Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0018799 (heart disease)
 34,133 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Background - Atrial fibrillation (AF) often arises from structural abnormalities in the left atria (LA). Annotation of the non-coding genome in human LA is limited, as are effects on gene expression and chromatin architecture. Many AF-associated genetic variants reside in noncoding regions; this knowledge gap impairs efforts to understand the molecular mechanisms of AF and cardiac conduction phenotypes. Methods - We generated a model of the LA non-coding genome by profiling 7 histone post-translational modifications (active: H3K4me3, H3K4me2, H3K4me1, H3K27ac, H3K36me3; repressive: H3K27me3, H3K9me3), CTCF binding, and gene expression in samples from 5 individuals without structural heart disease or AF. We used MACS2 to identify peak regions (P < 0.01), applied a Markov model to classify regulatory elements, and annotated this model with matched gene expression data. We intersected chromatin states with eQTL, DNA methylation, and Hi-C chromatin interaction data from LA and left ventricle. Finally, we integrated genome wide association data for AF and electrocardiographic traits, to link disease-related variants to genes. Results - Our model identified 21 epigenetic states, encompassing regulatory motifs such as promoters, enhancers, and repressed regions. Genes were regulated by proximal chromatin states; repressive states were associated with a significant reduction in gene expression (P < 2x10-16). Chromatin states were differentially methylated, promoters were less methylated than repressed regions (P < 2x10-16). We identified over 15,000 LA-specific enhancers, defined by homeobox family motifs, and annotated several CVD susceptibility loci. Intersecting AF and PR GWAS loci with long-range chromatin conformation data identified a gene interaction network dominated by NKX2-5, TBX3, ZFHX3, and SYNPO2L. Conclusions - Profiling the non-coding genome provides new insights into the gene expression and chromatin regulation in human LA tissue. These findings enabled identification of a gene network underlying AF; our experimental and analytic approach is extensible to identifying molecular mechanisms for other cardiac diseases and traits.
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PMID:Epigenetic Analyses of Human Left Atrial Tissue Identifies Gene Networks Underlying Atrial Fibrillation. 3315 27