Dataset Information

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

ABSTRACT: Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

SUBMITTER: Saha A

PROVIDER: S-EPMC5668942 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

Saha Ashis A Kim Yungil Y Gewirtz Ariel D H ADH Jo Brian B Gao Chuan C McDowell Ian C IC Engelhardt Barbara E BE Battle Alexis A

Genome research 20171011 11

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections ...[more]

PMID: 29021288

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Project description:BackgroundCaenorhabditis 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 findingsOverexpression 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/significanceThe expression profiles, co-expression networks and co-expression regulation of the 10 tissues studied and the tissue-specific analysis presented here are a valuable resource for studying tissue-specific biological functions in nematodes.

Project description:MotivationProtein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as downregulation of tumor suppressors and upregulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers.ResultsWe develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural and functional association of phosphosites, kinase-substrate associations and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific-weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry-based phosphorylation datasets for breast cancer (BC), and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive BC subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers.Availability and implementationCoPPNet is available at http://compbio.case.edu/coppnet/.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

Publications

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

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