WorldWideScience

Sample records for ancient gene network

  1. Recruitment and Remodeling of an ancient gene regulatory network during land plant evolution

    OpenAIRE

    Pires, Nuno D.; Yi, Keke; Breuninger, Holger; Catarino, Bruno; Menand, Benoît; Dolan, Liam

    2013-01-01

    The evolution of multicellular organisms was made possible by the evolution of underlying gene regulatory networks. In animals, the core of gene regulatory networks consists of kernels, stable subnetworks of transcription factors that are highly conserved in distantly related species. However, in plants it is not clear when and how kernels evolved. We show here that RSL (ROOT HAIR DEFECTIVE SIX-LIKE) transcription factors form an ancient land plant kernel controlling caulonema differentiation...

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

    Science.gov (United States)

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

    2016-06-01

    Hindbrain development is orchestrated by a vertebrate gene regulatory network that generates segmental patterning along the anterior-posterior axis via Hox genes. Here, we review analyses of vertebrate and invertebrate chordate models that inform upon the evolutionary origin and diversification of this network. Evidence from the sea lamprey reveals that the hindbrain regulatory network generates rhombomeric compartments with segmental Hox expression and an underlying Hox code. We infer that this basal feature was present in ancestral vertebrates and, as an evolutionarily constrained developmental state, is fundamentally important for patterning of the vertebrate hindbrain across diverse lineages. Despite the common ground plan, vertebrates exhibit neuroanatomical diversity in lineage-specific patterns, with different vertebrates revealing variations of Hox expression in the hindbrain that could underlie this diversification. Invertebrate chordates lack hindbrain segmentation but exhibit some conserved aspects of this network, with retinoic acid signaling playing a role in establishing nested domains of Hox expression. PMID:27027928

  3. Examining Ancient Inter-domain Horizontal Gene Transfer

    Directory of Open Access Journals (Sweden)

    Francisca C. Almeida

    2008-01-01

    Full Text Available Details of the genomic changes that occurred in the ancestors of Eukarya, Archaea and Bacteria are elusive. Ancient interdomain horizontal gene transfer (IDHGT amongst the ancestors of these three domains has been difficult to detect and analyze because of the extreme degree of divergence of genes in these three domains and because most evidence for such events are poorly supported. In addition, many researchers have suggested that the prevalence of IDHGT events early in the evolution of life would most likely obscure the patterns of divergence of major groups of organisms let alone allow the tracking of horizontal transfer at this level. In order to approach this problem, we mined the E. coli genome for genes with distinct paralogs. Using the 1,268 E. coli K-12 genes with 40% or higher similarity level to a paralog elsewhere in the E. coli genome we detected 95 genes found exclusively in Bacteria and Archaea and 86 genes found in Bacteria and Eukarya. These genes form the basis for our analysis of IDHGT. We also applied a newly developed statistical test (the node height test, to examine the robustness of these inferences and to corroborate the phylogenetically identifi ed cases of ancient IDHGT. Our results suggest that ancient inter domain HGT is restricted to special cases, mostly involving symbiosis in eukaryotes and specific adaptations in prokaryotes. Only three genes in the Bacteria + Eukarya class (Deoxyxylulose-5-phosphate synthase (DXPS, fructose 1,6-phosphate aldolase class II protein and glucosamine-6-phosphate deaminase and three genes–in the Bacteria + Archaea class (ABC-type FE3+ -siderophore transport system, ferrous iron transport protein B, and dipeptide transport protein showed evidence of ancient IDHGT. However, we conclude that robust estimates of IDHGT will be very difficult to obtain due to the methodological limitations and the extreme sequence saturation of the genes suspected of being involved in IDHGT.

  4. Characterization of an ancient lepidopteran lateral gene transfer.

    Directory of Open Access Journals (Sweden)

    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.

  5. Differentiation of the maize subgenomes by genome dominance and both ancient and ongoing gene loss

    OpenAIRE

    James C Schnable; Springer, Nathan M.; Freeling, Michael

    2011-01-01

    Ancient tetraploidies are found throughout the eukaryotes. After duplication, one copy of each duplicate gene pair tends to be lost (fractionate). For all studied tetraploidies, the loss of duplicated genes, known as homeologs, homoeologs, ohnologs, or syntenic paralogs, is uneven between duplicate regions. In maize, a species that experienced a tetraploidy 5–12 million years ago, we show that in addition to uneven ancient gene loss, the two complete genomes contained within maize are differe...

  6. Computation in gene networks

    Science.gov (United States)

    Ben-Hur, Asa; Siegelmann, Hava T.

    2004-03-01

    Genetic regulatory networks have the complex task of controlling all aspects of life. Using a model of gene expression by piecewise linear differential equations we show that this process can be considered as a process of computation. This is demonstrated by showing that this model can simulate memory bounded Turing machines. The simulation is robust with respect to perturbations of the system, an important property for both analog computers and biological systems. Robustness is achieved using a condition that ensures that the model equations, that are generally chaotic, follow a predictable dynamics.

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

    OpenAIRE

    Paul J. Mann; 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 characteristics of dissolved organic carbon from fluvial networks across the Kolyma River Basin (Siberia), and isotopic changes during bioincubation experiments. Microbial communities utilized ancient carbon (1...

  8. Clustering analysis of ancient celadon based on SOM neural network

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In the study, chemical compositions of 48 fragments of ancient ceramics excavated in 4 archaeological kiln sites which were located in 3 cities (Hangzhou, Cixi and Longquan in Zhejiang Province, China) have been examined by energy-dispersive X-ray fluorescence (EDXRF) technique. Then the method of SOM was introduced into the clustering analysis based on the major and minor element compositions of the bodies, the results manifested that 48 samples could be perfectly distributed into 3 locations, Hangzhou, Cixi and Longquan. Because the major and minor element compositions of two Royal Kilns were similar to each other, the classification accuracy over them was merely 76.92%. In view of this, the authors have made a SOM clustering analysis again based on the trace element compositions of the bodies, the classification accuracy rose to 84.61%. These results indicated that discrepancies in the trace element compositions of the bodies of the ancient ceramics excavated in two Royal Kiln sites were more distinct than those in the major and minor element compositions, which was in accordance with the fact. We argued that SOM could be employed in the clustering analysis of ancient ceramics.

  9. Ancient homeobox gene loss and the evolution of chordate brain and pharynx development : deductions from amphioxus gene expression

    OpenAIRE

    Butts, Thomas; Holland, Peter W. H.; Ferrier, David Ellard Keith

    2010-01-01

    Homeobox genes encode a large superclass of transcription factors with widespread roles in animal development. Within chordates there are over 100 homeobox genes in the invertebrate cephalochordate amphioxus and over 200 in humans. Set against this general trend of increasing gene number in vertebrate evolution, some ancient homeobox genes that were present in the last common ancestor of chordates have been lost from vertebrates. Here, we describe the embryonic expression of four amphioxus de...

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

    International Nuclear Information System (INIS)

    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)

  11. EGAN: exploratory gene association networks

    OpenAIRE

    Paquette, Jesse; Tokuyasu, Taku

    2009-01-01

    Summary: Exploratory Gene Association Networks (EGAN) is a Java desktop application that provides a point-and-click environment for contextual graph visualization of high-throughput assay results. By loading the entire network of genes, pathways, interactions, annotation terms and literature references directly into memory, EGAN allows a biologist to repeatedly query and interpret multiple experimental results without incurring additional delays for data download/integration. Other compelling...

  12. Logistic Growth for the Nuzi Cuneiform Tablets: Analyzing Family Networks in Ancient Mesopotamia

    OpenAIRE

    Ueda, Sumie; Makino, Kumi; Itoh, Yoshiaki; Tsuchiya, Takashi

    2013-01-01

    We reconstruct the year of publication of each cuneiform tablet of the Nuzi society in ancient Mesopotamia. The tablets, are on land transaction, marriage, loan, slavery contracts etc. The number of tablets seem to increase by logistic growth until saturation. It may show the dynamics of concentration of lands or other properties into few powerful families in a period of about twenty years. We reconstruct family trees and social networks of Nuzi and estimate the publication years of cuneiform...

  13. Maths Meets Myths: Network Investigations of Ancient Narratives

    CERN Document Server

    Kenna, R

    2015-01-01

    Three years ago, we initiated a programme of research in which ideas and tools from statistical physics and network theory were applied to the field of comparative mythology. The eclecticism of the work, together with the perspectives it delivered, led to widespread media coverage and academic discussion. Here we review some aspects of the project, contextualised with a brief history of the long relationship between science and the humanities. We focus in particular on an Irish epic, summarising some of the outcomes of our quantitative investigation. We also describe the emergence of a new sub-discipline and our hopes for its future.

  14. Maths Meets Myths: Network Investigations of Ancient Narratives

    Science.gov (United States)

    Kenna, Ralph; Mac Carron, Pádraig

    2016-02-01

    Three years ago, we initiated a programme of research in which ideas and tools from statistical physics and network theory were applied to the field of comparative mythology. The eclecticism of the work, together with the perspectives it delivered, led to widespread media coverage and academic discussion. Here we review some aspects of the project, contextualised with a brief history of the long relationship between science and the humanities. We focus in particular on an Irish epic, summarising some of the outcomes of our quantitative investigation. We also describe the emergence of a new sub-discipline and our hopes for its future.

  15. Logistic growth for the Nuzi cuneiform tablets: Analyzing family networks in ancient Mesopotamia

    Science.gov (United States)

    Ueda, Sumie; Makino, Kumi; Itoh, Yoshiaki; Tsuchiya, Takashi

    2015-03-01

    We reconstruct the published year of each cuneiform tablet of the Nuzi society in ancient Mesopotamia. The tablets are on land transaction, marriage, loan, slavery contracts, etc. The number of tablets seems to increase by logistic growth. It may show the dynamics of concentration of lands or other properties into few powerful families in a period of about sixty years and most of them are in about thirty years. We reconstruct family trees and social networks of Nuzi and estimate the published years of cuneiform tablets consistently with the trees and networks, formulating least squares problems with linear inequality constraints.

  16. Ancient eudicot hexaploidy meets ancestral eurosid gene order

    OpenAIRE

    Zheng, Chunfang; Chen, Eric; Albert, Victor A.; Lyons, Eric; Sankoff, David

    2013-01-01

    Background A hexaploidization event over 125 Mya underlies the evolutionary lineage of the majority of flowering plants, including very many species of agricultural importance. Half of these belong to the rosid subgrouping, containing severals whose genome sequences have been published. Although most duplicate and triplicate genes have been lost in all descendants, clear traces of the original chromosome triples can be discerned, their internal contiguity highly conserved in some genomes and ...

  17. Gene networks controlling petal organogenesis.

    Science.gov (United States)

    Huang, Tengbo; Irish, Vivian F

    2016-01-01

    One of the biggest unanswered questions in developmental biology is how growth is controlled. Petals are an excellent organ system for investigating growth control in plants: petals are dispensable, have a simple structure, and are largely refractory to environmental perturbations that can alter their size and shape. In recent studies, a number of genes controlling petal growth have been identified. The overall picture of how such genes function in petal organogenesis is beginning to be elucidated. This review will focus on studies using petals as a model system to explore the underlying gene networks that control organ initiation, growth, and final organ morphology. PMID:26428062

  18. Buffering in cyclic gene networks

    Science.gov (United States)

    Glyzin, S. D.; Kolesov, A. Yu.; Rozov, N. Kh.

    2016-06-01

    We consider cyclic chains of unidirectionally coupled delay differential-difference equations that are mathematical models of artificial oscillating gene networks. We establish that the buffering phenomenon is realized in these system for an appropriate choice of the parameters: any given finite number of stable periodic motions of a special type, the so-called traveling waves, coexist.

  19. Gene networks and liar paradoxes.

    Science.gov (United States)

    Isalan, Mark

    2009-10-01

    Network motifs are small patterns of connections, found over-represented in gene regulatory networks. An example is the negative feedback loop (e.g. factor A represses itself). This opposes its own state so that when 'on' it tends towards 'off' - and vice versa. Here, we argue that such self-opposition, if considered dimensionlessly, is analogous to the liar paradox: 'This statement is false'. When 'true' it implies 'false' - and vice versa. Such logical constructs have provided philosophical consternation for over 2000 years. Extending the analogy, other network topologies give strikingly varying outputs over different dimensions. For example, the motif 'A activates B and A. B inhibits A' can give switches or oscillators with time only, or can lead to Turing-type patterns with both space and time (spots, stripes or waves). It is argued here that the dimensionless form reduces to a variant of 'The following statement is true. The preceding statement is false'. Thus, merely having a static topological description of a gene network can lead to a liar paradox. Network diagrams are only snapshots of dynamic biological processes and apparent paradoxes can reveal important biological mechanisms that are far from paradoxical when considered explicitly in time and space. PMID:19722183

  20. The effects of subsampling gene trees on coalescent methods applied to ancient divergences.

    Science.gov (United States)

    Simmons, Mark P; Sloan, Daniel B; Gatesy, John

    2016-04-01

    Gene-tree-estimation error is a major concern for coalescent methods of phylogenetic inference. We sampled eight empirical studies of ancient lineages with diverse numbers of taxa and genes for which the original authors applied one or more coalescent methods. We found that the average pairwise congruence among gene trees varied greatly both between studies and also often within a study. We recommend that presenting plots of pairwise congruence among gene trees in a dataset be treated as a standard practice for empirical coalescent studies so that readers can readily assess the extent and distribution of incongruence among gene trees. ASTRAL-based coalescent analyses generally outperformed MP-EST and STAR with respect to both internal consistency (congruence between analyses of subsamples of genes with the complete dataset of all genes) and congruence with the concatenation-based topology. We evaluated the approach of subsampling gene trees that are, on average, more congruent with other gene trees as a method to reduce artifacts caused by gene-tree-estimation errors on coalescent analyses. We suggest that this method is well suited to testing whether gene-tree-estimation error is a primary cause of incongruence between concatenation- and coalescent-based results, to reconciling conflicting phylogenetic results based on different coalescent methods, and to identifying genes affected by artifacts that may then be targeted for reciprocal illumination. We provide scripts that automate the process of calculating pairwise gene-tree incongruence and subsampling trees while accounting for differential taxon sampling among genes. Finally, we assert that multiple tree-search replicates should be implemented as a standard practice for empirical coalescent studies that apply MP-EST. PMID:26768112

  1. Gene networks and haloperidol-induced catalepsy

    OpenAIRE

    Iancu, O. D.; Darakjian, P.; Malmanger, B.; Walter, N. A. R.; McWeeney, S.; Hitzemann, R

    2011-01-01

    The current study examined the changes in striatal gene network structure induced by short-term selective breeding from a heterogeneous stock for haloperidol response. Brain (striatum) gene expression data were obtained using the Illumina WG 8.2 array, and the datasets from responding and non-responding selected lines were independently interrogated using a weighted gene coexpression network analysis (WGCNA). We detected several gene modules (groups of coexpressed genes) in each dataset; the ...

  2. 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

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

    Directory of Open Access Journals (Sweden)

    Imke Schmitt

    Full Text Available BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS/SIGNIFICANCE: 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.

  4. Mutational Robustness of Gene Regulatory Networks

    OpenAIRE

    Dijk, van, G.; Mourik, van, J.A.; Ham, van, R.C.H.J.

    2012-01-01

    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....

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

    International Nuclear Information System (INIS)

    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

  6. Evolution of evolvability in gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Anton Crombach

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

  7. Incorporating existing network information into gene network inference.

    Directory of Open Access Journals (Sweden)

    Scott Christley

    Full Text Available One methodology that has met success to infer gene networks from gene expression data is based upon ordinary differential equations (ODE. However new types of data continue to be produced, so it is worthwhile to investigate how to integrate these new data types into the inference procedure. One such data is physical interactions between transcription factors and the genes they regulate as measured by ChIP-chip or ChIP-seq experiments. These interactions can be incorporated into the gene network inference procedure as a priori network information. In this article, we extend the ODE methodology into a general optimization framework that incorporates existing network information in combination with regularization parameters that encourage network sparsity. We provide theoretical results proving convergence of the estimator for our method and show the corresponding probabilistic interpretation also converges. We demonstrate our method on simulated network data and show that existing network information improves performance, overcomes the lack of observations, and performs well even when some of the existing network information is incorrect. We further apply our method to the core regulatory network of embryonic stem cells utilizing predicted interactions from two studies as existing network information. We show that including the prior network information constructs a more closely representative regulatory network versus when no information is provided.

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

    Directory of Open Access Journals (Sweden)

    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.

  9. Stabilizing gene regulatory networks through feedforward loops

    Science.gov (United States)

    Kadelka, C.; Murrugarra, D.; Laubenbacher, R.

    2013-06-01

    The global dynamics of gene regulatory networks are known to show robustness to perturbations in the form of intrinsic and extrinsic noise, as well as mutations of individual genes. One molecular mechanism underlying this robustness has been identified as the action of so-called microRNAs that operate via feedforward loops. We present results of a computational study, using the modeling framework of stochastic Boolean networks, which explores the role that such network motifs play in stabilizing global dynamics. The paper introduces a new measure for the stability of stochastic networks. The results show that certain types of feedforward loops do indeed buffer the network against stochastic effects.

  10. 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.

  11. Network of tRNA Gene Sequences

    Institute of Scientific and Technical Information of China (English)

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

    2008-01-01

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

  12. Sparsity in Model Gene Regulatory Networks

    International Nuclear Information System (INIS)

    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. Construction of gene regulatory networks using biclustering and bayesian networks

    OpenAIRE

    Alakwaa Fadhl M; Solouma Nahed H; Kadah Yasser M

    2011-01-01

    Abstract Background Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA mi...

  14. Circadian Gene Networks In Bone Regeneration

    OpenAIRE

    Hassan, Nathaniel

    2012-01-01

    BACKGROUND: Previous studies suggested that vitamin D played a significant role in bone regeneration, facilitating the establishment of implant osseointegration. A whole genome microarray study further suggested that the vitamin D axis might involve circadian rhythm gene expression in the bone peripheral tissue.OBJECTIVES: To identify key gene networks involved with vitamin D receptor in the bone regeneration process and to explore any correlation with circadian rhythm gene expression in bone...

  15. Ancient Genetic Signatures of Orang Asli Revealed by Killer Immunoglobulin-Like Receptor Gene Polymorphisms

    Science.gov (United States)

    NurWaliyuddin, Hanis Z. A.; Norazmi, Mohd N.; Edinur, Hisham A.; Chambers, Geoffrey K.; Panneerchelvam, Sundararajulu; Zafarina, Zainuddin

    2015-01-01

    The aboriginal populations of Peninsular Malaysia, also known as Orang Asli (OA), comprise three major groups; Semang, Senoi and Proto-Malays. Here, we analyzed for the first time KIR gene polymorphisms for 167 OA individuals, including those from four smallest OA subgroups (Che Wong, Orang Kanaq, Lanoh and Kensiu) using polymerase chain reaction-sequence specific primer (PCR-SSP) analyses. The observed distribution of KIR profiles of OA is heterogenous; Haplotype B is the most frequent in the Semang subgroups (especially Batek) while Haplotype A is the most common type in the Senoi. The Semang subgroups were clustered together with the Africans, Indians, Papuans and Australian Aborigines in a principal component analysis (PCA) plot and shared many common genotypes (AB6, BB71, BB73 and BB159) observed in these other populations. Given that these populations also display high frequencies of Haplotype B, it is interesting to speculate that Haplotype B may be generally more frequent in ancient populations. In contrast, the two Senoi subgroups, Che Wong and Semai are displaced toward Southeast Asian and African populations in the PCA scatter plot, respectively. Orang Kanaq, the smallest and the most endangered of all OA subgroups, has lost some degree of genetic variation, as shown by their relatively high frequency of the AB2 genotype (0.73) and a total absence of KIR2DL2 and KIR2DS2 genes. Orang Kanaq tradition that strictly prohibits intermarriage with outsiders seems to have posed a serious threat to their survival. This present survey is a demonstration of the value of KIR polymorphisms in elucidating genetic relationships among human populations. PMID:26565719

  16. Ancient Genetic Signatures of Orang Asli Revealed by Killer Immunoglobulin-Like Receptor Gene Polymorphisms.

    Science.gov (United States)

    NurWaliyuddin, Hanis Z A; Norazmi, Mohd N; Edinur, Hisham A; Chambers, Geoffrey K; Panneerchelvam, Sundararajulu; Zafarina, Zainuddin

    2015-01-01

    The aboriginal populations of Peninsular Malaysia, also known as Orang Asli (OA), comprise three major groups; Semang, Senoi and Proto-Malays. Here, we analyzed for the first time KIR gene polymorphisms for 167 OA individuals, including those from four smallest OA subgroups (Che Wong, Orang Kanaq, Lanoh and Kensiu) using polymerase chain reaction-sequence specific primer (PCR-SSP) analyses. The observed distribution of KIR profiles of OA is heterogenous; Haplotype B is the most frequent in the Semang subgroups (especially Batek) while Haplotype A is the most common type in the Senoi. The Semang subgroups were clustered together with the Africans, Indians, Papuans and Australian Aborigines in a principal component analysis (PCA) plot and shared many common genotypes (AB6, BB71, BB73 and BB159) observed in these other populations. Given that these populations also display high frequencies of Haplotype B, it is interesting to speculate that Haplotype B may be generally more frequent in ancient populations. In contrast, the two Senoi subgroups, Che Wong and Semai are displaced toward Southeast Asian and African populations in the PCA scatter plot, respectively. Orang Kanaq, the smallest and the most endangered of all OA subgroups, has lost some degree of genetic variation, as shown by their relatively high frequency of the AB2 genotype (0.73) and a total absence of KIR2DL2 and KIR2DS2 genes. Orang Kanaq tradition that strictly prohibits intermarriage with outsiders seems to have posed a serious threat to their survival. This present survey is a demonstration of the value of KIR polymorphisms in elucidating genetic relationships among human populations. PMID:26565719

  17. 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.

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

    Institute of Scientific and Technical Information of China (English)

    LIU Bei; MENG Fanjiang; LI Yong; LIU Liyan

    2008-01-01

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

  19. Mutated Genes in Schizophrenia Map to Brain Networks

    Science.gov (United States)

    ... 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal cortex area of the brain. ... of spontaneous mutations in genes that form a network in the front region of the brain. The ...

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-08-01

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

  2. Inferring gene regression networks with model trees

    Directory of Open Access Journals (Sweden)

    Aguilar-Ruiz Jesus S

    2010-10-01

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

  3. GeneNet: a database on structure and functional organisation of gene networks

    OpenAIRE

    Ananko, E A; Podkolodny, N. L.; Stepanenko, I. L.; Ignatieva, E. V.; Podkolodnaya, O. A.; Kolchanov, N. A.

    2002-01-01

    The GeneNet database is designed for accumulation of information on gene networks. Original technology applied in GeneNet enables description of not only a gene network structure and functional relationships between components, but also metabolic and signal transduction pathways. Specialised software, GeneNet Viewer, automatically displays the graphical diagram of gene networks described in the database. Current release 3.0 of GeneNet database contains descriptions of 25 gene networks, 945 pr...

  4. 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...... environments ranging from permafrost to desert, we demonstrate the presence of miscoding lesion damage in both the mtDNA and nuDNA, resulting in insertion of erroneous bases during amplification. Interestingly, no significant differences in the frequency of miscoding lesion damage are recorded between mtDNA...

  5. Integrating heterogeneous gene expression data for gene regulatory network modelling.

    Science.gov (United States)

    Sîrbu, Alina; Ruskin, Heather J; Crane, Martin

    2012-06-01

    Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets. PMID:21948152

  6. Genes and gene networks implicated in aggression related behaviour.

    Science.gov (United States)

    Malki, Karim; Pain, Oliver; Du Rietz, Ebba; Tosto, Maria Grazia; Paya-Cano, Jose; Sandnabba, Kenneth N; de Boer, Sietse; Schalkwyk, Leonard C; Sluyter, Frans

    2014-10-01

    Aggressive behaviour is a major cause of mortality and morbidity. Despite of moderate heritability estimates, progress in identifying the genetic factors underlying aggressive behaviour has been limited. There are currently three genetic mouse models of high and low aggression created using selective breeding. This is the first study to offer a global transcriptomic characterization of the prefrontal cortex across all three genetic mouse models of aggression. A systems biology approach has been applied to transcriptomic data across the three pairs of selected inbred mouse strains (Turku Aggressive (TA) and Turku Non-Aggressive (TNA), Short Attack Latency (SAL) and Long Attack Latency (LAL) mice and North Carolina Aggressive (NC900) and North Carolina Non-Aggressive (NC100)), providing novel insight into the neurobiological mechanisms and genetics underlying aggression. First, weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly correlated genes associated with aggression. Probe sets belonging to gene modules uncovered by WGCNA were carried forward for network analysis using ingenuity pathway analysis (IPA). The RankProd non-parametric algorithm was then used to statistically evaluate expression differences across the genes belonging to modules significantly associated with aggression. IPA uncovered two pathways, involving NF-kB and MAPKs. The secondary RankProd analysis yielded 14 differentially expressed genes, some of which have previously been implicated in pathways associated with aggressive behaviour, such as Adrbk2. The results highlighted plausible candidate genes and gene networks implicated in aggression-related behaviour. PMID:25142712

  7. 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.

  8. From gene expressions to genetic networks

    Science.gov (United States)

    Cieplak, Marek

    2009-03-01

    A method based on the principle of entropy maximization is used to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles [1]. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher order correlations. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabollic oscillations identifies a gene interaction network that reflects the intracellular communication pathways. These pathways adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. The time-dependent behavior of the genetic network is found to involve only a few fundamental modes [2,3]. [4pt] REFERENCES:[0pt] [1] T. R. Lezon, J. R. Banavar, M. Cieplak, A. Maritan, and N. Fedoroff, Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns, Proc. Natl. Acad. Sci. (USA) 103, 19033-19038 (2006) [0pt] [2] N. S. Holter, M. Mitra, A. Maritan, M. Cieplak, J. R. Banavar, and N. V. Fedoroff, Fundamental patterns underlying gene expression profiles: simplicity from complexity, Proc. Natl. Acad. Sci. USA 97, 8409-8414 (2000) [0pt] [3] N. S. Holter, A. Maritan, M. Cieplak, N. V. Fedoroff, and J. R. Banavar, Dynamic modeling of gene expression data, Proc. Natl. Acad. Sci. USA 98, 1693-1698 (2001)

  9. Gene Network Biological Validity Based on Gene-Gene Interaction Relevance

    OpenAIRE

    Francisco Gómez-Vela; Norberto Díaz-Díaz

    2014-01-01

    In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in...

  10. 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 characteristi

  11. Gene-interleaving patterns of synteny in the Saccharomyces cerevisiae genome: are they proof of an ancient genome duplication event?

    Directory of Open Access Journals (Sweden)

    Sun Feng-Jie

    2007-09-01

    Full Text Available Abstract Background Recent comparative genomic studies claim local syntenic gene-interleaving relationships in Ashbya gossypii and Kluyveromyces waltii are compelling evidence for an ancient whole-genome duplication event in Saccharomyces cerevisiae. We here test, using Hannenhalli-Pevzner rearrangement algorithms that address the multiple genome rearrangement problem, whether syntenic patterns are proof of paleopolyploidization. Results We focus on (1 pairwise comparison of gene arrangement sequences in A. gossypii and S. cerevisiae, (2 reconstruction of gene arrangements ancestral to A. gossypii, S. cerevisiae, and K. waltii, (3 synteny patterns arising within and between lineages, and (4 expected gene orientation of duplicate gene sets. The existence of syntenic patterns between ancestral gene sets and A. gossypii, S. cerevisiae, and K. waltii, and other evidence, suggests that gene-interleaving relationships are the natural consequence of topological rearrangements in chromosomes and that a more gradual scenario of genome evolution involving segmental duplication and recombination constitutes a more parsimonious explanation. Furthermore, phylogenetic trees reconstructed under alternative hypotheses placed the putative whole-genome duplication event after the divergence of the S. cerevisiae and K. waltii lineages, but in the lineage leading to K. waltii. This is clearly incompatible with an ancient genome duplication event in S. cerevisiae. Conclusion Because the presence of syntenic patterns appears to be a condition that is necessary, but not sufficient, to support the existence of the whole-genome duplication event, our results prompt careful re-evaluation of paleopolyploidization in the yeast lineage and the evolutionary meaning of syntenic patterns. Reviewers This article was reviewed by Kenneth H. Wolfe (nominated by Nicolas Galtier, Austin L. Hughes (nominated by Eugene Koonin, Mikhail S. Gelfand, and Mark Gerstein.

  12. An RNA-dependent RNA polymerase gene in bat genomes derived from an ancient negative-strand RNA virus.

    Science.gov (United States)

    Horie, Masayuki; Kobayashi, Yuki; Honda, Tomoyuki; Fujino, Kan; Akasaka, Takumi; Kohl, Claudia; Wibbelt, Gudrun; Mühldorfer, Kristin; Kurth, Andreas; Müller, Marcel A; Corman, Victor M; Gillich, Nadine; Suzuki, Yoshiyuki; Schwemmle, Martin; Tomonaga, Keizo

    2016-01-01

    Endogenous bornavirus-like L (EBLL) elements are inheritable sequences derived from ancient bornavirus L genes that encode a viral RNA-dependent RNA polymerase (RdRp) in many eukaryotic genomes. Here, we demonstrate that bats of the genus Eptesicus have preserved for more than 11.8 million years an EBLL element named eEBLL-1, which has an intact open reading frame of 1,718 codons. The eEBLL-1 coding sequence revealed that functional motifs essential for mononegaviral RdRp activity are well conserved in the EBLL-1 genes. Genetic analyses showed that natural selection operated on eEBLL-1 during the evolution of Eptesicus. Notably, we detected efficient transcription of eEBLL-1 in tissues from Eptesicus bats. To the best of our knowledge, this study is the first report showing that the eukaryotic genome has gained a riboviral polymerase gene from an ancient virus that has the potential to encode a functional RdRp. PMID:27174689

  13. Discovering Implicit Entity Relation with the Gene-Citation-Gene Network

    OpenAIRE

    Min Song; Nam-Gi Han; Yong-Hwan Kim; Ying Ding; Tamy Chambers

    2013-01-01

    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 c...

  14. 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...

  15. Recovering the urban network of ancient Sikyon through multi-component geophysical approaches

    OpenAIRE

    Sarris, Apostolos; Papadopoulos, Nikos; Trigkas, Vasilis

    2008-01-01

    A suite of different geophysical techniques was applied in the course of multidisciplinary research conducted within the framework of the Sikyon survey project, whose goal is the study of the landscape and human activity on the plateau of ancient Sikyon (NE Peloponnese). During the first 3 years of the geophysical campaign, more than 60,000 m2 of the city centre were covered using magnetic measurements,electrical resistivity mapping and tomography techniques, and ground penetrating radar. Con...

  16. 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.

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

    DEFF Research Database (Denmark)

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

    2007-01-01

    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...

  18. Construction of gene regulatory networks using biclustering and bayesian networks

    Directory of Open Access Journals (Sweden)

    Alakwaa Fadhl M

    2011-10-01

    Full Text Available Abstract Background Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling. Results In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method. Conclusions Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.

  19. 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.

  20. 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.

  1. An orphaned mammalian β-globin gene of ancient evolutionary origin

    OpenAIRE

    Wheeler, David; Hope, Rory; Cooper, Steven J. B.; Dolman, Gaynor; Webb, Graham C.; Bottema, Cynthia D. K.; Gooley, Andrew A; Goodman, Morris; Holland, Robert A. B.

    2001-01-01

    Mammals possess multiple, closely linked β-globin genes that differ in the timing of their expression during development. These genes have been thought to be derived from a single ancestral gene, by duplication events that occurred after the separation of the mammals and birds. We report the isolation and characterization of an atypical β-like globin gene (ω-globin) in marsupials that appears to be more closely related to avian β-globin genes than to other mammalian ...

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

    Science.gov (United States)

    Song, Min; Han, Nam-Gi; Kim, Yong-Hwan; Ding, Ying; Chambers, Tamy

    2013-01-01

    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. PMID:24358368

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

    Directory of Open Access Journals (Sweden)

    Min Song

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

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

    Directory of Open Access Journals (Sweden)

    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.

  5. Gene network-based cancer prognosis analysis with sparse boosting

    OpenAIRE

    Ma, Shuangge; Huang, Yuan; Huang, Jian; Fang, Kuangnan

    2012-01-01

    High-throughput gene profiling studies have been extensively conducted, searching for markers associated with cancer development and progression. In this study, we analyse cancer prognosis studies with right censored survival responses. With gene expression data, we adopt the weighted gene co-expression network analysis (WGCNA) to describe the interplay among genes. In network analysis, nodes represent genes. There are subsets of nodes, called modules, which are tightly connected to each othe...

  6. Modular composition of gene transcription networks.

    Science.gov (United States)

    Gyorgy, Andras; Del Vecchio, Domitilla

    2014-03-01

    Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems. PMID:24626132

  7. 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.

  8. An ancient evolutionary origin of the Rag1/2 gene locus

    OpenAIRE

    Fugmann, Sebastian D.; Messier, Cynthia; Novack, Laura A.; Cameron, R. Andrew; Rast, Jonathan P.

    2006-01-01

    The diversity of antigen receptors in the adaptive immune system of jawed vertebrates is generated by a unique process of somatic gene rearrangement known as V(D)J recombination. The Rag1 and Rag2 proteins are the key mediators of this process. They are encoded by a compact gene cluster that has exclusively been identified in animal species displaying V(D)J-mediated immunity, and no homologous gene pair has been identified in other organisms. This distinctly restricted phylogenetic distributi...

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

    Directory of Open Access Journals (Sweden)

    Kevin L Childs

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

  10. Arabidopsis gene co-expression network and its functional modules

    Directory of Open Access Journals (Sweden)

    Dash Sudhansu

    2009-10-01

    Full Text Available Abstract Background Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. Results We constructed a genome-wide Arabidopsis gene co-expression network (AGCN by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000 of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively. Conclusion This study reveals new insight into the topological properties of

  11. Differential gene co-expression networks via Bayesian biclustering models

    OpenAIRE

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

    2014-01-01

    Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes whose covariation may be observed in only a subset of the samples. Our biclustering me...

