Evidence for Coregulation of Myocardial Gene Expression by MEF2 and NFAT in Human Heart Failure / CLINICAL PERSPECTIVE

TitleEvidence for Coregulation of Myocardial Gene Expression by MEF2 and NFAT in Human Heart Failure / CLINICAL PERSPECTIVE
Publication TypeJournal Articles
Year of Publication2009
AuthorsPutt ME, Hannenhalli S, Lu Y, Haines P, Chandrupatla HR, Morrisey EE, Margulies KB, Cappola TP
JournalCirculation: Cardiovascular Genetics
Pagination212 - 219
Date Published2009/06/01/

Background— Pathological 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 understood. Here, we use genomic data to examine the evidence that 5 candidate TF families coregulate gene expression in human heart failure.Methods and Results— RNA isolates from failing (n=86) and nonfailing (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 9076 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 coregulation. 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 the highest odds ratios noted for genes containing both MEF2 and NFAT binding motifs together in the same promoter region (peak odds ratio, 3.47; P=0.005).Conclusions— These findings provide genomic evidence for coregulation 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 insight into human cardiovascular disorders.