What is a good (gene) network?

Chantier qualité GAInternational audienceWhat is a good (gene) network

Gene array and real time PCR analysis of the adrenal sensitivity to adrenocorticotropic hormone in pig

<p>Abstract</p> <p>Background</p> <p>Variability in hypothalamic-pituitary-adrenal (HPA) axis activity has been shown to be influenced by genetic factors and related to great metabolic differences such as obesity. The aim of this study was to investigate molecular bases of genetic variability of the adrenal sensitivity to ACTH, a major source of variability, in Meishan (MS) and Large White (LW) pigs, MS being reported to exhibit higher basal cortisol levels, response to ACTH and fatness than LW. A pig cDNA microarray was used to identify changes in gene expression in basal conditions and in response to ACTH stimulation.</p> <p>Results</p> <p>Genotype and/or ACTH affected the expression of 211 genes related to transcription, cell growth/maintenance, signal transduction, cell structure/adhesion/extra cellular matrix and protein kinase/phosphatase activity. No change in the expression of known key regulator proteins of the ACTH signaling pathway or of steroidogenic enzymes was found. However, <it>Mdh2</it>, <it>Sdha</it>, <it>Suclg2</it>, genes involved in the tricarboxylic acid (TCA) pathway, were over-expressed in MS pigs. Higher TCA cycle activity in MS than in LW may thus result in higher steroidogenic activity and thus explain the typically higher cortisol levels in MS compared to LW. Moreover, up-regulation of <it>Star </it>and <it>Ldlr </it>genes in MS and/or in response to ACTH suggest that differences in the adrenal function between MS and LW may also involve mechanisms requisite for cholesterol supply to steroidogenesis.</p> <p>Conclusion</p> <p>The present study provides new potential candidate genes to explain genetic variations in the adrenal sensitivity to ACTH and better understand relationship between HPA axis activity and obesity.</p

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

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

The structure of a gene network reveals 7 biological sub-graphs underlying eQTLs in pig

International audienceIntegrative and system biology is a very promising tool for deciphering the biological and genetic mechanisms underlying complex traits. Transcriptomic analyses, in combination with genomic polymorphism, for instance, can give interesting insight on the genetic control of gene expression (eQTL studies). When hundreds of genes are detected with a link between their expression and some genetic polymorphisms (eQTL), the following question raises: what are the biological underlying functions? One tool is the use of a gene network, displaying interactions between genes with a genetic control (having at least an eQTL). There exist several possibilities for inferring a gene network: literature mining (using softwares such as Ingenuity) or inference from gene expression data. Although the first framework is a useful tool, it has some limitations: there is still a serious problem of lack of annotation in the pig genome, and a bias in information provided by Ingenuity (literature mainly devoted to Human, Mouse and Rat). We will hence explore in this work the inference of gene network from expression data. One simple method of inference was focused on, that has proven useful: Gaussian networks (Schäfer and Strimmer 2005). The following problem to be faced is the interpretation of such a "large" network (more than 100 genes). The aim of this study is to propose an adequate method for deciphering the structure of large gene networks. With the use of a good clustering of graph, the structure of one graph can be highlighted, and can reveal several sub graphs, each corresponding to particular biological functions

L'analyse d'un réseau de co-expression génique met en valeur des groupes fonctionnels homogènes et des gènes importants relatifs a un phénotype d'intérêt

National audienceCet article présente l'analyse d'un réseau de co-expression entre gènes dont la particularité est d'être régulés génétiquement. Cette étude est menée selon deux axes : une classification des gènes impliqués dans le réseau permet de mettre en valeur des groupes fonctionnels homogènes. Par ailleurs, une analyse conjointe du réseau et d'un phénotype d'intérêt permet de mettre en évidence des gènes candidats importants

Pathway results from the chicken data set using GOTM, Pathway Studio and Ingenuity softwares

Background: As presented in the introduction paper, three sets of differentially regulated genes were found after the analysis of the chicken infection data set from EADGENE. Different methods were used to interpret these results.[br/] Results: GOTM, Pathway Studio and Ingenuity softwares were used to investigate the three lists of genes. The three softwares allowed the analysis of the data and highlighted different networks. However, only one set of genes, showing a differential expression between primary and secondary response gave significant biological interpretation.[br/] Conclusion: Combining these databases that were developed independently on different annotation sources supplies a useful tool for a global biological interpretation of microarray data, even if they may contain some imperfections (e.g. gene not or not well annotated)

Using microarrays to identify positional candidate genes for QTL: the case study of ACTH response in pigs

