pathogens and host immunity in the ancient human oral cavity

    Science.gov (United States)

    Warinner, Christina; Matias Rodrigues, João F.; Vyas, Rounak; Trachsel, Christian; Shved, Natallia; Grossmann, Jonas; Radini, Anita; Hancock, Y.; Tito, Raul Y.; Fiddyment, Sarah; Speller, Camilla; Hendy, Jessica; Charlton, Sophy; Luder, Hans Ulrich; Salazar-García, Domingo C.; Eppler, Elisabeth; Seiler, Roger; Hansen, Lars; Samaniego Castruita, José Alfredo; Barkow-Oesterreicher, Simon; Teoh, Kai Yik; Kelstrup, Christian; Olsen, Jesper V.; Nanni, Paolo; Kawai, Toshihisa; Willerslev, Eske; von Mering, Christian; Lewis, Cecil M.; Collins, Matthew J.; Gilbert, M. Thomas P.; Rühli, Frank; Cappellini, Enrico

    2014-01-01

    Calcified dental plaque (dental calculus) preserves for millennia and entraps biomolecules from all domains of life and viruses. We report the first high-resolution taxonomic and protein functional characterization of the ancient oral microbiome and demonstrate that the oral cavity has long served as a reservoir for bacteria implicated in both local and systemic disease. We characterize: (i) the ancient oral microbiome in a diseased state, (ii) 40 opportunistic pathogens, (iii) the first evidence of ancient human-associated putative antibiotic resistance genes, (iv) a genome reconstruction of the periodontal pathogen Tannerella forsythia, (v) 239 bacterial and 43 human proteins, allowing confirmation of a long-term association between host immune factors, “red-complex” pathogens, and periodontal disease, and (vi) DNA sequences matching dietary sources. Directly datable and nearly ubiquitous, dental calculus permits the simultaneous investigation of pathogen activity, host immunity, and diet, thereby extending the direct investigation of common diseases into the human evolutionary past. PMID:24562188

  14. [Weighted gene co-expression network analysis in biomedicine research].

    Science.gov (United States)

    Liu, Wei; Li, Li; Ye, Hua; Tu, Wei

    2017-11-25

    High-throughput biological technologies are now widely applied in biology and medicine, allowing scientists to monitor thousands of parameters simultaneously in a specific sample. However, it is still an enormous challenge to mine useful information from high-throughput data. The emergence of network biology provides deeper insights into complex bio-system and reveals the modularity in tissue/cellular networks. Correlation networks are increasingly used in bioinformatics applications. Weighted gene co-expression network analysis (WGCNA) tool can detect clusters of highly correlated genes. Therefore, we systematically reviewed the application of WGCNA in the study of disease diagnosis, pathogenesis and other related fields. First, we introduced principle, workflow, advantages and disadvantages of WGCNA. Second, we presented the application of WGCNA in disease, physiology, drug, evolution and genome annotation. Then, we indicated the application of WGCNA in newly developed high-throughput methods. We hope this review will help to promote the application of WGCNA in biomedicine research.

  15. Network-based association of hypoxia-responsive genes with cardiovascular diseases

    International Nuclear Information System (INIS)

    Wang, Rui-Sheng; Oldham, William M; Loscalzo, Joseph

    2014-01-01

    Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology. (paper)

  16. Apps for Ancient Civilizations

    Science.gov (United States)

    Thompson, Stephanie

    2011-01-01

    This project incorporates technology and a historical emphasis on science drawn from ancient civilizations to promote a greater understanding of conceptual science. In the Apps for Ancient Civilizations project, students investigate an ancient culture to discover how people might have used science and math smartphone apps to make their lives…

  17. 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.

  18. Co-expression network analysis of duplicate genes in maize (Zea mays L.) reveals no subgenome bias.

    Science.gov (United States)

    Li, Lin; Briskine, Roman; Schaefer, Robert; Schnable, Patrick S; Myers, Chad L; Flagel, Lex E; Springer, Nathan M; Muehlbauer, Gary J

    2016-11-04

    Gene duplication is prevalent in many species and can result in coding and regulatory divergence. Gene duplications can be classified as whole genome duplication (WGD), tandem and inserted (non-syntenic). In maize, WGD resulted in the subgenomes maize1 and maize2, of which maize1 is considered the dominant subgenome. However, the landscape of co-expression network divergence of duplicate genes in maize is still largely uncharacterized. To address the consequence of gene duplication on co-expression network divergence, we developed a gene co-expression network from RNA-seq data derived from 64 different tissues/stages of the maize reference inbred-B73. WGD, tandem and inserted gene duplications exhibited distinct regulatory divergence. Inserted duplicate genes were more likely to be singletons in the co-expression networks, while WGD duplicate genes were likely to be co-expressed with other genes. Tandem duplicate genes were enriched in the co-expression pattern where co-expressed genes were nearly identical for the duplicates in the network. Older gene duplications exhibit more extensive co-expression variation than younger duplications. Overall, non-syntenic genes primarily from inserted duplications show more co-expression divergence. Also, such enlarged co-expression divergence is significantly related to duplication age. Moreover, subgenome dominance was not observed in the co-expression networks - maize1 and maize2 exhibit similar levels of intra subgenome correlations. Intriguingly, the level of inter subgenome co-expression was similar to the level of intra subgenome correlations, and genes from specific subgenomes were not likely to be the enriched in co-expression network modules and the hub genes were not predominantly from any specific subgenomes in maize. Our work provides a comprehensive analysis of maize co-expression network divergence for three different types of gene duplications and identifies potential relationships between duplication types

  19. Identifying noncoding risk variants using disease-relevant gene regulatory networks.

    Science.gov (United States)

    Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

    2018-02-16

    Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

  20. Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

    Science.gov (United States)

    Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

    2005-04-15

    The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

  1. Integration of gene expression and methylation to unravel biological networks in glioblastoma patients.

    Science.gov (United States)

    Gadaleta, Francesco; Bessonov, Kyrylo; Van Steen, Kristel

    2017-02-01

    The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work, we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylation data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genomic or Expression-Methylation (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach, Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified, respectively, 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Although the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response, and several cancer types. Importantly, we observed significant overrepresentation of cancer-related pathways including glioma, especially in the XORnet network, suggesting a nonignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors. © 2016 WILEY PERIODICALS, INC.

  2. A reconstruction problem for a class of phylogenetic networks with lateral gene transfers.

    Science.gov (United States)

    Cardona, Gabriel; Pons, Joan Carles; Rosselló, Francesc

    2015-01-01

    Lateral, or Horizontal, Gene Transfers are a type of asymmetric evolutionary events where genetic material is transferred from one species to another. In this paper we consider LGT networks, a general model of phylogenetic networks with lateral gene transfers which consist, roughly, of a principal rooted tree with its leaves labelled on a set of taxa, and a set of extra secondary arcs between nodes in this tree representing lateral gene transfers. An LGT network gives rise in a natural way to a principal phylogenetic subtree and a set of secondary phylogenetic subtrees, which, roughly, represent, respectively, the main line of evolution of most genes and the secondary lines of evolution through lateral gene transfers. We introduce a set of simple conditions on an LGT network that guarantee that its principal and secondary phylogenetic subtrees are pairwise different and that these subtrees determine, up to isomorphism, the LGT network. We then give an algorithm that, given a set of pairwise different phylogenetic trees [Formula: see text] on the same set of taxa, outputs, when it exists, the LGT network that satisfies these conditions and such that its principal phylogenetic tree is [Formula: see text] and its secondary phylogenetic trees are [Formula: see text].

  3. Co-regulation of metabolic genes is better explained by flux coupling than by network distance.

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    Richard A Notebaart

    2008-01-01

    Full Text Available To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

  4. Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

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

    2017-08-01

    Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

  5. Characterization of WRKY co-regulatory networks in rice and Arabidopsis

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    Kikuchi Shoshi

    2009-09-01

    Full Text Available Abstract Background The WRKY transcription factor gene family has a very ancient origin and has undergone extensive duplications in the plant kingdom. Several studies have pointed out their involvement in a range of biological processes, revealing that a large number of WRKY genes are transcriptionally regulated under conditions of biotic and/or abiotic stress. To investigate the existence of WRKY co-regulatory networks in plants, a whole gene family WRKYs expression study was carried out in rice (Oryza sativa. This analysis was extended to Arabidopsis thaliana taking advantage of an extensive repository of gene expression data. Results The presented results suggested that 24 members of the rice WRKY gene family (22% of the total were differentially-regulated in response to at least one of the stress conditions tested. We defined the existence of nine OsWRKY gene clusters comprising both phylogenetically related and unrelated genes that were significantly co-expressed, suggesting that specific sets of WRKY genes might act in co-regulatory networks. This hypothesis was tested by Pearson Correlation Coefficient analysis of the Arabidopsis WRKY gene family in a large set of Affymetrix microarray experiments. AtWRKYs were found to belong to two main co-regulatory networks (COR-A, COR-B and two smaller ones (COR-C and COR-D, all including genes belonging to distinct phylogenetic groups. The COR-A network contained several AtWRKY genes known to be involved mostly in response to pathogens, whose physical and/or genetic interaction was experimentally proven. We also showed that specific co-regulatory networks were conserved between the two model species by identifying Arabidopsis orthologs of the co-expressed OsWRKY genes. Conclusion In this work we identified sets of co-expressed WRKY genes in both rice and Arabidopsis that are functionally likely to cooperate in the same signal transduction pathways. We propose that, making use of data from co

  6. Evolutionary conservation and network structure characterize genes of phenotypic relevance for mitosis in human.

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    Marek Ostaszewski

    Full Text Available The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research.

  7. Selling Internet Service: An Ancient Art Form on a New Canvas.

    Science.gov (United States)

    Maloff, Joel H.

    1992-01-01

    The Internet, no longer solely the domain of scientists and network engineers, is expanding rapidly to serve a diverse community of business professionals. Those marketing Internet services to these decision makers must practice the ancient art of salesmanship in a complex technological environment. Crucial is knowledge of Internet opportunities,…

  8. In-silico gene co-expression network analysis in Paracoccidioides brasiliensis with reference to haloacid dehalogenase superfamily hydrolase gene

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    Raghunath Satpathy

    2015-01-01

    Full Text Available Context: Paracoccidioides brasiliensis, a dimorphic fungus is the causative agent of paracoccidioidomycosis, a disease globally affecting millions of people. The haloacid dehalogenase (HAD superfamily hydrolases enzyme in the fungi, in particular, is known to be responsible in the pathogenesis by adhering to the tissue. Hence, identification of novel drug targets is essential. Aims: In-silico based identification of co-expressed genes along with HAD superfamily hydrolase in P. brasiliensis during the morphogenesis from mycelium to yeast to identify possible genes as drug targets. Materials and Methods: In total, four datasets were retrieved from the NCBI-gene expression omnibus (GEO database, each containing 4340 genes, followed by gene filtration expression of the data set. Further co-expression (CE study was performed individually and then a combination these genes were visualized in the Cytoscape 2. 8.3. Statistical Analysis Used: Mean and standard deviation value of the HAD superfamily hydrolase gene was obtained from the expression data and this value was subsequently used for the CE calculation purpose by selecting specific correlation power and filtering threshold. Results: The 23 genes that were thus obtained are common with respect to the HAD superfamily hydrolase gene. A significant network was selected from the Cytoscape network visualization that contains total 7 genes out of which 5 genes, which do not have significant protein hits, obtained from gene annotation of the expressed sequence tags by BLAST X. For all the protein PSI-BLAST was performed against human genome to find the homology. Conclusions: The gene co-expression network was obtained with respect to HAD superfamily dehalogenase gene in P. Brasiliensis.

  9. Long-term oil contamination alters the molecular ecological networks of soil microbial functional genes

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    Yuting eLiang

    2016-02-01

    Full Text Available With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001. Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential keystone genes, defined as either hubs or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions.

  10. 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

  11. Signatures of Low-Mo Ancient Ocean May be Preserved in Cyanobacterial Genomes

    Science.gov (United States)

    Glass, J. B.; Wolfe-Simon, F. L.; Poret-Peterson, A. T.; Anbar, A. D.

    2010-04-01

    Before 800 million years ago, Mo was scarce in the ocean. We present evidence that primitive cyanobacteria store Mo using a protein called "Mop." Mop genes preserved in modern genomes may be signatures of the imprint of ancient Mo limitation.

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

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    Castillo Luis F.

    2012-12-01

    Full Text Available 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.

  13. Text mining and network analysis to find functional associations of genes in high altitude diseases.

    Science.gov (United States)

    Bhasuran, Balu; Subramanian, Devika; Natarajan, Jeyakumar

    2018-05-02

    Travel to elevations above 2500 m is associated with the risk of developing one or more forms of acute altitude illness such as acute mountain sickness (AMS), high altitude cerebral edema (HACE) or high altitude pulmonary edema (HAPE). Our work aims to identify the functional association of genes involved in high altitude diseases. In this work we identified the gene networks responsible for high altitude diseases by using the principle of gene co-occurrence statistics from literature and network analysis. First, we mined the literature data from PubMed on high-altitude diseases, and extracted the co-occurring gene pairs. Next, based on their co-occurrence frequency, gene pairs were ranked. Finally, a gene association network was created using statistical measures to explore potential relationships. Network analysis results revealed that EPO, ACE, IL6 and TNF are the top five genes that were found to co-occur with 20 or more genes, while the association between EPAS1 and EGLN1 genes is strongly substantiated. The network constructed from this study proposes a large number of genes that work in-toto in high altitude conditions. Overall, the result provides a good reference for further study of the genetic relationships in high altitude diseases. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Transcriptional dynamics of a conserved gene expression network associated with craniofacial divergence in Arctic charr.

    Science.gov (United States)

    Ahi, Ehsan Pashay; Kapralova, Kalina Hristova; Pálsson, Arnar; Maier, Valerie Helene; Gudbrandsson, Jóhannes; Snorrason, Sigurdur S; Jónsson, Zophonías O; Franzdóttir, Sigrídur Rut

    2014-01-01

    Understanding the molecular basis of craniofacial variation can provide insights into key developmental mechanisms of adaptive changes and their role in trophic divergence and speciation. Arctic charr (Salvelinus alpinus) is a polymorphic fish species, and, in Lake Thingvallavatn in Iceland, four sympatric morphs have evolved distinct craniofacial structures. We conducted a gene expression study on candidates from a conserved gene coexpression network, focusing on the development of craniofacial elements in embryos of two contrasting Arctic charr morphotypes (benthic and limnetic). Four Arctic charr morphs were studied: one limnetic and two benthic morphs from Lake Thingvallavatn and a limnetic reference aquaculture morph. The presence of morphological differences at developmental stages before the onset of feeding was verified by morphometric analysis. Following up on our previous findings that Mmp2 and Sparc were differentially expressed between morphotypes, we identified a network of genes with conserved coexpression across diverse vertebrate species. A comparative expression study of candidates from this network in developing heads of the four Arctic charr morphs verified the coexpression relationship of these genes and revealed distinct transcriptional dynamics strongly correlated with contrasting craniofacial morphologies (benthic versus limnetic). A literature review and Gene Ontology analysis indicated that a significant proportion of the network genes play a role in extracellular matrix organization and skeletogenesis, and motif enrichment analysis of conserved noncoding regions of network candidates predicted a handful of transcription factors, including Ap1 and Ets2, as potential regulators of the gene network. The expression of Ets2 itself was also found to associate with network gene expression. Genes linked to glucocorticoid signalling were also studied, as both Mmp2 and Sparc are responsive to this pathway. Among those, several transcriptional

  15. The Drosophila melanogaster methuselah gene: a novel gene with ancient functions.

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    Ana Rita Araújo

    Full Text Available The Drosophila melanogaster G protein-coupled receptor gene, methuselah (mth, has been described as a novel gene that is less than 10 million years old. Nevertheless, it shows a highly specific expression pattern in embryos, larvae, and adults, and has been implicated in larval development, stress resistance, and in the setting of adult lifespan, among others. Although mth belongs to a gene subfamily with 16 members in D. melanogaster, there is no evidence for functional redundancy in this subfamily. Therefore, it is surprising that a novel gene influences so many traits. Here, we explore the alternative hypothesis that mth is an old gene. Under this hypothesis, in species distantly related to D. melanogaster, there should be a gene with features similar to those of mth. By performing detailed phylogenetic, synteny, protein structure, and gene expression analyses we show that the D. virilis GJ12490 gene is the orthologous of mth in species distantly related to D. melanogaster. We also show that, in D. americana (a species of the virilis group of Drosophila, a common amino acid polymorphism at the GJ12490 orthologous gene is significantly associated with developmental time, size, and lifespan differences. Our results imply that GJ12490 orthologous genes are candidates for developmental time and lifespan differences in Drosophila in general.

  16. CoryneRegNet 4.0 – A reference database for corynebacterial gene regulatory networks

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    Baumbach Jan

    2007-11-01

    Full Text Available Abstract Background Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Results Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user can be analyzed in the context of known

  17. The transcriptional and gene regulatory network of Lactococcus lactis MG1363 during growth in milk.

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    Anne de Jong

    Full Text Available In the present study we examine the changes in the expression of genes of Lactococcus lactis subspecies cremoris MG1363 during growth in milk. To reveal which specific classes of genes (pathways, operons, regulons, COGs are important, we performed a transcriptome time series experiment. Global analysis of gene expression over time showed that L. lactis adapted quickly to the environmental changes. Using upstream sequences of genes with correlated gene expression profiles, we uncovered a substantial number of putative DNA binding motifs that may be relevant for L. lactis fermentative growth in milk. All available novel and literature-derived data were integrated into network reconstruction building blocks, which were used to reconstruct and visualize the L. lactis gene regulatory network. This network enables easy mining in the chrono-transcriptomics data. A freely available website at http://milkts.molgenrug.nl gives full access to all transcriptome data, to the reconstructed network and to the individual network building blocks.

  18. A study on provenance relation between Jiaotanxia ancient Guan porcelain and Qingliangsi ancient Ru porcelain by NAA

    International Nuclear Information System (INIS)

    Li Rongwu; Feng Songlin; Huang Zhongxiang; Jia Xiuqin

    2004-01-01

    11 samples of ancient Chinese Ru porcelain from Qingliangsi kiln, 23 samples of ancient Chinese Guan porcelain from Jiaotanxia kiln and 4 samples of modern archaized Guan porcelain were obtained to determine the contents of elements in each of them by neutron activation analysis (NAA). The NAA data were further analyzed using fuzzy cluster analysis to obtain the fuzzy cluster trend diagrams for the bodies' samples and the glazes samples respectively. The analysis shows that the raw material origins of the Jiaotanxia ancient Chinese Guan porcelain bodies samples are very concentrated; those of the Qingliangsi ancient Chinese Ru porcelain bodies samples are a little dispersed; those of ancient Chinese Guan porcelain glazes samples are relatively concentrated; those of ancient Chinese Ru porcelain glazes samples are dispersed; and the origins of the raw material of ancient Chinese Guan porcelain glazes samples are obviously different from those of ancient Chinese Ru porcelain glazes samples. The bodies samples and glazes samples of Jiaotanxia ancient Chinese Guan porcelain and those of Qingliangsi ancient Chinese Ru porcelain have some difference but can be compared with each other. (authors)

  19. Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications.

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    Aaron R Wolen

    Full Text Available Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain across a highly diverse family of 27 isogenic mouse strains (BXD panel before and after treatment with ethanol.Acute ethanol altered the expression of ~2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol's effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2.The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol

  20. Statistical assessment of crosstalk enrichment between gene groups in biological networks.

    Science.gov (United States)

    McCormack, Theodore; Frings, Oliver; Alexeyenko, Andrey; Sonnhammer, Erik L L

    2013-01-01

    Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.

  1. Network-Guided Key Gene Discovery for a Given Cellular Process

    DEFF Research Database (Denmark)

    He, Feng Q; Ollert, Markus

    2018-01-01

    Identification of key genes for a given physiological or pathological process is an essential but still very challenging task for the entire biomedical research community. Statistics-based approaches, such as genome-wide association study (GWAS)- or quantitative trait locus (QTL)-related analysis...... have already made enormous contributions to identifying key genes associated with a given disease or phenotype, the success of which is however very much dependent on a huge number of samples. Recent advances in network biology, especially network inference directly from genome-scale data...

  2. Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

    Science.gov (United States)

    Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

    2014-01-16

    To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high

  3. Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles.

    Science.gov (United States)

    Mustafin, Zakhar Sergeevich; Lashin, Sergey Alexandrovich; Matushkin, Yury Georgievich; Gunbin, Konstantin Vladimirovich; Afonnikov, Dmitry Arkadievich

    2017-01-27

    There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape ( http://cytoscape.org/ ) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged 'network evolution' found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results. Orthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface. Orthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.

  4. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

    Science.gov (United States)

    Zhu, Mingzhu; Dahmen, Jeremy L; Stacey, Gary; Cheng, Jianlin

    2013-09-22

    High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.

  5. Robustness and accuracy in sea urchin developmental gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Smadar eBen-Tabou De-Leon

    2016-02-01

    Full Text Available Developmental gene regulatory networks robustly control the timely activation of regulatory and differentiation genes. The structure of these networks underlies their capacity to buffer intrinsic and extrinsic noise and maintain embryonic morphology. Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust control of cell fate decisions. The Wnt-βcatenin signaling pathway patterns the primary embryonic axis while the BMP signaling pathway patterns the secondary embryonic axis in the sea urchin embryo and across bilateria. Interestingly, in the sea urchin in both cases, the signaling pathway that defines the axis controls directly the expression of a set of downstream regulatory genes. I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell fate decision in the endoderm and in the dorsal ectoderm. The specification of the mesodermal pigment cell lineage is activated by Delta signaling that initiates a triple positive feedback loop that locks down the pigment specification state. I propose that the use of compound positive feedback circuitry provides the endodermal cells enough time to turn off mesodermal genes and ensures correct mesoderm vs. endoderm fate decision. Thus, I argue that understanding the control properties of repeatedly used regulatory architectures illuminates their role in embryogenesis and provides possible explanations to their resistance to evolutionary change.

  6. Cooperative adaptive responses in gene regulatory networks with many degrees of freedom.

    Science.gov (United States)

    Inoue, Masayo; Kaneko, Kunihiko

    2013-04-01

    Cells generally adapt to environmental changes by first exhibiting an immediate response and then gradually returning to their original state to achieve homeostasis. Although simple network motifs consisting of a few genes have been shown to exhibit such adaptive dynamics, they do not reflect the complexity of real cells, where the expression of a large number of genes activates or represses other genes, permitting adaptive behaviors. Here, we investigated the responses of gene regulatory networks containing many genes that have undergone numerical evolution to achieve high fitness due to the adaptive response of only a single target gene; this single target gene responds to changes in external inputs and later returns to basal levels. Despite setting a single target, most genes showed adaptive responses after evolution. Such adaptive dynamics were not due to common motifs within a few genes; even without such motifs, almost all genes showed adaptation, albeit sometimes partial adaptation, in the sense that expression levels did not always return to original levels. The genes split into two groups: genes in the first group exhibited an initial increase in expression and then returned to basal levels, while genes in the second group exhibited the opposite changes in expression. From this model, genes in the first group received positive input from other genes within the first group, but negative input from genes in the second group, and vice versa. Thus, the adaptation dynamics of genes from both groups were consolidated. This cooperative adaptive behavior was commonly observed if the number of genes involved was larger than the order of ten. These results have implications in the collective responses of gene expression networks in microarray measurements of yeast Saccharomyces cerevisiae and the significance to the biological homeostasis of systems with many components.

  7. Challenges for modeling global gene regulatory networks during development: insights from Drosophila.

    Science.gov (United States)

    Wilczynski, Bartek; Furlong, Eileen E M

    2010-04-15

    Development is regulated by dynamic patterns of gene expression, which are orchestrated through the action of complex gene regulatory networks (GRNs). Substantial progress has been made in modeling transcriptional regulation in recent years, including qualitative "coarse-grain" models operating at the gene level to very "fine-grain" quantitative models operating at the biophysical "transcription factor-DNA level". Recent advances in genome-wide studies have revealed an enormous increase in the size and complexity or GRNs. Even relatively simple developmental processes can involve hundreds of regulatory molecules, with extensive interconnectivity and cooperative regulation. This leads to an explosion in the number of regulatory functions, effectively impeding Boolean-based qualitative modeling approaches. At the same time, the lack of information on the biophysical properties for the majority of transcription factors within a global network restricts quantitative approaches. In this review, we explore the current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks. We suggest to integrate coarse- and find-grain approaches to model gene regulatory networks in cis. We focus on two very well-studied examples from Drosophila, which likely represent typical developmental regulatory modules across metazoans. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  8. Mining for novel candidate clock genes in the circadian regulatory network

    OpenAIRE

    Bhargava, Anuprabha; Herzel, Hanspeter; Ananthasubramaniam, Bharath

    2015-01-01

    Background Most physiological processes in mammals are temporally regulated by means of a master circadian clock in the brain and peripheral oscillators in most other tissues. A transcriptional-translation feedback network of clock genes produces near 24 h oscillations in clock gene and protein expression. Here, we aim to identify novel additions to the clock network using a meta-analysis of public chromatin immunoprecipitation sequencing (ChIP-seq), proteomics and protein-protein interaction...

  9. Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Takeshi Hase

    Full Text Available Elucidating gene regulatory network (GRN from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

  10. A gene network simulator to assess reverse engineering algorithms.

    Science.gov (United States)

    Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

    2009-03-01

    In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

  11. Detection of 400-year-old Yersinia pestis DNA in human dental pulp: an approach to the diagnosis of ancient septicemia.

    Science.gov (United States)

    Drancourt, M; Aboudharam, G; Signoli, M; Dutour, O; Raoult, D

    1998-10-13

    Ancient septicemic plague epidemics were reported to have killed millions of people for 2 millenniums. However, confident diagnosis of ancient septicemia solely on the basis of historical clinical observations is not possible. The lack of suitable infected material has prevented direct demonstration of ancient septicemia; thus, the history of most infections such as plague remains hypothetical. The durability of dental pulp, together with its natural sterility, makes it a suitable material on which to base such research. We hypothesized that it would be a lasting refuge for Yersinia pestis, the plague agent. DNA extracts were made from the dental pulp of 12 unerupted teeth extracted from skeletons excavated from 16th and 18th century French graves of persons thought to have died of plague ("plague teeth") and from 7 ancient negative control teeth. PCRs incorporating ancient DNA extracts and primers specific for the human beta-globin gene demonstrated the absence of inhibitors in these preparations. The incorporation of primers specific for Y. pestis rpoB (the RNA polymerase beta-subunit-encoding gene) and the recognized virulence-associated pla (the plasminogen activator-encoding gene) repeatedly yielded products that had a nucleotide sequence indistinguishable from that of modern day isolates of the bacterium. The specific pla sequence was obtained from 6 of 12 plague skeleton teeth but 0 of 7 negative controls (P plague was achieved for historically identified victims, and we have confirmed the presence of the disease at the end of 16th century in France. Dental pulp is an attractive target in the quest to determine the etiology of septicemic illnesses detected in ancient corpses. Molecular techniques could be applied to this material to resolve historical outbreaks.

  12. Generic Properties of Random Gene Regulatory Networks.

    Science.gov (United States)

    Li, Zhiyuan; Bianco, Simone; Zhang, Zhaoyang; Tang, Chao

    2013-12-01

    Modeling gene regulatory networks (GRNs) is an important topic in systems biology. Although there has been much work focusing on various specific systems, the generic behavior of GRNs with continuous variables is still elusive. In particular, it is not clear typically how attractors partition among the three types of orbits: steady state, periodic and chaotic, and how the dynamical properties change with network's topological characteristics. In this work, we first investigated these questions in random GRNs with different network sizes, connectivity, fraction of inhibitory links and transcription regulation rules. Then we searched for the core motifs that govern the dynamic behavior of large GRNs. We show that the stability of a random GRN is typically governed by a few embedding motifs of small sizes, and therefore can in general be understood in the context of these short motifs. Our results provide insights for the study and design of genetic networks.

  13. Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility.

    Science.gov (United States)

    Bagot, Rosemary C; Cates, Hannah M; Purushothaman, Immanuel; Lorsch, Zachary S; Walker, Deena M; Wang, Junshi; Huang, Xiaojie; Schlüter, Oliver M; Maze, Ian; Peña, Catherine J; Heller, Elizabeth A; Issler, Orna; Wang, Minghui; Song, Won-Min; Stein, Jason L; Liu, Xiaochuan; Doyle, Marie A; Scobie, Kimberly N; Sun, Hao Sheng; Neve, Rachael L; Geschwind, Daniel; Dong, Yan; Shen, Li; Zhang, Bin; Nestler, Eric J

    2016-06-01

    Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Population genomics of the Arabidopsis thaliana flowering time gene network.

    Science.gov (United States)

    Flowers, Jonathan M; Hanzawa, Yoshie; Hall, Megan C; Moore, Richard C; Purugganan, Michael D

    2009-11-01

    The time to flowering is a key component of the life-history strategy of the model plant Arabidopsis thaliana that varies quantitatively among genotypes. A significant problem for evolutionary and ecological genetics is to understand how natural selection may operate on this ecologically significant trait. Here, we conduct a population genomic study of resequencing data from 52 genes in the flowering time network. McDonald-Kreitman tests of neutrality suggested a strong excess of amino acid polymorphism when pooling across loci. This excess of replacement polymorphism across the flowering time network and a skewed derived frequency spectrum toward rare alleles for both replacement and noncoding polymorphisms relative to synonymous changes is consistent with a large class of deleterious polymorphisms segregating in these genes. Assuming selective neutrality of synonymous changes, we estimate that approximately 30% of amino acid polymorphisms are deleterious. Evidence of adaptive substitution is less prominent in our analysis. The photoperiod regulatory gene, CO, and a gibberellic acid transcription factor, AtMYB33, show evidence of adaptive fixation of amino acid mutations. A test for extended haplotypes revealed no examples of flowering time alleles with haplotypes comparable in length to those associated with the null fri(Col) allele reported previously. This suggests that the FRI gene likely has a uniquely intense or recent history of selection among the flowering time genes considered here. Although there is some evidence for adaptive evolution in these life-history genes, it appears that slightly deleterious polymorphisms are a major component of natural molecular variation in the flowering time network of A. thaliana.

  15. Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives

    International Nuclear Information System (INIS)

    Warmflash, Aryeh; Siggia, Eric D; Francois, Paul

    2012-01-01

    The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input–output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria. (paper)

  16. Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.

    Science.gov (United States)

    Warmflash, Aryeh; Francois, Paul; Siggia, Eric D

    2012-10-01

    The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.

  17. The ancient Virus World and evolution of cells

    Directory of Open Access Journals (Sweden)

    Dolja Valerian V

    2006-09-01

    with a specific model of precellular evolution under which the primordial gene pool dwelled in a network of inorganic compartments. Somewhat paradoxically, under this scenario, we surmise that selfish genetic elements ancestral to viruses evolved prior to typical cells, to become intracellular parasites once bacteria and archaea arrived at the scene. Selection against excessively aggressive parasites that would kill off the host ensembles of genetic elements would lead to early evolution of temperate virus-like agents and primitive defense mechanisms, possibly, based on the RNA interference principle. The emergence of the eukaryotic cell is construed as the second melting pot of virus evolution from which the major groups of eukaryotic viruses originated as a result of extensive recombination of genes from various bacteriophages, archaeal viruses, plasmids, and the evolving eukaryotic genomes. Again, this vision is predicated on a specific model of the emergence of eukaryotic cell under which archaeo-bacterial symbiosis was the starting point of eukaryogenesis, a scenario that appears to be best compatible with the data. Conclusion The existence of several genes that are central to virus replication and structure, are shared by a broad variety of viruses but are missing from cellular genomes (virus hallmark genes suggests the model of an ancient virus world, a flow of virus-specific genes that went uninterrupted from the precellular stage of life's evolution to this day. This concept is tightly linked to two key conjectures on evolution of cells: existence of a complex, precellular, compartmentalized but extensively mixing and recombining pool of genes, and origin of the eukaryotic cell by archaeo-bacterial fusion. The virus world concept and these models of major transitions in the evolution of cells provide complementary pieces of an emerging coherent picture of life's history. Reviewers W. Ford Doolittle, J. Peter Gogarten, and Arcady Mushegian.

  18. Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.

    Directory of Open Access Journals (Sweden)

    Jiny Nair

    Full Text Available Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD. Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC and node degree as key topological parameters, we identified interleukin-6 (IL-6, vascular endothelial growth factor A (VEGFA, interleukin-1 beta (IL-1B, tumor necrosis factor (TNF and prostaglandin-endoperoxide synthase 2 (PTGS2 as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1, signal transducer and activator of transcription 3 (STAT3 and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05. Thus, analysis of complex networks aid in the

  19. On the role of sparseness in the evolution of modularity in gene regulatory networks.

    Science.gov (United States)

    Espinosa-Soto, Carlos

    2018-05-01

    Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

  20. Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins.

    Directory of Open Access Journals (Sweden)

    Stefano Varrella

    Full Text Available Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure.

  1. Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

    Directory of Open Access Journals (Sweden)

    Borlawsky Tara B

    2010-10-01

    Full Text Available Abstract Background Chronic lymphocytic leukemia (CLL is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. Results In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network. Conclusions We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.

  2. Common gene-network signature of different neurological disorders and their potential implications to neuroAIDS.

    Directory of Open Access Journals (Sweden)

    Vidya Sagar

    Full Text Available The neurological complications of AIDS (neuroAIDS during the infection of human immunodeficiency virus (HIV are symptomized by non-specific, multifaceted neurological conditions and therefore, defining a specific diagnosis/treatment mechanism(s for this neuro-complexity at the molecular level remains elusive. Using an in silico based integrated gene network analysis we discovered that HIV infection shares convergent gene networks with each of twelve neurological disorders selected in this study. Importantly, a common gene network was identified among HIV infection, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and age macular degeneration. An mRNA microarray analysis in HIV-infected monocytes showed significant changes in the expression of several genes of this in silico derived common pathway which suggests the possible physiological relevance of this gene-circuit in driving neuroAIDS condition. Further, this unique gene network was compared with another in silico derived novel, convergent gene network which is shared by seven major neurological disorders (Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Age Macular Degeneration, Amyotrophic Lateral Sclerosis, Vascular Dementia, and Restless Leg Syndrome. These networks differed in their gene circuits; however, in large, they involved innate immunity signaling pathways, which suggests commonalities in the immunological basis of different neuropathogenesis. The common gene circuits reported here can provide a prospective platform to understand how gene-circuits belonging to other neuro-disorders may be convoluted during real-time neuroAIDS condition and it may elucidate the underlying-and so far unknown-genetic overlap between HIV infection and neuroAIDS risk. Also, it may lead to a new paradigm in understanding disease progression, identifying biomarkers, and developing therapies.

  3. Deregulation of an imprinted gene network in prostate cancer.

    Science.gov (United States)

    Ribarska, Teodora; Goering, Wolfgang; Droop, Johanna; Bastian, Klaus-Marius; Ingenwerth, Marc; Schulz, Wolfgang A

    2014-05-01

    Multiple epigenetic alterations contribute to prostate cancer progression by deregulating gene expression. Epigenetic mechanisms, especially differential DNA methylation at imprinting control regions (termed DMRs), normally ensure the exclusive expression of imprinted genes from one specific parental allele. We therefore wondered to which extent imprinted genes become deregulated in prostate cancer and, if so, whether deregulation is due to altered DNA methylation at DMRs. Therefore, we selected presumptive deregulated imprinted genes from a previously conducted in silico analysis and from the literature and analyzed their expression in prostate cancer tissues by qRT-PCR. We found significantly diminished expression of PLAGL1/ZAC1, MEG3, NDN, CDKN1C, IGF2, and H19, while LIT1 was significantly overexpressed. The PPP1R9A gene, which is imprinted in selected tissues only, was strongly overexpressed, but was expressed biallelically in benign and cancerous prostatic tissues. Expression of many of these genes was strongly correlated, suggesting co-regulation, as in an imprinted gene network (IGN) reported in mice. Deregulation of the network genes also correlated with EZH2 and HOXC6 overexpression. Pyrosequencing analysis of all relevant DMRs revealed generally stable DNA methylation between benign and cancerous prostatic tissues, but frequent hypo- and hyper-methylation was observed at the H19 DMR in both benign and cancerous tissues. Re-expression of the ZAC1 transcription factor induced H19, CDKN1C and IGF2, supporting its function as a nodal regulator of the IGN. Our results indicate that a group of imprinted genes are coordinately deregulated in prostate cancers, independently of DNA methylation changes.

  4. 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.

  5. Ground rules of the pluripotency gene regulatory network.

    KAUST Repository

    Li, Mo; Belmonte, Juan Carlos Izpisua

    2017-01-01

    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.

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

    Science.gov (United States)

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

    2018-07-01

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

  7. Memory functions reveal structural properties of gene regulatory networks

    Science.gov (United States)

    Perez-Carrasco, Ruben

    2018-01-01

    Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs. PMID:29470492

  8. Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment.

    Science.gov (United States)

    Yang, Yi; Maxwell, Andrew; Zhang, Xiaowei; Wang, Nan; Perkins, Edward J; Zhang, Chaoyang; Gong, Ping

    2013-01-01

    Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting. Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways. Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach

  9. Sundanese ancient manuscripts search engine using probability approach

    Science.gov (United States)

    Suryani, Mira; Hadi, Setiawan; Paulus, Erick; Nurma Yulita, Intan; Supriatna, Asep K.

    2017-10-01

    Today, Information and Communication Technology (ICT) has become a regular thing for every aspect of live include cultural and heritage aspect. Sundanese ancient manuscripts as Sundanese heritage are in damage condition and also the information that containing on it. So in order to preserve the information in Sundanese ancient manuscripts and make them easier to search, a search engine has been developed. The search engine must has good computing ability. In order to get the best computation in developed search engine, three types of probabilistic approaches: Bayesian Networks Model, Divergence from Randomness with PL2 distribution, and DFR-PL2F as derivative form DFR-PL2 have been compared in this study. The three probabilistic approaches supported by index of documents and three different weighting methods: term occurrence, term frequency, and TF-IDF. The experiment involved 12 Sundanese ancient manuscripts. From 12 manuscripts there are 474 distinct terms. The developed search engine tested by 50 random queries for three types of query. The experiment results showed that for the single query and multiple query, the best searching performance given by the combination of PL2F approach and TF-IDF weighting method. The performance has been evaluated using average time responds with value about 0.08 second and Mean Average Precision (MAP) about 0.33.

  10. An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

    Science.gov (United States)

    Botía, Juan A; Vandrovcova, Jana; Forabosco, Paola; Guelfi, Sebastian; D'Sa, Karishma; Hardy, John; Lewis, Cathryn M; Ryten, Mina; Weale, Michael E

    2017-04-12

    Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

  11. Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information.

    Directory of Open Access Journals (Sweden)

    Xinyan Wang

    Full Text Available Chronic obstructive pulmonary disease (COPD is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.

  12. Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

    Science.gov (United States)

    Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

    2014-12-01

    Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

  13. Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.

    Directory of Open Access Journals (Sweden)

    Emre Guney

    Full Text Available Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO analysis highlighted the role of functional diversity for such diseases.

  14. Structural influence of gene networks on their inference: analysis of C3NET

    Directory of Open Access Journals (Sweden)

    Emmert-Streib Frank

    2011-06-01

    Full Text Available Abstract Background The availability of large-scale high-throughput data possesses considerable challenges toward their functional analysis. For this reason gene network inference methods gained considerable interest. However, our current knowledge, especially about the influence of the structure of a gene network on its inference, is limited. Results In this paper we present a comprehensive investigation of the structural influence of gene networks on the inferential characteristics of C3NET - a recently introduced gene network inference algorithm. We employ local as well as global performance metrics in combination with an ensemble approach. The results from our numerical study for various biological and synthetic network structures and simulation conditions, also comparing C3NET with other inference algorithms, lead a multitude of theoretical and practical insights into the working behavior of C3NET. In addition, in order to facilitate the practical usage of C3NET we provide an user-friendly R package, called c3net, and describe its functionality. It is available from https://r-forge.r-project.org/projects/c3net and from the CRAN package repository. Conclusions The availability of gene network inference algorithms with known inferential properties opens a new era of large-scale screening experiments that could be equally beneficial for basic biological and biomedical research with auspicious prospects. The availability of our easy to use software package c3net may contribute to the popularization of such methods. Reviewers This article was reviewed by Lev Klebanov, Joel Bader and Yuriy Gusev.

  15. Ancient Greek with Thrasymachus: A Web Site for Learning Ancient Greek.

    Science.gov (United States)

    Barker, Alison

    2001-01-01

    Discusses a project that was begun as an attempt by two teachers of Ancient Greek to provide supplementary materials to accompany "Thrasymachus," a first-year textbook for learning ancient Greek. Provides a brief history and description of the project, the format of each chapter, a chronology for completion of materials for each chapter in the…

  16. Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

    Science.gov (United States)

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2010-03-01

    The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. 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. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

  17. A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders.

    Science.gov (United States)

    Jiang, Peng; Scarpa, Joseph R; Fitzpatrick, Karrie; Losic, Bojan; Gao, Vance D; Hao, Ke; Summa, Keith C; Yang, He S; Zhang, Bin; Allada, Ravi; Vitaterna, Martha H; Turek, Fred W; Kasarskis, Andrew

    2015-05-05

    Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  18. Ancient genomics

    DEFF Research Database (Denmark)

    Der Sarkissian, Clio; Allentoft, Morten Erik; Avila Arcos, Maria del Carmen

    2015-01-01

    throughput of next generation sequencing platforms and the ability to target short and degraded DNA molecules. Many ancient specimens previously unsuitable for DNA analyses because of extensive degradation can now successfully be used as source materials. Additionally, the analytical power obtained...... by increasing the number of sequence reads to billions effectively means that contamination issues that have haunted aDNA research for decades, particularly in human studies, can now be efficiently and confidently quantified. At present, whole genomes have been sequenced from ancient anatomically modern humans...

  19. Parallel logic gates in synthetic gene networks induced by non-Gaussian noise.

    Science.gov (United States)

    Xu, Yong; Jin, Xiaoqin; Zhang, Huiqing

    2013-11-01

    The recent idea of logical stochastic resonance is verified in synthetic gene networks induced by non-Gaussian noise. We realize the switching between two kinds of logic gates under optimal moderate noise intensity by varying two different tunable parameters in a single gene network. Furthermore, in order to obtain more logic operations, thus providing additional information processing capacity, we obtain in a two-dimensional toggle switch model two complementary logic gates and realize the transformation between two logic gates via the methods of changing different parameters. These simulated results contribute to improve the computational power and functionality of the networks.

  20. Genes and Gene Networks Involved in Sodium Fluoride-Elicited Cell Death Accompanying Endoplasmic Reticulum Stress in Oral Epithelial Cells

    Directory of Open Access Journals (Sweden)

    Yoshiaki Tabuchi

    2014-05-01

    Full Text Available Here, to understand the molecular mechanisms underlying cell death induced by sodium fluoride (NaF, we analyzed gene expression patterns in rat oral epithelial ROE2 cells exposed to NaF using global-scale microarrays and bioinformatics tools. A relatively high concentration of NaF (2 mM induced cell death concomitant with decreases in mitochondrial membrane potential, chromatin condensation and caspase-3 activation. Using 980 probe sets, we identified 432 up-regulated and 548 down-regulated genes, that were differentially expressed by >2.5-fold in the cells treated with 2 mM of NaF and categorized them into 4 groups by K-means clustering. Ingenuity® pathway analysis revealed several gene networks from gene clusters. The gene networks Up-I and Up-II included many up-regulated genes that were mainly associated with the biological function of induction or prevention of cell death, respectively, such as Atf3, Ddit3 and Fos (for Up-I and Atf4 and Hspa5 (for Up-II. Interestingly, knockdown of Ddit3 and Hspa5 significantly increased and decreased the number of viable cells, respectively. Moreover, several endoplasmic reticulum (ER stress-related genes including, Ddit3, Atf4 and Hapa5, were observed in these gene networks. These findings will provide further insight into the molecular mechanisms of NaF-induced cell death accompanying ER stress in oral epithelial cells.

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

    Science.gov (United States)

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

    2018-01-01

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

  2. Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis.

    Science.gov (United States)

    van Dam, Jesse C J; Schaap, Peter J; Martins dos Santos, Vitor A P; Suárez-Diez, María

    2014-09-26

    Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each separate set has an intrinsic value that is diluted and partly lost when building a consensus network. Here we present a methodology to generate co-expression networks and, instead of a consensus network, we propose an integration framework where the different networks are kept and analysed with additional tools to efficiently combine the information extracted from each network. We developed a workflow to efficiently analyse information generated by different inference and prediction methods. Our methodology relies on providing the user the means to simultaneously visualise and analyse the coexisting networks generated by different algorithms, heterogeneous datasets, and a suite of analysis tools. As a show case, we have analysed the gene co-expression networks of Mycobacterium tuberculosis generated using over 600 expression experiments. Regarding DNA damage repair, we identified SigC as a key control element, 12 new targets for LexA, an updated LexA binding motif, and a potential mismatch repair system. We expanded the DevR regulon with 27 genes while identifying 9 targets wrongly assigned to this regulon. We discovered 10 new genes linked to zinc uptake and a new regulatory mechanism for ZuR. The use of co-expression networks to perform system level analysis allows the development of custom made methodologies. As show cases we implemented a pipeline to integrate ChIP-seq data and another method to uncover multiple regulatory layers. Our workflow is based on representing the multiple types of information as network representations and presenting these networks in a synchronous framework that allows their simultaneous visualization while keeping specific associations from the different

  3. Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

    Science.gov (United States)

    Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju

    2017-04-27

    Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By

  4. Cloning the uteroglobin gene promoter from the relic volcano rabbit (Romerolagus diazi) reveals an ancient estrogen-response element.

    Science.gov (United States)

    Acosta-MontesdeOca, Adriana; Zariñán, Teresa; Macías, Héctor; Pérez-Solís, Marco A; Ulloa-Aguirre, Alfredo; Gutiérrez-Sagal, Rubén

    2012-05-01

    To gain further insight on the estrogen-dependent transcriptional regulation of the uteroglobin (UG) gene, we cloned the 5'-flanking region of the UG gene from the phylogenetically ancient volcano rabbit (Romerolagus diazi; Rd). The cloned region spans 812 base pairs (bp; -812/-1) and contains a noncanonical TATA box (TACA). The translation start site is 48 bp downstream from the putative transcription initiation site (AGA), and is preceded by a consensus Kozak box. Comparison of the Rd-UG gene with that previously isolated from rabbits (Oryctolagus cuniculus) showed 93% in sequence identity as well as a number of conserved cis-acting elements, including the estrogen-response element (ERE; -265/-251), which differs from the consensus by two nucleotides. In MCF-7 cells, 17β-estradiol (E(2)) induced transcription of a luciferase reporter driven by the Rd-UG promoter in a similar manner as in an equivalent rabbit UG reporter; the Rd-UG promoter was 30% more responsive to E(2) than the rabbit promoter. Mutagenesis studies on the Rd-ERE confirmed this cis-element as a target of E(2) as two luciferase mutant reporters of the Rd-promoter, one with the rabbit and the other with the consensus ERE, were more responsive to the hormone than the wild-type reporter. Gel shift and super-shift assays showed that estrogen receptor-α indeed binds to the imperfect palindromic sequence of the Rd-ERE. Copyright © 2012 Wiley Periodicals, Inc.

  5. Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

    DEFF Research Database (Denmark)

    Bergholdt, R.; Størling, Zenia, Marian; Hansen, Kasper Lage

    2007-01-01

    We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We...... identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases....

  6. 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.

  7. Ancient Pbx-Hox signatures define hundreds of vertebrate developmental enhancers

    Directory of Open Access Journals (Sweden)

    Parker Hugo J

    2011-12-01

    Full Text Available Abstract Background Gene regulation through cis-regulatory elements plays a crucial role in development and disease. A major aim of the post-genomic era is to be able to read the function of cis-regulatory elements through scrutiny of their DNA sequence. Whilst comparative genomics approaches have identified thousands of putative regulatory elements, our knowledge of their mechanism of action is poor and very little progress has been made in systematically de-coding them. Results Here, we identify ancient functional signatures within vertebrate conserved non-coding elements (CNEs through a combination of phylogenetic footprinting and functional assay, using genomic sequence from the sea lamprey as a reference. We uncover a striking enrichment within vertebrate CNEs for conserved binding-site motifs of the Pbx-Hox hetero-dimer. We further show that these predict reporter gene expression in a segment specific manner in the hindbrain and pharyngeal arches during zebrafish development. Conclusions These findings evoke an evolutionary scenario in which many CNEs evolved early in the vertebrate lineage to co-ordinate Hox-dependent gene-regulatory interactions that pattern the vertebrate head. In a broader context, our evolutionary analyses reveal that CNEs are composed of tightly linked transcription-factor binding-sites (TFBSs, which can be systematically identified through phylogenetic footprinting approaches. By placing a large number of ancient vertebrate CNEs into a developmental context, our findings promise to have a significant impact on efforts toward de-coding gene-regulatory elements that underlie vertebrate development, and will facilitate building general models of regulatory element evolution.

  8. 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

  9. A conserved mammalian protein interaction network.

    Directory of Open Access Journals (Sweden)

    Åsa Pérez-Bercoff

    Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

  10. Fractal gene regulatory networks for robust locomotion control of modular robots

    DEFF Research Database (Denmark)

    Zahadat, Payam; Christensen, David Johan; Schultz, Ulrik Pagh

    2010-01-01

    Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed and the ......Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed...

  11. Effects of threshold on the topology of gene co-expression networks.

    Science.gov (United States)

    Couto, Cynthia Martins Villar; Comin, César Henrique; Costa, Luciano da Fontoura

    2017-09-26

    Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection. Each of the methods considers a respective biologically-motivated criterion for electing a potentially suitable threshold. A set of 21 microarray experiments from different biological groups was used to investigate the effect of applying the five proposed criteria to several biological situations. For each experiment, we used the Pearson correlation coefficient to measure the relationship between each gene pair, and the resulting weight matrices were thresholded considering several values, generating respective adjacency matrices (co-expression networks). Each of the five proposed criteria was then applied in order to select the respective threshold value. The effects of these thresholding approaches on the topology of the resulting networks were compared by using several measurements, and we verified that, depending on the database, the impact on the topological properties can be large. However, a group of databases was verified to be similarly affected by most of the considered criteria. Based on such results, it can be suggested that when the generated networks present similar measurements, the thresholding method can be chosen with greater freedom. If the generated networks are markedly different, the thresholding method that better suits the interests of each specific research study represents a reasonable choice.

  12. 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

  13. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

    Directory of Open Access Journals (Sweden)

    Lan Chung-Yu

    2008-09-01

    Full Text Available Abstract Background Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs such as NF-κB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin. Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network

  14. Ancient aqueous sedimentation on Mars

    International Nuclear Information System (INIS)

    Goldspiel, J.M.; Squyres, S.W.

    1991-01-01

    Viking orbiter images are presently used to calculate approximate volumes for the inflow valleys of the ancient cratered terrain of Mars; a sediment-transport model is then used to conservatively estimate the amount of water required for the removal of this volume of debris from the valleys. The results obtained for four basins with well-developed inflow networks indicate basin sediment thicknesses of the order of tens to hundreds of meters. The calculations further suggest that the quantity of water required to transport the sediment is greater than that which could be produced by a single discharge of the associated aquifer, unless the material of the Martian highlands was very fine-grained and noncohesive to depths of hundreds of meters. 48 refs

  15. Network Analysis Reveals Putative Genes Affecting Meat Quality in Angus Cattle.

    Science.gov (United States)

    Mateescu, Raluca G; Garrick, Dorian J; Reecy, James M

    2017-01-01

    Improvements in eating satisfaction will benefit consumers and should increase beef demand which is of interest to the beef industry. Tenderness, juiciness, and flavor are major determinants of the palatability of beef and are often used to reflect eating satisfaction. Carcass qualities are used as indicator traits for meat quality, with higher quality grade carcasses expected to relate to more tender and palatable meat. However, meat quality is a complex concept determined by many component traits making interpretation of genome-wide association studies (GWAS) on any one component challenging to interpret. Recent approaches combining traditional GWAS with gene network interactions theory could be more efficient in dissecting the genetic architecture of complex traits. Phenotypic measures of 23 traits reflecting carcass characteristics, components of meat quality, along with mineral and peptide concentrations were used along with Illumina 54k bovine SNP genotypes to derive an annotated gene network associated with meat quality in 2,110 Angus beef cattle. The efficient mixed model association (EMMAX) approach in combination with a genomic relationship matrix was used to directly estimate the associations between 54k SNP genotypes and each of the 23 component traits. Genomic correlated regions were identified by partial correlations which were further used along with an information theory algorithm to derive gene network clusters. Correlated SNP across 23 component traits were subjected to network scoring and visualization software to identify significant SNP. Significant pathways implicated in the meat quality complex through GO term enrichment analysis included angiogenesis, inflammation, transmembrane transporter activity, and receptor activity. These results suggest that network analysis using partial correlations and annotation of significant SNP can reveal the genetic architecture of complex traits and provide novel information regarding biological mechanisms

  16. VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.

    Science.gov (United States)

    Jia, Peilin; Zhao, Zhongming

    2014-02-01

    A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.

  17. Genomic Evidence of Chemotrophic Metabolisms in Deep-Dwelling Chloroflexi Conferred by Ancient Horizontal Gene Transfer Events

    Science.gov (United States)

    Momper, L. M.; Magnabosco, C.; Amend, J.; Osburn, M. R.; Fournier, G. P.

    2017-12-01

    The marine and terrestrial subsurface biospheres represent quite likely the largest reservoirs for life on Earth, directly impacting surface processes and global cycles throughout Earth's history. In the deep subsurface biosphere (DSB) organic carbon and energy are often extremely scarce. However, archaea and bacteria are able to persist in the DSB to at least 3.5 km below surface [1]. Understanding how they persist, and by what metabolisms they subsist, are key questions in this biosphere. To address these questions we investigated 5 global DSB environments: one legacy mine in South Dakota, USA, 3 mines in South Africa and marine fluids circulating beneath the Juan de Fuca Ridge. Boreholes within these mines provided access to fluids buried beneath the earth's surface and sampled depths down to 3.1 km. Geochemical data were collected concomitantly with DNA for metagenomic sequencing. We examined genomes of the ancient and deeply branching Chloroflexi for metabolic capabilities and interrogated the geochemical drivers behind those metabolisms with in situ thermodynamic modeling of reaction energetics. In total, 23 Chloroflexi genomes were identified and analyzed from the 5 subsurface sites. Genes for nitrate reduction (nar) and sulfite reduction (dsr) were found in many of the South Africa Chloroflexi but were absent from genomes collected in South Dakota. Indeed, nitrate reduction was among the most energetically favorable reactions in South African fluids (10-14 kJ cell-1 sec -1 per mol of reactant) and sulfur reduction with Fe2+ or H2 was also exergonic [2]. Conversely, genes for nitrite and nitrous oxide reduction (nrf, nir and nos) were found in genomes collected in South Dakota and Juan de Fuca, but not South Africa. We examined the origin of genes conferring these metabolisms in the Chloroflexi genomes. We discovered evidence for horizontal gene transfer (HGT) for all of these putative metabolisms. Retention of these genes in Chloroflexi lineages indicates

  18. Portrait of Candida Species Biofilm Regulatory Network Genes.

    Science.gov (United States)

    Araújo, Daniela; Henriques, Mariana; Silva, Sónia

    2017-01-01

    Most cases of candidiasis have been attributed to Candida albicans, but Candida glabrata, Candida parapsilosis and Candida tropicalis, designated as non-C. albicans Candida (NCAC), have been identified as frequent human pathogens. Moreover, Candida biofilms are an escalating clinical problem associated with significant rates of mortality. Biofilms have distinct developmental phases, including adhesion/colonisation, maturation and dispersal, controlled by complex regulatory networks. This review discusses recent advances regarding Candida species biofilm regulatory network genes, which are key components for candidiasis. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

    Science.gov (United States)

    Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Castiglioni, Isabella

    2018-01-06

    Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

  20. Potential energy landscape and robustness of a gene regulatory network: toggle switch.

    Directory of Open Access Journals (Sweden)

    Keun-Young Kim

    2007-03-01

    Full Text Available Finding a multidimensional potential landscape is the key for addressing important global issues, such as the robustness of cellular networks. We have uncovered the underlying potential energy landscape of a simple gene regulatory network: a toggle switch. This was realized by explicitly constructing the steady state probability of the gene switch in the protein concentration space in the presence of the intrinsic statistical fluctuations due to the small number of proteins in the cell. We explored the global phase space for the system. We found that the protein synthesis rate and the unbinding rate of proteins to the gene were small relative to the protein degradation rate; the gene switch is monostable with only one stable basin of attraction. When both the protein synthesis rate and the unbinding rate of proteins to the gene are large compared with the protein degradation rate, two global basins of attraction emerge for a toggle switch. These basins correspond to the biologically stable functional states. The potential energy barrier between the two basins determines the time scale of conversion from one to the other. We found as the protein synthesis rate and protein unbinding rate to the gene relative to the protein degradation rate became larger, the potential energy barrier became larger. This also corresponded to systems with less noise or the fluctuations on the protein numbers. It leads to the robustness of the biological basins of the gene switches. The technique used here is general and can be applied to explore the potential energy landscape of the gene networks.

  1. rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks.

    Science.gov (United States)

    Guo, Liyuan; Wang, Jing

    2018-01-04

    Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element-target gene pairs (E-G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  2. Balancing Acts Between Ancient and Modern Cities: The Ancient Greek Cities Project of C. A. Doxiadis

    Directory of Open Access Journals (Sweden)

    Mantha Zarmakoupi

    2015-12-01

    Full Text Available This paper examines the inception and development of the Ancient Greek Cities (AGC research project (1963–77 of Constantinos A. Doxiadis and addresses the novelty of its methodological approach to the study of classical urbanism. With the AGC project, Doxiadis launched a comprehensive study of the ancient Greek built environment to provide an overview of the factors involved in its shaping. The project produced 24 published volumes — the first two laying out the historical and methodological parameters of the ensuing 22 monographs with case studies — as well as 12 unpublished manuscripts, and through international conferences initiated a wider dialogue on ancient cities beyond the classical Greek world. It was the first interdisciplinary study that attempted to tackle the environmental factors, together with the social and economic ones, underpinning the creation, development and operation of ancient Greek cities. Doxiadis’s innovative approach to the analysis of the ancient city was indebted to his practice as an architect and town planner and was informed by his theory of Ekistics. His purpose was to identify the urban planning principles of ancient Greek settlements in order to employ them in his projects. This paper examines the concept and methodology of the AGC project as well as the ways in which Doxiadis used the study of ancient cities in relation to his contemporary urban/architectural agendas, and explains this important moment in the historiography of ancient Greek urbanism.

  3. Ancient Transposable Elements Transformed the Uterine Regulatory Landscape and Transcriptome during the Evolution of Mammalian Pregnancy

    Directory of Open Access Journals (Sweden)

    Vincent J. Lynch

    2015-02-01

    Full Text Available A major challenge in biology is determining how evolutionarily novel characters originate; however, mechanistic explanations for the origin of new characters are almost completely unknown. The evolution of pregnancy is an excellent system in which to study the origin of novelties because mammals preserve stages in the transition from egg laying to live birth. To determine the molecular bases of this transition, we characterized the pregnant/gravid uterine transcriptome from tetrapods to trace the evolutionary history of uterine gene expression. We show that thousands of genes evolved endometrial expression during the origins of mammalian pregnancy, including genes that mediate maternal-fetal communication and immunotolerance. Furthermore, thousands of cis-regulatory elements that mediate decidualization and cell-type identity in decidualized stromal cells are derived from ancient mammalian transposable elements (TEs. Our results indicate that one of the defining mammalian novelties evolved from DNA sequences derived from ancient mammalian TEs co-opted into hormone-responsive regulatory elements distributed throughout the genome.

  4. Genes, Genomes, and Assemblages of Modern Anoxygenic Photosynthetic Cyanobacteria as Proxies for Ancient Cyanobacteria

    Science.gov (United States)

    Grim, S. L.; Dick, G.

    2015-12-01

    Oxygenic photosynthetic (OP) cyanobacteria were responsible for the production of O2 during the Proterozoic. However, the extent and degree of oxygenation of the atmosphere and oceans varied for over 2 Ga after OP cyanobacteria first appeared in the geologic record. Cyanobacteria capable of anoxygenic photosynthesis (AP) may have altered the trajectory of oxygenation, yet the scope of their role in the Proterozoic is not well known. Modern cyanobacterial populations from Middle Island Sinkhole (MIS), Michigan and a handful of cultured cyanobacterial strains, are capable of OP and AP. With their metabolic versatility, these microbes may approximate ancient cyanobacterial assemblages that mediated Earth's oxygenation. To better characterize the taxonomic and genetic signatures of these modern AP/OP cyanobacteria, we sequenced 16S rRNA genes and conducted 'omics analyses on cultured strains, lab mesocosms, and MIS cyanobacterial mat samples collected over multiple years from May to September. Diversity in the MIS cyanobacterial mat is low, with one member of Oscillatoriales dominating at all times. However, Planktothrix members are more abundant in the cyanobacterial community in late summer and fall. The shift in cyanobacterial community composition may be linked to seasonally changing light intensity. In lab mesocosms of MIS microbial mat, we observed a shift in dominant cyanobacterial groups as well as the emergence of Chlorobium, bacteria that specialize in AP. These shifts in microbial community composition and metabolism are likely in response to changing environmental parameters such as the availability of light and sulfide. Further research is needed to understand the impacts of the changing photosynthetic community on oxygen production and the entire microbial consortium. Our study connects genes and genomes of AP cyanobacteria to their environment, and improves understanding of cyanobacterial metabolic strategies that may have shaped Earth's redox evolution.

  5. Developmental evolution in social insects: regulatory networks from genes to societies.

    Science.gov (United States)

    Linksvayer, Timothy A; Fewell, Jennifer H; Gadau, Jürgen; Laubichler, Manfred D

    2012-05-01

    The evolution and development of complex phenotypes in social insect colonies, such as queen-worker dimorphism or division of labor, can, in our opinion, only be fully understood within an expanded mechanistic framework of Developmental Evolution. Conversely, social insects offer a fertile research area in which fundamental questions of Developmental Evolution can be addressed empirically. We review the concept of gene regulatory networks (GRNs) that aims to fully describe the battery of interacting genomic modules that are differentially expressed during the development of individual organisms. We discuss how distinct types of network models have been used to study different levels of biological organization in social insects, from GRNs to social networks. We propose that these hierarchical networks spanning different organizational levels from genes to societies should be integrated and incorporated into full GRN models to elucidate the evolutionary and developmental mechanisms underlying social insect phenotypes. Finally, we discuss prospects and approaches to achieve such an integration. © 2012 WILEY PERIODICALS, INC.

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

    Directory of Open Access Journals (Sweden)

    Jordan K. Boutilier

    2017-09-01

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

  7. Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.

    Science.gov (United States)

    Xenitidis, P; Seimenis, I; Kakolyris, S; Adamopoulos, A

    2017-08-07

    High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time domain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. Copyright © 2017 Elsevier

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-01-01

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

  9. Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation.

    Science.gov (United States)

    Li, Min; Zhang, Jiayi; Liu, Qing; Wang, Jianxin; Wu, Fang-Xiang

    2014-01-01

    Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN). Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia. Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.

  10. Changes in the topology of gene expression networks by human immunodeficiency virus type 1 (HIV-1) integration in macrophages.

    Science.gov (United States)

    Soto-Girón, María Juliana; García-Vallejo, Felipe

    2012-01-01

    One key step of human immunodeficiency virus type 1 (HIV-1) infection is the integration of its viral cDNA. This process is mediated through complex networks of host-virus interactions that alter several normal cell functions of the host. To study the complexity of disturbances in cell gene expression networks by HIV-1 integration, we constructed a network of human macrophage genes located close to chromatin regions rich in proviruses. To perform the network analysis, we selected 28 genes previously identified as the target of cDNA integration and their transcriptional profiles were obtained from GEO Profiles (NCBI). A total of 2770 interactions among the 28 genes located around the HIV-1 proviruses in human macrophages formed a highly dense main network connected to five sub-networks. The overall network was significantly enriched by genes associated with signal transduction, cellular communication and regulatory processes. To simulate the effects of HIV-1 integration in infected macrophages, five genes with the most number of interaction in the normal network were turned off by putting in zero the correspondent expression values. The HIV-1 infected network showed changes in its topology and alteration in the macrophage functions reflected in a re-programming of biosynthetic and general metabolic process. Understanding the complex virus-host interactions that occur during HIV-1 integration, may provided valuable genomic information to develop new antiviral treatments focusing on the management of some specific gene expression networks associated with viral integration. This is the first gene network which describes the human macrophages genes interactions related with HIV-1 integration. Copyright © 2011 Elsevier B.V. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Insuk Lee

    2007-10-01

    Full Text Available 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.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.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.

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

    Directory of Open Access Journals (Sweden)

    Marek eMutwil

    2014-08-01

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

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

    Science.gov (United States)

    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. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  14. Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.

    Directory of Open Access Journals (Sweden)

    Yinyin Yuan

    Full Text Available Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/.

  15. A guide to ancient protein studies

    DEFF Research Database (Denmark)

    Hendy, Jessica; Welker, Frido; Demarchi, Beatrice

    2018-01-01

    Palaeoproteomics is an emerging neologism used to describe the application of mass spectrometry-based approaches to the study of ancient proteomes. As with palaeogenomics (the study of ancient DNA), it intersects evolutionary biology, archaeology and anthropology, with applications ranging from....... Additionally, in contrast to the ancient DNA community, no consolidated guidelines have been proposed by which researchers, reviewers and editors can evaluate palaeoproteomics data, in part due to the novelty of the field. Here we present a series of precautions and standards for ancient protein research...

  16. 77 FR 37730 - Culturally Significant Objects Imported for Exhibition Determinations: “Nomads and Networks: The...

    Science.gov (United States)

    2012-06-22

    ... DEPARTMENT OF STATE [Public Notice 7928] Culturally Significant Objects Imported for Exhibition Determinations: ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan'' SUMMARY: Notice is hereby... objects to be included in the exhibition ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan...

  17. 77 FR 7229 - Culturally Significant Objects Imported for Exhibition Determinations: “Nomads and Networks: The...

    Science.gov (United States)

    2012-02-10

    ... DEPARTMENT OF STATE [Public Notice 7794] Culturally Significant Objects Imported for Exhibition Determinations: ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan'' SUMMARY: Notice is hereby... objects to be included in the exhibition ``Nomads and Networks: The Ancient Art and Culture of Kazakhstan...

  18. Pseudogenes regulate parental gene expression via ceRNA network.

    Science.gov (United States)

    An, Yang; Furber, Kendra L; Ji, Shaoping

    2017-01-01

    The concept of competitive endogenous RNA (ceRNA) was first proposed by Salmena and colleagues. Evidence suggests that pseudogene RNAs can act as a 'sponge' through competitive binding of common miRNA, releasing or attenuating repression through sequestering miRNAs away from parental mRNA. In theory, ceRNAs refer to all transcripts such as mRNA, tRNA, rRNA, long non-coding RNA, pseudogene RNA and circular RNA, because all of them may become the targets of miRNA depending on spatiotemporal situation. As binding of miRNA to the target RNA is not 100% complementary, it is possible that one miRNA can bind to multiple target RNAs and vice versa. All RNAs crosstalk through competitively binding to miRNAvia miRNA response elements (MREs) contained within the RNA sequences, thus forming a complex regulatory network. The ratio of a subset of miRNAs to the corresponding number of MREs determines repression strength on a given mRNA translation or stability. An increase in pseudogene RNA level can sequester miRNA and release repression on the parental gene, leading to an increase in parental gene expression. A massive number of transcripts constitute a complicated network that regulates each other through this proposed mechanism, though some regulatory significance may be mild or even undetectable. It is possible that the regulation of gene and pseudogene expression occurring in this manor involves all RNAs bearing common MREs. In this review, we will primarily discuss how pseudogene transcripts regulate expression of parental genes via ceRNA network and biological significance of regulation. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

  19. Aiding the Interpretation of Ancient Documents

    DEFF Research Database (Denmark)

    Roued-Cunliffe, Henriette

    How can Decision Support System (DSS) software aid the interpretation process involved in the reading of ancient documents? This paper discusses the development of a DSS prototype for the reading of ancient texts. In this context the term ‘ancient documents’ is used to describe mainly Greek...... tool it is important first to comprehend the interpretation process involved in reading ancient documents. This is not a linear process but rather a recursive process where the scholar moves between different levels of reading, such as ‘understanding the meaning of a character’ or ‘understanding...

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

    Science.gov (United States)

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

    2016-06-01

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

  1. Medicine in Ancient Assur

    DEFF Research Database (Denmark)

    Arbøll, Troels Pank

    This dissertation is a microhistorical study of a single individual named Kiṣir-Aššur who practiced medicine in the ancient city of Assur (modern northern Iraq) in the 7th century BCE. The study provides the first detailed analysis of one healer’s education and practice in ancient Mesopotamia...

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

    Directory of Open Access Journals (Sweden)

    Gao Shouguo

    2011-08-01

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

  3. The eye and its diseases in Ancient Egypt

    DEFF Research Database (Denmark)

    Andersen, S. Ry

    1997-01-01

    Ophthalmology, History of ophthalmology, eyes in the Ancient Egypt, eye disease in Ancient Egypt, porotic hyperostosis, mummification......Ophthalmology, History of ophthalmology, eyes in the Ancient Egypt, eye disease in Ancient Egypt, porotic hyperostosis, mummification...

  4. Predictive minimum description length principle approach to inferring gene regulatory networks.

    Science.gov (United States)

    Chaitankar, Vijender; Zhang, Chaoyang; Ghosh, Preetam; Gong, Ping; Perkins, Edward J; Deng, Youping

    2011-01-01

    Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold that defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we propose a new inference algorithm that incorporates mutual information (MI), conditional mutual information (CMI), and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm is evaluated using both synthetic time series data sets and a biological time series data set (Saccharomyces cerevisiae). The results show that the proposed algorithm produced fewer false edges and significantly improved the precision when compared to existing MDL algorithm.

  5. Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

    Directory of Open Access Journals (Sweden)

    Olszewski Kellen L

    2007-07-01

    Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

  6. Ancient Chinese Precedents in China

    National Research Council Canada - National Science Library

    Geddis, Robert

    1999-01-01

    ... classics from ancient china. The assumption is that since China's political and military leaders state openly that their strategy is based on traditional Chinese strategic concepts, a study of ancient classics on strategy...

  7. Dwarfs in ancient Egypt.

    Science.gov (United States)

    Kozma, Chahira

    2006-02-15

    Ancient Egypt was one of the most advanced and productive civilizations in antiquity, spanning 3000 years before the "Christian" era. Ancient Egyptians built colossal temples and magnificent tombs to honor their gods and religious leaders. Their hieroglyphic language, system of organization, and recording of events give contemporary researchers insights into their daily activities. Based on the record left by their art, the ancient Egyptians documented the presence of dwarfs in almost every facet of life. Due to the hot dry climate and natural and artificial mummification, Egypt is a major source of information on achondroplasia in the old world. The remains of dwarfs are abundant and include complete and partial skeletons. Dwarfs were employed as personal attendants, animal tenders, jewelers, and entertainers. Several high-ranking dwarfs especially from the Old Kingdom (2700-2190 BCE) achieved important status and had lavish burial places close to the pyramids. Their costly tombs in the royal cemeteries and the inscriptions on their statutes indicate their high-ranking position in Egyptian society and their close relation to the king. Some of them were Seneb, Pereniankh, Khnumhotpe, and Djeder. There were at least two dwarf gods, Ptah and Bes. The god Ptah was associated with regeneration and rejuvenation. The god Bes was a protector of sexuality, childbirth, women, and children. He was a favored deity particularly during the Greco-Roman period. His temple was recently excavated in the Baharia oasis in the middle of Egypt. The burial sites and artistic sources provide glimpses of the positions of dwarfs in daily life in ancient Egypt. Dwarfs were accepted in ancient Egypt; their recorded daily activities suggest assimilation into daily life, and their disorder was not shown as a physical handicap. Wisdom writings and moral teachings in ancient Egypt commanded respect for dwarfs and other individuals with disabilities. Copyright (c) 2005 Wiley-Liss, Inc.

  8. Gene regulatory networks in lactation: identification of global principles using bioinformatics

    Directory of Open Access Journals (Sweden)

    Pollard Katherine S

    2007-11-01

    Full Text Available Abstract Background The molecular events underlying mammary development during pregnancy, lactation, and involution are incompletely understood. Results Mammary gland microarray data, cellular localization data, protein-protein interactions, and literature-mined genes were integrated and analyzed using statistics, principal component analysis, gene ontology analysis, pathway analysis, and network analysis to identify global biological principles that govern molecular events during pregnancy, lactation, and involution. Conclusion Several key principles were derived: (1 nearly a third of the transcriptome fluctuates to build, run, and disassemble the lactation apparatus; (2 genes encoding the secretory machinery are transcribed prior to lactation; (3 the diversity of the endogenous portion of the milk proteome is derived from fewer than 100 transcripts; (4 while some genes are differentially transcribed near the onset of lactation, the lactation switch is primarily post-transcriptionally mediated; (5 the secretion of materials during lactation occurs not by up-regulation of novel genomic functions, but by widespread transcriptional suppression of functions such as protein degradation and cell-environment communication; (6 the involution switch is primarily transcriptionally mediated; and (7 during early involution, the transcriptional state is partially reverted to the pre-lactation state. A new hypothesis for secretory diminution is suggested – milk production gradually declines because the secretory machinery is not transcriptionally replenished. A comprehensive network of protein interactions during lactation is assembled and new regulatory gene targets are identified. Less than one fifth of the transcriptionally regulated nodes in this lactation network have been previously explored in the context of lactation. Implications for future research in mammary and cancer biology are discussed.

  9. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    Science.gov (United States)

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  10. Exploring the bZIP transcription factor regulatory network in Neurospora crassa.

    Science.gov (United States)

    Tian, Chaoguang; Li, Jingyi; Glass, N Louise

    2011-03-01

    Transcription factors (TFs) are key nodes of regulatory networks in eukaryotic organisms, including filamentous fungi such as Neurospora crassa. The 178 predicted DNA-binding TFs in N. crassa are distributed primarily among six gene families, which represent an ancient expansion in filamentous ascomycete genomes; 98 TF genes show detectable expression levels during vegetative growth of N. crassa, including 35 that show a significant difference in expression level between hyphae at the periphery versus hyphae in the interior of a colony. Regulatory networks within a species genome include paralogous TFs and their respective target genes (TF regulon). To investigate TF network evolution in N. crassa, we focused on the basic leucine zipper (bZIP) TF family, which contains nine members. We performed baseline transcriptional profiling during vegetative growth of the wild-type and seven isogenic, viable bZIP deletion mutants. We further characterized the regulatory network of one member of the bZIP family, NCU03905. NCU03905 encodes an Ap1-like protein (NcAp-1), which is involved in resistance to multiple stress responses, including oxidative and heavy metal stress. Relocalization of NcAp-1 from the cytoplasm to the nucleus was associated with exposure to stress. A comparison of the NcAp-1 regulon with Ap1-like regulons in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Aspergillus fumigatus showed both conservation and divergence. These data indicate how N. crassa responds to stress and provide information on pathway evolution.

  11. Ethylene-Related Gene Expression Networks in Wood Formation

    Directory of Open Access Journals (Sweden)

    Carolin Seyfferth

    2018-03-01

    Full Text Available Thickening of tree stems is the result of secondary growth, accomplished by the meristematic activity of the vascular cambium. Secondary growth of the stem entails developmental cascades resulting in the formation of secondary phloem outwards and secondary xylem (i.e., wood inwards of the stem. Signaling and transcriptional reprogramming by the phytohormone ethylene modifies cambial growth and cell differentiation, but the molecular link between ethylene and secondary growth remains unknown. We addressed this shortcoming by analyzing expression profiles and co-expression networks of ethylene pathway genes using the AspWood transcriptome database which covers all stages of secondary growth in aspen (Populus tremula stems. ACC synthase expression suggests that the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC is synthesized during xylem expansion and xylem cell maturation. Ethylene-mediated transcriptional reprogramming occurs during all stages of secondary growth, as deduced from AspWood expression profiles of ethylene-responsive genes. A network centrality analysis of the AspWood dataset identified EIN3D and 11 ERFs as hubs. No overlap was found between the co-expressed genes of the EIN3 and ERF hubs, suggesting target diversification and hence independent roles for these transcription factor families during normal wood formation. The EIN3D hub was part of a large co-expression gene module, which contained 16 transcription factors, among them several new candidates that have not been earlier connected to wood formation and a VND-INTERACTING 2 (VNI2 homolog. We experimentally demonstrated Populus EIN3D function in ethylene signaling in Arabidopsis thaliana. The ERF hubs ERF118 and ERF119 were connected on the basis of their expression pattern and gene co-expression module composition to xylem cell expansion and secondary cell wall formation, respectively. We hereby establish data resources for ethylene-responsive genes and

  12. Network Based Integrated Analysis of Phenotype-Genotype Data for Prioritization of Candidate Symptom Genes

    Directory of Open Access Journals (Sweden)

    Xing Li

    2014-01-01

    Full Text Available Background. Symptoms and signs (symptoms in brief are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM. To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. Methods. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. Results. The proposed method gets reliable gene rank list with AUC (area under curve 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Conclusions. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.

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

    Science.gov (United States)

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

    2016-01-26

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

  14. Insights gained from the reverse engineering of gene networks in keloid fibroblasts

    Directory of Open Access Journals (Sweden)

    Phan Toan

    2011-05-01

    Full Text Available Abstract Background Keloids are protrusive claw-like scars that have a propensity to recur even after surgery, and its molecular etiology remains elusive. The goal of reverse engineering is to infer gene networks from observational data, thus providing insight into the inner workings of a cell. However, most attempts at modeling biological networks have been done using simulated data. This study aims to highlight some of the issues involved in working with experimental data, and at the same time gain some insights into the transcriptional regulatory mechanism present in keloid fibroblasts. Methods Microarray data from our previous study was combined with microarray data obtained from the literature as well as new microarray data generated by our group. For the physical approach, we used the fREDUCE algorithm for correlating expression values to binding motifs. For the influence approach, we compared the Bayesian algorithm BANJO with the information theoretic method ARACNE in terms of performance in recovering known influence networks obtained from the KEGG database. In addition, we also compared the performance of different normalization methods as well as different types of gene networks. Results Using the physical approach, we found consensus sequences that were active in the keloid condition, as well as some sequences that were responsive to steroids, a commonly used treatment for keloids. From the influence approach, we found that BANJO was better at recovering the gene networks compared to ARACNE and that transcriptional networks were better suited for network recovery compared to cytokine-receptor interaction networks and intracellular signaling networks. We also found that the NFKB transcriptional network that was inferred from normal fibroblast data was more accurate compared to that inferred from keloid data, suggesting a more robust network in the keloid condition. Conclusions Consensus sequences that were found from this study are

  15. A novel gene network inference algorithm using predictive minimum description length approach.

    Science.gov (United States)

    Chaitankar, Vijender; Ghosh, Preetam; Perkins, Edward J; Gong, Ping; Deng, Youping; Zhang, Chaoyang

    2010-05-28

    Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold which defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we proposed a new inference algorithm which incorporated mutual information (MI), conditional mutual information (CMI) and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm was evaluated using both synthetic time series data sets and a biological time series data set for the yeast Saccharomyces cerevisiae. The benchmark quantities precision and recall were used as performance measures. The results show that the proposed algorithm produced less false edges and significantly improved the precision, as compared to the existing algorithm. For further analysis the performance of the algorithms was observed over different sizes of data. We have proposed a new algorithm that implements the PMDL principle for inferring gene regulatory networks from time series DNA microarray data that eliminates the need of a fine tuning parameter. The evaluation results obtained from both synthetic and actual biological data sets show that the

  16. Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks.

    Science.gov (United States)

    Wu, Mengmeng; Lin, Zhixiang; Ma, Shining; Chen, Ting; Jiang, Rui; Wong, Wing Hung

    2017-12-01

    Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.

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

    OpenAIRE

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

    2009-01-01

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

  18. Biomarker Gene Signature Discovery Integrating Network Knowledge

    Directory of Open Access Journals (Sweden)

    Holger Fröhlich

    2012-02-01

    Full Text Available Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches.

  19. Toward a new history and geography of human genes informed by ancient DNA.

    Science.gov (United States)

    Pickrell, Joseph K; Reich, David

    2014-09-01

    Genetic information contains a record of the history of our species, and technological advances have transformed our ability to access this record. Many studies have used genome-wide data from populations today to learn about the peopling of the globe and subsequent adaptation to local conditions. Implicit in this research is the assumption that the geographic locations of people today are informative about the geographic locations of their ancestors in the distant past. However, it is now clear that long-range migration, admixture, and population replacement subsequent to the initial out-of-Africa expansion have altered the genetic structure of most of the world's human populations. In light of this we argue that it is time to critically reevaluate current models of the peopling of the globe, as well as the importance of natural selection in determining the geographic distribution of phenotypes. We specifically highlight the transformative potential of ancient DNA. By accessing the genetic make-up of populations living at archaeologically known times and places, ancient DNA makes it possible to directly track migrations and responses to natural selection. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. 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.

  1. Causal structure of oscillations in gene regulatory networks: Boolean analysis of ordinary differential equation attractors.

    Science.gov (United States)

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

    2013-06-01

    A common approach to the modeling of gene regulatory networks is to represent activating or repressing interactions using ordinary differential equations for target gene concentrations that include Hill function dependences on regulator gene concentrations. An alternative formulation represents the same interactions using Boolean logic with time delays associated with each network link. We consider the attractors that emerge from the two types of models in the case of a simple but nontrivial network: a figure-8 network with one positive and one negative feedback loop. We show that the different modeling approaches give rise to the same qualitative set of attractors with the exception of a possible fixed point in the ordinary differential equation model in which concentrations sit at intermediate values. The properties of the attractors are most easily understood from the Boolean perspective, suggesting that time-delay Boolean modeling is a useful tool for understanding the logic of regulatory networks.

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

    KAUST Repository

    Salem, Saeed

    2013-05-01

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

  3. Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient.

    Science.gov (United States)

    Mohamed Salleh, Faridah Hani; Arif, Shereena Mohd; Zainudin, Suhaila; Firdaus-Raih, Mohd

    2015-12-01

    A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Ancient Biomolecules and Evolutionary Inference

    DEFF Research Database (Denmark)

    Cappellini, Enrico; Prohaska, Ana; Racimo, Fernando

    2018-01-01

    Over the last decade, studies of ancient biomolecules-particularly ancient DNA, proteins, and lipids-have revolutionized our understanding of evolutionary history. Though initially fraught with many challenges, the field now stands on firm foundations. Researchers now successfully retrieve nucleo...

  5. Leprosy: ancient disease remains a public health problem nowadays.

    Science.gov (United States)

    Noriega, Leandro Fonseca; Chiacchio, Nilton Di; Noriega, Angélica Fonseca; Pereira, Gilmayara Alves Abreu Maciel; Vieira, Marina Lino

    2016-01-01

    Despite being an ancient disease, leprosy remains a public health problem in several countries -particularly in India, Brazil and Indonesia. The current operational guidelines emphasize the evaluation of disability from the time of diagnosis and stipulate as fundamental principles for disease control: early detection and proper treatment. Continued efforts are needed to establish and improve quality leprosy services. A qualified primary care network that is integrated into specialized service and the development of educational activities are part of the arsenal in the fight against the disease, considered neglected and stigmatizing.

  6. Inference of gene regulatory networks from time series by Tsallis entropy

    Directory of Open Access Journals (Sweden)

    de Oliveira Evaldo A

    2011-05-01

    Full Text Available Abstract Background The inference of gene regulatory networks (GRNs from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information, a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5

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

    Science.gov (United States)

    Colaprico, Antonio; Bontempi, Gianluca; Castiglioni, Isabella

    2018-01-01

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

  8. Endogenous change: on cooperation and water in ancient history

    Science.gov (United States)

    Pande, S.; Ertsen, M.

    2013-04-01

    We propose and test the theory of endogenous change based on historical reconstructions of two ancient civilizations, Indus and Hohokam, in two water scarce basins, the Indus basin in the Indian subcontinent and the Lower Colorado basin in Southwestern United States. The endogenous institutional change sees changes in institutions as a sequence of equilibria brought about by changes in "quasi-parameters" such as rainfall, population density, soil and land use induced water resource availability. In the historical reconstructions of ancient civilizations, institutions are proximated by the scale of cooperation be it in the form of the extent of trade, sophisticated irrigation network, a centrally planned state or a loosely held state with a common cultural identity. The "quasi-parameters" either change naturally or are changed by humans and the changes affect the stability of cooperative structures over time. However, human influenced changes in the quasi-parameters itself are conditioned on the scale of existing cooperative structures. We thus provide insights into the quantitative dimensions of water access by ancient populations and its co-evolution with the socioeconomic and sociopolitical organization of the human past. We however do not suggest that water manipulation was the single most significant factor in stimulating social development and complexity - clearly this has been shown as highly reductionist, even misleading. The paper cautiously contributes to proximate prediction of hydrological change by attempting to understand the complexity of coupled human-hydrological systems.

  9. Identification of conserved drought stress responsive gene-network across tissues and developmental stages in rice.

    Science.gov (United States)

    Smita, Shuchi; Katiyar, Amit; Pandey, Dev Mani; Chinnusamy, Viswanathan; Archak, Sunil; Bansal, Kailash Chander

    2013-01-01

    Identification of genes that are coexpressed across various tissues and environmental stresses is biologically interesting, since they may play coordinated role in similar biological processes. Genes with correlated expression patterns can be best identified by using coexpression network analysis of transcriptome data. In the present study, we analyzed the temporal-spatial coordination of gene expression in root, leaf and panicle of rice under drought stress and constructed network using WGCNA and Cytoscape. Total of 2199 differentially expressed genes (DEGs) were identified in at least three or more tissues, wherein 88 genes have coordinated expression profile among all the six tissues under drought stress. These 88 highly coordinated genes were further subjected to module identification in the coexpression network. Based on chief topological properties we identified 18 hub genes such as ABC transporter, ATP-binding protein, dehydrin, protein phosphatase 2C, LTPL153 - Protease inhibitor, phosphatidylethanolaminebinding protein, lactose permease-related, NADP-dependent malic enzyme, etc. Motif enrichment analysis showed the presence of ABRE cis-elements in the promoters of > 62% of the coordinately expressed genes. Our results suggest that drought stress mediated upregulated gene expression was coordinated through an ABA-dependent signaling pathway across tissues, at least for the subset of genes identified in this study, while down regulation appears to be regulated by tissue specific pathways in rice.

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

    Science.gov (United States)

    Schaffter, Thomas; Marbach, Daniel; Floreano, Dario

    2011-08-15

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

  11. Serious Games Network

    Directory of Open Access Journals (Sweden)

    Carlos Vaz de Carvalho

    2013-11-01

    Full Text Available “Serious games” can be defined as (digital games used for purposes other than mere entertainment. Serious Games can be applied to a broad spectrum of areas, e.g. educational, healthcare, training in hazardous environments or situations. Game-based Learning, one aspect of Serious Games, are also more and more explored for all levels of education in different subjects, such as Ancient History. The SEGAN (SErious GAmes Network will create a Community of Practice on the Serious Games subject. The main objective is to create a stable (but expanding consortium to exchange ideas and experiences related to Serious Games. The SEGAN Network invites the people of the community of Archaeology, Cultural Heritage and Ancient History interested in Serious Games to join the net and to participate in their activities.

  12. Biophysical Constraints Arising from Compositional Context in Synthetic Gene Networks.

    Science.gov (United States)

    Yeung, Enoch; Dy, Aaron J; Martin, Kyle B; Ng, Andrew H; Del Vecchio, Domitilla; Beck, James L; Collins, James J; Murray, Richard M

    2017-07-26

    Synthetic gene expression is highly sensitive to intragenic compositional context (promoter structure, spacing regions between promoter and coding sequences, and ribosome binding sites). However, much less is known about the effects of intergenic compositional context (spatial arrangement and orientation of entire genes on DNA) on expression levels in synthetic gene networks. We compare expression of induced genes arranged in convergent, divergent, or tandem orientations. Induction of convergent genes yielded up to 400% higher expression, greater ultrasensitivity, and dynamic range than divergent- or tandem-oriented genes. Orientation affects gene expression whether one or both genes are induced. We postulate that transcriptional interference in divergent and tandem genes, mediated by supercoiling, can explain differences in expression and validate this hypothesis through modeling and in vitro supercoiling relaxation experiments. Treatment with gyrase abrogated intergenic context effects, bringing expression levels within 30% of each other. We rebuilt the toggle switch with convergent genes, taking advantage of supercoiling effects to improve threshold detection and switch stability. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Dynamic and modular gene regulatory networks drive the development of gametogenesis.

    Science.gov (United States)

    Che, Dongxue; Wang, Yang; Bai, Weiyang; Li, Leijie; Liu, Guiyou; Zhang, Liangcai; Zuo, Yongchun; Tao, Shiheng; Hua, Jinlian; Liao, Mingzhi

    2017-07-01

    Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

    Science.gov (United States)

    Obayashi, Takeshi; Kinoshita, Kengo

    2010-05-01

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

  15. Ancient and Current Chaos Theories

    Directory of Open Access Journals (Sweden)

    Güngör Gündüz

    2006-07-01

    Full Text Available Chaos theories developed in the last three decades have made very important contributions to our understanding of dynamical systems and natural phenomena. The meaning of chaos in the current theories and in the past is somewhat different from each other. In this work, the properties of dynamical systems and the evolution of chaotic systems were discussed in terms of the views of ancient philosophers. The meaning of chaos in Anaximenes’ philosophy and its role in the Ancient natural philosophy has been discussed in relation to other natural philosophers such as of Anaximander, Parmenides, Heraclitus, Empedocles, Leucippus (i.e. atomists and Aristotle. In addition, the fundamental concepts of statistical mechanics and the current chaos theories were discussed in relation to the views in Ancient natural philosophy. The roots of the scientific concepts such as randomness, autocatalysis, nonlinear growth, information, pattern, etc. in the Ancient natural philosophy were investigated.