  12. Transcriptome network analysis reveals candidate genes for renal cell carcinoma

    OpenAIRE

    Wei Zhai; Yun-Fei Xu; Min Liu; Jun-Hua Zheng

    2012-01-01

    Context: Renal cell carcinoma (RCC) is a kidney cancer that originates in renal parenchyma and it is the most common type of kidney cancer with approximately 80% lethal cases. Aims: To interpret the mechanism, explore the regulation of TF-target genes and TF-pathway, and identify the potential key genes of renal cell carcinoma. Settings and Design: After constructing a regulation network from differently expressed genes and transcription factors, pathway regulation network and gene onto...

  13. An ancient evolutionary origin of the Rag1/2 gene locus.

    Science.gov (United States)

    Fugmann, Sebastian D; Messier, Cynthia; Novack, Laura A; Cameron, R Andrew; Rast, Jonathan P

    2006-03-01

    The diversity of antigen receptors in the adaptive immune system of jawed vertebrates is generated by a unique process of somatic gene rearrangement known as V(D)J recombination. The Rag1 and Rag2 proteins are the key mediators of this process. They are encoded by a compact gene cluster that has exclusively been identified in animal species displaying V(D)J-mediated immunity, and no homologous gene pair has been identified in other organisms. This distinctly restricted phylogenetic distribution has led to the hypothesis that one or both of the Rag genes were coopted after horizontal gene transfer and assembled into a Rag1/2 gene cluster in a common jawed vertebrate ancestor. Here, we identify and characterize a closely linked pair of genes, SpRag1L and SpRag2L, from an invertebrate, the purple sea urchin (Strongylocentrotus purpuratus) with similarity in both sequence and genomic organization to the vertebrate Rag1 and Rag2 genes. They are coexpressed during development and in adult tissues, and recombinant versions of the proteins form a stable complex with each other as well as with Rag1 and Rag2 proteins from several vertebrate species. We thus conclude that SpRag1L and SpRag2L represent homologs of vertebrate Rag1 and Rag2. In combination with the apparent absence of V(D)J recombination in echinoderms, this finding strongly suggests that linked Rag1- and Rag2-like genes were already present and functioning in a different capacity in the common ancestor of living deuterostomes, and that their specific role in the adaptive immune system was acquired much later in an early jawed vertebrate. PMID:16505374

  14. Mosaic genome of endobacteria in arbuscular mycorrhizal fungi: Transkingdom gene transfer in an ancient mycoplasma-fungus association.

    Science.gov (United States)

    Torres-Cortés, Gloria; Ghignone, Stefano; Bonfante, Paola; Schüßler, Arthur

    2015-06-23

    For more than 450 million years, arbuscular mycorrhizal fungi (AMF) have formed intimate, mutualistic symbioses with the vast majority of land plants and are major drivers in almost all terrestrial ecosystems. The obligate plant-symbiotic AMF host additional symbionts, so-called Mollicutes-related endobacteria (MRE). To uncover putative functional roles of these widespread but yet enigmatic MRE, we sequenced the genome of DhMRE living in the AMF Dentiscutata heterogama. Multilocus phylogenetic analyses showed that MRE form a previously unidentified lineage sister to the hominis group of Mycoplasma species. DhMRE possesses a strongly reduced metabolic capacity with 55% of the proteins having unknown function, which reflects unique adaptations to an intracellular lifestyle. We found evidence for transkingdom gene transfer between MRE and their AMF host. At least 27 annotated DhMRE proteins show similarities to nuclear-encoded proteins of the AMF Rhizophagus irregularis, which itself lacks MRE. Nuclear-encoded homologs could moreover be identified for another AMF, Gigaspora margarita, and surprisingly, also the non-AMF Mortierella verticillata. Our data indicate a possible origin of the MRE-fungus association in ancestors of the Glomeromycota and Mucoromycotina. The DhMRE genome encodes an arsenal of putative regulatory proteins with eukaryotic-like domains, some of them encoded in putative genomic islands. MRE are highly interesting candidates to study the evolution and interactions between an ancient, obligate endosymbiotic prokaryote with its obligate plant-symbiotic fungal host. Our data moreover may be used for further targeted searches for ancient effector-like proteins that may be key components in the regulation of the arbuscular mycorrhiza symbiosis. PMID:25964335

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

    International Nuclear Information System (INIS)

    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.

  16. Ancient horizontal transfer of transaldolase-like protein gene and its role in plant vascular development

    OpenAIRE

    Yang, Zefeng; Zhou, Yong; Huang, Jinling; Hu, Yunyun; Zhang, Enying; Xie, Zhengwen; Ma, Sijia; Gao, Yun; Song, Song; Xu, Chenwu; Liang, Guohua

    2014-01-01

    A major event in land plant evolution is the origin of vascular tissues, which ensure the long-distance transport of water, nutrients and organic compounds. However, the molecular basis for the origin and evolution of plant vascular tissues remains largely unknown. Here, we investigate the evolution of the land plant TAL-type transaldolase (TAL) gene and its potential function in rice (Oryza sativa) based on phylogenetic analyses and transgenic experiments, respectively. TAL genes are only pr...

  17. Evolutionary diversification of an ancient gene family (rhs through C-terminal displacement

    Directory of Open Access Journals (Sweden)

    Parkhill Julian

    2009-12-01

    Full Text Available Abstract Background Rhs genes are prominent features of bacterial genomes that have previously been implicated in genomic rearrangements in E. coli. By comparing rhs repertoires across the Enterobacteriaceae, this study provides a robust explanation of rhs diversification and evolution, and a mechanistic model of how rhs diversity is gained and lost. Results Rhs genes are ubiquitous and comprise six structurally distinct lineages within the Enterobacteriaceae. There is considerable intergenomic variation in rhs repertoire; for instance, in Salmonella enterica, rhs are restricted to mobile elements, while in Escherichia coli one rhs lineage has diversified through transposition as older lineages have been deleted. Overall, comparative genomics reveals frequent, independent gene gains and losses, as well as occasional lateral gene transfer, in different genera. Furthermore, we demonstrate that Rhs 'core' domains and variable C-termini are evolutionarily decoupled, and propose that rhs diversity is driven by homologous recombination with circular intermediates. Existing C-termini are displaced by laterally acquired alternatives, creating long arrays of dissociated 'tips' that characterize the appearance of rhs loci. Conclusion Rhs repertoires are highly dynamic among Enterobacterial genomes, due to repeated gene gains and losses. In contrast, the primary structures of Rhs genes are evolutionarily conserved, indicating that rhs sequence diversity is driven, not by rapid mutation, but by the relatively slow evolution of novel core/tip combinations. Hence, we predict that a large pool of dissociated rhs C-terminal tips exists episomally and these are potentially transmitted across taxonomic boundaries.

  18. Network of Cancer Genes (NCG 3.0): integration and analysis of genetic and network properties of cancer genes

    OpenAIRE

    D'Antonio, Matteo; Pendino, Vera; Sinha, Shruti; Ciccarelli, Francesca D.

    2011-01-01

    The identification of a constantly increasing number of genes whose mutations are causally implicated in tumor initiation and progression (cancer genes) requires the development of tools to store and analyze them. The Network of Cancer Genes (NCG 3.0) collects information on 1494 cancer genes that have been found mutated in 16 different cancer types. These genes were collected from the Cancer Gene Census as well as from 18 whole exome and 11 whole-genome screenings of cancer samples. For each...

  19. Graphical Features of Functional Genes in Human Protein Interaction Network.

    Science.gov (United States)

    Wang, Pei; Chen, Yao; Lu, Jinhu; Wang, Qingyun; Yu, Xinghuo

    2016-06-01

    With the completion of the human genome project, it is feasible to investigate large-scale human protein interaction network (HPIN) with complex networks theory. Proteins are encoded by genes. Essential, viable, disease, conserved, housekeeping (HK) and tissue-enriched (TE) genes are functional genes, which are organized and functioned via interaction networks. Based on up-to-date data from various databases or literature, two large-scale HPINs and six subnetworks are constructed. We illustrate that the HPINs and most of the subnetworks are sparse, small-world, scale-free, disassortative and with hierarchical modularity. Among the six subnetworks, essential, disease and HK subnetworks are more densely connected than the others. Statistical analysis on the topological structures of the HPIN reveals that the lethal, the conserved, the HK and the TE genes are with hallmark graphical features. Receiver operating characteristic (ROC) curves indicate that the essential genes can be distinguished from the viable ones with accuracy as high as almost 70%. Closeness, semi-local and eigenvector centralities can distinguish the HK genes from the TE ones with accuracy around 82%. Furthermore, the Venn diagram, cluster dendgrams and classifications of disease genes reveal that some classes of disease genes are with hallmark graphical features, especially for cancer genes, HK disease genes and TE disease genes. The findings facilitate the identification of some functional genes via topological structures. The investigations shed some light on the characteristics of the compete interactome, which have potential implications in networked medicine and biological network control. PMID:26841412

  20. Ancient signals: comparative genomics of plant MAPK and MAPKK gene families

    DEFF Research Database (Denmark)

    Hamel, Louis-Philippe; Nicole, Marie-Claude; Sritubtim, Somrudee; Morency, Marie-Josée; Ellis, Margaret; Ehlting, Juergen; Beaudoin, Nathalie; Barbazuk, Brad; Klessig, Dan; Lee, Justin; Martin, Greg; Mundy, John; Ohashi, Yuko; Scheel, Dierk; Sheen, Jen; Xing, Tim; Zhang, Shuqun; Seguin, Armand; Ellis, Brian E

    2006-01-01

    MAPK signal transduction modules play crucial roles in regulating many biological processes in plants, and their components are encoded by highly conserved genes. The recent availability of genome sequences for rice and poplar now makes it possible to examine how well the previously described...

  1. Exhaustive Search for Fuzzy Gene Networks from Microarray Data

    Energy Technology Data Exchange (ETDEWEB)

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

    2003-07-07

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

  2. Ancient and recent adaptive evolution of primate non-homologous end joining genes.

    Directory of Open Access Journals (Sweden)

    Ann Demogines

    2010-10-01

    Full Text Available In human cells, DNA double-strand breaks are repaired primarily by the non-homologous end joining (NHEJ pathway. Given their critical nature, we expected NHEJ proteins to be evolutionarily conserved, with relatively little sequence change over time. Here, we report that while critical domains of these proteins are conserved as expected, the sequence of NHEJ proteins has also been shaped by recurrent positive selection, leading to rapid sequence evolution in other protein domains. In order to characterize the molecular evolution of the human NHEJ pathway, we generated large simian primate sequence datasets for NHEJ genes. Codon-based models of gene evolution yielded statistical support for the recurrent positive selection of five NHEJ genes during primate evolution: XRCC4, NBS1, Artemis, POLλ, and CtIP. Analysis of human polymorphism data using the composite of multiple signals (CMS test revealed that XRCC4 has also been subjected to positive selection in modern humans. Crystal structures are available for XRCC4, Nbs1, and Polλ; and residues under positive selection fall exclusively on the surfaces of these proteins. Despite the positive selection of such residues, biochemical experiments with variants of one positively selected site in Nbs1 confirm that functions necessary for DNA repair and checkpoint signaling have been conserved. However, many viruses interact with the proteins of the NHEJ pathway as part of their infectious lifecycle. We propose that an ongoing evolutionary arms race between viruses and NHEJ genes may be driving the surprisingly rapid evolution of these critical genes.

  3. Oxytocin Pathway Genes: Evolutionary Ancient System Impacting on Human Affiliation, Sociality, and Psychopathology.

    Science.gov (United States)

    Feldman, Ruth; Monakhov, Mikhail; Pratt, Maayan; Ebstein, Richard P

    2016-02-01

    Oxytocin (OT), a nonapeptide signaling molecule originating from an ancestral peptide, appears in different variants across all vertebrate and several invertebrate species. Throughout animal evolution, neuropeptidergic signaling has been adapted by organisms for regulating response to rapidly changing environments. The family of OT-like molecules affects both peripheral tissues implicated in reproduction, homeostasis, and energy balance, as well as neuromodulation of social behavior, stress regulation, and associative learning in species ranging from nematodes to humans. After describing the OT-signaling pathway, we review research on the three genes most extensively studied in humans: the OT receptor (OXTR), the structural gene for OT (OXT/neurophysin-I), and CD38. Consistent with the notion that sociality should be studied from the perspective of social life at the species level, we address human social functions in relation to OT-pathway genes, including parenting, empathy, and using social relationships to manage stress. We then describe associations between OT-pathway genes with psychopathologies involving social dysfunctions such as autism, depression, or schizophrenia. Human research particularly underscored the involvement of two OXTR single nucleotide polymorphisms (rs53576, rs2254298) with fewer studies focusing on other OXTR (rs7632287, rs1042778, rs2268494, rs2268490), OXT (rs2740210, rs4813627, rs4813625), and CD38 (rs3796863, rs6449197) single nucleotide polymorphisms. Overall, studies provide evidence for the involvement of OT-pathway genes in human social functions but also suggest that factors such as gender, culture, and early environment often confound attempts to replicate first findings. We conclude by discussing epigenetics, conceptual implications within an evolutionary perspective, and future directions, especially the need to refine phenotypes, carefully characterize early environments, and integrate observations of social behavior across

  4. C. elegans Metabolic Gene Regulatory Networks Govern the Cellular Economy

    Science.gov (United States)

    Watson, Emma; Walhout, Albertha J.M.

    2014-01-01

    Diet greatly impacts metabolism in health and disease. In response to the presence or absence of specific nutrients, metabolic gene regulatory networks sense the metabolic state of the cell and regulate metabolic flux accordingly, for instance by the transcriptional control of metabolic enzymes. Here we discuss recent insights regarding metazoan metabolic regulatory networks using the nematode Caenorhabditis elegans as a model, including the modular organization of metabolic gene regulatory networks, the prominent impact of diet on the transcriptome and metabolome, specialized roles of nuclear hormone receptors in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by microRNAs, and feedback between metabolic genes and their regulators. PMID:24731597

  5. Identifying disease candidate genes via large-scale gene network analysis.

    Science.gov (United States)

    Kim, Haseong; Park, Taesung; Gelenbe, Erol

    2014-01-01

    Gene Regulatory Networks (GRN) provide systematic views of complex living systems, offering reliable and large-scale GRNs to identify disease candidate genes. A reverse engineering technique, Bayesian Model Averaging-based Networks (BMAnet), which ensembles all appropriate linear models to tackle uncertainty in model selection that integrates heterogeneous biological data sets is introduced. Using network evaluation metrics, we compare the networks that are thus identified. The metric 'Random walk with restart (Rwr)' is utilised to search for disease genes. In a simulation our method shows better performance than elastic-net and Gaussian graphical models, but topological quantities vary among the three methods. Using real-data, brain tumour gene expression samples consisting of non-tumour, grade III and grade IV are analysed to estimate networks with a total of 4422 genes. Based on these networks, 169 brain tumour-related candidate genes were identified and some were found to relate to 'wound', 'apoptosis', and 'cell death' processes. PMID:25796737

  6. The Dispanins : A Novel Gene Family of Ancient Origin That Contains 14 Human Members

    OpenAIRE

    Markus Sällman Almén; Nathalie Bringeland; Robert Fredriksson; Helgi B Schiöth

    2012-01-01

    The Interferon induced transmembrane proteins (IFITM) are a family of transmembrane proteins that is known to inhibit cell invasion of viruses such as HIV-1 and influenza. We show that the IFITM genes are a subfamily in a larger family of transmembrane (TM) proteins that we call Dispanins, which refers to a common 2TM structure. We mined the Dispanins in 36 eukaryotic species, covering all major eukaryotic groups, and investigated their evolutionary history using Bayesian and maximum likeliho...

  7. An ancient dental gene set governs development and continuous regeneration of teeth in sharks.

    Science.gov (United States)

    Rasch, Liam J; Martin, Kyle J; Cooper, Rory L; Metscher, Brian D; Underwood, Charlie J; Fraser, Gareth J

    2016-07-15

    The evolution of oral teeth is considered a major contributor to the overall success of jawed vertebrates. This is especially apparent in cartilaginous fishes including sharks and rays, which develop elaborate arrays of highly specialized teeth, organized in rows and retain the capacity for life-long regeneration. Perpetual regeneration of oral teeth has been either lost or highly reduced in many other lineages including important developmental model species, so cartilaginous fishes are uniquely suited for deep comparative analyses of tooth development and regeneration. Additionally, sharks and rays can offer crucial insights into the characters of the dentition in the ancestor of all jawed vertebrates. Despite this, tooth development and regeneration in chondrichthyans is poorly understood and remains virtually uncharacterized from a developmental genetic standpoint. Using the emerging chondrichthyan model, the catshark (Scyliorhinus spp.), we characterized the expression of genes homologous to those known to be expressed during stages of early dental competence, tooth initiation, morphogenesis, and regeneration in bony vertebrates. We have found that expression patterns of several genes from Hh, Wnt/β-catenin, Bmp and Fgf signalling pathways indicate deep conservation over ~450 million years of tooth development and regeneration. We describe how these genes participate in the initial emergence of the shark dentition and how they are redeployed during regeneration of successive tooth generations. We suggest that at the dawn of the vertebrate lineage, teeth (i) were most likely continuously regenerative structures, and (ii) utilised a core set of genes from members of key developmental signalling pathways that were instrumental in creating a dental legacy redeployed throughout vertebrate evolution. These data lay the foundation for further experimental investigations utilizing the unique regenerative capacity of chondrichthyan models to answer evolutionary

  8. Novel primate miRNAs co-evolved with ancient target genes in germinal zone specific expression patterns

    Science.gov (United States)

    Arcila, Mary L; Betizeau, Marion; Cambronne, Xiaolu A; Guzman, Elmer; Doerflinger, Nathalie; Bouhallier, Frantz; Zhou, Hongjun; Wu, Bian; Rani, Neha; Bassett, Dani S; Borello, Ugo; Huissoud, Cyril; Goodman, Richard H; Dehay, Colette; Kosik, Kenneth S

    2014-01-01

    Summary Major non primate-primate differences in corticogenesis include the dimensions, precursor lineages and developmental timing of the germinal zones (GZ). microRNAs (miRNAs) of laser dissected GZ compartments and cortical plate (CP) from embryonic E80 macaque visual cortex were deep sequenced. The CP and the GZ including Ventricular Zone (VZ), outer and inner subcompartments of the Outer SubVentricular Zone (OSVZ) in area 17 displayed unique miRNA profiles. miRNAs present in primate, but absent in rodent, contributed disproportionately to the differential expression between GZ sub-regions. Prominent among the validated targets of these miRNAs were cell-cycle and neurogenesis regulators. Co-evolution between the emergent miRNAs and their targets suggested that novel miRNAs became integrated into ancient gene circuitry to exert additional control over proliferation. We conclude that multiple cell-cycle regulatory events contribute to the emergence of primate-specific cortical features, including the OSVZ, generated enlarged supragranular layers, largely responsible for the increased primate cortex computational abilities. PMID:24583023

  9. Novel primate miRNAs coevolved with ancient target genes in germinal zone-specific expression patterns.

    Science.gov (United States)

    Arcila, Mary L; Betizeau, Marion; Cambronne, Xiaolu A; Guzman, Elmer; Doerflinger, Nathalie; Bouhallier, Frantz; Zhou, Hongjun; Wu, Bian; Rani, Neha; Bassett, Danielle S; Borello, Ugo; Huissoud, Cyril; Goodman, Richard H; Dehay, Colette; Kosik, Kenneth S

    2014-03-19

    Major nonprimate-primate differences in cortico-genesis include the dimensions, precursor lineages, and developmental timing of the germinal zones (GZs). microRNAs (miRNAs) of laser-dissected GZ compartments and cortical plate (CP) from embryonic E80 macaque visual cortex were deep sequenced. The CP and the GZ including ventricular zone (VZ) and outer and inner subcompartments of the outer subventricular zone (OSVZ) in area 17 displayed unique miRNA profiles. miRNAs present in primate, but absent in rodent, contributed disproportionately to the differential expression between GZ subregions. Prominent among the validated targets of these miRNAs were cell-cycle and neurogenesis regulators. Coevolution between the emergent miRNAs and their targets suggested that novel miRNAs became integrated into ancient gene circuitry to exert additional control over proliferation. We conclude that multiple cell-cycle regulatory events contribute to the emergence of primate-specific cortical features, including the OSVZ, generated enlarged supragranular layers, largely responsible for the increased primate cortex computational abilities. PMID:24583023

  10. An ancient spliceosomal intron in the ribosomal protein L7a gene (Rpl7a of Giardia lamblia

    Directory of Open Access Journals (Sweden)

    Gray Michael W

    2005-08-01

    Full Text Available Abstract Background Only one spliceosomal-type intron has previously been identified in the unicellular eukaryotic parasite, Giardia lamblia (a diplomonad. This intron is only 35 nucleotides in length and is unusual in possessing a non-canonical 5' intron boundary sequence, CT, instead of GT. Results We have identified a second spliceosomal-type intron in G. lamblia, in the ribosomal protein L7a gene (Rpl7a, that possesses a canonical GT 5' intron boundary sequence. A comparison of the two known Giardia intron sequences revealed extensive nucleotide identity at both the 5' and 3' intron boundaries, similar to the conserved sequence motifs recently identified at the boundaries of spliceosomal-type introns in Trichomonas vaginalis (a parabasalid. Based on these observations, we searched the partial G. lamblia genome sequence for these conserved features and identified a third spliceosomal intron, in an unassigned open reading frame. Our comprehensive analysis of the Rpl7a intron in other eukaryotic taxa demonstrates that it is evolutionarily conserved and is an ancient eukaryotic intron. Conclusion An analysis of the phylogenetic distribution and properties of the Rpl7a intron suggests its utility as a phylogenetic marker to evaluate particular eukaryotic groupings. Additionally, analysis of the G. lamblia introns has provided further insight into some of the conserved and unique features possessed by the recently identified spliceosomal introns in related organisms such as T. vaginalis and Carpediemonas membranifera.

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

    OpenAIRE

    Chris eGaiteri; Etienne eSibille

    2011-01-01

    The structure of gene coexpression networks reflects the activation and interaction of multiple cellular systems. Since the pathology of neuropsychiatric disorders is influenced by diverse cellular systems and pathways, we investigated gene coexpression networks in major depression, and searched for putative unifying themes in network connectivity across neuropsychiatric disorders. Specifically, based on the prevalence of the lethality-centrality relationship in disease-related networks, we h...

  12. Differentially Expressed Genes in Major Depression Reside on the Periphery of Resilient Gene Coexpression Networks

    OpenAIRE

    Gaiteri, Chris; Sibille, Etienne

    2011-01-01

    The structure of gene coexpression networks reflects the activation and interaction of multiple cellular systems. Since the pathology of neuropsychiatric disorders is influenced by diverse cellular systems and pathways, we investigated gene coexpression networks in major depression, and searched for putative unifying themes in network connectivity across neuropsychiatric disorders. Specifically, based on the prevalence of the lethality–centrality relationship in disease-related networks, we h...

  13. Ancient exaptation of a CORE-SINE retroposon into a highly conserved mammalian neuronal enhancer of the proopiomelanocortin gene.

    Directory of Open Access Journals (Sweden)

    Andrea M Santangelo

    2007-10-01

    Full Text Available The proopiomelanocortin gene (POMC is expressed in the pituitary gland and the ventral hypothalamus of all jawed vertebrates, producing several bioactive peptides that function as peripheral hormones or central neuropeptides, respectively. We have recently determined that mouse and human POMC expression in the hypothalamus is conferred by the action of two 5' distal and unrelated enhancers, nPE1 and nPE2. To investigate the evolutionary origin of the neuronal enhancer nPE2, we searched available vertebrate genome databases and determined that nPE2 is a highly conserved element in placentals, marsupials, and monotremes, whereas it is absent in nonmammalian vertebrates. Following an in silico paleogenomic strategy based on genome-wide searches for paralog sequences, we discovered that opossum and wallaby nPE2 sequences are highly similar to members of the superfamily of CORE-short interspersed nucleotide element (SINE retroposons, in particular to MAR1 retroposons that are widely present in marsupial genomes. Thus, the neuronal enhancer nPE2 originated from the exaptation of a CORE-SINE retroposon in the lineage leading to mammals and remained under purifying selection in all mammalian orders for the last 170 million years. Expression studies performed in transgenic mice showed that two nonadjacent nPE2 subregions are essential to drive reporter gene expression into POMC hypothalamic neurons, providing the first functional example of an exapted enhancer derived from an ancient CORE-SINE retroposon. In addition, we found that this CORE-SINE family of retroposons is likely to still be active in American and Australian marsupial genomes and that several highly conserved exonic, intronic and intergenic sequences in the human genome originated from the exaptation of CORE-SINE retroposons. Together, our results provide clear evidence of the functional novelties that transposed elements contributed to their host genomes throughout evolution.

  14. Ancient Egypt.

    Science.gov (United States)

    Evers, Virginia

    This four-week fourth grade social studies unit dealing with religious dimensions in ancient Egyptian culture was developed by the Public Education Religion Studies Center at Wright State University. It seeks to help students understand ancient Egypt by looking at the people, the culture, and the people's world view. The unit begins with outlines…

  15. Evolutionary responses to a constructed niche: ancient Mesoamericans as a model of gene-culture coevolution.

    Directory of Open Access Journals (Sweden)

    Tábita Hünemeier

    Full Text Available Culture and genetics rely on two distinct but not isolated transmission systems. Cultural processes may change the human selective environment and thereby affect which individuals survive and reproduce. Here, we evaluated whether the modes of subsistence in Native American populations and the frequencies of the ABCA1*Arg230Cys polymorphism were correlated. Further, we examined whether the evolutionary consequences of the agriculturally constructed niche in Mesoamerica could be considered as a gene-culture coevolution model. For this purpose, we genotyped 229 individuals affiliated with 19 Native American populations and added data for 41 other Native American groups (n = 1905 to the analysis. In combination with the SNP cluster of a neutral region, this dataset was then used to unravel the scenario involved in 230Cys evolutionary history. The estimated age of 230Cys is compatible with its origin occurring in the American continent. The correlation of its frequencies with the archeological data on Zea pollen in Mesoamerica/Central America, the neutral coalescent simulations, and the F(ST-based natural selection analysis suggest that maize domestication was the driving force in the increase in the frequencies of 230Cys in this region. These results may represent the first example of a gene-culture coevolution involving an autochthonous American allele.

  16. Analysis of bHLH coding genes using gene co-expression network approach.

    Science.gov (United States)

    Srivastava, Swati; Sanchita; Singh, Garima; Singh, Noopur; Srivastava, Gaurava; Sharma, Ashok

    2016-07-01

    Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species. PMID:27178572

  17. Phenotype accessibility and noise in random threshold gene regulatory networks.

    Science.gov (United States)

    Pinho, Ricardo; Garcia, Victor; Feldman, Marcus W

    2014-01-01

    Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes

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

    Directory of Open Access Journals (Sweden)

    Chris eGaiteri

    2011-08-01

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

  19. Genes meet geology: fish phylogeographic pattern reflects ancient, rather than modern, drainage connections.

    Science.gov (United States)

    Waters, J M; Craw, D; Youngson, J H; Wallis, G P

    2001-09-01

    We used DNA analysis of the freshwater Galaxias vulgaris complex (Pisces: Galaxiidae) to test a geological hypothesis of drainage evolution in South Island, New Zealand. Geological evidence suggests that the presently north-flowing Nevis River branch of the Clutha/Kawarau River system (Otago) once flowed south into the Nokomai branch of the Mataura system (Southland). The flow reversal is thought to have resulted from fault and fold activity associated with post-Miocene uplift. Mitochondrial DNA sequence data (control region and cytochrome b genes; 76 individuals; maximum divergence 7.1%) corroborate this geomorphological hypothesis: The Nevis River retains a freshwater fish species (Galaxias gollumoides; five sites; 10 haplotypes) that is otherwise restricted to Southland (nine sites; 15 haplotypes). There is no indication that the Nevis River lineage of G. gollumoides lives elsewhere in the Clutha/ Kawarau system (> 30 sites). Likewise, two widespread Clutha lineages (G. 'sp D'; G. anomalus-G. pullus) are apparently absent from the Nevis (> 30 sites). In particular, G. 'sp D' lives throughout much of the Clutha (12 sites, 23 haplotypes), including a tributary of the Kawarau, but is absent from the Nevis itself. Conventional molecular clock calibrations (based on a minimum Nevis-Mataura haplotype divergence of 3.0%) indicate that the Nevis flow reversal may have occurred in the early-mid Pleistocene, which is roughly consistent with geological data. The broad phylogeographic structure evident in the Clutha system is consistent with the sedentary nature of nonmigratory galaxiids. Our study reinforces the value of combining biological and geological data for the formulation and testing of historical hypotheses. PMID:11681739

  20. Discovering cancer genes by integrating network and functional properties

    Directory of Open Access Journals (Sweden)

    Davis David P

    2009-09-01

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

  1. Gene networks from DNA microarray data: centrality and lethality

    OpenAIRE

    Provero, P.

    2002-01-01

    We construct a gene network based on expression data from DNA microarray experiments, by establishing a link between two genes whenever the Pearson's correlation coefficient between their expression profiles is higher than a certain cutoff. The resulting connectivity distribution is compatible with a power-law decay with exponent ~1, corrected by an exponential cutoff at large connectivity. The biological relevance of such network is demonstrated by showing that there is a strong statistical ...

  2. Comparing Statistical Methods for Constructing Large Scale Gene Networks

    OpenAIRE

    Jeffrey D Allen; Yang Xie; Min Chen; Luc Girard; Guanghua Xiao

    2012-01-01

    The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constr...

  3. Multiscale Embedded Gene Co-expression Network Analysis

    OpenAIRE

    Song, Won-Min; Zhang, Bin

    2015-01-01

    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...

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

    OpenAIRE

    Dajeong Lim; Nam-Kuk Kim; Seung-Hwan Lee; Hye-Sun Park; Yong-Min Cho; Han-Ha Chai; Heebal Kim

    2014-01-01

    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 d...

  5. Gene Regulatory Network Reconstruction Using Conditional Mutual Information

    Directory of Open Access Journals (Sweden)

    Xiaodong Wang

    2008-06-01

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

  6. Functional identification in correlation networks using gene ontology edge annotation.

    Science.gov (United States)

    Dempsey, Kathryn; Thapa, Ishwor; Bastola, Dhundy; Ali, Hesham

    2012-01-01

    Correlation networks identify mechanisms behind observed change in temporal data sets; however, it is often difficult to discriminate between causative versus coincidental structures in such networks. We propose a method to enhance causative relationships based on annotations derived from the Gene Ontology (GO). Enriching correlation networks with biological relationships is likely to conserve relevant signals while reducing the network size. The obtained results are structures enriched in GO functions, despite reduction in network size. Our proposed method annotates edges according to the shortest path between elements and the position of the deepest common parent in the GO tree. Our results show that such enrichment brings functional relationships to the forefront which allows for the identification of clusters with significant biological relevance. Further, this method impacts the identification of essential genes within a network model. This approach for uncovering true function of relationships provides annotation beyond traditional statistical analysis. PMID:23013651

  7. Listening to the noise: random fluctuations reveal gene network parameters.

    Science.gov (United States)

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2009-01-01

    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 show cell-to-cell variability that can manifest significant phenotypic differences. Noise-induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We show that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks. PMID:19888213

  8. Ancient DNA

    OpenAIRE

    Willerslev, Eske; Cooper, Alan

    2004-01-01

    In the past two decades, ancient DNA research has progressed from the retrieval of small fragments of mitochondrial DNA from a few late Holocene specimens, to large-scale studies of ancient populations, phenotypically important nuclear loci, and even whole mitochondrial genome sequences of extinct species. However, the field is still regularly marred by erroneous reports, which underestimate the extent of contamination within laboratories and samples themselves. An improved understanding of t...

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

    Science.gov (United States)

    Coronnello, Claudia; Tumminello, Michele; Miccichè, Salvatore

    2016-09-01

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

  10. Topological origin of global attractors in gene regulatory networks

    Science.gov (United States)

    Zhang, YunJun; Ouyang, Qi; Geng, Zhi

    2015-02-01

    Fixed-point attractors with global stability manifest themselves in a number of gene regulatory networks. This property indicates the stability of regulatory networks against small state perturbations and is closely related to other complex dynamics. In this paper, we aim to reveal the core modules in regulatory networks that determine their global attractors and the relationship between these core modules and other motifs. This work has been done via three steps. Firstly, inspired by the signal transmission in the regulation process, we extract the model of chain-like network from regulation networks. We propose a module of "ideal transmission chain (ITC)", which is proved sufficient and necessary (under certain condition) to form a global fixed-point in the context of chain-like network. Secondly, by examining two well-studied regulatory networks (i.e., the cell-cycle regulatory networks of Budding yeast and Fission yeast), we identify the ideal modules in true regulation networks and demonstrate that the modules have a superior contribution to network stability (quantified by the relative size of the biggest attraction basin). Thirdly, in these two regulation networks, we find that the double negative feedback loops, which are the key motifs of forming bistability in regulation, are connected to these core modules with high network stability. These results have shed new light on the connection between the topological feature and the dynamic property of regulatory networks.

  11. Weighted gene coexpression network analysis strategies applied to mouse weight

    OpenAIRE

    Fuller, Tova F; Ghazalpour, Anatole; Aten, Jason E.; Drake, Thomas A; Lusis, Aldons J.; Horvath, Steve

    2007-01-01

    Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an ...

  12. A complex network analysis of hypertension-related genes

    Science.gov (United States)

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

    2014-01-01

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

  13. Development of a synthetic gene network to modulate gene expression by mechanical forces.

    Science.gov (United States)

    Kis, Zoltán; Rodin, Tania; Zafar, Asma; Lai, Zhangxing; Freke, Grace; Fleck, Oliver; Del Rio Hernandez, Armando; Towhidi, Leila; Pedrigi, Ryan M; Homma, Takayuki; Krams, Rob

    2016-01-01

    The majority of (mammalian) cells in our body are sensitive to mechanical forces, but little work has been done to develop assays to monitor mechanosensor activity. Furthermore, it is currently impossible to use mechanosensor activity to drive gene expression. To address these needs, we developed the first mammalian mechanosensitive synthetic gene network to monitor endothelial cell shear stress levels and directly modulate expression of an atheroprotective transcription factor by shear stress. The technique is highly modular, easily scalable and allows graded control of gene expression by mechanical stimuli in hard-to-transfect mammalian cells. We call this new approach mechanosyngenetics. To insert the gene network into a high proportion of cells, a hybrid transfection procedure was developed that involves electroporation, plasmids replication in mammalian cells, mammalian antibiotic selection, a second electroporation and gene network activation. This procedure takes 1 week and yielded over 60% of cells with a functional gene network. To test gene network functionality, we developed a flow setup that exposes cells to linearly increasing shear stress along the length of the flow channel floor. Activation of the gene network varied logarithmically as a function of shear stress magnitude. PMID:27404994

  14. 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.

  15. Ancient genomics

    DEFF Research Database (Denmark)

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

    2015-01-01

    The past decade has witnessed a revolution in ancient DNA (aDNA) research. Although the field's focus was previously limited to mitochondrial DNA and a few nuclear markers, whole genome sequences from the deep past can now be retrieved. This breakthrough is tightly connected to the massive sequence...... 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......, archaic hominins, ancient pathogens and megafaunal species. Those have revealed important functional and phenotypic information, as well as unexpected adaptation, migration and admixture patterns. As such, the field of aDNA has entered the new era of genomics and has provided valuable information when...

  16. Comparing statistical methods for constructing large scale gene networks.

    Directory of Open Access Journals (Sweden)

    Jeffrey D Allen

    Full Text Available The gene regulatory network (GRN reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1 GeneNet, WGCNA (Weighted Correlation Network Analysis, and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks performed well in constructing the global network structure; (2 GeneNet and SPACE (Sparse PArtial Correlation Estimation performed well in identifying a few connections with high specificity.

  17. A spruce gene map infers ancient plant genome reshuffling and subsequent slow evolution in the gymnosperm lineage leading to extant conifers

    Directory of Open Access Journals (Sweden)

    Pavy Nathalie

    2012-10-01

    Full Text Available Abstract Background Seed plants are composed of angiosperms and gymnosperms, which diverged from each other around 300 million years ago. While much light has been shed on the mechanisms and rate of genome evolution in flowering plants, such knowledge remains conspicuously meagre for the gymnosperms. Conifers are key representatives of gymnosperms and the sheer size of their genomes represents a significant challenge for characterization, sequencing and assembling. Results To gain insight into the macro-organisation and long-term evolution of the conifer genome, we developed a genetic map involving 1,801 spruce genes. We designed a statistical approach based on kernel density estimation to analyse gene density and identified seven gene-rich isochors. Groups of co-localizing genes were also found that were transcriptionally co-regulated, indicative of functional clusters. Phylogenetic analyses of 157 gene families for which at least two duplicates were mapped on the spruce genome indicated that ancient gene duplicates shared by angiosperms and gymnosperms outnumbered conifer-specific duplicates by a ratio of eight to one. Ancient duplicates were much more translocated within and among spruce chromosomes than conifer-specific duplicates, which were mostly organised in tandem arrays. Both high synteny and collinearity were also observed between the genomes of spruce and pine, two conifers that diverged more than 100 million years ago. Conclusions Taken together, these results indicate that much genomic evolution has occurred in the seed plant lineage before the split between gymnosperms and angiosperms, and that the pace of evolution of the genome macro-structure has been much slower in the gymnosperm lineage leading to extent conifers than that seen for the same period of time in flowering plants. This trend is largely congruent with the contrasted rates of diversification and morphological evolution observed between these two groups of seed

  18. Tamil merchant in ancient Mesopotamia.

    Directory of Open Access Journals (Sweden)

    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.

  19. Tamil merchant in ancient Mesopotamia.

    Science.gov (United States)

    Palanichamy, Malliya Gounder; Mitra, Bikash; Debnath, Monojit; Agrawal, Suraksha; Chaudhuri, Tapas Kumar; Zhang, Ya-Ping

    2014-01-01

    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. PMID:25299580

  20. Using GeneReg to construct time delay gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Qian Ziliang

    2010-05-01

    Full Text Available Abstract Background Understanding gene expression and regulation is essential for understanding biological mechanisms. Because gene expression profiling has been widely used in basic biological research, especially in transcription regulation studies, we have developed GeneReg, an easy-to-use R package, to construct gene regulatory networks from time course gene expression profiling data; More importantly, this package can provide information about time delays between expression change in a regulator and that of its target genes. Findings The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. There are two parameters in the model, time delay and regulation coefficient. Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. Regulation coefficient expresses the regulation effect: a positive regulation coefficient indicates activation and negative indicates repression. GeneReg was implemented on a real Saccharomyces cerevisiae cell cycle dataset; more than thirty percent of the modeled regulations, based entirely on gene expression files, were found to be consistent with previous discoveries from known databases. Conclusions GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. It may be applied to study time-related biological processes such as cell cycle, cell differentiation, or causal inference.

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

    Science.gov (United States)

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

    2014-12-01

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

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

    Science.gov (United States)

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

    2013-12-01

    Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively. PMID:24140883

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

    KAUST Repository

    Fujii, Chisato

    2015-04-16

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

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

    Directory of Open Access Journals (Sweden)

    Thomas R Geiger

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

  5. 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

  6. Transcriptional Control in the Segmentation Gene Network of Drosophila

    Directory of Open Access Journals (Sweden)

    Schroeder Mark D

    2004-01-01

    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

  7. Ancient Duplications Have Led to Functional Divergence of Vitellogenin-Like Genes Potentially Involved in Inflammation and Oxidative Stress in Honey Bees.

    Science.gov (United States)

    Salmela, Heli; Stark, Taina; Stucki, Dimitri; Fuchs, Siiri; Freitak, Dalial; Dey, Alivia; Kent, Clement F; Zayed, Amro; Dhaygude, Kishor; Hokkanen, Heikki; Sundström, Liselotte

    2016-03-01

    Protection against inflammation and oxidative stress is key in slowing down aging processes. The honey bee (Apis mellifera) shows flexible aging patterns linked to the social role of individual bees. One molecular factor associated with honey bee aging regulation is vitellogenin, a lipoglycophosphoprotein with anti-inflammatory and antioxidant properties. Recently, we identified three genes in Hymenopteran genomes arisen from ancient insect vitellogenin duplications, named vg-like-A, -B, and -C. The function of these vitellogenin homologs is unclear. We hypothesize that some of them might share gene- and protein-level similarities and a longevity-supporting role with vitellogenin. Here, we show how the structure and modifications of the vg-like genes and proteins have diverged from vitellogenin. Furthermore, all three vg-like genes show signs of positive selection, but the spatial location of the selected protein sites differ from those found in vitellogenin. We show that all these genes are expressed in both long-lived winter worker bees and in summer nurse bees with intermediate life expectancy, yet only vg-like-A shows elevated expression in winter bees as found in vitellogenin. Finally, we show that vg-like-A responds more strongly than vitellogenin to inflammatory and oxidative conditions in summer nurse bees, and that also vg-like-B responds to oxidative stress. We associate vg-like-A and, to lesser extent, vg-like-B to the antiaging roles of vitellogenin, but that vg-like-C probably is involved in some other function. Our analysis indicates that an ancient duplication event facilitated the adaptive and functional divergence of vitellogenin and its paralogs in the honey bee. PMID:26961250

  8. Ancient Duplications Have Led to Functional Divergence of Vitellogenin-Like Genes Potentially Involved in Inflammation and Oxidative Stress in Honey Bees

    Science.gov (United States)

    Salmela, Heli; Stark, Taina; Stucki, Dimitri; Fuchs, Siiri; Freitak, Dalial; Dey, Alivia; Kent, Clement F.; Zayed, Amro; Dhaygude, Kishor; Hokkanen, Heikki; Sundström, Liselotte

    2016-01-01

    Protection against inflammation and oxidative stress is key in slowing down aging processes. The honey bee (Apis mellifera) shows flexible aging patterns linked to the social role of individual bees. One molecular factor associated with honey bee aging regulation is vitellogenin, a lipoglycophosphoprotein with anti-inflammatory and antioxidant properties. Recently, we identified three genes in Hymenopteran genomes arisen from ancient insect vitellogenin duplications, named vg-like-A, -B, and -C. The function of these vitellogenin homologs is unclear. We hypothesize that some of them might share gene- and protein-level similarities and a longevity-supporting role with vitellogenin. Here, we show how the structure and modifications of the vg-like genes and proteins have diverged from vitellogenin. Furthermore, all three vg-like genes show signs of positive selection, but the spatial location of the selected protein sites differ from those found in vitellogenin. We show that all these genes are expressed in both long-lived winter worker bees and in summer nurse bees with intermediate life expectancy, yet only vg-like-A shows elevated expression in winter bees as found in vitellogenin. Finally, we show that vg-like-A responds more strongly than vitellogenin to inflammatory and oxidative conditions in summer nurse bees, and that also vg-like-B responds to oxidative stress. We associate vg-like-A and, to lesser extent, vg-like-B to the antiaging roles of vitellogenin, but that vg-like-C probably is involved in some other function. Our analysis indicates that an ancient duplication event facilitated the adaptive and functional divergence of vitellogenin and its paralogs in the honey bee. PMID:26961250

  9. Propagation of genetic variation in gene regulatory networks

    OpenAIRE

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

    2013-01-01

    A future quantitative genetics theory should link genetic variation to phenotypic variation in a causally cohesive way based on how genes actually work and interact. We provide a theoretical framework for predicting and understanding the manifestation of genetic variation in haploid and diploid regulatory networks with arbitrary feedback structures and intra-locus and inter-locus functional dependencies. Using results from network and graph theory, we define propagation functions describing h...

  10. An empirical comparison of popular algorithms for learning gene networks

    Czech Academy of Sciences Publication Activity Database

    Djordjilović, V.; Chiogna, M.; Vomlel, Jiří

    Praha: Oeconomica, 2015 - (Kratochvíl, V.), s. 61-72 ISBN 978-80-245-2102-2. [WUPES 2015. Workshop on Uncertainty Processing /10./. Monínec (CZ), 16.09.2015-19.09.2015] Institutional support: RVO:67985556 Keywords : Bayesian networks * Gene networks * Biological pathways Subject RIV: IN - Informatics, Computer Science http://library.utia.cas.cz/separaty/2015/MTR/vomlel-0450559.pdf

  11. 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.

  12. In silico evolution of gene cooption in pattern-forming gene networks.

    Science.gov (United States)

    Spirov, Alexander V; Sabirov, Marat A; Holloway, David M

    2012-01-01

    Gene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN), comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Using evolutionary computations (EC), we investigate how cooption affects the evolvability, outgrowth and robustness of GRNs. We use a data-driven model of insect segmentation, for the fruit fly Drosophila, and evaluate fitness by robustness to maternal variability-a major constraint in biological development. We compare two mechanisms of gene cooption: a simpler one with gene Introduction and Withdrawal operators; and one in which GRN elements can be altered by transposon infection. Starting from a minimal 2-gene network, insufficient for fitting the Drosophila gene expression patterns, we find a general trend of coopting available genes into the GRN, in order to better fit the data. With the transposon mechanism, we find co-evolutionary oscillations between genes and their transposons. These oscillations may offer a new technique in EC for overcoming premature convergence. Finally, we comment on how a differential equations (in contrast to Boolean) approach is necessary for addressing realistic continuous variation in biochemical parameters. PMID:23365523

  13. In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks

    Directory of Open Access Journals (Sweden)

    Alexander V. Spirov

    2012-01-01

    Full Text Available Gene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN, comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Using evolutionary computations (EC, we investigate how cooption affects the evolvability, outgrowth and robustness of GRNs. We use a data-driven model of insect segmentation, for the fruit fly Drosophila, and evaluate fitness by robustness to maternal variability—a major constraint in biological development. We compare two mechanisms of gene cooption: a simpler one with gene Introduction and Withdrawal operators; and one in which GRN elements can be altered by transposon infection. Starting from a minimal 2-gene network, insufficient for fitting the Drosophila gene expression patterns, we find a general trend of coopting available genes into the GRN, in order to better fit the data. With the transposon mechanism, we find co-evolutionary oscillations between genes and their transposons. These oscillations may offer a new technique in EC for overcoming premature convergence. Finally, we comment on how a differential equations (in contrast to Boolean approach is necessary for addressing realistic continuous variation in biochemical parameters.

  14. Hysteresis in a synthetic mammalian gene network

    OpenAIRE

    Kramer, Beat P.; Fussenegger, Martin

    2005-01-01

    Bistable and hysteretic switches, enabling cells to adopt multiple internal expression states in response to a single external input signal, have a pivotal impact on biological systems, ranging from cell-fate decisions to cell-cycle control. We have designed a synthetic hysteretic mammalian transcription network. A positive feedback loop, consisting of a transgene and transactivator (TA) cotranscribed by TA's cognate promoter, is repressed by constitutive expression of a macrolide-dependent t...

  15. Multicolor labeling in developmental gene regulatory network analysis.

    Science.gov (United States)

    Sethi, Aditya J; Angerer, Robert C; Angerer, Lynne M

    2014-01-01

    The sea urchin embryo is an important model system for developmental gene regulatory network (GRN) analysis. This chapter describes the use of multicolor fluorescent in situ hybridization (FISH) as well as a combination of FISH and immunohistochemistry in sea urchin embryonic GRN studies. The methods presented here can be applied to a variety of experimental settings where accurate spatial resolution of multiple gene products is required for constructing a developmental GRN. PMID:24567220

  16. On the basic computational structure of gene regulatory networks

    OpenAIRE

    Rodriguez-Caso, Carlos; Corominas-Murtra, Bernat; Solé, Ricard V.

    2009-01-01

    Gene regulatory networks constitute the first layer of the cellular computation for cell adaptation and surveillance. In these webs, a set of causal relations is built up from thousands of interactions between transcription factors and their target genes. The large size of these webs and their entangled nature make difficult to achieve a global view of their internal organisation. Here, this problem has been addressed through a comparative study for {\\em Escherichia coli}, {\\em Bacillus subti...

  17. Epidermal differentiation gene regulatory networks controlled by MAF and MAFB.

    Science.gov (United States)

    Labott, Andrew T; Lopez-Pajares, Vanessa

    2016-06-01

    Numerous regulatory factors in epidermal differentiation and their role in regulating different cell states have been identified in recent years. However, the genetic interactions between these regulators over the dynamic course of differentiation have not been studied. In this Extra-View article, we review recent work by Lopez-Pajares et al. that explores a new regulatory network in epidermal differentiation. They analyze the changing transcriptome throughout epidermal regeneration to identify 3 separate gene sets enriched in the progenitor, early and late differentiation states. Using expression module mapping, MAF along with MAFB, are identified as transcription factors essential for epidermal differentiation. Through double knock-down of MAF:MAFB using siRNA and CRISPR/Cas9-mediated knockout, epidermal differentiation was shown to be impaired both in-vitro and in-vivo, confirming MAF:MAFB's role to activate genes that drive differentiation. Lopez-Pajares and collaborators integrated 42 published regulator gene sets and the MAF:MAFB gene set into the dynamic differentiation gene expression landscape and found that lncRNAs TINCR and ANCR act as upstream regulators of MAF:MAFB. Furthermore, ChIP-seq analysis of MAF:MAFB identified key transcription factor genes linked to epidermal differentiation as downstream effectors. Combined, these findings illustrate a dynamically regulated network with MAF:MAFB as a crucial link for progenitor gene repression and differentiation gene activation. PMID:27097296

  18. miRTargetLink—miRNAs, Genes and Interaction Networks

    Science.gov (United States)

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

    2016-01-01

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink. PMID:27089332

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

    Directory of Open Access Journals (Sweden)

    Bert van der Zwaag

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

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

    OpenAIRE

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

    2014-01-01

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

  1. Lists2Networks: Integrated analysis of gene/protein lists

    Directory of Open Access Journals (Sweden)

    Ma'ayan Avi

    2010-02-01

    Full Text Available Abstract Background Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses. Results We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system. Conclusions Lists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org.

  2. Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks.

    Science.gov (United States)

    Wu, Siqi; Joseph, Antony; Hammonds, Ann S; Celniker, Susan E; Yu, Bin; Frise, Erwin

    2016-04-19

    Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set ofDrosophilaearly embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation ofDrosophilaexpression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. The performance of PP with theDrosophiladata suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data. PMID:27071099

  3. Inferring the Gene Network Underlying the Branching of Tomato Inflorescence

    NARCIS (Netherlands)

    Astola, L.; Stigter, J.D.; Dijk, van A.D.J.; Daelen, van R.; Molenaar, J.

    2014-01-01

    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

  4. Differential regulation drives plasticity in sex determination gene networks

    Directory of Open Access Journals (Sweden)

    Seymour Robert M

    2010-12-01

    Full Text Available Abstract Background Sex determination networks evolve rapidly and have been studied intensely across many species, particularly in insects, thus presenting good models to study the evolutionary plasticity of gene networks. Results We study the evolution of an unlinked gene capable of regulating an existing diploid sex determination system. Differential gene expression determines phenotypic sex and fitness, dramatically reducing the number of assumptions of previous models. It allows us to make a quantitative evaluation of the full range of evolutionary outcomes of the system and an assessment of the likely contribution of sexual conflict to change in sex determination systems. Our results show under what conditions network mutations causing differential regulation can lead to the reshaping of sex determination networks. Conclusion The analysis demonstrates the complex relationship between mutation and outcome: the same mutation can produce many different evolved populations, while the same evolved population can be produced by many different mutations. Existing network structure alters the constraints and frequency of evolutionary changes, which include the recruitment of new regulators, changes in heterogamety, protected polymorphisms, and transitions to a new locus that controls sex determination.

  5. Obtain osteoarthritis related molecular signature genes through regulation network.

    Science.gov (United States)

    Li, Yawei; Wang, Bing; Lv, Guohua; Xiong, Guangzhong; Liu, Wei Dong; Li, Lei

    2012-01-01

    Osteoarthritis (OA), also known as degenerative joint disease or osteoarthrosis, is the most common form of arthritis. OA occurs when cartilage in the joints wears down over time. We used the GSE1919 series to identify potential genes that correlated to OA. The aim of our study was to obtain a molecular signature of OA through the regulation network based on differentially expressed genes. From the result of regulation network construction in OA, a number of transcription factors (TFs) and pathways closely related to OA were linked by our method. Peroxisome proliferator-activated receptor γ also arises as hub nodes in our transcriptome network and certain TFs containing CEBPD, EGR2 and ETS2 were shown to be related to OA by a previous study. PMID:21946934

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

    Institute of Scientific and Technical Information of China (English)

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

    2011-01-01

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

  7. Robustness and Accuracy in Sea Urchin Developmental Gene Regulatory Networks

    Science.gov (United States)

    Ben-Tabou de-Leon, Smadar

    2016-01-01

    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. PMID:26913048

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

    Directory of Open Access Journals (Sweden)

    Elena R Alvarez-Buylla

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

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

    Directory of Open Access Journals (Sweden)

    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.

  10. 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.

  11. Identifying disease feature genes based on cellular localized gene functional modules and regulation networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Min; ZHU Jing; GUO Zheng; LI Xia; YANG Da; WANG Lei; RAO Shaoqi

    2006-01-01

    Identifying disease-relevant genes and functional modules, based on gene expression profiles and gene functional knowledge, is of high importance for studying disease mechanisms and subtyping disease phenotypes. Using gene categories of biological process and cellular component in Gene Ontology, we propose an approach to selecting functional modules enriched with differentially expressed genes, and identifying the feature functional modules of high disease discriminating abilities. Using the differentially expressed genes in each feature module as the feature genes, we reveal the relevance of the modules to the studied diseases. Using three datasets for prostate cancer, gastric cancer, and leukemia, we have demonstrated that the proposed modular approach is of high power in identifying functionally integrated feature gene subsets that are highly relevant to the disease mechanisms. Our analysis has also shown that the critical disease-relevant genes might be better recognized from the gene regulation network, which is constructed using the characterized functional modules, giving important clues to the concerted mechanisms of the modules responding to complex disease states. In addition, the proposed approach to selecting the disease-relevant genes by jointly considering the gene functional knowledge suggests a new way for precisely classifying disease samples with clear biological interpretations, which is critical for the clinical diagnosis and the elucidation of the pathogenic basis of complex diseases.

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

    Science.gov (United States)

    Liu, Ji-Long; Zhao, Miao

    2016-01-01

    Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%-2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk-based computational method named TM-rank to prioritize ectopic pregnancy-related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes. PMID:26840308

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

    Science.gov (United States)

    Varrella, Stefano; Ruocco, Nadia; Ianora, Adrianna; Bentley, Matt G.; Costantini, Maria

    2016-01-01

    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. PMID:26914213

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

    Directory of Open Access Journals (Sweden)

    Djordje Djordjevic

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

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

    Science.gov (United States)

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

    2014-01-01

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

  16. Translational cross talk in gene networks.

    Science.gov (United States)

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

    2013-06-01

    It has been shown experimentally that competition for limited translational resources by upstream mRNAs can lead to an anticorrelation between protein counts. Here, we investigate a stochastic model for this phenomenon, in which gene transcripts of different types compete for a finite pool of ribosomes. Throughout, we utilize concepts from the theory of multiclass queues to describe a qualitative shift in protein count statistics as the system transitions from being underloaded (ribosomes exceed transcripts in number) to being overloaded (transcripts exceed ribosomes in number). The exact analytical solution of a simplified stochastic model, in which the numbers of competing mRNAs and ribosomes are fixed, exhibits weak positive correlations between steady-state protein counts when total transcript count slightly exceeds ribosome count, whereas the solution can exhibit strong negative correlations when total transcript count significantly exceeds ribosome count. Extending this analysis, we find approximate but reasonably accurate solutions for a more realistic model, in which abundances of mRNAs and ribosomes are allowed to fluctuate randomly. Here, ribosomal fluctuations contribute positively and mRNA fluctuations contribute negatively to correlations, and when mRNA fluctuations dominate ribosomal fluctuations, a strong anticorrelation extremum reliably occurs near the transition from the underloaded to the overloaded regime. PMID:23746529

  17. Vitamin D and gene networks in human osteoblasts

    Directory of Open Access Journals (Sweden)

    JohannesVan Leeuwen

    2014-04-01

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

  18. Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum.

    Science.gov (United States)

    Guo, Li; Zhao, Guoyi; Xu, Jin-Rong; Kistler, H Corby; Gao, Lixin; Ma, Li-Jun

    2016-07-01

    Head blight caused by Fusarium graminearum threatens world-wide wheat production, resulting in both yield loss and mycotoxin contamination. We reconstructed the global F. graminearum gene regulatory network (GRN) from a large collection of transcriptomic data using Bayesian network inference, a machine-learning algorithm. This GRN reveals connectivity between key regulators and their target genes. Focusing on key regulators, this network contains eight distinct but interwoven modules. Enriched for unique functions, such as cell cycle, DNA replication, transcription, translation and stress responses, each module exhibits distinct expression profiles. Evolutionarily, the F. graminearum genome can be divided into core regions shared with closely related species and variable regions harboring genes that are unique to F. graminearum and perform species-specific functions. Interestingly, the inferred top regulators regulate genes that are significantly enriched from the same genomic regions (P control strategies involving the targeting of master regulators in pathogens. The program can be used to construct GRNs of other plant pathogens. PMID:26990214

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Nolan Priedigkeit

    2015-02-01

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

  1. Identification of microRNA-regulated gene networks by expression analysis of target genes

    OpenAIRE

    Gennarino, Vincenzo Alessandro; D'Angelo, Giovanni; Dharmalingam, Gopuraja; Fernandez, Serena; Russolillo, Giorgio; Sanges, Remo; Mutarelli, Margherita; Belcastro, Vincenzo; Ballabio, Andrea; Verde, Pasquale; Sardiello, Marco; Banfi, Sandro

    2012-01-01

    MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assig...

  2. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function

    OpenAIRE

    Warde-Farley, David; Sylva L. Donaldson; Comes, Ovi; Zuberi, Khalid; Badrawi, Rashad; Chao, Pauline; Franz, Max; Grouios, Chris; Kazi, Farzana; Lopes, Christian Tannus; Maitland, Anson; Mostafavi, Sara; Montojo, Jason; Shao, Quentin; Wright, George

    2010-01-01

    GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis t...

  3. An estimation method for inference of gene regulatory net-work using Bayesian network with uniting of partial problems

    Directory of Open Access Journals (Sweden)

    Watanabe Yukito

    2012-01-01

    Full Text Available Abstract Background Bayesian networks (BNs have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures. Results In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures. Conclusions We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

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

    Directory of Open Access Journals (Sweden)

    Crane Martin

    2010-01-01

    Full Text Available 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.

  5. 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.

  6. Ancient Egypt

    Science.gov (United States)

    Swamy, Ashwin Balegar

    This thesis involves development of an interactive GIS (Geographic Information System) based application, which gives information about the ancient history of Egypt. The astonishing architecture, the strange burial rituals and their civilization were some of the intriguing questions that motivated me towards developing this application. The application is a historical timeline starting from 3100 BC, leading up to 664 BC, focusing on the evolution of the Egyptian dynasties. The tool holds information regarding some of the famous monuments which were constructed during that era and also about the civilizations that co-existed. It also provides details about the religions followed by their kings. It also includes the languages spoken during those periods. The tool is developed using JAVA, a programing language and MOJO (Map Objects Java Objects) a product of ESRI (Environmental Science Research Institute) to create map objects, to provide geographic information. JAVA Swing is used for designing the user interface. HTML (Hyper Text Markup Language) pages are created to provide the user with more information related to the historic period. CSS (Cascade Style Sheets) and JAVA Scripts are used with HTML5 to achieve creative display of content. The tool is kept simple and easy for the user to interact with. The tool also includes pictures and videos for the user to get a feel of the historic period. The application is built to motivate people to know more about one of the prominent and ancient civilization of the Mediterranean world.

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

    Directory of Open Access Journals (Sweden)

    Daniele Muraro

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

  8. Gene Profiling of Aortic Valve Interstitial Cells under Elevated Pressure Conditions: Modulation of Inflammatory Gene Networks

    Directory of Open Access Journals (Sweden)

    James N. Warnock

    2011-01-01

    Full Text Available The study aimed to identify mechanosensitive pathways and gene networks that are stimulated by elevated cyclic pressure in aortic valve interstitial cells (VICs and lead to detrimental tissue remodeling and/or pathogenesis. Porcine aortic valve leaflets were exposed to cyclic pressures of 80 or 120 mmHg, corresponding to diastolic transvalvular pressure in normal and hypertensive conditions, respectively. Linear, two-cycle amplification of total RNA, followed by microarray was performed for transcriptome analysis (with qRT-PCR validation. A combination of systems biology modeling and pathway analysis identified novel genes and molecular mechanisms underlying the biological response of VICs to elevated pressure. 56 gene transcripts related to inflammatory response mechanisms were differentially expressed. TNF-α, IL-1α, and IL-1β were key cytokines identified from the gene network model. Also of interest was the discovery that pentraxin 3 (PTX3 was significantly upregulated under elevated pressure conditions (41-fold change. In conclusion, a gene network model showing differentially expressed inflammatory genes and their interactions in VICs exposed to elevated pressure has been developed. This system overview has detected key molecules that could be targeted for pharmacotherapy of aortic stenosis in hypertensive patients.

  9. Oncogenes and tumor suppressor genes: comparative genomics and network perspectives

    OpenAIRE

    Zhu, Kevin; Liu, Qi; Zhou, Yubo; Tao, Cui; Zhao, Zhongming; Sun, Jingchun; Xu, Hua

    2015-01-01

    Background Defective tumor suppressor genes (TSGs) and hyperactive oncogenes (OCGs) heavily contribute to cell proliferation and apoptosis during cancer development through genetic variations such as somatic mutations and deletions. Moreover, they usually do not perform their cellular functions individually but rather execute jointly. Therefore, a comprehensive comparison of their mutation patterns and network properties may provide a deeper understanding of their roles in the cancer developm...

  10. Grouped graphical Granger modeling for gene expression regulatory networks discovery

    OpenAIRE

    Lozano, Aurélie C.; Abe, Naoki; Yan LIU; Rosset, Saharon

    2009-01-01

    We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of ‘Granger causality’ to make assertions on causality through inference on time-lagged effects. Existing algorithms, however, have neglected an important aspect of the problem—the grou...

  11. Topological effects of data incompleteness of gene regulatory networks

    CERN Document Server

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

    2012-01-01

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

  12. Identification of microRNA-regulated gene networks by expression analysis of target genes.

    Science.gov (United States)

    Gennarino, Vincenzo Alessandro; D'Angelo, Giovanni; Dharmalingam, Gopuraja; Fernandez, Serena; Russolillo, Giorgio; Sanges, Remo; Mutarelli, Margherita; Belcastro, Vincenzo; Ballabio, Andrea; Verde, Pasquale; Sardiello, Marco; Banfi, Sandro

    2012-06-01

    MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGFβ pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGFβ signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs. PMID:22345618

  13. Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation

    Science.gov (United States)

    Goode, Debbie K.; Obier, Nadine; Vijayabaskar, M.S.; Lie-A-Ling, Michael; Lilly, Andrew J.; Hannah, Rebecca; Lichtinger, Monika; Batta, Kiran; Florkowska, Magdalena; Patel, Rahima; Challinor, Mairi; Wallace, Kirstie; Gilmour, Jane; Assi, Salam A.; Cauchy, Pierre; Hoogenkamp, Maarten; Westhead, David R.; Lacaud, Georges; Kouskoff, Valerie; Göttgens, Berthold; Bonifer, Constanze

    2016-01-01

    Summary Metazoan development involves the successive activation and silencing of specific gene expression programs and is driven by tissue-specific transcription factors programming the chromatin landscape. To understand how this process executes an entire developmental pathway, we generated global gene expression, chromatin accessibility, histone modification, and transcription factor binding data from purified embryonic stem cell-derived cells representing six sequential stages of hematopoietic specification and differentiation. Our data reveal the nature of regulatory elements driving differential gene expression and inform how transcription factor binding impacts on promoter activity. We present a dynamic core regulatory network model for hematopoietic specification and demonstrate its utility for the design of reprogramming experiments. Functional studies motivated by our genome-wide data uncovered a stage-specific role for TEAD/YAP factors in mammalian hematopoietic specification. Our study presents a powerful resource for studying hematopoiesis and demonstrates how such data advance our understanding of mammalian development. PMID:26923725

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

    Science.gov (United States)

    Shaw, Harry C.

    2016-01-01

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

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

    Science.gov (United States)

    Shaw, Harry C.

    2016-01-01

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

  16. Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation.

    Science.gov (United States)

    Goode, Debbie K; Obier, Nadine; Vijayabaskar, M S; Lie-A-Ling, Michael; Lilly, Andrew J; Hannah, Rebecca; Lichtinger, Monika; Batta, Kiran; Florkowska, Magdalena; Patel, Rahima; Challinor, Mairi; Wallace, Kirstie; Gilmour, Jane; Assi, Salam A; Cauchy, Pierre; Hoogenkamp, Maarten; Westhead, David R; Lacaud, Georges; Kouskoff, Valerie; Göttgens, Berthold; Bonifer, Constanze

    2016-03-01

    Metazoan development involves the successive activation and silencing of specific gene expression programs and is driven by tissue-specific transcription factors programming the chromatin landscape. To understand how this process executes an entire developmental pathway, we generated global gene expression, chromatin accessibility, histone modification, and transcription factor binding data from purified embryonic stem cell-derived cells representing six sequential stages of hematopoietic specification and differentiation. Our data reveal the nature of regulatory elements driving differential gene expression and inform how transcription factor binding impacts on promoter activity. We present a dynamic core regulatory network model for hematopoietic specification and demonstrate its utility for the design of reprogramming experiments. Functional studies motivated by our genome-wide data uncovered a stage-specific role for TEAD/YAP factors in mammalian hematopoietic specification. Our study presents a powerful resource for studying hematopoiesis and demonstrates how such data advance our understanding of mammalian development. PMID:26923725

  17. Major Histocompatibility Complex Genes Map to Two Chromosomes in an Evolutionarily Ancient Reptile, the Tuatara Sphenodon punctatus.

    Science.gov (United States)

    Miller, Hilary C; O'Meally, Denis; Ezaz, Tariq; Amemiya, Chris; Marshall-Graves, Jennifer A; Edwards, Scott

    2015-07-01

    Major histocompatibility complex (MHC) genes are a central component of the vertebrate immune system and usually exist in a single genomic region. However, considerable differences in MHC organization and size exist between different vertebrate lineages. Reptiles occupy a key evolutionary position for understanding how variation in MHC structure evolved in vertebrates, but information on the structure of the MHC region in reptiles is limited. In this study, we investigate the organization and cytogenetic location of MHC genes in the tuatara (Sphenodon punctatus), the sole extant representative of the early-diverging reptilian order Rhynchocephalia. Sequencing and mapping of 12 clones containing class I and II MHC genes from a bacterial artificial chromosome library indicated that the core MHC region is located on chromosome 13q. However, duplication and translocation of MHC genes outside of the core region was evident, because additional class I MHC genes were located on chromosome 4p. We found a total of seven class I sequences and 11 class II β sequences, with evidence for duplication and pseudogenization of genes within the tuatara lineage. The tuatara MHC is characterized by high repeat content and low gene density compared with other species and we found no antigen processing or MHC framework genes on the MHC gene-containing clones. Our findings indicate substantial differences in MHC organization in tuatara compared with mammalian and avian MHCs and highlight the dynamic nature of the MHC. Further sequencing and annotation of tuatara and other reptile MHCs will determine if the tuatara MHC is representative of nonavian reptiles in general. PMID:25953959

  18. Phylogenetic analysis of bacterial and archaeal arsC gene sequences suggests an ancient, common origin for arsenate reductase

    Directory of Open Access Journals (Sweden)

    Dugas Sandra L

    2003-07-01

    Full Text Available Abstract Background The ars gene system provides arsenic resistance for a variety of microorganisms and can be chromosomal or plasmid-borne. The arsC gene, which codes for an arsenate reductase is essential for arsenate resistance and transforms arsenate into arsenite, which is extruded from the cell. A survey of GenBank shows that arsC appears to be phylogenetically widespread both in organisms with known arsenic resistance and those organisms that have been sequenced as part of whole genome projects. Results Phylogenetic analysis of aligned arsC sequences shows broad similarities to the established 16S rRNA phylogeny, with separation of bacterial, archaeal, and subsequently eukaryotic arsC genes. However, inconsistencies between arsC and 16S rRNA are apparent for some taxa. Cyanobacteria and some of the γ-Proteobacteria appear to possess arsC genes that are similar to those of Low GC Gram-positive Bacteria, and other isolated taxa possess arsC genes that would not be expected based on known evolutionary relationships. There is no clear separation of plasmid-borne and chromosomal arsC genes, although a number of the Enterobacteriales (γ-Proteobacteria possess similar plasmid-encoded arsC sequences. Conclusion The overall phylogeny of the arsenate reductases suggests a single, early origin of the arsC gene and subsequent sequence divergence to give the distinct arsC classes that exist today. Discrepancies between 16S rRNA and arsC phylogenies support the role of horizontal gene transfer (HGT in the evolution of arsenate reductases, with a number of instances of HGT early in bacterial arsC evolution. Plasmid-borne arsC genes are not monophyletic suggesting multiple cases of chromosomal-plasmid exchange and subsequent HGT. Overall, arsC phylogeny is complex and is likely the result of a number of evolutionary mechanisms.

