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A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

ABSTRACT: The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues.Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database.HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes.

SUBMITTER: Jambusaria A

PROVIDER: S-EPMC6019795 | biostudies-other | 2018 Jun

REPOSITORIES: biostudies-other

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A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

Jambusaria Ankit A Klomp Jeff J Hong Zhigang Z Rafii Shahin S Dai Yang Y Malik Asrar B AB Rehman Jalees J

BMC bioinformatics 20180607 1

<h4>Background</h4>The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues.<h4>Results</h4>Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis ...[more]

PMID: 29940845

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Project description:BACKGROUND:Transcription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Maize has been a model organism for over one hundred years, but little is known about its tissue-specific gene regulation through TFs. In this study, we used a network approach to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, SAM and seed) in maize. We utilized GENIE3, a machine-learning algorithm combined with large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Unlike some other techniques, this approach is not limited by high-quality Position Weighed Matrix (PWM), and can therefore predict GRNs for over 2000 TFs in maize. RESULTS:Although many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. Our GRNs were also validated by ChIP-Seq datasets (KN1, FEA4 and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. We also found functional modules in each network by clustering analysis with the MCL algorithm. CONCLUSIONS:By combining publicly available genome-wide expression data and network analysis, we can uncover GRNs at tissue-level resolution in maize. Since ChIP-Seq and PWMs are still limited in several model organisms, our study provides a uniform platform that can be adapted to any species with genome-wide expression data to construct GRNs. We also present a publicly available database, maize tissue-specific GRN (mGRN, https://www.bio.fsu.edu/mcginnislab/mgrn/ ), for easy querying. All source code and data are available at Github ( https://github.com/timedreamer/maize_tissue-specific_GRN ).

Project description:BackgroundTranscription factors (TFs) and miRNAs are essential for the regulation of gene expression; however, the global view of human gene regulatory networks remains poorly understood. For example, how is the expression of so many genes regulated by limited cohorts of regulators and how are genes differentially expressed in different tissues despite the genetic code being the same in all tissues?ResultsWe analyzed the network properties of housekeeping and tissue-specific genes in gene regulatory networks from seven human tissues. Our results show that different classes of genes behave quite differently in these networks. Tissue-specific miRNAs show a higher average target number compared with non-tissue specific miRNAs, which indicates that tissue-specific miRNAs tend to regulate different sets of targets. Tissue-specific TFs exhibit higher in-degree, out-degree, cluster coefficient and betweenness values, indicating that they occupy central positions in the regulatory network and that they transfer genetic information from upstream genes to downstream genes more quickly than other TFs. Housekeeping TFs tend to have higher cluster coefficients compared with other genes that are neither housekeeping nor tissue specific, indicating that housekeeping TFs tend to regulate their targets synergistically. Several topological properties of disease-associated miRNAs and genes were found to be significantly different from those of non-disease-associated miRNAs and genes.ConclusionsTissue-specific miRNAs, TFs and disease genes have particular topological properties within the transcriptional regulatory networks of the seven human tissues examined. The tendency of tissue-specific miRNAs to regulate different sets of genes shows that a particular tissue-specific miRNA and its target gene set may form a regulatory module to execute particular functions in the process of tissue differentiation. The regulatory patterns of tissue-specific TFs reflect their vital role in regulatory networks and their importance to biological functions in their respective tissues. The topological differences between disease and non-disease genes may aid the discovery of new disease genes or drug targets. Determining the network properties of these regulatory factors will help define the basic principles of human gene regulation and the molecular mechanisms of disease.

Project description:Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein-protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein-protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein-protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis.

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A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

Publications

A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

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