SummaryAlthough all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Highlights d Sex differences are evident in sample-specific gene regulatory networks d TF sex-biased targeting of genes is independent of their differential expression d Sex-biased target genes are enriched for tissue-related functions and diseases d Rich public resource that includes 8,279 gene regulatory networks of 29 tissues

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.GTEx | expression quantitative trait locus | eQTL | bipartite networks | GWAS M ore than a decade after the sequencing of the human genome, our understanding of the relationship between genetic variation and complex traits remains limited. Genomewide association studies (GWASs), which look for association between common genetic variants and phenotypic traits, have resoundingly shown that complex phenotypes are influenced by many variants of relatively small effect size (1, 2), the overwhelming majority of which (∼93%) lie in noncoding regions of the genome (3, 4). Those single-nucleotide polymorphisms (SNPs) associated with complex traits are enriched for variants likely to affect gene expression, as measured by expression quantitative trait locus (eQTL) analysis (5), suggesting that they influence phenotypes through changes in gene regulation (6, 7). Identifying the regulatory role of these variants likely also depends on the tissues relevant to the phenotype. For example, eQTL identified in skeletal muscle and adipose tissues for type 2 diabetes (T2D) have been shown to explain a greater proportion of the disease heritability than those identified across tissues (8). Furthermore, variants far from the transcriptional start site (TSS) of a gene, trans-eQTL, explain more of the heritability of T2D than those near the gene, cis-eQTL, and there is mounting evidence for the importance of these variants in a variety of phenotypes (9-11). That trans-eQTL, which can influence hundreds of genes in humans (12), might have an impact on the phenotype is consistent with similar observations in model organisms (13). However, large-scale detection of trans-eQTL across populations and tissues (14) has only recently become feasible in humans, and our understanding of how multiple cis-and trans-eQTL influence gene expression and cellular functions in different tissues is incomplete.We performed a systems genetics analysis of the regulatory effects of common [minor allele frequency (MAF) >5%] variants in 13 tissues collected by the Genotype-Tissue Expression (GTEx) consortium. By constructing tissue-leve...

Epigenetic mechanisms integrate both genetic variability and environmental exposures. However, comprehensive epigenome-wide analysis has not been performed across major childhood allergic phenotypes. We examined the association of epigenome-wide DNA methylation in mid-childhood peripheral blood (Illumina HumanMethyl450K) with mid-childhood atopic sensitization, environmental/inhalant and food allergen sensitization in 739 children in two birth cohorts (Project Viva-Boston, and the Generation R Study-Rotterdam). We performed covariate-adjusted epigenomewide association meta-analysis and employed pathway and regional analyses of results. Sevenhundred and five methylation sites (505 genes) were significantly cross-sectionally associated with mid-childhood atopic sensitization, 1411 (905 genes) for environmental and 45 (36 genes) for food allergen sensitization (FDR<0.05). We observed differential methylation across multiple genes for all three phenotypes, including genes implicated previously in innate immunity (DICER1), eosinophilic esophagitis and sinusitis (SIGLEC8), the atopic march (AP5B1) and asthma (EPX, IL4, IL5RA, PRG2, SIGLEC8, CLU). In addition, most of the associated methylation marks for all three phenotypes occur in putative transcription factor binding motifs. Pathway analysis identified multiple methylation sites associated with atopic sensitization and environmental allergen sensitization located in/near genes involved in asthma, mTOR signaling, and inositol phosphate metabolism. We identified multiple differentially methylated regions associated with atopic sensitization (8 regions) and environmental allergen sensitization (26 regions). A number of nominally significant methylation sites in the cord blood analysis were epigenome-wide significant in the mid-childhood analysis, and we observed significant methylationtime interactions among a subset of sites examined. Our findings provide insights into epigenetic regulatory pathways as markers of childhood allergic sensitization.

BackgroundCell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin.ResultsWe compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE.ConclusionsOur results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4111-x) contains supplementary material, which is available to authorized users.

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