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

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    Chen Xin

    2012-10-01

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

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

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    Jochen Hecht

    2008-03-01

    Full Text Available The skeleton is one of the most important features for the reconstruction of vertebrate phylogeny but few data are available to understand its molecular origin. In mammals the Runt genes are central regulators of skeletogenesis. Runx2 was shown to be essential for osteoblast differentiation, tooth development, and bone formation. Both Runx2 and Runx3 are essential for chondrocyte maturation. Furthermore, Runx2 directly regulates Indian hedgehog expression, a master coordinator of skeletal development. To clarify the correlation of Runt gene evolution and the emergence of cartilage and bone in vertebrates, we cloned the Runt genes from hagfish as representative of jawless fish (MgRunxA, MgRunxB and from dogfish as representative of jawed cartilaginous fish (ScRunx1-3. According to our phylogenetic reconstruction the stem species of chordates harboured a single Runt gene and thereafter Runt locus duplications occurred during early vertebrate evolution. All newly isolated Runt genes were expressed in cartilage according to quantitative PCR. In situ hybridisation confirmed high MgRunxA expression in hard cartilage of hagfish. In dogfish ScRunx2 and ScRunx3 were expressed in embryonal cartilage whereas all three Runt genes were detected in teeth and placoid scales. In cephalochordates (lancelets Runt, Hedgehog and SoxE were strongly expressed in the gill bars and expression of Runt and Hedgehog was found in endo- as well as ectodermal cells. Furthermore we demonstrate that the lancelet Runt protein binds to Runt binding sites in the lancelet Hedgehog promoter and regulates its activity. Together, these results suggest that Runt and Hedgehog were part of a core gene network for cartilage formation, which was already active in the gill bars of the common ancestor of cephalochordates and vertebrates and diversified after Runt duplications had occurred during vertebrate evolution. The similarities in expression patterns of Runt genes support the view

  1. Identification of Gene Modules Associated with Drought Response in Rice by Network-Based Analysis

    OpenAIRE

    Lida Zhang; Shunwu Yu; Kaijing Zuo; Lijun Luo; Kexuan Tang

    2012-01-01

    Understanding the molecular mechanisms that underlie plant responses to drought stress is challenging due to the complex interplay of numerous different genes. Here, we used network-based gene clustering to uncover the relationships between drought-responsive genes from large microarray datasets. We identified 2,607 rice genes that showed significant changes in gene expression under drought stress; 1,392 genes were highly intercorrelated to form 15 gene modules. These drought-responsive gene ...

  2. Signal Network Analysis of Plant Genes Responding to Ionizing Radiation

    International Nuclear Information System (INIS)

    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

  3. Gene Networks in Plant Ozone Stress Response and Tolerance

    Institute of Scientific and Technical Information of China (English)

    Agnieszka Ludwikow; Jan Sadowski

    2008-01-01

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

  4. Complex Dynamic Behavior in Simple Gene Regulatory Networks

    Science.gov (United States)

    Santillán Zerón, Moisés

    2007-02-01

    Knowing the complete genome of a given species is just a piece of the puzzle. To fully unveil the systems behavior of an organism, an organ, or even a single cell, we need to understand the underlying gene regulatory dynamics. Given the complexity of the whole system, the ultimate goal is unattainable for the moment. But perhaps, by analyzing the most simple genetic systems, we may be able to develop the mathematical techniques and procedures required to tackle more complex genetic networks in the near future. In the present work, the techniques for developing mathematical models of simple bacterial gene networks, like the tryptophan and lactose operons are introduced. Despite all of the underlying assumptions, such models can provide valuable information regarding gene regulation dynamics. Here, we pay special attention to robustness as an emergent property. These notes are organized as follows. In the first section, the long historical relation between mathematics, physics, and biology is briefly reviewed. Recently, the multidisciplinary work in biology has received great attention in the form of systems biology. The main concepts of this novel science are discussed in the second section. A very slim introduction to the essential concepts of molecular biology is given in the third section. In the fourth section, a brief introduction to chemical kinetics is presented. Finally, in the fifth section, a mathematical model for the lactose operon is developed and analyzed..

  5. 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.

  6. An ancient history of gene duplications, fusions and losses in the evolution of APOBEC3 mutators in mammals

    Directory of Open Access Journals (Sweden)

    Münk Carsten

    2012-05-01

    Full Text Available Abstract Background The APOBEC3 (A3 genes play a key role in innate antiviral defense in mammals by introducing directed mutations in the DNA. The human genome encodes for seven A3 genes, with multiple splice alternatives. Different A3 proteins display different substrate specificity, but the very basic question on how discerning self from non-self still remains unresolved. Further, the expression of A3 activity/ies shapes the way both viral and host genomes evolve. Results We present here a detailed temporal analysis of the origin and expansion of the A3 repertoire in mammals. Our data support an evolutionary scenario where the genome of the mammalian ancestor encoded for at least one ancestral A3 gene, and where the genome of the ancestor of placental mammals (and possibly of the ancestor of all mammals already encoded for an A3Z1-A3Z2-A3Z3 arrangement. Duplication events of the A3 genes have occurred independently in different lineages: humans, cats and horses. In all of them, gene duplication has resulted in changes in enzyme activity and/or substrate specificity, in a paradigmatic example of convergent adaptive evolution at the genomic level. Finally, our results show that evolutionary rates for the three A3Z1, A3Z2 and A3Z3 motifs have significantly decreased in the last 100 Mya. The analysis constitutes a textbook example of the evolution of a gene locus by duplication and sub/neofunctionalization in the context of virus-host arms race. Conclusions Our results provide a time framework for identifying ancestral and derived genomic arrangements in the APOBEC loci, and to date the expansion of this gene family for different lineages through time, as a response to changes in viral/retroviral/retrotransposon pressure.

  7. 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

  8. Identification of crucial genes in intracranial aneurysm based on weighted gene coexpression network analysis.

    Science.gov (United States)

    Zheng, X; Xue, C; Luo, G; Hu, Y; Luo, W; Sun, X

    2015-05-01

    The rupture of intracranial aneurysm (IA) is the leading cause for devastating subarachnoid hemorrhage. This study aimed to investigate genes related to IA and potential diagnosis targets. Two data sets (GSE15629 and GSE54083) were downloaded from Gene Expression Omnibus database. GSE15629 contained eight RI (ruptured IA), six UI (unruptured IA) and five control IA samples. GSE54083 included 8 RI, 5 UI and 10 superficial temporal artery samples. In total, 452 differentially expressed genes (DEGs) between RI and control, and 570 DEGs between UI and control, were identified. Protein-protein interaction networks for two kinds of DEGs related to RI and UI were constructed, respectively. Module networks were searched for DEGs related to RI or UI based on WGCNA (weighted gene coexpression network analysis). In the significant modules, FOS, CCL2, COL4A2 and CXCL5 were screened as crucial nodes with high degrees. Among them, FOS and CCL2 were enriched in immune response and COL4A2 was involved in the ECM (extracellular matrix) pathway, whereas CXCL5 was related to cytokine-cytokine receptor pathway. Taken together, FOS, CCL2, COL4A2 and CXCL5 might participate in the pathogenesis of RI or UI, and could serve as potential diagnosis targets. PMID:25721208

  9. Ancient Origin of the U2 Small Nuclear RNA Gene-Targeting Non-LTR Retrotransposons Utopia.

    Science.gov (United States)

    Kojima, Kenji K; Jurka, Jerzy

    2015-01-01

    Most non-long terminal repeat (non-LTR) retrotransposons encoding a restriction-like endonuclease show target-specific integration into repetitive sequences such as ribosomal RNA genes and microsatellites. However, only a few target-specific lineages of non-LTR retrotransposons are distributed widely and no lineage is found across the eukaryotic kingdoms. Here we report the most widely distributed lineage of target sequence-specific non-LTR retrotransposons, designated Utopia. Utopia is found in three supergroups of eukaryotes: Amoebozoa, SAR, and Opisthokonta. Utopia is inserted into a specific site of U2 small nuclear RNA genes with different strength of specificity for each family. Utopia families from oomycetes and wasps show strong target specificity while only a small number of Utopia copies from reptiles are flanked with U2 snRNA genes. Oomycete Utopia families contain an "archaeal" RNase H domain upstream of reverse transcriptase (RT), which likely originated from a plant RNase H gene. Analysis of Utopia from oomycetes indicates that multiple lineages of Utopia have been maintained inside of U2 genes with few copy numbers. Phylogenetic analysis of RT suggests the monophyly of Utopia, and it likely dates back to the early evolution of eukaryotes. PMID:26556480

  10. Some metallurgical aspects of ancient silver coins discovered in romania (original and imitations) - provenance, destination and commercial networks

    International Nuclear Information System (INIS)

    The analyses of source materials combined with analyses of archaeological objects could distinguish from pieces produced in different regions and periods. For coins, chemical differences that occur during preparation of alloys will affect the elemental composition and could be used for the identification of technologies and workshops and also to distinguish between originals and counterfeits. We illustrate with the case of Geto-Dacian coins (Thassos and Macedonian - Phillip II, Alexander the Greek and Phillip III 'barbarized' tetradrachms) and with Greek Apollonia and Dyrrhachium silver drachms emitted by these old cities for Pompejus during the First Roman Civil War between Julius Caesar and Pompejus, coins found on the actual territory of Romania (ancient Dacia), probably used as bursaries to pay the Dacian mercenaries allied with Pompejus. To analyze the chemical composition of these coins, we used two methods: Am-241 and Pu-238 gamma sources based X-Ray Fluorescence (XRF) and in vacuum 3 MeV protons Particle Induced X-ray Emission (PIXE). Some special measurements on the edge of some coins (to identify plated exemplaires) were done using the ATOMKI Debrecen Van de Graaf 2 MeV protons microprobe, in the frame of European Action COST G1. Concerning the Geto-Dacian coins, we observed: - There is a reduction of the fineness in time that is specific to almost every coin issue. - Tin concentration in coins increased in time; at the beginning of the coinage (250 - 150 B.C.) this was more or less proportionally to copper. This could suggest that bronze was used in alloying silver coins instead of copper. A very high correlation is not expected because the ratio Sn/Cu in ancient bronzes is far to be a constant. A value of the Cu/Sn ratio close to 1 is not surprising because such objects were common in antiquity. In the last issues (150-50 B.C.) seems that Sn replaced partially Cu. - It seems that tin alloying appeared first time in Transylvania around 150 BC and then

  11. Quantitative assessment of gene expression network module-validation methods.

    Science.gov (United States)

    Li, Bing; Zhang, Yingying; Yu, Yanan; Wang, Pengqian; Wang, Yongcheng; Wang, Zhong; Wang, Yongyan

    2015-01-01

    Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks. PMID:26470848

  12. A new gene co-expression network analysis based on Core Structure Detection (CSD)

    OpenAIRE

    Brunet, A-C; Azais, J-M; Loubes, J-M; Amar, J; Burcelin, R

    2016-01-01

    We propose a novel method to cluster gene networks. Based on a dissimilarity built using correlation structures, we consider networks that connect all the genes based on the strength of their dissimilarity. The large number of genes require the use of the threshold to find sparse structures in the graph. in this work, using the notion of graph coreness, we identify clusters of genes which are central in the network. Then we estimate a network that has these genes as main hubs. We use this new...

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

    Directory of Open Access Journals (Sweden)

    Chang Yu-Te

    2008-11-01

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

  14. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function.

    Science.gov (United States)

    Warde-Farley, David; Donaldson, Sylva L; Comes, Ovi; Zuberi, Khalid; Badrawi, Rashad; Chao, Pauline; Franz, Max; Grouios, Chris; Kazi, Farzana; Lopes, Christian Tannus; Maitland, Anson; Mostafavi, Sara; Montojo, Jason; Shao, Quentin; Wright, George; Bader, Gary D; Morris, Quaid

    2010-07-01

    GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist. PMID:20576703

  15. 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.

  16. CRISPR loci reveal networks of gene exchange in archaea

    Directory of Open Access Journals (Sweden)

    Brodt Avital

    2011-12-01

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

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

    Science.gov (United States)

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

    2016-01-01

    Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols. PMID:26965665

  18. Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

    Directory of Open Access Journals (Sweden)

    Koseska Aneta

    2011-07-01

    Full Text Available Abstract Background Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in

  19. Adipose Co-expression networks across Finns and Mexicans identify novel triglyceride-associated genes

    Directory of Open Access Journals (Sweden)

    Haas Blake E

    2012-12-01

    Full Text Available Abstract Background High serum triglyceride (TG levels is an established risk factor for coronary heart disease (CHD. Fat is stored in the form of TGs in human adipose tissue. We hypothesized that gene co-expression networks in human adipose tissue may be correlated with serum TG levels and help reveal novel genes involved in TG regulation. Methods Gene co-expression networks were constructed from two Finnish and one Mexican study sample using the blockwiseModules R function in Weighted Gene Co-expression Network Analysis (WGCNA. Overlap between TG-associated networks from each of the three study samples were calculated using a Fisher’s Exact test. Gene ontology was used to determine known pathways enriched in each TG-associated network. Results We measured gene expression in adipose samples from two Finnish and one Mexican study sample. In each study sample, we observed a gene co-expression network that was significantly associated with serum TG levels. The TG modules observed in Finns and Mexicans significantly overlapped and shared 34 genes. Seven of the 34 genes (ARHGAP30, CCR1, CXCL16, FERMT3, HCST, RNASET2, SELPG were identified as the key hub genes of all three TG modules. Furthermore, two of the 34 genes (ARHGAP9, LST1 reside in previous TG GWAS regions, suggesting them as the regional candidates underlying the GWAS signals. Conclusions This study presents a novel adipose gene co-expression network with 34 genes significantly correlated with serum TG across populations.

  20. Mosaic genome of endobacteria in arbuscular mycorrhizal fungi: Transkingdom gene transfer in an ancient mycoplasma-fungus association

    OpenAIRE

    Torres-Cortés, Gloria; Ghignone, Stefano; Bonfante, Paola; Schüßler, Arthur

    2015-01-01

    Obligate plant-symbiotic, arbuscular mycorrhizal fungi (AMF) are major drivers of terrestrial ecosystems and host enigmatic Mollicutes-related endobacteria (MRE) in their cytoplasm. The genome analysis of a MRE living in the AMF Dentiscutata heterogama revealed it to represent a previously unidentified bacterial lineage of Mycoplasma-related species. DhMRE shows strongly reduced metabolic capacity and underwent trans-kingdom gene transfer: its genome codes for an arsenal of eukaryotic-like pu...

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

    Science.gov (United States)

    Hinczewski, Michael; Thirumalai, D

    2016-07-01

    Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise. PMID:27095600

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

    OpenAIRE

    Marie-Pier eScott-Boyer; Benjamin eHaibe-Kains; Deschepper, Christian F.

    2013-01-01

    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 a...

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

    OpenAIRE

    Scott-Boyer, Marie-Pier; Haibe-Kains, Benjamin; Deschepper, Christian F.

    2013-01-01

    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 ana...

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

    OpenAIRE

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

    2012-01-01

    Background Fever is one of the most common adverse events of vaccines. The detailed mechanisms of fever and vaccine-associated gene interaction networks are not fully understood. In the present study, we employed a genome-wide, Centrality and Ontology-based Network Discovery using Literature data (CONDL) approach to analyse the genes and gene interaction networks associated with fever or vaccine-related fever responses. Results Over 170,000 fever-related articles from PubMed abstracts and tit...

  5. Discovery of core biotic stress responsive genes in Arabidopsis by weighted gene co-expression network analysis.

    Science.gov (United States)

    Amrine, Katherine C H; Blanco-Ulate, Barbara; Cantu, Dario

    2015-01-01

    Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant Arabidopsis thaliana with microbial pathogens. This work was conducted to identify (i) modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii) hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA) we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and cis-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups, under different

  6. Discovery of core biotic stress responsive genes in Arabidopsis by weighted gene co-expression network analysis.

    Directory of Open Access Journals (Sweden)

    Katherine C H Amrine

    Full Text Available Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant Arabidopsis thaliana with microbial pathogens. This work was conducted to identify (i modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and cis-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups

  7. Gene, protein and network of male sterility in rice

    Directory of Open Access Journals (Sweden)

    Wang eKun

    2013-04-01

    Full Text Available Rice is one of the most important model crop plants whose heterosis has been well exploited in commercial hybrid seed production via a variety of types of male sterile lines. Hybrid rice cultivation area is steadily expanding around the world, especially in Southern Asia. Characterization of genes and proteins related to male sterility aims to understand how and why the male sterility occurs, and which proteins are the key players for microspores abortion. Recently, a series of genes and proteins related to cytoplasmic male sterility, photoperiod sensitive male sterility, self-incompatibility and other types of microspores deterioration have been characterized through genetics or proteomics. Especially the latter, offers us a powerful and high throughput approach to discern the novel proteins involving in male-sterile pathways which may help us to breed artificial male-sterile system. This represents an alternative tool to meet the critical challenge of further development of hybrid rice. In this paper, we reviewed the recent developments in our understanding of male sterility in rice hybrid production across gene, protein and integrated network levels, and also, present a perspective on the engineering of male sterile lines for hybrid rice production.

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

    OpenAIRE

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

    2014-01-01

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

  9. Gene cloning of the 18S rRNA of an ancient viable moss from the permafrost of northeastern Siberia

    Science.gov (United States)

    Marsic, Damien; Hoover, Richard B.; Gilichinsky, David A.; Ng, Joseph D.

    1999-12-01

    A moss plant dating as much as 40,000 years old was collected from the permafrost of the Kolyma Lowlands of Northeastern Siberia. The plant tissue was revived and cultured for the extraction of its genomic DNA. Using the polymerase chain reaction technique, the 18S ribosomal RNA gene was cloned and its sequence studied. Comparative sequence analysis of the cloned ribosomal DNA to other known 18S RNA showed very high sequence identity and was revealed to be closest to the moss specie, Aulacomnium turgidum. The results of this study also show the ability of biological organisms to rest dormant in deep frozen environments where they can be revived and cultured under favorable conditions. This is significant in the notion that celestial icy bodies can be media to preserve biological function and genetic material during long term storage or transport.

  10. Gene interaction network analysis suggests differences between high and low doses of acetaminophen

    International Nuclear Information System (INIS)

    Bayesian networks for quantifying linkages between genes were applied to detect differences in gene expression interaction networks between multiple doses of acetaminophen at multiple time points. Seventeen (17) genes were selected from the gene expression profiles from livers of rats orally exposed to 50, 150 and 1500 mg/kg acetaminophen (APAP) at 6, 24 and 48 h after exposure using a variety of statistical and bioinformatics approaches. The selected genes are related to three biological categories: apoptosis, oxidative stress and other. Gene interaction networks between all 17 genes were identified for the nine dose-time observation points by the TAO-Gen algorithm. Using k-means clustering analysis, the estimated nine networks could be clustered into two consensus networks, the first consisting of the low and middle dose groups, and the second consisting of the high dose. The analysis suggests that the networks could be segregated by doses and were consistent in structure over time of observation within grouped doses. The consensus networks were quantified to calculate the probability distribution for the strength of the linkage between genes connected in the networks. The quantifying analysis showed that, at lower doses, the genes related to the oxidative stress signaling pathway did not interact with the apoptosis-related genes. In contrast, the high-dose network demonstrated significant interactions between the oxidative stress genes and the apoptosis genes and also demonstrated a different network between genes in the oxidative stress pathway. The approaches shown here could provide predictive information to understand high- versus low-dose mechanisms of toxicity

  11. OryzaExpress: An Integrated Database of Gene Expression Networks and Omics Annotations in Rice

    OpenAIRE

    Hamada, Kazuki; Hongo, Kohei; Suwabe, Keita; Shimizu, Akifumi; Nagayama, Taishi; Abe, Reina; Kikuchi, Shunsuke; Yamamoto, Naoki; Fujii, Takaaki; Yokoyama, Koji; Tsuchida, Hiroko; Sano, Kazumi; Mochizuki, Takako; Oki, Nobuhiko; Horiuchi, Youko

    2010-01-01

    Similarity of gene expression profiles provides important clues for understanding the biological functions of genes, biological processes and metabolic pathways related to genes. A gene expression network (GEN) is an ideal choice to grasp such expression profile similarities among genes simultaneously. For GEN construction, the Pearson correlation coefficient (PCC) has been widely used as an index to evaluate the similarities of expression profiles for gene pairs. However, calculation of PCCs...

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

    Indian Academy of Sciences (India)

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

    2014-06-01

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

  13. Cis-regulatory control of the nodal gene, initiator of the sea urchin oral ectoderm gene network

    OpenAIRE

    Nam, Jongmin; Su, Yi-Hsien; Lee, Pei Yun; Robertson, Anthony J; Coffman, James A.; Davidson, Eric H.

    2007-01-01

    Expression of the nodal gene initiates the gene regulatory network which establishes the transcriptional specification of the oral ectoderm in the sea urchin embryo. This gene encodes a TGFβ ligand, and in Strongylocentrotus purpuratus its transcription is activated in the presumptive oral ectoderm at about the 30-cell stage. Thereafter Nodal signaling occurs among all cells of the oral ectoderm territory, and nodal expression is required for expression of oral ectoderm regulatory genes. The ...

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

    OpenAIRE

    XING, YONGHUA; Zhang, Junling; Lu, Lu; Li, Deguan; Wang, Yueying; Huang, Song; Li, Chengcheng; ZHANG, ZHUBO; Li, Jianguo; Meng, Aimin

    2015-01-01

    Irradiation commonly causes pneumocyte senescence, which may lead to severe fatal lung injury characterized by pulmonary dysfunction and respiratory failure. However, the molecular mechanism underlying the induction of pneumocyte senescence by irradiation remains to be elucidated. In the present study, weighted gene co-expression network analysis (WGCNA) was used to screen for differentially expressed genes, and to identify the hub genes and gene modules, which may be critical for senescence....

  15. Construction of citrus gene coexpression networks from microarray data using random matrix theory.

    Science.gov (United States)

    Du, Dongliang; Rawat, Nidhi; Deng, Zhanao; Gmitter, Fred G

    2015-01-01

    After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus. PMID:26504573

  16. Study on Consumption Preference of Tourists of Ancient Town Zhouzhuang under the Perspective of Network Traveb%基于网络游记视角下古镇周庄旅游者消费偏好研究

    Institute of Scientific and Technical Information of China (English)

    王新亮

    2011-01-01

    Taking network travels as study samples, then, based on the characteristics of tourists of ancient town, distribution of travel time,diversity of travel motivation, travel diet, transport, accommodation, shopping content and preferences, travel evaluation and tourism satisfaction, the new consumption trends of tourists of ancient town were analyzed.%以网络游记为研究样本,通过古镇旅游者特征,旅游时间分布,旅游动机多样性,旅游饮食、交通、住宿、购物内容及偏好,旅游评价和旅游满意度,分析古镇旅游者消费新动向.

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

    Science.gov (United States)

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

    2016-06-01

    The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. PMID:26975659

  18. Gene network and familial analyses uncover a gene network involving Tbx5/Osr1/Pcsk6 interaction in the second heart field for atrial septation.

    Science.gov (United States)

    Zhang, Ke K; Xiang, Menglan; Zhou, Lun; Liu, Jielin; Curry, Nathan; Heine Suñer, Damian; Garcia-Pavia, Pablo; Zhang, Xiaohua; Wang, Qin; Xie, Linglin

    2016-03-15

    Atrial septal defects (ASDs) are a common human congenital heart disease (CHD) that can be induced by genetic abnormalities. Our previous studies have demonstrated a genetic interaction between Tbx5 and Osr1 in the second heart field (SHF) for atrial septation. We hypothesized that Osr1 and Tbx5 share a common signaling networking and downstream targets for atrial septation. To identify this molecular networks, we acquired the RNA-Seq transcriptome data from the posterior SHF of wild-type, Tbx5(+/) (-), Osr1(+/-), Osr1(-/-) and Tbx5(+/-)/Osr1(+/-) mutant embryos. Gene set analysis was used to identify the Kyoto Encyclopedia of Genes and Genomes pathways that were affected by the doses of Tbx5 and Osr1. A gene network module involving Tbx5 and Osr1 was identified using a non-parametric distance metric, distance correlation. A subset of 10 core genes and gene-gene interactions in the network module were validated by gene expression alterations in posterior second heart field (pSHF) of Tbx5 and Osr1 transgenic mouse embryos, a time-course gene expression change during P19CL6 cell differentiation. Pcsk6 was one of the network module genes that were linked to Tbx5. We validated the direct regulation of Tbx5 on Pcsk6 using immunohistochemical staining of pSHF, ChIP-quantitative polymerase chain reaction and luciferase reporter assay. Importantly, we identified Pcsk6 as a novel gene associated with ASD via a human genotyping study of an ASD family. In summary, our study implicated a gene network involving Tbx5, Osr1 and Pcsk6 interaction in SHF for atrial septation, providing a molecular framework for understanding the role of Tbx5 in CHD ontogeny. PMID:26744331

  19. Absence of the Filarial Endosymbiont Wolbachia in Seal Heartworm (Acanthocheilonema spirocauda) but Evidence of Ancient Lateral Gene Transfer.

    Science.gov (United States)

    Keroack, Caroline D; Wurster, Jenna I; Decker, Caroline G; Williams, Kalani M; Slatko, Barton E; Foster, Jeremy M; Williams, Steven A

    2016-06-01

    The symbiotic relationship of Wolbachia spp. was first observed in insects and subsequently in many parasitic filarial nematodes. This bacterium is believed to provide metabolic and developmental assistance to filarial parasitic nematodes, although the exact nature of this relationship remains to be fully elucidated. While Wolbachia is present in most filarial nematodes in the family Onchocercidae, it is absent in several disparate species such as the human parasite Loa loa . All tested members of the genus Acanthocheilonema, such as Acanthocheilonema viteae, have been shown to lack Wolbachia. Consistent with this, we show that Wolbachia is absent from the seal heartworm (Acanthocheilonema spirocauda), but lateral gene transfer (LGT) of DNA sequences between Wolbachia and A. spirocauda has occurred, indicating a past evolutionary association. Seal heartworm is an important pathogen of phocid seals and understanding its basic biology is essential for conservation of the host. The findings presented here may allow for the development of future treatments or diagnostics for the disease and also aid in clarification of the complicated nematode-Wolbachia relationship. PMID:26859724

  20. Eric Davidson: Steps to a gene regulatory network for development.

    Science.gov (United States)

    Rothenberg, Ellen V

    2016-04-15

    Eric Harris Davidson was a unique and creative intellectual force who grappled with the diversity of developmental processes used by animal embryos and wrestled them into an intelligible set of principles, then spent his life translating these process elements into molecularly definable terms through the architecture of gene regulatory networks. He took speculative risks in his theoretical writing but ran a highly organized, rigorous experimental program that yielded an unprecedentedly full characterization of a developing organism. His writings created logical order and a framework for mechanism from the complex phenomena at the heart of advanced multicellular organism development. This is a reminiscence of intellectual currents in his work as observed by the author through the last 30-35 years of Davidson's life. PMID:26825392

  1. Analysis of deterministic cyclic gene regulatory network models with delays

    CERN Document Server

    Ahsen, Mehmet Eren; Niculescu, Silviu-Iulian

    2015-01-01

    This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.

  2. 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…

  3. 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.

  4. Neural network predicts sequence of TP53 gene based on DNA chip

    DEFF Research Database (Denmark)

    Spicker, J.S.; Wikman, F.; Lu, M.L.;

    2002-01-01

    We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero and...

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

    Science.gov (United States)

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

    2016-01-01

    The sequential chain of interactions altering the binary state of a biomolecule represents the ‘information flow’ within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein–protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes—network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. PMID:26975659

  6. Construction of citrus gene coexpression networks from microarray data using random matrix theory

    OpenAIRE

    Dongliang Du; Nidhi Rawat; Zhanao Deng; Gmitter Jr., Fred G.

    2015-01-01

    After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 3...

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

    OpenAIRE

    Sivieng Jane; Sellmeier Julia; Leung Kin; Kadupitige Sidath; Catchpoole Daniel R; Bain Michael E; Gaëta Bruno A

    2009-01-01

    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...

  8. Iterative plug-and-play methodology for constructing and modifying synthetic gene networks

    OpenAIRE

    Litcofsky, Kevin D.; Afeyan, Raffi B.; Krom, Russell J.; Khalil, Ahmad S.; Collins, James J.

    2012-01-01

    We present a methodology for the design, construction, and modification of synthetic gene networks. This method emphasizes post-assembly modification of constructs based on network behavior, thus facilitating iterative design strategies and rapid tuning and repurposing of gene networks. The ease of post-construction modifications afforded by this approach and the ever-increasing repository of components within the framework will help to fast-track the development of functional genetic circuit...

  9. Reverse Engineering Gene Interaction Networks Using the Phi-Mixing Coefficient

    OpenAIRE

    Singh, Nitin Kumar; Ahsen, M. Eren; Mankala, Shiva; Kim, Hyun-Seok; White, Michael A; Vidyasagar, M.

    2012-01-01

    Constructing gene interaction networks (GINs) from high-throughput gene expression data is an important and challenging problem in systems biology. Existing algorithms produce networks that either have undirected and unweighted edges, or else are constrained to contain no cycles, both of which are biologically unrealistic. In the present paper we propose a new algorithm, based on a concept from probability theory known as the phi-mixing coefficient, that produces networks whose edges are weig...

  10. A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe.

    Science.gov (United States)

    Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

    2016-01-01

    Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies. PMID:27014338

  11. Global analysis of the human pathophenotypic similarity gene network merges disease module components.

    Science.gov (United States)

    Reyes-Palomares, Armando; Rodríguez-López, Rocío; Ranea, Juan A G; Sánchez-Jiménez, Francisca; Sánchez Jiménez, Francisca; Medina, Miguel Angel

    2013-01-01

    The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, "the human diseases networks" (HDN) and "the orphan disease networks" (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the "Human Phenotype Ontology". The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease

  12. Global analysis of the human pathophenotypic similarity gene network merges disease module components.

    Directory of Open Access Journals (Sweden)

    Armando Reyes-Palomares

    Full Text Available The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, "the human diseases networks" (HDN and "the orphan disease networks" (ODN. However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN and compared it with the unipartite projections (based on gene-to-gene edges similar to those used in previous network models (HDN and ODN. Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms in the "Human Phenotype Ontology". The resulting network contains 1075 genes (nodes and 26197 significant pathophenotypic similarities (edges. A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co

  13. Modeling gene expression regulatory networks with the sparse vector autoregressive model

    Directory of Open Access Journals (Sweden)

    Miyano Satoru

    2007-08-01

    Full Text Available Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters originating from a smaller number of microarray experiments (samples. Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    Notebaart, Richard A; Teusink, Bas; Siezen, Roland J; Papp, Balázs

    2008-01-01

    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. PMID:18225949

  16. System Review about Function Role of ESCC Driver Gene KDM6A by Network Biology Approach.

    Science.gov (United States)

    Ran, Jihua; Li, Hui; Li, Huiwu

    2016-01-01

    Background. KDM6A (Lysine (K)-Specific Demethylase 6A) is the driver gene related to esophageal squamous cell carcinoma (ESCC). In order to provide more biological insights into KDM6A, in this paper, we treat PPI (protein-protein interaction) network derived from KDM6A as a conceptual framework and follow it to review its biological function. Method. We constructed a PPI network with Cytoscape software and performed clustering of network with Clust&See. Then, we evaluate the pathways, which are statistically involved in the network derived from KDM6A. Lastly, gene ontology analysis of clusters of genes in the network was conducted. Result. The network includes three clusters that consist of 74 nodes connected via 453 edges. Fifty-five pathways are statistically involved in the network and most of them are functionally related to the processes of cell cycle, gene expression, and carcinogenesis. The biology themes of clusters 1, 2, and 3 are chromatin modification, regulation of gene expression by transcription factor complex, and control of cell cycle, respectively. Conclusion. The PPI network presents a panoramic view which can facilitate for us to understand the function role of KDM6A. It is a helpful way by network approach to perform system review on a certain gene. PMID:27294188

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

    OpenAIRE

    Poole, Matthew; Kentzoglanakis, Kyriakos

    2011-01-01

    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 modelling the dynamical behaviour of gene regulatory systems. More specifically, ACO is used for searching the discre...

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

    OpenAIRE

    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 WGCN...

  19. A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism

    Directory of Open Access Journals (Sweden)

    Carey Vincent J

    2009-05-01

    Full Text Available Abstract Background Graphical models (e.g., Bayesian networks have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs in genes. Results Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1 construction of a graphical Gaussian model (GGM based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2 extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3 identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4 evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2 and Interleukin 1B (IL1B, two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics. Conclusion Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.

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

    Directory of Open Access Journals (Sweden)

    Lodowski Kerrie H

    2009-01-01

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

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

    Science.gov (United States)

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

    2015-12-01

    Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi-tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment. PMID:26668231

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

    Science.gov (United States)

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

    2016-01-01

    Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues. PMID:27467526

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

    OpenAIRE

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

    2015-01-01

    Background 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. Methods 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 IN...

  4. 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.

  5. Limitations of Gene Duplication Models: Evolution of Modules in Protein Interaction Networks

    OpenAIRE

    Frank Emmert-Streib

    2012-01-01

    It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that the...

  6. What Transcription Factors Can't Do: On the Combinatorial Limits of Gene Regulatory Networks

    OpenAIRE

    Werner, Eric

    2013-01-01

    A proof is presented that gene regulatory networks (GRNs) based solely on transcription factors cannot control the development of complex multicellular life. GRNs alone cannot explain the evolution of multicellular life in the Cambrian Explosion. Networks are based on addressing systems which are used to construct network links. The more complex the network the greater the number of links and the larger the required address space. It has been assumed that combinations of transcription factors...

  7. The Transcriptional and Gene Regulatory Network of Lactococcus lactis MG1363 during Growth in Milk

    DEFF Research Database (Denmark)

    de Jong, Anne; Hansen, Morten Ejby; Kuipers, Oscar P.;

    2013-01-01

    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...... 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......://milkts.molgenrug.nl gives full access to all transcriptome data, to the reconstructed network and to the individual network building blocks....

