WorldWideScience

Sample records for coexpression networks reveal

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

    Directory of Open Access Journals (Sweden)

    Kohane Isaac S

    2005-09-01

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

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

    DEFF Research Database (Denmark)

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

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

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

    DEFF Research Database (Denmark)

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

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

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

    Science.gov (United States)

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

    2013-11-01

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

  5. Coexpression network analysis in chronic hepatitis B and C hepatic lesions reveals distinct patterns of disease progression to hepatocellular carcinoma

    Institute of Scientific and Technical Information of China (English)

    Danning He; Zhi-Ping Liu; Masao Honda; Shuichi Kaneko; Luonan Chen

    2012-01-01

    Chronic infections with the hepatitis B virus (HBV) and hepatitis C virus (HCV) are the major risks of hepatocellular carcinoma (HCC),and great efforts have been made towards the understanding of the different mechanisms that link the viral infection of hepatic lesions to HCC development.In this work,we developed a novel framework to identify distinct patterns of gene coexpression networks and inflammation-related modules from genome-scale microarray data upon viral infection,and further classified them into oncogenic and dysfunctional ones.The core of our framework lies in the comparative study on viral infection modules across different disease stages and disease types--the module preservation during disease progression is evaluated according to the change of network connectivity in different stages,while the similarity and difference in HBV and HCV are evaluated by comparing the overlap of gene compositions and functional annotations in HBV and HCV modules.In particular,we revealed two types of driving modules related to infection for carcinogenesis in HBV and HCV,respectively,i.e.pro-apoptosis modules that are oncogenic in HBV,and antiapoptosis and inflammation modules that are oncogenic in HCV,which are in concordance with the results of previous differential expression-based approaches.Moreover,we found that intracellular protein transmembrane transportation and the transmembrane receptor protein tyrosine kinase signaling pathway act as oncogenic factors in HBV-HCC.Our findings provide novel insights into viral hepatocarcinogenesis and disease progression,and also demonstrate the advantages of an integrative and comparative network analysis over the existing differential expression-based approach and virus-host interactome-based approach.

  6. Coexpression network analysis in abdominal and gluteal adipose tissue reveals regulatory genetic loci for metabolic syndrome and related phenotypes

    DEFF Research Database (Denmark)

    Min, Josine L; Nicholson, George; Halgrimsdottir, Ingileif

    2012-01-01

    Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue......, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS-associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD...... and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D(ABD-GLU) = 0.89), seven of which were associated with MetS (FDR P100,000 individuals; rs10282458, affecting expression of RARRES2...

  7. Transcriptional coexpression network reveals the involvement of varying stem cell features with different dysregulations in different gastric cancer subtypes.

    Science.gov (United States)

    Kalamohan, Kalaivani; Periasamy, Jayaprakash; Bhaskar Rao, Divya; Barnabas, Georgina D; Ponnaiyan, Srigayatri; Ganesan, Kumaresan

    2014-10-01

    Despite the advancements in the cancer therapeutics, gastric cancer ranks as the second most common cancers with high global mortality rate. Integrative functional genomic investigation is a powerful approach to understand the major dysregulations and to identify the potential targets toward the development of targeted therapeutics for various cancers. Intestinal and diffuse type gastric tumors remain the major subtypes and the molecular determinants and drivers of these distinct subtypes remain unidentified. In this investigation, by exploring the network of gene coexpression association in gastric tumors, mRNA expressions of 20,318 genes across 200 gastric tumors were categorized into 21 modules. The genes and the hub genes of the modules show gastric cancer subtype specific expression. The expression patterns of the modules were correlated with intestinal and diffuse subtypes as well as with the differentiation status of gastric tumors. Among these, G1 module has been identified as a major driving force of diffuse type gastric tumors with the features of (i) enriched mesenchymal, mesenchymal stem cell like, and mesenchymal derived multiple lineages, (ii) elevated OCT1 mediated transcription, (iii) involvement of Notch activation, and (iv) reduced polycomb mediated epigenetic repression. G13 module has been identified as key factor in intestinal type gastric tumors and found to have the characteristic features of (i) involvement of embryonic stem cell like properties, (ii) Wnt, MYC and E2F mediated transcription programs, and (iii) involvement of polycomb mediated repression. Thus the differential transcription programs, differential epigenetic regulation and varying stem cell features involved in two major subtypes of gastric cancer were delineated by exploring the gene coexpression network. The identified subtype specific dysregulations could be optimally employed in developing subtype specific therapeutic targeting strategies for gastric cancer.

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

    Directory of Open Access Journals (Sweden)

    Diogo Vieira da Silva Pellegrina

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

  10. Inequalities and Duality in Gene Coexpression Networks of HIV-1 Infection Revealed by the Combination of the Double-Connectivity Approach and the Gini's Method

    Directory of Open Access Journals (Sweden)

    Chuang Ma

    2011-01-01

    Full Text Available The symbiosis (Sym and pathogenesis (Pat is a duality problem of microbial infection, including HIV/AIDS. Statistical analysis of inequalities and duality in gene coexpression networks (GCNs of HIV-1 infection may gain novel insights into AIDS. In this study, we focused on analysis of GCNs of uninfected subjects and HIV-1-infected patients at three different stages of viral infection based on data deposited in the GEO database of NCBI. The inequalities and duality in these GCNs were analyzed by the combination of the double-connectivity (DC approach and the Gini's method. DC analysis reveals that there are significant differences between positive and negative connectivity in HIV-1 stage-specific GCNs. The inequality measures of negative connectivity and edge weight are changed more significantly than those of positive connectivity and edge weight in GCNs from the HIV-1 uninfected to the AIDS stages. With the permutation test method, we identified a set of genes with significant changes in the inequality and duality measure of edge weight. Functional analysis shows that these genes are highly enriched for the immune system, which plays an essential role in the Sym-Pat duality (SPD of microbial infections. Understanding of the SPD problems of HIV-1 infection may provide novel intervention strategies for AIDS.

  11. A null model for Pearson coexpression networks.

    Science.gov (United States)

    Gobbi, Andrea; Jurman, Giuseppe

    2015-01-01

    Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges.

  12. A null model for Pearson coexpression networks.

    Directory of Open Access Journals (Sweden)

    Andrea Gobbi

    Full Text Available Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges.

  13. Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

    Directory of Open Access Journals (Sweden)

    Junyi Li

    2016-01-01

    Full Text Available With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA based on gene coexpression network (GCN increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

  14. Eigengene networks for studying the relationships between co-expression modules

    Directory of Open Access Journals (Sweden)

    Horvath Steve

    2007-11-01

    Full Text Available Abstract Background There is evidence that genes and their protein products are organized into functional modules according to cellular processes and pathways. Gene co-expression networks have been used to describe the relationships between gene transcripts. Ample literature exists on how to detect biologically meaningful modules in networks but there is a need for methods that allow one to study the relationships between modules. Results We show that network methods can also be used to describe the relationships between co-expression modules and present the following methodology. First, we describe several methods for detecting modules that are shared by two or more networks (referred to as consensus modules. We represent the gene expression profiles of each module by an eigengene. Second, we propose a method for constructing an eigengene network, where the edges are undirected but maintain information on the sign of the co-expression information. Third, we propose methods for differential eigengene network analysis that allow one to assess the preservation of network properties across different data sets. We illustrate the value of eigengene networks in studying the relationships between consensus modules in human and chimpanzee brains; the relationships between consensus modules in brain, muscle, liver, and adipose mouse tissues; and the relationships between male-female mouse consensus modules and clinical traits. In some applications, we find that module eigengenes can be organized into higher level clusters which we refer to as meta-modules. Conclusion Eigengene networks can be effective and biologically meaningful tools for studying the relationships between modules of a gene co-expression network. The proposed methods may reveal a higher order organization of the transcriptome. R software tutorials, the data, and supplementary material can be found at the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/EigengeneNetwork.

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

    Science.gov (United States)

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

    2016-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Gao Haichun

    2007-08-01

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

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

    Science.gov (United States)

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

    2013-12-01

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

  18. 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.

  19. A developmental transcriptional network for maize defines coexpression modules.

    Science.gov (United States)

    Downs, Gregory S; Bi, Yong-Mei; Colasanti, Joseph; Wu, Wenqing; Chen, Xi; Zhu, Tong; Rothstein, Steven J; Lukens, Lewis N

    2013-04-01

    Here, we present a genome-wide overview of transcriptional circuits in the agriculturally significant crop species maize (Zea mays). We examined transcript abundance data at 50 developmental stages, from embryogenesis to senescence, for 34,876 gene models and classified genes into 24 robust coexpression modules. Modules were strongly associated with tissue types and related biological processes. Sixteen of the 24 modules (67%) have preferential transcript abundance within specific tissues. One-third of modules had an absence of gene expression in specific tissues. Genes within a number of modules also correlated with the developmental age of tissues. Coexpression of genes is likely due to transcriptional control. For a number of modules, key genes involved in transcriptional control have expression profiles that mimic the expression profiles of module genes, although the expression of transcriptional control genes is not unusually representative of module gene expression. Known regulatory motifs are enriched in several modules. Finally, of the 13 network modules with more than 200 genes, three contain genes that are notably clustered (P < 0.05) within the genome. This work, based on a carefully selected set of major tissues representing diverse stages of maize development, demonstrates the remarkable power of transcript-level coexpression networks to identify underlying biological processes and their molecular components.

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

    Science.gov (United States)

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

    2010-04-01

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

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

    Directory of Open Access Journals (Sweden)

    Zhang Weixiong

    2010-02-01

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

  2. Analyzing miRNA co-expression networks to explore TF-miRNA regulation

    Directory of Open Access Journals (Sweden)

    Bhattacharyya Malay

    2009-05-01

    clustering motivation is proposed in this paper. However, there remains a basic difference between the mining method and a clustering approach. The heuristic approach can produce priority modules (PM from an miRNA co-expression network, by employing a self-pruning phase, which are analyzed for statistical and biological significance. The mining algorithm minimizes the space/time complexity of the analysis, and also handles noise in the data. In addition, the mining method reveals promising results in the unsupervised analysis of TF-miRNA regulation.

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

    Science.gov (United States)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2015-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Emma Pierson

    2015-05-01

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

  6. 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

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

    Science.gov (United States)

    Tejera, Eduardo; Bernardes, João; Rebelo, Irene

    2013-11-12

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

  8. 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

  9. 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.

  10. Dissecting nutrient-related co-expression networks in phosphate starved poplars

    Science.gov (United States)

    Kavka, Mareike; Polle, Andrea

    2017-01-01

    Phosphorus (P) is an essential plant nutrient, but its availability is often limited in soil. Here, we studied changes in the transcriptome and in nutrient element concentrations in leaves and roots of poplars (Populus × canescens) in response to P deficiency. P starvation resulted in decreased concentrations of S and major cations (K, Mg, Ca), in increased concentrations of N, Zn and Al, while C, Fe and Mn were only little affected. In roots and leaves >4,000 and >9,000 genes were differently expressed upon P starvation. These genes clustered in eleven co-expression modules of which seven were correlated with distinct elements in the plant tissues. One module (4.7% of all differentially expressed genes) was strongly correlated with changes in the P concentration in the plant. In this module the GO term “response to P starvation” was enriched with phosphoenolpyruvate carboxylase kinases, phosphatases and pyrophosphatases as well as regulatory domains such as SPX, but no phosphate transporters. The P-related module was also enriched in genes of the functional category “galactolipid synthesis”. Galactolipids substitute phospholipids in membranes under P limitation. Two modules, one correlated with C and N and the other with biomass, S and Mg, were connected with the P-related module by co-expression. In these modules GO terms indicating “DNA modification” and “cell division” as well as “defense” and “RNA modification” and “signaling” were enriched; they contained phosphate transporters. Bark storage proteins were among the most strongly upregulated genes in the growth-related module suggesting that N, which could not be used for growth, accumulated in typical storage compounds. In conclusion, weighted gene coexpression network analysis revealed a hierarchical structure of gene clusters, which separated phosphate starvation responses correlated with P tissue concentrations from other gene modules, which most likely represented

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

    DEFF Research Database (Denmark)

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

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Marek eMutwil

    2014-08-01

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

  13. Gene coexpression network analysis of oil biosynthesis in an interspecific backcross of oil palm.

    Science.gov (United States)

    Guerin, Chloé; Joët, Thierry; Serret, Julien; Lashermes, Philippe; Vaissayre, Virginie; Agbessi, Mawussé D T; Beulé, Thierry; Severac, Dany; Amblard, Philippe; Tregear, James; Durand-Gasselin, Tristan; Morcillo, Fabienne; Dussert, Stéphane

    2016-09-01

    Global demand for vegetable oils is increasing at a dramatic rate, while our understanding of the regulation of oil biosynthesis in plants remains limited. To gain insights into the mechanisms that govern oil synthesis and fatty acid (FA) composition in the oil palm fruit, we used a multilevel approach combining gene coexpression analysis, quantification of allele-specific expression and joint multivariate analysis of transcriptomic and lipid data, in an interspecific backcross population between the African oil palm, Elaeis guineensis, and the American oil palm, Elaeis oleifera, which display contrasting oil contents and FA compositions. The gene coexpression network produced revealed tight transcriptional coordination of fatty acid synthesis (FAS) in the plastid with sugar sensing, plastidial glycolysis, transient starch storage and carbon recapture pathways. It also revealed a concerted regulation, along with FAS, of both the transfer of nascent FA to the endoplasmic reticulum, where triacylglycerol assembly occurs, and of the production of glycerol-3-phosphate, which provides the backbone of triacylglycerols. Plastid biogenesis and auxin transport were the two other biological processes most tightly connected to FAS in the network. In addition to WRINKLED1, a transcription factor (TF) known to activate FAS genes, two novel TFs, termed NF-YB-1 and ZFP-1, were found at the core of the FAS module. The saturated FA content of palm oil appeared to vary above all in relation to the level of transcripts of the gene coding for β-ketoacyl-acyl carrier protein synthase II. Our findings should facilitate the development of breeding and engineering strategies in this and other oil crops.

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Scott M Gibson

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

  16. Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex.

    Directory of Open Access Journals (Sweden)

    Sepideh Babaei

    2015-05-01

    Full Text Available The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale to chromatin compartment interactions (i.e. large-scale.

  17. 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.

  18. 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.

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

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Watson-Haigh, Nathan S.

    2012-01-01

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

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

    Science.gov (United States)

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

    2016-07-01

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

  1. Co-expression analysis reveals a group of genes potentially involved in regulation of plant response to iron-deficiency.

    Science.gov (United States)

    Li, Hua; Wang, Lei; Yang, Zhi Min

    2015-01-01

    Iron (Fe) is an essential element for plant growth and development. Iron deficiency results in abnormal metabolisms from respiration to photosynthesis. Exploration of Fe-deficient responsive genes and their networks is critically important to understand molecular mechanisms leading to the plant adaptation to soil Fe-limitation. Co-expression genes are a cluster of genes that have a similar expression pattern to execute relatively biological functions at a stage of development or under a certain environmental condition. They may share a common regulatory mechanism. In this study, we investigated Fe-starved-related co-expression genes from Arabidopsis. From the biological process GO annotation of TAIR (The Arabidopsis Information Resource), 180 iron-deficient responsive genes were detected. Using ATTED-II database, we generated six gene co-expression networks. Among these, two modules of PYE and IRT1 were successfully constructed. There are 30 co-expression genes that are incorporated in the two modules (12 in PYE-module and 18 in IRT1-module). Sixteen of the co-expression genes were well characterized. The remaining genes (14) are poorly or not functionally identified with iron stress. Validation of the 14 genes using real-time PCR showed differential expression under iron-deficiency. Most of the co-expression genes (23/30) could be validated in pye and fit mutant plants with iron-deficiency. We further identified iron-responsive cis-elements upstream of the co-expression genes and found that 22 out of 30 genes contain the iron-responsive motif IDE1. Furthermore, some auxin and ethylene-responsive elements were detected in the promoters of the co-expression genes. These results suggest that some of the genes can be also involved in iron stress response through the phytohormone-responsive pathways.

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

    Science.gov (United States)

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2017-02-01

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

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

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

    2015-01-01

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

  6. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Science.gov (United States)

    Colak, Recep; Moser, Flavia; Chu, Jeffrey Shih-Chieh; Schönhuth, Alexander; Chen, Nansheng; Ester, Martin

    2010-10-25

    Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large

  7. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Directory of Open Access Journals (Sweden)

    Recep Colak

    Full Text Available BACKGROUND: Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. METHODOLOGY/PRINCIPAL FINDINGS: We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. CONCLUSION/SIGNIFICANCE: We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-08-28

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

  9. 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...

  10. 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...

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

    CERN Document Server

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

    2012-01-01

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

  12. 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.

  13. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis.

    Science.gov (United States)

    Clarke, Colin; Madden, Stephen F; Doolan, Padraig; Aherne, Sinead T; Joyce, Helena; O'Driscoll, Lorraine; Gallagher, William M; Hennessy, Bryan T; Moriarty, Michael; Crown, John; Kennedy, Susan; Clynes, Martin

    2013-10-01

    Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

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

    Directory of Open Access Journals (Sweden)

    Li Yang

    2010-05-01

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

  15. Gene Coexpression Analysis Reveals Complex Metabolism of the Monoterpene Alcohol Linalool in Arabidopsis FlowersW

    NARCIS (Netherlands)

    Ginglinger, J.F.; Boachon, B.; Hofer, R.; Paetz, C.; Kollner, T.G.; Miesch, L.; Lugan, R.; Baltenweck, R.; Mutterer, J.; Ullman, P.; Verstappen, F.W.A.; Bouwmeester, H.J.

    2013-01-01

    The cytochrome P450 family encompasses the largest family of enzymes in plant metabolism, and the functions of many of its members in Arabidopsis thaliana are still unknown. Gene coexpression analysis pointed to two P450s that were coexpressed with two monoterpene synthases in flowers and were thus

  16. Gene Coexpression Analysis Reveals Complex Metabolism of the Monoterpene Alcohol Linalool in Arabidopsis FlowersW

    NARCIS (Netherlands)

    Ginglinger, J.F.; Boachon, B.; Hofer, R.; Paetz, C.; Kollner, T.G.; Miesch, L.; Lugan, R.; Baltenweck, R.; Mutterer, J.; Ullman, P.; Verstappen, F.W.A.; Bouwmeester, H.J.

    2013-01-01

    The cytochrome P450 family encompasses the largest family of enzymes in plant metabolism, and the functions of many of its members in Arabidopsis thaliana are still unknown. Gene coexpression analysis pointed to two P450s that were coexpressed with two monoterpene synthases in flowers and were thus

  17. CoExpNetViz: Comparative Co-expression Networks Construction and Visualization Tool

    Directory of Open Access Journals (Sweden)

    Oren eTzfadia

    2016-01-01

    Full Text Available Motivation: Comparative transcriptomics is a common approach in functional gene discovery efforts. It allows for finding conserved co-expression patterns between orthologous genes in closely related plant species, suggesting that these genes potentially share similar function and regulation. Several efficient co-expression-based tools have been commonly used in plant research but most of these pipelines are limited to data from model systems, which greatly limit their utility. Moreover, in addition, none of the existing pipelines allow plant researchers to make use of their own unpublished gene expression data for performing a comparative co-expression analysis and generate multi-species co-expression networks.Results: We introduce CoExpNetViz, a computational tool that uses a set of query or 'bait' genes as an input (chosen by the user and a minimum of one pre-processed gene expression dataset. The CoExpNetViz algorithm proceeds in three main steps; (i for every bait gene submitted, co-expression values are calculated using mutual information and Pearson correlation coefficients, (ii non-bait (or target genes are grouped based on cross-species orthology, and (iii output files are generated and results can be visualized as network graphs in Cytoscape.Availability: The CoExpNetViz tool is freely available both as a PHP web server (link: http://bioinformatics.psb.ugent.be/webtools/coexpr/ (implemented in C++ and as a Cytoscape plugin (implemented in Java. Both versions of the CoExpNetViz tool support LINUX and Windows platforms.

  18. CoExpNetViz: Comparative Co-Expression Networks Construction and Visualization Tool.

    Science.gov (United States)

    Tzfadia, Oren; Diels, Tim; De Meyer, Sam; Vandepoele, Klaas; Aharoni, Asaph; Van de Peer, Yves

    2015-01-01

    Comparative transcriptomics is a common approach in functional gene discovery efforts. It allows for finding conserved co-expression patterns between orthologous genes in closely related plant species, suggesting that these genes potentially share similar function and regulation. Several efficient co-expression-based tools have been commonly used in plant research but most of these pipelines are limited to data from model systems, which greatly limit their utility. Moreover, in addition, none of the existing pipelines allow plant researchers to make use of their own unpublished gene expression data for performing a comparative co-expression analysis and generate multi-species co-expression networks. We introduce CoExpNetViz, a computational tool that uses a set of query or "bait" genes as an input (chosen by the user) and a minimum of one pre-processed gene expression dataset. The CoExpNetViz algorithm proceeds in three main steps; (i) for every bait gene submitted, co-expression values are calculated using mutual information and Pearson correlation coefficients, (ii) non-bait (or target) genes are grouped based on cross-species orthology, and (iii) output files are generated and results can be visualized as network graphs in Cytoscape. The CoExpNetViz tool is freely available both as a PHP web server (link: http://bioinformatics.psb.ugent.be/webtools/coexpr/) (implemented in C++) and as a Cytoscape plugin (implemented in Java). Both versions of the CoExpNetViz tool support LINUX and Windows platforms.

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

    Science.gov (United States)

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

    2014-07-15

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

  20. Chemokine receptor co-expression reveals aberrantly distributed TH effector memory cells in GPA patients.

    Science.gov (United States)

    Lintermans, Lucas L; Rutgers, Abraham; Stegeman, Coen A; Heeringa, Peter; Abdulahad, Wayel H

    2017-06-14

    Persistent expansion of circulating CD4(+) effector memory T cells (TEM) in patients with granulomatosis with polyangiitis (GPA) suggests their fundamental role in disease pathogenesis. Recent studies have shown that distinct functional CD4(+) TEM cell subsets can be identified based on expression patterns of chemokine receptors. The current study aimed to determine different CD4(+) TEM cell subsets based on chemokine receptor expression in peripheral blood of GPA patients. Identification of particular circulating CD4(+) TEM cells subsets may reveal distinct contributions of specific CD4(+) TEM subsets to the disease pathogenesis in GPA. Peripheral blood of 63 GPA patients in remission and 42 age- and sex-matched healthy controls was stained immediately after blood withdrawal with fluorochrome-conjugated antibodies for cell surface markers (CD3, CD4, CD45RO) and chemokine receptors (CCR4, CCR6, CCR7, CRTh2, CXCR3) followed by flow cytometry analysis. CD4(+) TEM memory cells (CD3(+)CD4(+)CD45RO(+)CCR7(-)) were gated, and the expression patterns of chemokine receptors CXCR3(+)CCR4(-)CCR6(-)CRTh2(-), CXCR3(-)CCR4(+)CCR6(-)CRTh2(+), CXCR3(-)CCR4(+)CCR6(+)CRTh2(-), and CXCR3(+)CCR4(-)CCR6(+)CRTh2(-) were used to distinguish TEM1, TEM2, TEM17, and TEM17.1 cells, respectively. The percentage of CD4(+) TEM cells was significantly increased in GPA patients in remission compared to HCs. Chemokine receptor co-expression analysis within the CD4(+) TEM cell population demonstrated a significant increase in the proportion of TEM17 cells with a concomitant significant decrease in the TEM1 cells in GPA patients compared to HC. The percentage of TEM17 cells correlated negatively with TEM1 cells in GPA patients. Moreover, the circulating proportion of TEM17 cells showed a positive correlation with the number of organs involved and an association with the tendency to relapse in GPA patients. Interestingly, the aberrant distribution of TEM1 and TEM17 cells is modulated in CMV

  1. Integrative analysis of many weighted co-expression networks using tensor computation.

    Directory of Open Access Journals (Sweden)

    Wenyuan Li

    2011-06-01

    Full Text Available The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks.

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

    Directory of Open Access Journals (Sweden)

    Elizabeth A Osterndorff-Kahanek

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

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

    Directory of Open Access Journals (Sweden)

    Rajeevan Mangalathu S

    2008-11-01

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

  4. Meta-analysis of inter-species liver co-expression networks elucidates traits associated with common human diseases.

    Directory of Open Access Journals (Sweden)

    Kai Wang

    2009-12-01

    Full Text Available Co-expression networks are routinely used to study human diseases like obesity and diabetes. Systematic comparison of these networks between species has the potential to elucidate common mechanisms that are conserved between human and rodent species, as well as those that are species-specific characterizing evolutionary plasticity. We developed a semi-parametric meta-analysis approach for combining gene-gene co-expression relationships across expression profile datasets from multiple species. The simulation results showed that the semi-parametric method is robust against noise. When applied to human, mouse, and rat liver co-expression networks, our method out-performed existing methods in identifying gene pairs with coherent biological functions. We identified a network conserved across species that highlighted cell-cell signaling, cell-adhesion and sterol biosynthesis as main biological processes represented in genome-wide association study candidate gene sets for blood lipid levels. We further developed a heterogeneity statistic to test for network differences among multiple datasets, and demonstrated that genes with species-specific interactions tend to be under positive selection throughout evolution. Finally, we identified a human-specific sub-network regulated by RXRG, which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse. Taken together, our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only common information but also differences that are dataset-specific.

  5. Metabolic and co-expression network-based analyses associated with nitrate response in rice.

    Science.gov (United States)

    Coneva, Viktoriya; Simopoulos, Caitlin; Casaretto, José A; El-Kereamy, Ashraf; Guevara, David R; Cohn, Jonathan; Zhu, Tong; Guo, Lining; Alexander, Danny C; Bi, Yong-Mei; McNicholas, Paul D; Rothstein, Steven J

    2014-12-03

    Understanding gene expression and metabolic re-programming that occur in response to limiting nitrogen (N) conditions in crop plants is crucial for the ongoing progress towards the development of varieties with improved nitrogen use efficiency (NUE). To unravel new details on the molecular and metabolic responses to N availability in a major food crop, we conducted analyses on a weighted gene co-expression network and metabolic profile data obtained from leaves and roots of rice plants adapted to sufficient and limiting N as well as after shifting them to limiting (reduction) and sufficient (induction) N conditions. A gene co-expression network representing clusters of rice genes with similar expression patterns across four nitrogen conditions and two tissue types was generated. The resulting 18 clusters were analyzed for enrichment of significant gene ontology (GO) terms. Four clusters exhibited significant correlation with limiting and reducing nitrate treatments. Among the identified enriched GO terms, those related to nucleoside/nucleotide, purine and ATP binding, defense response, sugar/carbohydrate binding, protein kinase activities, cell-death and cell wall enzymatic activity are enriched. Although a subset of functional categories are more broadly associated with the response of rice organs to limiting N and N reduction, our analyses suggest that N reduction elicits a response distinguishable from that to adaptation to limiting N, particularly in leaves. This observation is further supported by metabolic profiling which shows that several compounds in leaves change proportionally to the nitrate level (i.e. higher in sufficient N vs. limiting N) and respond with even higher levels when the nitrate level is reduced. Notably, these compounds are directly involved in N assimilation, transport, and storage (glutamine, asparagine, glutamate and allantoin) and extend to most amino acids. Based on these data, we hypothesize that plants respond by rapidly mobilizing

  6. Comprehensive analysis of differential co-expression patterns reveal transcriptional dysregulation mechanism and identify novel prognostic lncRNAs in esophageal squamous cell carcinoma

    Directory of Open Access Journals (Sweden)

    Li Z

    2017-06-01

    Full Text Available Zhen Li,1 Qianlan Yao,1 Songjian Zhao,1 Yin Wang,2,3 Yixue Li,1,4 Zhen Wang4 1School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 2Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 3Collaborative Innovation Center for Genetics and Development, Fudan University, 4Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China Abstract: Esophageal squamous cell carcinoma (ESCC is one of the most common malignancies worldwide and occurs at a relatively high frequency in People’s Republic of China. However, the molecular mechanism underlying ESCC is still unclear. In this study, the mRNA and long non-coding RNA (lncRNA expression profiles of ESCC were downloaded from the Gene Expression Omnibus database, and then differential co-expression analysis was used to reveal the altered co-expression relationship of gene pairs in ESCC tumors. A total of 3,709 mRNAs and 923 lncRNAs were differentially co-expressed between normal and tumor tissues, and we found that most of the gene pairs lost associations in the tumor tissues. The differential regulatory networking approach deciphered that transcriptional dysregulation was ubiquitous in ESCC, and most of the differentially regulated links were modulated by 37 TFs. Our study also found that two novel lncRNAs (ADAMTS9-AS1 and AP000696.2 might be essential in the development of ectoderm and epithelial cells, which could significantly stratify ESCC patients into high-risk and low-risk groups, and were much better than traditional clinical tumor markers. Further inspection of two risk groups showed that the changes in TF-target regulation in the high-risk patients were significantly higher than those in the low-risk patients. In addition, four signal transduction-related DCmRNAs (ERBB3, ENSA, KCNK7, MFSD5

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

    Science.gov (United States)

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

    2017-01-19

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

  8. Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

    Science.gov (United States)

    Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

    2000-01-01

    Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

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

    Science.gov (United States)

    Fung, David C Y

    2008-01-01

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

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

    Directory of Open Access Journals (Sweden)

    David C.Y. Fung

    2008-01-01

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

  12. Coexpression Analysis Reveals Key Gene Modules and Pathway of Human Coronary Heart Disease.

    Science.gov (United States)

    Tang, Yu; Ke, Zun-Ping; Peng, Yi-Gen; Cai, Ping-Tai

    2017-08-31

    Coronary heart disease is a kind of disease which causes great injury to people world-widely. Although gene expression analyses had been performed previously, to our best knowledge, systemic co-expression analysis for this disease is still lacking to date. Microarray data of coronary heart disease was downloaded from NCBI with the accession number of GSE20681. Co-expression modules were constructed by WGCNA. Besides, the connectivity degree of eigengenes was analyzed. Furthermore, GO and KEGG enrichment analysis was performed on these eigengenes in these constructed modules. A total of 11 co-expression modules were constructed by the 3,000 up-regulated genes from the 99 samples with coronary heart disease. The average number of genes in these modules was 270. The interaction analysis indicated the relative independence of gene expression in these modules. The functional enrichment analysis showed that there was a significant difference in the enriched terms and degree among these 11 modules. The results showed that module 9 and module 10 played critical roles in the occurrence of coronary disease. Pathways of hsa00190(Oxidative phosphorylation)and (hsa01130: Biosynthesis of antibiotics) were thought to be closely related to the occurrence and development of coronary heart disease. Our result demonstrated that module 9 and module 10 were the most critical modules in the occurrence of coronary heart disease. Pathways as hsa00190(Oxidative phosphorylation) and (hsa01130: Biosynthesis of antibiotics) had the potential to serve as the prognostic and predictive marker of coronary heart disease. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

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

    OpenAIRE

    2013-01-01

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

  14. Recursive Indirect-Paths Modularity (RIP-M) for Detecting Community Structure in RNA-Seq Co-expression Networks.

    Science.gov (United States)

    Rahmani, Bahareh; Zimmermann, Michael T; Grill, Diane E; Kennedy, Richard B; Oberg, Ann L; White, Bill C; Poland, Gregory A; McKinney, Brett A

    2016-01-01

    Clusters of genes in co-expression networks are commonly used as functional units for gene set enrichment detection and increasingly as features (attribute construction) for statistical inference and sample classification. One of the practical challenges of clustering for these purposes is to identify an optimal partition of the network where the individual clusters are neither too large, prohibiting interpretation, nor too small, precluding general inference. Newman Modularity is a spectral clustering algorithm that automatically finds the number of clusters, but for many biological networks the cluster sizes are suboptimal. In this work, we generalize Newman Modularity to incorporate information from indirect paths in RNA-Seq co-expression networks. We implement a merge-and-split algorithm that allows the user to constrain the range of cluster sizes: large enough to capture genes in relevant pathways, yet small enough to resolve distinct functions. We investigate the properties of our recursive indirect-pathways modularity (RIP-M) and compare it with other clustering methods using simulated co-expression networks and RNA-seq data from an influenza vaccine response study. RIP-M had higher cluster assignment accuracy than Newman Modularity for finding clusters in simulated co-expression networks for all scenarios, and RIP-M had comparable accuracy to Weighted Gene Correlation Network Analysis (WGCNA). RIP-M was more accurate than WGCNA for modest hard thresholds and comparable for high, while WGCNA was slightly more accurate for soft thresholds. In the vaccine study data, RIP-M and WGCNA enriched for a comparable number of immunologically relevant pathways.

  15. Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

    NARCIS (Netherlands)

    Babaei, S.; Mahfouz, A.M.E.T.A.; Hulsman, M.; Lelieveldt, B.P.F.; De Ridder, J.; Reinders, M.J.T.

    2015-01-01

    The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequen

  16. Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

    Science.gov (United States)

    Wang, Yongcui; Zhao, Weiling; Zhou, Xiaobo

    2016-10-01

    Accurate identification of coherent transcriptional modules (subnetworks) in adipose and muscle tissues is important for revealing the related mechanisms and co-regulated pathways involved in the development of aging-related diseases. Here, we proposed a systematically computational approach, called ICEGM, to Identify the Co-Expression Gene Modules through a novel mathematical framework of Higher-Order Generalized Singular Value Decomposition (HO-GSVD). ICEGM was applied on the adipose, and heart and skeletal muscle tissues in old and young female African green vervet monkeys. The genes associated with the development of inflammation, cardiovascular and skeletal disorder diseases, and cancer were revealed by the ICEGM. Meanwhile, genes in the ICEGM modules were also enriched in the adipocytes, smooth muscle cells, cardiac myocytes, and immune cells. Comprehensive disease annotation and canonical pathway analysis indicated that immune cells, adipocytes, cardiomyocytes, and smooth muscle cells played a synergistic role in cardiac and physical functions in the aged monkeys by regulation of the biological processes associated with metabolism, inflammation, and atherosclerosis. In conclusion, the ICEGM provides an efficiently systematic framework for decoding the co-expression gene modules in multiple tissues. Analysis of genes in the ICEGM module yielded important insights on the cooperative role of multiple tissues in the development of diseases.

  17. Differential co-expression and regulation analyses reveal different mechanisms underlying major depressive disorder and subsyndromal symptomatic depression.

    Science.gov (United States)

    Xu, Fan; Yang, Jing; Chen, Jin; Wu, Qingyuan; Gong, Wei; Zhang, Jianguo; Shao, Weihua; Mu, Jun; Yang, Deyu; Yang, Yongtao; Li, Zhiwei; Xie, Peng

    2015-04-03

    Recent depression research has revealed a growing awareness of how to best classify depression into depressive subtypes. Appropriately subtyping depression can lead to identification of subtypes that are more responsive to current pharmacological treatment and aid in separating out depressed patients in which current antidepressants are not particularly effective. Differential co-expression analysis (DCEA) and differential regulation analysis (DRA) were applied to compare the transcriptomic profiles of peripheral blood lymphocytes from patients with two depressive subtypes: major depressive disorder (MDD) and subsyndromal symptomatic depression (SSD). Six differentially regulated genes (DRGs) (FOSL1, SRF, JUN, TFAP4, SOX9, and HLF) and 16 transcription factor-to-target differentially co-expressed gene links or pairs (TF2target DCLs) appear to be the key differential factors in MDD; in contrast, one DRG (PATZ1) and eight TF2target DCLs appear to be the key differential factors in SSD. There was no overlap between the MDD target genes and SSD target genes. Venlafaxine (Efexor™, Effexor™) appears to have a significant effect on the gene expression profile of MDD patients but no significant effect on the gene expression profile of SSD patients. DCEA and DRA revealed no apparent similarities between the differential regulatory processes underlying MDD and SSD. This bioinformatic analysis may provide novel insights that can support future antidepressant R&D efforts.

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

    Science.gov (United States)

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

    2013-09-20

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

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

    Science.gov (United States)

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-09-30

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

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Kai Shi

    2015-04-01

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

  3. Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression

    Directory of Open Access Journals (Sweden)

    Derow Catherine K

    2010-05-01

    algorithm is effective in identifying relatively independent co-expression modules from gene co-expression networks and the module-based approach illustrated in this study provides a robust, interpretable, and mechanistic characterization of transcriptional changes.

  4. An integrative approach predicted co-expression sub-networks regulating properties of stem cells and differentiation.

    Science.gov (United States)

    Sahu, Mousumi; Mallick, Bibekanand

    2016-10-01

    The differentiation of human Embryonic Stem Cells (hESCs) is accompanied by the formation of different intermediary cells, gradually losing its stemness and acquiring differentiation. The precise mechanisms underlying hESCs integrity and its differentiation into fibroblast (Fib) are still elusive. Here, we aimed to assess important genes and co-expression sub-networks responsible for stemness, early differentiation of hESCs into embryoid bodies (EBs) and its lineage specification into Fibs. To achieve this, we compared transcriptional profiles of hESCs-EBs and EBs-Fibs and obtained differentially expressed genes (DEGs) exclusive to hESCs-EBs (early differentiation), EBs-Fibs (late differentiation) and common DEGs in hESCs-EBs and EBs-Fibs. Then, we performed gene set enrichment analysis (GSEA) followed by overrepresentation study and identified key genes for each gene category. The regulations of these genes were studied by integrating ChIP-Seq data of core transcription factors (TFs) and histone methylation marks in hESCs. Finally, we identified co-expression sub-networks from key genes of each gene category using k-clique sub-network extraction method. Our study predicted seven genes edicting core stemness properties forming a co-expression network. From the pathway analysis of sub-networks of hESCs-EBs, we hypothesize that FGF2 is contributing to pluripotent transcription network of hESCs in association with DNMT3B and JARID2 thereby facilitating cell proliferation. On the contrary, FGF2 is found to promote cell migration in Fibs along with DDR2, CAV1, DAB2, and PARVA. Moreover, our study identified three k-clique sub-networks regulating TGF-β signaling pathway thereby promoting EBs to Fibs differentiation by: (i) modulating extracellular matrix involving ITGB1, TGFB1I1 and GBP1, (ii) regulating cell cycle remodeling involving CDKN1A, JUNB and DUSP1 and (iii) helping in epithelial to mesenchymal transition (EMT) involving THBS1, INHBA and LOX. This study put

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

    Directory of Open Access Journals (Sweden)

    Kathleen Oros Klein

    2016-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Zhang Weixiong

    2010-10-01

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

  7. Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to ‘Candidatus Liberibacter asiaticus’ infection

    Science.gov (United States)

    2013-01-01

    Background Huanglongbing (HLB) is arguably the most destructive disease for the citrus industry. HLB is caused by infection of the bacterium, Candidatus Liberibacter spp. Several citrus GeneChip studies have revealed thousands of genes that are up- or down-regulated by infection with Ca. Liberibacter asiaticus. However, whether and how these host genes act to protect against HLB remains poorly understood. Results As a first step towards a mechanistic view of citrus in response to the HLB bacterial infection, we performed a comparative transcriptome analysis and found that a total of 21 Probesets are commonly up-regulated by the HLB bacterial infection. In addition, a number of genes are likely regulated specifically at early, late or very late stages of the infection. Furthermore, using Pearson correlation coefficient-based gene coexpression analysis, we constructed a citrus HLB response network consisting of 3,507 Probesets and 56,287 interactions. Genes involved in carbohydrate and nitrogen metabolic processes, transport, defense, signaling and hormone response were overrepresented in the HLB response network and the subnetworks for these processes were constructed. Analysis of the defense and hormone response subnetworks indicates that hormone response is interconnected with defense response. In addition, mapping the commonly up-regulated HLB responsive genes into the HLB response network resulted in a core subnetwork where transport plays a key role in the citrus response to the HLB bacterial infection. Moreover, analysis of a phloem protein subnetwork indicates a role for this protein and zinc transporters or zinc-binding proteins in the citrus HLB defense response. Conclusion Through integrating transcriptome comparison and gene coexpression network analysis, we have provided for the first time a systems view of citrus in response to the Ca. Liberibacter spp. infection causing HLB. PMID:23324561

  8. Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to ‘Candidatus Liberibacter asiaticus’ infection

    Directory of Open Access Journals (Sweden)

    Zheng Zhi-Liang

    2013-01-01

    Full Text Available Abstract Background Huanglongbing (HLB is arguably the most destructive disease for the citrus industry. HLB is caused by infection of the bacterium, Candidatus Liberibacter spp. Several citrus GeneChip studies have revealed thousands of genes that are up- or down-regulated by infection with Ca. Liberibacter asiaticus. However, whether and how these host genes act to protect against HLB remains poorly understood. Results As a first step towards a mechanistic view of citrus in response to the HLB bacterial infection, we performed a comparative transcriptome analysis and found that a total of 21 Probesets are commonly up-regulated by the HLB bacterial infection. In addition, a number of genes are likely regulated specifically at early, late or very late stages of the infection. Furthermore, using Pearson correlation coefficient-based gene coexpression analysis, we constructed a citrus HLB response network consisting of 3,507 Probesets and 56,287 interactions. Genes involved in carbohydrate and nitrogen metabolic processes, transport, defense, signaling and hormone response were overrepresented in the HLB response network and the subnetworks for these processes were constructed. Analysis of the defense and hormone response subnetworks indicates that hormone response is interconnected with defense response. In addition, mapping the commonly up-regulated HLB responsive genes into the HLB response network resulted in a core subnetwork where transport plays a key role in the citrus response to the HLB bacterial infection. Moreover, analysis of a phloem protein subnetwork indicates a role for this protein and zinc transporters or zinc-binding proteins in the citrus HLB defense response. Conclusion Through integrating transcriptome comparison and gene coexpression network analysis, we have provided for the first time a systems view of citrus in response to the Ca. Liberibacter spp. infection causing HLB.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

  12. A Systems Approach Implicates a Brain Mitochondrial Oxidative Homeostasis Co-expression Network in Genetic Vulnerability to Alcohol Withdrawal

    Science.gov (United States)

    Walter, Nicole A. R.; Denmark, DeAunne L.; Kozell, Laura B.; Buck, Kari J.

