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Co-regulated transcripts associated to cooperating eSNPs define Bi-fan motifs in human gene networks.

ABSTRACT: Associations between the level of single transcripts and single corresponding genetic variants, expression single nucleotide polymorphisms (eSNPs), have been extensively studied and reported. However, most expression traits are complex, involving the cooperative action of multiple SNPs at different loci affecting multiple genes. Finding these cooperating eSNPs by exhaustive search has proven to be statistically challenging. In this paper we utilized availability of sequencing data with transcriptional profiles in the same cohorts to identify two kinds of usual suspects: eSNPs that alter coding sequences or eSNPs within the span of transcription factors (TFs). We utilize a computational framework for considering triplets, each comprised of a SNP and two associated genes. We examine pairs of triplets with such cooperating source eSNPs that are both associated with the same pair of target genes. We characterize such quartets through their genomic, topological and functional properties. We establish that this regulatory structure of cooperating quartets is frequent in real data, but is rarely observed in permutations. eSNP sources are mostly located on different chromosomes and away from their targets. In the majority of quartets, SNPs affect the expression of the two gene targets independently of one another, suggesting a mutually independent rather than a directionally dependent effect. Furthermore, the directions in which the minor allele count of the SNP affects gene expression within quartets are consistent, so that the two source eSNPs either both have the same effect on the target genes or both affect one gene in the opposite direction to the other. Same-effect eSNPs are observed more often than expected by chance. Cooperating quartets reported here in a human system might correspond to bi-fans, a known network motif of four nodes previously described in model organisms. Overall, our analysis offers insights regarding the fine motif structure of human regulatory networks.

SUBMITTER: Kreimer A

PROVIDER: S-EPMC4161301 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Co-regulated transcripts associated to cooperating eSNPs define Bi-fan motifs in human gene networks.

Kreimer Anat A Pe'er Itsik I

PLoS genetics 20140911 9

Associations between the level of single transcripts and single corresponding genetic variants, expression single nucleotide polymorphisms (eSNPs), have been extensively studied and reported. However, most expression traits are complex, involving the cooperative action of multiple SNPs at different loci affecting multiple genes. Finding these cooperating eSNPs by exhaustive search has proven to be statistically challenging. In this paper we utilized availability of sequencing data with transcrip ...[more]

PMID: 25210734

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Project description:BackgroundA large amount of computational and experimental work has been devoted to uncovering network motifs in gene regulatory networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a network motif must be specified, and the cis-promoter features that participate in the regulation must be determined.ResultsWe have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis-features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize network motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same network motif.ConclusionGlobal transcriptional regulators control multiple promoters by a variety of network motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or network motif, but also the cis-acting elements participating in each of these connexions.

Project description:MotivationA growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes.ResultsWe propose a cross-species bi-clustering approach which first denoises the gene expression data of each species into a data matrix. The rows of the data matrices of different species represent the same set of genes that are characterized by their expression patterns over the developmental stages of each species as columns. A novel bi-clustering method is then developed to cluster genes into subgroups by a joint sparse rank-one factorization of all the data matrices. This method decomposes a data matrix into a product of a column vector and a row vector where the column vector is a consistent indicator across the matrices (species) to identify the same gene cluster and the row vector specifies for each species the developmental stages that the clustered genes co-regulate. Efficient optimization algorithm has been developed with convergence analysis. This approach was first validated on synthetic data and compared to the two-step method and several recent joint clustering methods. We then applied this approach to two real world datasets of gene expression during the pre-implantation embryonic development of the human and mouse. Co-regulated genes consistent between the human and mouse were identified, offering insights into conserved functions, as well as similarities and differences in genome activation timing between the human and mouse embryos.Availability and implementationThe R package containing the implementation of the proposed method in C ++ is available at: https://github.com/JavonSun/mvbc.git and also at the R platform https://www.r-project.org/Contactjinbo@engr.uconn.edu.

Project description:Studies of transcriptional networks in multi-cellular organisms usually focus on isolated cells and typically assume that the discovered gene networks represent those present in connected cells within a complex organ like the brain. However, similar cell types connected in diverse anatomical networks could differentially influence transcriptional networks. Here, we used laser capture microdissection, expression arrays, genome mapping, and computational inference to explore behaviorally regulated gene networks in the brains of awake, behaving songbirds producing a skilled motor behavior, singing. We found that at baseline, in the absence of singing, a large proportion of genes (17%, >3000) are differentially expressed in the different brain regions of the neural circuit that controls singing. These genes predominantly code for cell communication and connectivity proteins, and non-coding RNAs. Remarkably, the act of singing was associated with differential regulation of ~10% of the coding and non-coding genome. However, less than 1% of genes were singing-regulated in most brain regions and these were largely immediate early genes (IEGs), which peaked early, including the inducible transcription factors EGR1 and FOS. The remaining vast majority of behaviorally regulated gene expression was specific to one or a subset of brain regions, which peaked later. Promoters of the baseline, common, and diverse singing regulated gene clusters were enriched for different combinations of post-translationally activated transcription factors, like CREB, SRF, MEF2, MZF, and the IEG transcription factors. The results suggest that diverse cell-to-cell interactions and differential combinatorial binding of a small group of transcription factors may influence regional diversity of gene networks in seemingly similar cell types. Thus, in highly integrated neural circuits of intact behaving animals, transcriptional network diversity appears to be the rule, rather than the exception.

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Co-regulated transcripts associated to cooperating eSNPs define Bi-fan motifs in human gene networks.

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Co-regulated transcripts associated to cooperating eSNPs define Bi-fan motifs in human gene networks.

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