  16. Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model

    Directory of Open Access Journals (Sweden)

    Ingkasuwan Papapit

    2012-08-01

    Full Text Available Abstract Background Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM. Results Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF. A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090, which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene. The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070 and constans-like (COL: At2g21320, were identified as positive regulators of starch synthase 4 (SS4: At4g18240. The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines. Conclusions In this study, we utilized a systematic approach of microarray

  17. Classification of ancient mammal individuals using dental pulp MALDI-TOF MS peptide profiling.

    Directory of Open Access Journals (Sweden)

    Thi-Nguyen-Ny Tran

    Full Text Available BACKGROUND: The classification of ancient animal corpses at the species level remains a challenging task for forensic scientists and anthropologists. Severe damage and mixed, tiny pieces originating from several skeletons may render morphological classification virtually impossible. Standard approaches are based on sequencing mitochondrial and nuclear targets. METHODOLOGY/PRINCIPAL FINDINGS: We present a method that can accurately classify mammalian species using dental pulp and mass spectrometry peptide profiling. Our work was organized into three successive steps. First, after extracting proteins from the dental pulp collected from 37 modern individuals representing 13 mammalian species, trypsin-digested peptides were used for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis. The resulting peptide profiles accurately classified every individual at the species level in agreement with parallel cytochrome b gene sequencing gold standard. Second, using a 279-modern spectrum database, we blindly classified 33 of 37 teeth collected in 37 modern individuals (89.1%. Third, we classified 10 of 18 teeth (56% collected in 15 ancient individuals representing five mammal species including human, from five burial sites dating back 8,500 years. Further comparison with an upgraded database comprising ancient specimen profiles yielded 100% classification in ancient teeth. Peptide sequencing yield 4 and 16 different non-keratin proteins including collagen (alpha-1 type I and alpha-2 type I in human ancient and modern dental pulp, respectively. CONCLUSIONS/SIGNIFICANCE: Mass spectrometry peptide profiling of the dental pulp is a new approach that can be added to the arsenal of species classification tools for forensics and anthropology as a complementary method to DNA sequencing. The dental pulp is a new source for collagen and other proteins for the species classification of modern and ancient mammal individuals.

  18. Classification of Ancient Mammal Individuals Using Dental Pulp MALDI-TOF MS Peptide Profiling

    Science.gov (United States)

    Tran, Thi-Nguyen-Ny; Aboudharam, Gérard; Gardeisen, Armelle; Davoust, Bernard; Bocquet-Appel, Jean-Pierre; Flaudrops, Christophe; Belghazi, Maya; Raoult, Didier; Drancourt, Michel

    2011-01-01

    Background The classification of ancient animal corpses at the species level remains a challenging task for forensic scientists and anthropologists. Severe damage and mixed, tiny pieces originating from several skeletons may render morphological classification virtually impossible. Standard approaches are based on sequencing mitochondrial and nuclear targets. Methodology/Principal Findings We present a method that can accurately classify mammalian species using dental pulp and mass spectrometry peptide profiling. Our work was organized into three successive steps. First, after extracting proteins from the dental pulp collected from 37 modern individuals representing 13 mammalian species, trypsin-digested peptides were used for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis. The resulting peptide profiles accurately classified every individual at the species level in agreement with parallel cytochrome b gene sequencing gold standard. Second, using a 279–modern spectrum database, we blindly classified 33 of 37 teeth collected in 37 modern individuals (89.1%). Third, we classified 10 of 18 teeth (56%) collected in 15 ancient individuals representing five mammal species including human, from five burial sites dating back 8,500 years. Further comparison with an upgraded database comprising ancient specimen profiles yielded 100% classification in ancient teeth. Peptide sequencing yield 4 and 16 different non-keratin proteins including collagen (alpha-1 type I and alpha-2 type I) in human ancient and modern dental pulp, respectively. Conclusions/Significance Mass spectrometry peptide profiling of the dental pulp is a new approach that can be added to the arsenal of species classification tools for forensics and anthropology as a complementary method to DNA sequencing. The dental pulp is a new source for collagen and other proteins for the species classification of modern and ancient mammal individuals. PMID:21364886

  19. The house spider genome reveals an ancient whole-genome duplication during arachnid evolution.

    Science.gov (United States)

    Schwager, Evelyn E; Sharma, Prashant P; Clarke, Thomas; Leite, Daniel J; Wierschin, Torsten; Pechmann, Matthias; Akiyama-Oda, Yasuko; Esposito, Lauren; Bechsgaard, Jesper; Bilde, Trine; Buffry, Alexandra D; Chao, Hsu; Dinh, Huyen; Doddapaneni, HarshaVardhan; Dugan, Shannon; Eibner, Cornelius; Extavour, Cassandra G; Funch, Peter; Garb, Jessica; Gonzalez, Luis B; Gonzalez, Vanessa L; Griffiths-Jones, Sam; Han, Yi; Hayashi, Cheryl; Hilbrant, Maarten; Hughes, Daniel S T; Janssen, Ralf; Lee, Sandra L; Maeso, Ignacio; Murali, Shwetha C; Muzny, Donna M; Nunes da Fonseca, Rodrigo; Paese, Christian L B; Qu, Jiaxin; Ronshaugen, Matthew; Schomburg, Christoph; Schönauer, Anna; Stollewerk, Angelika; Torres-Oliva, Montserrat; Turetzek, Natascha; Vanthournout, Bram; Werren, John H; Wolff, Carsten; Worley, Kim C; Bucher, Gregor; Gibbs, Richard A; Coddington, Jonathan; Oda, Hiroki; Stanke, Mario; Ayoub, Nadia A; Prpic, Nikola-Michael; Flot, Jean-François; Posnien, Nico; Richards, Stephen; McGregor, Alistair P

    2017-07-31

    The duplication of genes can occur through various mechanisms and is thought to make a major contribution to the evolutionary diversification of organisms. There is increasing evidence for a large-scale duplication of genes in some chelicerate lineages including two rounds of whole genome duplication (WGD) in horseshoe crabs. To investigate this further, we sequenced and analyzed the genome of the common house spider Parasteatoda tepidariorum. We found pervasive duplication of both coding and non-coding genes in this spider, including two clusters of Hox genes. Analysis of synteny conservation across the P. tepidariorum genome suggests that there has been an ancient WGD in spiders. Comparison with the genomes of other chelicerates, including that of the newly sequenced bark scorpion Centruroides sculpturatus, suggests that this event occurred in the common ancestor of spiders and scorpions, and is probably independent of the WGDs in horseshoe crabs. Furthermore, characterization of the sequence and expression of the Hox paralogs in P. tepidariorum suggests that many have been subject to neo-functionalization and/or sub-functionalization since their duplication. Our results reveal that spiders and scorpions are likely the descendants of a polyploid ancestor that lived more than 450 MYA. Given the extensive morphological diversity and ecological adaptations found among these animals, rivaling those of vertebrates, our study of the ancient WGD event in Arachnopulmonata provides a new comparative platform to explore common and divergent evolutionary outcomes of polyploidization events across eukaryotes.

  20. Effect of Duplicate Genes on Mouse Genetic Robustness: An Update

    Directory of Open Access Journals (Sweden)

    Zhixi Su

    2014-01-01

    Full Text Available In contrast to S. cerevisiae and C. elegans, analyses based on the current knockout (KO mouse phenotypes led to the conclusion that duplicate genes had almost no role in mouse genetic robustness. It has been suggested that the bias of mouse KO database toward ancient duplicates may possibly cause this knockout duplicate puzzle, that is, a very similar proportion of essential genes (PE between duplicate genes and singletons. In this paper, we conducted an extensive and careful analysis for the mouse KO phenotype data and corroborated a strong effect of duplicate genes on mouse genetics robustness. Moreover, the effect of duplicate genes on mouse genetic robustness is duplication-age dependent, which holds after ruling out the potential confounding effect from coding-sequence conservation, protein-protein connectivity, functional bias, or the bias of duplicates generated by whole genome duplication (WGD. Our findings suggest that two factors, the sampling bias toward ancient duplicates and very ancient duplicates with a proportion of essential genes higher than that of singletons, have caused the mouse knockout duplicate puzzle; meanwhile, the effect of genetic buffering may be correlated with sequence conservation as well as protein-protein interactivity.

  1. Using Morpholinos to Probe Gene Networks in Sea Urchin.

    Science.gov (United States)

    Materna, Stefan C

    2017-01-01

    The control processes that underlie the progression of development can be summarized in maps of gene regulatory networks (GRNs). A critical step in their assembly is the systematic perturbation of network candidates. In sea urchins the most important method for interfering with expression in a gene-specific way is application of morpholino antisense oligonucleotides (MOs). MOs act by binding to their sequence complement in transcripts resulting in a block in translation or a change in splicing and thus result in a loss of function. Despite the tremendous success of this technology, recent comparisons to mutants generated by genome editing have led to renewed criticism and challenged its reliability. As with all methods based on sequence recognition, MOs are prone to off-target binding that may result in phenotypes that are erroneously ascribed to the loss of the intended target. However, the slow progression of development in sea urchins has enabled extremely detailed studies of gene activity in the embryo. This wealth of knowledge paired with the simplicity of the sea urchin embryo enables careful analysis of MO phenotypes through a variety of methods that do not rely on terminal phenotypes. This article summarizes the use of MOs in probing GRNs and the steps that should be taken to assure their specificity.

  2. Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues.

    Science.gov (United States)

    Chen, Lei; Pan, Hongying; Zhang, Yu-Hang; Feng, Kaiyan; Kong, XiangYin; Huang, Tao; Cai, Yu-Dong

    2017-10-02

    Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein-protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method.

  3. Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks

    Directory of Open Access Journals (Sweden)

    Kohane Isaac S

    2005-09-01

    Full Text Available Abstract Background Biological processes are carried out by coordinated modules of interacting molecules. As clustering methods demonstrate that genes with similar expression display increased likelihood of being associated with a common functional module, networks of coexpressed genes provide one framework for assigning gene function. This has informed the guilt-by-association (GBA heuristic, widely invoked in functional genomics. Yet although the idea of GBA is accepted, the breadth of GBA applicability is uncertain. Results We developed methods to systematically explore the breadth of GBA across a large and varied corpus of expression data to answer the following question: To what extent is the GBA heuristic broadly applicable to the transcriptome and conversely how broadly is GBA captured by a priori knowledge represented in the Gene Ontology (GO? Our study provides an investigation of the functional organization of five coexpression networks using data from three mammalian organisms. Our method calculates a probabilistic score between each gene and each Gene Ontology category that reflects coexpression enrichment of a GO module. For each GO category we use Receiver Operating Curves to assess whether these probabilistic scores reflect GBA. This methodology applied to five different coexpression networks demonstrates that the signature of guilt-by-association is ubiquitous and reproducible and that the GBA heuristic is broadly applicable across the population of nine hundred Gene Ontology categories. We also demonstrate the existence of highly reproducible patterns of coexpression between some pairs of GO categories. Conclusion We conclude that GBA has universal value and that transcriptional control may be more modular than previously realized. Our analyses also suggest that methodologies combining coexpression measurements across multiple genes in a biologically-defined module can aid in characterizing gene function or in characterizing

  4. Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

    Science.gov (United States)

    Gong, Wuming; Koyano-Nakagawa, Naoko; Li, Tongbin; Garry, Daniel J

    2015-03-07

    Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion. We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes. The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p network inference model identified a region with an elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP

  5. Radiocarbon dating of ancient Japanese documents

    International Nuclear Information System (INIS)

    Oda, H.

    2001-01-01

    History is a reconstruction of past human activity, evidence of which is remained in the form of documents or relics. For the reconstruction of historic period, the radiocarbon dating of ancient documents provides important information. Although radiocarbon age is converted into calendar age with the calibration curve, the calibrated radiocarbon age is still different from the historical age when the document was written. The difference is known as 'old wood effect' for wooden cultural property. The discrepancy becomes more serious problem for recent sample which requires more accurate age determination. Using Tandetron accelerator mass spectrometer at Nagoya University, we have measured radiocarbon ages of Japanese ancient documents, sutras and printed books written dates of which are clarified from the paleographic standpoint. The purpose is to clarify the relation between calibrated radiocarbon age and historical age of ancient Japanese document by AMS radiocarbon dating. This paper reports 23 radiocarbon ages of ancient Japanese documents, sutras and printed books. The calibrated radiocarbon ages are in good agreement with the corresponding historical ages. It was shown by radiocarbon dating of the ancient documents that Japanese paper has little gap by 'old wood effect'; accordingly, ancient Japanese paper is a suitable sample for radiocarbon dating of recent historic period. (author)

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

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    Daniela Nitsch

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

  7. Authenticity in ancient DNA studies

    DEFF Research Database (Denmark)

    Gilbert, M Thomas P; Willerslev, Eske

    2006-01-01

    Ancient DNA studies represent a powerful tool that can be used to obtain genetic insights into the past. However, despite the publication of large numbers of apparently successful ancient DNA studies, a number of problems exist with the field that are often ignored. Therefore, questions exist as ...

  8. The History and Practice of Ancient Astronomy

    CERN Document Server

    Evans, James

    1998-01-01

    The History and Practice of Ancient Astronomy combines new scholarship with hands-on science to bring readers into direct contact with the work of ancient astronomers. While tracing ideas from ancient Babylon to sixteenth-century Europe, the book places its greatest emphasis on the Greek period, when astronomers developed the geometric and philosophical ideas that have determined the subsequent character of Western astronomy. The author approaches this history through the concrete details of ancient astronomical practice. Carefully organized and generously illustrated, the book can teach reade

  9. An investigation into the ancient abortion laws: comparing ancient Persia with ancient Greece and Rome.

    Science.gov (United States)

    Yarmohammadi, Hassan; Zargaran, Arman; Vatanpour, Azadeh; Abedini, Ehsan; Adhami, Siamak

    2013-01-01

    Since the dawn of medicine, medical rights and ethics have always been one of mankind's concerns. In any civilisation, attention paid to medical laws and ethics depends on the progress of human values and the advancement of medical science. The history of various civilisations teaches that each had its own views on medical ethics, but most had something in common. Ancient civilisations such as Greece, Rome, or Assyria did not consider the foetus to be alive and therefore to have human rights. In contrast, ancient Persians valued the foetus as a living person equal to others. Accordingly, they brought laws against abortion, even in cases of sexual abuse. Furthermore, abortion was considered to be a murder and punishments were meted out to the mother, father, and the person performing it.

  10. The Ancient Greece's roots of Olimpism

    Directory of Open Access Journals (Sweden)

    Bubka Sergej Nazarovich

    2011-10-01

    Full Text Available The paper focused on the phenomena of sport in Ancient Greece along with history, traditions, religion, education, culture and art. Economic and political conditions are analysed which promote or hamper development of Olympic Games in Ancient Greece. Exceptional stability of Ancient Olympic games during more than eleven centuries are noted as well as their influence on the life of Greek polices of those days. Hellenistic period needs of individual consideration.

  11. Urology and the scientific method in ancient Egypt.

    Science.gov (United States)

    Gordetsky, Jennifer; O'Brien, Jeanne

    2009-03-01

    To examine the practice of urology in ancient Egypt using various sources, including the Edwin Smith and Ebers Papyri. The sources of knowledge of ancient Egyptian medicine include medical papyri, paleopathology, art, and hieroglyphic carvings. A brief overview of the medical system in ancient Egypt was completed, in addition to an examination of the training and specialization of the physician in the ancient world. Urologic diseases treated in ancient Egypt and some of the first documented urologic surgeries are presented. Finally, we studied the role of the physician-priest and the intertwined use of religion and magic in ancient Egyptian medicine. The same medical conditions urologists treat in the office today were methodically documented thousands of years ago. Medical papyri show evidence that the ancient Egyptians practiced medicine using a scientific method based on the clinical observation of disease. This has been exemplified by the Edwin Smith Surgical Papyrus, a collection of surgical cases that gives a diagnosis, treatment, and prognosis for each ailment, and the discovery of medical specialization in ancient Egypt, giving us perhaps the world's first urologists. Intertwined with the scientific method was also the rich mysticism and religion of ancient Egypt, which were integral components of the healing process. We present an overview of the practice of urology in ancient Egypt, in terms of both pharmacologic and surgical intervention, as well as with a look into the religion of medicine practiced at that time.

  12. 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.

  13. Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

    Science.gov (United States)

    Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

    2017-10-03

    With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

  14. Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

    Directory of Open Access Journals (Sweden)

    Lemke Ney

    2009-09-01

    Full Text Available Abstract Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing

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

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    Li He

    2014-01-01

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

  16. A platform for rapid prototyping of synthetic gene networks in mammalian cells

    Science.gov (United States)

    Duportet, Xavier; Wroblewska, Liliana; Guye, Patrick; Li, Yinqing; Eyquem, Justin; Rieders, Julianne; Rimchala, Tharathorn; Batt, Gregory; Weiss, Ron

    2014-01-01

    Mammalian synthetic biology may provide novel therapeutic strategies, help decipher new paths for drug discovery and facilitate synthesis of valuable molecules. Yet, our capacity to genetically program cells is currently hampered by the lack of efficient approaches to streamline the design, construction and screening of synthetic gene networks. To address this problem, here we present a framework for modular and combinatorial assembly of functional (multi)gene expression vectors and their efficient and specific targeted integration into a well-defined chromosomal context in mammalian cells. We demonstrate the potential of this framework by assembling and integrating different functional mammalian regulatory networks including the largest gene circuit built and chromosomally integrated to date (6 transcription units, 27kb) encoding an inducible memory device. Using a library of 18 different circuits as a proof of concept, we also demonstrate that our method enables one-pot/single-flask chromosomal integration and screening of circuit libraries. This rapid and powerful prototyping platform is well suited for comparative studies of genetic regulatory elements, genes and multi-gene circuits as well as facile development of libraries of isogenic engineered cell lines. PMID:25378321

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

    Directory of Open Access Journals (Sweden)

    Xin Lai

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    Sîrbu, Alina; Crane, Martin; Ruskin, Heather J

    2015-05-14

    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.

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

    NARCIS (Netherlands)

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

    2009-01-01

    Background: Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2016-06-01

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

  3. Science and Library in the Ancient Age

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    Hasan Sacit Keseroğlu

    2016-09-01

    Full Text Available Science assumes its contemporary identity as a result of the stages of magic, religion and reason. The religious stage starts with the invention of writing and this stage leaves its place to reason with Thales in Ancient Greece. Knowledge eludes from religious beliefs. Ways to reach accurate, reliable and realistic knowledge are sought, along with the answer for what knowledge is. Therefore, beginning of the science is taken into consideration together with science and philosophy. The purpose of this study is to approach knowledge and science of the ancient age in Mesopotamia, Egypt and Ancient Greece in general terms and to determine the relationship between the knowledge produced in those places and libraries established. The hypothesis has been determined as “Egypt and Mesopotamia at the starting point of the history of science and science, and libraries in Ancient Greece have developed parallelly to each other.” The scope of the study has been limited to Mesopotamia, Egypt and Ancient Greece; and Ancient Greece has been explained, with descriptive method, in the frame of the topics of Ionia, Athens, Hellenistic Period and Rome. Many archives and libraries have been established in the ancient age. The difference between an archive and a library has been mentioned first, and then, various libraries have been introduced such as Nineveh in Mesopotamia, Alexandria in Ancient Greece and many others in Egypt. It has been clearly distinguished that there had been a very tight relationship between knowledge production and library, especially with the Library of Alexandria.

  4. Global gene expression profiling displays a network of dysregulated genes in non-atherosclerotic arterial tissue from patients with type 2 diabetes

    Directory of Open Access Journals (Sweden)

    Skov Vibe

    2012-02-01

    Full Text Available Abstract Background Generalized arterial alterations, such as endothelial dysfunction, medial matrix accumulations, and calcifications are associated with type 2 diabetes (T2D. These changes may render the vessel wall more susceptible to injury; however, the molecular characteristics of such diffuse pre-atherosclerotic changes in diabetes are only superficially known. Methods To identify the molecular alterations of the generalized arterial disease in T2D, DNA microarrays were applied to examine gene expression changes in normal-appearing, non-atherosclerotic arterial tissue from 10 diabetic and 11 age-matched non-diabetic men scheduled for a coronary by-pass operation. Gene expression changes were integrated with GO-Elite, GSEA, and Cytoscape to identify significant biological pathways and networks. Results Global pathway analysis revealed differential expression of gene-sets representing matrix metabolism, triglyceride synthesis, inflammation, insulin signaling, and apoptosis. The network analysis showed a significant cluster of dysregulated genes coding for both intra- and extra-cellular proteins associated with vascular cell functions together with genes related to insulin signaling and matrix remodeling. Conclusions Our results identify pathways and networks involved in the diffuse vasculopathy present in non-atherosclerotic arterial tissue in patients with T2D and confirmed previously observed mRNA-alterations. These abnormalities may play a role for the arterial response to injury and putatively for the accelerated atherogenesis among patients with diabetes.

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

    Directory of Open Access Journals (Sweden)

    Josefin Skogsberg

    2008-03-01

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

  6. Paleo-Environmental Reconstruction Using Ancient DNA

    DEFF Research Database (Denmark)

    Pedersen, Mikkel Winther

    The aim of this thesis has been to investigate and expand the methodology and applicability for using ancient DNA deposited in lake sediments to detect and determine its genetic sources for paleo-environmental reconstruction. The aim was furthermore to put this tool into an applicable context...... solving other scientifically interesting questions. Still in its childhood, ancient environmental DNA research has a large potential for still developing, improving and discovering its possibilities and limitations in different environments and for identifying various organisms, both in terms...... research on ancient and modern environmental DNA (Paper 1), secondly by setting up a comparative study (Paper 2) to investigate how an ancient plant DNA (mini)-barcode can reflect other traditional methods (e.g. pollen and macrofossils) for reconstructing floristic history. In prolongation of the results...

  7. AN INTERESTING CASE OF ANCIENT SCHWANNOMA

    Directory of Open Access Journals (Sweden)

    Binu

    2015-01-01

    Full Text Available INTRODUCTION : Schwannoma is a common benign tumour of nerve sheath. Degenerating type of schwannoma is called ancient schwannoma. Ancient schwannomas of scalp are rare and are often misdiagnosed as sebaceous cyst or dermoid cyst. CASE REPORT : We present a thirty two year old male presented with scalp swel ling of eight years duration. X - ray showed no intracranial extension. He underwent excision of the tumour and histopathology was reported as ancient schwannoma. DISCUSSION : Histopathologically , ancient schwannomas charecterised by cellular Antoni type A ar eas and less cellular Antoni type - B areas. 9 th , 7 th , 11 th , 5 th and 4 th cranial nerves are often affected and may be associated with multiple neuro fibramatosis (Von - Recklinghausen’s disease. Impact : Case is presented for its rarity and possible pre - operative misdiagnosis

  8. A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients.

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    Mingguang Shi

    Full Text Available Several studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes.We investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p = 0.002. Similar results were obtained with reversed training and testing datasets (p = 0.007. Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p = 0.006, but not beneficial to the low-risk patients (p = 0.491.The NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network

  9. A comparative study of covariance selection models for the inference of gene regulatory networks.

    Science.gov (United States)

    Stifanelli, Patrizia F; Creanza, Teresa M; Anglani, Roberto; Liuzzi, Vania C; Mukherjee, Sayan; Schena, Francesco P; Ancona, Nicola

    2013-10-01

    The inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology. In this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) 'ℓ(2C)' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that ℓ(2C) outperformed the other two methods having the most predictive partial correlation estimates and the highest values of sensitivity to infer conditional dependencies between genes even when a few number of observations was available. The application of this method for inferring gene networks of the isoprenoid biosynthesis pathways in Arabidopsis thaliana allowed to enlighten a negative partial correlation coefficient between the two hubs in the two isoprenoid pathways and, more importantly, provided an evidence of cross-talk between genes in the plastidial and the cytosolic pathways. When applied to gene expression data relative to a signature of HRAS oncogene in human cell cultures, the method revealed 9 genes (p-value<0.0005) directly interacting with HRAS, sharing the same Ras-responsive binding site for the transcription factor RREB1. This result suggests that the transcriptional activation of these genes is mediated by a common transcription factor downstream of Ras signaling. Software implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip. Copyright © 2013 The Authors. Published by

  10. Inferring Allele Frequency Trajectories from Ancient DNA Indicates That Selection on a Chicken Gene Coincided with Changes in Medieval Husbandry Practices.

