The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.

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.

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

The effects of estrogen on gene expression in mammary cells are mediated by interaction of the estrogen receptor (ER) with estrogen response elements in target DNA. Whereas the ER is the primary initiator of transcription, the recruitment of coregulatory proteins to the DNA-bound receptor influences estrogen responsiveness. To better understand how estrogen alters gene expression, we identified proteins associated with the DNA-bound ERalpha. Surprisingly, the antioxidant enzyme Cu/Zn superoxide dismutase (SOD1), which is known primarily as a scavenger of superoxide, was associated with the DNA-bound receptor. We have now demonstrated that SOD1 interacts with ERalpha from MCF-7 cell nuclear extracts and with purified ERalpha and that SOD1 enhances binding of ERalpha to estrogen response element-containing DNA. Although SOD1 decreases transcription of an estrogen-responsive reporter plasmid in transiently transfected U2 osteosarcoma cells, RNA interference assays demonstrate that SOD1 is required for effective estrogen responsiveness of the endogenous pS2, progesterone receptor, cyclin D1, and Cathepsin D genes in MCF-7 breast cancer cells. Furthermore, ERalpha and SOD1 are associated with regions of the pS2 and progesterone receptor genes involved in conferring estrogen-responsive gene expression. Interestingly, when MCF-7 cells are exposed to 17beta-estradiol and superoxide generated by addition of potassium superoxide (KO2) to the cell medium, SOD1 levels are increased and tyrosine nitration, which is an indicator of oxidative stress-induced protein damage, is significantly diminished. Our studies have identified a new role for SOD1 in regulating estrogen-responsive gene expression and suggest that the 17beta-estradiol- and KO2-induced increase in SOD1 may play a role in the survival of breast cancer cells and the progression of mammary tumors.