Cell ReportsGenomicsPeer-reviewed journal article
Genome-Wide Fetalization of Enhancer Architecture in Heart Disease
Cailyn H. SpurrelliD, Iros BarozziiD, Michael KosickiiD, Brandon J. MannioniD, Matthew J. BlowiD, Yoko Fukuda-YuzawaiD, Neil SlaveniD, Sarah Yasmeen AfzaliD, Jennifer A. AkiyamaiD, Veena AfzaliD, Stella TraniD, Ingrid Plajzer-Frick, Catherine S. Novak, Momoe KatoiD, Elizabeth A. LeeiD, Tyler H. GarviniD, Quan T. Pham, Anne N. Kronshage, Steven LisgoiD, James D Bristow, Thomas P. CappolaiD, Michael P. MorleyiD, Kenneth B. MarguliesiD, Len A. PennacchioiD, Diane E. DickeliD, Axel ViseliD
Abstract
Heart disease is associated with re-expression of key transcription factors normally active only during prenatal development of the heart. However, the impact of this reactivation on the genome-wide regulatory landscape in heart disease has remained obscure. Here we show that pervasive epigenomic changes occur in heart disease, with thousands of regulatory sequences reacquiring fetal-like chromatin signatures. We used RNA-seq and ChIP-seq targeting a histone modification associated with active transcriptional enhancers to generate genome-wide enhancer maps from left ventricle tissue from 18 healthy controls and 18 individuals with idiopathic dilated cardiomyopathy (DCM). Healthy individuals had a highly reproducible epigenomic landscape, consisting of more than 31,000 predicted heart enhancers. In contrast, we observed reproducible disease-associated gains or losses of activity at more than 7,500 predicted heart enhancers. Next, we profiled human fetal heart tissue by ChIP-seq and RNA-seq. Comparison with adult tissues revealed that the heart disease epigenome and transcrip-tome both shift toward a fetal-like state, with 3,400 individual enhancers sharing fetal regulatory properties. Our results demonstrate widespread epigenomic changes in DCM, and we provide a comprehensive data resource (http://heart.lbl.gov) for the mechanistic exploration of heart disease etiology.
- organism: human
- assay: ChIP-seq
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- Citations
- 51
- FWCI
- 2.12
- Field percentile
- top 11%
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