Background: Microarray studies can supplement QTL studies by suggesting potential candidate genes in the QTL regions, which by themselves are too large to provide a limited selection of candidate genes. Here we provide a case study where we explore ways to integrate QTL data and microarray data for the pig, which has only a partial genome sequence. We outline various procedures to localize differentially expressed genes on the pig genome and link this with information on published QTL. The starting point is a set of 237 differentially expressed cDNA clones in adrenal tissue from two pig breeds, before and after treatment with adrenocorticotropic hormone (ACTH). Results: Different approaches to localize the differentially expressed (DE) genes to the pig genome showed different levels of success and a clear lack of concordance for some genes between the various approaches. For a focused analysis on 12 genes, overlapping QTL from the public domain were presented. Also, differentially expressed genes underlying QTL for ACTH response were described. Using the latest version of the draft sequence, the differentially expressed genes were mapped to the pig genome. This enabled co-location of DE genes and previously studied QTL regions, but the draft genome sequence is still incomplete and will contain many errors. A further step to explore links between DE genes and QTL at the pathway level was largely unsuccessful due to the lack of annotation of the pig genome. This could be improved by further comparative mapping analyses but this would be time consuming. Conclusion: This paper provides a case study for the integration of QTL data and microarray data for a species with limited genome sequence information and annotation. The results illustrate the challenges that must be addressed but also provide a roadmap for future work that is applicable to other non-model species

Late Fetal Blood Transcriptomic Approach To Get Insight Into Biology Related To Birth Survival

In recent decades, improvement of prolificacy and body composition has been accompanied by a substantial increase in the mortality of piglets before weaning. The most critical period is the perinatal period, mostly during the first 24-48 hours following birth. The maturity of piglets, defined as the state of full development for survival at birth, is an important determinant of early mortality. The objective of our project is to take advantage of current knowledge about two pig breeds, Large White (LW) pigs selected for prolificacy and body composition and Meishan (MS) pigs being more robust. Maturity of several tissues and metabolite profiles of various fluids are analyzed on the fetuses (LW, MS and reciprocal F1) at day 90 or 110 of gestation (birth at day 114). Here we presented the transcriptomic analysis done on total blood samples (N=63). We did two different statistical analyses, a supervised one to reveal differential pathways for the interaction between gestational stages and genotypes and an unsupervised analysis (hclust and differential analyses) to identify potential predictors of a lesser maturity at birth. All p-values were adjusted with a Bonferroni correction < 1%. The 265 genes differential for the interaction (Bonferroni 1%) in blood samples revealed many genes for mitochondrial ATP synthesis, transcriptional regulation, and response to hypoxia (overexpressed in LW at day 110 of gestation)

Genetic variability of transcript abundance in pig peri-mortem skeletal muscle: eQTL localized genes involved in stress response, cell death, muscle disorders and metabolism

<p>Abstract</p> <p>Background</p> <p>The genetics of transcript-level variation is an exciting field that has recently given rise to many studies. Genetical genomics studies have mainly focused on cell lines, blood cells or adipose tissues, from human clinical samples or mice inbred lines. Few eQTL studies have focused on animal tissues sampled from outbred populations to reflect natural genetic variation of gene expression levels in animals. In this work, we analyzed gene expression in a whole tissue, pig skeletal muscle sampled from individuals from a half sib F2 family shortly after slaughtering.</p> <p>Results</p> <p>QTL detection on transcriptome measurements was performed on a family structured population. The analysis identified 335 eQTLs affecting the expression of 272 transcripts. The ontologic annotation of these eQTLs revealed an over-representation of genes encoding proteins involved in processes that are expected to be induced during muscle development and metabolism, cell morphology, assembly and organization and also in stress response and apoptosis. A gene functional network approach was used to evidence existing biological relationships between all the genes whose expression levels are influenced by eQTLs. eQTLs localization revealed a significant clustered organization of about half the genes located on segments of chromosome 1, 2, 10, 13, 16, and 18. Finally, the combined expression and genetic approaches pointed to putative <it>cis</it>-drivers of gene expression programs in skeletal muscle as <it>COQ4 </it>(SSC1), <it>LOC100513192 </it>(SSC18) where both the gene transcription unit and the eQTL affecting its expression level were shown to be localized in the same genomic region. This suggests <it>cis</it>-causing genetic polymorphims affecting gene expression levels, with (e.g. <it>COQ4</it>) or without (e.g. <it>LOC100513192</it>) potential pleiotropic effects that affect the expression of other genes (cluster of <it>trans</it>-eQTLs).</p> <p>Conclusion</p> <p>Genetic analysis of transcription levels revealed dependence among molecular phenotypes as being affected by variation at the same loci. We observed the genetic variation of molecular phenotypes in a specific situation of cellular stress thus contributing to a better description of muscle physiologic response. In turn, this suggests that large amounts of genetic variation, mediated through transcriptional networks, can drive transient cell response phenotypes and contribute to organismal adaptative potential.</p

Methods for interpreting lists of affected genes obstained in a DNA microarray experiment

Background - The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. Results - Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached. Conclusion - It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experimen