Dataset Information

Network analysis of functional genomics data: application to avian sex-biased gene expression.

ABSTRACT: Gene expression analysis is often used to investigate the molecular and functional underpinnings of a phenotype. However, differential expression of individual genes is limited in that it does not consider how the genes interact with each other in networks. To address this shortcoming we propose a number of network-based analyses that give additional functional insights into the studied process. These were applied to a dataset of sex-specific gene expression in the chicken gonad and brain at different developmental stages. We first constructed a global chicken interaction network. Combining the network with the expression data showed that most sex-biased genes tend to have lower network connectivity, that is, act within local network environments, although some interesting exceptions were found. Genes of the same sex bias were generally more strongly connected with each other than expected. We further studied the fates of duplicated sex-biased genes and found that there is a significant trend to keep the same pattern of sex bias after duplication. We also identified sex-biased modules in the network, which reveal pathways or complexes involved in sex-specific processes. Altogether, this work integrates evolutionary genomics with systems biology in a novel way, offering new insights into the modular nature of sex-biased genes.

SUBMITTER: Frings O

PROVIDER: S-EPMC3540752 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Network analysis of functional genomics data: application to avian sex-biased gene expression.

Frings Oliver O Mank Judith E JE Alexeyenko Andrey A Sonnhammer Erik L L EL

TheScientificWorldJournal 20121223

Gene expression analysis is often used to investigate the molecular and functional underpinnings of a phenotype. However, differential expression of individual genes is limited in that it does not consider how the genes interact with each other in networks. To address this shortcoming we propose a number of network-based analyses that give additional functional insights into the studied process. These were applied to a dataset of sex-specific gene expression in the chicken gonad and brain at dif ...[more]

PMID: 23319882

Similar Datasets

Project description:BackgroundMammalian gonadal sex is determined by the presence or absence of a Y chromosome and the subsequent production of sex hormones contributes to secondary sexual differentiation. However, sex chromosome-linked genes encoding dosage-sensitive transcription and epigenetic factors are expressed well before gonad formation and have the potential to establish sex-biased expression that persists beyond the appearance of gonadal hormones. Here, we apply a comparative bioinformatics analysis on a pair of published single-cell datasets from mouse and human during very early embryogenesis-from two-cell to pre-implantation stages-to characterize sex-specific signals and to assess the degree of conservation among early acting sex-specific genes and pathways.ResultsClustering and regression analyses of gene expression across samples reveal that sex initially plays a significant role in overall gene expression patterns at the earliest stages of embryogenesis which potentially may be the byproduct of signals from male and female gametes during fertilization. Although these transcriptional sex effects rapidly diminish, sex-biased genes appear to form sex-specific protein-protein interaction networks across pre-implantation stages in both mammals providing evidence that sex-biased expression of epigenetic enzymes may establish sex-specific patterns that persist beyond pre-implantation. Non-negative matrix factorization (NMF) on male and female transcriptomes generated clusters of genes with similar expression patterns across sex and developmental stages, including post-fertilization, epigenetic, and pre-implantation ontologies conserved between mouse and human. While the fraction of sex-differentially expressed genes (sexDEGs) in early embryonic stages is similar and functional ontologies are conserved, the genes involved are generally different in mouse and human.ConclusionsThis comparative study uncovers much earlier than expected sex-specific signals in mouse and human embryos that pre-date hormonal signaling from the gonads. These early signals are diverged with respect to orthologs yet conserved in terms of function with important implications in the use of genetic models for sex-specific disease.

Project description:BackgroundFunctional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks.ResultsHere we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks - HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers - the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition.ConclusionThe current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.

Project description:The evolution of sex-specific phenotypes is an important dimension of diversification and local adaptation. The sex-dependent regulation of gene expression is considered a key genomic mechanism facilitating sex-dependent adaptation. In many species, genes with male-biased expression evolve faster in DNA sequence and expression level than genes with female-biased or sexually monomorphic expression. While positive selection may be responsible for rapid DNA sequence evolution, why expression of male-biased genes also evolves rapidly remains unclear. Beyond sex differences in selection, some aspects of the genetic architecture of gene expression could contribute to the rapid evolution of male-biased gene expression. First, male-biased genes might simply have greater standing genetic variance than female-biased genes. Second, male-biased genes could be less constrained by pleiotropy, either within or between sexes. Here, we evaluate these alternative explanations on an intraspecific scale using a series of quantitative genetic experiments conducted on natural variation in male and female gene expression in the fly Drosophila serrata Male-biased genes had significantly higher genetic variance than female-biased genes and were generally more narrowly expressed across tissues, suggesting lower within-individual pleiotropy. However, consistent with stronger constraints due to between-sex pleiotropy, their between-sex genetic correlations, rMF, were higher than for female-biased genes and more strongly negatively associated with sex bias. Using an extensive clinal dataset, we tested whether sex differences in gene expression divergence among populations have been shaped by pleiotropy. Here too, male-biased gene divergence was more strongly associated with between-sex pleiotropy than was female-biased gene divergence. Systematic differences in genetic variance and pleiotropy may be important factors influencing sex-specific adaptation arising through changes in gene expression.This article is part of the theme issue 'Linking local adaptation with the evolution of sex differences'.

Dataset Information

Network analysis of functional genomics data: application to avian sex-biased gene expression.

Publications

Network analysis of functional genomics data: application to avian sex-biased gene expression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets