Background

Pathologic stresses induce heart failure in animal models through activation of multiple cardiac transcription factors (TFs) working cooperatively. However, interactions among TFs in human heart failure are less well understood. Here we use genomic data to examine the evidence that five candidate TF families co-regulate gene expression in human heart failure.

Methods and Results

RNA isolates from failing (n=86) and non-failing (n=16) human hearts were hybridized with Affymetrix HU133A arrays. For each gene on the array, we determined conserved MEF2, NFAT, NKX, GATA, and FOX binding motifs within the −1 kb promoter region using human-murine sequence alignments and the TRANSFAC database. Across 9,076 genes expressed in the heart, TF binding motifs tended to cluster together in nonrandom patterns within promoters of specific genes (P-values ranging from 10−2 to 10−21), suggesting co-regulation. We then modeled differential expression as a function of TF combinations present in promoter regions. Several combinations predicted increased odds of differential expression in the failing heart, with highest odds ratios noted for genes containing both MEF2 and NFAT bindings motifs together in the same promoter (peak OR 3.47, P=0.005).

Conclusions

These findings provide genomic evidence for co-regulation of myocardial gene expression by MEF2 and NFAT in human heart failure. In doing so, they extend the paradigm of combinatorial regulation of gene expression to the human heart and identify new target genes for mechanistic study. More broadly, we demonstrate how integrating diverse sources of genomic data yields novel insights into human cardiovascular disorders.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a “cosmopolitan” tagging approach to capture the genetic diversity across ∼2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.