  8. 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

    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......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...... and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to...

  9. 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. PMID:21576756

  10. Chaotic Gene Regulatory Networks Can Be Robust Against Mutations and Noise

    Science.gov (United States)

    Sevim, Volkan; Rikvold, Per Arne

    2008-03-01

    Robustness to mutations and noise has been shown to evolve through stabilizing selection for optimal phenotypes in model gene regulatory networks. The ability to evolve robust mutants is known to depend on the network architecture. How do the state-space structures of networks with high and low robustness differ? Here we present large-scale computer simulations of a Random Threshold Network model of gene regulatory networks undergoing biological evolution. We show using damage propagation analysis and an extensive statistical analysis of state spaces of these model gene networks that the change in their dynamical properties due to stabilizing selection is very small. Therefore, conventional measures of stability do not provide much information about robustness in model gene regulatory networks. Interestingly, the networks that are most robust to both mutations and noise are highly chaotic. Chaotic networks are able to produce large attractor basins, which can be useful for maintaining a stable gene-expression pattern.[1] V. Sevim and P. A. Rikvold (2007), e-print arXiv:0708.2244.[2] V. Sevim and P. A. Rikvold (2007), e-print arXiv:0711.1522.

  11. A gene-networked gel matrix-supported lipid bilayer as a synthetic nucleus system.

    Science.gov (United States)

    Bae, Sun Ju; Song, Woo Chul; Jung, Sung Hwan; Cho, Seung-Woo; Kim, Dong-Ik; Um, Soong Ho

    2012-12-11

    A spheroidal transgene-networked gel matrix was designed as a synthetic nucleus system. It was spheroidically manufactured using both advanced lithography and DNA nanotechnology. Stable Aqueorea coerulescens green fluorescent protein (AcGFP)-encoding gene cross-networks have been optimized in various parameters: the number of gene-networked gel (G-net-gel) spheroids, the concentration of a AcGFP plasmid in the scaffold, and the molar ratio between the X-DNA building blocks and the gene. It was then assessed that 800 units of the gene networked gel matrix at a 4000:1 molar ratio of X-DNA blocks and AcGFP gene components accomplished 20-fold enhanced in vitro protein expression efficiency for 36 h. Furthermore, once with lipid capping, it reproduced the natural nucleus system, demonstrating the 2-fold increased levels of messenger RNAs (mRNAs) relative to solution phase vectors. PMID:23148683

  12. Looking at the origin of phenotypic variation from pattern formation gene networks

    Indian Academy of Sciences (India)

    Isaac Salazar-Ciudad

    2009-10-01

    This article critically reviews some widespread views about the overall functioning of development. Special attention is devoted to views in developmental genetics about the superstructure of developmental gene networks. According to these views gene networks are hierarchic and multilayered. The highest layers partition the embryo in large coarse areas and control downstream genes that subsequently subdivide the embryo into smaller and smaller areas. These views are criticized on the bases of developmental and evolutionary arguments. First, these views, although detailed at the level of gene identities, do not incorporate morphogenetic mechanisms nor do they try to explain how morphology changes during development. Often, they assume that morphogenetic mechanisms are subordinate to cell signaling events. This is in contradiction to the evidence reviewed herein. Experimental evidence on pattern formation also contradicts the view that developmental gene networks are hierarchically multilayered and that their functioning is decodable from promoter analysis. Simple evolutionary arguments suggest that, indeed, developmental gene networks tend to be non-hierarchic. Re-use leads to extensive modularity in gene networks while developmental drift blurs this modularity. Evolutionary opportunism makes developmental gene networks very dependent on epigenetic factors.

  13. A parallel implementation of the network identification by multiple regression (NIR algorithm to reverse-engineer regulatory gene networks.

    Directory of Open Access Journals (Sweden)

    Francesco Gregoretti

    Full Text Available The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.

  14. A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.

    Science.gov (United States)

    Gregoretti, Francesco; Belcastro, Vincenzo; di Bernardo, Diego; Oliva, Gennaro

    2010-01-01

    The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications. PMID:20422008

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

    Directory of Open Access Journals (Sweden)

    Scott M Gibson

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

  16. Gene identification and analysis: an application of neural network-based information fusion

    Energy Technology Data Exchange (ETDEWEB)

    Matis, S.; Xu, Y.; Shah, M.B.; Mural, R.J.; Einstein, J.R.; Uberbacher, E.C.

    1996-10-01

    Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system called GRAIL. GRAIL is a multiple sensor-neural network based system. It localizes genes in anonymous DNA sequence by recognizing gene features related to protein-coding slice sites, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene mode. RNA polymerase II promoters can also be predicted. Through years of extensive testing, GRAIL consistently localizes about 90 percent of coding portions of test genes with a false positive rate of about 10 percent. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

  17. 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.

  18. Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

    Science.gov (United States)

    Zhu, Shijia; Wang, Yadong

    2015-12-01

    Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.

  19. 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.

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

    Science.gov (United States)

    Shin, Yong-Jun

    2016-01-01

    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. PMID:27102828

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

    Science.gov (United States)

    Shin, Yong-Jun

    2016-01-01

    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. PMID:27102828

  2. Patterns of human gene expression variance show strong associations with signaling network hierarchy

    Directory of Open Access Journals (Sweden)

    Ram Prahlad T

    2010-11-01

    Full Text Available Abstract Background Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV of genes and their relationship to cellular functions and physiological responses is poorly understood. Results To understand global principles of relationship between transcriptional regulation of human genes and their functions, we have leveraged large-scale datasets of human gene expression measurements across a wide spectrum of cell conditions. We report that human genes are highly diverse in terms of their EV; while some genes have highly variable expression pattern, some seem to be relatively ubiquitously expressed across a wide range of conditions. The wide spectrum of gene EV strongly correlates with the positioning of proteins within the signaling network hierarchy, such that, secreted extracellular receptor ligands and membrane receptors have the highest EV, and intracellular signaling proteins have the lowest EV in the genome. Our analysis shows that this pattern of EV reflects functional centrality: proteins with highly specific signaling functions are modulated more frequently than those with highly central functions in the network, which is also consistent with previous studies on tissue-specific gene expression. Interestingly, these patterns of EV along the signaling network hierarchy have significant correlations with promoter architectures of respective genes. Conclusion Our analyses suggest a generic systems level mechanism of regulation of the cellular signaling network at the transcriptional level.

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

    Science.gov (United States)

    Kim, Haseong; Gelenbe, Erol

    2012-09-01

    Gene regulatory networks provide the systematic view of molecular interactions in a complex living system. However, constructing large-scale gene regulatory networks is one of the most challenging problems in systems biology. Also large burst sets of biological data require a proper integration technique for reliable gene regulatory network construction. Here we present a new reverse engineering approach based on Bayesian model averaging which attempts to combine all the appropriate models describing interactions among genes. This Bayesian approach with a prior based on the Gibbs distribution provides an efficient means to integrate multiple sources of biological data. In a simulation study with maximum of 2000 genes, our method shows better sensitivity than previous elastic-net and Gaussian graphical models, with a fixed specificity of 0.99. The study also shows that the proposed method outperforms the other standard methods for a DREAM dataset generated by nonlinear stochastic models. In brain tumor data analysis, three large-scale networks consisting of 4422 genes were built using the gene expression of non-tumor, low and high grade tumor mRNA expression samples, along with DNA-protein binding affinity information. We found that genes having a large variation of degree distribution among the three tumor networks are the ones that see most involved in regulatory and developmental processes, which possibly gives a novel insight concerning conventional differentially expressed gene analysis. PMID:22987132

  4. Identification of hub genes and pathways associated with retinoblastoma based on co-expression network analysis.

    Science.gov (United States)

    Wang, Q L; Chen, X; Zhang, M H; Shen, Q H; Qin, Z M

    2015-01-01

    The objective of this paper was to identify hub genes and pathways associated with retinoblastoma using centrality analysis of the co-expression network and pathway-enrichment analysis. The co-expression network of retinoblastoma was constructed by weighted gene co-expression network analysis (WGCNA) based on differentially expressed (DE) genes, and clusters were obtained through the molecular complex detection (MCODE) algorithm. Degree centrality analysis of the co-expression network was performed to explore hub genes present in retinoblastoma. Pathway-enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Validation of hub gene expression in retinoblastoma was performed by reverse transcription-polymerase chain reaction (RT-PCR) analysis. The co-expression network based on 221 DE genes between retinoblastoma and normal controls consisted of 210 nodes and 3965 edges, and 5 clusters of the network were evaluated. By assessing the centrality analysis of the co-expression network, 21 hub genes were identified, such as SNORD115-41, RASSF2, and SNORD115-44. According to RT-PCR analysis, 16 of the 21 hub genes were differently expressed, including RASSF2 and CDCA7, and 5 were not differently expressed in retinoblastoma compared to normal controls. Pathway analysis showed that genes in 2 clusters were enriched in 3 pathways: purine metabolism, p53 signaling pathway, and melanogenesis. In this study, we successfully identified 16 hub genes and 3 pathways associated with retinoblastoma, which may be potential biomarkers for early detection and therapy for retinoblastoma. PMID:26662407

  5. 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.

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

    International Nuclear Information System (INIS)

    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)

  7. Meta-analysis on gene regulatory networks discovered by pairwise Granger causality

    OpenAIRE

    Tam, GHF; Hung, YS; Chang, C.

    2013-01-01

    Identifying regulatory genes partaking in disease development is important to medical advances. Since gene expression data of multiple experiments exist, combining results from multiple gene regulatory network discoveries offers higher sensitivity and specificity. However, data for multiple experiments on the same problem may not possess the same set of genes, and hence many existing combining methods are not applicable. In this paper, we approach this problem using a number of meta-analysis ...

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

    Science.gov (United States)

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

    2002-01-01

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

  9. Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.

    Directory of Open Access Journals (Sweden)

    Ville-Petteri Mäkinen

    2014-07-01

    Full Text Available The majority of the heritability of coronary artery disease (CAD remains unexplained, despite recent successes of genome-wide association studies (GWAS in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls with 1 genetics of gene expression studies of CAD-relevant tissues in humans, 2 metabolic and signaling pathways from public databases, and 3 data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1 and peptidylprolyl isomerase I (PPIL1, which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.

  10. 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.

  11. "Guilt by association" is the exception rather than the rule in gene networks.

    Directory of Open Access Journals (Sweden)

    Jesse Gillis

    Full Text Available Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.

  12. Performance Analysis of Network Model to Identify Healthy and Cancerous Colon Genes.

    Science.gov (United States)

    Roy, Tanusree; Barman, Soma

    2016-03-01

    Modeling of cancerous and healthy Homo Sapiens colon gene using electrical network is proposed to study their behavior. In this paper, the individual amino acid models are designed using hydropathy index of amino acid side chain. The phase and magnitude responses of genes are examined to screen out cancer from healthy genes. The performance of proposed modeling technique is judged using various performance measurement metrics such as accuracy, sensitivity, specificity, etc. The network model performance is increased with frequency, which is analyzed using the receiver operating characteristic curve. The accuracy of the model is tested on colon genes and achieved maximum 97% at 10-MHz frequency. PMID:25730835

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

    Indian Academy of Sciences (India)

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

    2013-12-01

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

  14. Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis.

    Science.gov (United States)

    Creanza, Teresa Maria; Liguori, Maria; Liuni, Sabino; Nuzziello, Nicoletta; Ancona, Nicola

    2016-01-01

    Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment. PMID:27314336

  15. Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Teresa Maria Creanza

    2016-06-01

    Full Text Available Differential gene expression analyses to investigate multiple sclerosis (MS molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1 that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment.

  16. New insight into genes in association with asthma: literature-based mining and network centrality analysis

    Institute of Scientific and Technical Information of China (English)

    LIANG Rui; WANG Lei; WANG Gang

    2013-01-01

    Background Asthma is a heterogeneous disease for which a strong genetic basis has been firmly established.Until now no studies have been undertaken to systemically explore the network of asthma-related genes using an internally developed literature-based discovery approach.This study was to explore asthma-related genes by using literaturebased mining and network centrality analysis.Methods Literature involving asthma-related genes were searched in PubMed from 2001 to 2011.Integration of natural language processing with network centrality analysis was used to identify asthma susceptibility genes and their interaction network.Asthma susceptibility genes were classified into three functional groups by gene ontology (GO) analysis and the key genes were confirmed by establishing asthma-related networks and pathways.Results Three hundred and twenty-six genes related with asthma such as IGHE (IgE),interleukin (IL)-4,5,6,10,13,17A,and tumor necrosis factor (TNF)-alpha were identified.GO analysis indicated some biological processes (developmental processes,signal transduction,death,etc.),cellular components (non-structural extracellular,plasma membrane and extracellular matrix),and molecular functions (signal transduction activity) that were involved in asthma.Furthermore,22 asthma-related pathways such as the Toll-like receptor signaling pathway,hematopoietic cell lineage,JAK-STAT signaling pathway,chemokine signaling pathway,and cytokine-cytokine receptor interaction,and 17 hub genes,such as JAK3,CCR1-3,CCR5-7,CCR8,were found.Conclusions Our study provides a remarkably detailed and comprehensive picture of asthma susceptibility genes and their interacting network.Further identification of these genes and molecular pathways may play a prominent role in establishing rational therapeutic approaches for asthma.

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

    DEFF Research Database (Denmark)

    van Leeuwen, D. M.; Pedersen, Marie; Knudsen, Lisbeth E.; Bonassi, S; Fenech, M; Kleinjans, J. C. S.; Jennen, D G J

    2011-01-01

    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...... 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 on the...... 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...

  18. Discrete dynamical system modelling for gene regulatory networks of 5-hydroxymethylfural tolerance for ethanologenic yeast

    Science.gov (United States)

    Composed of linear difference equations, a discrete dynamic system model was designed to reconstruct transcriptional regulations in gene regulatory networks in response to 5-hydroxymethylfurfural, a bioethanol conversion inhibitor for ethanologenic yeast Saccharomyces cerevisiae. The modeling aims ...

  19. 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. PMID:27181059

  20. What does biologically meaningful mean? A perspective on gene regulatory network validation

    OpenAIRE

    Walhout, Albertha JM

    2011-01-01

    Gene regulatory networks (GRNs) are rapidly being delineated, but their quality and biological meaning are often questioned. Here, I argue that biological meaning is challenging to define and discuss reasons why GRN validation should be interpreted cautiously.

  1. Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis.

    Directory of Open Access Journals (Sweden)

    Matthieu Vignes

    Full Text Available Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth "Dialogue for Reverse Engineering Assessments and Methods" (DREAM5 challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on "Systems Genetics" proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.

  2. Studying Ancient History.

    Science.gov (United States)

    Barrow, Robin

    1982-01-01

    Defends the value and relevance of the study of ancient history and classics in history curricula. The unique homogeneity of the classical period contributes to its instructional manageability. A year-long, secondary-level course on fifth-century Greece and Rome is described to illustrate effective approaches to teaching ancient history. (AM)

  3. Identification of Genes Related to Nasopharyngeal Carcinoma with the Help of Pathway-based Networks

    Institute of Scientific and Technical Information of China (English)

    Hui LI; Cai-Ping REN; Xiao-Jun TAN; Xu-Yu YANG; Hong-Bo ZHANG; Wen ZHOU; Kai-Tai YAO

    2006-01-01

    cDNA microarray is a powerful tool to analyze simultaneously the expression levels of tens of thousands of genes. Compared with normal nasopharynx (NP) tissues, 2210 genes were highly differentially expressed in nasopharyngeal carcinoma (NPC) tissues detected by cDNA microarray. Since signal pathway is widely used to describe the complex relationship between genes, a pathway-based network was constructed to visualize the connection between the genes obtained from microarray data in this report. We analyzed the targeted genes that may have more important influence on this gene network with statistical methods and found that some genes might have significant influence on this network, especially Ras-related nuclear protein (RAN), carboxyl ester lipase (CEL), v-rel reticuloendotheliosis viral oncogene homolog A (RELA) genes. To verify the results from pathway-based selection, reverse transcription-polymerase chain reaction (RT-PCR) and real-time RT-PCR were performed to detect the expression levels of RAN, CEL and RELA genes and it was found that the RAN and CEL genes were significantly up-regulated in more than 80%of NPC tissues. To further elucidate the function of the RAN gene, RAN expression was specifically suppressed in a 5-8F NPC cell line by RNA interference (RNAi). As expected, the depletion of RAN could effectively block the proliferation of tumor cells. Therefore, our study may open up a new way to analyze the vast microarray data.

  4. The impact of the metabotropic glutamate receptor and other gene family interaction networks on autism.

    OpenAIRE

    Hadley, Dexter; Wu, Zhi-Liang; Kao, Charlly; Kini, Akshata; Mohamed-Hadley, Alisha; Thomas, Kelly; Vazquez, Lyam; Qiu, Haijun; Mentch, Frank; Pellegrino, Renata; Kim, Cecilia; Connolly, John; Pinto, Dalila; Merikangas, Alison; Klei, Lambertus

    2014-01-01

    Although multiple reports show that defective genetic networks underlie the aetiology of autism, few have translated into pharmacotherapeutic opportunities. Since drugs compete with endogenous small molecules for protein binding, many successful drugs target large gene families with multiple drug binding sites. Here we search for defective gene family interaction networks (GFINs) in 6,742 patients with the ASDs relative to 12,544 neurologically normal controls, to find potentially druggable g...

  5. A comprehensive gene regulatory network for the diauxic shift in Saccharomyces cerevisiae

    OpenAIRE

    Geistlinger, Ludwig; Csaba, Gergely; Dirmeier, Simon; Küffner, Robert; Zimmer, Ralf

    2013-01-01

    Existing machine-readable resources for large-scale gene regulatory networks usually do not provide context information characterizing the activating conditions for a regulation and how targeted genes are affected. Although this information is essentially required for data interpretation, available networks are often restricted to not condition-dependent, non-quantitative, plain binary interactions as derived from high-throughput screens. In this article, we present a comprehensive Petri net ...

  6. Identification of Common Regulators of Genes in Co-Expression Networks Affecting Muscle and Meat Properties

    OpenAIRE

    Siriluck Ponsuksili; Puntita Siengdee; Yang Du; Nares Trakooljul; Eduard Murani; Manfred Schwerin; Klaus Wimmers

    2015-01-01

    Understanding the genetic contributions behind skeletal muscle composition and metabolism is of great interest in medicine and agriculture. Attempts to dissect these complex traits combine genome-wide genotyping, expression data analyses and network analyses. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of co-expression, which can be linked to phenotypes by correlation analysis of trait values and the module eigengenes, i.e. the first princi...

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

    OpenAIRE

    Rajeevan Mangalathu S; Suarez Charlyn J; Whistler Toni; Papp Jeanette C; Sobel Eric M; Presson Angela P; Vernon Suzanne D; Horvath Steve

    2008-01-01

    Abstract Background Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set. Results We combine WGCNA with genetic marker data to identify a disease-related pathway and its causa...

  8. Cancer attractors: A systems view of tumors from a gene network dynamics and developmental perspective

    OpenAIRE

    Huang, Sui; Ernberg, Ingemar; Kauffman, Stuart

    2009-01-01

    Cell lineage commitment and differentiation are governed by a complex gene regulatory network. Disruption of these processes by inappropriate regulatory signals and by mutational rewiring of the network can lead to tumorigenesis. Cancer cells often exhibit immature or embryonic traits and dysregulated developmental genes can act as oncogenes. However, the prevailing paradigm of somatic evolution and multi-step tumorigenesis, while useful in many instances, offers no logically coherent reason ...

  9. Transcriptome network analysis reveals potential candidate genes for squamous lung cancer.

    Science.gov (United States)

    Bai, Jing; Hu, Sheng

    2012-01-01

    Squamous lung cancer is a common type of lung cancer; however, its mechanism of oncogenesis is still unknown. The aim of this study was to screen candidate genes of squamous lung cancer using a bioinformatics strategy and elucidate the mechanism of squamous lung cancer. Published microarray data of the GSE3268 series was obtained from Gene Expression Omnibus (GEO). Significance analysis of microarrays was performed using the software R, and differentially expressed genes by R analysis were harvested. The relationship between transcription factors and target genes in cancer were collected from the Transcriptional regulatory element database. A transcriptome network analysis method was used to construct gene regulation networks and select the candidate genes for squamous lung cancer. SPI1, FLI1, FOS, ETS2, EGR1 and PPARG were defined as candidate genes for squamous lung cancer by the transcriptome network analysis method. Among them, 5 genes had been reported to be involved in lung cancer, except SPI1 and FLI1. Effective recall on previous knowledge conferred strong confidence in these methods. It is demonstrated that transcriptome network analysis is useful in the identification of candidate genes in disease. PMID:21922129

  10. 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.

  11. Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization

    Directory of Open Access Journals (Sweden)

    Huang Yufei

    2007-01-01

    Full Text Available We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map.

  12. Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization

    Directory of Open Access Journals (Sweden)

    Isabel Tienda Luna

    2007-06-01

    Full Text Available We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map.

  13. Network analysis of microRNAs, genes and their regulation in mantle cell lymphoma.

    Science.gov (United States)

    Deng, Si-Yu; Guo, Xiao-Xin; Wang, Ning; Wang, Kun-Hao; Wang, Shang

    2015-01-01

    The pathogenesis of mantle cell lymphoma, a special subtype of lymphoma that is invasive and indolent and has a median survival of 3 to 4 years, is still partially unexplained. Much research about genes and miRNAs has been conducted in recent years, but interactions and regulatory relations of genetic elements which may play a vital role in genesis of MCL have attracted only limited attention. The present study concentrated on regulatory relations about genes and miRNAs contributing to MCL pathogenesis. Numerous experimentally validated raw data were organized into three topology networks, comprising differentially expressed, associated and global examples. Comparison of similarities and dissimilarities of the three regulating networks, paired with the analysis of the interactions between pairs of elements in every network, revealed that the differentially expressed network illuminated the carcinogenicity mechanism of MCL and the related network further described the regulatory relations involved, including prevention, diagnosis, development and therapy. Three kinds of regulatory relations for host genes including miRNAs, miRNAs targeting genes and genes regulating miRNAs were concluded macroscopically. Regulation of the differentially expressed miRNAs was also analyzed, in terms of abnormal gene expression affecting the MCL pathogenesis. Special regulatory relations were uncovered. For example, auto-regulatory loops were found in the three topology networks, key pathways of the nodes being highlighted. The present study focused on a novel point of view revealing important influencing factors for MCL pathogenesis. PMID:25684471

  14. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  16. The Drosophila gap gene network is composed of two parallel toggle switches.

    Directory of Open Access Journals (Sweden)

    Dmitri Papatsenko

    Full Text Available Drosophila "gap" genes provide the first response to maternal gradients in the early fly embryo. Gap genes are expressed in a series of broad bands across the embryo during first hours of development. The gene network controlling the gap gene expression patterns includes inputs from maternal gradients and mutual repression between the gap genes themselves. In this study we propose a modular design for the gap gene network, involving two relatively independent network domains. The core of each network domain includes a toggle switch corresponding to a pair of mutually repressive gap genes, operated in space by maternal inputs. The toggle switches present in the gap network are evocative of the phage lambda switch, but they are operated positionally (in space by the maternal gradients, so the synthesis rates for the competing components change along the embryo anterior-posterior axis. Dynamic model, constructed based on the proposed principle, with elements of fractional site occupancy, required 5-7 parameters to fit quantitative spatial expression data for gap gradients. The identified model solutions (parameter combinations reproduced major dynamic features of the gap gradient system and explained gap expression in a variety of segmentation mutants.

  17. 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

    2016-01-01

    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. PMID:26707700

  18. Inferring Drosophila gap gene regulatory network: A parameter sensitivity and perturbation analysis

    NARCIS (Netherlands)

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

    2009-01-01

    Background: Inverse modelling of gene regulatory networks (GRNs) capable of simulating continuous spatio-temporal biological processes requires accurate data and a good description of the system. If quantitative relations between genes cannot be extracted from direct measurements, an efficient metho

  19. Net Venn - An integrated network analysis web platform for gene lists

    Science.gov (United States)

    Many lists containing biological identifiers such as gene lists have been generated in various genomics projects. Identifying the overlap among gene lists can enable us to understand the similarities and differences between the datasets. Here, we present an interactome network-based web application...

  20. Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

    OpenAIRE

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

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    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

  3. 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

  4. Dominant negative autoregulation limits steady-state repression levels in gene networks.

    Science.gov (United States)

    Semsey, Szabolcs; Krishna, Sandeep; Erdossy, János; Horváth, Péter; Orosz, László; Sneppen, Kim; Adhya, Sankar

    2009-07-01

    Many transcription factors repress transcription of their own genes. Negative autoregulation has been shown to reduce cell-cell variation in regulatory protein levels and speed up the response time in gene networks. In this work we examined transcription regulation of the galS gene and the function of its product, the GalS protein. We observed a unique operator preference of the GalS protein characterized by dominant negative autoregulation. We show that this pattern of regulation limits the repression level of the target genes in steady states. We suggest that transcription factors with dominant negative autoregulation are designed for regulating gene expression during environmental transitions. PMID:19429616

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    Angela Bruex

    2012-01-01

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

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

    Science.gov (United States)

    Pashaiasl, Maryam; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2016-09-01

    Diminished ovarian reserve (DOR) is one of the reasons for infertility that not only affects both older and young women. Ovarian reserve assessment can be used as a new prognostic tool for infertility treatment decision making. Here, up- and down-regulated gene expression profiles of granulosa cells were analysed to generate a putative interaction map of the involved genes. In addition, gene ontology (GO) analysis was used to get insight intol the biological processes and molecular functions of involved proteins in DOR. Eleven up-regulated genes and nine down-regulated genes were identified and assessed by constructing interaction networks based on their biological processes. PTGS2, CTGF, LHCGR, CITED, SOCS2, STAR and FSTL3 were the key nodes in the up-regulated networks, while the IGF2, AMH, GREM, and FOXC1 proteins were key in the down-regulated networks. MIRN101-1, MIRN153-1 and MIRN194-1 inhibited the expression of SOCS2, while CSH1 and BMP2 positively regulated IGF1 and IGF2. Ossification, ovarian follicle development, vasculogenesis, sequence-specific DNA binding transcription factor activity, and golgi apparatus are the major differential groups between up-regulated and down-regulated genes in DOR. Meta-analysis of publicly available transcriptomic data highlighted the high coexpression of CTGF, connective tissue growth factor, with the other key regulators of DOR. CTGF is involved in organ senescence and focal adhesion pathway according to GO analysis. These findings provide a comprehensive system biology based insight into the aetiology of DOR through network and gene ontology analyses. PMID:27324248

  8. Identification of Candidate Genes related to Bovine Marbling using Protein-Protein Interaction Networks

    OpenAIRE

    Lim, Dajeong; Kim, Nam-Kuk; Park, Hye-Sun; Lee, Seung-Hwan; Cho, Yong-Min; Oh, Sung Jong; Kim, Tae-Hun; Kim, Heebal

    2011-01-01

    Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The present study systemically analyzed genes associated with bovine marbling score and identified their relationships. The candidate nodes were obtained using MedScan text-mining tools and linked by protein-protein intera...

  9. Prior knowledge driven Granger causality analysis on gene regulatory network discovery

    OpenAIRE

    Yao, Shun; Yoo, Shinjae; Yu, Dantong

    2015-01-01

    Background Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. Results In this study, we proposed a new method, viz., CGC-2SPR (CGC ...

  10. Detection of gene communities in multi-networks reveals cancer drivers

    Science.gov (United States)

    Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

    2015-12-01

    We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

  11. The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs

    Science.gov (United States)

    Villa-Vialaneix, Nathalie; Liaubet, Laurence; Laurent, Thibault; Cherel, Pierre; Gamot, Adrien; SanCristobal, Magali

    2013-01-01

    What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology. PMID:23577081

  12. Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

    Directory of Open Access Journals (Sweden)

    John L. Hartman

    2015-02-01

    Full Text Available The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis. The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

  13. Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer.

    Science.gov (United States)

    Latysheva, Natasha S; Oates, Matt E; Maddox, Louis; Flock, Tilman; Gough, Julian; Buljan, Marija; Weatheritt, Robert J; Babu, M Madan

    2016-08-18

    Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer. PMID:27540857

  14. Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

    Science.gov (United States)

    Lezon, Timothy R.; Banavar, Jayanth R.; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V.

    2006-01-01

    We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. PMID:17138668

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

    Science.gov (United States)

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

    2016-07-01

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

  16. 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

  17. 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; Burger, Pamela A

    2016-06-14

    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

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

    Institute of Scientific and Technical Information of China (English)

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

    2015-01-01

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

  19. Comparison of Gene Regulatory Networks via Steady-State Trajectories

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    Seungchan Kim

    2007-05-01

    Full Text Available The modeling of genetic regulatory networks is becoming increasingly widespread in the study of biological systems. In the abstract, one would prefer quantitatively comprehensive models, such as a differential-equation model, to coarse models; however, in practice, detailed models require more accurate measurements for inference and more computational power to analyze than coarse-scale models. It is crucial to address the issue of model complexity in the framework of a basic scientific paradigm: the model should be of minimal complexity to provide the necessary predictive power. Addressing this issue requires a metric by which to compare networks. This paper proposes the use of a classical measure of difference between amplitude distributions for periodic signals to compare two networks according to the differences of their trajectories in the steady state. The metric is applicable to networks with both continuous and discrete values for both time and state, and it possesses the critical property that it allows the comparison of networks of different natures. We demonstrate application of the metric by comparing a continuous-valued reference network against simplified versions obtained via quantization.

  20. Comparison of Gene Regulatory Networks via Steady-State Trajectories

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    Choi Woonjung

    2007-01-01

    Full Text Available The modeling of genetic regulatory networks is becoming increasingly widespread in the study of biological systems. In the abstract, one would prefer quantitatively comprehensive models, such as a differential-equation model, to coarse models; however, in practice, detailed models require more accurate measurements for inference and more computational power to analyze than coarse-scale models. It is crucial to address the issue of model complexity in the framework of a basic scientific paradigm: the model should be of minimal complexity to provide the necessary predictive power. Addressing this issue requires a metric by which to compare networks. This paper proposes the use of a classical measure of difference between amplitude distributions for periodic signals to compare two networks according to the differences of their trajectories in the steady state. The metric is applicable to networks with both continuous and discrete values for both time and state, and it possesses the critical property that it allows the comparison of networks of different natures. We demonstrate application of the metric by comparing a continuous-valued reference network against simplified versions obtained via quantization.

  1. Identification of candidate target genes for human peripheral arterial disease using weighted gene co‑expression network analysis.

    Science.gov (United States)

    Yin, De-Xin; Zhao, Hao-Min; Sun, Da-Jun; Yao, Jian; Ding, Da-Yong

    2015-12-01

    The aim of the present study was to identify the potential treatment targets of peripheral arterial disease (PAD) and provide further insights into the underlying mechanism of PAD, based on a weighted gene co‑expression network analysis (WGCNA) method. The mRNA expression profiles (accession. no. GSE27034), which included 19 samples from patients with PAD and 18 samples from normal control individuals were extracted from the Gene Expression Omnibus database. Subsequently, the differentially expressed genes (DEGs) were obtained using the Limma package and the co‑expression network modules were screened using the WGCNA approach. In addition, the protein‑protein interaction network for the DEGs in the most significant module was constructed using Cytoscape software. Functional enrichment analyses of the DEGs in the most significant module were also performed using the Database for Annotation, Visualization and Integrated Discovery and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology‑Based Annotation System, respectively. A total of 148 DEGs were identified in PAD, which were used to construct the WGCN, in which two modules (gray module and turquoise module) were identified, with the gray module exhibiting a higher gene significance (GS) value than the turquoise module. In addition, a co‑expression network was constructed for 60 DEGs in the gray module. The functional enrichment results showed that the DEGs in the gray module were enriched in five Gene Ontology terms and four KEGG pathways. For example, cyclin‑dependent kinase inhibitor 1A (CDKN1A), FBJ murine osteosarcoma viral oncogene homolog (FOS) and prostaglandin‑endoperoxide synthase 2 (PTGS2) were enriched in response to glucocorticoid stimulus. The results of the present study suggested that DEGs in the gray module, including CDKN1A, FOS and PTGS2, may be associated with the pathogenesis of PAD, by modulating the cell cycle, and may offer potential for use as candidate treatment

  2. Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

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    Zhou Qing

    2009-07-01

    Full Text Available Abstract Background Recent work has revealed that a core group of transcription factors (TFs regulates the key characteristics of embryonic stem (ES cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes. Results We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA, we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status, which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation. Conclusion Our systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology.

  3. Esotericism Ancient and Modern

    OpenAIRE

    Frazer, Michael

    2006-01-01

    Leo Strauss presents at least two distinct accounts of the idea that the authors in the political-philosophical canon have often masked their true teachings. A weaker account of esotericism, dependent on the contingent fact of persecution, is attributed to the moderns, while a stronger account, stemming from a necessary conflict between philosophy and society, is attributed to the ancients. Although most interpreters agree that Strauss here sides with the ancients, this view fails to consider...

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

    Science.gov (United States)

    Sears, Karen E; Maier, Jennifer A; Rivas-Astroza, Marcelo; Poe, Rachel; Zhong, Sheng; Kosog, Kari; Marcot, Jonathan D; Behringer, Richard R; Cretekos, Chris J; Rasweiler, John J; Rapti, Zoi

    2015-08-01

    Variation among individuals is a prerequisite of evolution by natural selection. As such, identifying the origins of variation is a fundamental goal of biology. We investigated the link between gene interactions and variation in gene expression among individuals and species using the mammalian limb as a model system. We first built interaction networks for key genes regulating early (outgrowth; E9.5-11) and late (expansion and elongation; E11-13) limb development in mouse. This resulted in an Early (ESN) and Late (LSN) Stage Network. Computational perturbations of these networks suggest that the ESN is more robust. We then quantified levels of the same key genes among mouse individuals and found that they vary less at earlier limb stages and that variation in gene expression is heritable. Finally, we quantified variation in gene expression levels among four mammals with divergent limbs (bat, opossum, mouse and pig) and found that levels vary less among species at earlier limb stages. We also found that variation in gene expression levels among individuals and species are correlated for earlier and later limb development. In conclusion, results are consistent with the robustness of the ESN buffering among-individual variation in gene expression levels early in mammalian limb development, and constraining the evolution of early limb development among mammalian species. PMID:26317994

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

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    Oded Magger

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

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

    Science.gov (United States)

    van Oudenaarden, Alexander

    2003-03-01

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

  7. A Systems Approach Identifies Networks and Genes Linking Sleep and Stress: Implications for Neuropsychiatric Disorders

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    Peng Jiang

    2015-05-01

    Full Text Available 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.