    2017-01-01

    Genetic factors significantly affect vulnerability to alcohol dependence (alcoholism). We previously identified quantitative trait loci on distal mouse chromosome 1 with large effects on predisposition to alcohol physiological dependence and associated withdrawal following both chronic and acute alcohol exposure in mice (Alcdp1 and Alcw1, respectively). We fine-mapped these loci to a 1.1–1.7 Mb interval syntenic with human 1q23.2-23.3. Alcw1/Alcdp1 interval genes show remarkable genetic variation among mice derived from the C57BL/6J and DBA/2J strains, the two most widely studied genetic animal models for alcohol-related traits. Here, we report the creation of a novel recombinant Alcw1/Alcdp1 congenic model (R2) in which the Alcw1/Alcdp1 interval from a donor C57BL/6J strain is introgressed onto a uniform, inbred DBA/2J genetic background. As expected, R2 mice demonstrate significantly less severe alcohol withdrawal compared to wild-type littermates. Additionally, comparing R2 and background strain animals, as well as reciprocal congenic (R8) and appropriate background strain animals, we assessed Alcw1/Alcdp1 dependent brain gene expression using microarray and quantitative PCR analyses. To our knowledge this includes the first Weighted Gene Co-expression Network Analysis using reciprocal congenic models. Importantly, this allows detection of co-expression patterns limited to one or common to both genetic backgrounds with high or low predisposition to alcohol withdrawal severity. The gene expression patterns (modules) in common contain genes related to oxidative phosphorylation, building upon human and animal model studies that implicate involvement of oxidative phosphorylation in alcohol use disorders (AUDs). Finally, we demonstrate that administration of N-acetylcysteine, an FDA-approved antioxidant, significantly reduces symptoms of alcohol withdrawal (convulsions) in mice, thus validating a phenotypic role for this network. Taken together, these studies

  13. 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...

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

    Directory of Open Access Journals (Sweden)

    Mohammed Mamdani

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

  15. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular networks

    NARCIS (Netherlands)

    Colak, R.; Moser, F.; Shu, J.; Schoenhuth, A.; Chen, N.; Ester, M.

    2010-01-01

    Background Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustive

  16. VSNL1 Co-expression networks in aging include calcium signaling, synaptic plasticity, and Alzheimer’s disease pathways

    Directory of Open Access Journals (Sweden)

    C W Lin

    2015-03-01

    Full Text Available The Visinin-like 1 (VSNL1 gene encodes Visinin-like protein 1, a peripheral biomarker for Alzheimer disease (AD. Little is known, however, about normal VSNL1 expression in brain and the biologic networks in which it participates. Frontal cortex gray matter from 209 subjects without neurodegenerative or psychiatric illness, ranging in age from 16–91, were processed on Affymetrix GeneChip 1.1 ST and Human SNP Array 6.0. VSNL1 expression was unaffected by age and sex, and not significantly associated with SNPs in cis or trans. VSNL1 was significantly co-expressed with genes in pathways for Calcium Signaling, AD, Long Term Potentiation, Long Term Depression, and Trafficking of AMPA Receptors. The association with AD was driven, in part, by correlation with amyloid precursor protein (APP expression. These findings provide an unbiased link between VSNL1 and molecular mechanisms of AD, including pathways implicated in synaptic pathology in AD. Whether APP may drive increased VSNL1 expression, VSNL1 drives increased APP expression, or both are downstream of common pathogenic regulators will need to be evaluated in model systems.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Wang Shan

    2012-08-01

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

  19. Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis.

    Science.gov (United States)

    Cao, Mu-Shui; Liu, Bing-Ya; Dai, Wen-Tao; Zhou, Wei-Xin; Li, Yi-Xue; Li, Yuan-Yuan

    2015-01-01

    Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analysis. We first applied DCEA to a gene expression dataset of gastric normal mucosa, adenoma and carcinoma samples to identify gene interconnection changes during cancer progression, based on which we inferred normal, adenoma, and carcinoma-specific gene regulation networks by using linear regression model. It was observed that cancer genes and drug targets were enriched in each network. To investigate the dynamic changes of gene regulation during carcinogenesis, we then designed two quantitative methods to prioritize differentially regulated genes (DRGs) and gene pairs or links (DRLs) between adjacent stages. It was found that known cancer genes and drug targets are significantly higher ranked. The top 4% normal vs. adenoma DRGs (36 genes) and top 6% adenoma vs. carcinoma DRGs (56 genes) proved to be worthy of further investigation to explore their association with gastric cancer. Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. adenoma and adenoma vs. carcinoma comparisons, 15 have been reported to be gastric cancer or cancer related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of GATA6, ESRRG and their signaling pathways in gastric carcinogenesis. Compared with established approaches which build genome-scale GRNs, or sub-networks around differentially expressed genes, the present one proved to be better at enriching cancer genes and drug targets, and prioritizing

  20. The CesA gene family of barley. Quantitative analysis of transcripts reveals two groups of co-expressed genes.

    Science.gov (United States)

    Burton, Rachel A; Shirley, Neil J; King, Brendon J; Harvey, Andrew J; Fincher, Geoffrey B

    2004-01-01

    Sequence data from cDNA and genomic clones, coupled with analyses of expressed sequence tag databases, indicate that the CesA (cellulose synthase) gene family from barley (Hordeum vulgare) has at least eight members, which are distributed across the genome. Quantitative polymerase chain reaction has been used to determine the relative abundance of mRNA transcripts for individual HvCesA genes in vegetative and floral tissues, at different stages of development. To ensure accurate expression profiling, geometric averaging of multiple internal control gene transcripts has been applied for the normalization of transcript abundance. Total HvCesA mRNA levels are highest in coleoptiles, roots, and stems and much lower in floral tissues, early developing grain, and in the elongation zone of leaves. In most tissues, HvCesA1, HvCesA2, and HvCesA6 predominate, and their relative abundance is very similar; these genes appear to be coordinately transcribed. A second group, comprising HvCesA4, HvCesA7, and HvCesA8, also appears to be coordinately transcribed, most obviously in maturing stem and root tissues. The HvCesA3 expression pattern does not fall into either of these two groups, and HvCesA5 transcript levels are extremely low in all tissues. Thus, the HvCesA genes fall into two general groups of three genes with respect to mRNA abundance, and the co-expression of the groups identifies their products as candidates for the rosettes that are involved in cellulose biosynthesis at the plasma membrane. Phylogenetic analysis allows the two groups of genes to be linked with orthologous Arabidopsis CesA genes that have been implicated in primary and secondary wall synthesis.

  1. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jing; Ma, Zihao; Carr, Steven A.; Mertins, Philipp; Zhang, Hui; Zhang, Zhen; Chan, Daniel W.; Ellis, Matthew J. C.; Townsend, R. Reid; Smith, Richard D.; McDermott, Jason E.; Chen, Xian; Paulovich, Amanda G.; Boja, Emily S.; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Rodland, Karin D.; Liebler, Daniel C.; Zhang, Bing

    2016-11-11

    Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies

  2. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction*

    Science.gov (United States)

    Wang, Jing; Ma, Zihao; Carr, Steven A.; Mertins, Philipp; Zhang, Hui; Zhang, Zhen; Chan, Daniel W.; Ellis, Matthew J. C.; Townsend, R. Reid; Smith, Richard D.; McDermott, Jason E.; Chen, Xian; Paulovich, Amanda G.; Boja, Emily S.; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Rodland, Karin D.; Liebler, Daniel C.; Zhang, Bing

    2017-01-01

    Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies. PMID

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

    Science.gov (United States)

    Tian, Feng; Zhao, Jinlong; Kang, Zhenxing

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Tania Dottorini

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

  6. Revealing effective classifiers through network comparison

    CERN Document Server

    Gallos, Lazaros K

    2014-01-01

    The ability to compare complex systems can provide new insight into the fundamental nature of the processes captured in ways that are otherwise inaccessible to observation. Here, we introduce the $n$-tangle method to directly compare two networks for structural similarity, based on the distribution of edge density in network subgraphs. We demonstrate that this method can efficiently introduce comparative analysis into network science and opens the road for many new applications. For example, we show how the construction of a phylogenetic tree across animal taxa according to their social structure can reveal commonalities in the behavioral ecology of the populations, or how students create similar networks according to the University size. Our method can be expanded to study a multitude of additional properties, such as network classification, changes during time evolution, convergence of growth models, and detection of structural changes during damage.

  7. TF-Cluster: A pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM

    Directory of Open Access Journals (Sweden)

    Thomson James A

    2011-04-01

    Full Text Available Abstract Background Identifying the key transcription factors (TFs controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1 Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2 Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3 A TF can function in different cell types or developmental stages. Currently, the methods available for identifying TFs involved in biological processes are still very scarce, and the development of novel, more powerful methods is desperately needed. Results We developed a computational pipeline called TF-Cluster for identifying functionally coordinated TFs in two steps: (1 Construction of a shared coexpression connectivity matrix (SCCM, in which each entry represents the number of shared coexpressed genes between two TFs. This sparse and symmetric matrix embodies a new concept of coexpression networks in which genes are associated in the context of other shared coexpressed genes; (2 Decomposition of the SCCM using a novel heuristic algorithm termed "Triple-Link", which searches the highest connectivity in the SCCM, and then uses two connected TF as a primer for growing a TF cluster with a number of linking criteria. We applied TF-Cluster to microarray data from human stem cells and Arabidopsis roots, and then demonstrated that many of the resulting TF clusters contain functionally coordinated TFs that, based on existing literature, accurately represent

  8. PLANEX: the plant co-expression database.

    Science.gov (United States)

    Yim, Won Cheol; Yu, Yongbin; Song, Kitae; Jang, Cheol Seong; Lee, Byung-Moo

    2013-05-20

    The PLAnt co-EXpression database (PLANEX) is a new internet-based database for plant gene analysis. PLANEX (http://planex.plantbioinformatics.org) contains publicly available GeneChip data obtained from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI). PLANEX is a genome-wide co-expression database, which allows for the functional identification of genes from a wide variety of experimental designs. It can be used for the characterization of genes for functional identification and analysis of a gene's dependency among other genes. Gene co-expression databases have been developed for other species, but gene co-expression information for plants is currently limited. We constructed PLANEX as a list of co-expressed genes and functional annotations for Arabidopsis thaliana, Glycine max, Hordeum vulgare, Oryza sativa, Solanum lycopersicum, Triticum aestivum, Vitis vinifera and Zea mays. PLANEX reports Pearson's correlation coefficients (PCCs; r-values) that distribute from a gene of interest for a given microarray platform set corresponding to a particular organism. To support PCCs, PLANEX performs an enrichment test of Gene Ontology terms and Cohen's Kappa value to compare functional similarity for all genes in the co-expression database. PLANEX draws a cluster network with co-expressed genes, which is estimated using the k-mean method. To construct PLANEX, a variety of datasets were interpreted by the IBM supercomputer Advanced Interactive eXecutive (AIX) in a supercomputing center. PLANEX provides a correlation database, a cluster network and an interpretation of enrichment test results for eight plant species. A typical co-expressed gene generates lists of co-expression data that contain hundreds of genes of interest for enrichment analysis. Also, co-expressed genes can be identified and cataloged in terms of comparative genomics by using the 'Co-expression gene compare' feature. This type of analysis will help interpret

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

    Directory of Open Access Journals (Sweden)

    Zahra Zinati

    2016-12-01

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

  10. Microarray profiling and co-expression network analysis of circulating lncRNAs and mRNAs associated with major depressive disorder.

    Directory of Open Access Journals (Sweden)

    Zhifen Liu

    Full Text Available LncRNAs, which represent one of the most highly expressed classes of ncRNAs in the brain, are becoming increasingly interesting with regard to brain functions and disorders. However, changes in the expression of regulatory lncRNAs in Major Depressive Disorder (MDD have not yet been reported. Using microarrays, we profiled the expression of 34834 lncRNAs and 39224 mRNAs in peripheral blood sampled from MDD patients as well as demographically-matched controls. Among these, we found that 2007 lncRNAs and 1667 mRNAs were differentially expressed, 17 of which were documented as depression-related gene in previous studies. Gene Ontology (GO and pathway analyses indicated that the biological functions of differentially expressed mRNAs were related to fundamental metabolic processes and neurodevelopment diseases. To investigate the potential regulatory roles of the differentially expressed lncRNAs on the mRNAs, we also constructed co-expression networks composed of the lncRNAs and mRNAs, which shows significant correlated patterns of expression. In the MDD-derived network, there were a greater number of nodes and connections than that in the control-derived network. The lncRNAs located at chr10:874695-874794, chr10:75873456-75873642, and chr3:47048304-47048512 may be important factors regulating the expression of mRNAs as they have previously been reported associations with MDD. This study is the first to explore genome-wide lncRNA expression and co-expression with mRNA patterns in MDD using microarray technology. We identified circulating lncRNAs that are aberrantly expressed in MDD and the results suggest that lncRNAs may contribute to the molecular pathogenesis of MDD.

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

    Science.gov (United States)

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

    2015-08-01

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

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

    Science.gov (United States)

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

    2015-07-28

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

  13. Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network

    Directory of Open Access Journals (Sweden)

    Chamovitz Daniel A

    2009-09-01

    Full Text Available Abstract Background Analyses of gene expression data from microarray experiments has become a central tool for identifying co-regulated, functional gene modules. A crucial aspect of such analysis is the integration of data from different experiments and different laboratories. How to weigh the contribution of different experiments is an important point influencing the final outcomes. We have developed a novel method for this integration, and applied it to genome-wide data from multiple Arabidopsis microarray experiments performed under a variety of experimental conditions. The goal of this study is to identify functional globally co-regulated gene modules in the Arabidopsis genome. Results Following the analysis of 21,000 Arabidopsis genes in 43 datasets and about 2 × 108 gene pairs, we identified a globally co-expressed gene network. We found clusters of globally co-expressed Arabidopsis genes that are enriched for known Gene Ontology annotations. Two types of modules were identified in the regulatory network that differed in their sensitivity to the node-scoring parameter; we further showed these two pertain to general and specialized modules. Some of these modules were further investigated using the Genevestigator compendium of microarray experiments. Analyses of smaller subsets of data lead to the identification of condition-specific modules. Conclusion Our method for identification of gene clusters allows the integration of diverse microarray experiments from many sources. The analysis reveals that part of the Arabidopsis transcriptome is globally co-expressed, and can be further divided into known as well as novel functional gene modules. Our methodology is general enough to apply to any set of microarray experiments, using any scoring function.

  14. Identification of estrogen receptor dimer selective ligands reveals growth-inhibitory effects on cells that co-express ERα and ERβ.

    Directory of Open Access Journals (Sweden)

    Emily Powell

    Full Text Available Estrogens play essential roles in the progression of mammary and prostatic diseases. The transcriptional effects of estrogens are transduced by two estrogen receptors, ERα and ERβ, which elicit opposing roles in regulating proliferation: ERα is proliferative while ERβ is anti-proliferative. Exogenous expression of ERβ in ERα-positive cancer cell lines inhibits cell proliferation in response to estrogen and reduces xenografted tumor growth in vivo, suggesting that ERβ might oppose ERα's proliferative effects via formation of ERα/β heterodimers. Despite biochemical and cellular evidence of ERα/β heterodimer formation in cells co-expressing both receptors, the biological roles of the ERα/β heterodimer remain to be elucidated. Here we report the identification of two phytoestrogens that selectively activate ERα/β heterodimers at specific concentrations using a cell-based, two-step high throughput small molecule screen for ER transcriptional activity and ER dimer selectivity. Using ERα/β heterodimer-selective ligands at defined concentrations, we demonstrate that ERα/β heterodimers are growth inhibitory in breast and prostate cells which co-express the two ER isoforms. Furthermore, using Automated Quantitative Analysis (AQUA to examine nuclear expression of ERα and ERβ in human breast tissue microarrays, we demonstrate that ERα and ERβ are co-expressed in the same cells in breast tumors. The co-expression of ERα and ERβ in the same cells supports the possibility of ERα/β heterodimer formation at physio- and pathological conditions, further suggesting that targeting ERα/β heterodimers might be a novel therapeutic approach to the treatment of cancers which co-express ERα and ERβ.

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

    Science.gov (United States)

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

    2017-05-02

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

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

    Science.gov (United States)

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

    2016-12-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  18. Discovering functional modules across diverse maize transcriptomes using COB, the Co-expression Browser.

    Science.gov (United States)

    Schaefer, Robert J; Briskine, Roman; Springer, Nathan M; Myers, Chad L

    2014-01-01

    Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB), which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks.

  19. Discovering functional modules across diverse maize transcriptomes using COB, the Co-expression Browser.

    Directory of Open Access Journals (Sweden)

    Robert J Schaefer

    Full Text Available Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB, which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks.

  20. Revealing the Hidden Language of Complex Networks

    Science.gov (United States)

    Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Davis, Darren; Levnajic, Zoran; Janjic, Vuk; Karapandza, Rasa; Stojmirovic, Aleksandar; Pržulj, Nataša

    2014-04-01

    Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.

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

    Science.gov (United States)

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

    2012-01-01

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

  2. Discriminative topological features reveal biological network mechanisms

    Directory of Open Access Journals (Sweden)

    Levovitz Chaya

    2004-11-01

    Full Text Available Abstract Background Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. Results We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model. Conclusions Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.

  3. Revealing Hidden Connections in Recommendation Networks

    CERN Document Server

    Minhano, Rogerio; Kamienski, Carlos

    2016-01-01

    Companies have been increasingly seeking new mechanisms for making their electronic marketing campaigns to become viral, thus obtaining a cascading recommendation effect similar to word-of-mouth. We analysed a dataset of a magazine publisher that uses email as the main marketing strategy and found out that networks emerging from those campaigns form a very sparse graph. We show that online social networks can be effectively used as a means to expand recommendation networks. Starting from a set of users, called seeders, we crawled Google's Orkut and collected about 20 million users and 80 million relationships. Next, we extended the original recommendation network by adding new edges using Orkut relationships that built a much denser network. Therefore, we advocate that online social networks are much more effective than email-based marketing campaigns

  4. Revealing networks from dynamics: an introduction

    CERN Document Server

    Timme, Marc

    2014-01-01

    What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.

  5. Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle.

    Science.gov (United States)

    Cánovas, Angela; Reverter, Antonio; DeAtley, Kasey L; Ashley, Ryan L; Colgrave, Michelle L; Fortes, Marina R S; Islas-Trejo, Alma; Lehnert, Sigrid; Porto-Neto, Laercio; Rincón, Gonzalo; Silver, Gail A; Snelling, Warren M; Medrano, Juan F; Thomas, Milton G

    2014-01-01

    Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics

  6. Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle.

    Directory of Open Access Journals (Sweden)

    Angela Cánovas

    Full Text Available Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver. These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL, first service conception (FSC, and heifer pregnancy (HPG. In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS, RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes. Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP associated with ACL, FSC, and (or HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.. Results from these multi

  7. Network Physiology reveals relations between network topology and physiological function

    CERN Document Server

    Bashan, Amir; Kantelhardt, Jan W; Havlin, Shlomo; Ivanov, Plamen Ch; 10.1038/ncomms1705

    2012-01-01

    The human organism is an integrated network where complex physiologic systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. Identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge. Here, we develop a framework to probe interactions among diverse systems, and we identify a physiologic network. We find that each physiologic state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function. Across physiologic states the network undergoes topological transitions associated with fast reorganization of physiologic interactions on time scales of a few minutes, indicating high network flexibility in response to perturbations. The proposed system-wide integrative approach may facilitate the development of a new field, Network Physiology.

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

    Science.gov (United States)

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

    2016-11-01

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

  9. Network physiology reveals relations between network topology and physiological function

    OpenAIRE

    Bashan, Amir; Bartsch, Ronny P.; Kantelhardt, Jan W.; Havlin, Shlomo; Ivanov, Plamen Ch.

    2012-01-01

    The human organism is an integrated network where complex physiological systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. Identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge. Here we develop a framework to probe interactions among diverse systems, and we identify a physiological network. We find that each physiological state is...

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

    Directory of Open Access Journals (Sweden)

    Mario Huerta

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Alice Mühlroth

    2013-11-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Horvath Steve

    2010-01-01

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

  14. Brain rhythms reveal a hierarchical network organization.

    Directory of Open Access Journals (Sweden)

    G Karl Steinke

    2011-10-01

    Full Text Available Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or "virtual brains", whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic, in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states

  15. Revealing how network structure affects accuracy of link prediction

    Science.gov (United States)

    Yang, Jin-Xuan; Zhang, Xiao-Dong

    2017-08-01

    Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.

  16. Characterization of genome-wide enhancer-promoter interactions reveals co-expression of interacting genes and modes of higher order chromatin organization

    Institute of Scientific and Technical Information of China (English)

    Iouri Chepelev; Gang Wei; Dara Wangsa; Qingsong Tang; Keji Zhao

    2012-01-01

    Recent epigenomic studies have predicted thousands of potential enhancers in the human genome.However,there has not been systematic characterization of target promoters for these potential enhancers.Using H3K4me2 as a mark for active enhancers,we identified genome-wide EP interactions in human CD4+ T cells.Among the 6 520 longdistance chromatin interactions,we identify 2 067 enhancers that interact with 1 619 promoters and enhance their expression.These enhancers exist in accessible chromatin regions and are associated with various histone modifications and polymerase Ⅱ binding.The promoters with interacting enhancers are expressed at higher levels than those without interacting enhancers,and their expression levels are positively correlated with the number of interacting enhancers.Interestingly,interacting promoters are co-expressed in a tissue-specific manner.We also find that chromosomes are organized into multiple levels of interacting domains.Our results define a global view of EP interactions and provide a data set to further understand mechanisms of enhancer targeting and long-range chromatin organization.The Gene Expression Omnibus accession number for the raw and analyzed chromatin interaction data is GSE32677.

  17. Integrating phosphorylation network with transcriptional network reveals novel functional relationships.

    Directory of Open Access Journals (Sweden)

    Lin Wang

    Full Text Available Phosphorylation and transcriptional regulation events are critical for cells to transmit and respond to signals. In spite of its importance, systems-level strategies that couple these two networks have yet to be presented. Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets. Based on this understanding, we introduce a HeRS score (hetero-regulatory similarity score to systematically characterize the functional relevance of kinase/phosphatase involvement with transcription factor, and present an algorithm that predicts hetero-regulatory modules. When extended to signaling network, this approach confirmed the structure and cross talk of MAPK pathways, inferred a novel functional transcription factor Sok2 in high osmolarity glycerol pathway, and explained the mechanism of reduced mating efficiency upon Fus3 deletion. This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors.

  18. Resting-state brain organization revealed by functional covariance networks.

    Directory of Open Access Journals (Sweden)

    Zhiqiang Zhang

    Full Text Available BACKGROUND: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN and structural covariance network (SCN have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. METHODOLOGY AND PRINCIPAL FINDINGS: We proposed a functional covariance network (FCN method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. CONCLUSION: The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.

  19. Maps of random walks on complex networks reveal community structure.

    Science.gov (United States)

    Rosvall, Martin; Bergstrom, Carl T

    2008-01-29

    To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.

  20. Revealing catastrophic failure of leaf networks under stress.

    Science.gov (United States)

    Brodribb, Timothy J; Bienaimé, Diane; Marmottant, Philippe

    2016-04-26

    The intricate patterns of veins that adorn the leaves of land plants are among the most important networks in biology. Water flows in these leaf irrigation networks under tension and is vulnerable to embolism-forming cavitations, which cut off water supply, ultimately causing leaf death. Understanding the ways in which plants structure their vein supply network to protect against embolism-induced failure has enormous ecological and evolutionary implications, but until now there has been no way of observing dynamic failure in natural leaf networks. Here we use a new optical method that allows the initiation and spread of embolism bubbles in the leaf network to be visualized. Examining embolism-induced failure of architecturally diverse leaf networks, we found that conservative rules described the progression of hydraulic failure within veins. The most fundamental rule was that within an individual venation network, susceptibility to embolism always increased proportionally with the size of veins, and initial nucleation always occurred in the largest vein. Beyond this general framework, considerable diversity in the pattern of network failure was found between species, related to differences in vein network topology. The highest-risk network was found in a fern species, where single events caused massive disruption to leaf water supply, whereas safer networks in angiosperm leaves contained veins with composite properties, allowing a staged failure of water supply. These results reveal how the size structure of leaf venation is a critical determinant of the spread of embolism damage to leaves during drought.

  1. Revealing physical interaction networks from statistics of collective dynamics

    Science.gov (United States)

    Nitzan, Mor; Casadiego, Jose; Timme, Marc

    2017-01-01

    Revealing physical interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Current reconstruction methods require access to a system’s model or dynamical data at a level of detail often not available. We exploit changes in invariant measures, in particular distributions of sampled states of the system in response to driving signals, and use compressed sensing to reveal physical interaction networks. Dynamical observations following driving suffice to infer physical connectivity even if they are temporally disordered, are acquired at large sampling intervals, and stem from different experiments. Testing various nonlinear dynamic processes emerging on artificial and real network topologies indicates high reconstruction quality for existence as well as type of interactions. These results advance our ability to reveal physical interaction networks in complex synthetic and natural systems. PMID:28246630

  2. Phenotyping of human melanoma cells reveals a unique composition of receptor targets and a subpopulation co-expressing ErbB4, EPO-R and NGF-R.

    Science.gov (United States)

    Mirkina, Irina; Hadzijusufovic, Emir; Krepler, Clemens; Mikula, Mario; Mechtcheriakova, Diana; Strommer, Sabine; Stella, Alexander; Jensen-Jarolim, Erika; Höller, Christoph; Wacheck, Volker; Pehamberger, Hubert; Valent, Peter

    2014-01-01

    Malignant melanoma is a life-threatening skin cancer increasingly diagnosed in the western world. In advanced disease the prognosis is grave. Growth and metastasis formation in melanomas are regulated by a network of cytokines, cytokine-receptors, and adhesion molecules. However, little is known about surface antigens and target expression profiles in human melanomas. We examined the cell surface antigen profile of human skin melanoma cells by multicolor flow cytometry, and compared their phenotype with 4 melanoma cell lines (A375, 607B, Mel-Juso, SK-Mel28). Melanoma cells were defined as CD45-/CD31- cells co-expressing one or more melanoma-related antigens (CD63, CD146, CD166). In most patients, melanoma cells exhibited ErbB3/Her3, CD44/Pgp-1, ICAM-1/CD54 and IGF-1-R/CD221, but did not express CD20, ErbB2/Her2, KIT/CD117, AC133/CD133 or MDR-1/CD243. Melanoma cell lines were found to display a similar phenotype. In most patients, a distinct subpopulation of melanoma cells (4-40%) expressed the erythropoietin receptor (EPO-R) and ErbB4 together with PD-1 and NGF-R/CD271. Both the EPO-R+ and EPO-R- subpopulations produced melanoma lesions in NOD/SCID IL-2Rgamma(null) (NSG) mice in first and secondary recipients. Normal skin melanocytes did not express ErbB4 or EPO-R, but expressed a functional KIT receptor (CD117) as well as NGF-R, ErbB3/Her3, IGF-1-R and CD44. In conclusion, melanoma cells display a unique composition of surface target antigens and cytokine receptors. Malignant transformation of melanomas is accompanied by loss of KIT and acquisition of EPO-R and ErbB4, both of which are co-expressed with NGF-R and PD-1 in distinct subfractions of melanoma cells. However, expression of EPO-R/ErbB4/PD-1 is not indicative of a selective melanoma-initiating potential.

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

    Directory of Open Access Journals (Sweden)

    Henry D Priest

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

  4. Maximal Neighbor Similarity Reveals Real Communities in Networks

    Science.gov (United States)

    Žalik, Krista Rizman

    2015-12-01

    An important problem in the analysis of network data is the detection of groups of densely interconnected nodes also called modules or communities. Community structure reveals functions and organizations of networks. Currently used algorithms for community detection in large-scale real-world networks are computationally expensive or require a priori information such as the number or sizes of communities or are not able to give the same resulting partition in multiple runs. In this paper we investigate a simple and fast algorithm that uses the network structure alone and requires neither optimization of pre-defined objective function nor information about number of communities. We propose a bottom up community detection algorithm in which starting from communities consisting of adjacent pairs of nodes and their maximal similar neighbors we find real communities. We show that the overall advantage of the proposed algorithm compared to the other community detection algorithms is its simple nature, low computational cost and its very high accuracy in detection communities of different sizes also in networks with blurred modularity structure consisting of poorly separated communities. All communities identified by the proposed method for facebook network and E-Coli transcriptional regulatory network have strong structural and functional coherence.

  5. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

    Directory of Open Access Journals (Sweden)

    Hyun-Seob Song

    2015-03-01

    Full Text Available Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

  6. GeneCAT--novel webtools that combine BLAST and co-expression analyses

    DEFF Research Database (Denmark)

    Mutwil, Marek; Obro, Jens; Willats, William G T

    2008-01-01

    The gene co-expression analysis toolbox (GeneCAT) introduces several novel microarray data analyzing tools. First, the multigene co-expression analysis, combined with co-expressed gene networks, provides a more powerful data mining technique than standard, single-gene co-expression analysis. Second......, the high-throughput Map-O-Matic tool matches co-expression pattern of multiple query genes to genes present in user-defined subdatabases, and can therefore be used for gene mapping in forward genetic screens. Third, Rosetta combines co-expression analysis with BLAST and can be used to find 'true' gene...... orthologs in the plant model organisms Arabidopsis thaliana and Hordeum vulgare (Barley). GeneCAT is equipped with expression data for the model plant A. thaliana, and first to introduce co-expression mining tools for the monocot Barley. GeneCAT is available at http://genecat.mpg.de....

  7. 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.

  8. A methodology for the analysis of differential coexpression across the human lifespan

    Directory of Open Access Journals (Sweden)

    Gillis Jesse

    2009-09-01

    Full Text Available Abstract Background Differential coexpression is a change in coexpression between genes that may reflect 'rewiring' of transcriptional networks. It has previously been hypothesized that such changes might be occurring over time in the lifespan of an organism. While both coexpression and differential expression of genes have been previously studied in life stage change or aging, differential coexpression has not. Generalizing differential coexpression analysis to many time points presents a methodological challenge. Here we introduce a method for analyzing changes in coexpression across multiple ordered groups (e.g., over time and extensively test its validity and usefulness. Results Our method is based on the use of the Haar basis set to efficiently represent changes in coexpression at multiple time scales, and thus represents a principled and generalizable extension of the idea of differential coexpression to life stage data. We used published microarray studies categorized by age to test the methodology. We validated the methodology by testing our ability to reconstruct Gene Ontology (GO categories using our measure of differential coexpression and compared this result to using coexpression alone. Our method allows significant improvement in characterizing these groups of genes. Further, we examine the statistical properties of our measure of differential coexpression and establish that the results are significant both statistically and by an improvement in semantic similarity. In addition, we found that our method finds more significant changes in gene relationships compared to several other methods of expressing temporal relationships between genes, such as coexpression over time. Conclusion Differential coexpression over age generates significant and biologically relevant information about the genes producing it. Our Haar basis methodology for determining age-related differential coexpression performs better than other tested methods. The

  9. Revealing the structure of the world airline network

    CERN Document Server

    Verma, Trivik; Herrmann, Hans J

    2014-01-01

    Resilience of most critical infrastructures against failure of elements that appear insignificant is usually taken for granted. The World Airline Network (WAN) is an infrastructure that reduces the geographical gap between societies, both small and large, and brings forth economic gains. With the extensive use of a publicly maintained data set that contains information about airports and alternative connections between these airports, we empirically reveal that the WAN is a redundant and resilient network for long distance air travel, but otherwise breaks down completely due to removal of short and apparently insignificant connections. These short range connections with moderate number of passengers and alternate flights are the connections that keep remote parts of the world accessible. It is surprising, insofar as there exists a highly resilient and strongly connected core consisting of a small fraction of airports (around 2.3%) together with an extremely fragile star-like periphery. Yet, in spite of their ...

  10. A Network Based Methodology to Reveal Patterns in Knowledge Transfer

    Directory of Open Access Journals (Sweden)

    Orlando López-Cruz

    2015-12-01

    Full Text Available This paper motivates, presents and demonstrates in use a methodology based in complex network analysis to support research aimed at identification of sources in the process of knowledge transfer at the interorganizational level. The importance of this methodology is that it states a unified model to reveal knowledge sharing patterns and to compare results from multiple researches on data from different periods of time and different sectors of the economy. This methodology does not address the underlying statistical processes. To do this, national statistics departments (NSD provide documents and tools at their websites. But this proposal provides a guide to model information inferences gathered from data processing revealing links between sources and recipients of knowledge being transferred and that the recipient detects as main source to new knowledge creation. Some national statistics departments set as objective for these surveys the characterization of innovation dynamics in firms and to analyze the use of public support instruments. From this characterization scholars conduct different researches. Measures of dimensions of the network composed by manufacturing firms and other organizations conform the base to inquiry the structure that emerges from taking ideas from other organizations to incept innovations. These two sets of data are actors of a two- mode-network. The link between two actors (network nodes, one acting as the source of the idea. The second one acting as the destination comes from organizations or events organized by organizations that “provide” ideas to other group of firms. The resulting demonstrated design satisfies the objective of being a methodological model to identify sources in knowledge transfer of knowledge effectively used in innovation.

  11. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks.

    Science.gov (United States)

    Krumholz, Elias W; Libourel, Igor G L

    2015-07-31

    Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

  12. Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs

    Directory of Open Access Journals (Sweden)

    Ye Zhi-Qiang

    2011-08-01

    Full Text Available Abstract Background Differential coexpression analysis (DCEA is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance. Results We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links. Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis. Conclusions This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.

  13. Transcriptome and network analyses in Saccharomyces cerevisiae reveal that amphotericin B and lactoferrin synergy disrupt metal homeostasis and stress response

    Science.gov (United States)

    Pang, Chi Nam Ignatius; Lai, Yu-Wen; Campbell, Leona T.; Chen, Sharon C.-A.; Carter, Dee A.; Wilkins, Marc R.

    2017-01-01

    Invasive fungal infections are difficult to treat. The few available antifungal drugs have problems with toxicity or efficacy, and resistance is increasing. To overcome these challenges, existing therapies may be enhanced by synergistic combination with another agent. Previously, we found amphotericin B (AMB) and the iron chelator, lactoferrin (LF), were synergistic against a range of different fungal pathogens. This study investigates the mechanism of AMB-LF synergy, using RNA-seq and network analyses. AMB treatment resulted in increased expression of genes involved in iron homeostasis and ATP synthesis. Unexpectedly, AMB-LF treatment did not lead to increased expression of iron and zinc homeostasis genes. However, genes involved in adaptive response to zinc deficiency and oxidative stress had decreased expression. The clustering of co-expressed genes and network analysis revealed that many iron and zinc homeostasis genes are targets of transcription factors Aft1p and Zap1p. The aft1Δ and zap1Δ mutants were hypersensitive to AMB and H2O2, suggesting they are key regulators of the drug response. Mechanistically, AMB-LF synergy could involve AMB affecting the integrity of the cell wall and membrane, permitting LF to disrupt intracellular processes. We suggest that Zap1p- and Aft1p-binding molecules could be combined with existing antifungals to serve as synergistic treatments. PMID:28079179

  14. Transcriptome profiling of taproot reveals complex regulatory networks during taproot thickening in radish (Raphanus sativus L.

    Directory of Open Access Journals (Sweden)

    Rugang Yu

    2016-08-01

    Full Text Available Radish (Raphanus sativus L., is one of the most important vegetable crops worldwide. Taproot thickening represents a critical developmental period that determines yield and quality in radish life cycle. To isolate differentially expressed genes (DGEs involved in radish taproot thickening process and explored the molecular mechanism in underlying taproot development, three cDNA libraries from radish taproot collected at pre-cortex splitting stage (L1, cortex splitting stage (L2 and expanding stage (L3 were constructed and sequenced by RNA-Seq technology. More than seven million clean reads were obtained from the three libraries, respectively, from which 4,717,617 (L1, 65.35%, 4,809,588 (L2, 68.24% and 4,973,745 (L3, 69.45% reads were matched to the radish reference genes. A total of 85,939 transcripts were generated from three libraries, from which 10,450, 12,325 and 7,392 differentially expressed transcripts (DETs were detected in L1 vs. L2, L1 vs. L3, and L2 vs. L3 comparisons, respectively. Gene Ontology and pathway analysis showed that many DEGs, including EXPA9, Cyclin, CaM, Syntaxin, MADS-box, SAUR and CalS were involved in cell events, cell wall modification, regulation of plant hormone levels, signal transduction and metabolisms, which may relate to taproot thickening. Furthermore, the integrated analysis of mRNA-miRNA revealed that 43 miRNAs and 92 genes that formed 114 miRNA-target mRNA pairs were co-expressed, and three miRNA-target regulatory networks of taproot were constructed from different libraries. Finally, the expression patterns of 16 selected genes were confirmed using RT-qPCR analysis. A hypothetical model of genetic regulatory network associated with taproot thickening in radish was put forward. The taproot formation of radish is mainly contributed to cell differentiation, division and expansion, which are regulated and promoted by certain specific signal transduction pathways and metabolism possesses. These results could

  15. Inheritance Patterns in Citation Networks Reveal Scientific Memes

    Directory of Open Access Journals (Sweden)

    Tobias Kuhn

    2014-11-01

    Full Text Available Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

  16. Inheritance Patterns in Citation Networks Reveal Scientific Memes

    Science.gov (United States)

    Kuhn, Tobias; Perc, Matjaž; Helbing, Dirk

    2014-10-01

    Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

  17. Revealing the structure of the world airline network.

    Science.gov (United States)

    Verma, T; Araújo, N A M; Herrmann, H J

    2014-07-09

    Resilience of most critical infrastructures against failure of elements that appear insignificant is usually taken for granted. The World Airline Network (WAN) is an infrastructure that reduces the geographical gap between societies, both small and large, and brings forth economic gains. With the extensive use of a publicly maintained data set that contains information about airports and alternative connections between these airports, we empirically reveal that the WAN is a redundant and resilient network for long distance air travel, but otherwise breaks down completely due to removal of short and apparently insignificant connections. These short range connections with moderate number of passengers and alternate flights are the connections that keep remote parts of the world accessible. It is surprising, insofar as there exists a highly resilient and strongly connected core consisting of a small fraction of airports (around 2.3%) together with an extremely fragile star-like periphery. Yet, in spite of their relevance, more than 90% of the world airports are still interconnected upon removal of this core. With standard and unconventional removal measures we compare both empirical and topological perceptions for the fragmentation of the world. We identify how the WAN is organized into different classes of clusters based on the physical proximity of airports and analyze the consequence of this fragmentation.

  18. Metagenomics reveals flavour metabolic network of cereal vinegar microbiota.

    Science.gov (United States)

    Wu, Lin-Huan; Lu, Zhen-Ming; Zhang, Xiao-Juan; Wang, Zong-Min; Yu, Yong-Jian; Shi, Jin-Song; Xu, Zheng-Hong

    2017-04-01

    Multispecies microbial community formed through centuries of repeated batch acetic acid fermentation (AAF) is crucial for the flavour quality of traditional vinegar produced from cereals. However, the metabolism to generate and/or formulate the essential flavours by the multispecies microbial community is hardly understood. Here we used metagenomic approach to clarify in situ metabolic network of key microbes responsible for flavour synthesis of a typical cereal vinegar, Zhenjiang aromatic vinegar, produced by solid-state fermentation. First, we identified 3 organic acids, 7 amino acids, and 20 volatiles as dominant vinegar metabolites. Second, we revealed taxonomic and functional composition of the microbiota by metagenomic shotgun sequencing. A total of 86 201 predicted protein-coding genes from 35 phyla (951 genera) were involved in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of Metabolism (42.3%), Genetic Information Processing (28.3%), and Environmental Information Processing (10.1%). Furthermore, a metabolic network for substrate breakdown and dominant flavour formation in vinegar microbiota was constructed, and microbial distribution discrepancy in different metabolic pathways was charted. This study helps elucidating different metabolic roles of microbes during flavour formation in vinegar microbiota.

  19. Revealing the structure of the world airline network

    Science.gov (United States)

    Verma, T.; Araújo, N. A. M.; Herrmann, H. J.

    2014-07-01

    Resilience of most critical infrastructures against failure of elements that appear insignificant is usually taken for granted. The World Airline Network (WAN) is an infrastructure that reduces the geographical gap between societies, both small and large, and brings forth economic gains. With the extensive use of a publicly maintained data set that contains information about airports and alternative connections between these airports, we empirically reveal that the WAN is a redundant and resilient network for long distance air travel, but otherwise breaks down completely due to removal of short and apparently insignificant connections. These short range connections with moderate number of passengers and alternate flights are the connections that keep remote parts of the world accessible. It is surprising, insofar as there exists a highly resilient and strongly connected core consisting of a small fraction of airports (around 2.3%) together with an extremely fragile star-like periphery. Yet, in spite of their relevance, more than 90% of the world airports are still interconnected upon removal of this core. With standard and unconventional removal measures we compare both empirical and topological perceptions for the fragmentation of the world. We identify how the WAN is organized into different classes of clusters based on the physical proximity of airports and analyze the consequence of this fragmentation.

  20. An extensive (co-expression analysis tool for the cytochrome P450 superfamily in Arabidopsis thaliana

    Directory of Open Access Journals (Sweden)

    Provart Nicholas J

    2008-04-01

    Full Text Available Abstract Background Sequencing of the first plant genomes has revealed that cytochromes P450 have evolved to become the largest family of enzymes in secondary metabolism. The proportion of P450 enzymes with characterized biochemical function(s is however very small. If P450 diversification mirrors evolution of chemical diversity, this points to an unexpectedly poor understanding of plant metabolism. We assumed that extensive analysis of gene expression might guide towards the function of P450 enzymes, and highlight overlooked aspects of plant metabolism. Results We have created a comprehensive database, 'CYPedia', describing P450 gene expression in four data sets: organs and tissues, stress response, hormone response, and mutants of Arabidopsis thaliana, based on public Affymetrix ATH1 microarray expression data. P450 expression was then combined with the expression of 4,130 re-annotated genes, predicted to act in plant metabolism, for co-expression analyses. Based on the annotation of co-expressed genes from diverse pathway annotation databases, co-expressed pathways were identified. Predictions were validated for most P450s with known functions. As examples, co-expression results for P450s related to plastidial functions/photosynthesis, and to phenylpropanoid, triterpenoid and jasmonate metabolism are highlighted here. Conclusion The large scale hypothesis generation tools presented here provide leads to new pathways, unexpected functions, and regulatory networks for many P450s in plant metabolism. These can now be exploited by the community to validate the proposed functions experimentally using reverse genetics, biochemistry, and metabolic profiling.

