Although 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.

Significance: A core tenet in genetics is that genotype influences phenotype. In an individual, the same genome can be expressed in substantially different ways, depending on the tissue. Expression quantitative trait locus (eQTL) analysis, which associates genetic variants at millions of locations across the genome with the expression levels of each gene, can provide insight into genetic regulation of phenotype. In each of 13 tissues we performed an eQTL analysis, represented significant associations as edges in a network, and explored the structure of those networks. We found clusters of eQTL linked to shared functions across tissues and tissue-specific clusters linked to tissue-specific functions, driven by genetic variants with tissue-specific regulatory potential. Our findings provide unique insight into the genotype–phenotype relationship.

Cell 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. We 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. Our 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.

Calcific aortic valve disease (CAVD) is the most common valve disease, which consists of a chronic interplay of inflammation, fibrosis, and calcification. In this study, sortilin (SORT1) was identified as a novel key player in the pathophysiology of CAVD, and its role in the transformation of valvular interstitial cells (VICs) into pathological phenotypes is explored. An aortic valve (AV) wire injury (AVWI) mouse model with sortilin deficiency was used to determine the effects of sortilin on AV stenosis, fibrosis, and calcification. In vitro experiments employed human primary VICs cultured in osteogenic conditions for 7, 14, and 21 days; and processed for imaging, proteomics, and transcriptomics including single-cell RNA-sequencing (scRNA-seq). The AVWI mouse model showed reduced AV fibrosis, calcification, and stenosis in sortilin-deficient mice vs. littermate controls. Protein studies identified the transition of human VICs into a myofibroblast-like phenotype mediated by sortilin. Sortilin loss-of-function decreased in vitro VIC calcification. ScRNA-seq identified 12 differentially expressed cell clusters in human VIC samples, where a novel combined inflammatory myofibroblastic-osteogenic VIC (IMO-VIC) phenotype was detected with increased expression of SORT1, COL1A1, WNT5A, IL-6, and serum amyloid A1. VICs sequenced with sortilin deficiency showed decreased IMO-VIC phenotype. Sortilin promotes CAVD by mediating valvular fibrosis and calcification, and a newly identified phenotype (IMO-VIC). This is the first study to examine the role of sortilin in valvular calcification and it may render it a therapeutic target to inhibit IMO-VIC emergence by simultaneously reducing inflammation, fibrosis, and calcification, the three key pathological processes underlying CAVD. Multi-omic approach to identify the role of sortilin in mediating fibrosis and calcification in calcific aortic valve disease (CAVD). Aortic valve (AV) wire injury in sortilin wild-type (Sort1^+/+) and deficient mice (Sort1^−/−) showed decreased collagen deposition and calcification in mouse AVs. Valvular interstitial cells (VICs) were collected from human CAVD tissue and cultured in osteogenic conditions. VICs collected at varying time points of culture (Days 7, 14, and 21) were processed for flow cytometry, proteomics, and single-cell RNA-sequencing (scRNA-seq). Multiomics data identified increased sortilin expression following the osteogenic culture of VICs. Protein analysis and scRNA-seq identified increased expression of WNT5a, MAPK, YAP, and IL-6 regulated by the expression of sortilin. ScRNA-seq identified a transitionary VIC subpopulation with an activated myofibroblast phenotype that later transitioned into a combined myofibroblast and osteogenic phenotype. ScRNA-seq data identified an inflammatory myofibroblastic-osteogenic VIC (IMO-VIC) subpopulation that may be a key player in the pathogenesis of CAVD under the regulation of sortilin.

Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. An R package instantiating YARN is available at http://bioconductor.org/packages/yarn .

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell’s epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER’s predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.