Genetics of Myocardial Interstitial Fibrosis in the Human Heart and Association with Disease

ABSTRACT: Myocardial interstitial fibrosis is a common thread in multiple cardiovascular diseases including heart failure, atrial fibrillation, conduction disease and sudden cardiac death. To investigate the biologic pathways that underlie interstitial fibrosis in the human heart, we developed a machine learning model to measure myocardial T1 time, a marker of myocardial interstitial fibrosis, in 41,505 UK Biobank participants. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Mendelian randomization analysis supported a potential causal role for diabetes mellitus type 1 in myocardial interstitial fibrosis. In genome-wide association analysis, we identified 11 independent loci associated with native myocardial T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Transcriptome-wide association studies highlighted the role of expression of ADAMTSL1 and SLC2A12 in human cardiac tissue in modulating myocardial interstitial fibrosis. Using a TGFβ1-mediated cardiac fibroblast activation assay, we found that 9 out of the 11 genome-wide significant loci comprised genes that exhibited temporal changes in expression and/or open chromatin conformation supporting the biological relevance of these loci to myocardial fibrosis and myofibroblast cell state acquisition. Harnessing machine learning to perform large-scale phenotyping of interstitial fibrosis in the human heart, our results yield novel insights into biologically relevant pathways to myocardial fibrosis and prioritize a number of pathways for further investigation.

ORGANISM(S): Homo sapiens

PROVIDER: GSE225336 | GEO | 2023/03/11

REPOSITORIES: GEO