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

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    Rajeevan Mangalathu S

    2008-11-01

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

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

    OpenAIRE

    Crane Martin; Ruskin Heather J; Sîrbu Alina

    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...

  10. Gene, protein, and network of male sterility in rice

    OpenAIRE

    WANG Kun; Peng, Xiaojue; Ji, Yanxiao; Yang, Pingfang; Zhu, Yingguo; LI, SHAOQING

    2013-01-01

    Rice is one of the most important model crop plants whose heterosis has been well-exploited in commercial hybrid seed production via a variety of types of male-sterile lines. Hybrid rice cultivation area is steadily expanding around the world, especially in Southern Asia. Characterization of genes and proteins related to male sterility aims to understand how and why the male sterility occurs, and which proteins are the key players for microspores abortion. Recently, a series of genes and prot...

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

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    Gao Shouguo

    2011-08-01

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

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

    Science.gov (United States)

    Xing, Yonghua; Zhang, Junling; Lu, Lu; Li, Deguan; Wang, Yueying; Huang, Song; Li, Chengcheng; Zhang, Zhubo; Li, Jianguo; Meng, Aimin

    2016-01-01

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

  13. Mitochondrial network genes in the skeletal muscle of amyotrophic lateral sclerosis patients.

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    Camilla Bernardini

    Full Text Available Recent evidence suggested that muscle degeneration might lead and/or contribute to neurodegeneration, thus it possibly play a key role in the etiopathogenesis and progression of amyotrophic lateral sclerosis (ALS. To test this hypothesis, this study attempted to categorize functionally relevant genes within the genome-wide expression profile of human ALS skeletal muscle, using microarray technology and gene regulatory network analysis. The correlation network structures significantly change between patients and controls, indicating an increased inter-gene connection in patients compared to controls. The gene network observed in the ALS group seems to reflect the perturbation of muscle homeostasis and metabolic balance occurring in affected individuals. In particular, the network observed in the ALS muscles includes genes (PRKR1A, FOXO1, TRIM32, ACTN3, among others, whose functions connect the sarcomere integrity to mitochondrial oxidative metabolism. Overall, the analytical approach used in this study offer the possibility to observe higher levels of correlation (i.e. common expression trends among genes, whose function seems to be aberrantly activated during the progression of muscle atrophy.

  14. Modification of gene duplicability during the evolution of protein interaction network.

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    Matteo D'Antonio

    2011-04-01

    Full Text Available Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.

  15. Identification of Candidate Genes related to Bovine Marbling using Protein-Protein Interaction Networks

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    Dajeong Lim, Nam-Kuk Kim, Hye-Sun Park, Seung-Hwan Lee, Yong-Min Cho, Sung Jong Oh, Tae-Hun Kim, Heebal Kim

    2011-01-01

    Full Text Available Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The present study systemically analyzed genes associated with bovine marbling score and identified their relationships. The candidate nodes were obtained using MedScan text-mining tools and linked by protein-protein interaction (PPI from the Human Protein Reference Database (HPRD. To determine key node of marbling, the degree and betweenness centrality (BC were used. The hub nodes and biological pathways of our network are consistent with the previous reports about marbling traits, and also suggest unknown candidate genes associated with intramuscular fat. Five nodes were identified as hub genes, which was consistent with the network analysis using quantitative reverse-transcription PCR (qRT-PCR. Key nodes of the PPI network have positive roles (PPARγ, C/EBPα, and RUNX1T1 and negative roles (RXRA, CAMK2A in the development of intramuscular fat by several adipogenesis-related pathways. This study provides genetic information for identifying candidate genes for the marbling trait in bovine.

  16. 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. PMID:27036626

  17. Inference of nonlinear gene regulatory networks through optimized ensemble of support vector regression and dynamic Bayesian networks.

    Science.gov (United States)

    Akutekwe, Arinze; Seker, Huseyin

    2015-08-01

    Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in systems biology. Most methods for modeling and inferring the dynamics of GRNs, such as those based on state space models, vector autoregressive models and G1DBN algorithm, assume linear dependencies among genes. However, this strong assumption does not make for true representation of time-course relationships across the genes, which are inherently nonlinear. Nonlinear modeling methods such as the S-systems and causal structure identification (CSI) have been proposed, but are known to be statistically inefficient and analytically intractable in high dimensions. To overcome these limitations, we propose an optimized ensemble approach based on support vector regression (SVR) and dynamic Bayesian networks (DBNs). The method called SVR-DBN, uses nonlinear kernels of the SVR to infer the temporal relationships among genes within the DBN framework. The two-stage ensemble is further improved by SVR parameter optimization using Particle Swarm Optimization. Results on eight insilico-generated datasets, and two real world datasets of Drosophila Melanogaster and Escherichia Coli, show that our method outperformed the G1DBN algorithm by a total average accuracy of 12%. We further applied our method to model the time-course relationships of ovarian carcinoma. From our results, four hub genes were discovered. Stratified analysis further showed that the expression levels Prostrate differentiation factor and BTG family member 2 genes, were significantly increased by the cisplatin and oxaliplatin platinum drugs; while expression levels of Polo-like kinase and Cyclin B1 genes, were both decreased by the platinum drugs. These hub genes might be potential biomarkers for ovarian carcinoma. PMID:26738192

  18. Gene Expression Correlations in Human Cancer Cell Lines Define Molecular Interaction Networks for Epithelial Phenotype

    OpenAIRE

    Kohn, Kurt W.; Zeeberg, Barry M.; Reinhold, William C.; Pommier, Yves

    2014-01-01

    Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Bro...

  19. 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.

  20. Revealing gene regulation and association through biological networks

    Science.gov (United States)

    This review had first summarized traditional methods used by plant breeders for genetic improvement, such as QTL analysis and transcriptomic analysis. With accumulating data, we can draw a network that comprises all possible links between members of a community, including protein–protein interaction...

  1. Identification of gene networks underlying dystocia in dairy cattle

    Science.gov (United States)

    Dystocia is a trait with a high impact in the dairy industry. Among its risk factors are calf weight, gestation length, breed and conformation. Biological networks have been proposed to capture the genetic architecture of complex traits, where GWAS show limitations. The objective of this study was t...

  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. PMID:27064123

  3. 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...... of spurious information along the network are avoided. The proposed inference procedure is based on the minimization of the Bayesian Information Criterion (BIC) in the class of decomposable graphical models. This class of models can be used to represent complex relationships and has suitable...... properties 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...

  4. 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.

  5. Structure and function of gene regulatory networks associated with worker sterility in honeybees.

    Science.gov (United States)

    Sobotka, Julia A; Daley, Mark; Chandrasekaran, Sriram; Rubin, Benjamin D; Thompson, Graham J

    2016-03-01

    A characteristic of eusocial bees is a reproductive division of labor in which one or a few queens monopolize reproduction, while her worker daughters take on reproductively altruistic roles within the colony. The evolution of worker reproductive altruism involves indirect selection for the coordinated expression of genes that regulate personal reproduction, but evidence for this type of selection remains elusive. In this study, we tested whether genes coexpressed under queen-induced worker sterility show evidence of adaptive organization within a model brain transcriptional regulatory network (TRN). If so, this structured pattern would imply that indirect selection on nonreproductive workers has influenced the functional organization of genes within the network, specifically to regulate the expression of sterility. We found that literature-curated sets of candidate genes for sterility, ranging in size from 18 to 267, show strong evidence of clustering within the three-dimensional space of the TRN. This finding suggests that our candidate sets of genes for sterility form functional modules within the living bee brain's TRN. Moreover, these same gene sets colocate to a single, albeit large, region of the TRN's topology. This spatially organized and convergent pattern contrasts with a null expectation for functionally unrelated genes to be haphazardly distributed throughout the network. Our meta-genomic analysis therefore provides first evidence for a truly "social transcriptome" that may regulate the conditional expression of honeybee worker sterility. PMID:26925214

  6. 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.

  7. 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. PMID:26739559

  8. Discovering potential cancer driver genes by an integrated network-based approach.

    Science.gov (United States)

    Shi, Kai; Gao, Lin; Wang, Bingbo

    2016-08-16

    Although a lot of methods have been proposed to identify driver genes, how to separate the driver mutations from the passenger mutations is still a challenging problem in cancer genomics. The detection of driver genes with rare mutation and low accuracy is unsolved better. In this study, we present an integrated network-based approach to locate potential driver genes in a cohort of patients. The approach is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation. We analyze three cancer datasets including Glioblastoma multiforme, Ovarian cancer and Breast cancer. Our method has not only identified the known driver genes with high-frequency mutations, but also discovered the potential driver genes with a rare mutation. At the same time, validation by literature search and functional enrichment analysis reveal that the predicted genes are obviously related to these three kinds of cancers. PMID:27426053

  9. 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.

  10. Epigenetic modulation of brain gene networks for cocaine and alcohol abuse.

    Science.gov (United States)

    Farris, Sean P; Harris, Robert A; Ponomarev, Igor

    2015-01-01

    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. PMID:26041984

  11. BioNetwork Bench: Database and Software for Storage, Query, and Analysis of Gene and Protein Networks

    OpenAIRE

    Oksana Kohutyuk; Fadi Towfic; M. Heather West Greenlee; Vasant Honavar

    2012-01-01

    Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from high-throughput analyses. Although many tools and databases are currently available for accessing such data, they are left unutilized by bench scientists as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by scientists with limited computational expertise. We...

  12. CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks

    OpenAIRE

    Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, Md. Matiur; Chen, Ming

    2014-01-01

    Background Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. Results We developed a tool...

  13. Noise Propagation in Gene Regulation Networks Involving Interlinked Positive and Negative Feedback Loops

    OpenAIRE

    Zhang, Hui; Chen, Yueling; CHEN, YONG

    2012-01-01

    It is well known that noise is inevitable in gene regulatory networks due to the low-copy numbers of molecules and local environmental fluctuations. The prediction of noise effects is a key issue in ensuring reliable transmission of information. Interlinked positive and negative feedback loops are essential signal transduction motifs in biological networks. Positive feedback loops are generally believed to induce a switch-like behavior, whereas negative feedback loops are thought to suppress ...

  14. Combinatorial Limits of Transcription Factors and Gene Regulatory Networks in Development and Evolution

    OpenAIRE

    Werner, Eric

    2015-01-01

    Gene Regulatory Networks (GRNs) consisting of combinations of transcription factors (TFs) and their cis promoters are assumed to be sufficient to direct the development of organisms. Mutations in GRNs are assumed to be the primary drivers for the evolution of multicellular life. Here it is proven that neither of these assumptions is correct. They are inconsistent with fundamental principles of combinatorics of bounded encoded networks. It is shown there are inherent complexity and control cap...

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

    Science.gov (United States)

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

    2016-01-01

    The spatial distribution of microbes on our planet is famously formulated in the Baas Becking hypothesis as "everything is everywhere but the environment selects." While this hypothesis does not strictly rule out patterns caused by geographical effects on ecology and historical founder effects, it does propose that the remarkable dispersal potential of microbes leads to distributions generally shaped by environmental factors rather than geographical distance. By constructing sequence similarity networks from uncultured environmental samples, we show that microbial gene pool distributions are not influenced nearly as much by geography as ecology, thus extending the Bass Becking hypothesis from whole organisms to microbial genes. We find that gene pools are shaped by their broad ecological niche (such as sea water, fresh water, host, and airborne). We find that freshwater habitats act as a gene exchange bridge between otherwise disconnected habitats. Finally, certain antibiotic resistance genes deviate from the general trend of habitat specificity by exhibiting a high degree of cross-habitat mobility. The strong cross-habitat mobility of antibiotic resistance genes is a cause for concern and provides a paradigmatic example of the rate by which genes colonize new habitats when new selective forces emerge. PMID:27190206

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

    Science.gov (United States)

    Himmelstein, Daniel S; Baranzini, Sergio E

    2015-07-01

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

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

    Directory of Open Access Journals (Sweden)

    Daniel S Himmelstein

    2015-07-01

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

  18. Mining of vaccine-associated IFN-γ gene interaction networks using the Vaccine Ontology

    OpenAIRE

    2011-01-01

    Background Interferon-gamma (IFN-γ) is vital in vaccine-induced immune defense against bacterial and viral infections and tumor. Our recent study demonstrated the power of a literature-based discovery method in extraction and comparison of the IFN-γ and vaccine-mediated gene interaction networks. The Vaccine Ontology (VO) contains a hierarchy of vaccine names. It is hypothesized that the application of VO will enhance the prediction of IFN-γ and vaccine-mediated gene interaction network. Resu...

  19. Identification of common regulators of genes in co-expression networks affecting muscle and meat properties.

    Directory of Open Access Journals (Sweden)

    Siriluck Ponsuksili

    Full Text Available Understanding the genetic contributions behind skeletal muscle composition and metabolism is of great interest in medicine and agriculture. Attempts to dissect these complex traits combine genome-wide genotyping, expression data analyses and network analyses. Weighted gene co-expression network analysis (WGCNA groups genes into modules based on patterns of co-expression, which can be linked to phenotypes by correlation analysis of trait values and the module eigengenes, i.e. the first principal component of a given module. Network hub genes and regulators of the genes in the modules are likely to play an important role in the emergence of respective traits. In order to detect common regulators of genes in modules showing association with meat quality traits, we identified eQTL for each of these genes, including the highly connected hub genes. Additionally, the module eigengene values were used for association analyses in order to derive a joint eQTL for the respective module. Thereby major sites of orchestrated regulation of genes within trait-associated modules were detected as hotspots of eQTL of many genes of a module and of its eigengene. These sites harbor likely common regulators of genes in the modules. We exemplarily showed the consistent impact of candidate common regulators on the expression of members of respective modules by RNAi knockdown experiments. In fact, Cxcr7 was identified and validated as a regulator of genes in a module, which is involved in the function of defense response in muscle cells. Zfp36l2 was confirmed as a regulator of genes of a module related to cell death or apoptosis pathways. The integration of eQTL in module networks enabled to interpret the differentially-regulated genes from a systems perspective. By integrating genome-wide genomic and transcriptomic data, employing co-expression and eQTL analyses, the study revealed likely regulators that are involved in the fine-tuning and synchronization of genes with

  20. Identification of common regulators of genes in co-expression networks affecting muscle and meat properties.

    Science.gov (United States)

    Ponsuksili, Siriluck; Siengdee, Puntita; Du, Yang; Trakooljul, Nares; Murani, Eduard; Schwerin, Manfred; Wimmers, Klaus

    2015-01-01

    Understanding the genetic contributions behind skeletal muscle composition and metabolism is of great interest in medicine and agriculture. Attempts to dissect these complex traits combine genome-wide genotyping, expression data analyses and network analyses. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of co-expression, which can be linked to phenotypes by correlation analysis of trait values and the module eigengenes, i.e. the first principal component of a given module. Network hub genes and regulators of the genes in the modules are likely to play an important role in the emergence of respective traits. In order to detect common regulators of genes in modules showing association with meat quality traits, we identified eQTL for each of these genes, including the highly connected hub genes. Additionally, the module eigengene values were used for association analyses in order to derive a joint eQTL for the respective module. Thereby major sites of orchestrated regulation of genes within trait-associated modules were detected as hotspots of eQTL of many genes of a module and of its eigengene. These sites harbor likely common regulators of genes in the modules. We exemplarily showed the consistent impact of candidate common regulators on the expression of members of respective modules by RNAi knockdown experiments. In fact, Cxcr7 was identified and validated as a regulator of genes in a module, which is involved in the function of defense response in muscle cells. Zfp36l2 was confirmed as a regulator of genes of a module related to cell death or apoptosis pathways. The integration of eQTL in module networks enabled to interpret the differentially-regulated genes from a systems perspective. By integrating genome-wide genomic and transcriptomic data, employing co-expression and eQTL analyses, the study revealed likely regulators that are involved in the fine-tuning and synchronization of genes with trait

  1. Social networking between mobile introns and their host genes

    OpenAIRE

    Stoddard, Barry; Belfort, Marlene

    2010-01-01

    Homing endonucleases have long been known as the orchestrators of intron mobility. However, the extent of their influence on the intron and its genetic and cellular environment is still being elucidated. The accompanying paper emphasizes the importance of temporal control of endonuclease expression on splicing, expression of the host gene and cellular metabolism, while it raises questions to guide future inquiry.

  2. Gene-to-metabolite networks for terpenoid indole alkaloid biosynthesis in Catharanthus roseus cells

    Science.gov (United States)

    Rischer, Heiko; Orešič, Matej; Seppänen-Laakso, Tuulikki; Katajamaa, Mikko; Lammertyn, Freya; Ardiles-Diaz, Wilson; Van Montagu, Marc C. E.; Inzé, Dirk; Oksman-Caldentey, Kirsi-Marja; Goossens, Alain

    2006-01-01

    Rational engineering of complicated metabolic networks involved in the production of biologically active plant compounds has been greatly impeded by our poor understanding of the regulatory and metabolic pathways underlying the biosynthesis of these compounds. Whereas comprehensive genome-wide functional genomics approaches can be successfully applied to analyze a select number of model plants, these holistic approaches are not yet available for the study of nonmodel plants that include most, if not all, medicinal plants. We report here a comprehensive profiling analysis of the Madagascar periwinkle (Catharanthus roseus), a source of the anticancer drugs vinblastine and vincristine. Genome-wide transcript profiling by cDNA-amplified fragment-length polymorphism combined with metabolic profiling of elicited C. roseus cell cultures yielded a collection of known and previously undescribed transcript tags and metabolites associated with terpenoid indole alkaloids. Previously undescribed gene-to-gene and gene-to-metabolite networks were drawn up by searching for correlations between the expression profiles of 417 gene tags and the accumulation profiles of 178 metabolite peaks. These networks revealed that the different branches of terpenoid indole alkaloid biosynthesis and various other metabolic pathways are subject to differing hormonal regulation. These networks also served to identify a select number of genes and metabolites likely to be involved in the biosynthesis of terpenoid indole alkaloids. This study provides the basis for a better understanding of periwinkle secondary metabolism and increases the practical potential of metabolic engineering of this important medicinal plant. PMID:16565214

  3. A comprehensive gene regulatory network for the diauxic shift in Saccharomyces cerevisiae.

    Science.gov (United States)

    Geistlinger, Ludwig; Csaba, Gergely; Dirmeier, Simon; Küffner, Robert; Zimmer, Ralf

    2013-10-01

    Existing machine-readable resources for large-scale gene regulatory networks usually do not provide context information characterizing the activating conditions for a regulation and how targeted genes are affected. Although this information is essentially required for data interpretation, available networks are often restricted to not condition-dependent, non-quantitative, plain binary interactions as derived from high-throughput screens. In this article, we present a comprehensive Petri net based regulatory network that controls the diauxic shift in Saccharomyces cerevisiae. For 100 specific enzymatic genes, we collected regulations from public databases as well as identified and manually curated >400 relevant scientific articles. The resulting network consists of >300 multi-input regulatory interactions providing (i) activating conditions for the regulators; (ii) semi-quantitative effects on their targets; and (iii) classification of the experimental evidence. The diauxic shift network compiles widespread distributed regulatory information and is available in an easy-to-use machine-readable form. Additionally, we developed a browsable system organizing the network into pathway maps, which allows to inspect and trace the evidence for each annotated regulation in the model. PMID:23873954

  4. Listening to the noise: random fluctuations reveal gene network parameters

    OpenAIRE

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2009-01-01

    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 show cell-to-cell variability that can manifest significant phenotypic differences. Noise-induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far...

  5. Bistability in a stochastic RNA-mediated gene network

    Science.gov (United States)

    Lloyd-Price, Jason; Ribeiro, Andre S.

    2013-09-01

    Small regulatory RNAs (srRNAs) are important regulators of gene expression in eukaryotes and prokaryotes. A common motif containing srRNA is a bistable two-gene motif where one gene codes for a transcription factor (TF) which represses the transcription of the second gene, whose transcript is a srRNA which targets the first gene's transcript. Here, we investigate the properties of this motif in a stochastic model which takes the low copy numbers of the RNA components into account. First, we examine the conditions for stability of the two “noisy attractors.” We find that for realistic low copy numbers, extreme, but within realistic intervals, mutual repression strengths are required to compensate for the variability of the RNA numbers and thus, achieve long-term bistability. Second, the promoter initiation kinetics is found to strongly influence the bistability of the switch. Super-Poissonian RNA production disrupts the ability of the srRNA to silence its target, though sub-Poissonian RNA production does not rule out the need for strong mutual repression. Finally, we show that asymmetry between the two interactions forming the switch allows an external input to induce the transition from “high srRNA” to “‘high TF” more easily (i.e., with a shorter input) than in the opposite direction. We hypothesize that this asymmetric switching property allows these circuits to be more sensitive to one external input, without sacrificing the stability of one of the noisy attractors.

  6. Mitochondrial DNA analysis of ancient Sampula population in Xinjiang

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The archaeological site of Sampula cemetery was located about 14 km to the southwest of the Luo County in Xinjiang Khotan, China, belonging to the ancient Yutian kingdom. 14C analysis showed that this cemetery was used from 217 B.C. to 283 A.D.Ancient DNA was analyzed by 364 bp of the mitochondrial DNA hypervariable region Ⅰ (mtDNA HVR-Ⅰ), and by six restriction fragment length polymorphism (RFLP) sites of mtDNA coding region. We successfully extracted and sequenced intact stretches of maternally inherited mtDNA from 13 out of 16 ancient Sampula samples. The analysis of mtDNA haplogroup distribution showed that the ancient Sampula was a complex population with both European and Asian characteristics. Median joining network of U3 sub-haplogroup and multi-dimensional scaling analysis all showed that the ancient Sampula had maternal relationship with Ossetian and Iranian.

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

    Directory of Open Access Journals (Sweden)

    Marek Mutwil

    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.

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

    Directory of Open Access Journals (Sweden)

    Iryna Kozmikova

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

  9. 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

  10. Modularity of gene-regulatory networks revealed in sea-star development

    OpenAIRE

    Degnan Bernard M; McDougall Carmel

    2011-01-01

    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 h...

  11. A novel model-free approach for reconstruction of time-delayed gene regulatory networks

    Institute of Scientific and Technical Information of China (English)

    JIANG; Wei; LI; Xia; GUO; Zheng; LI; Chuanxing; WANG; Lihong

    2006-01-01

    Reconstruction of genetic networks is one of the key scientific challenges in functional genomics. This paper describes a novel approach for addressing the regulatory dependencies between genes whose activities can be delayed by multiple units of time. The aim of the proposed approach termed TdGRN (time-delayed gene regulatory networking) is to reversely engineer the dynamic mechanisms of gene regulations, which is realized by identifying the time-delayed gene regulations through supervised decision-tree analysis of the newly designed time-delayed gene expression matrix, derived from the original time-series microarray data. A permutation technique is used to determine the statistical classification threshold of a tree, from which a gene regulatory rule(s) is extracted. The proposed TdGRN is a model-free approach that attempts to learn the underlying regulatory rules without relying on any model assumptions. Compared with model-based approaches, it has several significant advantages: it requires neither any arbitrary threshold for discretization of gene transcriptional values nor the definition of the number of regulators (k). We have applied this novel method to the publicly available data for budding yeast cell cycling. The numerical results demonstrate that most of the identified time-delayed gene regulations have current biological knowledge supports.

  12. Reverse Engineering Sparse Gene Regulatory Networks Using Cubature Kalman Filter and Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Amina Noor

    2013-01-01

    Full Text Available This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF and Kalman filter (KF techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.

  13. 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.

  14. Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions.

    Science.gov (United States)

    Werhli, Adriano V; Husmeier, Dirk

    2008-06-01

    There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach is based on pioneering work by Imoto et al. where the prior knowledge is expressed in terms of energy functions, from which a prior distribution over network structures is obtained in the form of a Gibbs distribution. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. We have derived and tested a Markov chain Monte Carlo (MCMC) scheme for sampling networks and hyperparameters simultaneously from the posterior distribution, thereby automatically learning how to trade off information from the prior knowledge and the data. We have extended this approach to a Bayesian coupling scheme for learning gene regulatory networks from a combination of related data sets, which were obtained under different experimental conditions and are therefore potentially associated with different active subpathways. The proposed coupling scheme is a compromise between (1) learning networks from the different subsets separately, whereby no information between the different experiments is shared; and (2) learning networks from a monolithic fusion of the individual data sets, which does not provide any mechanism for uncovering differences between the network structures associated with the different experimental conditions. We have assessed the viability of all proposed methods on data related to the Raf signaling pathway, generated both synthetically and in cytometry experiments. PMID:18574862

  15. Deconvoluting lung evolution: from phenotypes to gene regulatory networks

    OpenAIRE

    Torday, John S.; Rehan, Virender K.; Hicks, James W.; Wang, Tobias; Maina, John; Weibel, Ewald R.; Hsia, Connie C.W.; Sommer, Ralf J.; Perry, Steven F.

    2007-01-01

    Speakers in this symposium presented examples of respiratory regulation that broadly illustrate principles of evolution from whole organ to genes. The swim bladder and lungs of aquatic and terrestrial organisms arose independently from a common primordial “respiratory pharynx” but not from each other. Pathways of lung evolution are similar between crocodiles and birds but a low compliance of mammalian lung may have driven the development of the diaphragm to permit lung inflation during inspir...

  16. Data integration for microarrays: enhanced inference for gene regulatory networks

    OpenAIRE

    Alina Sîrbu; Martin Crane; Ruskin, Heather J

    2015-01-01

    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 l...

  17. Regulatory Networks Involving YABBY Genes in Rice Shoot Development

    OpenAIRE

    Dai, Mingqiu; Hu, Yongfeng; Zhao, Yu; Zhou, Dao-Xiu

    2007-01-01

    Shoot development is regulated by specific gene expression programs depending on the interplay between transcription factors and growth hormones that function in specific domains of the meristem and lateral organs. Functional relationship between different regulators is not clearly established. In the May issue of Plant Physiology (2007) we have shown that Wuschel-like Homeobox3 and YABBY3 are coexpressed in the leaf primordia and young leaves, and that WOX3 functions as a transcriptional rep...

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

    OpenAIRE

    Jochen Hecht; Sigmar Stricker; Ulrike Wiecha; Asita Stiege; Georgia Panopoulou; Lars Podsiadlowski; Poustka, Albert J.; Christoph Dieterich; Siegfried Ehrich; Julia Suvorova; Stefan Mundlos; Volkhard Seitz

    2008-01-01

    The skeleton is one of the most important features for the reconstruction of vertebrate phylogeny but few data are available to understand its molecular origin. In mammals the Runt genes are central regulators of skeletogenesis. Runx2 was shown to be essential for osteoblast differentiation, tooth development, and bone formation. Both Runx2 and Runx3 are essential for chondrocyte maturation. Furthermore, Runx2 directly regulates Indian hedgehog expression, a master coordinator of skeletal dev...

  19. [Psychiatry in ancient Mexico].

    Science.gov (United States)

    Calderón Narváez, G

    1992-12-01

    Using studies on prehispanic and early post-conquest documents of Ancient Mexico--such as the Badianus Manuscript, also known as Libellus de Medicinalibus Indorum Herbis, and Brother Bernardino de Sahagún's famous work History of the Things of the New Spain, a description of some existing medical and psychiatric problems, and treatments Ancient Aztecs resorted to, is presented. The structure of the Aztec family, their problems with the excessive ingestion of alcoholic beverages, and the punishments native authorities had implemented in order to check alcoholism up are also described. PMID:1341125

  20. Mosaic gene network modelling identified new regulatory mechanisms in HCV infection.

    Science.gov (United States)

    Popik, Olga V; Petrovskiy, Evgeny D; Mishchenko, Elena L; Lavrik, Inna N; Ivanisenko, Vladimir A

    2016-06-15

    Modelling of gene networks is widely used in systems biology to study the functioning of complex biological systems. Most of the existing mathematical modelling techniques are useful for analysis of well-studied biological processes, for which information on rates of reactions is available. However, complex biological processes such as those determining the phenotypic traits of organisms or pathological disease processes, including pathogen-host interactions, involve complicated cross-talk between interacting networks. Furthermore, the intrinsic details of the interactions between these networks are often missing. In this study, we developed an approach, which we call mosaic network modelling, that allows the combination of independent mathematical models of gene regulatory networks and, thereby, description of complex biological systems. The advantage of this approach is that it allows us to generate the integrated model despite the fact that information on molecular interactions between parts of the model (so-called mosaic fragments) might be missing. To generate a mosaic mathematical model, we used control theory and mathematical models, written in the form of a system of ordinary differential equations (ODEs). In the present study, we investigated the efficiency of this method in modelling the dynamics of more than 10,000 simulated mosaic regulatory networks consisting of two pieces. Analysis revealed that this approach was highly efficient, as the mean deviation of the dynamics of mosaic network elements from the behaviour of the initial parts of the model was less than 10%. It turned out that for construction of the control functional, data on perturbation of one or two vertices of the mosaic piece are sufficient. Further, we used the developed method to construct a mosaic gene regulatory network including hepatitis C virus (HCV) as the first piece and the tumour necrosis factor (TNF)-induced apoptosis and NF-κB induction pathways as the second piece. Thus

  1. A genome-wide association study identifies a gene network of ADAMTS genes in the predisposition to pediatric stroke.

    Science.gov (United States)

    Arning, Astrid; Hiersche, Milan; Witten, Anika; Kurlemann, Gerhard; Kurnik, Karin; Manner, Daniela; Stoll, Monika; Nowak-Göttl, Ulrike

    2012-12-20

    Pediatric stroke is a rare but highly penetrant disease with a strong genetic background. Although there are an increasing number of genome-wide association studies (GWASs) for stroke in adults, such studies for stroke of pediatric onset are lacking. Here we report the results of the first GWAS on pediatric stroke using a large cohort of 270 family-based trios. GWAS was performed using the Illumina 370 CNV single nucleotide polymorphisms array and analyzed using the transmission disequilibrium test as implemented in PLINK. An enrichment analysis was performed to identify additional true association signals among lower P value signals and searched for cumulatively associated genes within protein interaction data using dmGWAS. We observed clustering of association signals in 4 genes belonging to one family of metalloproteinases at high (ADAMTS12, P = 2.9 × 10(-6); ADAMTS2, P = 8.0 × 10(-6)) and moderate (ADAMTS13, P = 9.3 × 10(-4); ADAMTS17, P = 8.5 × 10(-4)) significance levels. Over-representation and gene-network analyses highlight the importance of the extracellular matrix in conjunction with members of the phosphoinositide and calcium signaling pathways in the susceptibility for pediatric stroke. Associated extracellular matrix components, such as ADAMTS proteins, in combination with misbalanced coagulation signals as unveiled by gene network analysis suggest a major role of postnatal vascular injury with subsequent thrombus formation as the leading cause of pediatric stroke. PMID:22990015

  2. Identification of genes and networks driving cardiovascular and metabolic phenotypes in a mouse F2 intercross.

    Directory of Open Access Journals (Sweden)

    Jonathan M J Derry

    Full Text Available To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6JxA/J F2 (B6AF2 cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs including loci driving variation in cardiac function on chromosomes 2 and 6 and a hotspot for adiposity, energy metabolism, and glucose traits on chromosome 8. Integration of adipose gene expression data identified a core set of genes that drive the chromosome 8 adiposity QTL. This chromosome 8 trans eQTL signature contains genes associated with mitochondrial function and oxidative phosphorylation and maps to a subnetwork with conserved function in humans that was previously implicated in human obesity. In addition, human eSNPs corresponding to orthologous genes from the signature show enrichment for association to type II diabetes in the DIAGRAM cohort, supporting the idea that the chromosome 8 locus perturbs a molecular network that in humans senses variations in DNA and in turn affects metabolic disease risk. We functionally validate predictions from this approach by demonstrating metabolic phenotypes in knockout mice for three genes from the trans eQTL signature, Akr1b8, Emr1, and Rgs2. In addition we show that the transcriptional signatures for knockout of two of these genes, Akr1b8 and Rgs2, map to the F2 network modules associated with the chromosome 8 trans eQTL signature and that these modules are in turn very significantly correlated with adiposity in the F2 population. Overall this study demonstrates how integrating gene expression data with QTL analysis in a network-based framework can aid in the elucidation of the molecular drivers of disease that can be translated from mice to humans.

  3. 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.

  4. 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.

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

    Science.gov (United States)

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

    2016-01-01

    Laboratory studies of social behavior have typically focused on dyadic interactions occurring within a limited spatiotemporal context. However, this strategy prevents analyses of the dynamics of group social behavior and constrains identification of the biological pathways mediating individual differences in behavior. In the current study, we aimed to identify the spatiotemporal dynamics and hierarchical organization of a large social network of male mice. We also sought to determine if standard assays of social and exploratory behavior are predictive of social behavior in this social network and whether individual network position was associated with the mRNA expression of two plasticity-related genes, DNA methyltransferase 1 and 3a. Mice were observed to form a hierarchically organized social network and self-organized into two separate social network communities. Members of both communities exhibited distinct patterns of socio-spatial organization within the vivaria that was not limited to only agonistic interactions. We further established that exploratory and social behaviors in standard behavioral assays conducted prior to placing the mice into the large group was predictive of initial network position and behavior but were not associated with final social network position. Finally, we determined that social network position is associated with variation in mRNA levels of two neural plasticity genes, DNMT1 and DNMT3a, in the hippocampus but not the mPOA. This work demonstrates the importance of understanding the role of social context and complex social dynamics in determining the relationship between individual differences in social behavior and brain gene expression. PMID:27540359

  6. BisoGenet: a new tool for gene network building, visualization and analysis

    Directory of Open Access Journals (Sweden)

    Miranda Jamilet

    2010-02-01

    Full Text Available Abstract Background The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics. Results BisoGenet is a multi-tier application for visualization and analysis of biomolecular relationships. The system consists of three tiers. In the data tier, an in-house database stores genomics information, protein-protein interactions, protein-DNA interactions, gene ontology and metabolic pathways. In the middle tier, a global network is created at server startup, representing the whole data on bioentities and their relationships retrieved from the database. The client tier is a Cytoscape plugin, which manages user input, communication with the Web Service, visualization and analysis of the resulting network. Conclusion BisoGenet is able to build and visualize biological networks in a fast and user-friendly manner. A feature of Bisogenet is the possibility to include coding relations to distinguish between genes and their products. This feature could be instrumental to achieve a finer grain representation of the bioentities and their relationships. The client application includes network analysis tools and interactive network expansion capabilities. In addition, an option is provided to allow other networks to be converted to BisoGenet. This feature facilitates the integration of our software with other tools available in the Cytoscape platform. BisoGenet is available at http://bio.cigb.edu.cu/bisogenet-cytoscape/.

