Plasma cortisol-linked gene networks in hepatic and adipose tissues implicate corticosteroid-binding globulin in modulating tissue glucocorticoid action and cardiovascular risk

Abstract

Genome-wide association meta-analysis (GWAMA) by the Cortisol Network (CORNET) consortium identified genetic variants spanning the SERPINA6/SERPINA1 locus on chromosome 14 associated with morning plasma cortisol, cardiovascular disease (CVD), and SERPINA6 mRNA expression encoding corticosteroid-binding globulin (CBG) in the liver. These and other findings indicate that higher plasma cortisol levels are causally associated with CVD; however, the mechanisms by which variations in CBG lead to CVD are undetermined. Using genomic and transcriptomic data from The Stockholm Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET) study, we identified plasma cortisol-linked single-nucleotide polymorphisms (SNPs) that are trans-associated with genes from seven different vascular and metabolic tissues, finding the highest representation of trans-genes in the liver, subcutaneous fat, and visceral abdominal fat, [false discovery rate (FDR) = 15%]. We identified a subset of cortisol-associated trans-genes that are putatively regulated by the glucocorticoid receptor (GR), the primary transcription factor activated by cortisol. Using causal inference, we identified GR-regulated trans-genes that are responsible for the regulation of tissue-specific gene networks. Cis-expression Quantitative Trait Loci (eQTLs) were used as genetic instruments for identification of pairwise causal relationships from which gene networks could be reconstructed. Gene networks were identified in the liver, subcutaneous fat, and visceral abdominal fat, including a high confidence gene network specific to subcutaneous adipose (FDR = 10%) under the regulation of the interferon regulatory transcription factor, IRF2. These data identify a plausible pathway through which variation in the liver CBG production perturbs cortisol-regulated gene networks in peripheral tissues and thereby promote CVD.

Keywords: causal inference; corticosteroid-binding globulin; cortisol; gene networks; systems genetics.

Figure 1

Identification of cortisol-associated trans-genes across STARNET tissues (FDR = 15%). (A) Upset plot showing the distribution of trans-genes across STARNET tissues, including genes shared by multiple tissues. Tissues include the atherosclerotic aortic root (AOR), skeletal muscle (SKLM), internal mammary artery (MAM), blood (Blood), liver (LIV), subcutaneous fat (SF), and visceral abdominal fat (VAF). (B) Distribution of trans-eQTLs across tissues and colored by genomic locus (LD block) of associated SNP. (C) LocusZoom plot (30) showing the location of cortisol-associated SNPs within defined LD blocks. (D) Venn diagrams where groupings represent different sources used to identify GR-linked trans-genes in the liver, (E) visceral abdominal fat, and (F) subcutaneous fat. These sources include transcription factor databases (db), ChIP-seq from perturbation-based experiments (23), and differential expression of dexamethasone-treated mice (24).

Figure 2

The 10% FDR gene networks in STARNET across different tissues. (A) Gene expression boxplot in the liver showing trans-association with cortisol-linked SNP rs4905194 and CPEB2, (B) in subcutaneous fat between rs11622665 and RNF13 and (C) rs8022616 and IRF2 (p-value obtained from Kruskal–Wallis test statistic). Box shows quarterlies of the dataset, with whiskers indicating the upper and lower variability of the distribution. (D) Causal gene network reconstructed from pairwise interactions from GR-regulated trans-genes against all other genes in the corresponding tissue for CPEB2, (E) RNF13, and (F) IRF2. Edges represent Bayesian posterior probabilities of pairwise interaction between genes (nodes) exceeding 10% global FDR. Arrow indicates direction of regulation, and interactions were only retained where parent node had at least four targets.

Figure 3

Replication of cortisol-associated gene networks in independent datasets. (A) Correlation heatmap showing pairwise Pearson correlations between CPEB2, (B) IRF2, and (C) RNF13 network targets. Hierarchical clustering of genes in STARNET (discovery) was applied to the same genes within replication datasets. (D) Correlations between network targets in discovery vs. replication datasets for CPEB2, (E) IRF2, and (F) RNF13 networks. The Kruskal–Wallis test calculated for the distribution of correlations between network targets compared to correlations within random gene sets of the same size.