    Science.gov (United States)

    Loog, Liisa; Thomas, Mark G; Barnett, Ross; Allen, Richard; Sykes, Naomi; Paxinos, Ptolemaios D; Lebrasseur, Ophélie; Dobney, Keith; Peters, Joris; Manica, Andrea; Larson, Greger; Eriksson, Anders

    2017-08-01

    Ancient DNA provides an opportunity to infer the drivers of natural selection by linking allele frequency changes to temporal shifts in environment or cultural practices. However, analyses have often been hampered by uneven sampling and uncertainties in sample dating, as well as being confounded by demographic processes. Here, we present a Bayesian statistical framework for quantifying the timing and strength of selection using ancient DNA that explicitly addresses these challenges. We applied this method to time series data for two loci: TSHR and BCDO2, both hypothesised to have undergone strong and recent selection in domestic chickens. The derived variant in TSHR, associated with reduced aggression to conspecifics and faster onset of egg laying, shows strong selection beginning around 1,100 years ago, coincident with archaeological evidence for intensified chicken production and documented changes in egg and chicken consumption. To our knowledge, this is the first example of preindustrial domesticate trait selection in response to a historically attested cultural shift in food preference. For BCDO2, we find support for selection, but demonstrate that the recent rise in allele frequency could also have been driven by gene flow from imported Asian chickens during more recent breed formations. Our findings highlight that traits found ubiquitously in modern domestic species may not necessarily have originated during the early stages of domestication. In addition, our results demonstrate the importance of precise estimation of allele frequency trajectories through time for understanding the drivers of selection. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  11. Pluripotency gene network dynamics: System views from parametric analysis.

    Science.gov (United States)

    Akberdin, Ilya R; Omelyanchuk, Nadezda A; Fadeev, Stanislav I; Leskova, Natalya E; Oschepkova, Evgeniya A; Kazantsev, Fedor V; Matushkin, Yury G; Afonnikov, Dmitry A; Kolchanov, Nikolay A

    2018-01-01

    Multiple experimental data demonstrated that the core gene network orchestrating self-renewal and differentiation of mouse embryonic stem cells involves activity of Oct4, Sox2 and Nanog genes by means of a number of positive feedback loops among them. However, recent studies indicated that the architecture of the core gene network should also incorporate negative Nanog autoregulation and might not include positive feedbacks from Nanog to Oct4 and Sox2. Thorough parametric analysis of the mathematical model based on this revisited core regulatory circuit identified that there are substantial changes in model dynamics occurred depending on the strength of Oct4 and Sox2 activation and molecular complexity of Nanog autorepression. The analysis showed the existence of four dynamical domains with different numbers of stable and unstable steady states. We hypothesize that these domains can constitute the checkpoints in a developmental progression from naïve to primed pluripotency and vice versa. During this transition, parametric conditions exist, which generate an oscillatory behavior of the system explaining heterogeneity in expression of pluripotent and differentiation factors in serum ESC cultures. Eventually, simulations showed that addition of positive feedbacks from Nanog to Oct4 and Sox2 leads mainly to increase of the parametric space for the naïve ESC state, in which pluripotency factors are strongly expressed while differentiation ones are repressed.

  12. On Ancient Babylonian Algebra and Geometry

    Indian Academy of Sciences (India)

    ber system prevalent during the ancient Mesopotamian civilization. In this article, we study the ... civilization provides a better insight into the thought processes of the ancient Babylonian mathematicians. In this context, consider the following ...

  13. Evolution of competitive ability: an adaptation speed vs. accuracy tradeoff rooted in gene network size.

    Science.gov (United States)

    Malcom, Jacob W

    2011-04-25

    Ecologists have increasingly come to understand that evolutionary change on short time-scales can alter ecological dynamics (and vice-versa), and this idea is being incorporated into community ecology research programs. Previous research has suggested that the size and topology of the gene network underlying a quantitative trait should constrain or facilitate adaptation and thereby alter population dynamics. Here, I consider a scenario in which two species with different genetic architectures compete and evolve in fluctuating environments. An important trade-off emerges between adaptive accuracy and adaptive speed, driven by the size of the gene network underlying the ecologically-critical trait and the rate of environmental change. Smaller, scale-free networks confer a competitive advantage in rapidly-changing environments, but larger networks permit increased adaptive accuracy when environmental change is sufficiently slow to allow a species time to adapt. As the differences in network characteristics increase, the time-to-resolution of competition decreases. These results augment and refine previous conclusions about the ecological implications of the genetic architecture of quantitative traits, emphasizing a role of adaptive accuracy. Along with previous work, in particular that considering the role of gene network connectivity, these results provide a set of expectations for what we may observe as the field of ecological genomics develops.

  14. Ancient Biomolecules and Evolutionary Inference.

    Science.gov (United States)

    Cappellini, Enrico; Prohaska, Ana; Racimo, Fernando; Welker, Frido; Pedersen, Mikkel Winther; Allentoft, Morten E; de Barros Damgaard, Peter; Gutenbrunner, Petra; Dunne, Julie; Hammann, Simon; Roffet-Salque, Mélanie; Ilardo, Melissa; Moreno-Mayar, J Víctor; Wang, Yucheng; Sikora, Martin; Vinner, Lasse; Cox, Jürgen; Evershed, Richard P; Willerslev, Eske

    2018-04-25

    Over the last decade, studies of ancient biomolecules-particularly ancient DNA, proteins, and lipids-have revolutionized our understanding of evolutionary history. Though initially fraught with many challenges, the field now stands on firm foundations. Researchers now successfully retrieve nucleotide and amino acid sequences, as well as lipid signatures, from progressively older samples, originating from geographic areas and depositional environments that, until recently, were regarded as hostile to long-term preservation of biomolecules. Sampling frequencies and the spatial and temporal scope of studies have also increased markedly, and with them the size and quality of the data sets generated. This progress has been made possible by continuous technical innovations in analytical methods, enhanced criteria for the selection of ancient samples, integrated experimental methods, and advanced computational approaches. Here, we discuss the history and current state of ancient biomolecule research, its applications to evolutionary inference, and future directions for this young and exciting field. Expected final online publication date for the Annual Review of Biochemistry Volume 87 is June 20, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

  15. A core filamentation response network in Candida albicans is restricted to eight genes.

    Directory of Open Access Journals (Sweden)

    Ronny Martin

    Full Text Available Although morphological plasticity is a central virulence trait of Candida albicans, the number of filament-associated genes and the interplay of mechanisms regulating their expression remain unknown. By correlation-based network modeling of the transcriptional response to different defined external stimuli for morphogenesis we identified a set of eight genes with highly correlated expression patterns, forming a core filamentation response. This group of genes included ALS3, ECE1, HGT2, HWP1, IHD1 and RBT1 which are known or supposed to encode for cell- wall associated proteins as well as the Rac1 guanine nucleotide exchange factor encoding gene DCK1 and the unknown function open reading frame orf19.2457. The validity of network modeling was confirmed using a dataset of advanced complexity that describes the transcriptional response of C. albicans during epithelial invasion as well as comparing our results with other previously published transcriptome studies. Although the set of core filamentation response genes was quite small, several transcriptional regulators are involved in the control of their expression, depending on the environmental condition.

  16. Trepanation in Ancient China.

    Science.gov (United States)

    Hobert, Leah; Binello, Emanuela

    2017-05-01

    Trepanation, the process of making a burr hole in the skull to access the brain, is an ancient form of a primitive craniotomy. There is widespread evidence of contributions made to this practice by ancient civilizations in Europe, Africa, and South America, where archaeologists have unearthed thousands of trepanned skulls dating back to the Neolithic period. Little is known about trepanation in China, and it is commonly believed that the Chinese used only traditional Chinese medicine and nonsurgical methods for treating brain injuries. However, a thorough analysis of the available archeological and literary evidence reveals that trepanation was widely practiced throughout China thousands of years ago. A significant number of trepanned Chinese skulls have been unearthed showing signs of healing and suggesting that patients survived after surgery. Trepanation was likely performed for therapeutic and spiritual reasons. Medical and historical works from Chinese literature contain descriptions of primitive neurosurgical procedures, including stories of surgeons, such as the legendary Hua Tuo, and surgical techniques used for the treatment of brain pathologies. The lack of translation of Chinese reports into the English language and the lack of publications on this topic in the English language may have contributed to the misconception that ancient China was devoid of trepanation. This article summarizes the available evidence attesting to the performance of successful primitive cranial surgery in ancient China. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Mechanisms in ancient Chinese books with illustrations

    CERN Document Server

    Hsiao, Kuo-Hung

    2014-01-01

    This book presents a unique approach for studying mechanisms and machines with drawings that were depicted unclearly in ancient Chinese books. The historical, cultural and technical backgrounds of the mechanisms are explained, and various mechanisms described and illustrated in ancient books are introduced. By utilizing the idea for the conceptual design of modern mechanisms, all feasible designs of ancient mechanisms with uncertain members and joints that meet the technical standards of the subjects’ time periods are synthesized systematically. Ancient Chinese crossbows (the original crossbow and repeating crossbows), textile mechanisms (silk-reeling mechanism, spinning mechanisms, and looms), and many other artisan's tool mechanisms are used as illustrated examples.  Such an approach provides a logical method for the reconstruction designs of ancient mechanisms with uncertain structures. It also provides an innovative direction for researchers to further identify the original structures of mechanisms...

  18. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Science.gov (United States)

    Park, Chihyun; Ahn, Jaegyoon; Kim, Hyunjin; Park, Sanghyun

    2014-01-01

    The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  19. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Chihyun Park

    Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  20. Endocrinology in ancient Sparta.

    Science.gov (United States)

    Tsoulogiannis, Ioannis N; Spandidos, Demetrios A

    2007-01-01

    This article attempts to analyze the crucial link between the plant Agnus castus and human health, particularly hormonal status, with special reference to the needs of the society of ancient Sparta. The ancient Spartans used Agnus both as a cure for infertility and as a remedy to treat battle wounds. These special properties were recognized by the sanctuary of Asclepios Agnita, which was located in Sparta, as well as by medical practitioners in Sparta during the classical, Hellenistic and Roman ages.

  1. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

    Directory of Open Access Journals (Sweden)

    Xingli Guo

    Full Text Available The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation.

  2. 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.

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

    KAUST Repository

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

    2013-01-01

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

  4. 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.

  5. A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.

    Science.gov (United States)

    Choi, Yoonha; Coram, Marc; Peng, Jie; Tang, Hua

    2017-07-01

    Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, GGMs are not appropriate for non-Gaussian data, such as those generated in RNA-seq experiments. We propose a novel statistical framework that maximizes a penalized likelihood, in which the observed count data follow a Poisson log-normal distribution. To overcome the computational challenges, we use Laplace's method to approximate the likelihood and its gradients, and apply the alternating directions method of multipliers to find the penalized maximum likelihood estimates. The proposed method is evaluated and compared with GGMs using both simulated and real RNA-seq data. The proposed method shows improved performance in detecting edges that represent covarying pairs of genes, particularly for edges connecting low-abundant genes and edges around regulatory hubs.

  6. Tracing ancient hydrogeological fracture network age and compartmentalisation using noble gases

    Science.gov (United States)

    Warr, Oliver; Sherwood Lollar, Barbara; Fellowes, Jonathan; Sutcliffe, Chelsea N.; McDermott, Jill M.; Holland, Greg; Mabry, Jennifer C.; Ballentine, Christopher J.

    2018-02-01

    We show that fluid volumes residing within the Precambrian crystalline basement account for ca 30% of the total groundwater inventory of the Earth (> 30 million km3). The residence times and scientific importance of this groundwater are only now receiving attention with ancient fracture fluids identified in Canada and South Africa showing: (1) microbial life which has existed in isolation for millions of years; (2) significant hydrogen and hydrocarbon production via water-rock reactions; and (3) preserving noble gas components from the early atmosphere. Noble gas (He, Ne, Ar, Kr, Xe) abundance and isotopic compositions provide the primary evidence for fluid mean residence time (MRT). Here we extend the noble gas data from the Kidd Creek Mine in Timmins Ontario Canada, a volcanogenic massive sulfide (VMS) deposit formed at 2.7 Ga, in which fracture fluids with MRTs of 1.1-1.7 Ga were identified at 2.4 km depth (Holland et al., 2013); to fracture fluids at 2.9 km depth. We compare here the Kidd Creek Mine study with noble gas compositions determined in fracture fluids taken from two mines (Mine 1 & Mine 2) at 1.7 and 1.4 km depth below surface in the Sudbury Basin formed by a meteorite impact at 1.849 Ga. The 2.9 km samples at Kidd Creek Mine show the highest radiogenic isotopic ratios observed to date in free fluids (e.g. 21Ne/22Ne = 0.6 and 40Ar/36Ar = 102,000) and have MRTs of 1.0-2.2 Ga. In contrast, resampled 2.4 km fluids indicated a less ancient MRT (0.2-0.6 Ga) compared with the previous study (1.1-1.7 Ga). This is consistent with a change in the age distribution of fluids feeding the fractures as they drain, with a decreasing proportion of the most ancient end-member fluids. 129Xe/136Xe ratios for these fluids confirm that boreholes at 2.4 km versus 2.9 km are sourced from hydrogeologically distinct systems. In contrast, results for the Sudbury mines have MRTs of 0.2-0.6 and 0.2-0.9 Ga for Mines 1 and 2 respectively. While still old compared to almost all

  7. Topography and the road network in the territory of ancient Naissus

    Directory of Open Access Journals (Sweden)

    Ljubomirović Irena V.

    2016-01-01

    Full Text Available Archaeological research revealed that ancient Naissus was located on the right bank of the Nišava, on the territory partially covered by the Niš Fortress. The ancient town developed on a wide and flat terrain, which offered good conditions for settlement, but also for raising fortifications. According to modern scholars, the urban settlement on the right bank of the Nišava was preceded by a small native village (vicus, which was important for the erection of the town fortifications. Numerous roads were linking Naissus with the surrounding regions and villas in the county side. We learn about them on the basis of epigraphic and archaeological material milestones and remains of roads, but also on the basis of the location of the necropolises, which in the classical period often sprang up near the suburban roads. The road leading to the east crossed the Nišava by a stone bridge, whose remains were visible not far from today's 'Benetton' factory. The road led further over 'Gabrovac land', intersected with streams over which the remains of three stone bridges from the Roman period were found. This route led to Mediana, a suburb with villas three miles distant from the city. Another important road connecting the region Pomoravlje with the southern parts of the province of Dalmatia was the road Naissus-Lissus. TA section leding to Macedonia and the harbour of Thessalonica over Scupi branched from it south of Naissus: Ad Herculem, Hammeum Ad Fines, Vindenae and Vicianum. From Vicianum station (Vučitrn one section ran towards Lissus and another towards Scupi. Throughout the Timok valley stretched one of the most important roads (Naissus-Ratiaria that linked Naissus and the central Balkan areas with the region of Podunavlje (the Danube basin. The road led from Naissus to the East, along the right bank of the Nišava (across the areas of Jagodin mala and Vrežina and, at the modern village of Malča, it turned towards the North, following the route of

  8. 5C analysis of the Epidermal Differentiation Complex locus reveals distinct chromatin interaction networks between gene-rich and gene-poor TADs in skin epithelial cells.

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    Krzysztof Poterlowicz

    2017-09-01

    Full Text Available Mammalian genomes contain several dozens of large (>0.5 Mbp lineage-specific gene loci harbouring functionally related genes. However, spatial chromatin folding, organization of the enhancer-promoter networks and their relevance to Topologically Associating Domains (TADs in these loci remain poorly understood. TADs are principle units of the genome folding and represents the DNA regions within which DNA interacts more frequently and less frequently across the TAD boundary. Here, we used Chromatin Conformation Capture Carbon Copy (5C technology to characterize spatial chromatin interaction network in the 3.1 Mb Epidermal Differentiation Complex (EDC locus harbouring 61 functionally related genes that show lineage-specific activation during terminal keratinocyte differentiation in the epidermis. 5C data validated by 3D-FISH demonstrate that the EDC locus is organized into several TADs showing distinct lineage-specific chromatin interaction networks based on their transcription activity and the gene-rich or gene-poor status. Correlation of the 5C results with genome-wide studies for enhancer-specific histone modifications (H3K4me1 and H3K27ac revealed that the majority of spatial chromatin interactions that involves the gene-rich TADs at the EDC locus in keratinocytes include both intra- and inter-TAD interaction networks, connecting gene promoters and enhancers. Compared to thymocytes in which the EDC locus is mostly transcriptionally inactive, these interactions were found to be keratinocyte-specific. In keratinocytes, the promoter-enhancer anchoring regions in the gene-rich transcriptionally active TADs are enriched for the binding of chromatin architectural proteins CTCF, Rad21 and chromatin remodeler Brg1. In contrast to gene-rich TADs, gene-poor TADs show preferential spatial contacts with each other, do not contain active enhancers and show decreased binding of CTCF, Rad21 and Brg1 in keratinocytes. Thus, spatial interactions between gene

  9. A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

    Energy Technology Data Exchange (ETDEWEB)

    Santra, Tapesh, E-mail: tapesh.santra@ucd.ie [Systems Biology Ireland, University College Dublin, Dublin (Ireland)

    2014-05-20

    Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

  10. A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

    International Nuclear Information System (INIS)

    Santra, Tapesh

    2014-01-01

    Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

  11. Neural model of gene regulatory network: a survey on supportive meta-heuristics.

    Science.gov (United States)

    Biswas, Surama; Acharyya, Sriyankar

    2016-06-01

    Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

  12. Application of neutron activation analysis in study of ancient ceramics

    International Nuclear Information System (INIS)

    Li Guoxia; Zhao Weijuan; Gao Zhengyao; Xie Jianzhong; Huang Zhongxiang; Jia Xiuqin; Han Song

    2000-01-01

    Trace-elements in ancient ceramics and imitative ancient ceramics were determined by neutron activation analysis (NAA). The NAA data are then analyzed by fuzzy cluster method and the trend cluster diagram is obtained. The raw material sources of ancient ceramics and imitative ancient ceramics are determined. The path for improving quality of imitative ancient ceramics is found

  13. Comparative scaffolding and gap filling of ancient bacterial genomes applied to two ancient Yersinia pestis genomes

    Science.gov (United States)

    Doerr, Daniel; Chauve, Cedric

    2017-01-01

    Yersinia pestis is the causative agent of the bubonic plague, a disease responsible for several dramatic historical pandemics. Progress in ancient DNA (aDNA) sequencing rendered possible the sequencing of whole genomes of important human pathogens, including the ancient Y. pestis strains responsible for outbreaks of the bubonic plague in London in the 14th century and in Marseille in the 18th century, among others. However, aDNA sequencing data are still characterized by short reads and non-uniform coverage, so assembling ancient pathogen genomes remains challenging and often prevents a detailed study of genome rearrangements. It has recently been shown that comparative scaffolding approaches can improve the assembly of ancient Y. pestis genomes at a chromosome level. In the present work, we address the last step of genome assembly, the gap-filling stage. We describe an optimization-based method AGapEs (ancestral gap estimation) to fill in inter-contig gaps using a combination of a template obtained from related extant genomes and aDNA reads. We show how this approach can be used to refine comparative scaffolding by selecting contig adjacencies supported by a mix of unassembled aDNA reads and comparative signal. We applied our method to two Y. pestis data sets from the London and Marseilles outbreaks, for which we obtained highly improved genome assemblies for both genomes, comprised of, respectively, five and six scaffolds with 95 % of the assemblies supported by ancient reads. We analysed the genome evolution between both ancient genomes in terms of genome rearrangements, and observed a high level of synteny conservation between these strains. PMID:29114402

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

    Directory of Open Access Journals (Sweden)

    Lionikas Arimantas

    2012-11-01

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

  15. Babylonian Resistor Networks

    Science.gov (United States)

    Mungan, Carl E.; Lipscombe, Trevor C.

    2012-01-01

    The ancient Babylonians had an iterative technique for numerically approximating the values of square roots. Their method can be physically implemented using series and parallel resistor networks. A recursive formula for the equivalent resistance R[subscript eq] is developed and converted into a nonrecursive solution for circuits using…

  16. Retroviral DNA Sequences as a Means for Determining Ancient Diets.

    Directory of Open Access Journals (Sweden)

    Jessica I Rivera-Perez

    Full Text Available For ages, specialists from varying fields have studied the diets of the primeval inhabitants of our planet, detecting diet remains in archaeological specimens using a range of morphological and biochemical methods. As of recent, metagenomic ancient DNA studies have allowed for the comparison of the fecal and gut microbiomes associated to archaeological specimens from various regions of the world; however the complex dynamics represented in those microbial communities still remain unclear. Theoretically, similar to eukaryote DNA the presence of genes from key microbes or enzymes, as well as the presence of DNA from viruses specific to key organisms, may suggest the ingestion of specific diet components. In this study we demonstrate that ancient virus DNA obtained from coprolites also provides information reconstructing the host's diet, as inferred from sequences obtained from pre-Columbian coprolites. This depicts a novel and reliable approach to determine new components as well as validate the previously suggested diets of extinct cultures and animals. Furthermore, to our knowledge this represents the first description of the eukaryotic viral diversity found in paleofaeces belonging to pre-Columbian cultures.