  1. Co-expression Analysis Identifies CRC and AP1 the Regulator of Arabidopsis Fatty Acid Biosynthesis

    Institute of Scientific and Technical Information of China (English)

    Xinxin Han; Linlin Yin; Hongwei Xue

    2012-01-01

    Fatty acids (FAs) play crucial rules in signal transduction and plant development,however,the regulation of FA metabolism is still poorly understood.To study the relevant regulatory network,fifty-eight FA biosynthesis genes including de novo synthases,desaturases and elongases were selected as "guide genes" to construct the co-expression network.Calculation of the correlation between all Arabidopsis thaliana (L.) genes with each guide gene by Arabidopsis co-expression dating mining tools (ACT)identifies 797 candidate FA-correlated genes.Gene ontology (GO) analysis of these co-expressed genes showed they are tightly correlated to photosynthesis and carbohydrate metabolism,and function in many processes.Interestingly,63 transcription factors (TFs) were identified as candidate FA biosynthesis regulators and 8 TF families are enriched.Two TF genes,CRC and AP1,both correlating with 8 FA guide genes,were further characterized.Analyses of the ap1 and crc mutant showed the altered total FA composition of mature seeds.The contents of palmitoleic acid,stearic acid,arachidic acid and eicosadienoic acid are decreased,whereas that of oleic acid is increased in ap1 and crc seeds,which is consistent with the qRT-PCR analysis revealing the suppressed expression of the corresponding guide genes.In addition,yeast one-hybrid analysis and electrophoretic mobility shift assay (EMSA) revealed that CRC can bind to the promoter regions of KCS7 and KCS15,indicating that CRC may directly regulate FA biosynthesis.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  3. Network analyses reveal novel aspects of ALS pathogenesis.

    Directory of Open Access Journals (Sweden)

    Mario Sanhueza

    2015-03-01

    Full Text Available Amyotrophic Lateral Sclerosis (ALS is a fatal neurodegenerative disease characterized by selective loss of motor neurons, muscle atrophy and paralysis. Mutations in the human VAMP-associated protein B (hVAPB cause a heterogeneous group of motor neuron diseases including ALS8. Despite extensive research, the molecular mechanisms underlying ALS pathogenesis remain largely unknown. Genetic screens for key interactors of hVAPB activity in the intact nervous system, however, represent a fundamental approach towards understanding the in vivo function of hVAPB and its role in ALS pathogenesis. Targeted expression of the disease-causing allele leads to neurodegeneration and progressive decline in motor performance when expressed in the adult Drosophila, eye or in its entire nervous system, respectively. By using these two phenotypic readouts, we carried out a systematic survey of the Drosophila genome to identify modifiers of hVAPB-induced neurotoxicity. Modifiers cluster in a diverse array of biological functions including processes and genes that have been previously linked to hVAPB function, such as proteolysis and vesicular trafficking. In addition to established mechanisms, the screen identified endocytic trafficking and genes controlling proliferation and apoptosis as potent modifiers of ALS8-mediated defects. Surprisingly, the list of modifiers was mostly enriched for proteins linked to lipid droplet biogenesis and dynamics. Computational analysis reveals that most modifiers can be linked into a complex network of interacting genes, and that the human genes homologous to the Drosophila modifiers can be assembled into an interacting network largely overlapping with that in flies. Identity markers of the endocytic process were also found to abnormally accumulate in ALS patients, further supporting the relevance of the fly data for human biology. Collectively, these results not only lead to a better understanding of hVAPB function but also point to

  4. Power graph compression reveals dominant relationships in genetic transcription networks.

    Science.gov (United States)

    Ahnert, Sebastian E

    2013-11-01

    We introduce a framework for the discovery of dominant relationship patterns in transcription networks, by compressing the network into a power graph with overlapping power nodes. Our application of this approach to the transcription networks of S. cerevisiae and E. coli, paired with GO term enrichment analysis, provides a highly informative overview of the most prominent relationships in the gene regulatory networks of these two organisms.

  5. 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

  6. Enhancer networks revealed by correlated DNAse hypersensitivity states of enhancers.

    Science.gov (United States)

    Malin, Justin; Aniba, Mohamed Radhouane; Hannenhalli, Sridhar

    2013-08-01

    Mammalian gene expression is often regulated by distal enhancers. However, little is known about higher order functional organization of enhancers. Using ∼100 K P300-bound regions as candidate enhancers, we investigated their correlated activity across 72 cell types based on DNAse hypersensitivity. We found widespread correlated activity between enhancers, which decreases with increasing inter-enhancer genomic distance. We found that correlated enhancers tend to share common transcription factor (TF) binding motifs, and several chromatin modification enzymes preferentially interact with these TFs. Presence of shared motifs in enhancer pairs can predict correlated activity with 73% accuracy. Also, genes near correlated enhancers exhibit correlated expression and share common function. Correlated enhancers tend to be spatially proximal. Interestingly, weak enhancers tend to correlate with significantly greater numbers of other enhancers relative to strong enhancers. Furthermore, strong/weak enhancers preferentially correlate with strong/weak enhancers, respectively. We constructed enhancer networks based on shared motif and correlated activity and show significant functional enrichment in their putative target gene clusters. Overall, our analyses show extensive correlated activity among enhancers and reveal clusters of enhancers whose activities are coordinately regulated by multiple potential mechanisms involving shared TF binding, chromatin modifying enzymes and 3D chromatin structure, which ultimately co-regulate functionally linked genes.

  7. Process-based network decomposition reveals backbone motif structure.

    Science.gov (United States)

    Wang, Guanyu; Du, Chenghang; Chen, Hao; Simha, Rahul; Rong, Yongwu; Xiao, Yi; Zeng, Chen

    2010-06-08

    A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated).

  8. Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes

    Science.gov (United States)

    Jacob, Yael; Winetraub, Yonatan; Raz, Gal; Ben-Simon, Eti; Okon-Singer, Hadas; Rosenberg-Katz, Keren; Hendler, Talma; Ben-Jacob, Eshel

    2016-01-01

    Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (DEPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the DEPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the DEPNA correctly captures the network’s hierarchy of influence. Applying DEPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that DEPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions. PMID:27271458

  9. Altered Anatomical Network in Early Blindness Revealed by Diffusion Tensor Tractography

    OpenAIRE

    Ni Shu; Yong Liu; Jun Li; Yonghui Li; Chunshui Yu; Tianzi Jiang

    2009-01-01

    The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. Diffusion MRI studies have revealed the efficient small-world properties and modular structure of the anatomical network in normal subjects. However, no previous study has used diffusion MRI to reveal changes in the brain anatomical network in e...

  10. Compression of flow can reveal overlapping modular organization in networks

    CERN Document Server

    Esquivel, Alcides Viamontes

    2011-01-01

    To better understand the overlapping modular organization of large networks with respect to flow, here we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy search algorithm, we find that some networks, for example, the neural network of C. Elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse road network of California, have a highly overlapping modular organization. To compare our approach with other clustering algorithms, we sugg...

  11. A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression

    Directory of Open Access Journals (Sweden)

    Mao Yu

    2009-07-01

    Full Text Available Abstract Background The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression. Results In this investigation we studied gene pair co-expression change by using a stochastic process model for approximating the underlying dynamic procedure of the co-expression change during cancer progression. Also, we presented a novel analytical method named 'Stochastic process model for Identifying differentially co-expressed Gene pair' (SIG method. This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous. From these data sets, 428,582 gene pairs and 303,992 gene pairs were identified respectively. Afterwards, we used two different current statistical methods to the same data sets, which were developed to identify gene pair differential co-expression and did not consider cancer progression in algorithm. We then compared these results from three different perspectives: progression analysis, gene pair identification effectiveness analysis, and pathway enrichment analysis. Statistical methods were used to quantify the quality and performance of these different perspectives. They included: Re-identification Scale (RS and Progression Score (PS in progression analysis, True Positive Rate (TPR in gene pair analysis, and Pathway Enrichment Score (PES in pathway analysis. Our results show small values of RS and large values of PS, TPR, and PES; thus, suggesting that gene pairs identified by the SIG method are highly correlated with cancer progression, and highly enriched in disease-specific pathways. From

  12. Global Geometric Affinity for Revealing High Fidelity Protein Interaction Network

    Science.gov (United States)

    Fang, Yi; Benjamin, William; Sun, Mengtian; Ramani, Karthik

    2011-01-01

    Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries. PMID:21559288

  13. Network analysis reveals multiscale controls on streamwater chemistry

    Science.gov (United States)

    Kevin J. McGuire; Christian E. Torgersen; Gene E. Likens; Donald C. Buso; Winsor H. Lowe; Scott W. Bailey

    2014-01-01

    By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in...

  14. Gene coexpression measures in large heterogeneous samples using count statistics.

    Science.gov (United States)

    Wang, Y X Rachel; Waterman, Michael S; Huang, Haiyan

    2014-11-18

    With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.

  15. A parietal memory network revealed by multiple MRI methods.

    Science.gov (United States)

    Gilmore, Adrian W; Nelson, Steven M; McDermott, Kathleen B

    2015-09-01

    The manner by which the human brain learns and recognizes stimuli is a matter of ongoing investigation. Through examination of meta-analyses of task-based functional MRI and resting state functional connectivity MRI, we identified a novel network strongly related to learning and memory. Activity within this network at encoding predicts subsequent item memory, and at retrieval differs for recognized and unrecognized items. The direction of activity flips as a function of recent history: from deactivation for novel stimuli to activation for stimuli that are familiar due to recent exposure. We term this network the 'parietal memory network' (PMN) to reflect its broad involvement in human memory processing. We provide a preliminary framework for understanding the key functional properties of the network. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Hierarchicality of trade flow networks reveals complexity of products.

    Science.gov (United States)

    Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

    2014-01-01

    With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.

  17. Hierarchicality of trade flow networks reveals complexity of products.

    Directory of Open Access Journals (Sweden)

    Peiteng Shi

    Full Text Available With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.

  18. Communicability reveals a transition to coordinated behavior in multiplex networks

    CERN Document Server

    Estrada, Ernesto

    2013-01-01

    We analyse the flow of information in multiplex networks by means of the communicability function. First, we generalize this measure from its definition from simple graphs to multiplex networks. Then, we study its relevance for the analysis of real-world systems by studying a social multiplex where information flows using formal/informal channels and an air transportation system where the layers represent different air companies. Accordingly, the communicability, which is essential for the good performance of these complex systems, emerges at a systemic operation point in the multiplex where the performance of the layers operates in a coordinated way very differently from the state represented by a collection of unconnected networks.

  19. Communicability reveals a transition to coordinated behavior in multiplex networks

    Science.gov (United States)

    Estrada, Ernesto; Gómez-Gardeñes, Jesús

    2014-04-01

    We analyze the flow of information in multiplex networks by means of the communicability function. First, we generalize this measure from its definition from simple graphs to multiplex networks. Then, we study its relevance for the analysis of real-world systems by studying a social multiplex where information flows using formal-informal channels and an air transportation system where the layers represent different air companies. Accordingly, the communicability, which is essential for the good performance of these complex systems, emerges at a systemic operation point in the multiplex where the performance of the layers operates in a coordinated way very differently from the state represented by a collection of unconnected networks.

  20. Remote synchronization reveals network symmetries and functional modules

    CERN Document Server

    Nicosia, Vincenzo; Chavez, Mario; Díaz-Guilera, Albert; Latora, Vito

    2012-01-01

    We study a Kuramoto model in which the oscillators are associated to the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization. The system organizes into a regime of remote synchronization where pairs of nodes with the same network symmetry are fully synchronized, despite their distance on the graph. We provide analytical arguments to explain this result and we show how the frustration parameter affects the distribution of phases. An application to brain networks suggests that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.

  1. The contribution of cis-regulatory elements to head-to-head gene pairs’ co-expression pattern

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Transcription regulation is one of the most critical pipelines in biological process,in which cis-elements play the role as gene expression regulators.We attempt to deduce the principles underlying the co-expression of "head-to-head" gene pairs by analyzing activities or behaviors of the shared cis-elements.A network component analysis was performed to estimate the impact of cis-elements on gene promoters and their activities under different conditions.Our discoveries reveal how biological system uses those regulatory elements to control the expression pattern of "head-to-head" gene pairs and the whole transcription regulation system.

  2. Probabilistic diffusion tractography reveals improvement of structural network in musicians.

    Directory of Open Access Journals (Sweden)

    Jianfu Li

    Full Text Available PURPOSE: Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. METHODS: Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. RESULTS: Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. CONCLUSIONS: We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.

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

    Energy Technology Data Exchange (ETDEWEB)

    Munsky, Brian [Los Alamos National Laboratory; Khammash, Mustafa [UCSB

    2009-01-01

    The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations of cells exhibit cell-to-cell variability that frequently manifests as significant phenotypic differences within the cellular population. The stochastic fluctuations in cellular constituents induced by noise can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.

  4. Expression analysis of mammaglobin A (SCGB2A2 and lipophilin B (SCGB1D2 in more than 300 human tumors and matching normal tissues reveals their co-expression in gynecologic malignancies

    Directory of Open Access Journals (Sweden)

    Kristiansen Glen

    2006-04-01

    Full Text Available Abstract Background Mammaglobin A (SCGB2A2 and lipophilin B (SCGB1D2, two members of the secretoglobin superfamily, are known to be co-expressed in breast cancer, where their proteins form a covalent complex. Based on the relatively high tissue-specific expression pattern, it has been proposed that the mammaglobin A protein and/or its complex with lipophilin B could be used in breast cancer diagnosis and treatment. In view of these clinical implications, the aim of the present study was to analyze the expression of both genes in a large panel of human solid tumors (n = 309, corresponding normal tissues (n = 309 and cell lines (n = 11, in order to evaluate their tissue specific expression and co-expression pattern. Methods For gene and protein expression analyses, northern blot, dot blot hybridization of matched tumor/normal arrays (cancer profiling arrays, quantitative RT-PCR, non-radioisotopic RNA in situ hybridization and immunohistochemistry were used. Results Cancer profiling array data demonstrated that mammaglobin A and lipophilin B expression is not restricted to normal and malignant breast tissue. Both genes were abundantly expressed in tumors of the female genital tract, i.e. endometrial, ovarian and cervical cancer. In these four tissues the expression pattern of mammaglobin A and lipophilin B was highly concordant, with both genes being down-, up- or not regulated in the same tissue samples. In breast tissue, mammaglobin A expression was down-regulated in 49% and up-regulated in 12% of breast tumor specimens compared with matching normal tissues, while lipophilin B was down-regulated in 59% and up-regulated in 3% of cases. In endometrial tissue, expression of mammaglobin A and lipophilin B was clearly up-regulated in tumors (47% and 49% respectively. Both genes exhibited down-regulation in 22% of endometrial tumors. The only exceptions to this concordance of mammaglobin A/lipophilin B expression were normal and malignant tissues of

  5. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    Full Text Available Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2 or L(1. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are

  6. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    Full Text Available Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2 or L(1. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are

  7. Mesoscopic structures reveal the network between the layers of multiplex data sets

    Science.gov (United States)

    Iacovacci, Jacopo; Wu, Zhihao; Bianconi, Ginestra

    2015-10-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks, or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex data sets, forming a "network of networks." We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network, and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collaborating on different subjects and publishing in the American Physical Society journals. The analysis of this data set reveals the interplay between the collaboration networks and the organization of knowledge in physics.

  8. Mesoscopic structures reveal the network between the layers of multiplex data sets.

    Science.gov (United States)

    Iacovacci, Jacopo; Wu, Zhihao; Bianconi, Ginestra

    2015-10-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks, or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex data sets, forming a "network of networks." We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network, and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collaborating on different subjects and publishing in the American Physical Society journals. The analysis of this data set reveals the interplay between the collaboration networks and the organization of knowledge in physics.

  9. Representational Similarity Analysis Reveals Heterogeneous Networks Supporting Speech Motor Control

    DEFF Research Database (Denmark)

    Zheng, Zane; Cusack, Rhodri; Johnsrude, Ingrid

    The everyday act of speaking involves the complex processes of speech motor control. One important feature of such control is regulation of articulation when auditory concomitants of speech do not correspond to the intended motor gesture. While theoretical accounts of speech monitoring posit...... is supported by a complex neural network that is involved in linguistic, motoric and sensory processing. With the aid of novel real-time acoustic analyses and representational similarity analyses of fMRI signals, our data show functionally differentiated networks underlying auditory feedback control of speech....... multiple functional components required for detection of errors in speech planning (e.g., Levelt, 1983), neuroimaging studies generally indicate either single brain regions sensitive to speech production errors, or small, discrete networks. Here we demonstrate that the complex system controlling speech...

  10. CERN tests reveal security flaws with industrial network devices

    CERN Document Server

    Lüders, Stefan

    2006-01-01

    The CERN high energy particle physics facility at Geneva, Switzerland will incorporate a wide range of COTS industrial control systems within its next generation particle collider, the LHC. In particular, the Internet will be used to facilitate the remote access for accelerator and particle physicists and system experts based at several hundred locations around the globe. The integration of Industrial Ethernet and COTS PLCs within the LHC program focuses extreme attention on the industrial network cyber-security requirement. CERN's response has been to conduct operational research on the security resilience of networked industrial devices. As test team lead Stefan Lüders reports here, industrial networked devices put through the organisation's test procedures have generally shown up unexpected vulnerabilities.

  11. Network analysis reveals multiscale controls on streamwater chemistry

    Science.gov (United States)

    McGuire, Kevin J.; Torgersen, Christian E.; Likens, Gene E.; Buso, Donald C.; Lowe, Winsor H.; Bailey, Scott W.

    2014-01-01

    By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.

  12. Phase resetting reveals network dynamics underlying a bacterial cell cycle.

    Directory of Open Access Journals (Sweden)

    Yihan Lin

    Full Text Available Genomic and proteomic methods yield networks of biological regulatory interactions but do not provide direct insight into how those interactions are organized into functional modules, or how information flows from one module to another. In this work we introduce an approach that provides this complementary information and apply it to the bacterium Caulobacter crescentus, a paradigm for cell-cycle control. Operationally, we use an inducible promoter to express the essential transcriptional regulatory gene ctrA in a periodic, pulsed fashion. This chemical perturbation causes the population of cells to divide synchronously, and we use the resulting advance or delay of the division times of single cells to construct a phase resetting curve. We find that delay is strongly favored over advance. This finding is surprising since it does not follow from the temporal expression profile of CtrA and, in turn, simulations of existing network models. We propose a phenomenological model that suggests that the cell-cycle network comprises two distinct functional modules that oscillate autonomously and couple in a highly asymmetric fashion. These features collectively provide a new mechanism for tight temporal control of the cell cycle in C. crescentus. We discuss how the procedure can serve as the basis for a general approach for probing network dynamics, which we term chemical perturbation spectroscopy (CPS.

  13. Integration of metabolome data with metabolic networks reveals reporter reactions

    DEFF Research Database (Denmark)

    Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen;

    2006-01-01

    Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...

  14. RNA-seq analysis of apical meristem reveals integrative regulatory network of ROS and chilling potentially related to flowering in Litchi chinensis.

    Science.gov (United States)

    Lu, Xingyu; Li, Jingjing; Chen, Houbin; Hu, Jiaqi; Liu, Pengxu; Zhou, Biyan

    2017-08-31

    Litchi is an important woody fruit tree. Floral initiation in litchi is triggered by low temperatures. However, defective flowering is a major challenge for litchi production in times of climate change and global warming. Previous studies have shown that the reactive oxygen species (ROS) generated by methyl viologen dichloride hydrate (MV) promotes flowering. In this study, potted trees were transferred to growth chambers for low-temperature (LT), medium-temperature (MT), and high-temperature (HT) treatments. Trees at MT were subjected to ROS treatment to promote flowering, and those at LT were induced to flower. RNA-sequencing was applied to obtain a global transcriptome of the apical meristem and reveal potential gene networks controlling the transformation from vegetative meristems (VM) into inflorescence meristems (IM). We assembled 73,117 unigenes with a mean size of 790 bp and 11741 unigenes were identified as both chilling and ROS responsive genes (CRRGs), of which 48 were identified as flowering-related CRRGs, 59 were plant hormone signal transduction CRRGs, and 146 were plant hormone biosynthesis-related CRRGs. Genes co-expression network analysis indicated inner relationships, suggesting that ROS and chilling promotes the VM to IM transition through a regulatory gene network of transcription factors, hormones, and flowering regulators.

  15. Network based approaches reveal clustering in protein point patterns

    Science.gov (United States)

    Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang

    2014-03-01

    Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.

  16. Comparison of protein interaction networks reveals species conservation and divergence

    Directory of Open Access Journals (Sweden)

    Teng Maikun

    2006-10-01

    Full Text Available Abstract Background Recent progresses in high-throughput proteomics have provided us with a first chance to characterize protein interaction networks (PINs, but also raised new challenges in interpreting the accumulating data. Results Motivated by the need of analyzing and interpreting the fast-growing data in the field of proteomics, we propose a comparative strategy to carry out global analysis of PINs. We compare two PINs by combining interaction topology and sequence similarity to identify conserved network substructures (CoNSs. Using this approach we perform twenty-one pairwise comparisons among the seven recently available PINs of E.coli, H.pylori, S.cerevisiae, C.elegans, D.melanogaster, M.musculus and H.sapiens. In spite of the incompleteness of data, PIN comparison discloses species conservation at the network level and the identified CoNSs are also functionally conserved and involve in basic cellular functions. We investigate the yeast CoNSs and find that many of them correspond to known complexes. We also find that different species harbor many conserved interaction regions that are topologically identical and these regions can constitute larger interaction regions that are topologically different but similar in framework. Based on the species-to-species difference in CoNSs, we infer potential species divergence. It seems that different species organize orthologs in similar but not necessarily the same topology to achieve similar or the same function. This attributes much to duplication and divergence of genes and their associated interactions. Finally, as the application of CoNSs, we predict 101 protein-protein interactions (PPIs, annotate 339 new protein functions and deduce 170 pairs of orthologs. Conclusion Our result demonstrates that the cross-species comparison strategy we adopt is powerful for the exploration of biological problems from the perspective of networks.

  17. Probabilistic Diffusion Tractography Reveals Improvement of Structural Network in Musicians

    OpenAIRE

    Jianfu Li; Cheng Luo; Yueheng Peng; Qiankun Xie; Jinnan Gong; Li Dong; Yongxiu Lai; Hong Li; Dezhong Yao

    2014-01-01

    PURPOSE: Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. METHODS: Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the...

  18. Estimating Network Economies in Retail Chains: A Revealed Preference Approach

    OpenAIRE

    Paul B. Ellickson; Stephanie Houghton; Christopher Timmins

    2010-01-01

    We measure the effects of chain economies, business stealing, and heterogeneous firms' comparative advantages in the discount retail industry. Traditional entry models are ill-suited for this high-dimensional problem of strategic interaction. Building upon recently developed profit inequality techniques, our model admits any number of potential rivals and stores per location, an endogenous distribution network, and unobserved (to the econometrician) location attributes that may cause firms to...

  19. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems

    CERN Document Server

    Rosvall, M

    2010-01-01

    To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation that reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network, the optimal number of levels and modular partition at each level, with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines:...

  20. Sequence similarity network reveals common ancestry of multidomain proteins.

    Science.gov (United States)

    Song, Nan; Joseph, Jacob M; Davis, George B; Durand, Dannie

    2008-05-16

    We address the problem of homology identification in complex multidomain families with varied domain architectures. The challenge is to distinguish sequence pairs that share common ancestry from pairs that share an inserted domain but are otherwise unrelated. This distinction is essential for accuracy in gene annotation, function prediction, and comparative genomics. There are two major obstacles to multidomain homology identification: lack of a formal definition and lack of curated benchmarks for evaluating the performance of new methods. We offer preliminary solutions to both problems: 1) an extension of the traditional model of homology to include domain insertions; and 2) a manually curated benchmark of well-studied families in mouse and human. We further present Neighborhood Correlation, a novel method that exploits the local structure of the sequence similarity network to identify homologs with great accuracy based on the observation that gene duplication and domain shuffling leave distinct patterns in the sequence similarity network. In a rigorous, empirical comparison using our curated data, Neighborhood Correlation outperforms sequence similarity, alignment length, and domain architecture comparison. Neighborhood Correlation is well suited for automated, genome-scale analyses. It is easy to compute, does not require explicit knowledge of domain architecture, and classifies both single and multidomain homologs with high accuracy. Homolog predictions obtained with our method, as well as our manually curated benchmark and a web-based visualization tool for exploratory analysis of the network neighborhood structure, are available at http://www.neighborhoodcorrelation.org. Our work represents a departure from the prevailing view that the concept of homology cannot be applied to genes that have undergone domain shuffling. In contrast to current approaches that either focus on the homology of individual domains or consider only families with identical domain

  1. Sequence similarity network reveals common ancestry of multidomain proteins.

    Directory of Open Access Journals (Sweden)

    Nan Song

    2008-05-01

    Full Text Available We address the problem of homology identification in complex multidomain families with varied domain architectures. The challenge is to distinguish sequence pairs that share common ancestry from pairs that share an inserted domain but are otherwise unrelated. This distinction is essential for accuracy in gene annotation, function prediction, and comparative genomics. There are two major obstacles to multidomain homology identification: lack of a formal definition and lack of curated benchmarks for evaluating the performance of new methods. We offer preliminary solutions to both problems: 1 an extension of the traditional model of homology to include domain insertions; and 2 a manually curated benchmark of well-studied families in mouse and human. We further present Neighborhood Correlation, a novel method that exploits the local structure of the sequence similarity network to identify homologs with great accuracy based on the observation that gene duplication and domain shuffling leave distinct patterns in the sequence similarity network. In a rigorous, empirical comparison using our curated data, Neighborhood Correlation outperforms sequence similarity, alignment length, and domain architecture comparison. Neighborhood Correlation is well suited for automated, genome-scale analyses. It is easy to compute, does not require explicit knowledge of domain architecture, and classifies both single and multidomain homologs with high accuracy. Homolog predictions obtained with our method, as well as our manually curated benchmark and a web-based visualization tool for exploratory analysis of the network neighborhood structure, are available at http://www.neighborhoodcorrelation.org. Our work represents a departure from the prevailing view that the concept of homology cannot be applied to genes that have undergone domain shuffling. In contrast to current approaches that either focus on the homology of individual domains or consider only families with

  2. Systems genetics reveals a transcriptional network associated with susceptibility in the maize-grey leaf spot pathosystem.

    Science.gov (United States)

    Christie, Nanette; Myburg, Alexander A; Joubert, Fourie; Murray, Shane L; Carstens, Maryke; Lin, Yao-Cheng; Meyer, Jacqueline; Crampton, Bridget G; Christensen, Shawn A; Ntuli, Jean F; Wighard, Sara S; Van de Peer, Yves; Berger, Dave K

    2017-02-01

    We used a systems genetics approach to elucidate the molecular mechanisms of the responses of maize to grey leaf spot (GLS) disease caused by Cercospora zeina, a threat to maize production globally. Expression analysis of earleaf samples in a subtropical maize recombinant inbred line population (CML444 × SC Malawi) subjected in the field to C. zeina infection allowed detection of 20 206 expression quantitative trait loci (eQTLs). Four trans-eQTL hotspots coincided with GLS disease QTLs mapped in the same field experiment. Co-expression network analysis identified three expression modules correlated with GLS disease scores. The module (GY-s) most highly correlated with susceptibility (r = 0.71; 179 genes) was enriched for the glyoxylate pathway, lipid metabolism, diterpenoid biosynthesis and responses to pathogen molecules such as chitin. The GY-s module was enriched for genes with trans-eQTLs in hotspots on chromosomes 9 and 10, which also coincided with phenotypic QTLs for susceptibility to GLS. This transcriptional network has significant overlap with the GLS susceptibility response of maize line B73, and may reflect pathogen manipulation for nutrient acquisition and/or unsuccessful defence responses, such as kauralexin production by the diterpenoid biosynthesis pathway. The co-expression module that correlated best with resistance (TQ-r; 1498 genes) was enriched for genes with trans-eQTLs in hotspots coinciding with GLS resistance QTLs on chromosome 9. Jasmonate responses were implicated in resistance to GLS through co-expression of COI1 and enrichment of genes with the Gene Ontology term 'cullin-RING ubiquitin ligase complex' in the TQ-r module. Consistent with this, JAZ repressor expression was highly correlated with the severity of GLS disease in the GY-s susceptibility network.

  3. Analytical reasoning task reveals limits of social learning in networks.

    Science.gov (United States)

    Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

    2014-04-06

    Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

  4. Communities in Neuronal Complex Networks Revealed by Activation Patterns

    CERN Document Server

    Costa, Luciano da Fontoura

    2008-01-01

    Recently, it has been shown that the communities in neuronal networks of the integrate-and-fire type can be identified by considering patterns containing the beginning times for each cell to receive the first non-zero activation. The received activity was integrated in order to facilitate the spiking of each neuron and to constrain the activation inside the communities, but no time decay of such activation was considered. The present article shows that, by taking into account exponential decays of the stored activation, it is possible to identify the communities also in terms of the patterns of activation along the initial steps of the transient dynamics. The potential of this method is illustrated with respect to complex neuronal networks involving four communities, each of a different type (Erd\\H{o}s-R\\'eny, Barab\\'asi-Albert, Watts-Strogatz as well as a simple geographical model). Though the consideration of activation decay has been found to enhance the communities separation, too intense decays tend to y...

  5. "Clicking" on the lights to reveal bacterial social networking.

    Science.gov (United States)

    Clevenger, Kenneth D; Fast, Walter

    2012-03-05

    "No man is an island." With apologies to John Donne, the same could be said for a bacterium. The discovery of bacterial quorum sensing and its relevance to microbial ecology and pathogenesis have fueled the increasing scrutiny of the molecular mechanisms responsible for the apparent group behavior of microbes. A number of chemically diverse small molecules act as diffusible signaling molecules that regulate gene expression in a population-dependent manner. Some of these signals, such as the N-acyl-L-homoserine lactones, are produced and sensed by others in the same or closely related species, and other chemical classes of signals are used more broadly for interspecies and even interkingdom communication. As a field, the study of these microbial social networks has been termed "sociomicrobiology".

  6. A comparative study of Pointed and Yan expression reveals new complexity to the transcriptional networks downstream of receptor tyrosine kinase signaling.

    Science.gov (United States)

    Boisclair Lachance, Jean-François; Peláez, Nicolás; Cassidy, Justin J; Webber, Jemma L; Rebay, Ilaria; Carthew, Richard W

    2014-01-15

    The biochemical regulatory network downstream of receptor tyrosine kinase (RTK) signaling is controlled by two opposing ETS family members: the transcriptional activator Pointed (Pnt) and the transcriptional repressor Yan. A bistable switch model has been invoked to explain how pathway activation can drive differentiation by shifting the system from a high-Yan/low-Pnt activity state to a low-Yan/high-Pnt activity state. Although the model explains yan and pnt loss-of-function phenotypes in several different cell types, how Yan and Pointed protein expression dynamics contribute to these and other developmental transitions remains poorly understood. Toward this goal we have used a functional GFP-tagged Pnt transgene (Pnt-GFP) to perform a comparative study of Yan and Pnt protein expression throughout Drosophila development. Consistent with the prevailing model of the Pnt-Yan network, we found numerous instances where Pnt-GFP and Yan adopt a mutually exclusive pattern of expression. However we also observed many examples of co-expression. While some co-expression occurred in cells where RTK signaling is presumed low, other co-expression occurred in cells with high RTK signaling. The instances of co-expressed Yan and Pnt-GFP in tissues with high RTK signaling cannot be explained by the current model, and thus they provide important contexts for future investigation of how context-specific differences in RTK signaling, network topology, or responsiveness to other signaling inputs, affect the transcriptional response.

  7. Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems

    Science.gov (United States)

    De Domenico, Manlio; Lancichinetti, Andrea; Arenas, Alex; Rosvall, Martin

    2015-01-01

    To comprehend interconnected systems across the social and natural sciences, researchers have developed many powerful methods to identify functional modules. For example, with interaction data aggregated into a single network layer, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow processes. However, many interconnected systems consist of agents or components that exhibit multiple layers of interactions, possibly from several different processes. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here, we propose a method based on a compression of network flows that can identify modular flows both within and across layers in nonaggregated multilayer networks. Our numerical experiments on synthetic multilayer networks, with some layers originating from the same interaction process, show that the analysis fails in aggregated networks or when treating the layers separately, whereas the multilayer method can accurately identify modules across layers that originate from the same interaction process. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks with topics as layers: scientists affiliated with the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method uncovers connected topics and reveals smaller modules with more overlap that better capture the actual organization.

  8. Cross tissue trait-pathway network reveals the importance of oxidative stress and inflammation pathways in obesity-induced diabetes in mouse.

    Directory of Open Access Journals (Sweden)

    Shouguo Gao

    Full Text Available Complex disorders often involve dysfunctions in multiple tissue organs. Elucidating the communication among them is important to understanding disease pathophysiology. In this study we integrate multiple tissue gene expression and quantitative trait measurements of an obesity-induced diabetes mouse model, with databases of molecular interaction networks, to construct a cross tissue trait-pathway network. The animals belong to two strains of mice (BTBR or B6, of two obesity status (obese or lean, and at two different ages (4 weeks and 10 weeks. Only 10 week obese BTBR animals are diabetic. The expression data was first utilized to determine the state of every pathway in each tissue, which is subsequently utilized to construct a pathway co-expression network and to define trait-relevant and trait-linking pathways. Among the six tissues profiled, the adipose contains the largest number of trait-linking pathways. Among the eight traits measured, the body weight and plasma insulin level possess the most number of relevant and linking pathways. Topological analysis of the trait-pathway network revealed that the glycolysis/gluconeogenesis pathway in liver and the insulin signaling pathway in muscle are of top importance to the information flow in the network, with the highest degrees and betweenness centralities. Interestingly, pathways related to metabolism and oxidative stress actively interact with many other pathways in all animals, whereas, among the 10 week animals, the inflammation pathways were preferentially interactive in the diabetic ones only. In summary, our method offers a systems approach to delineate disease trait relevant intra- and cross tissue pathway interactions, and provides insights to the molecular basis of the obesity-induced diabetes.

  9. PLANEX: the plant co-expression database

    OpenAIRE

    Yim, Won Cheol; Yu, YongBin; Song, Kitae; Jang, Cheol Seong; Lee, Byung-Moo

    2013-01-01

    Background The PLAnt co-EXpression database (PLANEX) is a new internet-based database for plant gene analysis. PLANEX (http://planex.plantbioinformatics.org) contains publicly available GeneChip data obtained from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI). PLANEX is a genome-wide co-expression database, which allows for the functional identification of genes from a wide variety of experimental designs. It can be used for the characterization...

  10. Differentially co-expressed interacting protein pairs discriminate samples under distinct stages of HIV type 1 infection.

    Science.gov (United States)

    Yoon, Dukyong; Kim, Hyosil; Suh-Kim, Haeyoung; Park, Rae Woong; Lee, KiYoung

    2011-01-01

    Microarray analyses based on differentially expressed genes (DEGs) have been widely used to distinguish samples across different cellular conditions. However, studies based on DEGs have not been able to clearly determine significant differences between samples of pathophysiologically similar HIV-1 stages, e.g., between acute and chronic progressive (or AIDS) or between uninfected and clinically latent stages. We here suggest a novel approach to allow such discrimination based on stage-specific genetic features of HIV-1 infection. Our approach is based on co-expression changes of genes known to interact. The method can identify a genetic signature for a single sample as contrasted with existing protein-protein-based analyses with correlational designs. Our approach distinguishes each sample using differentially co-expressed interacting protein pairs (DEPs) based on co-expression scores of individual interacting pairs within a sample. The co-expression score has positive value if two genes in a sample are simultaneously up-regulated or down-regulated. And the score has higher absolute value if expression-changing ratios are similar between the two genes. We compared characteristics of DEPs with that of DEGs by evaluating their usefulness in separation of HIV-1 stage. And we identified DEP-based network-modules and their gene-ontology enrichment to find out the HIV-1 stage-specific gene signature. Based on the DEP approach, we observed clear separation among samples from distinct HIV-1 stages using clustering and principal component analyses. Moreover, the discrimination power of DEPs on the samples (70-100% accuracy) was much higher than that of DEGs (35-45%) using several well-known classifiers. DEP-based network analysis also revealed the HIV-1 stage-specific network modules; the main biological processes were related to "translation," "RNA splicing," "mRNA, RNA, and nucleic acid transport," and "DNA metabolism." Through the HIV-1 stage-related modules, changing

  11. Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

    Directory of Open Access Journals (Sweden)

    Masanao Sato

    Full Text Available Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2. This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i the components of the network are highly interconnected; and (ii negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector

  12. A genome wide dosage suppressor network reveals genomic robustness

    Science.gov (United States)

    Patra, Biranchi; Kon, Yoshiko; Yadav, Gitanjali; Sevold, Anthony W.; Frumkin, Jesse P.; Vallabhajosyula, Ravishankar R.; Hintze, Arend; Østman, Bjørn; Schossau, Jory; Bhan, Ashish; Marzolf, Bruz; Tamashiro, Jenna K.; Kaur, Amardeep; Baliga, Nitin S.; Grayhack, Elizabeth J.; Adami, Christoph; Galas, David J.; Raval, Alpan; Phizicky, Eric M.; Ray, Animesh

    2017-01-01

    Genomic robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of genomic robustness in budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggests a surprising degree of functional plasticity of macromolecular complexes, and the existence of numerous degenerate pathways for circumventing the effects of potentially lethal mutations. These results imply that organisms and cancer are likely able to exploit the genomic robustness properties, due the persistence of cryptic gene and pathway functions, to generate variation and adapt to selective pressures. PMID:27899637

  13. Weighted gene co-expression based biomarker discovery for psoriasis detection.

    Science.gov (United States)

    Sundarrajan, Sudharsana; Arumugam, Mohanapriya

    2016-11-15

    Psoriasis is a chronic inflammatory disease of the skin with an unknown aetiology. The disease manifests itself as red and silvery scaly plaques distributed over the scalp, lower back and extensor aspects of the limbs. After receiving scant consideration for quite a few years, psoriasis has now become a prominent focus for new drug development. A group of closely connected and differentially co-expressed genes may act in a network and may serve as molecular signatures for an underlying phenotype. A weighted gene coexpression network analysis (WGCNA), a system biology approach has been utilized for identification of new molecular targets for psoriasis. Gene coexpression relationships were investigated in 58 psoriatic lesional samples resulting in five gene modules, clustered based on the gene coexpression patterns. The coexpression pattern was validated using three psoriatic datasets. 10 highly connected and informative genes from each module was selected and termed as psoriasis specific hub signatures. A random forest based binary classifier built using the expression profiles of signature genes robustly distinguished psoriatic samples from the normal samples in the validation set with an accuracy of 0.95 to 1. These signature genes may serve as potential candidates for biomarker discovery leading to new therapeutic targets. WGCNA, the network based approach has provided an alternative path to mine out key controllers and drivers of psoriasis. The study principle from the current work can be extended to other pathological conditions.

  14. Altered anatomical network in early blindness revealed by diffusion tensor tractography.

    Science.gov (United States)

    Shu, Ni; Liu, Yong; Li, Jun; Li, Yonghui; Yu, Chunshui; Jiang, Tianzi

    2009-09-28

    The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. Diffusion MRI studies have revealed the efficient small-world properties and modular structure of the anatomical network in normal subjects. However, no previous study has used diffusion MRI to reveal changes in the brain anatomical network in early blindness. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 17 early blind subjects and 17 age- and gender-matched sighted controls. We established the existence of structural connections between any pair of the 90 cortical and sub-cortical regions using deterministic tractography. Compared with controls, early blind subjects showed a decreased degree of connectivity, a reduced global efficiency, and an increased characteristic path length in their brain anatomical network, especially in the visual cortex. Moreover, we revealed some regions with motor or somatosensory function have increased connections with other brain regions in the early blind, which suggested experience-dependent compensatory plasticity. This study is the first to show alterations in the topological properties of the anatomical network in early blindness. From the results, we suggest that analyzing the brain's anatomical network obtained using diffusion MRI data provides new insights into the understanding of the brain's re-organization in the specific population with early visual deprivation.

  15. Altered anatomical network in early blindness revealed by diffusion tensor tractography.

    Directory of Open Access Journals (Sweden)

    Ni Shu

    Full Text Available The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. Diffusion MRI studies have revealed the efficient small-world properties and modular structure of the anatomical network in normal subjects. However, no previous study has used diffusion MRI to reveal changes in the brain anatomical network in early blindness. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 17 early blind subjects and 17 age- and gender-matched sighted controls. We established the existence of structural connections between any pair of the 90 cortical and sub-cortical regions using deterministic tractography. Compared with controls, early blind subjects showed a decreased degree of connectivity, a reduced global efficiency, and an increased characteristic path length in their brain anatomical network, especially in the visual cortex. Moreover, we revealed some regions with motor or somatosensory function have increased connections with other brain regions in the early blind, which suggested experience-dependent compensatory plasticity. This study is the first to show alterations in the topological properties of the anatomical network in early blindness. From the results, we suggest that analyzing the brain's anatomical network obtained using diffusion MRI data provides new insights into the understanding of the brain's re-organization in the specific population with early visual deprivation.

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

    Science.gov (United States)

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

    2011-11-01

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

  17. Virtual mutagenesis of the yeast cyclins genetic network reveals complex dynamics of transcriptional control networks.

    Directory of Open Access Journals (Sweden)

    Eliska Vohradska

    Full Text Available Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network--the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly.

  18. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems.

    Directory of Open Access Journals (Sweden)

    Martin Rosvall

    Full Text Available To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network--the optimal number of levels and modular partition at each level--with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.

  19. Transcriptional network analysis reveals that AT1 and AT2 angiotensin II receptors are both involved in the regulation of genes essential for glioma progression.

    Science.gov (United States)

    Azevedo, Hátylas; Fujita, André; Bando, Silvia Yumi; Iamashita, Priscila; Moreira-Filho, Carlos Alberto

    2014-01-01

    Gliomas are aggressive primary brain tumors with high infiltrative potential. The expression of Angiotensin II (Ang II) receptors has been associated with poor prognosis in human astrocytomas, the most common type of glioma. In this study, we investigated the role of Angiotensin II in glioma malignancy through transcriptional profiling and network analysis of cultured C6 rat glioma cells exposed to Ang II and to inhibitors of its membrane receptor subtypes. C6 cells were treated with Ang II and specific antagonists of AT1 and AT2 receptors. Total RNA was isolated after three and six hours of Ang II treatment and analyzed by oligonucleotide microarray technology. Gene expression data was evaluated through transcriptional network modeling to identify how differentially expressed (DE) genes are connected to each other. Moreover, other genes co-expressing with the DE genes were considered in these analyses in order to support the identification of enriched functions and pathways. A hub-based network analysis showed that the most connected nodes in Ang II-related networks exert functions associated with cell proliferation, migration and invasion, key aspects for glioma progression. The subsequent functional enrichment analysis of these central genes highlighted their participation in signaling pathways that are frequently deregulated in gliomas such as ErbB, MAPK and p53. Noteworthy, either AT1 or AT2 inhibitions were able to down-regulate different sets of hub genes involved in protumoral functions, suggesting that both Ang II receptors could be therapeutic targets for intervention in glioma. Taken together, our results point out multiple actions of Ang II in glioma pathogenesis and reveal the participation of both Ang II receptors in the regulation of genes relevant for glioma progression. This study is the first one to provide systems-level molecular data for better understanding the protumoral effects of Ang II in the proliferative and infiltrative behavior of

  20. Transcriptional network analysis reveals that AT1 and AT2 angiotensin II receptors are both involved in the regulation of genes essential for glioma progression.

    Directory of Open Access Journals (Sweden)

    Hátylas Azevedo

    Full Text Available Gliomas are aggressive primary brain tumors with high infiltrative potential. The expression of Angiotensin II (Ang II receptors has been associated with poor prognosis in human astrocytomas, the most common type of glioma. In this study, we investigated the role of Angiotensin II in glioma malignancy through transcriptional profiling and network analysis of cultured C6 rat glioma cells exposed to Ang II and to inhibitors of its membrane receptor subtypes. C6 cells were treated with Ang II and specific antagonists of AT1 and AT2 receptors. Total RNA was isolated after three and six hours of Ang II treatment and analyzed by oligonucleotide microarray technology. Gene expression data was evaluated through transcriptional network modeling to identify how differentially expressed (DE genes are connected to each other. Moreover, other genes co-expressing with the DE genes were considered in these analyses in order to support the identification of enriched functions and pathways. A hub-based network analysis showed that the most connected nodes in Ang II-related networks exert functions associated with cell proliferation, migration and invasion, key aspects for glioma progression. The subsequent functional enrichment analysis of these central genes highlighted their participation in signaling pathways that are frequently deregulated in gliomas such as ErbB, MAPK and p53. Noteworthy, either AT1 or AT2 inhibitions were able to down-regulate different sets of hub genes involved in protumoral functions, suggesting that both Ang II receptors could be therapeutic targets for intervention in glioma. Taken together, our results point out multiple actions of Ang II in glioma pathogenesis and reveal the participation of both Ang II receptors in the regulation of genes relevant for glioma progression. This study is the first one to provide systems-level molecular data for better understanding the protumoral effects of Ang II in the proliferative and infiltrative

  1. Cross-species transcriptional network analysis reveals conservation and variation in response to metal stress in cyanobacteria

    Science.gov (United States)

    2013-01-01

    Background As one of the most dominant bacterial groups on Earth, cyanobacteria play a pivotal role in the global carbon cycling and the Earth atmosphere composition. Understanding their molecular responses to environmental perturbations has important scientific and environmental values. Since important biological processes or networks are often evolutionarily conserved, the cross-species transcriptional network analysis offers a useful strategy to decipher conserved and species-specific transcriptional mechanisms that cells utilize to deal with various biotic and abiotic disturbances, and it will eventually lead to a better understanding of associated adaptation and regulatory networks. Results In this study, the Weighted Gene Co-expression Network Analysis (WGCNA) approach was used to establish transcriptional networks for four important cyanobacteria species under metal stress, including iron depletion and high copper conditions. Cross-species network comparison led to discovery of several core response modules and genes possibly essential to metal stress, as well as species-specific hub genes for metal stresses in different cyanobacteria species, shedding light on survival strategies of cyanobacteria responding to different environmental perturbations. Conclusions The WGCNA analysis demonstrated that the application of cross-species transcriptional network analysis will lead to novel insights to molecular response to environmental changes which will otherwise not be achieved by analyzing data from a single species. PMID:23421563

  2. Master stability functions reveal diffusion-driven instabilities in multi-layer networks

    CERN Document Server

    Brechtel, Andreas; Ritterskamp, Daniel; Drossel, Barbara; Gross, Thilo

    2016-01-01

    Many systems in science and technology can be described as multilayer networks, which are known to exhibit phenomena such as catastrophic failure cascades and pattern-forming instabilities. A particular class of multilayer networks describes systems where different interacting copies of a local network exist in different spatial locations, including for instance regulatory and metabolic networks of identical cells and interacting habitats of ecological populations. Here, we show that such systems can be analyzed by a master stability function (MSF) approach, which reveals conditions for diffusion-driven instabilities (DDIs). We demonstrate the methodology on the example of state-of-the-art meta-foodweb models, where it reveals diffusion-driven instabilities that lead to localized dynamics and spatial patterns. This type of approach can be applied to a variety of systems from nature, science and engineering to aid the understanding and design of complex self-organizing systems.

  3. Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function.

    Science.gov (United States)

    Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Zhang, Shu; Zhao, Shijie; Chen, Hanbo; Zhang, Tuo; Hu, Xintao; Han, Junwei; Ye, Jieping; Guo, Lei; Liu, Tianming

    2015-04-01

    For decades, it has been largely unknown to what extent multiple functional networks spatially overlap/interact with each other and jointly realize the total cortical function. Here, by developing novel sparse representation of whole-brain fMRI signals and by using the recently publicly released large-scale Human Connectome Project high-quality fMRI data, we show that a number of reproducible and robust functional networks, including both task-evoked and resting state networks, are simultaneously distributed in distant neuroanatomic areas and substantially spatially overlapping with each other, thus forming an initial collection of holistic atlases of functional networks and interactions (HAFNIs). More interestingly, the HAFNIs revealed two distinct patterns of highly overlapped regions and highly specialized regions and exhibited that these two patterns of areas are reciprocally localized, revealing a novel organizational principle of cortical function.

  4. The Information-Based View on Business Network Performance: Revealing the Performance of Interorganizational Networks

    NARCIS (Netherlands)

    S.R. Koendjbiharie (Sarita)

    2014-01-01

    markdownabstract__Abstract__ In the last thirty years, global developments including advancements in ICT and process and product modularization have made the network form of organization more widespread than ever before. In many industries large vertically integrated organizations have been supplan

  5. Transcriptional networks in single perivascular cells sorted from human adipose tissue reveal a hierarchy of mesenchymal stem cells.

    Science.gov (United States)

    Hardy, W Reef; Moldovan, Nicanor I; Moldovan, Leni; Livak, Kenneth J; Datta; Goswami, Chirayu; Corselli, Mirko; Traktuev, Dmitry O; Murray, Iain R; Péault, Bruno; March, Keith

    2017-02-24

    Adipose tissue is a rich source of multipotent mesenchymal stem-like cells, located in the perivascular niche. Based on their surface markers, these have been assigned to two main categories: CD34+CD31-CD45-CD146- cells (adventitial stromal/stem cells, ASCs), and CD146+CD31-CD34-CD45- cells (pericytes, PCs). These populations display heterogeneity of unknown significance. We hypothesized that aldehyde dehydrogenase (ALDH) activity, a functional marker of primitivity, could help to better define ASC and PC subclasses. To this end, the stromal vascular fraction from a human lipoaspirate was simultaneously stained with fluorescent antibodies to CD31, CD45, CD34, and CD146 antigens and the ALDH substrate Aldefluor®, then sorted by FACS. Individual ASCs (n=67) and PCs (n=73) selected from the extremities of the ALDH-staining spectrum were transcriptionally profiled by Fluidigm single-cell quantitative PCR for a predefined set (n=429) of marker genes. To these single-cell data, we applied differential expression and principal component and clustering analysis, as well as an original gene co-expression network reconstruction algorithm. Despite the stochasticity at the single-cell level, covariation gene expression analysis yielded multiple network connectivity parameters suggesting that these perivascular progenitor cell subclasses possess the following order of maturity: i) ALDH(br) ASC (most primitive); ii) ALDH(dim) ASC; iii) ALDH(br) PC; iv) ALDH(dim) PC (least primitive). This order was independently supported by specific combinations of class-specific expressed genes and further confirmed by the analysis of associated signaling pathways. In conclusion, single-cell transcriptional analysis of four populations isolated from fat by surface markers and enzyme activity suggests a developmental hierarchy among perivascular mesenchymal stem cells supported by markers and co-expression networks. This article is protected by copyright. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Xiujing He

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

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

    Science.gov (United States)

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

    2016-01-01

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

  8. Network catastrophe: self-organized patterns reveal both the instability and the structure of complex networks.

    Science.gov (United States)

    Moon, Hankyu; Lu, Tsai-Ching

    2015-03-30

    Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of-how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description - of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible.

  9. A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.

    Science.gov (United States)

    Niida, Atsushi; Imoto, Seiya; Nagasaki, Masao; Yamaguchi, Rui; Miyano, Satoru

    2010-01-01

    Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks. Starting from functional TF binding motifs and expression modules identified by EEM, we predict cancer transcriptional networks containing regulatory TFs, associated GO terms, and interactions between TF binding motifs. To systematically analyze transcriptional programs in broad types of cancer, we applied our EEM-based network prediction method to 122 microarray datasets collected from public databases. The data sets contain about 15000 experiments for tumor samples of various tissue origins including breast, colon, lung etc. This EEM based meta-analysis successfully revealed a prevailing cancer transcriptional network which functions in a large fraction of cancer transcriptomes; they include cell-cycle and immune related sub-networks. This study demonstrates broad applicability of EEM, and opens a way to comprehensive understanding of transcriptional networks in cancer cells.

  10. Maps of sparse Markov chains efficiently reveal community structure in network flows with memory

    CERN Document Server

    Persson, Christian; Edler, Daniel; Rosvall, Martin

    2016-01-01

    To better understand the flows of ideas or information through social and biological systems, researchers develop maps that reveal important patterns in network flows. In practice, network flow models have implied memoryless first-order Markov chains, but recently researchers have introduced higher-order Markov chain models with memory to capture patterns in multi-step pathways. Higher-order models are particularly important for effectively revealing actual, overlapping community structure, but higher-order Markov chain models suffer from the curse of dimensionality: their vast parameter spaces require exponentially increasing data to avoid overfitting and therefore make mapping inefficient already for moderate-sized systems. To overcome this problem, we introduce an efficient cross-validated mapping approach based on network flows modeled by sparse Markov chains. To illustrate our approach, we present a map of citation flows in science with research fields that overlap in multidisciplinary journals. Compared...

  11. Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma

    Science.gov (United States)

    Azevedo, Hátylas; Moreira-Filho, Carlos Alberto

    2015-11-01

    Biological networks display high robustness against random failures but are vulnerable to targeted attacks on central nodes. Thus, network topology analysis represents a powerful tool for investigating network susceptibility against targeted node removal. Here, we built protein interaction networks associated with chemoresistance to temozolomide, an alkylating agent used in glioma therapy, and analyzed their modular structure and robustness against intentional attack. These networks showed functional modules related to DNA repair, immunity, apoptosis, cell stress, proliferation and migration. Subsequently, network vulnerability was assessed by means of centrality-based attacks based on the removal of node fractions in descending orders of degree, betweenness, or the product of degree and betweenness. This analysis revealed that removing nodes with high degree and high betweenness was more effective in altering networks’ robustness parameters, suggesting that their corresponding proteins may be particularly relevant to target temozolomide resistance. In silico data was used for validation and confirmed that central nodes are more relevant for altering proliferation rates in temozolomide-resistant glioma cell lines and for predicting survival in glioma patients. Altogether, these results demonstrate how the analysis of network vulnerability to topological attack facilitates target prioritization for overcoming cancer chemoresistance.

  12. Co-expressed Pathways DataBase for Tomato: a database to predict pathways relevant to a query gene.

    Science.gov (United States)

    Narise, Takafumi; Sakurai, Nozomu; Obayashi, Takeshi; Ohta, Hiroyuki; Shibata, Daisuke

    2017-06-05

    Gene co-expression, the similarity of gene expression profiles under various experimental conditions, has been used as an indicator of functional relationships between genes, and many co-expression databases have been developed for predicting gene functions. These databases usually provide users with a co-expression network and a list of strongly co-expressed genes for a query gene. Several of these databases also provide functional information on a set of strongly co-expressed genes (i.e., provide biological processes and pathways that are enriched in these strongly co-expressed genes), which is generally analyzed via over-representation analysis (ORA). A limitation of this approach may be that users can predict gene functions only based on the strongly co-expressed genes. In this study, we developed a new co-expression database that enables users to predict the function of tomato genes from the results of functional enrichment analyses of co-expressed genes while considering the genes that are not strongly co-expressed. To achieve this, we used the ORA approach with several thresholds to select co-expressed genes, and performed gene set enrichment analysis (GSEA) applied to a ranked list of genes ordered by the co-expression degree. We found that internal correlation in pathways affected the significance levels of the enrichment analyses. Therefore, we introduced a new measure for evaluating the relationship between the gene and pathway, termed the percentile (p)-score, which enables users to predict functionally relevant pathways without being affected by the internal correlation in pathways. In addition, we evaluated our approaches using receiver operating characteristic curves, which concluded that the p-score could improve the performance of the ORA. We developed a new database, named Co-expressed Pathways DataBase for Tomato, which is available at http://cox-path-db.kazusa.or.jp/tomato . The database allows users to predict pathways that are relevant to a

  13. The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles.

    Science.gov (United States)

    Durek, Pawel; Walther, Dirk

    2008-11-25

    The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems. Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved

  14. Altered brain structural networks in attention deficit/hyperactivity disorder children revealed by cortical thickness.

    Science.gov (United States)

    Liu, Tian; Chen, Yanni; Li, Chenxi; Li, Youjun; Wang, Jue

    2017-01-18

    This study investigated the cortical thickness and topological features of human brain anatomical networks related to attention deficit/hyperactivity disorder. Data were collected from 40 attention deficit/hyperactivity disorder children and 40 normal control children. Interregional correlation matrices were established by calculating the correlations of cortical thickness between all pairs of cortical regions (68 regions) of the whole brain. Further thresholds were applied to create binary matrices to construct a series of undirected and unweighted graphs, and global, local, and nodal efficiencies were computed as a function of the network cost. These experimental results revealed abnormal cortical thickness and correlations in attention deficit/hyperactivity disorder, and showed that the brain structural networks of attention deficit/hyperactivity disorder subjects had inefficient small-world topological features. Furthermore, their topological properties were altered abnormally. In particular, decreased global efficiency combined with increased local efficiency in attention deficit/hyperactivity disorder children led to a disorder-related shift of the network topological structure toward regular networks. In addition, nodal efficiency, cortical thickness, and correlation analyses revealed that several brain regions were altered in attention deficit/hyperactivity disorder patients. These findings are in accordance with a hypothesis of dysfunctional integration and segregation of the brain in patients with attention deficit/hyperactivity disorder and provide further evidence of brain dysfunction in attention deficit/hyperactivity disorder patients by observing cortical thickness on magnetic resonance imaging.

  15. Functional splicing network reveals extensive regulatory potential of the core spliceosomal machinery.

    Science.gov (United States)

    Papasaikas, Panagiotis; Tejedor, J Ramón; Vigevani, Luisa; Valcárcel, Juan

    2015-01-08

    Pre-mRNA splicing relies on the poorly understood dynamic interplay between >150 protein components of the spliceosome. The steps at which splicing can be regulated remain largely unknown. We systematically analyzed the effect of knocking down the components of the splicing machinery on alternative splicing events relevant for cell proliferation and apoptosis and used this information to reconstruct a network of functional interactions. The network accurately captures known physical and functional associations and identifies new ones, revealing remarkable regulatory potential of core spliceosomal components, related to the order and duration of their recruitment during spliceosome assembly. In contrast with standard models of regulation at early steps of splice site recognition, factors involved in catalytic activation of the spliceosome display regulatory properties. The network also sheds light on the antagonism between hnRNP C and U2AF, and on targets of antitumor drugs, and can be widely used to identify mechanisms of splicing regulation.

  16. Network analysis of oyster transcriptome revealed a cascade of cellular responses during recovery after heat shock.

    Directory of Open Access Journals (Sweden)

    Lingling Zhang

    Full Text Available Oysters, as a major group of marine bivalves, can tolerate a wide range of natural and anthropogenic stressors including heat stress. Recent studies have shown that oysters pretreated with heat shock can result in induced heat tolerance. A systematic study of cellular recovery from heat shock may provide insights into the mechanism of acquired thermal tolerance. In this study, we performed the first network analysis of oyster transcriptome by reanalyzing microarray data from a previous study. Network analysis revealed a cascade of cellular responses during oyster recovery after heat shock and identified responsive gene modules and key genes. Our study demonstrates the power of network analysis in a non-model organism with poor gene annotations, which can lead to new discoveries that go beyond the focus on individual genes.

  17. Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography.

    Science.gov (United States)

    Shu, Ni; Liu, Yaou; Duan, Yunyun; Li, Kuncheng

    2015-01-01

    The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain.

  18. Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

    Directory of Open Access Journals (Sweden)

    Ni Shu

    2015-01-01

    Full Text Available The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain.

  19. Whole genome co-expression analysis of soybean cytochrome P450 genes identifies nodulation-specific P450 monooxygenases

    Directory of Open Access Journals (Sweden)

    Pandey Sona

    2010-11-01

    isoflavone synthase gene is co-expressed with several genes encoding isoflavonoid-related metabolic enzymes. We then focused on nodulation-induced P450s and found that CYP728H1 was co-expressed with the genes involved in phenylpropanoid metabolism. Similarly, CYP736A34 was highly co-expressed with lipoxygenase, lectin and CYP83D1, all of which are involved in root and nodule development. Conclusions The genome scale analysis of P450s in soybean reveals many unique features of these important enzymes in this crop although the functions of most of them are largely unknown. Gene co-expression analysis proves to be a useful tool to infer the function of uncharacterized genes. Our work presented here could provide important leads toward functional genomics studies of soybean P450s and their regulatory network through the integration of reverse genetics, biochemistry, and metabolic profiling tools. The identification of nodule-specific P450s and their further exploitation may help us to better understand the intriguing process of soybean and rhizobium interaction.

  20. Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion.

    Science.gov (United States)

    Rosenthal, Sara Brin; Twomey, Colin R; Hartnett, Andrew T; Wu, Hai Shan; Couzin, Iain D

    2015-04-14

    Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.

  1. Structured teleconnections reveal the South American monsoon onset: A network approach

    Science.gov (United States)

    Ciemer, Catrin; Ekhtiari, Nikoo; Barbosa, Henrique; Boers, Niklas; Donner, Reik; Kurths, Jürgen; Rammig, Anja; Winkelmann, Ricarda

    2017-04-01

    The regional onset dates of the global monsoon systems are, to first order, determined by the seasonal shift of the intertropical convergence zone. However, precise onset dates vary substantially from year to year due to the complexity of the involved mechanisms. In this study, we investigate processes determining the onset of the South American monsoon system (SAMS). In recent years, a trend towards later onset dates of the SAMS has been observed. A later onset of the monsoon can have severe impacts on agriculture and infrastructure such as farming, water transport routes, and the stability of the Amazon rainforest in the long term. Possible reasons for this shift involve a multitude of climatic phenomena and variables relevant for the SAMS. To account for the highly interactive nature of the SAMS, we here investigate it with the help of complex networks. By studying the temporal changes of the correlation structure in spatial rainfall networks, we are able to determine coherent areas of similar precipitation patterns, spot teleconnections in terms of strongly correlated areas, detect key regions for precipitation correlations, and finally reveal the monsoon onset by an abrupt shift from an unordered to an ordered correlation structure of the network. To further evaluate the shift in the monsoon onset, we couple our rainfall network to a network of climate networks using sea surface temperature as a second variable. We are thereby able to emphasize oceanic regions that are particularly important for the SAMS and anticipate the influence of future changes of sea-surface temperature on the SAMS.

  2. Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

    Science.gov (United States)

    Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  3. Large-scale co-expression approach to dissect secondary cell wall formation across plant species

    Directory of Open Access Journals (Sweden)

    Colin eRuprecht

    2011-07-01

    Full Text Available Plant cell walls are complex composites largely consisting of carbohydrate-based polymers, and are generally divided into primary and secondary walls based on content and characteristics. Cellulose microfibrils constitute a major component of both primary and secondary cell walls and are synthesized at the plasma membrane by cellulose synthase (CESA complexes. Several studies in Arabidopsis have demonstrated the power of co-expression analyses to identify new genes associated with secondary wall cellulose biosynthesis. However, across-species comparative co-expression analyses remain largely unexplored. Here, we compared co-expressed gene vicinity networks of primary and secondary wall CESAs in Arabidopsis, barley, rice, poplar, soybean, Medicago and wheat, and identified gene families that are consistently co-regulated with cellulose biosynthesis. In addition to the expected polysaccharide acting enzymes, we also found many gene families associated with cytoskeleton, signaling, transcriptional regulation, oxidation and protein degradation. Based on these analyses, we selected and biochemically analyzed T-DNA insertion lines corresponding to approximately twenty genes from gene families that re-occur in the co-expressed gene vicinity networks of secondary wall CESAs across the seven species. We developed a statistical pipeline using principal component analysis (PCA and optimal clustering based on silhouette width to analyze sugar profiles. One of the mutants, corresponding to a pinoresinol reductase gene, displayed disturbed xylem morphology and held lower levels of lignin molecules. We propose that this type of large-scale co-expression approach, coupled with statistical analysis of the cell wall contents, will be useful to facilitate rapid knowledge transfer across plant species.

  4. Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

    CERN Document Server

    Racah, Evan; Sadowski, Peter; Bhimji, Wahid; Tull, Craig; Oh, Sang-Yun; Baldi, Pierre; Prabhat,

    2016-01-01

    Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.

  5. Facilitators on networks reveal the optimal interplay between information exchange and reciprocity

    CERN Document Server

    Szolnoki, Attila; Mobilia, Mauro

    2014-01-01

    Reciprocity is firmly established as an important mechanism that promotes cooperation. An efficient information exchange is likewise important, especially on structured populations, where interactions between players are limited. Motivated by these two facts, we explore the role of facilitators in social dilemmas on networks. Facilitators are here mirrors to their neighbors -- they cooperate with cooperators and defect with defectors -- but they do not participate in the exchange of strategies. As such, in addition to introducing direct reciprocity, they also obstruct information exchange. In well-mixed populations, facilitators favor the replacement and invasion of defection by cooperation as long as their number exceeds a critical value. In structured populations, on the other hand, there exists a delicate balance between the benefits of reciprocity and the deterioration of information exchange. Extensive Monte Carlo simulations of social dilemmas on various interaction networks reveal that there exists an ...

  6. Network modularity reveals critical scales for connectivity in ecology and evolution.

    Science.gov (United States)

    Fletcher, Robert J; Revell, Andre; Reichert, Brian E; Kitchens, Wiley M; Dixon, Jeremy D; Austin, James D

    2013-01-01

    For nearly a century, biologists have emphasized the profound importance of spatial scale for ecology, evolution and conservation. Nonetheless, objectively identifying critical scales has proven incredibly challenging. Here we extend new techniques from physics and social sciences that estimate modularity on networks to identify critical scales for movement and gene flow in animals. Using four species that vary widely in dispersal ability and include both mark-recapture and population genetic data, we identify significant modularity in three species, two of which cannot be explained by geographic distance alone. Importantly, the inclusion of modularity in connectivity and population viability assessments alters conclusions regarding patch importance to connectivity and suggests higher metapopulation viability than when ignoring this hidden spatial scale. We argue that network modularity reveals critical meso-scales that are probably common in populations, providing a powerful means of identifying fundamental scales for biology and for conservation strategies aimed at recovering imperilled species.

  7. Network modularity reveals critical scales for connectivity in ecology and evolution

    Science.gov (United States)

    Fletcher, Robert J.; Revell, Andre; Reichert, Brian E.; Kitchens, Wiley M.; Dixon, J.; Austin, James D.

    2013-01-01

    For nearly a century, biologists have emphasized the profound importance of spatial scale for ecology, evolution and conservation. Nonetheless, objectively identifying critical scales has proven incredibly challenging. Here we extend new techniques from physics and social sciences that estimate modularity on networks to identify critical scales for movement and gene flow in animals. Using four species that vary widely in dispersal ability and include both mark-recapture and population genetic data, we identify significant modularity in three species, two of which cannot be explained by geographic distance alone. Importantly, the inclusion of modularity in connectivity and population viability assessments alters conclusions regarding patch importance to connectivity and suggests higher metapopulation viability than when ignoring this hidden spatial scale. We argue that network modularity reveals critical meso-scales that are probably common in populations, providing a powerful means of identifying fundamental scales for biology and for conservation strategies aimed at recovering imperilled species.

  8. Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior.

    Science.gov (United States)

    Portugues, Ruben; Feierstein, Claudia E; Engert, Florian; Orger, Michael B

    2014-03-19

    Most behaviors, even simple innate reflexes, are mediated by circuits of neurons spanning areas throughout the brain. However, in most cases, the distribution and dynamics of firing patterns of these neurons during behavior are not known. We imaged activity, with cellular resolution, throughout the whole brains of zebrafish performing the optokinetic response. We found a sparse, broadly distributed network that has an elaborate but ordered pattern, with a bilaterally symmetrical organization. Activity patterns fell into distinct clusters reflecting sensory and motor processing. By correlating neuronal responses with an array of sensory and motor variables, we find that the network can be clearly divided into distinct functional modules. Comparing aligned data from multiple fish, we find that the spatiotemporal activity dynamics and functional organization are highly stereotyped across individuals. These experiments systematically reveal the functional architecture of neural circuits underlying a sensorimotor behavior in a vertebrate brain.

  9. Combined genome-wide expression profiling and targeted RNA interference in primary mouse macrophages reveals perturbation of transcriptional networks associated with interferon signalling

    Directory of Open Access Journals (Sweden)

    Craigon Marie

    2009-08-01

    Full Text Available Abstract Background Interferons (IFNs are potent antiviral cytokines capable of reprogramming the macrophage phenotype through the induction of interferon-stimulated genes (ISGs. Here we have used targeted RNA interference to suppress the expression of a number of key genes associated with IFN signalling in murine macrophages prior to stimulation with interferon-gamma. Genome-wide changes in transcript abundance caused by siRNA activity were measured using exon-level microarrays in the presence or absence of IFNγ. Results Transfection of murine bone-marrow derived macrophages (BMDMs with a non-targeting (control siRNA and 11 sequence-specific siRNAs was performed using a cationic lipid transfection reagent (Lipofectamine2000 prior to stimulation with IFNγ. Total RNA was harvested from cells and gene expression measured on Affymetrix GeneChip Mouse Exon 1.0 ST Arrays. Network-based analysis of these data revealed six siRNAs to cause a marked shift in the macrophage transcriptome in the presence or absence IFNγ. These six siRNAs targeted the Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2 transcripts. The perturbation of the transcriptome by the six siRNAs was highly similar in each case and affected the expression of over 600 downstream transcripts. Regulated transcripts were clustered based on co-expression into five major groups corresponding to transcriptional networks associated with the type I and II IFN response, cell cycle regulation, and NF-KB signalling. In addition we have observed a significant non-specific immune stimulation of cells transfected with siRNA using Lipofectamine2000, suggesting use of this reagent in BMDMs, even at low concentrations, is enough to induce a type I IFN response. Conclusion Our results provide evidence that the type I IFN response in murine BMDMs is dependent on Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2, and that siRNAs targeted to these genes results in perturbation of key transcriptional networks associated

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

    Science.gov (United States)

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

    2009-09-01

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

  11. Genetic networking of the Bemisia tabaci cryptic species complex reveals pattern of biological invasions.

    Directory of Open Access Journals (Sweden)

    Paul De Barro

    Full Text Available BACKGROUND: A challenge within the context of cryptic species is the delimitation of individual species within the complex. Statistical parsimony network analytics offers the opportunity to explore limits in situations where there are insufficient species-specific morphological characters to separate taxa. The results also enable us to explore the spread in taxa that have invaded globally. METHODOLOGY/PRINCIPAL FINDINGS: Using a 657 bp portion of mitochondrial cytochrome oxidase 1 from 352 unique haplotypes belonging to the Bemisia tabaci cryptic species complex, the analysis revealed 28 networks plus 7 unconnected individual haplotypes. Of the networks, 24 corresponded to the putative species identified using the rule set devised by Dinsdale et al. (2010. Only two species proposed in Dinsdale et al. (2010 departed substantially from the structure suggested by the analysis. The analysis of the two invasive members of the complex, Mediterranean (MED and Middle East - Asia Minor 1 (MEAM1, showed that in both cases only a small number of haplotypes represent the majority that have spread beyond the home range; one MEAM1 and three MED haplotypes account for >80% of the GenBank records. Israel is a possible source of the globally invasive MEAM1 whereas MED has two possible sources. The first is the eastern Mediterranean which has invaded only the USA, primarily Florida and to a lesser extent California. The second are western Mediterranean haplotypes that have spread to the USA, Asia and South America. The structure for MED supports two home range distributions, a Sub-Saharan range and a Mediterranean range. The MEAM1 network supports the Middle East - Asia Minor region. CONCLUSION/SIGNIFICANCE: The network analyses show a high level of congruence with the species identified in a previous phylogenetic analysis. The analysis of the two globally invasive members of the complex support the view that global invasion often involve very small portions of

  12. Genome Neighborhood Network Reveals Insights into Enediyne Biosynthesis and Facilitates Prediction and Prioritization for Discovery

    Science.gov (United States)

    Rudolf, Jeffrey D.; Yan, Xiaohui; Shen, Ben

    2015-01-01

    The enediynes are one of the most fascinating families of bacterial natural products given their unprecedented molecular architecture and extraordinary cytotoxicity. Enediynes are rare with only 11 structurally characterized members and four additional members isolated in their cycloaromatized form. Recent advances in DNA sequencing have resulted in an explosion of microbial genomes. A virtual survey of the GenBank and JGI genome databases revealed 87 enediyne biosynthetic gene clusters from 78 bacteria strains, implying enediynes are more common than previously thought. Here we report the construction and analysis of an enediyne genome neighborhood network (GNN) as a high-throughput approach to analyze secondary metabolite gene clusters. Analysis of the enediyne GNN facilitated rapid gene cluster annotation, revealed genetic trends in enediyne biosynthetic gene clusters resulting in a simple prediction scheme to determine 9- vs 10-membered enediyne gene clusters, and supported a genomic-based strain prioritization method for enediyne discovery. PMID:26318027

  13. Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation.

    Science.gov (United States)

    Maier, Ezekiel J; Haynes, Brian C; Gish, Stacey R; Wang, Zhuo A; Skowyra, Michael L; Marulli, Alyssa L; Doering, Tamara L; Brent, Michael R

    2015-05-01

    Key steps in understanding a biological process include identifying genes that are involved and determining how they are regulated. We developed a novel method for identifying transcription factors (TFs) involved in a specific process and used it to map regulation of the key virulence factor of a deadly fungus-its capsule. The map, built from expression profiles of 41 TF mutants, includes 20 TFs not previously known to regulate virulence attributes. It also reveals a hierarchy comprising executive, midlevel, and "foreman" TFs. When grouped by temporal expression pattern, these TFs explain much of the transcriptional dynamics of capsule induction. Phenotypic analysis of TF deletion mutants revealed complex relationships among virulence factors and virulence in mice. These resources and analyses provide the first integrated, systems-level view of capsule regulation and biosynthesis. Our methods dramatically improve the efficiency with which transcriptional networks can be analyzed, making genomic approaches accessible to laboratories focused on specific physiological processes.

  14. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation.

    Science.gov (United States)

    Xue, Jia; Schmidt, Susanne V; Sander, Jil; Draffehn, Astrid; Krebs, Wolfgang; Quester, Inga; De Nardo, Dominic; Gohel, Trupti D; Emde, Martina; Schmidleithner, Lisa; Ganesan, Hariharasudan; Nino-Castro, Andrea; Mallmann, Michael R; Labzin, Larisa; Theis, Heidi; Kraut, Michael; Beyer, Marc; Latz, Eicke; Freeman, Tom C; Ulas, Thomas; Schultze, Joachim L

    2014-02-20

    Macrophage activation is associated with profound transcriptional reprogramming. Although much progress has been made in the understanding of macrophage activation, polarization, and function, the transcriptional programs regulating these processes remain poorly characterized. We stimulated human macrophages with diverse activation signals, acquiring a data set of 299 macrophage transcriptomes. Analysis of this data set revealed a spectrum of macrophage activation states extending the current M1 versus M2-polarization model. Network analyses identified central transcriptional regulators associated with all macrophage activation complemented by regulators related to stimulus-specific programs. Applying these transcriptional programs to human alveolar macrophages from smokers and patients with chronic obstructive pulmonary disease (COPD) revealed an unexpected loss of inflammatory signatures in COPD patients. Finally, by integrating murine data from the ImmGen project we propose a refined, activation-independent core signature for human and murine macrophages. This resource serves as a framework for future research into regulation of macrophage activation in health and disease.

  15. Mesoscopic Structures Reveal the Network Between the Layers of Multiplex Datasets

    CERN Document Server

    Iacovacci, Jacopo; Bianconi, Ginestra

    2015-01-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex datasets, forming a "network of networks". We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collab...

  16. Facilitators on networks reveal optimal interplay between information exchange and reciprocity

    Science.gov (United States)

    Szolnoki, Attila; Perc, Matjaž; Mobilia, Mauro

    2014-04-01

    Reciprocity is firmly established as an important mechanism that promotes cooperation. An efficient information exchange is likewise important, especially on structured populations, where interactions between players are limited. Motivated by these two facts, we explore the role of facilitators in social dilemmas on networks. Facilitators are here mirrors to their neighbors—they cooperate with cooperators and defect with defectors—but they do not participate in the exchange of strategies. As such, in addition to introducing direct reciprocity, they also obstruct information exchange. In well-mixed populations, facilitators favor the replacement and invasion of defection by cooperation as long as their number exceeds a critical value. In structured populations, on the other hand, there exists a delicate balance between the benefits of reciprocity and the deterioration of information exchange. Extensive Monte Carlo simulations of social dilemmas on various interaction networks reveal that there exists an optimal interplay between reciprocity and information exchange, which sets in only when a small number of facilitators occupy the main hubs of the scale-free network. The drawbacks of missing cooperative hubs are more than compensated for by reciprocity and, at the same time, the compromised information exchange is routed via the auxiliary hubs with only marginal losses in effectivity. These results indicate that it is not always optimal for the main hubs to become leaders of the masses, but rather to exploit their highly connected state to promote tit-for-tat-like behavior.

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

    Science.gov (United States)

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

    2016-05-01

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

  18. Network topology reveal high connectance levels and few key microbial genera within soils

    Directory of Open Access Journals (Sweden)

    Manoeli eLupatini

    2014-05-01

    Full Text Available Microbes have a central role in soil global biogeochemical process, yet specific microbe-microbe relationships are largely unknown. Analytical approaches as network analysis may shed new lights in understanding of microbial ecology and environmental microbiology. We investigated the soil bacterial community interactions through cultivation-independent methods in several land uses common in two Brazilian biomes. Using correlation network analysis we identified bacterial genera that presented important microbial associations within the soil community. The associations revealed nonrandomly structured microbial communities and clusters of operational taxonomic units (OTUs that reflected relevant bacterial relationships. Possible keystone genera were found in each soil. Irrespective of the biome or land use studied only a small portion of OTUs showed positive or negative interaction with other members of the soil bacterial community. The more interactive genera were also more abundant however, within those genera, the abundance was not related to taxon importance as measured by the Betweenness Centrality. Most of the soil bacterial genera were important to the overall connectance of the network, whereas only few genera play a key role as connectors, mainly belonged to phyla Proteobacteria and Actinobacteria. Finally it was observed that each land use presented a different set of keystone genera and that no keystone genus presented a generalized distribution. Taking into account that species interactions could be more important to soil processes than species richness and abundance, especially in complex ecosystems, this approach might represent a step forward in microbial ecology beyond the conventional studies of microbial richness and abundance.

  19. Sequence similarity network reveals the imprints of major diversification events in the evolution of microbial life

    Directory of Open Access Journals (Sweden)

    Shu eCheng

    2014-11-01

    Full Text Available Ancient transitions, such as between life that evolved in a reducing versus an oxidizing atmosphere precipitated by the Great Oxygenation Event (GOE ca. 2.4 billion years ago, fundamentally altered the space in which prokaryotes could derive metabolic energy. Despite fundamental changes in Earth’s redox state, there are very few comprehensive, proteome-wide analyses about the effects of these changes on gene content and evolution. Here, using a pan-proteome sequence similarity network applied to broadly sampled lifestyles of 84 prokaryotes that were categorized into four different redox groups (i.e., methanogens, obligate anaerobes, facultative anaerobes, and obligate aerobes, we reconstructed the genetic inventory of major respiratory communities. We show that a set of putative core homologs that is highly conserved in prokaryotic proteomes is characterized by the loss of canonical network connections and low conductance that correlates with differences in respiratory phenotypes. We suggest these different network patterns observed for different respiratory communities could be explained by two major evolutionary diversification events in the history of microbial life. The first event (M is a divergence between methanogenesis and other anaerobic lifestyles in prokaryotes (archaebacteria and eubacteria. The second diversification event (OX is from anaerobic to aerobic lifestyles that left a proteome-wide footprint among prokaryotes. Additional analyses revealed that oxidoreductase evolution played a central role in these two diversification events. Distinct cofactor binding domains were frequently recombined, allowing these enzymes to utilize increasingly oxidized substrates with high specificity.

  20. Identification of unstable network modules reveals disease modules associated with the progression of Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Masataka Kikuchi

    Full Text Available Alzheimer's disease (AD, the most common cause of dementia, is associated with aging, and it leads to neuron death. Deposits of amyloid β and aberrantly phosphorylated tau protein are known as pathological hallmarks of AD, but the underlying mechanisms have not yet been revealed. A high-throughput gene expression analysis previously showed that differentially expressed genes accompanying the progression of AD were more down-regulated than up-regulated in the later stages of AD. This suggested that the molecular networks and their constituent modules collapsed along with AD progression. In this study, by using gene expression profiles and protein interaction networks (PINs, we identified the PINs expressed in three brain regions: the entorhinal cortex (EC, hippocampus (HIP and superior frontal gyrus (SFG. Dividing the expressed PINs into modules, we examined the stability of the modules with AD progression and with normal aging. We found that in the AD modules, the constituent proteins, interactions and cellular functions were not maintained between consecutive stages through all brain regions. Interestingly, the modules were collapsed with AD progression, specifically in the EC region. By identifying the modules that were affected by AD pathology, we found the transcriptional regulation-associated modules that interact with the proteasome-associated module via UCHL5 hub protein, which is a deubiquitinating enzyme. Considering PINs as a system made of network modules, we found that the modules relevant to the transcriptional regulation are disrupted in the EC region, which affects the ubiquitin-proteasome system.

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

    Directory of Open Access Journals (Sweden)

    Qingying Meng

    2016-05-01

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

  2. Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola

    Science.gov (United States)

    Wan, Huafang; Cui, Yixin; Ding, Yijuan; Mei, Jiaqin; Dong, Hongli; Zhang, Wenxin; Wu, Shiqi; Liang, Ying; Zhang, Chunyu; Li, Jiana; Xiong, Qing; Qian, Wei

    2017-01-01

    Understanding the regulation of lipid metabolism is vital for genetic engineering of canola (Brassica napus L.) to increase oil yield or modify oil composition. We conducted time-series analyses of transcriptomes and proteomes to uncover the molecular networks associated with oil accumulation and dynamic changes in these networks in canola. The expression levels of genes and proteins were measured at 2, 4, 6, and 8 weeks after pollination (WAP). Our results show that the biosynthesis of fatty acids is a dominant cellular process from 2 to 6 WAP, while the degradation mainly happens after 6 WAP. We found that genes in almost every node of fatty acid synthesis pathway were significantly up-regulated during oil accumulation. Moreover, significant expression changes of two genes, acetyl-CoA carboxylase and acyl-ACP desaturase, were detected on both transcriptomic and proteomic levels. We confirmed the temporal expression patterns revealed by the transcriptomic analyses using quantitative real-time PCR experiments. The gene set association analysis show that the biosynthesis of fatty acids and unsaturated fatty acids are the most significant biological processes from 2-4 WAP and 4-6 WAP, respectively, which is consistent with the results of time-series analyses. These results not only provide insight into the mechanisms underlying lipid metabolism, but also reveal novel candidate genes that are worth further investigation for their values in the genetic engineering of canola. PMID:28119706

  3. ALE meta-analysis reveals dissociable networks for affective and discriminative aspects of touch.

    Science.gov (United States)

    Morrison, India

    2016-04-01

    Emotionally-laden tactile stimulation-such as a caress on the skin or the feel of velvet-may represent a functionally distinct domain of touch, underpinned by specific cortical pathways. In order to determine whether, and to what extent, cortical functional neuroanatomy supports a distinction between affective and discriminative touch, an activation likelihood estimate (ALE) meta-analysis was performed. This meta-analysis statistically mapped reported functional magnetic resonance imaging (fMRI) activations from 17 published affective touch studies in which tactile stimulation was associated with positive subjective evaluation (n = 291, 34 experimental contrasts). A separate ALE meta-analysis mapped regions most likely to be activated by tactile stimulation during detection and discrimination tasks (n = 1,075, 91 experimental contrasts). These meta-analyses revealed dissociable regions for affective and discriminative touch, with posterior insula (PI) more likely to be activated for affective touch, and primary somatosensory cortices (SI) more likely to be activated for discriminative touch. Secondary somatosensory cortex had a high likelihood of engagement by both affective and discriminative touch. Further, meta-analytic connectivity (MCAM) analyses investigated network-level co-activation likelihoods independent of task or stimulus, across a range of domains and paradigms. Affective-related PI and discriminative-related SI regions co-activated with different networks, implicated in dissociable functions, but sharing somatosensory co-activations. Taken together, these meta-analytic findings suggest that affective and discriminative touch are dissociable both on the regional and network levels. However, their degree of shared activation likelihood in somatosensory cortices indicates that this dissociation reflects functional biases within tactile processing networks, rather than functionally and anatomically distinct pathways.

  4. Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling.

    Directory of Open Access Journals (Sweden)

    Christine T Ferrara

    2008-03-01

    Full Text Available Although numerous quantitative trait loci (QTL influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin(ob/ob and the diabetes-susceptible BTBR leptin(ob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines. We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

  5. Lifespan Development of the Human Brain Revealed by Large-Scale Network Eigen-Entropy

    Directory of Open Access Journals (Sweden)

    Yiming Fan

    2017-09-01

    Full Text Available Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying functional connectivity patterns of the developing and aging brain. Normal brain development is characterized by continuous and significant network evolution through infancy, childhood, and adolescence, following specific maturational patterns. Normal aging is related to some resting state brain networks disruption, which are associated with certain cognitive decline. It is a big challenge to design an integral metric to track connectome evolution patterns across the lifespan, which is to understand the principles of network organization in the human brain. In this study, we first defined a brain network eigen-entropy (NEE based on the energy probability (EP of each brain node. Next, we used the NEE to characterize the lifespan orderness trajectory of the whole-brain functional connectivity of 173 healthy individuals ranging in age from 7 to 85 years. The results revealed that during the lifespan, the whole-brain NEE exhibited a significant non-linear decrease and that the EP distribution shifted from concentration to wide dispersion, implying orderness enhancement of functional connectome over age. Furthermore, brain regions with significant EP changes from the flourishing (7–20 years to the youth period (23–38 years were mainly located in the right prefrontal cortex and basal ganglia, and were involved in emotion regulation and executive function in coordination with the action of the sensory system, implying that self-awareness and voluntary control performance significantly changed during neurodevelopment. However, the changes from the youth period to middle age (40–59 years were located in the mesial temporal lobe and caudate, which are associated with long-term memory, implying that the memory of the human brain begins to decline with age during this period. Overall, the findings suggested that the human connectome

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

    Science.gov (United States)

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

    2015-10-19

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

  7. SLC9A9 Co-expression modules in autism-associated brain regions.

    Science.gov (United States)

    Patak, Jameson; Hess, Jonathan L; Zhang-James, Yanli; Glatt, Stephen J; Faraone, Stephen V

    2016-07-21

    SLC9A9 is a sodium hydrogen exchanger present in the recycling endosome and highly expressed in the brain. It is implicated in neuropsychiatric disorders, including autism spectrum disorders (ASDs). Little research concerning its gene expression patterns and biological pathways has been conducted. We sought to investigate its possible biological roles in autism-associated brain regions throughout development. We conducted a weighted gene co-expression network analysis on RNA-seq data downloaded from Brainspan. We compared prenatal and postnatal gene expression networks for three ASD-associated brain regions known to have high SLC9A9 gene expression. We also performed an ASD-associated single nucleotide polymorphism enrichment analysis and a cell signature enrichment analysis. The modules showed differences in gene constituents (membership), gene number, and connectivity throughout time. SLC9A9 was highly associated with immune system functions, metabolism, apoptosis, endocytosis, and signaling cascades. Gene list comparison with co-immunoprecipitation data was significant for multiple modules. We found a disproportionately high autism risk signal among genes constituting the prenatal hippocampal module. The modules were enriched with astrocyte and oligodendrocyte markers. SLC9A9 is potentially involved in the pathophysiology of ASDs. Our investigation confirmed proposed functions for SLC9A9, such as endocytosis and immune regulation, while also revealing potential roles in mTOR signaling and cell survival.. By providing a concise molecular map and interactions, evidence of cell type and implicated brain regions we hope this will guide future research on SLC9A9. Autism Res 2016. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

  8. Genome-wide tissue-specific gene expression, co-expression and regulation of co-expressed genes in adult nematode Ascaris suum.

    Directory of Open Access Journals (Sweden)

    Bruce A Rosa

    2014-02-01

    Full Text Available BACKGROUND: Caenorhabditis elegans has traditionally been used as a model for studying nematode biology, but its small size limits the ability for researchers to perform some experiments such as high-throughput tissue-specific gene expression studies. However, the dissection of individual tissues is possible in the parasitic nematode Ascaris suum due to its relatively large size. Here, we take advantage of the recent genome sequencing of Ascaris suum and the ability to physically dissect its separate tissues to produce a wide-scale tissue-specific nematode RNA-seq datasets, including data on three non-reproductive tissues (head, pharynx, and intestine in both male and female worms, as well as four reproductive tissues (testis, seminal vesicle, ovary, and uterus. We obtained fundamental information about the biology of diverse cell types and potential interactions among tissues within this multicellular organism. METHODOLOGY/PRINCIPAL FINDINGS: Overexpression and functional enrichment analyses identified many putative biological functions enriched in each tissue studied, including functions which have not been previously studied in detail in nematodes. Putative tissue-specific transcriptional factors and corresponding binding motifs that regulate expression in each tissue were identified, including the intestine-enriched ELT-2 motif/transcription factor previously described in nematode intestines. Constitutively expressed and novel genes were also characterized, with the largest number of novel genes found to be overexpressed in the testis. Finally, a putative acetylcholine-mediated transcriptional network connecting biological activity in the head to the male reproductive system is described using co-expression networks, along with a similar ecdysone-mediated system in the female. CONCLUSIONS/SIGNIFICANCE: The expression profiles, co-expression networks and co-expression regulation of the 10 tissues studied and the tissue-specific analysis

  9. Adhesion protein networks reveal functions proximal and distal to cell-matrix contacts.

    Science.gov (United States)

    Byron, Adam; Frame, Margaret C

    2016-04-01

    Cell adhesion to the extracellular matrix is generally mediated by integrin receptors, which bind to intracellular adhesion proteins that form multi-molecular scaffolding and signalling complexes. The networks of proteins, and their interactions, are dynamic, mechanosensitive and extremely complex. Recent efforts to characterise adhesions using a variety of technologies, including imaging, proteomics and bioinformatics, have provided new insights into their composition, organisation and how they are regulated, and have also begun to reveal unexpected roles for so-called adhesion proteins in other cellular compartments (for example, the nucleus or centrosomes) in diseases such as cancer. We believe this is opening a new chapter on understanding the wider functions of adhesion proteins, both proximal and distal to cell-matrix contacts.

  10. Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej; Pers, Tune Hannes; Pinho Soares, Simao Pedro;

    2010-01-01

    Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular...... with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment...... of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected...

  11. Network-based diffusion analysis reveals cultural transmission of lobtail feeding in humpback whales.

    Science.gov (United States)

    Allen, Jenny; Weinrich, Mason; Hoppitt, Will; Rendell, Luke

    2013-04-26

    We used network-based diffusion analysis to reveal the cultural spread of a naturally occurring foraging innovation, lobtail feeding, through a population of humpback whales (Megaptera novaeangliae) over a period of 27 years. Support for models with a social transmission component was 6 to 23 orders of magnitude greater than for models without. The spatial and temporal distribution of sand lance, a prey species, was also important in predicting the rate of acquisition. Our results, coupled with existing knowledge about song traditions, show that this species can maintain multiple independently evolving traditions in its populations. These insights strengthen the case that cetaceans represent a peak in the evolution of nonhuman culture, independent of the primate lineage.

  12. Information processing reveals how microscopic components affect the macroscopic system-state in complex networks

    CERN Document Server

    Quax, Rick; Sloot, Peter M A

    2013-01-01

    Nature processes information. We observe this from physical systems, which register information in the system state, transfer information through interactions, and lose information due to thermal noise. Being able to quantify this information processing could lead to a unifying framework for a better understanding of complex systems. In this letter we present a formalism to describe to what extent a macroscopic system is affected by the microstates of its constituents. We study this numerically for a scale-free network of Ising-spins, a prototypical complex system, and present an answer to the unexplained phenomenon that real systems with a heterogeneous topology are mainly controlled by nodes with fewer connections. Counter to intuition we find that due to selective information dissipation, not the hubs but rather the intermediately connected nodes are remembered best by the system. Our study reveals that the framework of information processing improves our understanding of complex systems at large.

  13. Scalably Revealing the Dynamics of Soft Community Structure in Complex Networks

    Institute of Scientific and Technical Information of China (English)

    LI Huijia; LI Huiying

    2016-01-01

    Revealing the dynamics of community structure is of great concern for scientists from many fields.Specifically,how to quantify the dynamic details of soft community structure is a very interesting topic.In this paper,the authors propose a novel framework to study the scalable dynamic behavior of the soft community structure.First,the authors model the Potts dynamics to detect community structure using a "soft" Markov process.Then the soft stability of in a multiscale view is proposed to naturally uncover the local uniform behavior of spin values across multiple hierarchical levels.Finally,a new partition index is developed to detect fuzzy communities based on the stability and the dynamical information.Experiments on the both synthetically generated and real-world networks verify that the framework can be used to uncover hierarchical community structures effectively and efficiently.

  14. Quantitative mass spectrometry reveals plasticity of metabolic networks in Mycobacterium smegmatis.

    Science.gov (United States)

    Chopra, Tarun; Hamelin, Romain; Armand, Florence; Chiappe, Diego; Moniatte, Marc; McKinney, John D

    2014-11-01

    Mycobacterium tuberculosis has a remarkable ability to persist within the human host as a clinically inapparent or chronically active infection. Fatty acids are thought to be an important carbon source used by the bacteria during long term infection. Catabolism of fatty acids requires reprogramming of metabolic networks, and enzymes central to this reprogramming have been targeted for drug discovery. Mycobacterium smegmatis, a nonpathogenic relative of M. tuberculosis, is often used as a model system because of the similarity of basic cellular processes in these two species. Here, we take a quantitative proteomics-based approach to achieve a global view of how the M. smegmatis metabolic network adjusts to utilization of fatty acids as a carbon source. Two-dimensional liquid chromatography and mass spectrometry of isotopically labeled proteins identified a total of 3,067 proteins with high confidence. This number corresponds to 44% of the predicted M. smegmatis proteome and includes most of the predicted metabolic enzymes. Compared with glucose-grown cells, 162 proteins showed differential abundance in acetate- or propionate-grown cells. Among these, acetate-grown cells showed a higher abundance of proteins that could constitute a functional glycerate pathway. Gene inactivation experiments confirmed that both the glyoxylate shunt and the glycerate pathway are operational in M. smegmatis. In addition to proteins with annotated functions, we demonstrate carbon source-dependent differential abundance of proteins that have not been functionally characterized. These proteins might play as-yet-unidentified roles in mycobacterial carbon metabolism. This study reveals several novel features of carbon assimilation in M. smegmatis, which suggests significant functional plasticity of metabolic networks in this organism.

  15. Rhythms of consciousness: binocular rivalry reveals large-scale oscillatory network dynamics mediating visual perception.

    Directory of Open Access Journals (Sweden)

    Sam M Doesburg

    Full Text Available Consciousness has been proposed to emerge from functionally integrated large-scale ensembles of gamma-synchronous neural populations that form and dissolve at a frequency in the theta band. We propose that discrete moments of perceptual experience are implemented by transient gamma-band synchronization of relevant cortical regions, and that disintegration and reintegration of these assemblies is time-locked to ongoing theta oscillations. In support of this hypothesis we provide evidence that (1 perceptual switching during binocular rivalry is time-locked to gamma-band synchronizations which recur at a theta rate, indicating that the onset of new conscious percepts coincides with the emergence of a new gamma-synchronous assembly that is locked to an ongoing theta rhythm; (2 localization of the generators of these gamma rhythms reveals recurrent prefrontal and parietal sources; (3 theta modulation of gamma-band synchronization is observed between and within the activated brain regions. These results suggest that ongoing theta-modulated-gamma mechanisms periodically reintegrate a large-scale prefrontal-parietal network critical for perceptual experience. Moreover, activation and network inclusion of inferior temporal cortex and motor cortex uniquely occurs on the cycle immediately preceding responses signaling perceptual switching. This suggests that the essential prefrontal-parietal oscillatory network is expanded to include additional cortical regions relevant to tasks and perceptions furnishing consciousness at that moment, in this case image processing and response initiation, and that these activations occur within a time frame consistent with the notion that conscious processes directly affect behaviour.

  16. Visualising the invisible: a network approach to reveal the informal social side of student learning.

    Science.gov (United States)

    Hommes, J; Rienties, B; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

    2012-12-01

    World-wide, universities in health sciences have transformed their curriculum to include collaborative learning and facilitate the students' learning process. Interaction has been acknowledged to be the synergistic element in this learning context. However, students spend the majority of their time outside their classroom and interaction does not stop outside the classroom. Therefore we studied how informal social interaction influences student learning. Moreover, to explore what really matters in the students learning process, a model was tested how the generally known important constructs-prior performance, motivation and social integration-relate to informal social interaction and student learning. 301 undergraduate medical students participated in this cross-sectional quantitative study. Informal social interaction was assessed using self-reported surveys following the network approach. Students' individual motivation, social integration and prior performance were assessed by the Academic Motivation Scale, the College Adaption Questionnaire and students' GPA respectively. A factual knowledge test represented student' learning. All social networks were positively associated with student learning significantly: friendships (β = 0.11), providing information to other students (β = 0.16), receiving information from other students (β = 0.25). Structural equation modelling revealed a model in which social networks increased student learning (r = 0.43), followed by prior performance (r = 0.31). In contrast to prior literature, students' academic motivation and social integration were not associated with students' learning. Students' informal social interaction is strongly associated with students' learning. These findings underline the need to change our focus from the formal context (classroom) to the informal context to optimize student learning and deliver modern medics.

  17. Whole-brain circuit dissection in free-moving animals reveals cell-specific mesocorticolimbic networks

    Science.gov (United States)

    Michaelides, Michael; Anderson, Sarah Ann R.; Ananth, Mala; Smirnov, Denis; Thanos, Panayotis K.; Neumaier, John F.; Wang, Gene-Jack; Volkow, Nora D.; Hurd, Yasmin L.

    2013-01-01

    The ability to map the functional connectivity of discrete cell types in the intact mammalian brain during behavior is crucial for advancing our understanding of brain function in normal and disease states. We combined designer receptor exclusively activated by designer drug (DREADD) technology and behavioral imaging with μPET and [18F]fluorodeoxyglucose (FDG) to generate whole-brain metabolic maps of cell-specific functional circuits during the awake, freely moving state. We have termed this approach DREADD-assisted metabolic mapping (DREAMM) and documented its ability in rats to map whole-brain functional anatomy. We applied this strategy to evaluating changes in the brain associated with inhibition of prodynorphin-expressing (Pdyn-expressing) and of proenkephalin-expressing (Penk-expressing) medium spiny neurons (MSNs) of the nucleus accumbens shell (NAcSh), which have been implicated in neuropsychiatric disorders. DREAMM revealed discrete behavioral manifestations and concurrent engagement of distinct corticolimbic networks associated with dysregulation of Pdyn and Penk in MSNs of the NAcSh. Furthermore, distinct neuronal networks were recruited in awake versus anesthetized conditions. These data demonstrate that DREAMM is a highly sensitive, molecular, high-resolution quantitative imaging approach. PMID:24231358

  18. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.

    Science.gov (United States)

    Li, Jingjing; Shi, Minyi; Ma, Zhihai; Zhao, Shuchun; Euskirchen, Ghia; Ziskin, Jennifer; Urban, Alexander; Hallmayer, Joachim; Snyder, Michael

    2014-12-30

    Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.

  19. Dynamic Changes in Amygdala Psychophysiological Connectivity Reveal Distinct Neural Networks for Facial Expressions of Basic Emotions

    Science.gov (United States)

    Diano, Matteo; Tamietto, Marco; Celeghin, Alessia; Weiskrantz, Lawrence; Tatu, Mona-Karina; Bagnis, Arianna; Duca, Sergio; Geminiani, Giuliano; Cauda, Franco; Costa, Tommaso

    2017-01-01

    The quest to characterize the neural signature distinctive of different basic emotions has recently come under renewed scrutiny. Here we investigated whether facial expressions of different basic emotions modulate the functional connectivity of the amygdala with the rest of the brain. To this end, we presented seventeen healthy participants (8 females) with facial expressions of anger, disgust, fear, happiness, sadness and emotional neutrality and analyzed amygdala’s psychophysiological interaction (PPI). In fact, PPI can reveal how inter-regional amygdala communications change dynamically depending on perception of various emotional expressions to recruit different brain networks, compared to the functional interactions it entertains during perception of neutral expressions. We found that for each emotion the amygdala recruited a distinctive and spatially distributed set of structures to interact with. These changes in amygdala connectional patters characterize the dynamic signature prototypical of individual emotion processing, and seemingly represent a neural mechanism that serves to implement the distinctive influence that each emotion exerts on perceptual, cognitive, and motor responses. Besides these differences, all emotions enhanced amygdala functional integration with premotor cortices compared to neutral faces. The present findings thus concur to reconceptualise the structure-function relation between brain-emotion from the traditional one-to-one mapping toward a network-based and dynamic perspective. PMID:28345642

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

    Directory of Open Access Journals (Sweden)

    Loor Juan J

    2010-03-01

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

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

    Science.gov (United States)

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

    2010-03-09

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

  2. Identifying modular flows on multilayer networks reveals highly overlapping organization in social systems

    CERN Document Server

    De Domenico, Manlio; Arenas, Alex; Rosvall, Martin

    2014-01-01

    Unveiling the community structure of networks is a powerful methodology to comprehend interconnected systems across the social and natural sciences. To identify different types of functional modules in interaction data aggregated in a single network layer, researchers have developed many powerful methods. For example, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow in the systems they represent. However, many networked systems consist of agents or components that exhibit multiple layers of interactions. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here we propose a method based on compression of network flows that can identify modular flows in non-aggregated multilayer networks. Our numerical experiments on synthetic networks show that the method can accurately identify modules that cannot be identified in agg...

  3. Iron homeostasis in Arabidopsis thaliana: transcriptomic analyses reveal novel FIT-regulated genes, iron deficiency marker genes and functional gene networks.

    Science.gov (United States)

    Mai, Hans-Jörg; Pateyron, Stéphanie; Bauer, Petra

    2016-10-03

    FIT (FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR) is the central regulator of iron uptake in Arabidopsis thaliana roots. We performed transcriptome analyses of six day-old seedlings and roots of six week-old plants using wild type, a fit knock-out mutant and a FIT over-expression line grown under iron-sufficient or iron-deficient conditions. We compared genes regulated in a FIT-dependent manner depending on the developmental stage of the plants. We assembled a high likelihood dataset which we used to perform co-expression and functional analysis of the most stably iron deficiency-induced genes. 448 genes were found FIT-regulated. Out of these, 34 genes were robustly FIT-regulated in root and seedling samples and included 13 novel FIT-dependent genes. Three hundred thirty-one genes showed differential regulation in response to the presence and absence of FIT only in the root samples, while this was the case for 83 genes in the seedling samples. We assembled a virtual dataset of iron-regulated genes based on a total of 14 transcriptomic analyses of iron-deficient and iron-sufficient wild-type plants to pinpoint the best marker genes for iron deficiency and analyzed this dataset in depth. Co-expression analysis of this dataset revealed 13 distinct regulons part of which predominantly contained functionally related genes. We could enlarge the list of FIT-dependent genes and discriminate between genes that are robustly FIT-regulated in roots and seedlings or only in one of those. FIT-regulated genes were mostly induced, few of them were repressed by FIT. With the analysis of a virtual dataset we could filter out and pinpoint new candidates among the most reliable marker genes for iron deficiency. Moreover, co-expression and functional analysis of this virtual dataset revealed iron deficiency-induced and functionally distinct regulons.

  4. Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma

    OpenAIRE

    Hátylas Azevedo; Carlos Alberto Moreira-Filho

    2015-01-01

    Biological networks display high robustness against random failures but are vulnerable to targeted attacks on central nodes. Thus, network topology analysis represents a powerful tool for investigating network susceptibility against targeted node removal. Here, we built protein interaction networks associated with chemoresistance to temozolomide, an alkylating agent used in glioma therapy, and analyzed their modular structure and robustness against intentional attack. These networks showed fu...

  5. Preservation Analysis of Macrophage Gene Coexpression Between Human and Mouse Identifies PARK2 as a Genetically Controlled Master Regulator of Oxidative Phosphorylation in Humans

    Science.gov (United States)

    Codoni, Veronica; Blum, Yuna; Civelek, Mete; Proust, Carole; Franzén, Oscar; Björkegren, Johan L. M.; Le Goff, Wilfried; Cambien, Francois; Lusis, Aldons J.; Trégouët, David-Alexandre

    2016-01-01

    Macrophages are key players involved in numerous pathophysiological pathways and an in-depth characterization of their gene regulatory networks can help in better understanding how their dysfunction may impact on human diseases. We here conducted a cross-species network analysis of macrophage gene expression data between human and mouse to identify conserved networks across both species, and assessed whether such networks could reveal new disease-associated regulatory mechanisms. From a sample of 684 individuals processed for genome-wide macrophage gene expression profiling, we identified 27 groups of coexpressed genes (modules). Six modules were found preserved (P Parkinson, and Huntington diseases. We further conducted an expression quantitative trait loci analysis to identify SNP that could regulate macrophage OXPHOS gene expression in humans. This analysis identified the PARK2 rs192804963 as a trans-acting variant influencing (minimal P-value = 4.3 × 10−8) the expression of most OXPHOS genes in humans. Further experimental work demonstrated that PARK2 knockdown expression was associated with increased OXPHOS gene expression in THP1 human macrophages. This work provided strong new evidence that PARK2 participates to the regulatory networks associated with oxidative phosphorylation and suggested that PARK2 genetic variations could act as a trans regulator of OXPHOS gene macrophage expression in humans. PMID:27558669

  6. Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

    DEFF Research Database (Denmark)

    Österlund, Tobias; Bordel, Sergio; Nielsen, Jens

    2015-01-01

    Transcriptional regulation is the most committed type of regulation in living cells where transcription factors (TFs) control the expression of their target genes and TF expression is controlled by other TFs forming complex transcriptional regulatory networks that can be highly interconnected. Here...... we analyze the topology and organization of nine transcriptional regulatory networks for E. coli, yeast, mouse and human, and we evaluate how the structure of these networks influences two of their key properties, namely controllability and stability. We calculate the controllability for each network...... as a measure of the organization and interconnectivity of the network. We find that the number of driver nodes n(D) needed to control the whole network is 64% of the TFs in the E. coli transcriptional regulatory network in contrast to only 17% for the yeast network, 4% for the mouse network and 8...

  7. scMRI reveals large-scale brain network abnormalities in autism.

    Directory of Open Access Journals (Sweden)

    Brandon A Zielinski

    Full Text Available Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a 'posteriorization' of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI.

  8. Dynamic Proteomic Characteristics and Network Integration Revealing Key Proteins for Two Kernel Tissue Developments in Popcorn.

    Directory of Open Access Journals (Sweden)

    Yongbin Dong

    Full Text Available The formation and development of maize kernel is a complex dynamic physiological and biochemical process that involves the temporal and spatial expression of many proteins and the regulation of metabolic pathways. In this study, the protein profiles of the endosperm and pericarp at three important developmental stages were analyzed by isobaric tags for relative and absolute quantification (iTRAQ labeling coupled with LC-MS/MS in popcorn inbred N04. Comparative quantitative proteomic analyses among developmental stages and between tissues were performed, and the protein networks were integrated. A total of 6,876 proteins were identified, of which 1,396 were nonredundant. Specific proteins and different expression patterns were observed across developmental stages and tissues. The functional annotation of the identified proteins revealed the importance of metabolic and cellular processes, and binding and catalytic activities for the development of the tissues. The whole, endosperm-specific and pericarp-specific protein networks integrated 125, 9 and 77 proteins, respectively, which were involved in 54 KEGG pathways and reflected their complex metabolic interactions. Confirmation for the iTRAQ endosperm proteins by two-dimensional gel electrophoresis showed that 44.44% proteins were commonly found. However, the concordance between mRNA level and the protein abundance varied across different proteins, stages, tissues and inbred lines, according to the gene cloning and expression analyses of four relevant proteins with important functions and different expression levels. But the result by western blot showed their same expression tendency for the four proteins as by iTRAQ. These results could provide new insights into the developmental mechanisms of endosperm and pericarp, and grain formation in maize.

  9. Simultaneous application of heat, drought, and virus to Arabidopsis plants reveals significant shifts in signaling networks.

    Science.gov (United States)

    Prasch, Christian Maximilian; Sonnewald, Uwe

    2013-08-01

    Considering global climate change, the incidence of combined drought and heat stress is likely to increase in the future and will considerably influence plant-pathogen interactions. Until now, little has been known about plants exposed to simultaneously occurring abiotic and biotic stresses. To shed some light on molecular plant responses to multiple stress factors, a versatile multifactorial test system, allowing simultaneous application of heat, drought, and virus stress, was developed in Arabidopsis (Arabidopsis thaliana). Comparative analysis of single, double, and triple stress responses by transcriptome and metabolome analysis revealed that gene expression under multifactorial stress is not predictable from single stress treatments. Hierarchical cluster and principal component analyses identified heat as the major stress factor, clearly separating heat-stressed from non-heat-stressed plants. We identified 11 genes differentially regulated in all stress combinations as well as 23 genes specifically regulated under triple stress. Furthermore, we showed that virus-treated plants displayed enhanced expression of defense genes, which was abolished in plants additionally subjected to heat and drought stress. Triple stress also reduced the expression of genes involved in the R-mediated disease response and increased the cytoplasmic protein response, which was not seen under single stress conditions. These observations suggested that abiotic stress factors significantly altered turnip mosaic virus-specific signaling networks, which led to a deactivation of defense responses and a higher susceptibility of plants. Collectively, our transcriptome and metabolome data provide a powerful resource to study plant responses during multifactorial stress and allow identifying metabolic processes and functional networks involved in tripartite interactions of plants with their environment.

  10. Reliable Attention Network Scores and Mutually Inhibited Inter-network Relationships Revealed by Mixed Design and Non-orthogonal Method.

    Science.gov (United States)

    Wang, Yi-Feng; Jing, Xiu-Juan; Liu, Feng; Li, Mei-Ling; Long, Zhi-Liang; Yan, Jin H; Chen, Hua-Fu

    2015-05-21

    The attention system can be divided into alerting, orienting, and executive control networks. The efficiency and independence of attention networks have been widely tested with the attention network test (ANT) and its revised versions. However, many studies have failed to find effects of attention network scores (ANSs) and inter-network relationships (INRs). Moreover, the low reliability of ANSs can not meet the demands of theoretical and empirical investigations. Two methodological factors (the inter-trial influence in the event-related design and the inter-network interference in orthogonal contrast) may be responsible for the unreliability of ANT. In this study, we combined the mixed design and non-orthogonal method to explore ANSs and directional INRs. With a small number of trials, we obtained reliable and independent ANSs (split-half reliability of alerting: 0.684; orienting: 0.588; and executive control: 0.616), suggesting an individual and specific attention system. Furthermore, mutual inhibition was observed when two networks were operated simultaneously, indicating a differentiated but integrated attention system. Overall, the reliable and individual specific ANSs and mutually inhibited INRs provide novel insight into the understanding of the developmental, physiological and pathological mechanisms of attention networks, and can benefit future experimental and clinical investigations of attention using ANT.

  11. Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

    Science.gov (United States)

    López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

    2015-04-15

    Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Resting State fMRI in Mice Reveals Anesthesia Specific Signatures of Brain Functional Networks and Their Interactions

    Science.gov (United States)

    Bukhari, Qasim; Schroeter, Aileen; Cole, David M.; Rudin, Markus

    2017-01-01

    fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under

  13. Resting State fMRI in Mice Reveals Anesthesia Specific Signatures of Brain Functional Networks and Their Interactions.

    Science.gov (United States)

    Bukhari, Qasim; Schroeter, Aileen; Cole, David M; Rudin, Markus

    2017-01-01

    fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under

  14. Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors.

    Science.gov (United States)

    Österlund, Tobias; Bordel, Sergio; Nielsen, Jens

    2015-05-01

    Transcriptional regulation is the most committed type of regulation in living cells where transcription factors (TFs) control the expression of their target genes and TF expression is controlled by other TFs forming complex transcriptional regulatory networks that can be highly interconnected. Here we analyze the topology and organization of nine transcriptional regulatory networks for E. coli, yeast, mouse and human, and we evaluate how the structure of these networks influences two of their key properties, namely controllability and stability. We calculate the controllability for each network as a measure of the organization and interconnectivity of the network. We find that the number of driver nodes nD needed to control the whole network is 64% of the TFs in the E. coli transcriptional regulatory network in contrast to only 17% for the yeast network, 4% for the mouse network and 8% for the human network. The high controllability (low number of drivers needed to control the system) in yeast, mouse and human is due to the presence of internal loops in their regulatory networks where the TFs regulate each other in a circular fashion. We refer to these internal loops as circular control motifs (CCM). The E. coli transcriptional regulatory network, which does not have any CCMs, shows a hierarchical structure of the transcriptional regulatory network in contrast to the eukaryal networks. The presence of CCMs also has influence on the stability of these networks, as the presence of cycles can be associated with potential unstable steady-states where even small changes in binding affinities can cause dramatic rearrangements of the state of the network.

  15. Exploring Plant Co-Expression and Gene-Gene Interactions with CORNET 3.0.

    Science.gov (United States)

    Van Bel, Michiel; Coppens, Frederik

    2017-01-01

    Selecting and filtering a reference expression and interaction dataset when studying specific pathways and regulatory interactions can be a very time-consuming and error-prone task. In order to reduce the duplicated efforts required to amass such datasets, we have created the CORNET (CORrelation NETworks) platform which allows for easy access to a wide variety of data types: coexpression data, protein-protein interactions, regulatory interactions, and functional annotations. The CORNET platform outputs its results in either text format or through the Cytoscape framework, which is automatically launched by the CORNET website.CORNET 3.0 is the third iteration of the web platform designed for the user exploration of the coexpression space of plant genomes, with a focus on the model species Arabidopsis thaliana. Here we describe the platform: the tools, data, and best practices when using the platform. We indicate how the platform can be used to infer networks from a set of input genes, such as upregulated genes from an expression experiment. By exploring the network, new target and regulator genes can be discovered, allowing for follow-up experiments and more in-depth study. We also indicate how to avoid common pitfalls when evaluating the networks and how to avoid over interpretation of the results.All CORNET versions are available at http://bioinformatics.psb.ugent.be/cornet/ .

  16. Adiponectin fine-tuning of liver regeneration dynamics revealed through cellular network modeling.

    Science.gov (United States)

    Correnti, Jason M; Cook, Daniel; Aksamitiene, Edita; Swarup, Aditi; Ogunnaike, Babatunde; Vadigepalli, Rajanikanth; Hoek, Jan B

    2014-11-10

    Following partial hepatectomy, the liver initiates a regenerative program involving hepatocyte priming and replication driven by coordinated cytokine and growth factor actions. We investigated the mechanisms underlying Adiponectin's (Adn) regulation of liver regeneration through modulation of these mediators. Adn-/- mice showed delayed onset of hepatocyte replication, but accelerated cell cycle progression relative to wild-type mice, suggesting Adn has multiple effects fine-tuning the kinetics of liver regeneration. We developed a computational model describing the molecular and physiological kinetics of liver regeneration in Adn-/- mice. We employed this computational model to evaluate the underlying regulatory mechanisms. Our analysis predicted that Adn is required for an efficient early cytokine response to partial hepatectomy, but is inhibitory to later growth factor actions. Consistent with this prediction, Adn knockout reduced hepatocyte responses to IL-6 during the priming phase, but enhanced growth factor levels through peak hepatocyte replication. By contrast, supraphysiological concentrations of Adn resulting from rosiglitazone treatment suppressed regeneration by reducing growth factor levels during S phase, consistent with computational predictions. Together, these results revealed that Adn fine-tunes the progression of liver regeneration through dynamically modulating molecular mediator networks and cellular interactions within the liver. This article is protected by copyright. All rights reserved.

  17. Adiponectin fine-tuning of liver regeneration dynamics revealed through cellular network modelling.

    Science.gov (United States)

    Correnti, Jason M; Cook, Daniel; Aksamitiene, Edita; Swarup, Aditi; Ogunnaike, Babatunde; Vadigepalli, Rajanikanth; Hoek, Jan B

    2015-01-15

    Following partial hepatectomy, the liver initiates a regenerative programme involving hepatocyte priming and replication driven by the coordinated actions of cytokine and growth factors. We investigated the mechanisms underlying adiponectin's (Adn) regulation of liver regeneration through modulation of these mediators. Adn(-/-) mice showed delayed onset of hepatocyte replication, but accelerated cell cycle progression relative to wild-type mice, suggesting Adn has multiple effects fine-tuning the kinetics of liver regeneration. We developed a computational model describing the molecular and physiological kinetics of liver regeneration in Adn(-/-) mice. We employed this computational model to evaluate the underlying regulatory mechanisms. Our analysis predicted that Adn is required for an efficient early cytokine response to partial hepatectomy, but is inhibitory to later growth factor actions. Consistent with this prediction, Adn knockout reduced hepatocyte responses to interleukin-6 during the priming phase, but enhanced growth factor levels through peak hepatocyte replication. By contrast, supraphysiological concentrations of Adn resulting from rosiglitazone treatment suppressed regeneration by reducing growth factor levels during S phase, consistent with computational predictions. Together, these results revealed that Adn fine-tunes the progression of liver regeneration through dynamically modulating molecular mediator networks and cellular interactions within the liver.

  18. RNA sequencing analysis of human podocytes reveals glucocorticoid regulated gene networks targeting non-immune pathways

    Science.gov (United States)

    Jiang, Lulu; Hindmarch, Charles C. T.; Rogers, Mark; Campbell, Colin; Waterfall, Christy; Coghill, Jane; Mathieson, Peter W.; Welsh, Gavin I.

    2016-01-01

    Glucocorticoids are steroids that reduce inflammation and are used as immunosuppressive drugs for many diseases. They are also the mainstay for the treatment of minimal change nephropathy (MCN), which is characterised by an absence of inflammation. Their mechanisms of action remain elusive. Evidence suggests that immunomodulatory drugs can directly act on glomerular epithelial cells or ‘podocytes’, the cell type which is the main target of injury in MCN. To understand the nature of glucocorticoid effects on non-immune cell functions, we generated RNA sequencing data from human podocyte cell lines and identified the genes that are significantly regulated in dexamethasone-treated podocytes compared to vehicle-treated cells. The upregulated genes are of functional relevance to cytoskeleton-related processes, whereas the downregulated genes mostly encode pro-inflammatory cytokines and growth factors. We observed a tendency for dexamethasone-upregulated genes to be downregulated in MCN patients. Integrative analysis revealed gene networks composed of critical signaling pathways that are likely targeted by dexamethasone in podocytes. PMID:27774996

  19. Mapping drug physico-chemical features to pathway activity reveals molecular networks linked to toxicity outcome.

    Directory of Open Access Journals (Sweden)

    Philipp Antczak

    Full Text Available The identification of predictive biomarkers is at the core of modern toxicology. So far, a number of approaches have been proposed. These rely on statistical inference of toxicity response from either compound features (i.e., QSAR, in vitro cell based assays or molecular profiling of target tissues (i.e., expression profiling. Although these approaches have already shown the potential of predictive toxicology, we still do not have a systematic approach to model the interaction between chemical features, molecular networks and toxicity outcome. Here, we describe a computational strategy designed to address this important need. Its application to a model of renal tubular degeneration has revealed a link between physico-chemical features and signalling components controlling cell communication pathways, which in turn are differentially modulated in response to toxic chemicals. Overall, our findings are consistent with the existence of a general toxicity mechanism operating in synergy with more specific single-target based mode of actions (MOAs and provide a general framework for the development of an integrative approach to predictive toxicology.

  20. Characterization of Phototransduction Gene Knockouts Revealed Important Signaling Networks in the Light-Induced Retinal Degeneration

    Directory of Open Access Journals (Sweden)

    Jayalakshmi Krishnan

    2008-01-01

    Full Text Available Understanding the molecular pathways mediating neuronal function in retinas can be greatly facilitated by the identification of genes regulated in the retinas of different mutants under various light conditions. We attempted to conduct a gene chip analysis study on the genes regulated during rhodopsin kinase (Rhok-/- and arrestin (Sag-/- knockout and double knockouts in mice retina. Hence, mice were exposed to constant illumination of 450 lux or 6,000 lux on dilated pupils for indicated periods. The retinas were removed after the exposure and processed for microarray analysis. Double knockout was associated with immense changes in gene expression regulating a number of apoptosis inducing transcription factors. Subsequently, network analysis revealed that during early exposure the transcription factors, p53, c-MYC, c-FOS, JUN, and, in late phase, NF-B, appeared to be essential for the initiation of light-induced retinal rod loss, and some other classical pro- and antipoptotic genes appeared to be significantly important as well.

  1. Phosphoproteome reveals an atlas of protein signaling networks during osteoblast adhesion.

    Science.gov (United States)

    Milani, Renato; Ferreira, Carmen V; Granjeiro, José M; Paredes-Gamero, Edgar J; Silva, Rodrigo A; Justo, Giselle Z; Nader, Helena B; Galembeck, Eduardo; Peppelenbosch, Maikel P; Aoyama, Hiroshi; Zambuzzi, Willian F

    2010-04-01

    Cell adhesion on surfaces is a fundamental process in the emerging biomaterials field and developmental events as well. However, the mechanisms regulating this biological process in osteoblasts are not fully understood. Reversible phosphorylation catalyzed by kinases is probably the most important regulatory mechanism in eukaryotes. Therefore, the goal of this study is to assess osteoblast adhesion through a molecular prism under a peptide array technology, revealing essential signaling proteins governing adhesion-related events. First, we showed that there are main morphological changes on osteoblast shape during adhesion up to 3 h. Second, besides classical proteins activated upon integrin activation, our results showed a novel network involving signaling proteins such as Rap1A, PKA, PKC, and GSK3beta during osteoblast adhesion on polystyrene. Third, these proteins were grouped in different signaling cascades including focal adhesion establishment, cytoskeleton rearrangement, and cell-cycle arrest. We have thus provided evidence that a global phosphorylation screening is able to yield a systems-oriented look at osteoblast adhesion, providing new insights for understanding of bone formation and improvement of cell-substratum interactions. Altogether, these statements are necessary means for further intervention and development of new approaches for the progress of tissue engineering.

  2. Genome-Wide Analysis Revealed the Complex Regulatory Network of Brassinosteroid Effects in Photomorphogenesis

    Institute of Scientific and Technical Information of China (English)

    Li Song; Xiao-Yi Zhou; Li Li; Liang-Jiao Xue; Xi Yang; Hong-Wei Xue

    2009-01-01

    Light and brassinosteroids (BRs) have been proved to be crucial in regulating plant growth and development;however,the mechanism of how they synergistically function is still largely unknown.To explore the underlying mechanisms in photomorphogenesis,genome-wide analyses were carried out through examining the gene expressions of the dark-grown WT or BR biosynthesis-defective mutant det2 seedlings in the presence of light stimuli or exogenous Brassinolide (BL).Results showed that BR deficiency stimulates,while BL treatment suppresses,the expressions of lightresponsive genes and photomorphogenesis,confirming the negative effects of BR in photomorphogenesis.This is consistent with the specific effects of BR on the expression of genes involved in cell wall modification,cellular metabolism and energy utilization during dark-light transition.Further analysis revealed that hormone biosynthesis and signaling-related genes,especially those of auxin,were altered under BL treatment or light stimuli,indicating that BR may modulate photomorphogenesis through synergetic regulation with other hormones.Additionally,suppressed ubiquitin-cycle pathway during light-dark transition hinted the presence of a complicated network among light,hormone,and protein degradation.The study provides the direct evidence of BR effects in photomorphogenesis and identified the genes involved in BR and light signaling pathway,which will help to elucidate the molecular mechanism of plant photomorphogenesis.

  3. Genome-wide analysis of LXRα activation reveals new transcriptional networks in human atherosclerotic foam cells.

    Science.gov (United States)

    Feldmann, Radmila; Fischer, Cornelius; Kodelja, Vitam; Behrens, Sarah; Haas, Stefan; Vingron, Martin; Timmermann, Bernd; Geikowski, Anne; Sauer, Sascha

    2013-04-01

    Increased physiological levels of oxysterols are major risk factors for developing atherosclerosis and cardiovascular disease. Lipid-loaded macrophages, termed foam cells, are important during the early development of atherosclerotic plaques. To pursue the hypothesis that ligand-based modulation of the nuclear receptor LXRα is crucial for cell homeostasis during atherosclerotic processes, we analysed genome-wide the action of LXRα in foam cells and macrophages. By integrating chromatin immunoprecipitation-sequencing (ChIP-seq) and gene expression profile analyses, we generated a highly stringent set of 186 LXRα target genes. Treatment with the nanomolar-binding ligand T0901317 and subsequent auto-regulatory LXRα activation resulted in sequence-dependent sharpening of the genome-binding patterns of LXRα. LXRα-binding loci that correlated with differential gene expression revealed 32 novel target genes with potential beneficial effects, which in part explained the implications of disease-associated genetic variation data. These observations identified highly integrated LXRα ligand-dependent transcriptional networks, including the APOE/C1/C4/C2-gene cluster, which contribute to the reversal of cholesterol efflux and the dampening of inflammation processes in foam cells to prevent atherogenesis.

  4. Network analysis reveals ecological links between N-fixing bacteria and wood-decaying fungi.

    Directory of Open Access Journals (Sweden)

    Björn Hoppe

    Full Text Available Nitrogen availability in dead wood is highly restricted and associations with N-fixing bacteria are thought to enable wood-decaying fungi to meet their nitrogen requirements for vegetative and generative growth. We assessed the diversity of nifH (dinitrogenase reductase genes in dead wood of the common temperate tree species Fagus sylvatica and Picea abies from differently managed forest plots in Germany using molecular tools. By incorporating these genes into a large compilation of published nifH sequences and subsequent phylogenetic analyses of deduced proteins we verified the presence of diverse pools corresponding to functional nifH, almost all of which are new to science. The distribution of nifH genes strongly correlated with tree species and decay class, but not with forest management, while higher fungal fructification was correlated with decreasing nitrogen content of the dead wood and positively correlated with nifH diversity, especially during the intermediate stage of wood decay. Network analyses based on non-random species co-occurrence patterns revealed interactions among fungi and N-fixing bacteria in the dead wood and strongly indicate the occurrence of at least commensal relationships between these taxa.

  5. Microbial dark matter ecogenomics reveals complex synergistic networks in a methanogenic bioreactor.

    Science.gov (United States)

    Nobu, Masaru K; Narihiro, Takashi; Rinke, Christian; Kamagata, Yoichi; Tringe, Susannah G; Woyke, Tanja; Liu, Wen-Tso

    2015-08-01

    Ecogenomic investigation of a methanogenic bioreactor degrading terephthalate (TA) allowed elucidation of complex synergistic networks of uncultivated microorganisms, including those from candidate phyla with no cultivated representatives. Our previous metagenomic investigation proposed that Pelotomaculum and methanogens may interact with uncultivated organisms to degrade TA; however, many members of the community remained unaddressed because of past technological limitations. In further pursuit, this study employed state-of-the-art omics tools to generate draft genomes and transcriptomes for uncultivated organisms spanning 15 phyla and reports the first genomic insight into candidate phyla Atribacteria, Hydrogenedentes and Marinimicrobia in methanogenic environments. Metabolic reconstruction revealed that these organisms perform fermentative, syntrophic and acetogenic catabolism facilitated by energy conservation revolving around H2 metabolism. Several of these organisms could degrade TA catabolism by-products (acetate, butyrate and H2) and syntrophically support Pelotomaculum. Other taxa could scavenge anabolic products (protein and lipids) presumably derived from detrital biomass produced by the TA-degrading community. The protein scavengers expressed complementary metabolic pathways indicating syntrophic and fermentative step-wise protein degradation through amino acids, branched-chain fatty acids and propionate. Thus, the uncultivated organisms may interact to form an intricate syntrophy-supported food web with Pelotomaculum and methanogens to metabolize catabolic by-products and detritus, whereby facilitating holistic TA mineralization to CO2 and CH4.