  7. Protein Interaction Networks Reveal Novel Autism Risk Genes within GWAS Statistical Noise

    Science.gov (United States)

    Correia, Catarina; Oliveira, Guiomar; Vicente, Astrid M.

    2014-01-01

    Genome-wide association studies (GWAS) for Autism Spectrum Disorder (ASD) thus far met limited success in the identification of common risk variants, consistent with the notion that variants with small individual effects cannot be detected individually in single SNP analysis. To further capture disease risk gene information from ASD association studies, we applied a network-based strategy to the Autism Genome Project (AGP) and the Autism Genetics Resource Exchange GWAS datasets, combining family-based association data with Human Protein-Protein interaction (PPI) data. Our analysis showed that autism-associated proteins at higher than conventional levels of significance (P<0.1) directly interact more than random expectation and are involved in a limited number of interconnected biological processes, indicating that they are functionally related. The functionally coherent networks generated by this approach contain ASD-relevant disease biology, as demonstrated by an improved positive predictive value and sensitivity in retrieving known ASD candidate genes relative to the top associated genes from either GWAS, as well as a higher gene overlap between the two ASD datasets. Analysis of the intersection between the networks obtained from the two ASD GWAS and six unrelated disease datasets identified fourteen genes exclusively present in the ASD networks. These are mostly novel genes involved in abnormal nervous system phenotypes in animal models, and in fundamental biological processes previously implicated in ASD, such as axon guidance, cell adhesion or cytoskeleton organization. Overall, our results highlighted novel susceptibility genes previously hidden within GWAS statistical “noise” that warrant further analysis for causal variants. PMID:25409314

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

    Directory of Open Access Journals (Sweden)

    Tauch Andreas

    2009-01-01

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

  9. 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.

    2015-01-01

    Background Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by co-expression may also be enriched for additional EOC risk associations. Methods 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 co-expressed with each selected TF gene in the unified microarray data set of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this data set 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). Results 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. Conclusion We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Impact Network analysis integrating large, context-specific data sets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. PMID:26209509

  10. 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). The...... thesis balances between the use of IT tools to aid Humanities research and the understanding that Humanities research must involve human beings. It does not attempt to develop a system that can automate the reading of ancient documents. Instead it seeks to demonstrate and develop tools that can support......, 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...

  11. A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network.

    Science.gov (United States)

    Ruan, Xiyun; Li, Hongyun; Liu, Bo; Chen, Jie; Zhang, Shibao; Sun, Zeqiang; Liu, Shuangqing; Sun, Fahai; Liu, Qingyong

    2015-08-01

    The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson's correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson's correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis. PMID:26058425

  12. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.

  13. 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...

  14. Sensor-coupled fractal gene regulatory networks for locomotion control of a modular snake robot

    DEFF Research Database (Denmark)

    Zahadat, Payam; Christensen, David Johan; Katebi, Serajeddin;

    2013-01-01

    In this paper we study fractal gene regulatory network (FGRN) controllers based on sensory information. The FGRN controllers are evolved to control a snake robot consisting of seven simulated ATRON modules. Each module contains three tilt sensors which represent the direction of gravity in the...

  15. Radioresistance related genes screened by protein-protein interaction network analysis in nasopharyngeal carcinoma

    International Nuclear Information System (INIS)

    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)

  16. Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis.

    Science.gov (United States)

    Xing, Heming; McDonagh, Paul D; Bienkowska, Jadwiga; Cashorali, Tanya; Runge, Karl; Miller, Robert E; Decaprio, Dave; Church, Bruce; Roubenoff, Ronenn; Khalil, Iya G; Carulli, John

    2011-03-01

    Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86--a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28. PMID:21423713

  17. Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis.

    Directory of Open Access Journals (Sweden)

    Heming Xing

    2011-03-01

    Full Text Available Tumor necrosis factor α (TNF-α is a key regulator of inflammation and rheumatoid arthritis (RA. TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28 score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86--a component of the signaling axis targeted by Abatacept (CTLA4-Ig, and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28.

  18. Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis

    Science.gov (United States)

    Xing, Heming; McDonagh, Paul D.; Bienkowska, Jadwiga; Cashorali, Tanya; Runge, Karl; Miller, Robert E.; DeCaprio, Dave; Church, Bruce; Roubenoff, Ronenn; Khalil, Iya G.; Carulli, John

    2011-01-01

    Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28. PMID:21423713

  19. 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

  20. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    Science.gov (United States)

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. PMID:26701126

  1. 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.

  2. Adoption in ancient times

    OpenAIRE

    Bisha Eugena

    2015-01-01

    Since in ancient times, in all human cultures, children transfered from biological parents to parents that want them to create family, for political alliances, for inheritance, for a future marriage, or to care for elderly parents. The practice of adoption was fairly common in different places and periods. Adoption is mention on Bible and Quran. Greeks, Romans, Egyptians and Babylonians had adoption systems.

  3. Ancient deforestation revisited.

    Science.gov (United States)

    Hughes, J Donald

    2011-01-01

    The image of the classical Mediterranean environment of the Greeks and Romans had a formative influence on the art, literature, and historical perception of modern Europe and America. How closely does is this image congruent with the ancient environment as it in reality existed? In particular, how forested was the ancient Mediterranean world, was there deforestation, and if so, what were its effects? The consensus of historians, geographers, and other scholars from the mid-nineteenth century through the first three quarters of the twentieth century was that human activities had depleted the forests to a major extent and caused severe erosion. My research confirmed this general picture. Since then, revisionist historians have questioned these conclusions, maintaining instead that little environmental damage was done to forests and soils in ancient Greco-Roman times. In a reconsideration of the question, this paper looks at recent scientific work providing proxy evidence for the condition of forests at various times in ancient history. I look at three scientific methodologies, namely anthracology, palynology, and computer modeling. Each of these avenues of research offers support for the concept of forest change, both in abundance and species composition, and episodes of deforestation and erosion, and confirms my earlier work. PMID:20669043

  4. A Vibrant Ancient City

    Institute of Scientific and Technical Information of China (English)

    WANGTONG

    2004-01-01

    LIJIANG is a small city onthe Yunnan-Guizhou Plateau in southern Chinawith an 800-year history.Word of its ancient language and music, and unique natural scenery has spread over the decades, and Lijiang is now known throughout the world. It was added

  5. Ancient Egypt: History 380.

    Science.gov (United States)

    Turk, Laraine D.

    "Ancient Egypt," an upper-division, non-required history course covering Egypt from pre-dynastic time through the Roman domination is described. General descriptive information is presented first, including the method of grading, expectation of student success rate, long-range course objectives, procedures for revising the course, major course…

  6. Ancient Egypt: Personal Perspectives.

    Science.gov (United States)

    Wolinski, Arelene

    This teacher resource book provides information on ancient Egypt via short essays, photographs, maps, charts, and drawings. Egyptian social and religious life, including writing, art, architecture, and even the practice of mummification, is conveniently summarized for the teacher or other practitioner in a series of one to three page articles with…

  7. Creative Ventures: Ancient Civilizations.

    Science.gov (United States)

    Stark, Rebecca

    The open-ended activities in this book are designed to extend the imagination and creativity of students and encourage students to examine their feelings and values about historic eras. Civilizations addressed include ancient Egypt, Greece, Rome, Mayan, Stonehenge, and Mesopotamia. The activities focus upon the cognitive and affective pupil…

  8. Ancient ports of Kalinga

    Digital Repository Service at National Institute of Oceanography (India)

    Tripati, S.

    which plied between Kalinga and south east Asian countries. Nanda Raja, is said to have attacked Kalinga with the intention of getting access to the sea for the landlocked Kingdom of Magadha (Bihar). The ancient texa Artha Sastra (3rd-4th century B...

  9. Geoglyphs of Titicaca as an ancient example of graphic design

    OpenAIRE

    Sparavigna, Amelia Carolina

    2010-01-01

    The paper proposes an ancient landscape design as an example of graphic design for an age and place where no written documents existed. It is created by a network of earthworks, which constitute the remains of an extensive ancient agricultural system. It can be seen by means of the Google satellite imagery on the Peruvian region near the Titicaca Lake, as a texture superimposed to the background landform. In this texture, many drawings (geoglyphs) can be observed.

  10. Lead in ancient Rome’s city waters

    OpenAIRE

    Delile, Hugo; Blichert-Toft, Janne; Goiran, Jean-Philippe; Keay, Simon; Albarède, Francis

    2014-01-01

    International audience It is now universally accepted that utilization of lead for domestic purposes and water distribution presents a major health hazard. The ancient Roman world was unaware of these risks. How far the gigantic network of lead pipes used in ancient Rome compromised public health in the city is unknown. Lead isotopes in sediments from the harbor of Imperial Rome register the presence of a strong anthropogenic component during the beginning of the Common Era and the Early M...

  11. Dynamics of simple gene-network motifs subject to extrinsic fluctuations

    Science.gov (United States)

    Roberts, Elijah; Be'er, Shay; Bohrer, Chris; Sharma, Rati; Assaf, Michael

    2015-12-01

    Cellular processes do not follow deterministic rules; even in identical environments genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise (IN) of these networks. Yet, especially for high-copy-number biomolecules, extrinsic or environmental noise (EN) has been experimentally shown to dominate the variation. Here, we develop an analytical formalism that allows for calculation of the effect of EN on gene-expression motifs. We introduce a method for modeling bounded EN as an auxiliary species in the master equation. The method is fully generic and is not limited to systems with small EN magnitudes. We focus our study on motifs that can be viewed as the building blocks of genetic switches: a nonregulated gene, a self-inhibiting gene, and a self-promoting gene. The role of the EN properties (magnitude, correlation time, and distribution) on the statistics of interest are systematically investigated, and the effect of fluctuations in different reaction rates is compared. Due to its analytical nature, our formalism can be used to quantify the effect of EN on the dynamics of biochemical networks and can also be used to improve the interpretation of data from single-cell gene-expression experiments.

  12. Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells.

    Science.gov (United States)

    Yamane, Junko; Aburatani, Sachiyo; Imanishi, Satoshi; Akanuma, Hiromi; Nagano, Reiko; Kato, Tsuyoshi; Sone, Hideko; Ohsako, Seiichiroh; Fujibuchi, Wataru

    2016-07-01

    Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5-100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development. PMID:27207879

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

    International Nuclear Information System (INIS)

    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)

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-10-15

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

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

    Directory of Open Access Journals (Sweden)

    Thimm Markus

    2010-09-01

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

  16. Gene-Disease Interaction Retrieval from Multiple Sources: A Network Based Method

    Science.gov (United States)

    Huang, Lan; Wang, Yan

    2016-01-01

    The number of gene-related databases has been growing largely along with the research on genes of bioinformatics. Those databases are filled with various gene functions, pathways, interactions, and so forth, while much biomedical knowledge about human diseases is stored as text in all kinds of literatures. Researchers have developed many methods to extract structured biomedical knowledge. Some study and improve text mining algorithms to achieve efficiency in order to cover as many data sources as possible, while some build open source database to accept individual submissions in order to achieve accuracy. This paper combines both efforts and biomedical ontologies to build an interaction network of multiple biomedical ontologies, which guarantees its robustness as well as its wide coverage of biomedical publications. Upon the network, we accomplish an algorithm which discovers paths between concept pairs and shows potential relations. PMID:27478829

  17. Effects of Gene Dose, Chromatin, and Network Topology on Expression in Drosophila melanogaster.

    Science.gov (United States)

    Lee, Hangnoh; Cho, Dong-Yeon; Whitworth, Cale; Eisman, Robert; Phelps, Melissa; Roote, John; Kaufman, Thomas; Cook, Kevin; Russell, Steven; Przytycka, Teresa; Oliver, Brian

    2016-09-01

    Deletions, commonly referred to as deficiencies by Drosophila geneticists, are valuable tools for mapping genes and for genetic pathway discovery via dose-dependent suppressor and enhancer screens. More recently, it has become clear that deviations from normal gene dosage are associated with multiple disorders in a range of species including humans. While we are beginning to understand some of the transcriptional effects brought about by gene dosage changes and the chromosome rearrangement breakpoints associated with them, much of this work relies on isolated examples. We have systematically examined deficiencies of the left arm of chromosome 2 and characterize gene-by-gene dosage responses that vary from collapsed expression through modest partial dosage compensation to full or even over compensation. We found negligible long-range effects of creating novel chromosome domains at deletion breakpoints, suggesting that cases of gene regulation due to altered nuclear architecture are rare. These rare cases include trans de-repression when deficiencies delete chromatin characterized as repressive in other studies. Generally, effects of breakpoints on expression are promoter proximal (~100bp) or in the gene body. Effects of deficiencies genome-wide are in genes with regulatory relationships to genes within the deleted segments, highlighting the subtle expression network defects in these sensitized genetic backgrounds. PMID:27599372

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

    OpenAIRE

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

    2016-01-01

    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 regulator...

  19. Rat Hepatocytes Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Liver Regeneration after Partial Hepatectomy

    OpenAIRE

    Yun Zhou; Jiucheng Xu; Yunqing Liu; Juntao Li; Cuifang Chang; Cunshuan Xu

    2014-01-01

    The recovery of liver mass is mainly mediated by proliferation of hepatocytes after 2/3 partial hepatectomy (PH) in rats. Studying the gene expression profiles of hepatocytes after 2/3 PH will be helpful to investigate the molecular mechanisms of liver regeneration (LR). We report here the first application of weighted gene co-expression network analysis (WGCNA) to analyze the biological implications of gene expression changes associated with LR. WGCNA identifies 12 specific gene modules and ...

  20. Neural networks for modeling gene-gene interactions in association studies

    Directory of Open Access Journals (Sweden)

    Bammann Karin

    2009-12-01

    Full Text Available Abstract Background Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons and five logistic regression models (the null model, three main effect models, and the full model with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied. Results The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction. Conclusions Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.

  1. Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding

    OpenAIRE

    Kommadath, Arun; Bao, Hua; Arantes, Adriano S; Plastow, Graham S.; Christopher K Tuggle; Bearson, Shawn MD; Luo Guan, Le; Stothard, Paul

    2014-01-01

    Background Salmonella enterica serovar Typhimurium is a gram-negative bacterium that can colonise the gut of humans and several species of food producing farm animals to cause enteric or septicaemic salmonellosis. While many studies have looked into the host genetic response to Salmonella infection, relatively few have used correlation of shedding traits with gene expression patterns to identify genes whose variable expression among different individuals may be associated with differences in ...

  2. 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....

  3. Genome evolution in an ancient bacteria-ant symbiosis: parallel gene loss among Blochmannia spanning the origin of the ant tribe Camponotini

    Directory of Open Access Journals (Sweden)

    Laura E. Williams

    2015-04-01

    Full Text Available Stable associations between bacterial endosymbionts and insect hosts provide opportunities to explore genome evolution in the context of established mutualisms and assess the roles of selection and genetic drift across host lineages and habitats. Blochmannia, obligate endosymbionts of ants of the tribe Camponotini, have coevolved with their ant hosts for ∼40 MY. To investigate early events in Blochmannia genome evolution across this ant host tribe, we sequenced Blochmannia from two divergent host lineages, Colobopsis obliquus and Polyrhachis turneri, and compared them with four published genomes from Blochmannia of Camponotus sensu stricto. Reconstructed gene content of the last common ancestor (LCA of these six Blochmannia genomes is reduced (690 protein coding genes, consistent with rapid gene loss soon after establishment of the symbiosis. Differential gene loss among Blochmannia lineages has affected cellular functions and metabolic pathways, including DNA replication and repair, vitamin biosynthesis and membrane proteins. Blochmannia of P. turneri (i.e., B. turneri encodes an intact DnaA chromosomal replication initiation protein, demonstrating that loss of dnaA was not essential for establishment of the symbiosis. Based on gene content, B. obliquus and B. turneri are unable to provision hosts with riboflavin. Of the six sequenced Blochmannia, B. obliquus is the earliest diverging lineage (i.e., the sister group of other Blochmannia sampled and encodes the fewest protein-coding genes and the most pseudogenes. We identified 55 genes involved in parallel gene loss, including glutamine synthetase, which may participate in nitrogen recycling. Pathways for biosynthesis of coenzyme A, terpenoids and riboflavin were lost in multiple lineages, suggesting relaxed selection on the pathway after inactivation of one component. Analysis of Illumina read datasets did not detect evidence of plasmids encoding missing functions, nor the presence of

  4. Genome evolution in an ancient bacteria-ant symbiosis: parallel gene loss among Blochmannia spanning the origin of the ant tribe Camponotini.

    Science.gov (United States)

    Williams, Laura E; Wernegreen, Jennifer J

    2015-01-01

    Stable associations between bacterial endosymbionts and insect hosts provide opportunities to explore genome evolution in the context of established mutualisms and assess the roles of selection and genetic drift across host lineages and habitats. Blochmannia, obligate endosymbionts of ants of the tribe Camponotini, have coevolved with their ant hosts for ∼40 MY. To investigate early events in Blochmannia genome evolution across this ant host tribe, we sequenced Blochmannia from two divergent host lineages, Colobopsis obliquus and Polyrhachis turneri, and compared them with four published genomes from Blochmannia of Camponotus sensu stricto. Reconstructed gene content of the last common ancestor (LCA) of these six Blochmannia genomes is reduced (690 protein coding genes), consistent with rapid gene loss soon after establishment of the symbiosis. Differential gene loss among Blochmannia lineages has affected cellular functions and metabolic pathways, including DNA replication and repair, vitamin biosynthesis and membrane proteins. Blochmannia of P. turneri (i.e., B. turneri) encodes an intact DnaA chromosomal replication initiation protein, demonstrating that loss of dnaA was not essential for establishment of the symbiosis. Based on gene content, B. obliquus and B. turneri are unable to provision hosts with riboflavin. Of the six sequenced Blochmannia, B. obliquus is the earliest diverging lineage (i.e., the sister group of other Blochmannia sampled) and encodes the fewest protein-coding genes and the most pseudogenes. We identified 55 genes involved in parallel gene loss, including glutamine synthetase, which may participate in nitrogen recycling. Pathways for biosynthesis of coenzyme A, terpenoids and riboflavin were lost in multiple lineages, suggesting relaxed selection on the pathway after inactivation of one component. Analysis of Illumina read datasets did not detect evidence of plasmids encoding missing functions, nor the presence of coresident symbionts

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

    International Nuclear Information System (INIS)

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

  6. An extended phylogenetic analysis reveals ancient origin of "non-green" phosphoribulokinase genes from two lineages of "green" secondary photosynthetic eukaryotes: Euglenophyta and Chlorarachniophyta

    Directory of Open Access Journals (Sweden)

    Sekimoto Hiroyuki

    2011-09-01

    Full Text Available Abstract Background Euglenophyta and Chlorarachniophyta are groups of photosynthetic eukaryotes harboring secondary plastids of distinct green algal origins. Although previous phylogenetic analyses of genes encoding Calvin cycle enzymes demonstrated the presence of genes apparently not derived from green algal endosymbionts in the nuclear genomes of Euglena gracilis (Euglenophyta and Bigelowiella natans (Chlorarachniophyta, the origins of these "non-green" genes in "green" secondary phototrophs were unclear due to the limited taxon sampling. Results Here, we sequenced five new phosphoribulokinase (PRK genes (from one euglenophyte, two chlorarachniophytes, and two glaucophytes and performed an extended phylogenetic analysis of the genes based on a phylum-wide taxon sampling from various photosynthetic eukaryotes. Our phylogenetic analyses demonstrated that the PRK sequences form two genera of Euglenophyta formed a robust monophyletic group within a large clade including stramenopiles, haptophytes and a cryptophyte, and three genera of Chlorarachniophyta were placed within the red algal clade. These "non-green" affiliations were supported by the taxon-specific insertion/deletion sequences in the PRK alignment, especially between euglenophytes and stramenopiles. In addition, phylogenetic analysis of another Calvin cycle enzyme, plastid-targeted sedoheptulose-bisphosphatase (SBP, showed that the SBP sequences from two genera of Chlorarachniophyta were positioned within a red algal clade. Conclusions Our results suggest that PRK genes may have been transferred from a "stramenopile" ancestor to Euglenophyta and from a "red algal" ancestor to Chlorarachniophyta before radiation of extant taxa of these two "green" secondary phototrophs. The presence of two of key Calvin cycle enzymes, PRK and SBP, of red algal origins in Chlorarachniophyta indicate that the contribution of "non-green" algae to the plastid proteome in the "green" secondary phototrophs is

  7. Exploiting resource use efficiency and resilience in ancient wheat species

    OpenAIRE

    Parmar, Anisha

    2014-01-01

    Modern bread wheat (Triticum aestivum) initially derived from wild progenitors which underwent hybridisation and domestication events. It is hypothesised that modern plant breeding has reduced the genetic variation among modern cultivars (Sparkes, 2010). Ancient wheat species form a conduit between wild ancient wheat and cultivated Triticum species, and may harbour the genetic variation required to supplement the modern bread wheat gene pool. The current work investigated a range of morpholog...

  8. Alcohol-induced histone acetylation reveals a gene network involved in alcohol tolerance.

    Directory of Open Access Journals (Sweden)

    Alfredo Ghezzi

    Full Text Available Sustained or repeated exposure to sedating drugs, such as alcohol, triggers homeostatic adaptations in the brain that lead to the development of drug tolerance and dependence. These adaptations involve long-term changes in the transcription of drug-responsive genes as well as an epigenetic restructuring of chromosomal regions that is thought to signal and maintain the altered transcriptional state. Alcohol-induced epigenetic changes have been shown to be important in the long-term adaptation that leads to alcohol tolerance and dependence endophenotypes. A major constraint impeding progress is that alcohol produces a surfeit of changes in gene expression, most of which may not make any meaningful contribution to the ethanol response under study. Here we used a novel genomic epigenetic approach to find genes relevant for functional alcohol tolerance by exploiting the commonalities of two chemically distinct alcohols. In Drosophila melanogaster, ethanol and benzyl alcohol induce mutual cross-tolerance, indicating that they share a common mechanism for producing tolerance. We surveyed the genome-wide changes in histone acetylation that occur in response to these drugs. Each drug induces modifications in a large number of genes. The genes that respond similarly to either treatment, however, represent a subgroup enriched for genes important for the common tolerance response. Genes were functionally tested for behavioral tolerance to the sedative effects of ethanol and benzyl alcohol using mutant and inducible RNAi stocks. We identified a network of genes that are essential for the development of tolerance to sedation by alcohol.

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

    Science.gov (United States)

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

    2016-01-01

    Organisms respond to environmental variation partly through changes in gene expression, which underlie both homeostatic and acclimatory responses to environmental stress. In some cases, so many genes change in expression in response to different influences that understanding expression patterns for all these individual genes becomes difficult. To reduce this problem, we use a systems genetics approach to show that variation in the expression of thousands of genes of reef-building corals can be explained as variation in the expression of a small number of coexpressed "modules." Modules were often enriched for specific cellular functions and varied predictably among individuals, experimental treatments, and physiological state. We describe two transcriptional modules for which expression levels immediately after heat stress predict bleaching a day later. One of these early "bleaching modules" is enriched for sequence-specific DNA-binding proteins, particularly E26 transformation-specific (ETS)-family transcription factors. The other module is enriched for extracellular matrix proteins. These classes of bleaching response genes are clear in the modular gene expression analysis we conduct but are much more difficult to discern in single gene analyses. Furthermore, the ETS-family module shows repeated differences in expression among coral colonies grown in the same common garden environment, suggesting a heritable genetic or epigenetic basis for these expression polymorphisms. This finding suggests that these corals harbor high levels of gene-network variation, which could facilitate rapid evolution in the face of environmental change. PMID:26710855

  10. Identification of candidate genes in Populus cell wall biosynthesis using text-mining, co-expression network and comparative genomics

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Xiaohan [ORNL; Ye, Chuyu [ORNL; Bisaria, Anjali [ORNL; Tuskan, Gerald A [ORNL; Kalluri, Udaya C [ORNL

    2011-01-01

    Populus is an important bioenergy crop for bioethanol production. A greater understanding of cell wall biosynthesis processes is critical in reducing biomass recalcitrance, a major hindrance in efficient generation of ethanol from lignocellulosic biomass. Here, we report the identification of candidate cell wall biosynthesis genes through the development and application of a novel bioinformatics pipeline. As a first step, via text-mining of PubMed publications, we obtained 121 Arabidopsis genes that had the experimental evidences supporting their involvement in cell wall biosynthesis or remodeling. The 121 genes were then used as bait genes to query an Arabidopsis co-expression database and additional genes were identified as neighbors of the bait genes in the network, increasing the number of genes to 548. The 548 Arabidopsis genes were then used to re-query the Arabidopsis co-expression database and re-construct a network that captured additional network neighbors, expanding to a total of 694 genes. The 694 Arabidopsis genes were computationally divided into 22 clusters. Queries of the Populus genome using the Arabidopsis genes revealed 817 Populus orthologs. Functional analysis of gene ontology and tissue-specific gene expression indicated that these Arabidopsis and Populus genes are high likelihood candidates for functional genomics in relation to cell wall biosynthesis.

  11. Finding missing interactions of the Arabidopsis thaliana root stem cell niche gene regulatory network

    Directory of Open Access Journals (Sweden)

    Eugenio eAzpeitia

    2013-04-01

    Full Text Available AbstractOver the last few decades, the Arabidopsis thaliana root stem cell niche has become a model system for the study of plant development and the stem cell niche. Currently, many of the molecular mechanisms involved in root stem cell niche maintenance and development have been described. A few years ago, we published a gene regulatory network model integrating this information. This model suggested that there were missing components or interactions. Upon updating the model, the observed stable gene configurations of the root stem cell niche could not be recovered, indicating that there are additional missing components or interactions in the model. In fact, due to the lack of experimental data, gene regulatory networks inferred from published data are usually incomplete. However, predicting the location and nature of the missing data is a not trivial task. Here, we propose a set of procedures for detecting and predicting missing interactions in Boolean networks. We used these procedures to predict putative missing interactions in the A. thaliana root stem cell niche network model. Using our approach, we identified three necessary interactions to recover the reported gene activation configurations that have been experimentally uncovered for the different cell types within the root stem cell niche: 1 a regulation of PHABULOSA to restrict its expression domain to the vascular cells, 2 a self-regulation of WOX5, possibly by an indirect mechanism through the auxin signalling pathway and 3 a positive regulation of JACKDAW by MAGPIE. The procedures proposed here greatly reduce the number of possible Boolean functions that are biologically meaningful and experimentally testable and that do not contradict previous data. We believe that these procedures can be used on any Boolean network. However, because the procedures were designed for the specific case of the root stem cell niche, formal demonstrations of the procedures should be shown in future

  12. Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium.

    Science.gov (United States)

    Moskvin, Oleg V; Bolotin, Dmitry; Wang, Andrew; Ivanov, Pavel S; Gomelsky, Mark

    2011-02-01

    We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (-400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org. PMID:21070832

  13. Modeling suggests that gene circuit architecture controls phenotypic variability in a bacterial persistence network

    Directory of Open Access Journals (Sweden)

    Koh Rachel S

    2012-05-01

    Full Text Available Abstract Background Bacterial persistence is a non-inherited bet-hedging mechanism where a subpopulation of cells enters a dormant state, allowing those cells to survive environmental stress such as treatment with antibiotics. Persister cells are not mutants; they are formed by natural stochastic variation in gene expression. Understanding how regulatory architecture influences the level of phenotypic variability can help us explain how the frequency of persistence events can be tuned. Results We present a model of the regulatory network controlling the HipBA toxin-antitoxin system from Escherichia coli. Using a biologically realistic model we first determine that the persistence phenotype is not the result of bistability within the network. Next, we develop a stochastic model and show that cells can enter persistence due to random fluctuations in transcription, translation, degradation, and complex formation. We then examine alternative gene circuit architectures for controlling hipBA expression and show that networks with more noise (more persisters and less noise (fewer persisters are straightforward to achieve. Thus, we propose that the gene circuit architecture can be used to tune the frequency of persistence, a trait that can be selected for by evolution. Conclusions We develop deterministic and stochastic models describing how the regulation of toxin and antitoxin expression influences phenotypic variation within a population. Persistence events are the result of stochastic fluctuations in toxin levels that cross a threshold, and their frequency is controlled by the regulatory topology governing gene expression.

  14. Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens

    Science.gov (United States)

    Guthke, Reinhard; Gerber, Silvia; Conrad, Theresia; Vlaic, Sebastian; Durmuş, Saliha; Çakır, Tunahan; Sevilgen, F. E.; Shelest, Ekaterina; Linde, Jörg

    2016-01-01

    In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility to predict central (hub) genes and thereby indicate potential biomarkers and drug targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene regulatory interactions, which have to be validated experimentally. Due to the lack of sufficient quantity and quality of both experimental data and prior knowledge about regulator–target gene relations, the genome-wide modeling still remains problematic for fungal pathogens. While a first genome-wide GRN model has already been published for Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated in the present article. Based on this evaluation, opinions are drawn on future directions of GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is the experimental evidence, both used for inferring the networks (omics ‘first-hand’ data as well as literature data used as prior knowledge) and for validation and evaluation of the inferred network models. PMID:27148247

  15. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses

    OpenAIRE

    Gilman, Sarah R; Iossifov, Ivan; Levy, Dan; Ronemus, Michael; Wigler, Michael; Vitkup, Dennis

    2011-01-01

    Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study we develop a method for NETwork-Based Analysis of Genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting and neuron motility. The identified network is strongly related to genes previously imp...

  16. Networks of neurons, networks of genes: An integrated view of memory consolidation

    Czech Academy of Sciences Publication Activity Database

    Miyashita, T.; Kubík, Štěpán; Lewandowski, G.; Guzowski, J. F.

    2008-01-01

    Roč. 89, č. 3 (2008), s. 269-284. ISSN 1074-7427 Institutional research plan: CEZ:AV0Z50110509 Keywords : hippocampus * immediate-early genes * arc Subject RIV: FH - Neurology Impact factor: 3.757, year: 2008

  17. Copy number variations in alternative splicing gene networks impact lifespan.

    Directory of Open Access Journals (Sweden)

    Joseph T Glessner

    Full Text Available Longevity has a strong genetic component evidenced by family-based studies. Lipoprotein metabolism, FOXO proteins, and insulin/IGF-1 signaling pathways in model systems have shown polygenic variations predisposing to shorter lifespan. To test the hypothesis that rare variants could influence lifespan, we compared the rates of CNVs in healthy children (0-18 years of age with individuals 67 years or older. CNVs at a significantly higher frequency in the pediatric cohort were considered risk variants impacting lifespan, while those enriched in the geriatric cohort were considered longevity protective variants. We performed a whole-genome CNV analysis on 7,313 children and 2,701 adults of European ancestry genotyped with 302,108 SNP probes. Positive findings were evaluated in an independent cohort of 2,079 pediatric and 4,692 geriatric subjects. We detected 8 deletions and 10 duplications that were enriched in the pediatric group (P=3.33×10(-8-1.6×10(-2 unadjusted, while only one duplication was enriched in the geriatric cohort (P=6.3×10(-4. Population stratification correction resulted in 5 deletions and 3 duplications remaining significant (P=5.16×10(-5-4.26×10(-2 in the replication cohort. Three deletions and four duplications were significant combined (combined P=3.7×10(-4-3.9×10(-2. All associated loci were experimentally validated using qPCR. Evaluation of these genes for pathway enrichment demonstrated ~50% are involved in alternative splicing (P=0.0077 Benjamini and Hochberg corrected. We conclude that genetic variations disrupting RNA splicing could have long-term biological effects impacting lifespan.

  18. Functional Ecological Gene Networks to Reveal the Changes Among Microbial Interactions Under Elevated Carbon Dioxide Conditions

    Energy Technology Data Exchange (ETDEWEB)

    Deng, Ye; Zhou, Jizhong; Luo, Feng; He, Zhili; Tu, Qichao; Zhi, Xiaoyang

    2010-05-17

    Biodiversity and its responses to environmental changes is a central issue in ecology, and for society. Almost all microbial biodiversity researches focus on species richness and abundance but ignore the interactions among different microbial species/populations. However, determining the interactions and their relationships to environmental changes in microbial communities is a grand challenge, primarily due to the lack of information on the network structure among different microbial species/populations. Here, a novel random matrix theory (RMT)-based conceptual framework for identifying functional ecological gene networks (fEGNs) is developed with the high throughput functional gene array hybridization data from the grassland microbial communities in a long-term FACE (Free Air CO2 Enrichment) experiment. Both fEGNs under elevated CO2 (eCO2) and ambient CO2 (aCO2) possessed general characteristics of many complex systems such as scale-free, small-world, modular and hierarchical. However, the topological structure of the fEGNs is distinctly different between eCO2 and aCO2, suggesting that eCO2 dramatically altered the interactions among different microbial functional groups/populations. In addition, the changes in network structure were significantly correlated with soil carbon and nitrogen dynamics, and plant productivity, indicating the potential importance of network interactions in ecosystem functioning. Elucidating network interactions in microbial communities and their responses to environmental changes are fundamentally important for research in microbial ecology, systems microbiology, and global change.