  17. RNAi-Based Identification of Gene-Specific Nuclear Cofactor Networks Regulating Interleukin-1 Target Genes

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    Johanna Meier-Soelch

    2018-04-01

    Full Text Available The potent proinflammatory cytokine interleukin (IL-1 triggers gene expression through the NF-κB signaling pathway. Here, we investigated the cofactor requirements of strongly regulated IL-1 target genes whose expression is impaired in p65 NF-κB-deficient murine embryonic fibroblasts. By two independent small-hairpin (shRNA screens, we examined 170 genes annotated to encode nuclear cofactors for their role in Cxcl2 mRNA expression and identified 22 factors that modulated basal or IL-1-inducible Cxcl2 levels. The functions of 16 of these factors were validated for Cxcl2 and further analyzed for their role in regulation of 10 additional IL-1 target genes by RT-qPCR. These data reveal that each inducible gene has its own (quantitative requirement of cofactors to maintain basal levels and to respond to IL-1. Twelve factors (Epc1, H2afz, Kdm2b, Kdm6a, Mbd3, Mta2, Phf21a, Ruvbl1, Sin3b, Suv420h1, Taf1, and Ube3a have not been previously implicated in inflammatory cytokine functions. Bioinformatics analysis indicates that they are components of complex nuclear protein networks that regulate chromatin functions and gene transcription. Collectively, these data suggest that downstream from the essential NF-κB signal each cytokine-inducible target gene has further subtle requirements for individual sets of nuclear cofactors that shape its transcriptional activation profile.

  18. A Genome-Wide Association Study and Complex Network Identify Four Core Hub Genes in Bipolar Disorder

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    Zengyan Xie

    2017-12-01

    Full Text Available Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To find core hub genes, we introduce a network analysis after the GWAS was conducted. Six thousand four hundred fifty eight single nucleotide polymorphisms (SNPs with p < 0.01 were sifted out from Wellcome Trust Case Control Consortium (WTCCC dataset and mapped to 2045 genes, which are then compared with the protein–protein network. One hundred twelve genes with a degree >17 were chosen as hub genes from which five significant modules and four core hub genes (FBXL13, WDFY2, bFGF, and MTHFD1L were found. These core hub genes have not been reported to be directly associated with BD but may function by interacting with genes directly related to BD. Our method engenders new thoughts on finding genes indirectly associated with, but important for, complex diseases.

  19. Geologically ancient DNA: fact or artefact?

    DEFF Research Database (Denmark)

    Hebsgaard, Martin Bay; Phillips, Matthew J.; Willerslev, Eske

    2005-01-01

    Studies continue to report ancient DNA sequences and viable microbial cells that are many millions of years old. In this paper we evaluate some of the most extravagant claims of geologically ancient DNA. We conclude that although exciting, the reports suffer from inadequate experimental setup and...

  20. Bioinformatics, interaction network analysis, and neural networks to characterize gene expression of radicular cyst and periapical granuloma.

    Science.gov (United States)

    Poswar, Fabiano de Oliveira; Farias, Lucyana Conceição; Fraga, Carlos Alberto de Carvalho; Bambirra, Wilson; Brito-Júnior, Manoel; Sousa-Neto, Manoel Damião; Santos, Sérgio Henrique Souza; de Paula, Alfredo Maurício Batista; D'Angelo, Marcos Flávio Silveira Vasconcelos; Guimarães, André Luiz Sena

    2015-06-01

    Bioinformatics has emerged as an important tool to analyze the large amount of data generated by research in different diseases. In this study, gene expression for radicular cysts (RCs) and periapical granulomas (PGs) was characterized based on a leader gene approach. A validated bioinformatics algorithm was applied to identify leader genes for RCs and PGs. Genes related to RCs and PGs were first identified in PubMed, GenBank, GeneAtlas, and GeneCards databases. The Web-available STRING software (The European Molecular Biology Laboratory [EMBL], Heidelberg, Baden-Württemberg, Germany) was used in order to build the interaction map among the identified genes by a significance score named weighted number of links. Based on the weighted number of links, genes were clustered using k-means. The genes in the highest cluster were considered leader genes. Multilayer perceptron neural network analysis was used as a complementary supplement for gene classification. For RCs, the suggested leader genes were TP53 and EP300, whereas PGs were associated with IL2RG, CCL2, CCL4, CCL5, CCR1, CCR3, and CCR5 genes. Our data revealed different gene expression for RCs and PGs, suggesting that not only the inflammatory nature but also other biological processes might differentiate RCs and PGs. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  1. Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

    Directory of Open Access Journals (Sweden)

    Cielito C Reyes-Gibby

    Full Text Available Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA, a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive and thymine degradation pathways (p = 1.06-08 were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis. The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67. In conclusion, gene network analysis identified novel molecules and

  2. Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

    Science.gov (United States)

    Reyes-Gibby, Cielito C; Melkonian, Stephanie C; Wang, Jian; Yu, Robert K; Shelburne, Samuel A; Lu, Charles; Gunn, Gary Brandon; Chambers, Mark S; Hanna, Ehab Y; Yeung, Sai-Ching J; Shete, Sanjay

    2017-01-01

    Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA), a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive) and thymine degradation pathways (p = 1.06-08) were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis). The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67). In conclusion, gene network analysis identified novel molecules and biological

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

    Science.gov (United States)

    Zhang, Qiang; Andersen, Melvin E

    2007-03-02

    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, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on

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

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2007-03-01

    Full Text Available 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, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear depends on changes in the specific values of local response coefficients (gains distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear

  5. The conscious of Nightmares in ancient China

    OpenAIRE

    西林, 眞紀子

    2006-01-01

    The analaysis concerns Nightmares in ancient China. People in ancient China were very afraid of Nightmares. Nightmares are described in the『春秋左氏傳』etc. The exocis Nightmares is described in the『周禮』. The ceremony "難" of exocis Nightmares in the『禮記』. In the characters Meng (夢) had the conscious of Nightmares in ancient China. The analaysis is about the characters 'Meng', about the characters of the relationship 'Meng'

  6. Unriddling of ancient-medieval culture by PIXE

    International Nuclear Information System (INIS)

    Uda, M.

    1997-01-01

    Some examples are given for unriddling of ancient-medieval culture by PIXE. Effectiveness of PIXE to analyze art and archaeological objects is also explained. Objects employed here are 1) red, yellow, blue and white pigments painted on sun-dried bricks excavated in Egypt, 2) ancient glass beads used in the Near East, 3) South American mummy hair, 4) ancient slag excavated from Kansai-district, Japan 5) ink used by Galileo Galilei and 6) Renaissance style enameled gold jewelry. (author)

  7. Carnegie Mellon's STUDIO for Creative Inquiry [and] The Interdisciplinary Teaching Network (ITeN) [and] Interactive Fiction [and] The Networked Virtual Art Museum.

    Science.gov (United States)

    Holden, Lynn; And Others

    1992-01-01

    Explains the STUDIO for Creative Inquiry, an interdisciplinary center at Carnegie Mellon University that supports experimental activities in the arts, and its Interdisciplinary Teaching Network. Three STUDIO projects are described: the Ancient Egypt Prototype application of the network; an interactive fiction system based on artificial…

  8. Algebraic model checking for Boolean gene regulatory networks.

    Science.gov (United States)

    Tran, Quoc-Nam

    2011-01-01

    We present a computational method in which modular and Groebner bases (GB) computation in Boolean rings are used for solving problems in Boolean gene regulatory networks (BN). In contrast to other known algebraic approaches, the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulting in a significant improvement in running time and memory space consumption. We also show how calculation in temporal logic for model checking can be done by means of our direct and efficient Groebner basis computation in Boolean rings. We present our experimental results in finding attractors and control strategies of Boolean networks to illustrate our theoretical arguments. The results are promising. Our algebraic approach is more efficient than the state-of-the-art model checker NuSMV on BNs. More importantly, our approach finds all solutions for the BN problems.

  9. Gene Acquisition Convergence between Entomopoxviruses and Baculoviruses

    Directory of Open Access Journals (Sweden)

    Julien Thézé

    2015-04-01

    Full Text Available Organisms from diverse phylogenetic origins can thrive within the same ecological niches. They might be induced to evolve convergent adaptations in response to a similar landscape of selective pressures. Their genomes should bear the signature of this process. The study of unrelated virus lineages infecting the same host panels guarantees a clear identification of phyletically independent convergent adaptation. Here, we investigate the evolutionary history of genes in the accessory genome shared by unrelated insect large dsDNA viruses: the entomopoxviruses (EPVs, Poxviridae and the baculoviruses (BVs. EPVs and BVs have overlapping ecological niches and have independently evolved similar infection processes. They are, in theory, subjected to the same selective pressures from their host’s immune responses. Their accessory genomes might, therefore, bear analogous genomic signatures of convergent adaption and could point out key genomic mechanisms of adaptation hitherto undetected in viruses. We uncovered 32 homologous, yet independent acquisitions of genes originating from insect hosts, different eukaryotes, bacteria and viruses. We showed different evolutionary levels of gene acquisition convergence in these viruses, underlining a continuous evolutionary process. We found both recent and ancient gene acquisitions possibly involved to the adaptation to both specific and distantly related hosts. Multidirectional and multipartite gene exchange networks appear to constantly drive exogenous gene assimilations, bringing key adaptive innovations and shaping the life histories of large DNA viruses. This evolutionary process might lead to genome level adaptive convergence.

  10. Modularity of gene-regulatory networks revealed in sea-star development

    Directory of Open Access Journals (Sweden)

    Degnan Bernard M

    2011-01-01

    Full Text Available Abstract Evidence that conserved developmental gene-regulatory networks can change as a unit during deutersostome evolution emerges from a study published in BMC Biology. This shows that genes consistently expressed in anterior brain patterning in hemichordates and chordates are expressed in a similar spatial pattern in another deuterostome, an asteroid echinoderm (sea star, but in a completely different developmental context (the animal-vegetal axis. This observation has implications for hypotheses on the type of development present in the deuterostome common ancestor. See research article: http://www.biomedcentral.com/1741-7007/8/143/abstract

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

    Science.gov (United States)

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

    2016-07-27

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

  12. Ancestral regulatory circuits governing ectoderm patterning downstream of Nodal and BMP2/4 revealed by gene regulatory network analysis in an echinoderm.

    Directory of Open Access Journals (Sweden)

    Alexandra Saudemont

    2010-12-01

    Full Text Available Echinoderms, which are phylogenetically related to vertebrates and produce large numbers of transparent embryos that can be experimentally manipulated, offer many advantages for the analysis of the gene regulatory networks (GRN regulating germ layer formation. During development of the sea urchin embryo, the ectoderm is the source of signals that pattern all three germ layers along the dorsal-ventral axis. How this signaling center controls patterning and morphogenesis of the embryo is not understood. Here, we report a large-scale analysis of the GRN deployed in response to the activity of this signaling center in the embryos of the Mediterranean sea urchin Paracentrotus lividus, in which studies with high spatial resolution are possible. By using a combination of in situ hybridization screening, overexpression of mRNA, recombinant ligand treatments, and morpholino-based loss-of-function studies, we identified a cohort of transcription factors and signaling molecules expressed in the ventral ectoderm, dorsal ectoderm, and interposed neurogenic ("ciliary band" region in response to the known key signaling molecules Nodal and BMP2/4 and defined the epistatic relationships between the most important genes. The resultant GRN showed a number of striking features. First, Nodal was found to be essential for the expression of all ventral and dorsal marker genes, and BMP2/4 for all dorsal genes. Second, goosecoid was identified as a central player in a regulatory sub-circuit controlling mouth formation, while tbx2/3 emerged as a critical factor for differentiation of the dorsal ectoderm. Finally, and unexpectedly, a neurogenic ectoderm regulatory circuit characterized by expression of "ciliary band" genes was triggered in the absence of TGF beta signaling. We propose a novel model for ectoderm regionalization, in which neural ectoderm is the default fate in the absence of TGF beta signaling, and suggest that the stomodeal and neural subcircuits that we

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

    Science.gov (United States)

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

    2016-01-01

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

  14. Reconstructing ancient genomes and epigenomes

    DEFF Research Database (Denmark)

    Orlando, Ludovic Antoine Alexandre; Gilbert, M. Thomas P.; Willerslev, Eske

    2015-01-01

    DNA studies have now progressed to whole-genome sequencing for an increasing number of ancient individuals and extinct species, as well as to epigenomic characterization. Such advances have enabled the sequencing of specimens of up to 1 million years old, which, owing to their extensive DNA damage...... and contamination, were previously not amenable to genetic analyses. In this Review, we discuss these varied technical challenges and solutions for sequencing ancient genomes and epigenomes....

  15. Signal Network Analysis of Plant Genes Responding to Ionizing Radiation

    International Nuclear Information System (INIS)

    Kim, Dong Sub; Kim, Jinbaek; Kim, Sang Hoon

    2012-12-01

    In this project, we irradiated Arabidopsis plants with various doses of gamma-rays at the vegetative and reproductive stages to assess their radiation sensitivity. After the gene expression profiles and an analysis of the antioxidant response, we selected several Arabidopsis genes for uses of 'Radio marker genes (RMG)' and conducted over-expression and knock-down experiments to confirm the radio sensitivity. Based on these results, we applied two patents for the detection of two RMG (At3g28210 and At4g37990) and development of transgenic plants. Also, we developed a Genechip for use of high-throughput screening of Arabidopsis genes responding only to ionizing radiation and identified RMG to detect radiation leaks. Based on these results, we applied two patents associated with the use of Genechip for different types of radiation and different growth stages. Also, we conducted co-expression network study of specific expressed probes against gamma-ray stress and identified expressed patterns of duplicated genes formed by whole/500kb segmental genome duplication

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

    Directory of Open Access Journals (Sweden)

    Guo Xiuyun

    2011-09-01

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

  17. Radioresistance related genes screened by protein-protein interaction network analysis in nasopharyngeal carcinoma

    International Nuclear Information System (INIS)

    Zhu Xiaodong; Guo Ya; Qu Song; Li Ling; Huang Shiting; Li Danrong; Zhang Wei

    2012-01-01

    Objective: To discover radioresistance associated molecular biomarkers and its mechanism in nasopharyngeal carcinoma by protein-protein interaction network analysis. Methods: Whole genome expression microarray was applied to screen out differentially expressed genes in two cell lines CNE-2R and CNE-2 with different radiosensitivity. Four differentially expressed genes were randomly selected for further verification by the semi-quantitative RT-PCR analysis with self-designed primers. The common differentially expressed genes from two experiments were analyzed with the SNOW online database in order to find out the central node related to the biomarkers of nasopharyngeal carcinoma radioresistance. The expression of STAT1 in CNE-2R and CNE-2 cells was measured by Western blot. Results: Compared with CNE-2 cells, 374 genes in CNE-2R cells were differentially expressed while 197 genes showed significant differences. Four randomly selected differentially expressed genes were verified by RT-PCR and had same change trend in consistent with the results of chip assay. Analysis with the SNOW database demonstrated that those 197 genes could form a complicated interaction network where STAT1 and JUN might be two key nodes. Indeed, the STAT1-α expression in CNE-2R was higher than that in CNE-2 (t=4.96, P<0.05). Conclusions: The key nodes of STAT1 and JUN may be the molecular biomarkers leading to radioresistance in nasopharyngeal carcinoma, and STAT1-α might have close relationship with radioresistance. (authors)

  18. Identifying miRNA and gene modules of colon cancer associated with pathological stage by weighted gene co-expression network analysis

    Directory of Open Access Journals (Sweden)

    Zhou X

    2018-05-01

    Full Text Available Xian-guo Zhou,1,2,* Xiao-liang Huang,1,2,* Si-yuan Liang,1–3 Shao-mei Tang,1,2 Si-kao Wu,1,2 Tong-tong Huang,1,2 Zeng-nan Mo,1,2,4 Qiu-yan Wang1,2,5 1Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 2Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 3Department of Colorectal Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 4Department of Urology and Nephrology, The First Affiliated Hospital of Guangxi, Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 5Guangxi Colleges and Universities Key Laboratory of Biological Molecular Medicine Research, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China *These authors contributed equally to this work Introduction: Colorectal cancer (CRC is the fourth most common cause of cancer-related mortality worldwide. The tumor, node, metastasis (TNM stage remains the standard for CRC prognostication. Identification of meaningful microRNA (miRNA and gene modules or representative biomarkers related to the pathological stage of colon cancer helps to predict prognosis and reveal the mechanisms behind cancer progression.Materials and methods: We applied a systems biology approach by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA to detect the pathological stage-related miRNA and gene modules and construct a miRNA–gene network. The Cancer Genome Atlas (TCGA colon adenocarcinoma (CAC RNA-sequencing data and miRNA-sequencing data were subjected to WGCNA analysis, and the GSE29623, GSE35602 and GSE39396 were utilized to validate and

  19. [Ancient Greek in modern language of medicine].

    Science.gov (United States)

    Marković, Vera

    2007-01-01

    In order to standardize language of medicine, it is essential to have a good command of ancient Greek and Latin. We cannot deny a huge impact of ancient Greek medicine on medical terminology. Compounds of Greek origin related to terms for organs, illnesses, inflammations, surgical procedures etc. have been listed as examples. They contain Greek prefixes and suffixes transcribed into Latin and they have been analysed. It may be concluded that the modern language of medicine basically represents the ancient Greek language transcribed into Latin.

  20. Vascular Gene Expression: A Hypothesis

    Directory of Open Access Journals (Sweden)

    Angélica Concepción eMartínez-Navarro

    2013-07-01

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

  1. Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network.

    Science.gov (United States)

    Liu, Chenglin; Cui, Peng; Huang, Tao

    2017-01-01

    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 are 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 to the cell cycle mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  2. Sieve-based relation extraction of gene regulatory networks from biological literature.

    Science.gov (United States)

    Žitnik, Slavko; Žitnik, Marinka; Zupan, Blaž; Bajec, Marko

    2015-01-01

    Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming

  3. Deep sequencing of RNA from ancient maize kernels

    DEFF Research Database (Denmark)

    Fordyce, Sarah Louise; Avila Arcos, Maria del Carmen; Rasmussen, Morten

    2013-01-01

    The characterization of biomolecules from ancient samples can shed otherwise unobtainable insights into the past. Despite the fundamental role of transcriptomal change in evolution, the potential of ancient RNA remains unexploited - perhaps due to dogma associated with the fragility of RNA. We hy...... maize kernels. The results suggest that ancient seed transcriptomics may offer a powerful new tool with which to study plant domestication....

  4. The provenance investigation on ancient chinese Ru porcelains by NAA

    International Nuclear Information System (INIS)

    Gao Zhengyao; Wang Jie; Chen Songhua

    1997-01-01

    The 28 samples of glazes and bodies of ancient Chinese Ru porcelains are analyzed by neutron activation. The 36 element contents in each sample are determined. The neutron activation analysis (NAA) data are analyzed by fuzzy cluster. The trend cluster diagram is obtained. The result shows that the ancient Chinese Ru porcelains were most probably from the same raw material source though they were from different time, fired in different kilns and in different colors. The near provenance relation between ancient Jun porcelain and ancient Ru porcelain is preliminarily analyzed. The two modern Ru porcelains approximate to ancient Ru porcelains, one becomes estranged from ancient Ru porcelains. Jingdezhen porcelain is unconcerned with Ru porcelains

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

    Science.gov (United States)

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

    2016-08-18

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

  6. Ancient Egyptian Medicine: A Systematic Review

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    Samuel Adu-Gyamfi

    2015-12-01

    Full Text Available Our present day knowledge in the area of medicine in Ancient Egypt has been severally sourced from medical papyri several of which have been deduced and analyzed by different scholars. For educational purposes it is always imperative to consult different literature or sources in the teaching of ancient Egypt and medicine in particular. To avoid subjectivity the author has found the need to re-engage the efforts made by several scholars in adducing evidences from medical papyri. In the quest to re-engage the efforts of earlier writers and commentaries on the medical papyri, we are afforded the opportunity to be informed about the need to ask further questions to enable us to construct or reconstruct both past and modern views on ancient Egyptian medical knowledge. It is this vocation the author sought to pursue in the interim, through a preliminary review, to highlight, comment and reinvigorate in the reader or researcher the need for a continuous engagement of some pertinent documentary sources on Ancient Egyptian medical knowledge for educational and research purposes. The study is based on qualitative review of published literature. The selection of those articles as sources was based on the focus of the review, in order to purposively select and comment on articles that were published based either on information from a medical papyrus or focused on medical specialization among the ancient Egyptians as well as ancient Egyptian knowledge on diseases and medicine. It was found that the Egyptians developed relatively sophisticated medical practices covering significant medical fields such as herbal medicine, gynecology and obstetrics, anatomy and physiology, mummification and even the preliminary form of surgery. These practices, perhaps, were developed as remedies for the prevailing diseases and the accidents that might have occurred during the construction of their giant pyramids. It must be stated that they were not without flaws. Also, the

  7. Protein-Protein Interaction Network and Gene Ontology

    Science.gov (United States)

    Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

    Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

  8. Statistical identification of gene association by CID in application of constructing ER regulatory network

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    Lien Huang-Chun

    2009-03-01

    Full Text Available Abstract Background A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID, is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs (X and their downstream genes (Y based on clinical data. More specifically, we use estrogen receptor α (ERα as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A. Results The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC, Student's t-test (STT, coefficient of determination (CoD, and mutual information (MI. When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y against a discrete variable (X, it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. Conclusion CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the

  9. Ancient Greek in modern language of medicine

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    Marković Vera

    2007-01-01

    Full Text Available In order to standardize language of medicine, it is essential to have a good command of ancient Greek and Latin. We cannot deny a huge impact of ancient Greek medicine on medical terminology. Compounds of Greek origin related to terms for organs, illnesses, inflammations, surgical procedures etc. have been listed as examples. They contain Greek prefixes and suffixes transcribed into Latin and they have been analyzed. It may be concluded that the modern language of medicine basically represents the ancient Greek language transcribed into Latin.

  10. The characterization of Helicobacter pylori DNA associated with ancient human remains recovered from a Canadian glacier.

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    Treena Swanston

    2011-02-01

    Full Text Available Helicobacter pylori is a gram-negative bacterium that colonizes the stomach of nearly half of the world's population. Genotypic characterization of H. pylori strains involves the analysis of virulence-associated genes, such as vacA, which has multiple alleles. Previous phylogenetic analyses have revealed a connection between modern H. pylori strains and the movement of ancient human populations. In this study, H. pylori DNA was amplified from the stomach tissue of the Kwäday Dän Ts'ìnchi individual. This ancient individual was recovered from the Samuel Glacier in Tatshenshini-Alsek Park, British Columbia, Canada on the traditional territory of the Champagne and Aishihik First Nations and radiocarbon dated to a timeframe of approximately AD 1670 to 1850. This is the first ancient H. pylori strain to be characterized with vacA sequence data. The Tatshenshini H. pylori strain has a potential hybrid vacA m2a/m1d middle (m region allele and a vacA s2 signal (s region allele. A vacA s2 allele is more commonly identified with Western strains, and this suggests that European strains were present in northwestern Canada during the ancient individual's time. Phylogenetic analysis indicated that the vacA m1d region of the ancient strain clusters with previously published novel Native American strains that are closely related to Asian strains. This indicates a past connection between the Kwäday Dän Ts'ìnchi individual and the ancestors who arrived in the New World thousands of years ago.

  11. Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach

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    Fei Long

    2015-01-01

    Full Text Available Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC and non-small-cell lung cancer (NSCLC that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.