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

    Science.gov (United States)

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

    2017-01-01

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

  7. Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle

    Directory of Open Access Journals (Sweden)

    Dalrymple Brian P

    2011-01-01

    Full Text Available Abstract Background Gene regulation by transcription factors (TF is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information. Results We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs. We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively. Conclusion The pivotal implication of our research is two-fold: (1 there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2 this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

  8. 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.

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

    Science.gov (United States)

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

    2013-01-01

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

  10. Magnetoencephalography Reveals a Widespread Increase in Network Connectivity in Idiopathic/Genetic Generalized Epilepsy.

    Directory of Open Access Journals (Sweden)

    Adham Elshahabi

    Full Text Available Idiopathic/genetic generalized epilepsy (IGE/GGE is characterized by seizures, which start and rapidly engage widely distributed networks, and result in symptoms such as absences, generalized myoclonic and primary generalized tonic-clonic seizures. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in IGE/GGE patients using diffusion tensor imaging and voxel-based morphometry. Changes have also been reported in functional networks during generalized spike wave discharges. However, network function in the resting-state without epileptiforme discharges has been less well studied. We hypothesize that resting-state networks are more representative of the underlying pathophysiology and abnormal network synchrony. We studied functional network connectivity derived from whole-brain magnetoencephalography recordings in thirteen IGE/GGE and nineteen healthy controls. Using graph theoretical network analysis, we found a widespread increase in connectivity in patients compared to controls. These changes were most pronounced in the motor network, the mesio-frontal and temporal cortex. We did not, however, find any significant difference between the normalized clustering coefficients, indicating preserved gross network architecture. Our findings suggest that increased resting state connectivity could be an important factor for seizure spread and/or generation in IGE/GGE, and could serve as a biomarker for the disease.

  11. Magnetoencephalography Reveals a Widespread Increase in Network Connectivity in Idiopathic/Genetic Generalized Epilepsy.

    Science.gov (United States)

    Elshahabi, Adham; Klamer, Silke; Sahib, Ashish Kaul; Lerche, Holger; Braun, Christoph; Focke, Niels K

    2015-01-01

    Idiopathic/genetic generalized epilepsy (IGE/GGE) is characterized by seizures, which start and rapidly engage widely distributed networks, and result in symptoms such as absences, generalized myoclonic and primary generalized tonic-clonic seizures. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in IGE/GGE patients using diffusion tensor imaging and voxel-based morphometry. Changes have also been reported in functional networks during generalized spike wave discharges. However, network function in the resting-state without epileptiforme discharges has been less well studied. We hypothesize that resting-state networks are more representative of the underlying pathophysiology and abnormal network synchrony. We studied functional network connectivity derived from whole-brain magnetoencephalography recordings in thirteen IGE/GGE and nineteen healthy controls. Using graph theoretical network analysis, we found a widespread increase in connectivity in patients compared to controls. These changes were most pronounced in the motor network, the mesio-frontal and temporal cortex. We did not, however, find any significant difference between the normalized clustering coefficients, indicating preserved gross network architecture. Our findings suggest that increased resting state connectivity could be an important factor for seizure spread and/or generation in IGE/GGE, and could serve as a biomarker for the disease.

  12. Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion.

    Science.gov (United States)

    Baggio, Jacopo A; BurnSilver, Shauna B; Arenas, Alex; Magdanz, James S; Kofinas, Gary P; De Domenico, Manlio

    2016-11-29

    Network analysis provides a powerful tool to analyze complex influences of social and ecological structures on community and household dynamics. Most network studies of social-ecological systems use simple, undirected, unweighted networks. We analyze multiplex, directed, and weighted networks of subsistence food flows collected in three small indigenous communities in Arctic Alaska potentially facing substantial economic and ecological changes. Our analysis of plausible future scenarios suggests that changes to social relations and key households have greater effects on community robustness than changes to specific wild food resources.

  13. Genome-wide mRNA and miRNA expression profiling reveal multiple regulatory networks in colorectal cancer

    DEFF Research Database (Denmark)

    Vishnubalaji, R; Hamam, R; Abdulla, M-H;

    2015-01-01

    upregulated and 1902 downregulated genes in CRC. Pathway analysis revealed significant enrichment in cell cycle, integrated cancer, Wnt (wingless-type MMTV integration site family member), matrix metalloproteinase, and TGF-β pathways in CRC. Pharmacological inhibition of Wnt (using XAV939 or IWP-2) or TGF......-β (using SB-431542) pathways led to dose- and time-dependent inhibition of CRC cell growth. Similarly, our data revealed up- (42) and downregulated (61) microRNAs in the same matched samples. Using target prediction and bioinformatics, ~77% of the upregulated genes were predicted to be targeted by micro...... in cell proliferation, and migration in vitro. Concordantly, small interfering RNA-mediated knockdown of EZH2 led to similar effects on CRC cell growth in vitro. Therefore, our data have revealed several hundred potential miRNA-mRNA regulatory networks in CRC and suggest targeting relevant networks...

  14. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI.

    Science.gov (United States)

    He, Yong; Chen, Zhang J; Evans, Alan C

    2007-10-01

    An important issue in neuroscience is the characterization for the underlying architectures of complex brain networks. However, little is known about the network of anatomical connections in the human brain. Here, we investigated large-scale anatomical connection patterns of the human cerebral cortex using cortical thickness measurements from magnetic resonance images. Two areas were considered anatomically connected if they showed statistically significant correlations in cortical thickness and we constructed the network of such connections using 124 brains from the International Consortium for Brain Mapping database. Significant short- and long-range connections were found in both intra- and interhemispheric regions, many of which were consistent with known neuroanatomical pathways measured by human diffusion imaging. More importantly, we showed that the human brain anatomical network had robust small-world properties with cohesive neighborhoods and short mean distances between regions that were insensitive to the selection of correlation thresholds. Additionally, we also found that this network and the probability of finding a connection between 2 regions for a given anatomical distance had both exponentially truncated power-law distributions. Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings. To our knowledge, this study provides the first report of small-world properties and degree distribution of anatomical networks in the human brain using cortical thickness measurements.

  15. Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics.

    Science.gov (United States)

    Bruno, Jennifer Lynn; Hosseini, S M Hadi; Saggar, Manish; Quintin, Eve-Marie; Raman, Mira Michelle; Reiss, Allan L

    2016-03-22

    Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS.

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

    Directory of Open Access Journals (Sweden)

    Jignesh R Parikh

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

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

    Science.gov (United States)

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

    2012-01-01

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

  18. Resting state functional MRI reveals abnormal network connectivity in orthostatic tremor.

    Science.gov (United States)

    Benito-León, Julián; Louis, Elan D; Manzanedo, Eva; Hernández-Tamames, Juan Antonio; Álvarez-Linera, Juan; Molina-Arjona, José Antonio; Matarazzo, Michele; Romero, Juan Pablo; Domínguez-González, Cristina; Domingo-Santos, Ángela; Sánchez-Ferro, Álvaro

    2016-07-01

    Very little is known about the pathogenesis of orthostatic tremor (OT). We have observed that OT patients might have deficits in specific aspects of neuropsychological function, particularly those thought to rely on the integrity of the prefrontal cortex, which suggests a possible involvement of frontocerebellar circuits. We examined whether resting-state functional magnetic resonance imaging (fMRI) might provide further insights into the pathogenesis on OT. Resting-state fMRI data in 13 OT patients (11 women and 2 men) and 13 matched healthy controls were analyzed using independent component analysis, in combination with a "dual-regression" technique, to identify group differences in several resting-state networks (RSNs). All participants also underwent neuropsychological testing during the same session. Relative to healthy controls, OT patients showed increased connectivity in RSNs involved in cognitive processes (default mode network [DMN] and frontoparietal networks), and decreased connectivity in the cerebellum and sensorimotor networks. Changes in network integrity were associated not only with duration (DMN and medial visual network), but also with cognitive function. Moreover, in at least 2 networks (DMN and medial visual network), increased connectivity was associated with worse performance on different cognitive domains (attention, executive function, visuospatial ability, visual memory, and language). In this exploratory study, we observed selective impairments of RSNs in OT patients. This and other future resting-state fMRI studies might provide a novel method to understand the pathophysiological mechanisms of motor and nonmotor features of OT.

  19. Genome-wide system analysis reveals stable yet flexible network dynamics in yeast.

    Science.gov (United States)

    Gustafsson, M; Hörnquist, M; Björkegren, J; Tegnér, J

    2009-07-01

    Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.

  20. Characterization of CIPK family in Asian pear (Pyrus bretschneideri Rehd and co-expression analysis related to salt and osmotic stress responses

    Directory of Open Access Journals (Sweden)

    Jun Tang

    2016-09-01

    Full Text Available Asian pear (Pyrus bretschneideri is one of the most important fruit crops in the world, and its growth and productivity are frequently affected by abiotic stresses. Calcineurin B-like interacting protein kinases (CIPKs as caladium-sensor protein kinases interact with Ca2+-binding CBLs to extensively mediate abiotic stress responses in plants. Although the pear genome sequence has been released, little information is available about the CIPK genes in pear, especially in response to salt and osmotic stresses. In this study, we systematically identified 28 CIPK family members from the sequenced pear genome and analyzed their organization, phylogeny, gene structure, protein motif, and synteny duplication divergences. Most duplicated PbCIPKs underwent purifying selection, and their evolutionary divergences accompanied with the pear whole genome duplication. We also investigated stress -responsive expression patterns and co-expression networks of CIPK family under salt and osmotic stresses, and the distribution of stress-related cis-regulatory elements in promoter regions. Our results suggest that most PbCIPKs could play important roles in the abiotic stress responses. Some PbCIPKs, such as PbCIPK22, -19, -18, -15, -8, and -6 can serve as core regulators in response to salt and osmotic stresses based on co-expression networks of PbCIPKs. Some sets of genes that were involved in response to salt did not overlap with those in response to osmotic responses, suggesting the sub-functionalization of CIPK genes in stress responses. This study revealed some candidate genes that play roles in early responses to salt and osmotic stress for further characterization of abiotic stress responses medicated by CIPKs in pear.

  1. Cardioinductive network guiding stem cell differentiation revealed by proteomic cartography of tumor necrosis factor alpha-primed endodermal secretome.

    Science.gov (United States)

    Arrell, D Kent; Niederländer, Nicolas J; Faustino, Randolph S; Behfar, Atta; Terzic, Andre

    2008-02-01

    In the developing embryo, instructive guidance from the ventral endoderm secures cardiac program induction within the anterolateral mesoderm. Endoderm-guided cardiogenesis, however, has yet to be resolved at the proteome level. Here, through cardiopoietic priming of the endoderm with the reprogramming cytokine tumor necrosis factor alpha (TNFalpha), candidate effectors of embryonic stem cell cardiac differentiation were delineated by comparative proteomics. Differential two-dimensional gel electrophoretic mapping revealed that more than 75% of protein species increased >1.5-fold in the TNFalpha-primed versus unprimed endodermal secretome. Protein spot identification by linear ion trap quadrupole (LTQ) tandem mass spectrometry (MS/MS) and validation by shotgun LTQ-Fourier transform MS/MS following multidimensional chromatography mapped 99 unique proteins from 153 spot assignments. A definitive set of 48 secretome proteins was deduced by iterative bioinformatic screening using algorithms for detection of canonical and noncanonical indices of secretion. Protein-protein interaction analysis, in conjunction with respective expression level changes, revealed a nonstochastic TNFalpha-centric secretome network with a scale-free hierarchical architecture. Cardiovascular development was the primary developmental function of the resolved TNFalpha-anchored network. Functional cooperativity of the derived cardioinductive network was validated through direct application of the TNFalpha-primed secretome on embryonic stem cells, potentiating cardiac commitment and sarcomerogenesis. Conversely, inhibition of primary network hubs negated the procardiogenic effects of TNFalpha priming. Thus, proteomic cartography establishes a systems biology framework for the endodermal secretome network guiding stem cell cardiopoiesis.

  2. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy

    Directory of Open Access Journals (Sweden)

    Jonathan Wirsich

    2016-01-01

    In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

  3. Analysis of functional and pathway association of differential co-expressed genes: a case study in drug addiction.

    Science.gov (United States)

    Li, Zi-hui; Liu, Yu-feng; Li, Ke-ning; Duanmu, Hui-zi; Chang, Zhi-qiang; Li, Zhen-qi; Zhang, Shan-zhen; Xu, Yan

    2012-02-01

    Drug addiction has been considered as a kind of chronic relapsing brain disease influenced by both genetic and environmental factors. At present, many causative genes and pathways related to diverse kinds of drug addiction have been discovered, while less attention has been paid to common mechanisms shared by different drugs underlying addiction. By applying a co-expression meta-analysis method to mRNA expression profiles of alcohol, cocaine, heroin addicted and normal samples, we identified significant gene co-expression pairs. As co-expression networks of drug group and control group constructed, associated function term pairs and pathway pairs reflected by co-expression pattern changes were discovered by integrating functional and pathway information respectively. The results indicated that respiratory electron transport chain, synaptic transmission, mitochondrial electron transport, signal transduction, locomotory behavior, response to amphetamine, negative regulation of cell migration, glucose regulation of insulin secretion, signaling by NGF, diabetes pathways, integration of energy metabolism, dopamine receptors may play an important role in drug addiction. In addition, the results can provide theory support for studies of addiction mechanisms.

  4. Intrinsic brain network abnormalities in migraines without aura revealed in resting-state fMRI.

    Directory of Open Access Journals (Sweden)

    Ting Xue

    Full Text Available BACKGROUND: Previous studies have defined low-frequency, spatially consistent intrinsic connectivity networks (ICN in resting functional magnetic resonance imaging (fMRI data which reflect functional interactions among distinct brain areas. We sought to explore whether and how repeated migraine attacks influence intrinsic brain connectivity, as well as how activity in these networks correlates with clinical indicators of migraine. METHODS/PRINCIPAL FINDINGS: Resting-state fMRI data in twenty-three patients with migraines without aura (MwoA and 23 age- and gender-matched healthy controls (HC were analyzed using independent component analysis (ICA, in combination with a "dual-regression" technique to identify the group differences of three important pain-related networks [default mode network (DMN, bilateral central executive network (CEN, salience network (SN] between the MwoA patients and HC. Compared with the HC, MwoA patients showed aberrant intrinsic connectivity within the bilateral CEN and SN, and greater connectivity between both the DMN and right CEN (rCEN and the insula cortex - a critical region involving in pain processing. Furthermore, greater connectivity between both the DMN and rCEN and the insula correlated with duration of migraine. CONCLUSIONS: Our findings may provide new insights into the characterization of migraine as a condition affecting brain activity in intrinsic connectivity networks. Moreover, the abnormalities may be the consequence of a persistent central neural system dysfunction, reflecting cumulative brain insults due to frequent ongoing migraine attacks.

  5. Co-expression of mitosis-regulating genes contributes to malignant progression and prognosis in oligodendrogliomas.

    Science.gov (United States)

    Liu, Yanwei; Hu, Huimin; Zhang, Chuanbao; Wang, Haoyuan; Zhang, Wenlong; Wang, Zheng; Li, Mingyang; Zhang, Wei; Zhou, Dabiao; Jiang, Tao

    2015-11-10

    The clinical prognosis of patients with glioma is determined by tumor grades, but tumors of different subtypes with equal malignancy grade usually have different prognosis that is largely determined by genetic abnormalities. Oligodendrogliomas (ODs) are the second most common type of gliomas. In this study, integrative analyses found that distribution of TCGA transcriptomic subtypes was associated with grade progression in ODs. To identify critical gene(s) associated with tumor grades and TCGA subtypes, we analyzed 34 normal brain tissue (NBT), 146 WHO grade II and 130 grade III ODs by microarray and RNA sequencing, and identified a co-expression network of six genes (AURKA, NDC80, CENPK, KIAA0101, TIMELESS and MELK) that was associated with tumor grades and TCGA subtypes as well as Ki-67 expression. Validation of the six genes was performed by qPCR in additional 28 ODs. Importantly, these genes also were validated in four high-grade recurrent gliomas and the initial lower-grade gliomas resected from the same patients. Finally, the RNA data on two genes with the highest discrimination potential (AURKA and NDC80) and Ki-67 were validated on an independent cohort (5 NBTs and 86 ODs) by immunohistochemistry. Knockdown of AURKA and NDC80 by siRNAs suppressed Ki-67 expression and proliferation of gliomas cells. Survival analysis showed that high expression of the six genes corporately indicated a poor survival outcome. Correlation and protein interaction analysis provided further evidence for this co-expression network. These data suggest that the co-expression of the six mitosis-regulating genes was associated with malignant progression and prognosis in ODs.

  6. Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration.

    Science.gov (United States)

    Kim, Heejung; Hahm, Jarang; Lee, Hyekyoung; Kang, Eunjoo; Kang, Hyejin; Lee, Dong Soo

    2015-05-01

    The human brain naturally integrates audiovisual information to improve speech perception. However, in noisy environments, understanding speech is difficult and may require much effort. Although the brain network is supposed to be engaged in speech perception, it is unclear how speech-related brain regions are connected during natural bimodal audiovisual or unimodal speech perception with counterpart irrelevant noise. To investigate the topological changes of speech-related brain networks at all possible thresholds, we used a persistent homological framework through hierarchical clustering, such as single linkage distance, to analyze the connected component of the functional network during speech perception using functional magnetic resonance imaging. For speech perception, bimodal (audio-visual speech cue) or unimodal speech cues with counterpart irrelevant noise (auditory white-noise or visual gum-chewing) were delivered to 15 subjects. In terms of positive relationship, similar connected components were observed in bimodal and unimodal speech conditions during filtration. However, during speech perception by congruent audiovisual stimuli, the tighter couplings of left anterior temporal gyrus-anterior insula component and right premotor-visual components were observed than auditory or visual speech cue conditions, respectively. Interestingly, visual speech is perceived under white noise by tight negative coupling in the left inferior frontal region-right anterior cingulate, left anterior insula, and bilateral visual regions, including right middle temporal gyrus, right fusiform components. In conclusion, the speech brain network is tightly positively or negatively connected, and can reflect efficient or effortful processes during natural audiovisual integration or lip-reading, respectively, in speech perception.

  7. Complex network models reveal correlations among network metrics, exercise intensity and role of body changes in the fatigue process

    Science.gov (United States)

    Pereira, Vanessa Helena; Gama, Maria Carolina Traina; Sousa, Filipe Antônio Barros; Lewis, Theodore Gyle; Gobatto, Claudio Alexandre; Manchado-Gobatto, Fúlvia Barros

    2015-05-01

    The aims of the present study were analyze the fatigue process at distinct intensity efforts and to investigate its occurrence as interactions at distinct body changes during exercise, using complex network models. For this, participants were submitted to four different running intensities until exhaustion, accomplished in a non-motorized treadmill using a tethered system. The intensities were selected according to critical power model. Mechanical (force, peak power, mean power, velocity and work) and physiological related parameters (heart rate, blood lactate, time until peak blood lactate concentration (lactate time), lean mass, anaerobic and aerobic capacities) and IPAQ score were obtained during exercises and it was used to construction of four complex network models. Such models have both, theoretical and mathematical value, and enables us to perceive new insights that go beyond conventional analysis. From these, we ranked the influences of each node at the fatigue process. Our results shows that nodes, links and network metrics are sensibility according to increase of efforts intensities, been the velocity a key factor to exercise maintenance at models/intensities 1 and 2 (higher time efforts) and force and power at models 3 and 4, highlighting mechanical variables in the exhaustion occurrence and even training prescription applications.

  8. Complex network models reveal correlations among network metrics, exercise intensity and role of body changes in the fatigue process.

    Science.gov (United States)

    Pereira, Vanessa Helena; Gama, Maria Carolina Traina; Sousa, Filipe Antônio Barros; Lewis, Theodore Gyle; Gobatto, Claudio Alexandre; Manchado-Gobatto, Fúlvia Barros

    2015-05-21

    The aims of the present study were analyze the fatigue process at distinct intensity efforts and to investigate its occurrence as interactions at distinct body changes during exercise, using complex network models. For this, participants were submitted to four different running intensities until exhaustion, accomplished in a non-motorized treadmill using a tethered system. The intensities were selected according to critical power model. Mechanical (force, peak power, mean power, velocity and work) and physiological related parameters (heart rate, blood lactate, time until peak blood lactate concentration (lactate time), lean mass, anaerobic and aerobic capacities) and IPAQ score were obtained during exercises and it was used to construction of four complex network models. Such models have both, theoretical and mathematical value, and enables us to perceive new insights that go beyond conventional analysis. From these, we ranked the influences of each node at the fatigue process. Our results shows that nodes, links and network metrics are sensibility according to increase of efforts intensities, been the velocity a key factor to exercise maintenance at models/intensities 1 and 2 (higher time efforts) and force and power at models 3 and 4, highlighting mechanical variables in the exhaustion occurrence and even training prescription applications.

  9. Using higher-order Markov models to reveal flow-based communities in networks

    CERN Document Server

    Salnikov, Vsevolod; Lambiotte, Renaud

    2016-01-01

    Complex systems made of interacting elements are commonly abstracted as networks, in which nodes are associated with dynamic state variables, whose evolution is driven by interactions mediated by the edges. Markov processes have been the prevailing paradigm to model such a network-based dynamics, for instance in the form of random walks or other types of diffusions. Despite the success of this modelling perspective for numerous applications, it represents an over-simplification of several real-world systems. Importantly, simple Markov models lack memory in their dynamics, an assumption often not realistic in practice. Here, we explore possibilities to enrich the system description by means of second-order Markov models, exploiting empirical pathway information. We focus on the problem of community detection and show that standard network algorithms can be generalized in order to extract novel temporal information about the system under investigation. We also apply our methodology to temporal networks, where w...

  10. Revealing latent factors of temporal networks for mesoscale intervention in epidemic spread

    CERN Document Server

    Gauvin, Laetitia; Barrat, Alain; Cattuto, Ciro

    2015-01-01

    The customary perspective to reason about epidemic mitigation in temporal networks hinges on the identification of nodes with specific features or network roles. The ensuing individual-based control strategies, however, are difficult to carry out in practice and ignore important correlations between topological and temporal patterns. Here we adopt a mesoscopic perspective and present a principled framework to identify collective features at multiple scales and rank their importance for epidemic spread. We use tensor decomposition techniques to build an additive representation of a temporal network in terms of mesostructures, such as cohesive clusters and temporally-localized mixing patterns. This representation allows to determine the impact of individual mesostructures on epidemic spread and to assess the effect of targeted interventions that remove chosen structures. We illustrate this approach using high-resolution social network data on face-to-face interactions in a school and show that our method afford...

  11. Network-based analysis reveals functional connectivity related to internet addiction tendency

    OpenAIRE

    Tanya eWen; Shulan eHsieh

    2016-01-01

    IntroductionPreoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual’s level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated wi...

  12. Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients

    Directory of Open Access Journals (Sweden)

    Qiu eXiangzhe

    2016-05-01

    Full Text Available Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM. However, the DM-related changes in the topological properties in functional brain networks are almost unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs, followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized shortest path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing the functional evidence for the abnormalities of brain networks in DM.

  13. Context-specificity in causal signaling networks revealed by phosphoprotein profiling

    Science.gov (United States)

    Hill, Steven M.; Nesser, Nicole K.; Johnson-Camacho, Katie; Jeffress, Mara; Johnson, Aimee; Boniface, Chris; Spencer, Simon E. F.; Lu, Yiling; Heiser, Laura M.; Lawrence, Yancey; Pande, Nupur T.; Korkola, James E.; Gray, Joe W.; Mills, Gordon B.; Mukherjee, Sach; Spellman, Paul T.

    2017-01-01

    Summary Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context-specificity of signaling networks within a causal conceptual framework, using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts—four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ~70,000 phosphoprotein and ~260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting. PMID:28017544

  14. Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach

    Directory of Open Access Journals (Sweden)

    Buer Jan

    2004-12-01

    Full Text Available Abstract Background Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear. Results In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators. Conclusion The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E

  15. Global terrestrial water storage connectivity revealed using complex climate network analyses

    Directory of Open Access Journals (Sweden)

    A. Y. Sun

    2015-04-01

    Full Text Available Terrestrial water storage (TWS exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationships exist between precipitation and TWS, the latter also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and hydrologic cycle, but also provide new model calibration constraints for improving the current land surface models. In this work, the connectivity of TWS is quantified using the climate network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS datasets, a remote-sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE satellite mission, and a model-generated dataset from the global land data assimilation system's NOAH model (GLDAS-NOAH. Both datasets have 1 ° × 1 ° resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a statistical cutoff threshold to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show TWS hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two datasets indicate that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide important insights for constraining land surface models, especially in data sparse regions.

  16. Network-based analysis reveals functional connectivity related to internet addiction tendency

    Directory of Open Access Journals (Sweden)

    Tanya eWen

    2016-02-01

    Full Text Available IntroductionPreoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual’s level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills. Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction.

  17. Network-Based Analysis Reveals Functional Connectivity Related to Internet Addiction Tendency.

    Science.gov (United States)

    Wen, Tanya; Hsieh, Shulan

    2016-01-01

    Preoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual's level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills). Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction.

  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. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.

    Directory of Open Access Journals (Sweden)

    Peng Fang

    Full Text Available Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients=86.4%, controls=96.2%; permutation test, p<0.0001 of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.

  20. Modeling reveals bistability and low-pass filtering in the network module determining blood stem cell fate.

    Directory of Open Access Journals (Sweden)

    Jatin Narula

    2010-05-01

    Full Text Available Combinatorial regulation of gene expression is ubiquitous in eukaryotes with multiple inputs converging on regulatory control elements. The dynamic properties of these elements determine the functionality of genetic networks regulating differentiation and development. Here we propose a method to quantitatively characterize the regulatory output of distant enhancers with a biophysical approach that recursively determines free energies of protein-protein and protein-DNA interactions from experimental analysis of transcriptional reporter libraries. We apply this method to model the Scl-Gata2-Fli1 triad-a network module important for cell fate specification of hematopoietic stem cells. We show that this triad module is inherently bistable with irreversible transitions in response to physiologically relevant signals such as Notch, Bmp4 and Gata1 and we use the model to predict the sensitivity of the network to mutations. We also show that the triad acts as a low-pass filter by switching between steady states only in response to signals that persist for longer than a minimum duration threshold. We have found that the auto-regulation loops connecting the slow-degrading Scl to Gata2 and Fli1 are crucial for this low-pass filtering property. Taken together our analysis not only reveals new insights into hematopoietic stem cell regulatory network functionality but also provides a novel and widely applicable strategy to incorporate experimental measurements into dynamical network models.

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

    Science.gov (United States)

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

    2013-11-01

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

  2. Noise Response Data Reveal Novel Controllability Gramian for Nonlinear Network Dynamics.

    Science.gov (United States)

    Kashima, Kenji

    2016-06-06

    Control of nonlinear large-scale dynamical networks, e.g., collective behavior of agents interacting via a scale-free connection topology, is a central problem in many scientific and engineering fields. For the linear version of this problem, the so-called controllability Gramian has played an important role to quantify how effectively the dynamical states are reachable by a suitable driving input. In this paper, we first extend the notion of the controllability Gramian to nonlinear dynamics in terms of the Gibbs distribution. Next, we show that, when the networks are open to environmental noise, the newly defined Gramian is equal to the covariance matrix associated with randomly excited, but uncontrolled, dynamical state trajectories. This fact theoretically justifies a simple Monte Carlo simulation that can extract effectively controllable subdynamics in nonlinear complex networks. In addition, the result provides a novel insight into the relationship between controllability and statistical mechanics.

  3. Temporal motifs reveal homophily, gender-specific patterns and group talk in mobile communication networks

    CERN Document Server

    Kovanen, Lauri; Kertész, János; Saramäki, Jari

    2013-01-01

    Electronic communication records provide detailed information about temporal aspects of human interaction. Previous studies have shown that individuals' communication patterns have complex temporal structure, and that this structure has system-wide effects. In this paper we use mobile phone records to show that interaction patterns involving multiple individuals have non-trivial temporal structure that cannot be deduced from a network presentation where only interaction frequencies are taken into account. We apply a recently introduced method, temporal motifs, to identify interaction patterns in a temporal network where nodes have additional attributes such as gender and age. We then develop a null model that allows identifying differences between various types of nodes so that these differences are independent of the network based on interaction frequencies. We find gender-related differences in communication patters, and show the existence of temporal homophily, the tendency of similar individuals to partic...

  4. Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases

    DEFF Research Database (Denmark)

    Tan, Chris Soon Heng; Bodenmiller, Bernd; Pasculescu, Adrian

    2009-01-01

    approximately 600 million years of evolution and hence are likely to be involved in fundamental cellular processes. This sequence-alignment analysis suggested that many phosphorylation sites evolve rapidly and therefore do not display strong evolutionary conservation in terms of sequence position in distantly...... related organisms. Thus, we devised a network-alignment approach to reconstruct conserved kinase-substrate networks, which identified 778 phosphorylation events in 698 human proteins. Both methods identified proteins tightly regulated by phosphorylation as well as signal integration hubs, and both types...... of phosphoproteins were enriched in proteins encoded by disease-associated genes. We analyzed the cellular functions and structural relationships for these conserved signaling events, noting the incomplete nature of current phosphoproteomes. Assessing phosphorylation conservation at both site and network levels...

  5. Gene network analysis of bone marrow mononuclear cells reveals activation of multiple kinase pathways in human systemic lupus erythematosus.

    Directory of Open Access Journals (Sweden)

    Magdalene Nakou

    Full Text Available BACKGROUND: Gene profiling studies provide important information for key molecules relevant to a disease but are less informative of protein-protein interactions, post-translational modifications and regulation by targeted subcellular localization. Integration of genomic data and construction of functional gene networks may provide additional insights into complex diseases such as systemic lupus erythematosus (SLE. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed gene expression microarray data of bone marrow mononuclear cells (BMMCs from 20 SLE patients (11 with active disease and 10 controls. Gene networks were constructed using the bioinformatic tool Ingenuity Gene Network Analysis. In SLE patients, comparative analysis of BMMCs genes revealed a network with 19 central nodes as major gene regulators including ERK, JNK, and p38 MAP kinases, insulin, Ca(2+ and STAT3. Comparison between active versus inactive SLE identified 30 central nodes associated with immune response, protein synthesis, and post-transcriptional modification. A high degree of identity between networks in active SLE and non-Hodgkin's lymphoma (NHL patients was found, with overlapping central nodes including kinases (MAPK, ERK, JNK, PKC, transcription factors (NF-kappaB, STAT3, and insulin. In validation studies, western blot analysis in splenic B cells from 5-month-old NZB/NZW F1 lupus mice showed activation of STAT3, ITGB2, HSPB1, ERK, JNK, p38, and p32 kinases, and downregulation of FOXO3 and VDR compared to normal C57Bl/6 mice. CONCLUSIONS/SIGNIFICANCE: Gene network analysis of lupus BMMCs identified central gene regulators implicated in disease pathogenesis which could represent targets of novel therapies in human SLE. The high similarity between active SLE and NHL networks provides a molecular basis for the reported association of the former with lymphoid malignancies.

  6. A Multiparameter Network Reveals Extensive Divergence between C. elegans bHLH Transcription Factors

    DEFF Research Database (Denmark)

    Grove, C.; De Masi, Federico; Newburger, Daniel;

    2009-01-01

    parameters remain undetermined. We comprehensively identify dimerization partners, spatiotemporal expression patterns, and DNA-binding specificities for the C. elegans bHLH family of TFs, and model these data into an integrated network. This network displays both specificity and promiscuity, as some b......HLH proteins, DNA sequences, and tissues are highly connected, whereas others are not. By comparing all bHLH TFs, we find extensive divergence and that all three parameters contribute equally to bHLH divergence. Our approach provides a framework for examining divergence for other protein families in C. elegans...

  7. Assessment and integration of publicly available SAGE, cDNA microarray, and oligonucleotide microarray expression data for global coexpression analyses.

    Science.gov (United States)

    Griffith, Obi L; Pleasance, Erin D; Fulton, Debra L; Oveisi, Mehrdad; Ester, Martin; Siddiqui, Asim S; Jones, Steven J M

    2005-10-01

    Large amounts of gene expression data from several different technologies are becoming available to the scientific community. A common practice is to use these data to calculate global gene coexpression for validation or integration of other "omic" data. To assess the utility of publicly available datasets for this purpose we have analyzed Homo sapiens data from 1202 cDNA microarray experiments, 242 SAGE libraries, and 667 Affymetrix oligonucleotide microarray experiments. The three datasets compared demonstrate significant but low levels of global concordance (rc<0.11). Assessment against Gene Ontology (GO) revealed that all three platforms identify more coexpressed gene pairs with common biological processes than expected by chance. As the Pearson correlation for a gene pair increased it was more likely to be confirmed by GO. The Affymetrix dataset performed best individually with gene pairs of correlation 0.9-1.0 confirmed by GO in 74% of cases. However, in all cases, gene pairs confirmed by multiple platforms were more likely to be confirmed by GO. We show that combining results from different expression platforms increases reliability of coexpression. A comparison with other recently published coexpression studies found similar results in terms of performance against GO but with each method producing distinctly different gene pair lists.

  8. Cortical network dynamics with time delays reveals functional connectivity in the resting brain.

    NARCIS (Netherlands)

    Ghosh, A.; Rho, Y.; McIntosh, A.R.; Kotter, R.; Jirsa, V.K.

    2008-01-01

    In absence of all goal-directed behavior, a characteristic network of cortical regions involving prefrontal and cingulate cortices consistently shows temporally coherent fluctuations. The origin of these fluctuations is unknown, but has been hypothesized to be of stochastic nature. In the present pa

  9. Network topology reveals high connectance levels and few key microbial genera within soils

    NARCIS (Netherlands)

    Lupatini, M.; Suleiman, A.; Jacques, R.; Antoniolli, Z.; de Siqueira Ferreira, A.; Kuramae, E.E.; Roesch, L.

    2014-01-01

    Microbes have a central role in soil global biogeochemical process, yet specific microbe–microbe relationships are largely unknown. Analytical approaches as network analysis may shed new lights in understanding of microbial ecology and environmental microbiology. We investigated the soil bacterial

  10. Brain network analysis reveals affected connectome structure in bipolar I disorder

    NARCIS (Netherlands)

    Collin, Guusje; van den Heuvel, Martijn P.; Abramovic, Lucija; Vreeker, Annabel; de Reus, Marcel A.; van Haren, Neeltje E M; Boks, Marco P M; Ophoff, Roel A.; Kahn, René S.

    The notion that healthy brain function emerges from coordinated neural activity constrained by the brain's network of anatomical connections-i.e., the connectome-suggests that alterations in the connectome's wiring pattern may underlie brain disorders. Corroborating this hypothesis, studies in

  11. Functional MRI reveals compromised neural integrity of the face processing network in congenital prosopagnosia.

    Science.gov (United States)

    Avidan, Galia; Behrmann, Marlene

    2009-07-14

    The summed activity of multiple nodes of a distributed cortical network supports face recognition in humans, including "core" ventral occipitotemporal cortex (VOTC) regions, and "extended" regions outside VOTC. Many individuals with congenital prosopagnosia-an impairment in face processing-exhibit normal blood oxygenation level-dependent (BOLD) activation in the core VOTC regions. These individuals evince a reduction in the structural integrity of the white matter tracts connecting VOTC to anterior temporal and frontal cortices, part of the "extended" face network. The impairment in congenital prosopagnosia may arise, therefore, not from a dysfunction of the core VOTC areas but from a failure to propagate signals between the intact VOTC and the extended nodes of the network. Using the fMR adaptation paradigm with famous and unknown faces, we show that individuals with congenital prosopagnosia evince normal adaptation effects in VOTC, indicating sensitivity to facial identity, but show no differential activation for familiar versus unknown faces outside VOTC, particularly in the precuneus/posterior cingulate cortex and the anterior paracingulate cortex. Normal BOLD activation in VOTC is thus insufficient to subserve intact face recognition, and disrupted information propagation between VOTC and the extended face processing network may explain the functional impairment in congenital prosopagnosia.

  12. Social Network Perspectives Reveal Strength of Academic Developers as Weak Ties

    Science.gov (United States)

    Matthews, Kelly E.; Crampton, Andrea; Hill, Matthew; Johnson, Elizabeth D.; Sharma, Manjula D.; Varsavsky, Cristina

    2015-01-01

    Social network perspectives acknowledge the influence of disciplinary cultures on academics' teaching beliefs and practices with implications for academic developers. The contribution of academic developers in 18 scholarship of teaching and learning (SoTL) projects situated in the sciences are explored by drawing on data from a two-year national…

  13. Global phosphoproteome profiling reveals unanticipated networks responsive to cisplatin treatment of embryonic stem cells

    DEFF Research Database (Denmark)

    Pines, Alex; Kelstrup, Christian D; Vrouwe, Mischa G

    2011-01-01

    (stable isotope labeling by amino acids in cell culture)-labeled murine embryonic stem cells with the anticancer drug cisplatin. Network and pathway analyses indicated that processes related to the DNA damage response and cytoskeleton organization were significantly affected. Although the ATM (ataxia...

  14. Multivoxel Patterns Reveal Functionally Differentiated Networks Underlying Auditory Feedback Processing of Speech

    DEFF Research Database (Denmark)

    Zheng, Zane Z.; Vicente-Grabovetsky, Alejandro; MacDonald, Ewen N.

    2013-01-01

    within a multivoxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was used to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while...

  15. Computational Analysis of Transcriptional Circuitries in Human Embryonic Stem Cells Reveals Multiple and Independent Networks

    Directory of Open Access Journals (Sweden)

    Xiaosheng Wang

    2014-01-01

    Full Text Available It has been known that three core transcription factors (TFs, NANOG, OCT4, and SOX2, collaborate to form a transcriptional circuitry to regulate pluripotency and self-renewal of human embryonic stem (ES cells. Similarly, MYC also plays an important role in regulating pluripotency and self-renewal of human ES cells. However, the precise mechanism by which the transcriptional regulatory networks control the activity of ES cells remains unclear. In this study, we reanalyzed an extended core network, which includes the set of genes that are cobound by the three core TFs and additional TFs that also bind to these cobound genes. Our results show that beyond the core transcriptional network, additional transcriptional networks are potentially important in the regulation of the fate of human ES cells. Several gene families that encode TFs play a key role in the transcriptional circuitry of ES cells. We also demonstrate that MYC acts independently of the core module in the regulation of the fate of human ES cells, consistent with the established argument. We find that TP53 is a key connecting molecule between the core-centered and MYC-centered modules. This study provides additional insights into the underlying regulatory mechanisms involved in the fate determination of human ES cells.

  16. Analysis of HRCT-derived xylem network reveals reverse flow in some vessels

    Science.gov (United States)

    Flow in xylem vessels is modeled based on constructions of three dimensional xylem networks derived from High Resolution Computed Tomography (HRCT) images of grapevine (Vitis vinifera) stems. Flow in 6-14% of the vessels was found to be oriented in the opposite direction to the bulk flow under norma...

  17. New patterns in human biogeography revealed by networks of contacts between linguistic groups.

    Science.gov (United States)

    Capitán, José A; Bock Axelsen, Jacob; Manrubia, Susanna

    2015-03-07

    Human languages differ broadly in abundance and are distributed highly unevenly on the Earth. In many qualitative and quantitative aspects, they strongly resemble biodiversity distributions. An intriguing and previously unexplored issue is the architecture of the neighbouring relationships between human linguistic groups. Here we construct and characterize these networks of contacts and show that they represent a new kind of spatial network with uncommon structural properties. Remarkably, language networks share a meaningful property with food webs: both are quasi-interval graphs. In food webs, intervality is linked to the existence of a niche space of low dimensionality; in language networks, we show that the unique relevant variable is the area occupied by the speakers of a language. By means of a range model analogous to niche models in ecology, we show that a geometric restriction of perimeter covering by neighbouring linguistic domains explains the structural patterns observed. Our findings may be of interest in the development of models for language dynamics or regarding the propagation of cultural innovations. In relation to species distribution, they pose the question of whether the spatial features of species ranges share architecture, and eventually generating mechanism, with the distribution of human linguistic groups. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  18. Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas

    Directory of Open Access Journals (Sweden)

    Nicholas eFurl

    2015-05-01

    Full Text Available Visual category perception is thought to depend on brain areas that respond specifically when certain categories are viewed. These category-sensitive areas are often assumed to be modules (with some degree of processing autonomy and to act predominantly on feedforward visual input. This modular view can be complemented by a view that treats brain areas as elements within more complex networks and as influenced by network properties. This network-oriented viewpoint is emerging from studies using either diffusion tensor imaging to map structural connections or effective connectivity analyses to measure how their functional responses influence each other. This literature motivates several hypotheses that predict category-sensitive activity based on network properties. Large, long-range fiber bundles such as inferior fronto-occipital, arcuate and inferior longitudinal fasciculi are associated with behavioural recognition and could play crucial roles in conveying backward influences on visual cortex from anterior temporal and frontal areas. Such backward influences could support top-down functions such as visual search and emotion-based visual modulation. Within visual cortex itself, areas sensitive to different categories appear well-connected (e.g., face areas connect to object- and motion sensitive areas and their responses can be predicted by backward modulation. Evidence supporting these propositions remains incomplete and underscores the need for better integration of DTI and functional imaging.

  19. Network topology reveals high connectance levels and few key microbial genera within soils

    NARCIS (Netherlands)

    Lupatini, M.; Suleiman, A.; Jacques, R.; Antoniolli, Z.; de Siqueira Ferreira, A.; Kuramae, E.E.; Roesch, L.

    2014-01-01

    Microbes have a central role in soil global biogeochemical process, yet specific microbe–microbe relationships are largely unknown. Analytical approaches as network analysis may shed new lights in understanding of microbial ecology and environmental microbiology. We investigated the soil bacterial c

  20. Social Network Perspectives Reveal Strength of Academic Developers as Weak Ties

    Science.gov (United States)

    Matthews, Kelly E.; Crampton, Andrea; Hill, Matthew; Johnson, Elizabeth D.; Sharma, Manjula D.; Varsavsky, Cristina

    2015-01-01

    Social network perspectives acknowledge the influence of disciplinary cultures on academics' teaching beliefs and practices with implications for academic developers. The contribution of academic developers in 18 scholarship of teaching and learning (SoTL) projects situated in the sciences are explored by drawing on data from a two-year national…

  1. Multiplex lexical networks reveal patterns in early word acquisition in children

    CERN Document Server

    Stella, Massimo; Brede, Markus

    2016-01-01

    Network models of language have provided a way of linking cognitive processes to the structure and connectivity of language. However, one shortcoming of current approaches is focusing on only one type of linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N=529 words/nodes are connected according to four types of relationships: (i) free associations, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We provide analysis of the topology of the resulting multiplex and then proceed to evaluate single layers as well as the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the emerging multiplex network topology is an important proxy of the cognitive processes of acquisition, capable of captur...