  19. Floral Transcriptomes in Woodland Strawberry Uncover Developing Receptacle and Anther Gene Networks.

    Science.gov (United States)

    Hollender, Courtney A; Kang, Chunying; Darwish, Omar; Geretz, Aviva; Matthews, Benjamin F; Slovin, Janet; Alkharouf, Nadim; Liu, Zhongchi

    2014-05-14

    Flowers are reproductive organs and precursors to fruits and seeds. While the basic tenets of the ABCE model of flower development are conserved in angiosperms, different flowering plants exhibit different and sometimes unique characteristics. A distinct feature of strawberry (Fragaria spp.) flowers is the development of several hundreds of individual apocarpous (unfused) carpels. These individual carpels are arranged in a spiral pattern on the subtending stem tip, the receptacle. Therefore, the receptacle is an integral part of the strawberry flower and is of significant agronomic importance, being the precursor to strawberry fruit. Taking advantage of next-generation sequencing and laser capture microdissection, we generated different tissue- and stage-transcriptomic profiling of woodland strawberry (Fragaria vesca) flower development. Using pairwise comparisons and weighted gene coexpression network analysis, we identified modules of coexpressed genes and hub genes of tissue-specific networks. Of particular importance is the discovery of a developing receptacle-specific module exhibiting similar molecular features to those of young floral meristems. The strawberry homologs of a number of meristem regulators, including LOST MERISTEM and WUSCHEL, are identified as hub genes operating in the developing receptacle network. Furthermore, almost 25% of the F-box genes in the genome are transiently induced in developing anthers at the meiosis stage, indicating active protein degradation. Together, this work provides important insights into the molecular networks underlying strawberry's unique reproductive developmental processes. This extensive floral transcriptome data set is publicly available and can be readily queried at the project Web site, serving as an important genomic resource for the plant biology research community. PMID:24828307

  20. p42.3 gene expression in gastric cancer cell and its protein regulatory network analysis

    Directory of Open Access Journals (Sweden)

    Zhang Jianhua

    2012-12-01

    Full Text Available Abstract Background To analyze the p42.3 gene expression in gastric cancer (GC cell, find the relationship between protein structure and function, establish the regulatory network of p42.3 protein molecule and then to obtain the optimal regulatory pathway. Methods The expression of p42.3 gene was analyzed by RT-PCR, Western Blot and other biotechnologies. The relationship between the spatial conformation of p42.3 protein molecule and its function was analyzed using bioinformatics, MATLAB and related knowledge about protein structure and function. Furthermore, based on similarity algorithm of spatial layered spherical coordinate, we compared p42.3 molecule with several similar structured proteins which are known for the function, screened the characteristic nodes related to tumorigenesis and development, and established the multi variable relational model between p42.3 protein expression, cell cycle regulation and biological characteristics in the level of molecular regulatory networks. Finally, the optimal regulatory network was found by using Bayesian network. Results (1 The expression amount of p42.3 in G1 and M phase was higher than that in S and G2 phase; (2 The space coordinate systems of different structural domains of p42.3 protein were established in Matlab7.0 software; (3 The optimal pathway of p42.3 gene in protein regulatory network in gastric cancer is Ras protein, Raf-1 protein, MEK, MAPK kinase, MAPK, tubulin, spindle protein, centromere protein and tumor. Conclusion It is of vital significance for mechanism research to find out the action pathway of p42.3 in protein regulatory network, since p42.3 protein plays an important role in the generation and development of GC.

  1. Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks

    OpenAIRE

    Grzegorczyk, M.; Husmeier, D.

    2009-01-01

    Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be learned. However, we show that the widely applied BGe score is susceptible to learning spurious feedback loops, which are a consequence of non-linear regulation and autocorrelation in the data. We propose a non-linear generalisation of the BGe m...

  2. Mining the Medication Law of Ancient Analgesic Formulas Based on Complex Network%基于复杂网络挖掘古代止痛方剂用药规律

    Institute of Scientific and Technical Information of China (English)

    孟凡红; 李明; 李敬华; 牛亚华

    2013-01-01

    目的 通过复杂网络挖掘技术,总结古代止痛方剂的核心药物、配伍规律及用药特点,以期为疼痛的临床治疗及新药开发提供参考.方法 筛选汉代到金元时期的代表性方书著作14部,收集止痛方剂2 746首,建立中药止痛方剂数据库并进行术语规范;利用复方药物配伍的无尺度网络规律,构建止痛方剂复杂网络,分析止痛方剂的核心药物及配伍规律.结果 按疼痛部位分类,挖掘出腹痛、胸心痛、头痛、肢节痛、腰痛、胁痛、眼目痛、咽痛、全身痛、齿痛在汉唐、金宋元时期排名前10位的高频单味药和药对.结论 运用复杂网络挖掘技术,得到了汉唐、宋金元时期治疗各类痛证的核心药物、配伍药对以及用药特点,为今后进一步深入挖掘历代止痛方剂的用药配伍规律起到了示范作用.%Objective To summarize the core medicinal, composition law and medication characteristics of the ancient analgesic formulas through data mining in complex network and provide a reference for treating pain and new drug development. Methods Totally 2 746 formulas were selected from 14 typical formulary books during the Han to Jin-Yuan Dynasties. The analgesic formula database was established and the terms were standardized. The complex network of analgesic formulas was built according to the scale-free networks law of formula composition and the core medicinal and composition law was analyzed. Results The top 10 frequent single herbs and couplet medicines during the Han, Tang, Song, Jin and Yuan Dynasties were found according to the pain location such as abdominal pain, chest pain, heartache, headache, limb pain, lower back pain, hypochondriac pain, eyes pain, sore throat, body aches and tooth pain. Conclusion The core medicinal, composition law and medication characteristics of analgesic formulas during the Han, Tang, Song, Jin and Yuan Dynasties are found through data mining in complex network. This

  3. Ancient human microbiomes

    Science.gov (United States)

    Warinner, Christina; Speller, Camilla; Collins, Matthew J.; Lewis, Cecil M.

    2015-01-01

    Very recently, we discovered a vast new microbial self: the human microbiome. Our native microbiota interface with our biology and culture to influence our health, behavior, and quality of life, and yet we know very little about their origin, evolution, or ecology. With the advent of industrialization, globalization, and modern sanitation, it is intuitive that we have changed our relationship with microbes, but we have little information about the ancestral state of our microbiome, and therefore, we lack a foundation for characterizing this change. High-throughput sequencing has opened up new opportunities in the field of paleomicrobiology, allowing us to investigate the evolution of the complex microbial ecologies that inhabit our bodies. By focusing on recent coprolite and dental calculus research, we explore how emerging research on ancient human microbiomes is changing the way we think about ancient disease and how archaeological studies can contribute to a medical understanding of health and nutrition today. PMID:25559298

  4. Comets in ancient India

    CERN Document Server

    Gupta, Patrick Das

    2014-01-01

    The Indo-aryans of ancient India observed stars and constellations for ascertaining auspicious times for sacrificial rites ordained by vedas. It is but natural that they would have recounted in the vedic texts about comets. In Rigveda ($\\sim $ 1700 - 1500 BC) and Atharvaveda ($\\sim $ 1150 BC), there are references to dhumaketus and ketus, which stand for comets in Sanskrit. Varahamihira in 550 AD and Ballala Sena ($\\sim $ 1100 - 1200 AD) have described a large number of comets recorded by ancient seers such as Parashara, Vriddha Garga, Narada, Garga, etc. In this article, I conjecture that an episode narrated in Mahabharata of a radiant king, Nahusha, ruling the heavens, and later turning into a serpent after he had kicked the seer Agastya (also the star Canopus), is a mythological retelling of a cometary event.

  5. Screening genes crucial for pediatric pilocytic astrocytoma using weighted gene coexpression network analysis combined with methylation data analysis.

    Science.gov (United States)

    Zhao, H; Cai, W; Su, S; Zhi, D; Lu, J; Liu, S

    2014-10-01

    To identify novel genes associated with pediatric pilocytic astrocytoma (PA) for better understanding the molecular mechanism underlying the pediatric PA pathogenesis. Gene expression profile data of GSE50161 and GSE44971 and the methylation data of GSE44684 were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between PA and normal control samples were screened using the limma package in R, and then used to construct weighted gene coexpression network (WGCN) using the WGCN analysis (WGCNA) package in R. Significant modules of DEGs were selected using the clustering analysis. Function enrichment analysis of the DEGs in significant modules were performed using the WGCNA package and clusterprofiler package in R. Correlation between methylation sites of DEGs and PA was analyzed using the CpGassoc package in R. Totally, 3479 DEGs were screened in PA samples. Thereinto, 3424 DEGs were used to construct the WGCN. Several significant modules of DEGs were selected based on the WGCN, in which the turquoise module was positively related to PA, whereas blue module was negatively related to PA. DEGs (for example, DOCK2 (dedicator of cytokinesis 2), DOCK8 and FCGR2A (Fc fragment of IgG, low affinity IIa)) in blue module were mainly involved in Fc gamma R-mediated phagocytosis pathway and natural killer cell-mediated cytotoxicity pathway. Methylations of 14 DEGs among the top 30 genes in blue module were related to PA. Our data suggest that DOCK2, DOCK8 and FCGR2A may represent potential therapeutic targets in PA that merits further investigation. PMID:25257306

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

    Directory of Open Access Journals (Sweden)

    David C.Y. Fung

    2008-01-01

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

  7. The probability of a gene tree topology within a phylogenetic network with applications to hybridization detection.

    Directory of Open Access Journals (Sweden)

    Yun Yu

    Full Text Available Gene tree topologies have proven a powerful data source for various tasks, including species tree inference and species delimitation. Consequently, methods for computing probabilities of gene trees within species trees have been developed and widely used in probabilistic inference frameworks. All these methods assume an underlying multispecies coalescent model. However, when reticulate evolutionary events such as hybridization occur, these methods are inadequate, as they do not account for such events. Methods that account for both hybridization and deep coalescence in computing the probability of a gene tree topology currently exist for very limited cases. However, no such methods exist for general cases, owing primarily to the fact that it is currently unknown how to compute the probability of a gene tree topology within the branches of a phylogenetic network. Here we present a novel method for computing the probability of gene tree topologies on phylogenetic networks and demonstrate its application to the inference of hybridization in the presence of incomplete lineage sorting. We reanalyze a Saccharomyces species data set for which multiple analyses had converged on a species tree candidate. Using our method, though, we show that an evolutionary hypothesis involving hybridization in this group has better support than one of strict divergence. A similar reanalysis on a group of three Drosophila species shows that the data is consistent with hybridization. Further, using extensive simulation studies, we demonstrate the power of gene tree topologies at obtaining accurate estimates of branch lengths and hybridization probabilities of a given phylogenetic network. Finally, we discuss identifiability issues with detecting hybridization, particularly in cases that involve extinction or incomplete sampling of taxa.

  8. Gene network analyses point to the importance of human tissue kallikreins in melanoma progression

    Directory of Open Access Journals (Sweden)

    Landman Gilles

    2011-10-01

    Full Text Available Abstract Background A wide variety of high-throughput microarray platforms have been used to identify molecular targets associated with biological and clinical tumor phenotypes by comparing samples representing distinct pathological states. Methods The gene expression profiles of human cutaneous melanomas were determined by cDNA microarray analysis. Next, a robust analysis to determine functional classifications and make predictions based on data-oriented hypotheses was performed. Relevant networks that may be implicated in melanoma progression were also considered. Results In this study we aimed to analyze coordinated gene expression changes to find molecular pathways involved in melanoma progression. To achieve this goal, ontologically-linked modules with coordinated expression changes in melanoma samples were identified. With this approach, we detected several gene networks related to different modules that were induced or repressed during melanoma progression. Among them we observed high coordinated expression levels of genes involved in a cell communication (KRT4, VWF and COMP; b epidermal development (KLK7, LAMA3 and EVPL; and c functionally related to kallikreins (EVPL, KLK6, KLK7, KLK8, SERPINB13, SERPING1 and SLPI. Our data also indicated that hKLK7 protein expression was significantly associated with good prognosis and survival. Conclusions Our findings, derived from a different type of analysis of microarray data, highlight the importance of analyzing coordinated gene expression to find molecular pathways involved in melanoma progression.

  9. 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.

  10. Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer

    OpenAIRE

    Chou, Wei-Chun; Cheng, An-Lin; Brotto, Marco; Chuang, Chun-Yu

    2014-01-01

    Background Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy,...

  11. The obesity gene, TMEM18, is of ancient origin, found in majority of neuronal cells in all major brain regions and associated with obesity in severely obese children

    Directory of Open Access Journals (Sweden)

    Levine Allen S

    2010-04-01

    Full Text Available Abstract Background TMEM18 is a hypothalamic gene that has recently been linked to obesity and BMI in genome wide association studies. However, the functional properties of TMEM18 are obscure. Methods The evolutionary history of TMEM18 was inferred using phylogenetic and bioinformatic methods. The gene's expression profile was investigated with real-time PCR in a panel of rat and mouse tissues and with immunohistochemistry in the mouse brain. Also, gene expression changes were analyzed in three feeding-related mouse models: food deprivation, reward and diet-induced increase in body weight. Finally, we genotyped 502 severely obese and 527 healthy Swedish children for two SNPs near TMEM18 (rs6548238 and rs756131. Results TMEM18 was found to be remarkably conserved and present in species that diverged from the human lineage over 1500 million years ago. The TMEM18 gene was widely expressed and detected in the majority of cells in all major brain regions, but was more abundant in neurons than other cell types. We found no significant changes in the hypothalamic and brainstem expression in the feeding-related mouse models. There was a strong association for two SNPs (rs6548238 and rs756131 of the TMEM18 locus with an increased risk for obesity (p = 0.001 and p = 0.002. Conclusion We conclude that TMEM18 is involved in both adult and childhood obesity. It is one of the most conserved human obesity genes and it is found in the majority of all brain sites, including the hypothalamus and the brain stem, but it is not regulated in these regions in classical energy homeostatic models.

  12. Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Bi-Qing Li

    2013-01-01

    Full Text Available Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC and nonsmall cell lung cancer (NSCLC. In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation P value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

  13. Modularity of gene-regulatory networks revealed in sea-star development

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    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

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

    OpenAIRE

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

    2015-01-01

    Background 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 t...

  15. Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

    DEFF Research Database (Denmark)

    Brorsson, C.; Hansen, Niclas Tue; Hansen, Kasper Lage;

    2009-01-01

    genes. We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC...... region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein...

  16. A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network

    OpenAIRE

    Ruan, Xiyun; Li, Hongyun; Liu, Bo; Chen, Jie; ZHANG, SHIBAO; Sun, Zeqiang; LIU, SHUANGQING; SUN, FAHAI; Liu, Qingyong

    2015-01-01

    The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Pro...

  17. OryzaExpress: An Integrated Database of Gene Expression Networks and Omics Annotations in Rice

    Science.gov (United States)

    Hamada, Kazuki; Hongo, Kohei; Suwabe, Keita; Shimizu, Akifumi; Nagayama, Taishi; Abe, Reina; Kikuchi, Shunsuke; Yamamoto, Naoki; Fujii, Takaaki; Yokoyama, Koji; Tsuchida, Hiroko; Sano, Kazumi; Mochizuki, Takako; Oki, Nobuhiko; Horiuchi, Youko; Fujita, Masahiro; Watanabe, Masao; Matsuoka, Makoto; Kurata, Nori; Yano, Kentaro

    2011-01-01

    Similarity of gene expression profiles provides important clues for understanding the biological functions of genes, biological processes and metabolic pathways related to genes. A gene expression network (GEN) is an ideal choice to grasp such expression profile similarities among genes simultaneously. For GEN construction, the Pearson correlation coefficient (PCC) has been widely used as an index to evaluate the similarities of expression profiles for gene pairs. However, calculation of PCCs for all gene pairs requires large amounts of both time and computer resources. Based on correspondence analysis, we developed a new method for GEN construction, which takes minimal time even for large-scale expression data with general computational circumstances. Moreover, our method requires no prior parameters to remove sample redundancies in the data set. Using the new method, we constructed rice GENs from large-scale microarray data stored in a public database. We then collected and integrated various principal rice omics annotations in public and distinct databases. The integrated information contains annotations of genome, transcriptome and metabolic pathways. We thus developed the integrated database OryzaExpress for browsing GENs with an interactive and graphical viewer and principal omics annotations (http://riceball.lab.nig.ac.jp/oryzaexpress/). With integration of Arabidopsis GEN data from ATTED-II, OryzaExpress also allows us to compare GENs between rice and Arabidopsis. Thus, OryzaExpress is a comprehensive rice database that exploits powerful omics approaches from all perspectives in plant science and leads to systems biology. PMID:21186175

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

    Science.gov (United States)

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

    2010-02-01

    Gene expression microarrays allow for the high throughput analysis of huge numbers of gene transcripts and this technology has been widely applied to the molecular and biological classification of cancer patients and in predicting clinical outcome. A potential handicap of such data intensive molecular technologies is the translation to clinical application in routine practice. In using an artificial neural network bioinformatic approach, we have reduced a 70 gene signature to just 9 genes capable of accurately predicting distant metastases in the original dataset. Upon validation in a follow-up cohort, this signature was an independent predictor of metastases free and overall survival in the presence of the 70 gene signature and other factors. Interestingly, the ANN signature and CA9 expression also split the groups defined by the 70 gene signature into prognostically distinct groups. Subsequently, the presence of protein for the principal prognosticator gene was categorically assessed in breast cancer tissue of an experimental and independent validation patient cohort, using immunohistochemistry. Importantly our principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis. PMID:19347577

  19. Integrated Analysis of Gene Network in Childhood Leukemia from Microarray and Pathway Databases

    Directory of Open Access Journals (Sweden)

    Amphun Chaiboonchoe

    2014-01-01

    Full Text Available Glucocorticoids (GCs have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B- and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    of reaction kinetics in metabolite concentration control is well studied at the level of individual reactions, the contribution of network connectivity has remained relatively unclear. Here we report a modeling framework that integrates both reaction kinetics and network connectivity constraints...... for describing the interplay between metabolite concentrations and mRNA levels. We used this framework to investigate correlations between the gene expression and the metabolite concentration changes in Saccharomyces cerevisiae during its metabolic cycle, as well as in response to three fundamentally different......One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First...

  1. 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

  2. Statistical identification of gene association by CID in application of constructing ER regulatory network

    Directory of Open Access Journals (Sweden)

    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

  3. Using giant scarlet runner bean embryos to uncover regulatory networks controlling suspensor gene activity

    OpenAIRE

    Henry, Kelli F.; Goldberg, Robert B.

    2015-01-01

    One of the major unsolved issues in plant development is understanding the regulatory networks that control the differential gene activity that is required for the specification and development of the two major embryonic regions, the embryo proper and suspensor. Historically, the giant embryo of scarlet runner bean (SRB), Phaseolus coccineus, has been used as a model system to investigate the physiological events that occur early in embryogenesis—focusing on the question of what role the susp...

  4. EGIA–evolutionary optimisation of gene regulatory networks, an integrative approach

    OpenAIRE

    Sirbu, Alina; Crane, Martin; Ruskin, Heather J

    2013-01-01

    Quantitative modelling of gene regulatory networks (GRNs) is still limited by data issues such as noise and the restricted length of available time series, creating an under-determination problem. However, large amounts of other types of biological data and knowledge are available, such as knockout experiments, annotations and so on, and it has been postulated that integration of these can improve model quality. However, integration has not been fully explored, to date. Here, we present...

  5. Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

    OpenAIRE

    Liwei Qian; Haoran Zheng; Hong Zhou; Ruibin Qin; Jinlong Li

    2013-01-01

    The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture mode...

  6. Adaptive walks in a gene network model of morphogenesis: insights into the Cambrian explosion

    OpenAIRE

    Sole, Ricard V.; Fernandez, Pau; Kauffman, Stuart A.

    2003-01-01

    The emergence of complex patterns of organization close to the Cambrian boundary is known to have happened over a (geologically) short period of time. It involved the rapid diversification of body plans and stands as one of the major transitions in evolution. How it took place is a controversial issue. Here we explore this problem by considering a simple model of pattern formation in multicellular organisms. By modeling gene network-based morphogenesis and its evolution through adaptive walks...

  7. Transcriptome analysis of genes and gene networks involved in aggressive behavior in mouse and zebrafish.

    Science.gov (United States)

    Malki, Karim; Du Rietz, Ebba; Crusio, Wim E; Pain, Oliver; Paya-Cano, Jose; Karadaghi, Rezhaw L; Sluyter, Frans; de Boer, Sietse F; Sandnabba, Kenneth; Schalkwyk, Leonard C; Asherson, Philip; Tosto, Maria Grazia

    2016-09-01

    Despite moderate heritability estimates, the molecular architecture of aggressive behavior remains poorly characterized. This study compared gene expression profiles from a genetic mouse model of aggression with zebrafish, an animal model traditionally used to study aggression. A meta-analytic, cross-species approach was used to identify genomic variants associated with aggressive behavior. The Rankprod algorithm was used to evaluated mRNA differences from prefrontal cortex tissues of three sets of mouse lines (N = 18) selectively bred for low and high aggressive behavior (SAL/LAL, TA/TNA, and NC900/NC100). The same approach was used to evaluate mRNA differences in zebrafish (N = 12) exposed to aggressive or non-aggressive social encounters. Results were compared to uncover genes consistently implicated in aggression across both studies. Seventy-six genes were differentially expressed (PFP behaviors. Moreover, the HDAC4 receptor is a drug target for valproic acid, which is being employed as an effective pharmacological treatment for aggressive behavior in geriatric, psychiatric, and brain-injury patients. © 2016 Wiley Periodicals, Inc. PMID:27090961

  8. Network-Based Enriched Gene Subnetwork Identification: A Game-Theoretic Approach

    Science.gov (United States)

    Razi, Abolfazl; Afghah, Fatemeh; Singh, Salendra; Varadan, Vinay

    2016-01-01

    Identifying subsets of genes that jointly mediate cancer etiology, progression, or therapy response remains a challenging problem due to the complexity and heterogeneity in cancer biology, a problem further exacerbated by the relatively small number of cancer samples profiled as compared with the sheer number of potential molecular factors involved. Pure data-driven methods that merely rely on multiomics data have been successful in discovering potentially functional genes but suffer from high false-positive rates and tend to report subsets of genes whose biological interrelationships are unclear. Recently, integrative data-driven models have been developed to integrate multiomics data with signaling pathway networks in order to identify pathways associated with clinical or biological phenotypes. However, these approaches suffer from an important drawback of being restricted to previously discovered pathway structures and miss novel genomic interactions as well as potential crosstalk among the pathways. In this article, we propose a novel coalition-based game-theoretic approach to overcome the challenge of identifying biologically relevant gene subnetworks associated with disease phenotypes. The algorithm starts from a set of seed genes and traverses a protein–protein interaction network to identify modulated subnetworks. The optimal set of modulated subnetworks is identified using Shapley value that accounts for both individual and collective utility of the subnetwork of genes. The algorithm is applied to two illustrative applications, including the identification of subnetworks associated with (i) disease progression risk in response to platinum-based therapy in ovarian cancer and (ii) immune infiltration in triple-negative breast cancer. The results demonstrate an improved predictive power of the proposed method when compared with state-of-the-art feature selection methods, with the added advantage of identifying novel potentially functional gene subnetworks

  9. 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. PMID:27280975

  10. Ancient Land Routes On The Paximadhi Peninsula, Karystos, Euboea

    Science.gov (United States)

    Keller, D.; Hom, E.

    Recent regional surface surveys have placed more focus on rural investigations, but the means of transport and communication within those rural surroundings has not always received adequate attention. The Southern Euboea Exploration Project has undertaken a new phase of research in the Karystos area with the goal of developing a methodology that allows for a more detailed record of the pre-modern land routes. On the Paximadhi peninsula it was possible to identify numerous fragments of suspected ancient routes dating to the Classical and Hellenistic periods. In the majority of cases these fragments were closely associated with adjacent datable ancient sites. By taking into consideration the evidence recorded during the survey it was sometimes possible to propose the extension of these ancient segments and to theorize the directions, lengths, and purposes of ancient networks.

  11. Genome-wide analyses for dissecting gene regulatory networks in the shoot apical meristem.

    Science.gov (United States)

    Bustamante, Mariana; Matus, José Tomás; Riechmann, José Luis

    2016-04-01

    Shoot apical meristem activity is controlled by complex regulatory networks in which components such as transcription factors, miRNAs, small peptides, hormones, enzymes and epigenetic marks all participate. Many key genes that determine the inherent characteristics of the shoot apical meristem have been identified through genetic approaches. Recent advances in genome-wide studies generating extensive transcriptomic and DNA-binding datasets have increased our understanding of the interactions within the regulatory networks that control the activity of the meristem, identifying new regulators and uncovering connections between previously unlinked network components. In this review, we focus on recent studies that illustrate the contribution of whole genome analyses to understand meristem function. PMID:26956505

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

    Directory of Open Access Journals (Sweden)

    Shubhada R Hegde

    2008-11-01

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

  13. 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.

  14. Differences in Human Cortical Gene Expression Match the Temporal Properties of Large-Scale Functional Networks

    Science.gov (United States)

    Cioli, Claudia; Abdi, Hervé; Beaton, Derek; Burnod, Yves; Mesmoudi, Salma

    2014-01-01

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

  15. 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.

  16. 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.

  17. Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models

    Directory of Open Access Journals (Sweden)

    Tsuda Koji

    2007-11-01

    Full Text Available Abstract Background Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., gene switches, RNAi, heterozygotes is expensive. It is thus desirable to use an identification method that effectively incorporates available prior knowledge – such as sparse connectivity – and that allows to design experiments such that maximal information is gained from each one. Results Our main contributions are twofold: a method for consistent inference of network structure is provided, incorporating prior knowledge about sparse connectivity. The algorithm is time efficient and robust to violations of model assumptions. Moreover, we show how to use it for optimal experimental design, reducing the number of required experiments substantially. We employ sparse linear models, and show how to perform full Bayesian inference for these. We not only estimate a single maximum likelihood network, but compute a posterior distribution over networks, using a novel variant of the expectation propagation method. The representation of uncertainty enables us to do effective experimental design in a standard statistical setting: experiments are selected such that the experiments are maximally informative. Conclusion Few methods have addressed the design issue so far. Compared to the most well-known one, our method is more transparent, and is shown to perform qualitatively superior. In the former, hard and unrealistic constraints have to be placed on the network structure for mere computational tractability, while such are not required in our method. We demonstrate reconstruction and optimal experimental design capabilities on tasks generated from realistic non-linear network simulators. The methods described in the paper are available as a Matlab package at http://www.kyb.tuebingen.mpg.de/sparselinearmodel.

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

    Directory of Open Access Journals (Sweden)

    Wei-Yi Ong

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

  19. Dance in Ancient Greek Culture

    OpenAIRE

    Spalva, Rita

    2015-01-01

    The greatness and harmony of ancient Greece has had an impact upon the development of the Western European culture to this day. The ancient Greek culture has influenced contemporary literature genres and systems of philosophy, principles of architecture, sculpture and drama and has formed basis for such sciences as astronomy and mathematics. The art of ancient Greece with its penchant for beauty and clarity has been the example of the humanity’s search for an aesthetic ideal. Despite only bei...

  20. Statistical completion of a partially identified graph with applications for the estimation of gene regulatory networks.

    Science.gov (United States)

    Yu, Donghyeon; Son, Won; Lim, Johan; Xiao, Guanghua

    2015-10-01

    We study the estimation of a Gaussian graphical model whose dependent structures are partially identified. In a Gaussian graphical model, an off-diagonal zero entry in the concentration matrix (the inverse covariance matrix) implies the conditional independence of two corresponding variables, given all other variables. A number of methods have been proposed to estimate a sparse large-scale Gaussian graphical model or, equivalently, a sparse large-scale concentration matrix. In practice, the graph structure to be estimated is often partially identified by other sources or a pre-screening. In this paper, we propose a simple modification of existing methods to take into account this information in the estimation. We show that the partially identified dependent structure reduces the error in estimating the dependent structure. We apply the proposed method to estimating the gene regulatory network from lung cancer data, where protein-protein interactions are partially identified from the human protein reference database. The application shows that proposed method identified many important cancer genes as hub genes in the constructed lung cancer network. In addition, we validated the prognostic importance of a newly identified cancer gene, PTPN13, in four independent lung cancer datasets. The results indicate that the proposed method could facilitate studying underlying lung cancer mechanisms and identifying reliable biomarkers for lung cancer prognosis. PMID:25837438

  1. Pitx2 modulates a Tbx5-dependent gene regulatory network to maintain atrial rhythm.

    Science.gov (United States)

    Nadadur, Rangarajan D; Broman, Michael T; Boukens, Bastiaan; Mazurek, Stefan R; Yang, Xinan; van den Boogaard, Malou; Bekeny, Jenna; Gadek, Margaret; Ward, Tarsha; Zhang, Min; Qiao, Yun; Martin, James F; Seidman, Christine E; Seidman, Jon; Christoffels, Vincent; Efimov, Igor R; McNally, Elizabeth M; Weber, Christopher R; Moskowitz, Ivan P

    2016-08-31

    Cardiac rhythm is extremely robust, generating 2 billion contraction cycles during the average human life span. Transcriptional control of cardiac rhythm is poorly understood. We found that removal of the transcription factor gene Tbx5 from the adult mouse caused primary spontaneous and sustained atrial fibrillation (AF). Atrial cardiomyocytes from the Tbx5-mutant mice exhibited action potential abnormalities, including spontaneous depolarizations, which were rescued by chelating free calcium. We identified a multitiered transcriptional network that linked seven previously defined AF risk loci: TBX5 directly activated PITX2, and TBX5 and PITX2 antagonistically regulated membrane effector genes Scn5a, Gja1, Ryr2, Dsp, and Atp2a2 In addition, reduced Tbx5 dose by adult-specific haploinsufficiency caused decreased target gene expression, myocardial automaticity, and AF inducibility, which were all rescued by Pitx2 haploinsufficiency in mice. These results defined a transcriptional architecture for atrial rhythm control organized as an incoherent feed-forward loop, driven by TBX5 and modulated by PITX2. TBX5/PITX2 interplay provides tight control of atrial rhythm effector gene expression, and perturbation of the co-regulated network caused AF susceptibility. This work provides a model for the molecular mechanisms underpinning the genetic implication of multiple AF genome-wide association studies loci and will contribute to future efforts to stratify patients for AF risk by genotype. PMID:27582060

  2. Detection of Critical Genes Associated with Overall Survival (OS) and Progression-Free Survival (PFS) in Reconstructed Canine B-Cell Lymphoma Gene Regulatory Network (GRN).

    Science.gov (United States)

    Zamani-Ahmadmahmudi, Mohamad; Najafi, Ali; Nassiri, Seyed Mahdi

    2016-01-01

    Canine B-cell lymphoma GRN was reconstructed from gene expression data in the STRING and MiMI databases. Critical genes of networks were identified and correlations of critical genes with overall survival (OS) and progression-free survival (PFS) were evaluated. Significant changes were detected in the expressions of GLUL, CD44, CD79A, ARF3, FOS, BLOC1S1, FYN, GZMB, GALNT3, IFI44, CD3G, GNG2, ESRP1, and CCND1 in the STRING network and of PECAM1, GLUL, CD44, GDI1, E2F4, TLE1, CD79A, UCP2, CCND1, FYN, RHOQ, BIN1, and A2M in the MiMI network. Final survival analysis highlighted CCND1 and FOS as genes with significant correlations with OS and PFS. PMID:26818715

  3. Ancestry of modern Europeans: contributions of ancient DNA.

    Science.gov (United States)

    Lacan, Marie; Keyser, Christine; Crubézy, Eric; Ludes, Bertrand

    2013-07-01

    Understanding the peopling history of Europe is crucial to comprehend the origins of modern populations. Of course, the analysis of current genetic data offers several explanations about human migration patterns which occurred on this continent, but it fails to explain precisely the impact of each demographic event. In this context, direct access to the DNA of ancient specimens allows the overcoming of recent demographic phenomena, which probably highly modified the constitution of the current European gene pool. In recent years, several DNA studies have been successfully conducted from ancient human remains thanks to the improvement of molecular techniques. They have brought new fundamental information on the peopling of Europe and allowed us to refine our understanding of European prehistory. In this review, we will detail all the ancient DNA studies performed to date on ancient European DNA from the Middle Paleolithic to the beginning of the protohistoric period. PMID:23052219

  4. Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.

    Directory of Open Access Journals (Sweden)

    Xin Wang

    Full Text Available Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the 'search space' for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package

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

    Science.gov (United States)

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

    2016-01-01

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

  6. Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients

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    DeYoung Joseph

    2009-08-01

    Full Text Available Abstract Background Amyotrophic Lateral Sclerosis (ALS is a lethal disorder characterized by progressive degeneration of motor neurons in the brain and spinal cord. Diagnosis is mainly based on clinical symptoms, and there is currently no therapy to stop the disease or slow its progression. Since access to spinal cord tissue is not possible at disease onset, we investigated changes in gene expression profiles in whole blood of ALS patients. Results Our transcriptional study showed dramatic changes in blood of ALS patients; 2,300 probes (9.4% showed significant differential expression in a discovery dataset consisting of 30 ALS patients and 30 healthy controls. Weighted gene co-expression network analysis (WGCNA was used to find disease-related networks (modules and disease related hub genes. Two large co-expression modules were found to be associated with ALS. Our findings were replicated in a second (30 patients and 30 controls and third dataset (63 patients and 63 controls, thereby demonstrating a highly significant and consistent association of two large co-expression modules with ALS disease status. Ingenuity Pathway Analysis of the ALS related module genes implicates enrichment of functional categories related to genetic disorders, neurodegeneration of the nervous system and inflammatory disease. The ALS related modules contain a number of candidate genes possibly involved in pathogenesis of ALS. Conclusion This first large-scale blood gene expression study in ALS observed distinct patterns between cases and controls which may provide opportunities for biomarker development as well as new insights into the molecular mechanisms of the disease.

  7. Gnomons in Ancient China

    Science.gov (United States)

    Li, Geng

    Gnomon shadow measurement was one of the most fundamental astronomical observations in ancient China. It was crucial for calendar making, which constituted an important aspect of imperial governance. A painted stick discovered from a prehistoric (2300 BC) astronomical site of Taosi (see Chap. 201, "Taosi Observatory", 10.1007/978-1-4614-6141-8_215") is the oldest gnomon known of China. From second century BC onward, gnomon shadow measurements have been essential part of calendrical practice. Various historical measurements are discussed in this chapter.

  8. Climate and Ancient Societies

    DEFF Research Database (Denmark)

    Climate, and human responses to it, have a strongly interconnected relationship. This when climate change occurs, the result of either natural or human causes, societies should react and adapt to these. But do they? If so, what is the nature of that change, and are the responses positive...... or negative for the long-term survival of social groups? In this volume, scholars from diverse disciplines including archaeology, geology and climate sciences explore scientific and material evidence for climate changes in the past, their causes, their effects on ancient societies and how those societies...

  9. Mathematics in ancient Greece

    CERN Document Server

    Dantzig, Tobias

    2006-01-01

    More than a history of mathematics, this lively book traces mathematical ideas and processes to their sources, stressing the methods used by the masters of the ancient world. Author Tobias Dantzig portrays the human story behind mathematics, showing how flashes of insight in the minds of certain gifted individuals helped mathematics take enormous forward strides. Dantzig demonstrates how the Greeks organized their precursors' melange of geometric maxims into an elegantly abstract deductive system. He also explains the ways in which some of the famous mathematical brainteasers of antiquity led

  10. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases.

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    Anna Bauer-Mehren

    Full Text Available BACKGROUND: Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. PRINCIPAL FINDINGS: We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. CONCLUSIONS: For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a