  12. The Toll-like receptor gene family is integrated into human DNA damage and p53 networks.

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    Daniel Menendez

    2011-03-01

    Full Text Available In recent years the functions that the p53 tumor suppressor plays in human biology have been greatly extended beyond "guardian of the genome." Our studies of promoter response element sequences targeted by the p53 master regulatory transcription factor suggest a general role for this DNA damage and stress-responsive regulator in the control of human Toll-like receptor (TLR gene expression. The TLR gene family mediates innate immunity to a wide variety of pathogenic threats through recognition of conserved pathogen-associated molecular motifs. Using primary human immune cells, we have examined expression of the entire TLR gene family following exposure to anti-cancer agents that induce the p53 network. Expression of all TLR genes, TLR1 to TLR10, in blood lymphocytes and alveolar macrophages from healthy volunteers can be induced by DNA metabolic stressors. However, there is considerable inter-individual variability. Most of the TLR genes respond to p53 via canonical as well as noncanonical promoter binding sites. Importantly, the integration of the TLR gene family into the p53 network is unique to primates, a recurrent theme raised for other gene families in our previous studies. Furthermore, a polymorphism in a TLR8 response element provides the first human example of a p53 target sequence specifically responsible for endogenous gene induction. These findings-demonstrating that the human innate immune system, including downstream induction of cytokines, can be modulated by DNA metabolic stress-have many implications for health and disease, as well as for understanding the evolution of damage and p53 responsive networks.

  13. Suicide in ancient Greece.

    Science.gov (United States)

    Laios, K; Tsoukalas, G; Kontaxaki, M-I; Karamanou, M; Androutsos, G

    2014-01-01

    The theme of suicide appears several times in ancient Greek literature. However, each such reference acquires special significance depending on the field from which it originates. Most of the information found in mythology, but the suicide in a mythological tale, although in terms of motivation and mental situation of heroes may be in imitation of similar incidents of real life, in fact is linked with the principles of the ancient Greek religion. In ancient drama and mainly in tragedies suicide conduces to the tragic hypostasis of the heroes and to the evolution of the plot and also is a tool in order to be presented the ideas of poets for the relations of the gods, the relation among gods and men and the relation among the men. In ancient Greek philosophy there were the deniers of suicide, who were more concerned about the impact of suicide on society and also these who accepted it, recognizing the right of the individual to put an end to his life, in order to avoid personal misfortunes. Real suicides will be found mostly from historical sources, but most of them concern leading figures of the ancient world. Closer to the problem of suicide in the everyday life of antiquity are ancient Greek medicines, who studied the phenomenon more general without references to specific incidents. Doctors did not approve in principal the suicide and dealt with it as insane behavior in the development of the mental diseases, of melancholia and mania. They considered that the discrepancy of humors in the organ of logic in the human body will cause malfunction, which will lead to the absurdity and consequently to suicide, either due to excessive concentration of black bile in melancholia or due to yellow bile in mania. They believed that greater risk to commit suicide had women, young people and the elderly. As therapy they used the drugs of their time with the intention to induce calm and repression in the ill person, therefore they mainly used mandragora. In general, we would say

  14. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

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    Hyun-Seob Song

    2015-03-01

    Full Text Available Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

  15. The properties of genome conformation and spatial gene interaction and regulation networks of normal and malignant human cell types.

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    Zheng Wang

    Full Text Available The spatial conformation of a genome plays an important role in the long-range regulation of genome-wide gene expression and methylation, but has not been extensively studied due to lack of genome conformation data. The recently developed chromosome conformation capturing techniques such as the Hi-C method empowered by next generation sequencing can generate unbiased, large-scale, high-resolution chromosomal interaction (contact data, providing an unprecedented opportunity to investigate the spatial structure of a genome and its applications in gene regulation, genomics, epigenetics, and cell biology. In this work, we conducted a comprehensive, large-scale computational analysis of this new stream of genome conformation data generated for three different human leukemia cells or cell lines by the Hi-C technique. We developed and applied a set of bioinformatics methods to reliably generate spatial chromosomal contacts from high-throughput sequencing data and to effectively use them to study the properties of the genome structures in one-dimension (1D and two-dimension (2D. Our analysis demonstrates that Hi-C data can be effectively applied to study tissue-specific genome conformation, chromosome-chromosome interaction, chromosomal translocations, and spatial gene-gene interaction and regulation in a three-dimensional genome of primary tumor cells. Particularly, for the first time, we constructed genome-scale spatial gene-gene interaction network, transcription factor binding site (TFBS - TFBS interaction network, and TFBS-gene interaction network from chromosomal contact information. Remarkably, all these networks possess the properties of scale-free modular networks.

  16. Exploring Ancient Skies An Encyclopedic Survey of Archaeoastronomy

    CERN Document Server

    Kelley, David H

    2005-01-01

    Exploring Ancient Skies brings together the methods of archaeology and the insights of modern astronomy to explore the science of astronomy as it was practiced in various cultures prior to the invention of the telescope. The book reviews an enormous and growing body of literature on the cultures of the ancient Mediterranean, the Far East, and the New World (particularly Mesoamerica), putting the ancient astronomical materials into their archaeological and cultural contexts. The authors begin with an overview of the field and proceed to essential aspects of naked-eye astronomy, followed by an examination of specific cultures. The book concludes by taking into account the purposes of ancient astronomy: astrology, navigation, calendar regulation, and (not least) the understanding of our place and role in the universe. Skies are recreated to display critical events as they would have appeared to ancient observers - events such as the supernova of 1054, the 'lion horoscope' or the 'Star of Bethlehem.' Exploring An...

  17. Past climate change on Sky Islands drives novelty in a core developmental gene network and its phenotype.

    Science.gov (United States)

    Favé, Marie-Julie; Johnson, Robert A; Cover, Stefan; Handschuh, Stephan; Metscher, Brian D; Müller, Gerd B; Gopalan, Shyamalika; Abouheif, Ehab

    2015-09-04

    A fundamental and enduring problem in evolutionary biology is to understand how populations differentiate in the wild, yet little is known about what role organismal development plays in this process. Organismal development integrates environmental inputs with the action of gene regulatory networks to generate the phenotype. Core developmental gene networks have been highly conserved for millions of years across all animals, and therefore, organismal development may bias variation available for selection to work on. Biased variation may facilitate repeatable phenotypic responses when exposed to similar environmental inputs and ecological changes. To gain a more complete understanding of population differentiation in the wild, we integrated evolutionary developmental biology with population genetics, morphology, paleoecology and ecology. This integration was made possible by studying how populations of the ant species Monomorium emersoni respond to climatic and ecological changes across five 'Sky Islands' in Arizona, which are mountain ranges separated by vast 'seas' of desert. Sky Islands represent a replicated natural experiment allowing us to determine how repeatable is the response of M. emersoni populations to climate and ecological changes at the phenotypic, developmental, and gene network levels. We show that a core developmental gene network and its phenotype has kept pace with ecological and climate change on each Sky Island over the last ~90,000 years before present (BP). This response has produced two types of evolutionary change within an ant species: one type is unpredictable and contingent on the pattern of isolation of Sky lsland populations by climate warming, resulting in slight changes in gene expression, organ growth, and morphology. The other type is predictable and deterministic, resulting in the repeated evolution of a novel wingless queen phenotype and its underlying gene network in response to habitat changes induced by climate warming. Our

  18. Beyond frontiers: Ancient Rome and the Eurasian trade networks

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    Marco Galli

    2017-01-01

    Full Text Available During the second half of the 19th century, the Roman Empire was already considered one of the key players inside the Eurasian networks. This research focuses on four relevant points. From a historiographical perspective, the reconstruction of the trading routes represented a central theme in the history of the relationship between the Roman Empire and the Far East. Imagining a plurality of itineraries and combinations of overland and sea routes, it is possible to reconstruct a complex reality in which the Eurasian networks during the Early Roman Empire developed. As far as economics is concerned, new documentation demonstrates the wide range and the extraordinary impact of the Eastern products on Roman markets. A final focus on the process of Chinese silk unravelling and reweaving provides an important clue on how complex and absolutely not mono-directional were the interactions and the exchanges in the Eurasian networks during the first centuries of the Roman Empire.

  19. The Integration of Epistasis Network and Functional Interactions in a GWAS Implicates RXR Pathway Genes in the Immune Response to Smallpox Vaccine.

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    Brett A McKinney

    Full Text Available Although many diseases and traits show large heritability, few genetic variants have been found to strongly separate phenotype groups by genotype. Complex regulatory networks of variants and expression of multiple genes lead to small individual-variant effects and difficulty replicating the effect of any single variant in an affected pathway. Interaction network modeling of GWAS identifies effects ignored by univariate models, but population differences may still cause specific genes to not replicate. Integrative network models may help detect indirect effects of variants in the underlying biological pathway. In this study, we used gene-level functional interaction information from the Integrative Multi-species Prediction (IMP tool to reveal important genes associated with a complex phenotype through evidence from epistasis networks and pathway enrichment. We test this method for augmenting variant-based network analyses with functional interactions by applying it to a smallpox vaccine immune response GWAS. The integrative analysis spotlights the role of genes related to retinoid X receptor alpha (RXRA, which has been implicated in a previous epistasis network analysis of smallpox vaccine.

  20. Twins in Ancient Greece: a synopsis.

    Science.gov (United States)

    Malamitsi-Puchner, Ariadne

    2016-01-01

    This brief outline associates twins with several aspects of life in Ancient Greece. In Greek mythology twins caused ambivalent reactions and were believed to have ambivalent feelings for each other. Very often, they were viewed as the representatives of the dualistic nature of the universe. Heteropaternal superfecundation, which dominates in ancient myths, explains on one hand, the god-like qualities and, on the other hand, the mortal nature of many twins. An assumption is presented that legends referring to twins might reflect the territorial expansions of Ancient Greeks in Northern Mediterranean, around the Black Sea, in Asia Minor, as well as North East Africa. In conclusion, in Greek antiquity, twins have been used as transitional figures between myth and reality.

  1. Intervention in gene regulatory networks with maximal phenotype alteration.

    Science.gov (United States)

    Yousefi, Mohammadmahdi R; Dougherty, Edward R

    2013-07-15

    A basic issue for translational genomics is to model gene interaction via gene regulatory networks (GRNs) and thereby provide an informatics environment to study the effects of intervention (say, via drugs) and to derive effective intervention strategies. Taking the view that the phenotype is characterized by the long-run behavior (steady-state distribution) of the network, we desire interventions to optimally move the probability mass from undesirable to desirable states Heretofore, two external control approaches have been taken to shift the steady-state mass of a GRN: (i) use a user-defined cost function for which desirable shift of the steady-state mass is a by-product and (ii) use heuristics to design a greedy algorithm. Neither approach provides an optimal control policy relative to long-run behavior. We use a linear programming approach to optimally shift the steady-state mass from undesirable to desirable states, i.e. optimization is directly based on the amount of shift and therefore must outperform previously proposed methods. Moreover, the same basic linear programming structure is used for both unconstrained and constrained optimization, where in the latter case, constraints on the optimization limit the amount of mass that may be shifted to 'ambiguous' states, these being states that are not directly undesirable relative to the pathology of interest but which bear some perceived risk. We apply the method to probabilistic Boolean networks, but the theory applies to any Markovian GRN. Supplementary materials, including the simulation results, MATLAB source code and description of suboptimal methods are available at http://gsp.tamu.edu/Publications/supplementary/yousefi13b. edward@ece.tamu.edu Supplementary data are available at Bioinformatics online.

  2. Deep convolutional neural networks for annotating gene expression patterns in the mouse brain.

    Science.gov (United States)

    Zeng, Tao; Li, Rongjian; Mukkamala, Ravi; Ye, Jieping; Ji, Shuiwang

    2015-05-07

    Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach. Deep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.

  3. A co-expression gene network associated with developmental regulation of apple fruit acidity.

    Science.gov (United States)

    Bai, Yang; Dougherty, Laura; Cheng, Lailiang; Xu, Kenong

    2015-08-01

    Apple fruit acidity, which affects the fruit's overall taste and flavor to a large extent, is primarily determined by the concentration of malic acid. Previous studies demonstrated that the major QTL malic acid (Ma) on chromosome 16 is largely responsible for fruit acidity variations in apple. Recent advances suggested that a natural mutation that gives rise to a premature stop codon in one of the two aluminum-activated malate transporter (ALMT)-like genes (called Ma1) is the genetic causal element underlying Ma. However, the natural mutation does not explain the developmental changes of fruit malate levels in a given genotype. Using RNA-seq data from the fruit of 'Golden Delicious' taken at 14 developmental stages from 1 week after full-bloom (WAF01) to harvest (WAF20), we characterized their transcriptomes in groups of high (12.2 ± 1.6 mg/g fw, WAF03-WAF08), mid (7.4 ± 0.5 mg/g fw, WAF01-WAF02 and WAF10-WAF14) and low (5.4 ± 0.4 mg/g fw, WAF16-WAF20) malate concentrations. Detailed analyses showed that a set of 3,066 genes (including Ma1) were expressed not only differentially (P FDR < 0.05) between the high and low malate groups (or between the early and late developmental stages) but also in significant (P < 0.05) correlation with malate concentrations. The 3,066 genes fell in 648 MapMan (sub-) bins or functional classes, and 19 of them were significantly (P FDR < 0.05) co-enriched or co-suppressed in a malate dependent manner. Network inferring using the 363 genes encompassed in the 19 (sub-) bins, identified a major co-expression network of 239 genes. Since the 239 genes were also differentially expressed between the early (WAF03-WAF08) and late (WAF16-WAF20) developmental stages, the major network was considered to be associated with developmental regulation of apple fruit acidity in 'Golden Delicious'.

  4. Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

    International Nuclear Information System (INIS)

    Blenk, Steffen; Engelmann, Julia C; Pinkert, Stefan; Weniger, Markus; Schultz, Jörg; Rosenwald, Andreas; Müller-Hermelink, Hans K; Müller, Tobias; Dandekar, Thomas

    2008-01-01

    Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis. We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation. On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL. The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data

  5. The Ancient Kemetic Roots of Library and Information Science.

    Science.gov (United States)

    Zulu, Itibari M.

    This paper argues that the ancient people of Kemet (Egypt), "the black land," built and operated the first major libraries and institutions of higher education in the world. Topics of discussion include the Ancient Egyptians as an African people; a chronology of Ancient Kemet; literature in Kemet; a history of Egyptian Librarianship; the…

  6. Wall paintings facies and their possible genetic correlates in the ancient Pompeii: A bio-anthropologic message from the past?

    Science.gov (United States)

    Ponti, Giovanni; Manfredini, Marco; Ruini, Cristel

    2016-09-10

    The figurative arts and precisely the ancient Pompeian wall paintings portraits can provide an additional source of information in supplementing bio-anthropological studies. There are several genetic diseases with a wide spectrum of congenital bone stigmata in association to distinctive facial features. Gorlin-Goltz syndrome, also named nevoid basal cell carcinoma syndrome, is an autosomal dominant syndrome characterized by unusual skeletal changes, such as macrocephaly, facial asymmetry, hypertelorism, frontal and parietal bossing caused by germline mutations of the gene PTCH1. The Gorlin syndrome, clinically defined in 1963, existed during Dynastic Egyptian times, as revealed by a spectrum of skeletal findings compatible with the syndrome in mummies dating back to three thousand years ago and, most likely, in the ancient population of Pompeii. In the present research, we discuss the potential relationship between Pompeian wall paintings portrait and the cranio-metric bone changes revealed among the Pompeian skull collections assuming that the ancient portraits can constitute an important tool that should be strictly integrated with osteologic and biomolecular data in order to argue a syndromic diagnosis in ancient population. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

    Science.gov (United States)

    Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

  8. Robust Learning of High-dimensional Biological Networks with Bayesian Networks

    Science.gov (United States)

    Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

    Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

  9. Heart morphogenesis gene regulatory networks revealed by temporal expression analysis.

    Science.gov (United States)

    Hill, Jonathon T; Demarest, Bradley; Gorsi, Bushra; Smith, Megan; Yost, H Joseph

    2017-10-01

    During embryogenesis the heart forms as a linear tube that then undergoes multiple simultaneous morphogenetic events to obtain its mature shape. To understand the gene regulatory networks (GRNs) driving this phase of heart development, during which many congenital heart disease malformations likely arise, we conducted an RNA-seq timecourse in zebrafish from 30 hpf to 72 hpf and identified 5861 genes with altered expression. We clustered the genes by temporal expression pattern, identified transcription factor binding motifs enriched in each cluster, and generated a model GRN for the major gene batteries in heart morphogenesis. This approach predicted hundreds of regulatory interactions and found batteries enriched in specific cell and tissue types, indicating that the approach can be used to narrow the search for novel genetic markers and regulatory interactions. Subsequent analyses confirmed the GRN using two mutants, Tbx5 and nkx2-5 , and identified sets of duplicated zebrafish genes that do not show temporal subfunctionalization. This dataset provides an essential resource for future studies on the genetic/epigenetic pathways implicated in congenital heart defects and the mechanisms of cardiac transcriptional regulation. © 2017. Published by The Company of Biologists Ltd.

  10. Model checking optimal finite-horizon control for probabilistic gene regulatory networks.

    Science.gov (United States)

    Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan

    2017-12-14

    Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

  11. Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC mouse

    Directory of Open Access Journals (Sweden)

    Belknap John

    2010-10-01

    Full Text Available Abstract Background The current study focused on the extent genetic diversity within a species (Mus musculus affects gene co-expression network structure. To examine this issue, we have created a new mouse resource, a heterogeneous stock (HS formed from the same eight inbred strains that have been used to create the collaborative cross (CC. The eight inbred strains capture > 90% of the genetic diversity available within the species. For contrast with the HS-CC, a C57BL/6J (B6 × DBA/2J (D2 F2 intercross and the HS4, derived from crossing the B6, D2, BALB/cJ and LP/J strains, were used. Brain (striatum gene expression data were obtained using the Illumina Mouse WG 6.1 array, and the data sets were interrogated using a weighted gene co-expression network analysis (WGCNA. Results Genes reliably detected as expressed were similar in all three data sets as was the variability of expression. As measured by the WGCNA, the modular structure of the transcriptome networks was also preserved both on the basis of module assignment and from the perspective of the topological overlap maps. Details of the HS-CC gene modules are provided; essentially identical results were obtained for the HS4 and F2 modules. Gene ontology annotation of the modules revealed a significant overrepresentation in some modules for neuronal processes, e.g., central nervous system development. Integration with known protein-protein interactions data indicated significant enrichment among co-expressed genes. We also noted significant overlap with markers of central nervous system cell types (neurons, oligodendrocytes and astrocytes. Using the Allen Brain Atlas, we found evidence of spatial co-localization within the striatum for several modules. Finally, for some modules it was possible to detect an enrichment of transcription binding sites. The binding site for Wt1, which is associated with neurodegeneration, was the most significantly overrepresented. Conclusions Despite the marked

  12. Ancient and modern women in the "Woman's World".

    Science.gov (United States)

    Hurst, Isobel

    2009-01-01

    Under the editorship of Oscar Wilde, the "Woman's World" exemplified the popular dissemination of Hellenism through periodical culture. Addressing topics such as marriage, politics, and education in relation to the lives of women in the ancient world, the magazine offered an unfamiliar version of the reception of ancient Greece and Rome in late-Victorian aestheticism, one that was accessible to a wide readership because it was often based on images rather than texts. The classical scholar Jane Ellen Harrison addressed herself to this audience of women readers, discussing the similarities between modern collegiate life and the "woman's world" that enabled Sappho to flourish in ancient Greece. The "Woman's World" thus questions gender stereotypes by juxtaposing ancient and modern women, implicitly endorsing varied models of womanhood.

  13. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

    Science.gov (United States)

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

  14. Defining Astrology in Ancient and Classical History

    Science.gov (United States)

    Campion, Nicholas

    2015-05-01

    Astrology in the ancient and classical worlds can be partly defined by its role, and partly by the way in which scholars spoke about it. The problem is complicated by the fact that the word is Greek - it has no Babylonian or Egyptian cognates - and even in Greece it was interchangeable with its cousin, 'astronomy'. Yet if we are to understand the role of the sky, stars and planets in culture, debates about the nature of ancient astrology, by both classical and modern scholars, must be taken into account. This talk will consider modern scholars' typologies of ancient astrology, together with ancient debates from Cicero in the 1st century BC, to Plotinus (204/5-270 AD) and Isidore of Seville (c. 560 - 4 April 636). It will consider the implications for our understanding of astronomy's role in culture, and conclude that in the classical period astrology may be best understood through its diversity and allegiance to competing philosophies, and that its functions were therefore similarly varied.

  15. The fingerprint element analysis on provenance of ancient chinese Jun porcelain

    International Nuclear Information System (INIS)

    Gao Zhengyao; Chen Songhua; Wang Jie; Huang Zhongxiang; Jia Xiuqin; Han Song

    1997-01-01

    Forty-three samples of ancient Jun porcelains and so on were chosen. Neutron activation analysis (NAA) was used to measure the 36 trace elements in every sample. Seven elements were chosen as the 'fingerprint elements'. The provenance of the glazes and bodies of ancient Chinese Jun porcelain were investigated by the fingerprint element analysis method. The result shows that although the ancient Chinese Jun porcelain samples have been leapt over six hundred years, and glaze colors are utterly different and are from many different kilns, there are long term, stable and same mainly raw material source. The near provenance relation between ancient Jun porcelain and ancient Ru porcelain is preliminarily analyzed. A few modern Jun porcelains approximate from ancient Jun porcelains, the majority become estranged from ancient Jun porcelain

  16. NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

    Science.gov (United States)

    Bellot, Pau; Olsen, Catharina; Salembier, Philippe; Oliveras-Vergés, Albert; Meyer, Patrick E

    2015-09-29

    In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

  17. Protein Annotation from Protein Interaction Networks and Gene Ontology

    OpenAIRE

    Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.

    2011-01-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...

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

    Directory of Open Access Journals (Sweden)

    Deussing Jan M

    2010-10-01

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

  19. Microanalysis study on ancient Wiangkalong Pottery

    Science.gov (United States)

    Won-in, K.; Tancharakorn, S.; Dararutana, P.

    2017-09-01

    Wiangkalong is one of major ceramic production cities in northern of Thailand, once colonized by the ancient Lanna Kingdom (1290 A.D.). Ancient Wiangkalong potteries were produced with shapes and designs as similar as those of the Chinese Yuan and Ming Dynasties. Due to the complex nature of materials and objects, extremely sensitive, spatially resolved, multi-elemental and versatile analytical instruments using non-destructive and non-sampling methods to analyze theirs composition are need. In this work, micro-beam X-ray fluorescence spectroscopy based on synchrotron radiation was firstly used to characterize the elemental composition of the ancient Wiangkalong pottery. The results showed the variations in elemental composition of the body matrix, the glaze and the underglaze painting, such as K, Ca, Ti, V, Cr, Mn and Fe.

  20. Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks.

    Science.gov (United States)

    Morrison, Erin S; Badyaev, Alexander V

    2018-05-01

    Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.