  2. Linking social and spatial networks to viral community phylogenetics reveal subtype specific transmission dynamics in African lions.

    Science.gov (United States)

    Fountain-Jones, Nicholas M; Packer, Craig; Troyer, Jennifer L; VanderWaal, Kimberly; Robinson, Stacie; Jacquot, Maude; Craft, Meggan E

    2017-09-08

    1.Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. 2.Here we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype specific transmission pathways in a social carnivore. 3.We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIVPle ) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIVPle molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father and/or mother offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype specific FIVPle co-occurrence networks and assessed what combination of social networks, spatial networks, or co-infection best structured the FIVPle network. 4.While social organization (i.e., pride) was an important component of FIVPle transmission pathways at all scales, we find that FIVPle subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIVPle transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype specific. 5.Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting

  3. Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state.

    Science.gov (United States)

    Wu, Xia; Yu, Xinyu; Yao, Li; Li, Rui

    2014-01-01

    Functional magnetic resonance imaging (fMRI) studies have converged to reveal the default mode network (DMN), a constellation of regions that display co-activation during resting-state but co-deactivation during attention-demanding tasks in the brain. Here, we employed a Bayesian network (BN) analysis method to construct a directed effective connectivity model of the DMN and compared the organizational architecture and interregional directed connections under both resting-state and task-state. The analysis results indicated that the DMN was consistently organized into two closely interacting subsystems in both resting-state and task-state. The directed connections between DMN regions, however, changed significantly from the resting-state to task-state condition. The results suggest that the DMN intrinsically maintains a relatively stable structure whether at rest or performing tasks but has different information processing mechanisms under varied states.

  4. Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

    Science.gov (United States)

    Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

    2016-06-01

    Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

  5. Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality.

    Science.gov (United States)

    Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

    2016-06-01

    Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

  6. Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines

    Science.gov (United States)

    2014-01-01

    Background Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations. Dimerisation with other receptors allows to bypass pathway blockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. We used longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplified breast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab or pertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modelling approach, signalling networks were reconstructed based on these data in a cell line and time course specific manner, including prior literature knowledge. Finally, we simulated network response to inhibitor combinations to detect signalling nodes reflecting growth inhibition. Results The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. In BT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPK signalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncovered feedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cell line. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT and p70S6K. The results indicated a pathway specific drug response, driven by the type of growth factor stimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumab inefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB and RPS6, relating to phenotypic events like cell growth or proliferation

  7. Hybrid ICA-Bayesian network approach reveals distinct effective connectivity differences in schizophrenia.

    Science.gov (United States)

    Kim, D; Burge, J; Lane, T; Pearlson, G D; Kiehl, K A; Calhoun, V D

    2008-10-01

    We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions

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

    Science.gov (United States)

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

    2011-06-01

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

  9. 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

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

    Science.gov (United States)

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

    2015-01-01

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

  11. Non-Linear Behaviour Of Gelatin Networks Reveals A Hierarchical Structure

    KAUST Repository

    Yang, Zhi

    2015-12-14

    We investigate the strain hardening behaviour of various gelatin networks - namely physically-crosslinked gelatin gel, chemically-crosslinked gelatin gels, and a hybrid gels made of a combination of the former two - under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillation shear protocols. Further, the internal structures of physically-crosslinked gelatin gel and chemically-crosslinked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically-crosslinked network, whereas in the physically-crosslinked gels a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as correlation length (ξ), cross-sectional polymer chain radius (Rc), and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physically-crosslinked and chemically crosslinked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized non-linear elastic theory we used to fit our stress-strain curves. The chemical crosslinking that generates coils and aggregates hinders the free stretching of the triple helices bundles in the physically-crosslinked gels.

  12. Fibrillar organization in tendons: A pattern revealed by percolation characteristics of the respective geometric network

    Directory of Open Access Journals (Sweden)

    Daniel Andres Dos Santos

    2014-06-01

    Full Text Available Since the tendon is composed by collagen fibrils of various sizes connected between them through molecular cross-links, it sounds logical to model it via a heterogeneous network of fibrils. Using cross sectional images, that network is operatively inferred from the respective Gabriel graph of the fibril mass centers. We focus on network percolation characteristics under an ordered activation of fibrils (progressive recruitment going from the smallest to the largest fibril. Analyses of percolation were carried out on a repository of images of digital flexor tendons obtained from samples of lizards and frogs. Observed percolation thresholds were compared against values derived from hypothetical scenarios of random activation of nodes. Strikingly, we found a significant delay for the occurrence of percolation in actual data. We interpret this finding as the consequence of some non-random packing of fibrillar units into a size-constrained geometric pattern. We erect an ideal geometric model of balanced interspersion of polymorphic units that accounts for the delayed percolating instance. We also address the circumstance of being percolation curves mirrored by the empirical curves of stress-strain obtained from the same studied tendons. By virtue of this isomorphism, we hypothesize that the inflection points of both curves are different quantitative manifestations of a common transitional process during mechanical load transference.

  13. Dynamical network of residue-residue contacts reveals coupled allosteric effects in recognition, catalysis, and mutation.

    Science.gov (United States)

    Doshi, Urmi; Holliday, Michael J; Eisenmesser, Elan Z; Hamelberg, Donald

    2016-04-26

    Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis remains ambiguous. Here, we simulate CypA using multiple-microsecond-long atomistic molecular dynamics in explicit solvent and carry out NMR experiments. We analyze a large amount of time-dependent multidimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue-residue contacts. The effects of substrate binding are observed to be largely sensed at a location over 15 Å from the active site, implying its importance in allostery. Using NMR experiments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of interresidue contacts is found to be coupled and temporally dispersed, ranging over 4 to 5 orders of magnitude. Finally, using network centrality measures we demonstrate the changes in the communication network, connectivity, and influence of CypA residues upon substrate binding, mutation, and during catalysis. We identify key residues that potentially act as a bottleneck in the communication flow through the distinct regions in CypA and, therefore, as targets for future mutational studies. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins, in general.

  14. Network analysis reveals strongly localized impacts of El Niño

    Science.gov (United States)

    Fan, Jingfang; Meng, Jun; Ashkenazy, Yosef; Havlin, Shlomo; Schellnhuber, Hans Joachim

    2017-07-01

    Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network “in”-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger in-weighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Niño/La Niña events and also may be applied in the investigation of other climate variables.

  15. Networks of neuroblastoma cells on porous silicon substrates reveal a small world topology

    KAUST Repository

    Marinaro, Giovanni

    2015-01-01

    The human brain is a tightly interweaving network of neural cells where the complexity of the network is given by the large number of its constituents and its architecture. The topological structure of neurons in the brain translates into its increased computational capabilities, low energy consumption, and nondeterministic functions, which differentiate human behavior from artificial computational schemes. In this manuscript, we fabricated porous silicon chips with a small pore size ranging from 8 to 75 nm and large fractal dimensions up to Df ∼ 2.8. In culturing neuroblastoma N2A cells on the described substrates, we found that those cells adhere more firmly to and proliferate on the porous surfaces compared to the conventional nominally flat silicon substrates, which were used as controls. More importantly, we observed that N2A cells on the porous substrates create highly clustered, small world topology patterns. We conjecture that neurons with a similar architecture may elaborate information more efficiently than in random or regular grids. Moreover, we hypothesize that systems of neurons on nano-scale geometry evolve in time to form networks in which the propagation of information is maximized. This journal is

  16. Multiplex lexical networks reveal patterns in early word acquisition in children

    Science.gov (United States)

    Stella, Massimo; Beckage, Nicole M.; Brede, Markus

    2017-04-01

    Network models of language have provided a way of linking cognitive processes to language structure. However, current approaches focus only on one linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N = 529 words/nodes are connected according to four relationship: (i) free association, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We investigate the topology of the resulting multiplex and then proceed to evaluate single layers and the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the multiplex topology is an important proxy of the cognitive processes of acquisition, capable of capturing emergent lexicon structure. In fact, we show that the multiplex structure is fundamentally more powerful than individual layers in predicting the ordering with which words are acquired. Furthermore, multiplex analysis allows for a quantification of distinct phases of lexical acquisition in early learners: while initially all the multiplex layers contribute to word learning, after about month 23 free associations take the lead in driving word acquisition.

  17. 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.

  18. Distinct configurations of protein complexes and biochemical pathways revealed by epistatic interaction network motifs

    LENUS (Irish Health Repository)

    Casey, Fergal

    2011-08-22

    Abstract Background Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks. Results We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter. Conclusion We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.

  19. Brain network analysis reveals affected connectome structure in bipolar I disorder.

    Science.gov (United States)

    Collin, Guusje; van den Heuvel, Martijn P; Abramovic, Lucija; Vreeker, Annabel; de Reus, Marcel A; van Haren, Neeltje E M; Boks, Marco P M; Ophoff, Roel A; Kahn, René S

    2016-01-01

    The notion that healthy brain function emerges from coordinated neural activity constrained by the brain's network of anatomical connections--i.e., the connectome--suggests that alterations in the connectome's wiring pattern may underlie brain disorders. Corroborating this hypothesis, studies in schizophrenia are indicative of altered connectome architecture including reduced communication efficiency, disruptions of central brain hubs, and affected "rich club" organization. Whether similar deficits are present in bipolar disorder is currently unknown. This study examines structural connectome topology in 216 bipolar I disorder patients as compared to 144 healthy controls, focusing in particular on central regions (i.e., brain hubs) and connections (i.e., rich club connections, interhemispheric connections) of the brain's network. We find that bipolar I disorder patients exhibit reduced global efficiency (-4.4%, P =0.002) and that this deficit relates (r = 0.56, P brain hub connections in general, or of connections spanning brain hubs (i.e., "rich club" connections) in particular (all P > 0.1). These findings highlight a role for aberrant brain network architecture in bipolar I disorder with reduced global efficiency in association with disruptions in interhemispheric connectivity, while the central "rich club" system appears not to be particularly affected.

  20. A perimotor framework reveals functional segmentation in the motoneuronal network controlling locomotion in Caenorhabditis elegans.

    Science.gov (United States)

    Haspel, Gal; O'Donovan, Michael J

    2011-10-12

    The neuronal connectivity dataset of the nematode Caenorhabditis elegans attracts wide attention from computational neuroscientists and experimentalists. However, the dataset is incomplete. The ventral and dorsal nerve cords of a single nematode were reconstructed halfway along the body and the posterior data are missing, leaving 21 of 75 motoneurons of the locomotor network with partial or no connectivity data. Using a new framework for network analysis, the perimotor space, we identified rules of connectivity that allowed us to approximate the missing data by extrapolation. Motoneurons were mapped into perimotor space in which each motoneuron is located according to the muscle cells it innervates. In this framework, a pattern of iterative connections emerges which includes most (0.90) of the connections. We identified a repeating unit consisting of 12 motoneurons and 12 muscle cells. The cell bodies of the motoneurons of such a unit are not necessarily anatomical neighbors and there is no obvious anatomical segmentation. A connectivity model, composed of six repeating units, is a description of the network that is both simplified (modular and without noniterative connections) and more complete (includes the posterior part) than the original dataset. The perimotor framework of observed connectivity and the segmented connectivity model give insights and advance the study of the neuronal infrastructure underlying locomotion in C. elegans. Furthermore, we suggest that the tools used herein may be useful to interpret, simplify, and represent connectivity data of other motor systems.

  1. A brassinosteroid transcriptional network revealed by genome-wide identification of BESI target genes in Arabidopsis thaliana.

    Science.gov (United States)

    Yu, Xiaofei; Li, Lei; Zola, Jaroslaw; Aluru, Maneesha; Ye, Huaxun; Foudree, Andrew; Guo, Hongqing; Anderson, Sarah; Aluru, Srinivas; Liu, Peng; Rodermel, Steve; Yin, Yanhai

    2011-02-01

    Brassinosteroids (BRs) are important regulators for plant growth and development. BRs signal to control the activities of the BES1 and BZR1 family transcription factors. The transcriptional network through which BES1 and BZR regulate large number of target genes is mostly unknown. By combining chromatin immunoprecipitation coupled with Arabidopsis tiling arrays (ChIP-chip) and gene expression studies, we have identified 1609 putative BES1 target genes, 404 of which are regulated by BRs and/or in gain-of-function bes1-D mutant. BES1 targets contribute to BR responses and interactions with other hormonal or light signaling pathways. Computational modeling of gene expression data using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) reveals that BES1-targeted transcriptional factors form a gene regulatory network (GRN). Mutants of many genes in the network displayed defects in BR responses. Moreover, we found that BES1 functions to inhibit chloroplast development by repressing the expression of GLK1 and GLK2 transcription factors, confirming a hypothesis generated from the GRN. Our results thus provide a global view of BR regulated gene expression and a GRN that guides future studies in understanding BR-regulated plant growth.

  2. Network modelling reveals the mechanism underlying colitis-associated colon cancer and identifies novel combinatorial anti-cancer targets.

    Science.gov (United States)

    Lu, Junyan; Zeng, Hanlin; Liang, Zhongjie; Chen, Limin; Zhang, Liyi; Zhang, Hao; Liu, Hong; Jiang, Hualiang; Shen, Bairong; Huang, Ming; Geng, Meiyu; Spiegel, Sarah; Luo, Cheng

    2015-10-08

    The connection between inflammation and tumourigenesis has been well established. However, the detailed molecular mechanism underlying inflammation-associated tumourigenesis remains unknown because this process involves a complex interplay between immune microenvironments and epithelial cells. To obtain a more systematic understanding of inflammation-associated tumourigenesis as well as to identify novel therapeutic approaches, we constructed a knowledge-based network describing the development of colitis-associated colon cancer (CAC) by integrating the extracellular microenvironment and intracellular signalling pathways. Dynamic simulations of the CAC network revealed a core network module, including P53, MDM2, and AKT, that may govern the malignant transformation of colon epithelial cells in a pro-tumor inflammatory microenvironment. Furthermore, in silico mutation studies and experimental validations led to a novel finding that concurrently targeting ceramide and PI3K/AKT pathway by chemical probes or marketed drugs achieves synergistic anti-cancer effects. Overall, our network model can guide further mechanistic studies on CAC and provide new insights into the design of combinatorial cancer therapies in a rational manner.

  3. Dynamic network of transcription and pathway crosstalk to reveal molecular mechanism of MGd-treated human lung cancer cells.

    Directory of Open Access Journals (Sweden)

    Liyan Shao

    Full Text Available Recent research has revealed various molecular markers in lung cancer. However, the organizational principles underlying their genetic regulatory networks still await investigation. Here we performed Network Component Analysis (NCA and Pathway Crosstalk Analysis (PCA to construct a regulatory network in human lung cancer (A549 cells which were treated with 50 uM motexafin gadolinium (MGd, a metal cation-containing chemotherapeutic drug for 4, 12, and 24 hours. We identified a set of key TFs, known target genes for these TFs, and signaling pathways involved in regulatory networks. Our work showed that putative interactions between these TFs (such as ESR1/Sp1, E2F1/Sp1, c-MYC-ESR, Smad3/c-Myc, and NFKB1/RELA, between TFs and their target genes (such as BMP41/Est1, TSC2/Myc, APE1/Sp1/p53, RARA/HOXA1, and SP1/USF2, and between signaling pathways (such as PPAR signaling pathway and Adipocytokines signaling pathway. These results will provide insights into the regulatory mechanism of MGd-treated human lung cancer cells.

  4. Social network-based recruitment successfully reveals HIV-1 transmission networks among high-risk individuals in El Salvador.

    Science.gov (United States)

    Dennis, Ann M; Murillo, Wendy; de Maria Hernandez, Flor; Guardado, Maria Elena; Nieto, Ana Isabel; Lorenzana de Rivera, Ivette; Eron, Joseph J; Paz-Bailey, Gabriela

    2013-05-01

    HIV in Central America is concentrated among certain groups such as men who have sex with men (MSM) and female sex workers (FSWs). We compared social recruitment chains and HIV transmission clusters from 699 MSM and 787 FSWs to better understand factors contributing to ongoing HIV transmission in El Salvador. Phylogenies were reconstructed using pol sequences from 119 HIV-positive individuals recruited by respondent-driven sampling (RDS) and compared with RDS chains in 3 cities in El Salvador. Transmission clusters with a mean pairwise genetic distance ≤ 0.015 and Bayesian posterior probabilities =1 were identified. Factors associated with cluster membership were evaluated among MSM. Sequences from 34 (43%) MSM and 4 (10%) FSW grouped in 14 transmission clusters. Clusters were defined by risk group (12 MSM clusters) and geographic residence (only 1 spanned separate cities). In 4 MSM clusters (all n = 2), individuals were also members of the same RDS chain, but only 2 had members directly linked through recruitment. All large clusters (n ≥ 3) spanned >1 RDS chain. Among MSM, factors independently associated with cluster membership included recent infection by BED assay (P = 0.02), sex with stable male partners (P = 0.02), and sex with ≥ 3 male partners in the past year (P = 0.04). We found few HIV transmissions corresponding directly with the social recruitment. However, we identified clustering in nearly one-half of MSM suggesting that RDS recruitment was indirectly but successfully uncovering transmission networks, particularly among recent infections. Interrogating RDS chains with phylogenetic analyses may help refine methods for identifying transmission clusters.

  5. Social network based recruitment successfully reveals HIV-1 transmission networks among high risk individuals in El Salvador

    Science.gov (United States)

    Dennis, Ann M.; Murillo, Wendy; de Maria Hernandez, Flor; Guardado, Maria Elena; Nieto, Ana Isabel; de Rivera, Ivette Lorenzana; Eron, Joseph J.; Paz-Bailey, Gabriela

    2013-01-01

    Objective HIV in Central America is concentrated among certain groups such as men who have sex with men (MSM) and female sex workers (FSW). We compared social recruitment chains and HIV transmission clusters from 699 MSM and 757 FSW to better understand factors contributing to ongoing HIV transmission in El Salvador. Methods Phylogenies were reconstructed using pol sequences from 119 HIV-positive individuals recruited by respondent driven sampling (RDS) and compared to RDS chains in three cities in El Salvador. Transmission clusters with a mean pairwise genetic distance ≤0.015 and Bayesian posterior probabilities=1 were identified. Factors associated with cluster membership were evaluated among MSM. Results Sequences from 34 (43%) MSM and 4 (10%) FSW grouped in 14 transmission clusters. Clusters were defined by risk group (12 MSM clusters) and geographic residence (only one spanned separate cities). In 4 MSM clusters (all n=2), individuals were also members of the same RDS chain but only 2 had members directly linked through recruitment. All large clusters (n≥3) spanned more than one RDS chain. Among MSM, factors independently associated with cluster membership included recent infection by BED assay (P=0.02), sex with stable male partners (P=0.02), and sex with ≥3 male partners in past year (P=0.04). Conclusions We found few HIV transmissions corresponding directly with the social recruitment. However, we identified clustering in nearly one half of MSM suggesting RDS recruitment was indirectly but successfully uncovering transmission networks, particularly among recent infections. Interrogating RDS chains with phylogenetic analyses may help refine methods for identifying transmission clusters. PMID:23364512

  6. Adding protein context to the human protein-protein interaction network to reveal meaningful interactions.

    Directory of Open Access Journals (Sweden)

    Martin H Schaefer

    Full Text Available Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs, which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the

  7. A Comprehensive Immunoreceptor Phosphotyrosine-based Signaling Network Revealed by Reciprocal Protein–Peptide Array Screening*

    Science.gov (United States)

    Liu, Huadong; Li, Lei; Voss, Courtney; Wang, Feng; Liu, Juewen; Li, Shawn Shun-Cheng

    2015-01-01

    Cells of the immune system communicate with their environment through immunoreceptors. These receptors often harbor intracellular tyrosine residues, which, when phosphorylated upon receptor activation, serve as docking sites to recruit downstream signaling proteins containing the Src Homology 2 (SH2) domain. A systematic investigation of interactions between the SH2 domain and the immunoreceptor tyrosine-based regulatory motifs (ITRM), including inhibitory (ITIM), activating (ITAM), or switching (ITSM) motifs, is critical for understanding cellular signal transduction and immune function. Using the B cell inhibitory receptor CD22 as an example, we developed an approach that combines reciprocal or bidirectional phosphopeptide and SH2 domain array screens with in-solution binding assays to identify a comprehensive SH2-CD22 interaction network. Extending this approach to 194 human ITRM sequences and 78 SH2 domains led to the identification of a high-confidence immunoreceptor interactome containing 1137 binary interactions. Besides recapitulating many previously reported interactions, our study uncovered numerous novel interactions. The resulting ITRM-SH2 interactome not only helped to fill many gaps in the immune signaling network, it also allowed us to associate different SH2 domains to distinct immune functions. Detailed analysis of the NK cell ITRM-mediated interactions led to the identification of a network nucleated by the Vav3 and Fyn SH2 domains. We showed further that these SH2 domains have distinct functions in cytotoxicity. The bidirectional protein-peptide array approach described herein may be applied to the numerous other peptide-binding modules to identify potential protein–protein interactions in a systematic and reliable manner. PMID:25907764

  8. A Comprehensive Immunoreceptor Phosphotyrosine-based Signaling Network Revealed by Reciprocal Protein-Peptide Array Screening.

    Science.gov (United States)

    Liu, Huadong; Li, Lei; Voss, Courtney; Wang, Feng; Liu, Juewen; Li, Shawn Shun-Cheng

    2015-07-01

    Cells of the immune system communicate with their environment through immunoreceptors. These receptors often harbor intracellular tyrosine residues, which, when phosphorylated upon receptor activation, serve as docking sites to recruit downstream signaling proteins containing the Src Homology 2 (SH2) domain. A systematic investigation of interactions between the SH2 domain and the immunoreceptor tyrosine-based regulatory motifs (ITRM), including inhibitory (ITIM), activating (ITAM), or switching (ITSM) motifs, is critical for understanding cellular signal transduction and immune function. Using the B cell inhibitory receptor CD22 as an example, we developed an approach that combines reciprocal or bidirectional phosphopeptide and SH2 domain array screens with in-solution binding assays to identify a comprehensive SH2-CD22 interaction network. Extending this approach to 194 human ITRM sequences and 78 SH2 domains led to the identification of a high-confidence immunoreceptor interactome containing 1137 binary interactions. Besides recapitulating many previously reported interactions, our study uncovered numerous novel interactions. The resulting ITRM-SH2 interactome not only helped to fill many gaps in the immune signaling network, it also allowed us to associate different SH2 domains to distinct immune functions. Detailed analysis of the NK cell ITRM-mediated interactions led to the identification of a network nucleated by the Vav3 and Fyn SH2 domains. We showed further that these SH2 domains have distinct functions in cytotoxicity. The bidirectional protein-peptide array approach described herein may be applied to the numerous other peptide-binding modules to identify potential protein-protein interactions in a systematic and reliable manner. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

  9. Preservation Analysis of Macrophage Gene Coexpression Between Human and Mouse Identifies PARK2 as a Genetically Controlled Master Regulator of Oxidative Phosphorylation in Humans

    Directory of Open Access Journals (Sweden)

    Veronica Codoni

    2016-10-01

    Full Text Available Macrophages are key players involved in numerous pathophysiological pathways and an in-depth characterization of their gene regulatory networks can help in better understanding how their dysfunction may impact on human diseases. We here conducted a cross-species network analysis of macrophage gene expression data between human and mouse to identify conserved networks across both species, and assessed whether such networks could reveal new disease-associated regulatory mechanisms. From a sample of 684 individuals processed for genome-wide macrophage gene expression profiling, we identified 27 groups of coexpressed genes (modules. Six modules were found preserved (P < 10−4 in macrophages from 86 mice of the Hybrid Mouse Diversity Panel. One of these modules was significantly [false discovery rate (FDR = 8.9 × 10−11] enriched for genes belonging to the oxidative phosphorylation (OXPHOS pathway. This pathway was also found significantly (FDR < 10−4 enriched in susceptibility genes for Alzheimer, Parkinson, and Huntington diseases. We further conducted an expression quantitative trait loci analysis to identify SNP that could regulate macrophage OXPHOS gene expression in humans. This analysis identified the PARK2 rs192804963 as a trans-acting variant influencing (minimal P-value = 4.3 × 10−8 the expression of most OXPHOS genes in humans. Further experimental work demonstrated that PARK2 knockdown expression was associated with increased OXPHOS gene expression in THP1 human macrophages. This work provided strong new evidence that PARK2 participates to the regulatory networks associated with oxidative phosphorylation and suggested that PARK2 genetic variations could act as a trans regulator of OXPHOS gene macrophage expression in humans.

  10. Network Analysis Reveals the Recognition Mechanism for Mannose-binding Lectins

    Science.gov (United States)

    Zhao, Yunjie; Jian, Yiren; Zeng, Chen; Computational Biophysics Lab Team

    The specific carbohydrate binding of mannose-binding lectin (MBL) protein in plants makes it a very useful molecular tool for cancer cell detection and other applications. The biological states of most MBL proteins are dimeric. Using dynamics network analysis on molecular dynamics (MD) simulations on the model protein of MBL, we elucidate the short- and long-range driving forces behind the dimer formation. The results are further supported by sequence coevolution analysis. We propose a general framework for deciphering the recognition mechanism underlying protein-protein interactions that may have potential applications in signaling pathways.

  11. Using data- and network science to reveal iterations and phase-transitions in the design process

    DEFF Research Database (Denmark)

    Piccolo, Sebastiano; Jørgensen, Sune Lehmann; Maier, Anja

    2017-01-01

    Understanding the role of iterations is a prevalent topic in both design research and design practice. Furthermore, the increasing amount of data produced and stored by companies leaves traces and enables the application of data science to learn from past design processes. In this article, we...... analyse a documentlog to show the temporal evolution of a real design process of a power plant by using exploratory data analysis and network analysis. We show how the iterative nature of the design process is reflected in archival data and how one might re-construct the design process, involving...

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

    Directory of Open Access Journals (Sweden)

    Degnan Bernard M

    2011-01-01

    Full Text Available Abstract Evidence that conserved developmental gene-regulatory networks can change as a unit during deutersostome evolution emerges from a study published in BMC Biology. This shows that genes consistently expressed in anterior brain patterning in hemichordates and chordates are expressed in a similar spatial pattern in another deuterostome, an asteroid echinoderm (sea star, but in a completely different developmental context (the animal-vegetal axis. This observation has implications for hypotheses on the type of development present in the deuterostome common ancestor. See research article: http://www.biomedcentral.com/1741-7007/8/143/abstract

  13. Multiligand specificity and wide tissue expression of GPRC6A reveals new endocrine networks.

    Science.gov (United States)

    Pi, Min; Quarles, L Darryl

    2012-05-01

    Emerging evidence supports the hypothesis that the skeleton is an endocrine organ that regulates energy metabolism through the release of the osteoblast-derived hormone, osteocalcin (Ocn). This bone-pancreas endocrine network is controversial because important gaps remain to be filled in our knowledge of the physiological effects of Ocn in multiple organs and the complex alterations in other hormonal networks induced by Ocn administration. A key step toward understanding the integrative regulation of energy metabolism by bone is the identification of GPCR family C group 6 member A (GPRC6A) as the Ocn receptor. GPRC6A is an amino acid-sensing G protein-coupled receptor highly expressed in β-cells and is activated by recombinant Ocn in vitro and in vivo but that is widely expressed in tissues other than the pancreas and is capable of sensing multiple structurally unrelated ligands, including l-amino acids, cations, and anabolic steroids in addition to Ocn. The broad expression and multiligand specificity of GPRC6A is identifying both systemic and paracrine regulation of seemingly disparate biological processes, ranging from energy metabolism, sexual reproduction, hypothalamic-pituitary function, bone formation, and prostate cancer. Consistent with the existence of more complex endocrine networks, ablation of GPRC6A in Gprc6a(-/-) mice results in complex metabolic abnormalities, including obesity, glucose intolerance, hepatic steatosis, insulin resistance, hyperphosphatemia, osteopenia, plus several hormonal abnormalities, including decreased circulating testosterone, IGF-I, and insulin and increased estradiol, LH, GH, and leptin. Recombinant Ocn also regulates testosterone production by the testes and male fertility through a GPRC6A-dependent mechanism, and testosterone regulation of LH secretion is abnormal in Gprc6a(-/-) mice. Thus, GPRC6A, as the biologically relevant receptor for Ocn, defines not only a molecular mechanism for linking bone metabolism with

  14. Co-expression of calretinin and parvalbumin in the rat facial nucleus

    Institute of Scientific and Technical Information of China (English)

    Qiben Wang; Linfeng Zheng; Qinghong Huang; Yanbin Meng; Manyuan Kuang

    2008-01-01

    BACKGROUND: Calretinin and parvalbumin are members of the intracellular calcium binding protein family, which transform Ca2+ bioinformation into regulation of neuronal and neural network activities. OBJECTIVE: To observe expression and co-expression of calretinin and parvalbumin in rat facial nucleus neurons. DESIGN, TIME AND SETTING: Neuronal morphology experiment was performed at the Research Laboratory of Applied Anatomy, Department Neurobiology and Anatomy, Xiangya Medical College of Central South University from August to October 2007. MATERIALS: Five healthy, adult Sprague Dawley rats were selected. Polyclonal rabbit-anti-parvalbumin and mouse-anti-calretinin were provided by Sigma, USA. METHODS: Rat brains were obtained and cut into coronal slices using a freezing microtome. Slices from the experimental group were immunofluorescent stained with polyclonal rabbit-anti-parvalbumin and mouse-anti-calretinin antibodies. The control group sections were stained with normal rabbit and mouse sera. MAIN OUTCOME MEASURES: lmmunofluorescent double-staining was used to detect calretinin and parvalbumin expression. Nissi staining was utilized for facial nucleus localization and neuronal morphology analysis. RESULTS: The majority of facial motor neurons was polygon-shaped, and expressed calretinin and parvalbumin. The calretinin-immunopositive neurons also exhibited parvalbumin immunoreactivity, that is, calretinin and parvalbumin were co-expressed in the same neuron. CONCLUSION: Calretinin and parvalbumin were expressed in facial nucleus neurons, with varied distribution.

  15. 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.

  16. Coexpression of receptor-tyrosine-kinases in gastric adenocarcinoma-a rationale for a molecular targeting strategy?

    Institute of Scientific and Technical Information of China (English)

    Daniel Drescher; Thomas Wehler; Andreas Teufel; Kerstin Herzer; Thomas Fischer; Martin R Berger; Theodor Junginger; Peter R Galle; Carl C Schimanski; Markus Moehler; Ines Gockel; Kirsten Frerichs; Annett Müller; Friedrich Dünschede; Thomas Borschitz; Stefan Biesterfeld; Martin Holtmann

    2007-01-01

    AIM: To define the (co-)expression pattern of target receptor-tyrosine-kinases (RTK) in human gastric adenocarcinoma.METHODS: The (co-)expression pattern of VEGFR1-3,PDGFRα/β and EGFR1 was analyzed by RT-PCR in 51 human gastric adenocarcinomas. In addition, IHC staining was applied for confirmation of expression and analysis of RTK Iocalisation.RESULTS: The majority of samples revealed a VEGFR1(98%), VEGFR2 (80%), VEGFR3 (67%), PDGFRα(82%) and PDGFRβ (82%) expression, whereas only 62% exhibited an EGFR1 expression. 78% of cancers expressed at least four out of six RTKs. While VEGFR1-3and PDGFRα revealed a predominantly cytoplasmatic staining in tumor cells, accompanied by an additional nuclear staining for VEGFR3, EGFR1 was almost exclusively detected on the membrane of tumor cells.PDGFRβ was restricted to stromal pericytes, which also depicted a PDGFRα expression.CONCLUSION: Our results reveal a high rate of receptor-tyrosine-kinases coexpression in gastric adenocarcinoma and might therefore encourage an application of multiple-target RTK-inhibitors within a combination therapy.

  17. Transcriptome comparison reveals a genetic network regulating the lower temperature limit in fish

    Science.gov (United States)

    Hu, Peng; Liu, Mingli; Liu, Yimeng; Wang, Jinfeng; Zhang, Dong; Niu, Hongbo; Jiang, Shouwen; Wang, Jian; Zhang, Dongsheng; Han, Bingshe; Xu, Qianghua; Chen, Liangbiao

    2016-01-01

    Transcriptional plasticity is a major driver of phenotypic differences between species. The lower temperature limit (LTL), namely the lower end of survival temperature, is an important trait delimiting the geographical distribution of a species, however, the genetic mechanisms are poorly understood. We investigated the inter-species transcriptional diversification in cold responses between zebrafish Danio rerio and tilapia Oreochromis niloticus, which were reared at a common temperature (28 °C) but have distinct LTLs. We identified significant expressional divergence between the two species in the orthologous genes from gills when the temperature cooled to the LTL of tilapia (8 °C). Five KEGG pathways were found sequentially over-represented in the zebrafish/tilapia divergently expressed genes in the duration (12 hour) of 8 °C exposure, forming a signaling cascade from metabolic regulation to apoptosis via FoxO signaling. Consistently, we found differential progression of apoptosis in the gills of the two species in which zebrafish manifested a delayed and milder apoptotic phenotype than tilapia, corresponding with a lower LTL of zebrafish. We identified diverged expression in 25 apoptosis-related transcription factors between the two species which forms an interacting network with diverged factors involving the FoxO signaling and metabolic regulation. We propose a genetic network which regulates LTL in fishes. PMID:27356472

  18. Assembly and interrogation of Alzheimer's disease genetic networks reveal novel regulators of progression.

    Directory of Open Access Journals (Sweden)

    Soline Aubry

    Full Text Available Alzheimer's disease (AD is a complex multifactorial disorder with poorly characterized pathogenesis. Our understanding of this disease would thus benefit from an approach that addresses this complexity by elucidating the regulatory networks that are dysregulated in the neural compartment of AD patients, across distinct brain regions. Here, we use a Systems Biology (SB approach, which has been highly successful in the dissection of cancer related phenotypes, to reverse engineer the transcriptional regulation layer of human neuronal cells and interrogate it to infer candidate Master Regulators (MRs responsible for disease progression. Analysis of gene expression profiles from laser-captured neurons from AD and controls subjects, using the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe, yielded an interactome consisting of 488,353 transcription-factor/target interactions. Interrogation of this interactome, using the Master Regulator INference algorithm (MARINa, identified an unbiased set of candidate MRs causally responsible for regulating the transcriptional signature of AD progression. Experimental assays in autopsy-derived human brain tissue showed that three of the top candidate MRs (YY1, p300 and ZMYM3 are indeed biochemically and histopathologically dysregulated in AD brains compared to controls. Our results additionally implicate p53 and loss of acetylation homeostasis in the neurodegenerative process. This study suggests that an integrative, SB approach can be applied to AD and other neurodegenerative diseases, and provide significant novel insight on the disease progression.

  19. An Atlas of Network Topologies Reveals Design Principles for Caenorhabditis elegans Vulval Precursor Cell Fate Patterning.

    Science.gov (United States)

    Ping, Xianfeng; Tang, Chao

    2015-01-01

    The vulval precursor cell (VPC) fate patterning in Caenorhabditis elegans is a classic model experimental system for cell fate determination and patterning in development. Despite its apparent simplicity (six neighboring cells arranged in one dimension) and many experimental and computational efforts, the patterning strategy and mechanism remain controversial due to incomplete knowledge of the complex biology. Here, we carry out a comprehensive computational analysis and obtain a reservoir of all possible network topologies that are capable of VPC fate patterning under the simulation of various biological environments and regulatory rules. We identify three patterning strategies: sequential induction, morphogen gradient and lateral antagonism, depending on the features of the signal secreted from the anchor cell. The strategy of lateral antagonism, which has not been reported in previous studies of VPC patterning, employs a mutual inhibition of the 2° cell fate in neighboring cells. Robust topologies are built upon minimal topologies with basic patterning strategies and have more flexible and redundant implementations of modular functions. By simulated mutation, we find that all three strategies can reproduce experimental error patterns of mutants. We show that the topology derived by mapping currently known biochemical pathways to our model matches one of our identified functional topologies. Furthermore, our robustness analysis predicts a possible missing link related to the lateral antagonism strategy. Overall, we provide a theoretical atlas of all possible functional networks in varying environments, which may guide novel discoveries of the biological interactions in vulval development of Caenorhabditis elegans and related species.

  20. The Neural Dynamics of Fronto-Parietal Networks in Childhood Revealed using Magnetoencephalography.

    Science.gov (United States)

    Astle, Duncan E; Luckhoo, Henry; Woolrich, Mark; Kuo, Bo-Cheng; Nobre, Anna C; Scerif, Gaia

    2015-10-01

    Our ability to hold information in mind is limited, requires a high degree of cognitive control, and is necessary for many subsequent cognitive processes. Children, in particular, are highly variable in how, trial-by-trial, they manage to recruit cognitive control in service of memory. Fronto-parietal networks, typically recruited under conditions where this cognitive control is needed, undergo protracted development. We explored, for the first time, whether dynamic changes in fronto-parietal activity could account for children's variability in tests of visual short-term memory (VSTM). We recorded oscillatory brain activity using magnetoencephalography (MEG) as 9- to 12-year-old children and adults performed a VSTM task. We combined temporal independent component analysis (ICA) with general linear modeling to test whether the strength of fronto-parietal activity correlated with VSTM performance on a trial-by-trial basis. In children, but not adults, slow frequency theta (4-7 Hz) activity within a right lateralized fronto-parietal network in anticipation of the memoranda predicted the accuracy with which those memory items were subsequently retrieved. These findings suggest that inconsistent use of anticipatory control mechanism contributes significantly to trial-to-trial variability in VSTM maintenance performance.

  1. Transcriptional Network Analysis Reveals Drought Resistance Mechanisms of AP2/ERF Transgenic Rice

    Directory of Open Access Journals (Sweden)

    Hongryul Ahn

    2017-06-01

    Full Text Available This study was designed to investigate at the molecular level how a transgenic version of rice “Nipponbare” obtained a drought-resistant phenotype. Using multi-omics sequencing data, we compared wild-type rice (WT and a transgenic version (erf71 that had obtained a drought-resistant phenotype by overexpressing OsERF71, a member of the AP2/ERF transcription factor (TF family. A comprehensive bioinformatics analysis pipeline, including TF networks and a cascade tree, was developed for the analysis of multi-omics data. The results of the analysis showed that the presence of OsERF71 at the source of the network controlled global gene expression levels in a specific manner to make erf71 survive longer than WT. Our analysis of the time-series transcriptome data suggests that erf71 diverted more energy to survival-critical mechanisms related to translation, oxidative response, and DNA replication, while further suppressing energy-consuming mechanisms, such as photosynthesis. To support this hypothesis further, we measured the net photosynthesis level under physiological conditions, which confirmed the further suppression of photosynthesis in erf71. In summary, our work presents a comprehensive snapshot of transcriptional modification in transgenic rice and shows how this induced the plants to acquire a drought-resistant phenotype.

  2. Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

    Science.gov (United States)

    Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

    2016-11-01

    Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes.

  3. Construction of adeno-associated virus coexpression system for human angiopoietin-1 and VEGF gene

    Institute of Scientific and Technical Information of China (English)

    陈德杰; 谭最; 谢友利; 刘芳

    2004-01-01

    Background Ischemic disease is one of the leading causes of death in the world. In order to further study gene therapy for ischemic disease, we constructed a recombinant plasmid for co-expression of human angiopoietin-1 and vascular endothelial growth factor 165 (VEGF165) gene in adeno-associated virus (AAV) gene delivery system.Methods Human angiopoietin 1 and VEGF165 gene were obtained using PCR. The upstream of angiopoietin 1 contained restriction enzyme site Hind Ⅲ, and the downstream of angiopoietin 1contained restriction enzyme site BamH Ⅰ. The upstream of VEGF165 contained restriction enzyme site Bgl Ⅱ, and the downstream of VEGF165 contained restriction enzyme site BamH Ⅰ . Using the multiple cloning sites (MCS) in plasmid pZero ++ such as BamH Ⅰ , Bgl Ⅱ, Hind Ⅲ, Not Ⅰ , XhoⅠ,Xba Ⅰ , Sal Ⅰ , BspH Ⅰ , Ksp Ⅰ and the corresponding MCS in plasmid pAAV-MCS, angiopoietin 1 and VEGF165 gene were subcloned into pAAV-MCS.Results DNA sequencing revealed that the PCR- amplified angiopoietin 1 and VEGF165 were consistent with NCBI Gene Bank. The recombinant plasmid was identified using PCR and digestion,which proved to be consistent with our hypothesis. In recombinant plasmid, angiopoietin1 and VEGF possessed a CMV promoter and polyA terminator system respectively, thus assuring co-expression of the two genes.Conclusion Successful construction of AAV co-expression system for human angiopoietin 1 and VEGF165 gene will provide the foundation for gene therapy to cure severe ischemic disease.

  4. Human-specific transcriptional networks in the brain.

    Science.gov (United States)

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

    2012-08-23

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

  5. Metagenomic signatures of a tropical mining-impacted stream reveal complex microbial and metabolic networks.

    Science.gov (United States)

    Reis, Mariana P; Dias, Marcela F; Costa, Patrícia S; Ávila, Marcelo P; Leite, Laura R; de Araújo, Flávio M G; Salim, Anna C M; Bucciarelli-Rodriguez, Mônica; Oliveira, Guilherme; Chartone-Souza, Edmar; Nascimento, Andréa M A

    2016-10-01

    Bacteria from aquatic ecosystems significantly contribute to biogeochemical cycles, but details of their community structure in tropical mining-impacted environments remain unexplored. In this study, we analyzed a bacterial community from circumneutral-pH tropical stream sediment by 16S rRNA and shotgun deep sequencing. Carrapatos stream sediment, which has been exposed to metal stress due to gold and iron mining (21 [g Fe]/kg), revealed a diverse community, with predominance of Proteobacteria (39.4%), Bacteroidetes (12.2%), and Parcubacteria (11.4%). Among Proteobacteria, the most abundant reads were assigned to neutrophilic iron-oxidizing taxa, such as Gallionella, Sideroxydans, and Mariprofundus, which are involved in Fe cycling and harbor several metal resistance genes. Functional analysis revealed a large number of genes participating in nitrogen and methane metabolic pathways despite the low concentrations of inorganic nitrogen in the Carrapatos stream. Our findings provide important insights into bacterial community interactions in a mining-impacted environment.

  6. Unbiased transcriptome signature of in vivo cell proliferation reveals pro- and antiproliferative gene networks.

    Science.gov (United States)

    Cohen, Meital; Vecsler, Manuela; Liberzon, Arthur; Noach, Meirav; Zlotorynski, Eitan; Tzur, Amit

    2013-09-15

    Different types of mature B-cell lymphocytes are overall highly similar. Nevertheless, some B cells proliferate intensively, while others rarely do. Here, we demonstrate that a simple binary classification of gene expression in proliferating vs. resting B cells can identify, with remarkable selectivity, global in vivo regulators of the mammalian cell cycle, many of which are also post-translationally regulated by the APC/C E3 ligase. Consequently, we discover a novel regulatory network between the APC/C and the E2F transcription factors and discuss its potential impact on the G1-S transition of the cell cycle. In addition, by focusing on genes whose expression inversely correlates with proliferation, we demonstrate the inherent ability of our approach to also identify in vivo regulators of cell differentiation, cell survival, and other antiproliferative processes. Relying on data sets of wt, non-transgenic animals, our approach can be applied to other cell lineages and human data sets.

  7. Social Network Analysis Reveals Potential Fission-Fusion Behavior in a Shark

    Science.gov (United States)

    Haulsee, Danielle E.; Fox, Dewayne A.; Breece, Matthew W.; Brown, Lori M.; Kneebone, Jeff; Skomal, Gregory B.; Oliver, Matthew J.

    2016-01-01

    Complex social networks and behaviors are difficult to observe for free-living marine species, especially those that move great distances. Using implanted acoustic transceivers to study the inter- and intraspecific interactions of sand tiger sharks Carcharias taurus, we observed group behavior that has historically been associated with higher order mammals. We found evidence strongly suggestive of fission-fusion behavior, or changes in group size and composition of sand tigers, related to five behavioral modes (summering, south migration, community bottleneck, dispersal, north migration). Our study shows sexually dimorphic behavior during migration, in addition to presenting evidence of a potential solitary phase for these typically gregarious sharks. Sand tigers spent up to 95 consecutive and 335 cumulative hours together, with the strongest relationships occurring between males. Species that exhibit fission-fusion group dynamics pose a particularly challenging issue for conservation and management because changes in group size and composition affect population estimates and amplify anthropogenic impacts. PMID:27686155

  8. Revealing the Supramolecular Nature of Side-Chain Terpyridine-Functionalized Polymer Networks

    Directory of Open Access Journals (Sweden)

    Jérémy Brassinne

    2015-01-01

    Full Text Available Nowadays, finely controlling the mechanical properties of polymeric materials is possible by incorporating supramolecular motifs into their architecture. In this context, the synthesis of a side-chain terpyridine-functionalized poly(2-(dimethylaminoethyl methacrylate is reported via reversible addition-fragmentation chain transfer polymerization. By addition of transition metal ions, concentrated aqueous solutions of this polymer turn into metallo-supramolecular hydrogels whose dynamic mechanical properties are investigated by rotational rheometry. Hence, the possibility for the material to relax mechanical constrains via dissociation of transient cross-links is brought into light. In addition, the complex phenomena occurring under large oscillatory shear are interpreted in the context of transient networks.

  9. Global effects of kinase inhibitors on signaling networks revealed by quantitative phosphoproteomics

    DEFF Research Database (Denmark)

    Pan, Cuiping; Olsen, Jesper V; Daub, Henrik;

    2009-01-01

    to identify the direct targets of kinase inhibitors upon affinity purification from cellular extracts. Here we introduce a complementary approach to evaluate the effects of kinase inhibitors on the entire cell signaling network. We used triple labeling SILAC (stable isotope labeling by amino acids in cell...... culture) to compare cellular phosphorylation levels for control, epidermal growth factor stimulus, and growth factor combined with kinase inhibitors. Of thousands of phosphopeptides, less than 10% had a response pattern indicative of targets of U0126 and SB202190, two widely used MAPK inhibitors....... Interestingly, 83% of the growth factor-induced phosphorylation events were affected by either or both inhibitors, showing quantitatively that early signaling processes are predominantly transmitted through the MAPK cascades. In contrast to MAPK inhibitors, dasatinib, a clinical drug directed against BCR...

  10. Musical Creativity "Revealed" in Brain Structure: Interplay between Motor, Default Mode, and Limbic Networks.

    Science.gov (United States)

    Bashwiner, David M; Wertz, Christopher J; Flores, Ranee A; Jung, Rex E

    2016-02-18

    Creative behaviors are among the most complex that humans engage in, involving not only highly intricate, domain-specific knowledge and skill, but also domain-general processing styles and the affective drive to create. This study presents structural imaging data indicating that musically creative people (as indicated by self-report) have greater cortical surface area or volume in a) regions associated with domain-specific higher-cognitive motor activity and sound processing (dorsal premotor cortex, supplementary and pre-supplementary motor areas, and planum temporale), b) domain-general creative-ideation regions associated with the default mode network (dorsomedial prefrontal cortex, middle temporal gyrus, and temporal pole), and c) emotion-related regions (orbitofrontal cortex, temporal pole, and amygdala). These findings suggest that domain-specific musical expertise, default-mode cognitive processing style, and intensity of emotional experience might all coordinate to motivate and facilitate the drive to create music.

  11. Network information analysis reveals risk perception transmission in a behaviour-influenza dynamics system.

    Science.gov (United States)

    Liao, C-M; You, S-H; Cheng, Y-H

    2015-01-01

    Influenza poses a significant public health burden worldwide. Understanding how and to what extent people would change their behaviour in response to influenza outbreaks is critical for formulating public health policies. We incorporated the information-theoretic framework into a behaviour-influenza (BI) transmission dynamics system in order to understand the effects of individual behavioural change on influenza epidemics. We showed that information transmission of risk perception played a crucial role in the spread of health-seeking behaviour throughout influenza epidemics. Here a network BI model provides a new approach for understanding the risk perception spread and human behavioural change during disease outbreaks. Our study allows simultaneous consideration of epidemiological, psychological, and social factors as predictors of individual perception rates in behaviour-disease transmission systems. We suggest that a monitoring system with precise information on risk perception should be constructed to effectively promote health behaviours in preparation for emerging disease outbreaks.

  12. RNAi screening reveals a large signaling network controlling the Golgi apparatus in human cells.

    Science.gov (United States)

    Chia, Joanne; Goh, Germaine; Racine, Victor; Ng, Susanne; Kumar, Pankaj; Bard, Frederic

    2012-01-01

    The Golgi apparatus has many important physiological functions, including sorting of secretory cargo and biosynthesis of complex glycans. These functions depend on the intricate and compartmentalized organization of the Golgi apparatus. To investigate the mechanisms that regulate Golgi architecture, we developed a quantitative morphological assay using three different Golgi compartment markers and quantitative image analysis, and performed a kinome- and phosphatome-wide RNAi screen in HeLa cells. Depletion of 159 signaling genes, nearly 20% of genes assayed, induced strong and varied perturbations in Golgi morphology. Using bioinformatics data, a large regulatory network could be constructed. Specific subnetworks are involved in phosphoinositides regulation, acto-myosin dynamics and mitogen activated protein kinase signaling. Most gene depletion also affected Golgi functions, in particular glycan biosynthesis, suggesting that signaling cascades can control glycosylation directly at the Golgi level. Our results provide a genetic overview of the signaling pathways that control the Golgi apparatus in human cells.

  13. Social Network Analysis Reveals Potential Fission-Fusion Behavior in a Shark

    Science.gov (United States)

    Haulsee, Danielle E.; Fox, Dewayne A.; Breece, Matthew W.; Brown, Lori M.; Kneebone, Jeff; Skomal, Gregory B.; Oliver, Matthew J.

    2016-09-01

    Complex social networks and behaviors are difficult to observe for free-living marine species, especially those that move great distances. Using implanted acoustic transceivers to study the inter- and intraspecific interactions of sand tiger sharks Carcharias taurus, we observed group behavior that has historically been associated with higher order mammals. We found evidence strongly suggestive of fission-fusion behavior, or changes in group size and composition of sand tigers, related to five behavioral modes (summering, south migration, community bottleneck, dispersal, north migration). Our study shows sexually dimorphic behavior during migration, in addition to presenting evidence of a potential solitary phase for these typically gregarious sharks. Sand tigers spent up to 95 consecutive and 335 cumulative hours together, with the strongest relationships occurring between males. Species that exhibit fission-fusion group dynamics pose a particularly challenging issue for conservation and management because changes in group size and composition affect population estimates and amplify anthropogenic impacts.

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

    Science.gov (United States)

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

    2014-04-24

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

  15. RNA-Sequencing Reveals Biological Networks during Table Grapevine (‘Fujiminori’) Fruit Development

    Science.gov (United States)

    Shangguan, Lingfei; Mu, Qian; Fang, Xiang; Zhang, Kekun; Jia, Haifeng; Li, Xiaoying; Bao, Yiqun; Fang, Jinggui

    2017-01-01

    Grapevine berry development is a complex and genetically controlled process, with many morphological, biochemical and physiological changes occurring during the maturation process. Research carried out on grapevine berry development has been mainly concerned with wine grape, while barely focusing on table grape. ‘Fujiminori’ is an important table grapevine cultivar, which is cultivated in most provinces of China. In order to uncover the dynamic networks involved in anthocyanin biosynthesis, cell wall development, lipid metabolism and starch-sugar metabolism in ‘Fujiminori’ fruit, we employed RNA-sequencing (RNA-seq) and analyzed the whole transcriptome of grape berry during development at the expanding period (40 days after full bloom, 40DAF), véraison period (65DAF), and mature period (90DAF). The sequencing depth in each sample was greater than 12×, and the expression level of nearly half of the expressed genes were greater than 1. Moreover, greater than 64% of the clean reads were aligned to the Vitis vinifera reference genome, and 5,620, 3,381, and 5,196 differentially expressed genes (DEGs) were identified between different fruit stages, respectively. Results of the analysis of DEGs showed that the most significant changes in various processes occurred from the expanding stage to the véraison stage. The expression patterns of F3’H and F3’5’H were crucial in determining red or blue color of the fruit skin. The dynamic networks of cell wall development, lipid metabolism and starch-sugar metabolism were also constructed. A total of 4,934 SSR loci were also identified from 4,337 grapevine genes, which may be helpful for the development of phylogenetic analysis in grapevine and other fruit trees. Our work provides the foundation for developmental research of grapevine fruit as well as other non-climacteric fruits. PMID:28118385

  16. Large-scale brain network abnormalities in Huntington's disease revealed by structural covariance.

    Science.gov (United States)

    Minkova, Lora; Eickhoff, Simon B; Abdulkadir, Ahmed; Kaller, Christoph P; Peter, Jessica; Scheller, Elisa; Lahr, Jacob; Roos, Raymund A; Durr, Alexandra; Leavitt, Blair R; Tabrizi, Sarah J; Klöppel, Stefan

    2016-01-01

    Huntington's disease (HD) is a progressive neurodegenerative disorder that can be diagnosed with certainty decades before symptom onset. Studies using structural MRI have identified grey matter (GM) loss predominantly in the striatum, but also involving various cortical areas. So far, voxel-based morphometric studies have examined each brain region in isolation and are thus unable to assess the changes in the interrelation of brain regions. Here, we examined the structural covariance in GM volumes in pre-specified motor, working memory, cognitive flexibility, and social-affective networks in 99 patients with manifest HD (mHD), 106 presymptomatic gene mutation carriers (pre-HD), and 108 healthy controls (HC). After correction for global differences in brain volume, we found that increased GM volume in one region was associated with increased GM volume in another. When statistically comparing the groups, no differences between HC and pre-HD were observed, but increased positive correlations were evident for mHD, relative to pre-HD and HC. These findings could be explained by a HD-related neuronal loss heterogeneously affecting the examined network at the pre-HD stage, which starts to dominate structural covariance globally at the manifest stage. Follow-up analyses identified structural connections between frontoparietal motor regions to be linearly modified by disease burden score (DBS). Moderator effects of disease load burden became significant at a DBS level typically associated with the onset of unequivocal HD motor signs. Together with existing findings from functional connectivity analyses, our data indicates a critical role of these frontoparietal regions for the onset of HD motor signs.

  17. Photopigment coexpression in mammals: comparative and developmental aspects.

    Science.gov (United States)

    Lukáts, A; Szabó, A; Röhlich, P; Vígh, B; Szél, A

    2005-04-01

    In mammals, each cone had been thought to contain only one single type of photopigment. It was not until the early 1990s that photopigment coexpression was reported. In the house mouse, the distribution of color cones shows a characteristic division. Whereas in the upper retinal field the ratio of short wave to middle-to-long wave cones falls in the usual range (1:10), in the ventral retinal field M/L-pigment expression is completely missing. In the transitional zone, numerous dual cones are detectable (spatial coexpression). In other species without retinal division, dual cones appear during development, suggesting that M/L-cones develop from S-cones. Dual elements represent a transitory stage in M/L-cone differentiation that disappear with maturation (transitory coexpression). These two phenomena seem to be mutually exclusive in the species studied so far. In the comparative part of this report the retinal cone distribution of eight rodent species is reported. In two species dual cones appear in adult specimens without retinal division, and dual elements either occupy the dorsal peripheral retina, or make up the entire cone population. This is the first observation proving that all cones of a retina are of dual nature. These species are good models for the study of molecular control of opsin expression and renders them suitable sources of dual cones for investigations on the role and neural connections of this peculiar cone type. In the developmental part, the retinal maturation of other species is examined to test the hypothesis of transitory coexpression. In these species S-pigment expression precedes that of the M/L-pigment, but dual cones are either identified in a small number or they are completely missing from the developing retina. These results exclude a common mechanism for M/L-cone maturation: they either transdifferentiate from S-cones or develop independently.

  18. Photopigment coexpression in mammals: comparative and developmental aspects

    OpenAIRE

    Lukáts, A.; Szabó, A.; Röhlich, P.; Vígh, B.; Szél, A

    2005-01-01

    In mammals, each cone had been thought to contain only one single type of photopigment. It was not until the early 1990s that photopigment coexpression was reported. In the house mouse, the distribution of color cones shows a characteristic division. Whereas in the upper retinal field the ratio of short wave to middleto- long wave cones falls in the usual range (1:10), in the ventral retinal field M/L-pigment expression is completely missing. In the transitiona...

  19. Community structure analysis of transcriptional networks reveals distinct molecular pathways for early- and late-onset temporal lobe epilepsy with childhood febrile seizures.

    Directory of Open Access Journals (Sweden)

    Carlos Alberto Moreira-Filho

    Full Text Available Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS constitute an initial precipitating insult (IPI commonly associated with mesial temporal lobe epilepsy (MTLE. FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E or late (L disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i the visualization and analysis of differentially expressed (DE and complete (CO - all valid GO annotated transcripts - GCNs for the E and L groups; ii the study of interactions between all the system's constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less

  20. Community structure analysis of transcriptional networks reveals distinct molecular pathways for early- and late-onset temporal lobe epilepsy with childhood febrile seizures.

    Science.gov (United States)

    Moreira-Filho, Carlos Alberto; Bando, Silvia Yumi; Bertonha, Fernanda Bernardi; Iamashita, Priscila; Silva, Filipi Nascimento; Costa, Luciano da Fontoura; Silva, Alexandre Valotta; Castro, Luiz Henrique Martins; Wen, Hung-Tzu

    2015-01-01

    Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system's constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to

  1. Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas.

    Science.gov (United States)

    Castillo, Jessica A; Epps, Clinton W; Jeffress, Mackenzie R; Ray, Chris; Rodhouse, Thomas J; Schwalm, Donelle

    2016-09-01

    Landscape connectivity is essential for maintaining viable populations, particularly for species restricted to fragmented habitats or naturally arrayed in metapopulations and facing rapid climate change. The importance of assessing both structural connectivity (physical distribution of favorable habitat patches) and functional connectivity (how species move among habitat patches) for managing such species is well understood. However, the degree to which functional connectivity for a species varies among landscapes, and the resulting implications for conservation, have rarely been assessed. We used a landscape genetics approach to evaluate resistance to gene flow and, thus, to determine how landscape and climate-related variables influence gene flow for American pikas (Ochotona princeps) in eight federally managed sites in the western United States. We used empirically derived, individual-based landscape resistance models in conjunction with predictive occupancy models to generate patch-based network models describing functional landscape connectivity. Metareplication across landscapes enabled identification of limiting factors for dispersal that would not otherwise have been apparent. Despite the cool microclimates characteristic of pika habitat, south-facing aspects consistently represented higher resistance to movement, supporting the previous hypothesis that exposure to relatively high temperatures may limit dispersal in American pikas. We found that other barriers to dispersal included areas with a high degree of topographic relief, such as cliffs and ravines, as well as streams and distances greater than 1-4 km depending on the site. Using the empirically derived network models of habitat patch connectivity, we identified habitat patches that were likely disproportionately important for maintaining functional connectivity, areas in which habitat appeared fragmented, and locations that could be targeted for management actions to improve functional connectivity

  2. Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia.

    Science.gov (United States)

    Meda, Shashwath A; Ruaño, Gualberto; Windemuth, Andreas; O'Neil, Kasey; Berwise, Clifton; Dunn, Sabra M; Boccaccio, Leah E; Narayanan, Balaji; Kocherla, Mohan; Sprooten, Emma; Keshavan, Matcheri S; Tamminga, Carol A; Sweeney, John A; Clementz, Brett A; Calhoun, Vince D; Pearlson, Godfrey D

    2014-05-13

    The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.

  3. Novel insights into embryonic stem cell self-renewal revealed through comparative human and mouse systems biology networks.

    Science.gov (United States)

    Dowell, Karen G; Simons, Allen K; Bai, Hao; Kell, Braden; Wang, Zack Z; Yun, Kyuson; Hibbs, Matthew A

    2014-05-01

    Embryonic stem cells (ESCs), characterized by their ability to both self-renew and differentiate into multiple cell lineages, are a powerful model for biomedical research and developmental biology. Human and mouse ESCs share many features, yet have distinctive aspects, including fundamental differences in the signaling pathways and cell cycle controls that support self-renewal. Here, we explore the molecular basis of human ESC self-renewal using Bayesian network machine learning to integrate cell-type-specific, high-throughput data for gene function discovery. We integrated high-throughput ESC data from 83 human studies (~1.8 million data points collected under 1,100 conditions) and 62 mouse studies (~2.4 million data points collected under 1,085 conditions) into separate human and mouse predictive networks focused on ESC self-renewal to analyze shared and distinct functional relationships among protein-coding gene orthologs. Computational evaluations show that these networks are highly accurate, literature validation confirms their biological relevance, and reverse transcriptase polymerase chain reaction (RT-PCR) validation supports our predictions. Our results reflect the importance of key regulatory genes known to be strongly associated with self-renewal and pluripotency in both species (e.g., POU5F1, SOX2, and NANOG), identify metabolic differences between species (e.g., threonine metabolism), clarify differences between human and mouse ESC developmental signaling pathways (e.g., leukemia inhibitory factor (LIF)-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated fibroblast growth factor in human), and reveal many novel genes and pathways predicted to be functionally associated with self-renewal in each species. These interactive networks are available online at www.StemSight.org for stem cell researchers to develop new hypotheses, discover potential mechanisms involving sparsely annotated genes, and prioritize genes of interest for experimental validation

  4. Traveling salesman problems with PageRank Distance on complex networks reveal community structure

    Science.gov (United States)

    Jiang, Zhongzhou; Liu, Jing; Wang, Shuai

    2016-12-01

    In this paper, we propose a new algorithm for community detection problems (CDPs) based on traveling salesman problems (TSPs), labeled as TSP-CDA. Since TSPs need to find a tour with minimum cost, cities close to each other are usually clustered in the tour. This inspired us to model CDPs as TSPs by taking each vertex as a city. Then, in the final tour, the vertices in the same community tend to cluster together, and the community structure can be obtained by cutting the tour into a couple of paths. There are two challenges. The first is to define a suitable distance between each pair of vertices which can reflect the probability that they belong to the same community. The second is to design a suitable strategy to cut the final tour into paths which can form communities. In TSP-CDA, we deal with these two challenges by defining a PageRank Distance and an automatic threshold-based cutting strategy. The PageRank Distance is designed with the intrinsic properties of CDPs in mind, and can be calculated efficiently. In the experiments, benchmark networks with 1000-10,000 nodes and varying structures are used to test the performance of TSP-CDA. A comparison is also made between TSP-CDA and two well-established community detection algorithms. The results show that TSP-CDA can find accurate community structure efficiently and outperforms the two existing algorithms.

  5. Network Modeling Reveals Cross Talk of MAP Kinases during Adaptation to Caspofungin Stress in Aspergillus fumigatus.

    Science.gov (United States)

    Altwasser, Robert; Baldin, Clara; Weber, Jakob; Guthke, Reinhard; Kniemeyer, Olaf; Brakhage, Axel A; Linde, Jörg; Valiante, Vito

    2015-01-01

    Mitogen activated protein kinases (MAPKs) are highly conserved in eukaryotic organisms. In pathogenic fungi, their activities were assigned to different physiological functions including drug adaptation and resistance. Aspergillus fumigatus is a human pathogenic fungus, which causes life-threatening invasive infections. Therapeutic options against invasive mycoses are still limited. One of the clinically used drugs is caspofungin, which specifically targets the fungal cell wall biosynthesis. A systems biology approach, based on comprehensive transcriptome data sets and mathematical modeling, was employed to infer a regulatory network and identify key interactions during adaptation to caspofungin stress in A. fumigatus. Mathematical modeling and experimental validations confirmed an intimate cross talk occurring between the cell wall-integrity and the high osmolarity-glycerol signaling pathways. Specifically, increased concentrations of caspofungin promoted activation of these signalings. Moreover, caspofungin affected the intracellular transport, which caused an additional osmotic stress that is independent of glucan inhibition. High concentrations of caspofungin reduced this osmotic stress, and thus decreased its toxic activity. Our results demonstrated that MAPK signaling pathways play a key role during caspofungin adaptation and are contributing to the paradoxical effect exerted by this drug.

  6. High-frequency oscillations in distributed neural networks reveal the dynamics of human decision making

    Directory of Open Access Journals (Sweden)

    Adrian G Guggisberg

    2008-03-01

    Full Text Available We examine the relative timing of numerous brain regions involved in human decisions that are based on external criteria, learned information, personal preferences, or unconstrained internal considerations. Using magnetoencephalography (MEG and advanced signal analysis techniques, we were able to non-invasively reconstruct oscillations of distributed neural networks in the high-gamma frequency band (60–150 Hz. The time course of the observed neural activity suggested that two-alternative forced choice tasks are processed in four overlapping stages: processing of sensory input, option evaluation, intention formation, and action execution. Visual areas are activated fi rst, and show recurring activations throughout the entire decision process. The temporo-occipital junction and the intraparietal sulcus are active during evaluation of external values of the options, 250–500 ms after stimulus presentation. Simultaneously, personal preference is mediated by cortical midline structures. Subsequently, the posterior parietal and superior occipital cortices appear to encode intention, with different subregions being responsible for different types of choice. The cerebellum and inferior parietal cortex are recruited for internal generation of decisions and actions, when all options have the same value. Action execution was accompanied by activation peaks in the contralateral motor cortex. These results suggest that high-gamma oscillations as recorded by MEG allow a reliable reconstruction of decision processes with excellent spatiotemporal resolution.

  7. Comparative Phosphoproteomics Reveals an Important Role of MKK2 in Banana (Musa spp.) Cold Signal Network

    Science.gov (United States)

    Gao, Jie; Zhang, Sheng; He, Wei-Di; Shao, Xiu-Hong; Li, Chun-Yu; Wei, Yue-Rong; Deng, Gui-Ming; Kuang, Rui-Bin; Hu, Chun-Hua; Yi, Gan-Jun; Yang, Qiao-Song

    2017-01-01

    Low temperature is one of the key environmental stresses, which greatly affects global banana production. However, little is known about the global phosphoproteomes in Musa spp. and their regulatory roles in response to cold stress. In this study, we conducted a comparative phosphoproteomic profiling of cold-sensitive Cavendish Banana and relatively cold tolerant Dajiao under cold stress. Phosphopeptide abundances of five phosphoproteins involved in MKK2 interaction network, including MKK2, HY5, CaSR, STN7 and kinesin-like protein, show a remarkable difference between Cavendish Banana and Dajiao in response to cold stress. Western blotting of MKK2 protein and its T31 phosphorylated peptide verified the phosphoproteomic results of increased T31 phosphopeptide abundance with decreased MKK2 abundance in Daojiao for a time course of cold stress. Meanwhile increased expression of MKK2 with no detectable T31 phosphorylation was found in Cavendish Banana. These results suggest that the MKK2 pathway in Dajiao, along with other cold-specific phosphoproteins, appears to be associated with the molecular mechanisms of high tolerance to cold stress in Dajiao. The results also provide new evidence that the signaling pathway of cellular MKK2 phosphorylation plays an important role in abiotic stress tolerance that likely serves as a universal plant cold tolerance mechanism. PMID:28106078

  8. Monkey drumming reveals common networks for perceiving vocal and nonvocal communication sounds.

    Science.gov (United States)

    Remedios, Ryan; Logothetis, Nikos K; Kayser, Christoph

    2009-10-20

    Salient sounds such as those created by drumming can serve as means of nonvocal acoustic communication in addition to vocal sounds. Despite the ubiquity of drumming across human cultures, its origins and the brain regions specialized in processing such signals remain unexplored. Here, we report that an important animal model for vocal communication, the macaque monkey, also displays drumming behavior, and we exploit this finding to show that vocal and nonvocal communication sounds are represented by overlapping networks in the brain's temporal lobe. Observing social macaque groups, we found that these animals use artificial objects to produce salient periodic sounds, similar to acoustic gestures. Behavioral tests confirmed that these drumming sounds attract the attention of listening monkeys similarly as conspecific vocalizations. Furthermore, in a preferential looking experiment, drumming sounds influenced the way monkeys viewed their conspecifics, suggesting that drumming serves as a multimodal signal of social dominance. Finally, by using high-resolution functional imaging we identified those brain regions preferentially activated by drumming sounds or by vocalizations and found that the representations of both these communication sounds overlap in caudal auditory cortex and the amygdala. The similar behavioral responses to drumming and vocal sounds, and their shared neural representation, suggest a common origin of primate vocal and nonvocal communication systems and support the notion of a gestural origin of speech and music.

  9. 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.

  10. Mapping the Nephronophthisis-Joubert-Meckel-Gruber Protein Network Reveals Ciliopathy Disease Genes and Pathways

    Science.gov (United States)

    Sang, Liyun; Miller, Julie J.; Corbit, Kevin C.; Giles, Rachel H.; Brauer, Matthew J.; Otto, Edgar A.; Baye, Lisa M.; Wen, Xiaohui; Scales, Suzie J.; Kwong, Mandy; Huntzicker, Erik G.; Sfakianos, Mindan K.; Sandoval, Wendy; Bazan, J. Fernando; Kulkarni, Priya; Garcia-Gonzalo, Francesc R.; Seol, Allen D.; O'Toole, John F.; Held, Susanne; Reutter, Heiko M.; Lane, William S.; Rafiq, Muhammad Arshad; Noor, Abdul; Ansar, Muhammad; Devi, Akella Radha Rama; Sheffield, Val C.; Slusarski, Diane C.; Vincent, John B.; Doherty, Daniel A.; Hildebrandt, Friedhelm; Reiter, Jeremy F.; Jackson, Peter K.

    2011-01-01

    Nephronophthisis (NPHP), Joubert (JBTS) and Meckel-Gruber (MKS) syndromes are autosomal-recessive ciliopathies presenting with cystic kidneys, retinal degeneration, and cerebellar/neural tube malformation. Whether defects in kidney, retinal, or neural disease primarily involve ciliary, Hedgehog, or cell polarity pathways remains unclear. Using high-confidence proteomics, we identified 850 interactors copurifying with nine NPHP/JBTS/MKS proteins, and discovered three connected modules: “NPHP1-4-8” functioning at the apical surface; “NPHP5-6” at centrosomes; and “MKS” linked to Hedgehog signaling. Assays for ciliogenesis and epithelial morphogenesis in 3D renal cultures link renal cystic disease to apical organization defects, whereas ciliary and Hedgehog pathway defects lead to retinal or neural deficits. Using 38 interactors as candidates, linkage and sequencing analysis of 250 patients identified ATXN10 and TCTN2 as new NPHP-JBTS genes and our Tctn2 mouse knockout shows neural tube and Hedgehog signaling defects. Our study further illustrates the power of linking proteomic networks and human genetics to uncover critical disease pathways. PMID:21565611

  11. Combined techniques for characterising pasta structure reveals how the gluten network slows enzymic digestion rate.

    Science.gov (United States)

    Zou, Wei; Sissons, Mike; Gidley, Michael J; Gilbert, Robert G; Warren, Frederick J

    2015-12-01

    The aim of the present study is to characterise the influence of gluten structure on the kinetics of starch hydrolysis in pasta. Spaghetti and powdered pasta were prepared from three different cultivars of durum semolina, and starch was also purified from each cultivar. Digestion kinetic parameters were obtained through logarithm-of-slope analysis, allowing identification of sequential digestion steps. Purified starch and semolina were digested following a single first-order rate constant, while pasta and powdered pasta followed two sequential first-order rate constants. Rate coefficients were altered by pepsin hydrolysis. Confocal microscopy revealed that, following cooking, starch granules were completely swollen for starch, semolina and pasta powder samples. In pasta, they were completely swollen in the external regions, partially swollen in the intermediate region and almost intact in the pasta strand centre. Gluten entrapment accounts for sequential kinetic steps in starch digestion of pasta; the compact microstructure of pasta also reduces digestion rates.

  12. β-catenin promoter ChIP-chip reveals potential schizophrenia and bipolar disorder gene network.

    Science.gov (United States)

    Pedrosa, Erika; Shah, Abhishek; Tenore, Christopher; Capogna, Michael; Villa, Catalina; Guo, Xingyi; Zheng, Deyou; Lachman, Herbert M

    2010-12-01

    Therapeutic concentrations of lithium salts inhibit glycogen synthase kinase 3 beta (GSK3β) and phosphoinositide (PI) signaling suggesting that abnormal activation of these pathways could be a factor in the pathophysiology of bipolar disorder (BD). Involvement of these pathways is also supported by recent genome-wide association studies (GWASs). One way investigators have investigated the molecular basis of BD and the therapeutic action of lithium is by microarray expression studies, since both GSK3β- and PI-mediated signal transduction pathways are coupled to transcriptional activation and inhibition. However, expression profiling has some limitations and investigators cannot use the approach to analyze fetal brain tissue, arguably the most relevant biological structure related to the development of genetically based psychiatric disorders. To address these shortcomings, the authors have taken a novel approach using chromatin immunoprecipitation-enriched material annealed to microarrays (ChIP-chip) targeting genes in fetal brain tissue bound by β-catenin, a transcription factor that is directly regulated by GSK3β. The promoters for 640 genes were found to be bound by β-catenin, many of which are known schizophrenia (SZ), autism spectrum disorder (ASD), and BD candidates, including CACNA1B, NRNG, SNAP29, FGFR1, PCDH9, and nine others identified in recently published GWASs and genome-wide searches for copy number variants (CNVs). The findings suggest that seemingly disparate candidate genes for SZ and BD can be incorporated into a common molecular network revolving around GSK3β/β-catenin signaling. In addition, the finding that a putative lithium-responsive pathway may influence a subgroup of SZ and ASD candidate genes could have therapeutic implications.

  13. Deficits in episodic memory retrieval reveal impaired default mode network connectivity in amnestic mild cognitive impairment

    Directory of Open Access Journals (Sweden)

    Cameron J. Dunn

    2014-01-01

    Full Text Available Amnestic mild cognitive impairment (aMCI is believed to represent a transitional stage between normal healthy ageing and the development of dementia. In particular, aMCI patients have been shown to have higher annual transition rates to Alzheimer's Disease (AD than individuals without cognitive impairment. Despite intensifying interest investigating the neuroanatomical basis of this transition, there remain a number of questions regarding the pathophysiological process underlying aMCI itself. A number of recent studies in aMCI have shown specific impairments in connectivity within the default mode network (DMN, which is a group of regions strongly related to episodic memory capacities. However to date, no study has investigated the integrity of the DMN between patients with aMCI and those with a non-amnestic pattern of MCI (naMCI, who have cognitive impairment, but intact memory storage systems. In this study, we contrasted the DMN connectivity in 24 aMCI and 33 naMCI patients using seed-based resting state fMRI. The two groups showed no statistical difference in their DMN intra-connectivity. However when connectivity was analysed according to performance on measures of episodic memory retrieval, the two groups were separable, with aMCI patients demonstrating impaired functional connectivity between the hippocampal formation and the posterior cingulate cortex. We provide evidence that this lack of connectivity is driven by impaired communication from the posterior cingulate hub and does not simply represent hippocampal atrophy, suggesting that posterior cingulate degeneration is the driving force behind impaired DMN connectivity in aMCI.

  14. Deficits in episodic memory retrieval reveal impaired default mode network connectivity in amnestic mild cognitive impairment

    Science.gov (United States)

    Dunn, Cameron J.; Duffy, Shantel L; Hickie, Ian B; Lagopoulos, Jim; Lewis, Simon J.G.; Naismith, Sharon L.; Shine, James M.

    2014-01-01

    Amnestic mild cognitive impairment (aMCI) is believed to represent a transitional stage between normal healthy ageing and the development of dementia. In particular, aMCI patients have been shown to have higher annual transition rates to Alzheimer's Disease (AD) than individuals without cognitive impairment. Despite intensifying interest investigating the neuroanatomical basis of this transition, there remain a number of questions regarding the pathophysiological process underlying aMCI itself. A number of recent studies in aMCI have shown specific impairments in connectivity within the default mode network (DMN), which is a group of regions strongly related to episodic memory capacities. However to date, no study has investigated the integrity of the DMN between patients with aMCI and those with a non-amnestic pattern of MCI (naMCI), who have cognitive impairment, but intact memory storage systems. In this study, we contrasted the DMN connectivity in 24 aMCI and 33 naMCI patients using seed-based resting state fMRI. The two groups showed no statistical difference in their DMN intra-connectivity. However when connectivity was analysed according to performance on measures of episodic memory retrieval, the two groups were separable, with aMCI patients demonstrating impaired functional connectivity between the hippocampal formation and the posterior cingulate cortex. We provide evidence that this lack of connectivity is driven by impaired communication from the posterior cingulate hub and does not simply represent hippocampal atrophy, suggesting that posterior cingulate degeneration is the driving force behind impaired DMN connectivity in aMCI. PMID:24634833

  15. The Intrinsic Dynamics and Unfolding Process of an Antibody Fab Fragment Revealed by Elastic Network Model

    Directory of Open Access Journals (Sweden)

    Ji-Guo Su

    2015-12-01

    Full Text Available Antibodies have been increasingly used as pharmaceuticals in clinical treatment. Thermal stability and unfolding process are important properties that must be considered in antibody design. In this paper, the structure-encoded dynamical properties and the unfolding process of the Fab fragment of the phosphocholine-binding antibody McPC603 are investigated by use of the normal mode analysis of Gaussian network model (GNM. Firstly, the temperature factors for the residues of the protein were calculated with GNM and then compared with the experimental measurements. A good result was obtained, which provides the validity for the use of GNM to study the dynamical properties of the protein. Then, with this approach, the mean-square fluctuation (MSF of the residues, as well as the MSF in the internal distance (MSFID between all pairwise residues, was calculated to investigate the mobility and flexibility of the protein, respectively. It is found that the mobility and flexibility of the constant regions are higher than those of the variable regions, and the six complementarity-determining regions (CDRs in the variable regions also exhibit relative large mobility and flexibility. The large amplitude motions of the CDRs are considered to be associated with the immune function of the antibody. In addition, the unfolding process of the protein was simulated by iterative use of the GNM. In our method, only the topology of protein native structure is taken into account, and the protein unfolding process is simulated through breaking the native contacts one by one according to the MSFID values between the residues. It is found that the flexible regions tend to unfold earlier. The sequence of the unfolding events obtained by our method is consistent with the hydrogen-deuterium exchange experimental results. Our studies imply that the unfolding behavior of the Fab fragment of antibody McPc603 is largely determined by the intrinsic dynamics of the protein.

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

    Directory of Open Access Journals (Sweden)

    Gokhan eErtaylan

    2014-12-01

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

  17. Neuropeptidomics Mass Spectrometry Reveals Signaling Networks Generated by Distinct Protease Pathways in Human Systems

    Science.gov (United States)

    Hook, Vivian; Bandeira, Nuno

    2015-12-01

    Neuropeptides regulate intercellular signaling as neurotransmitters of the central and peripheral nervous systems, and as peptide hormones in the endocrine system. Diverse neuropeptides of distinct primary sequences of various lengths, often with post-translational modifications, coordinate and integrate regulation of physiological functions. Mass spectrometry-based analysis of the diverse neuropeptide structures in neuropeptidomics research is necessary to define the full complement of neuropeptide signaling molecules. Human neuropeptidomics has notable importance in defining normal and dysfunctional neuropeptide signaling in human health and disease. Neuropeptidomics has great potential for expansion in translational research opportunities for defining neuropeptide mechanisms of human diseases, providing novel neuropeptide drug targets for drug discovery, and monitoring neuropeptides as biomarkers of drug responses. In consideration of the high impact of human neuropeptidomics for health, an observed gap in this discipline is the few published articles in human neuropeptidomics compared with, for example, human proteomics and related mass spectrometry disciplines. Focus on human neuropeptidomics will advance new knowledge of the complex neuropeptide signaling networks participating in the fine control of neuroendocrine systems. This commentary review article discusses several human neuropeptidomics accomplishments that illustrate the rapidly expanding diversity of neuropeptides generated by protease processing of pro-neuropeptide precursors occurring within the secretory vesicle proteome. Of particular interest is the finding that human-specific cathepsin V participates in producing enkephalin and likely other neuropeptides, indicating unique proteolytic mechanisms for generating human neuropeptides. The field of human neuropeptidomics has great promise to solve new mechanisms in disease conditions, leading to new drug targets and therapeutic agents for human

  18. Face-name association task reveals memory networks in patients with left and right hippocampal sclerosis.

    Science.gov (United States)

    Klamer, Silke; Milian, Monika; Erb, Michael; Rona, Sabine; Lerche, Holger; Ethofer, Thomas

    2017-01-01

    We aimed to identify reorganization processes of episodic memory networks in patients with left and right temporal lobe epilepsy (TLE) due to hippocampal sclerosis as well as their relations to neuropsychological memory performance. We investigated 28 healthy subjects, 12 patients with left TLE (LTLE) and 9 patients with right TLE (RTLE) with hippocampal sclerosis by means of functional magnetic resonance imaging (fMRI) using a face-name association task, which combines verbal and non-verbal memory functions. Regions-of-interest (ROIs) were defined based on the group results of the healthy subjects. In each ROI, fMRI activations were compared across groups and correlated with verbal and non-verbal memory scores. The face-name association task yielded activations in bilateral hippocampus (HC), left inferior frontal gyrus (IFG), left superior frontal gyrus (SFG), left superior temporal gyrus, bilateral angular gyrus (AG), bilateral medial prefrontal cortex and right anterior temporal lobe (ATL). LTLE patients demonstrated significantly less activation in the left HC and left SFG, whereas RTLE patients showed significantly less activation in the HC bilaterally, the left SFG and right AG. Verbal memory scores correlated with activations in the left and right HC, left SFG and right ATL and non-verbal memory scores with fMRI activations in the left and right HC and left SFG. The face-name association task can be employed to examine functional alterations of hippocampal activation during encoding of both verbal and non-verbal material in one fMRI paradigm. Further, the left SFG seems to be a convergence region for encoding of verbal and non-verbal material.

  19. Temporal change of EIA asymmetry revealed by a beacon receiver network in Southeast Asia

    Science.gov (United States)

    Watthanasangmechai, Kornyanat; Yamamoto, Mamoru; Saito, Akinori; Maruyama, Takashi; Yokoyama, Tatsuhiro; Nishioka, Michi; Ishii, Mamoru

    2015-05-01

    To reveal the temporal change of the equatorial ionization anomaly (EIA) asymmetry, a multipoint satellite-ground beacon experiment was conducted along the meridional plane of the Thailand-Indonesia sector. The observation includes one station near the magnetic equator and four stations at off-equator latitudes. This is the first EIA asymmetry study with high spatial resolution using GNU Radio Beacon Receiver (GRBR) observations in Southeast Asia. GRBR-total electron contents (TECs) from 97 polar-orbit satellite passes in March 2012 were analyzed in this study. Successive passes captured rapid evolution of EIA asymmetry, especially during geomagnetic disturbances. The penetrating electric fields that occur during geomagnetic disturbed days are not the cause of the asymmetry. Instead, high background TEC associated with an intense electric field empowers the neutral wind to produce severe asymmetry of the EIA. Such rapid evolution of EIA asymmetry was not seen during nighttime, when meridional wind mainly controlled the asymmetric structures. Additional data are necessary to identify the source of the variations, i.e., atmospheric waves. Precisely capturing the locations of the crests and the evolution of the asymmetry enhances understanding of the temporal change of EIA asymmetry at the local scale and leads to a future local modeling for TEC prediction in Southeast Asia.

  20. Profiling DNA damage-induced phosphorylation in budding yeast reveals diverse signaling networks.

    Science.gov (United States)

    Zhou, Chunshui; Elia, Andrew E H; Naylor, Maria L; Dephoure, Noah; Ballif, Bryan A; Goel, Gautam; Xu, Qikai; Ng, Aylwin; Chou, Danny M; Xavier, Ramnik J; Gygi, Steven P; Elledge, Stephen J

    2016-06-28

    The DNA damage response (DDR) is regulated by a protein kinase signaling cascade that orchestrates DNA repair and other processes. Identifying the substrate effectors of these kinases is critical for understanding the underlying physiology and mechanism of the response. We have used quantitative mass spectrometry to profile DDR-dependent phosphorylation in budding yeast and genetically explored the dependency of these phosphorylation events on the DDR kinases MEC1, RAD53, CHK1, and DUN1. Based on these screens, a database containing many novel DDR-regulated phosphorylation events has been established. Phosphorylation of many of these proteins has been validated by quantitative peptide phospho-immunoprecipitation and examined for functional relevance to the DDR through large-scale analysis of sensitivity to DNA damage in yeast deletion strains. We reveal a link between DDR signaling and the metabolic pathways of inositol phosphate and phosphatidyl inositol synthesis, which are required for resistance to DNA damage. We also uncover links between the DDR and TOR signaling as well as translation regulation. Taken together, these data shed new light on the organization of DDR signaling in budding yeast.