Background:Atrial fibrillation (AF) often arises from structural abnormalities in the left atria (LA). Annotation of the noncoding 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 noncoding 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 expression quantitative trait locus, DNA methylation, and HiC 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<2×10⁻¹⁶). Chromatin states were differentially methylated, promoters were less methylated than repressed regions (P<2×10⁻¹⁶). We identified over 15 000 LA-specific enhancers, defined by homeobox family motifs, and annotated several cardiovascular disease susceptibility loci. Intersecting AF and PR genome-wide association studies loci with long-range chromatin conformation data identified a gene interaction network dominated by NKX2-5, TBX3, ZFHX3, and SYNPO2L.Conclusions:Profiling the noncoding 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 can be extended to identify molecular mechanisms for other cardiac diseases and traits.

中文翻译：

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人类左心房组织的表观遗传学分析确定了心房颤动背后的基因网络


背景：心房颤动（AF）通常由左心房（LA）的结构异常引起。人类 LA 中非编码基因组的注释是有限的，对基因表达和染色质结构的影响也是有限的。许多与 AF 相关的遗传变异存在于非编码区；这一知识差距损害了理解 AF 和心脏传导表型分子机制的努力。 方法：我们通过分析 7 个组蛋白翻译后修饰（活性：H3K4me3、H3K4me2、H3K4me1、H3K27ac、H3K36me3；抑制性： ：5 个无结构性心脏病或 AF 个体样本中的 H3K27me3、H3K9me3)、 CTCF结合和基因表达。我们使用 MACS2 来识别峰区 ( P <0.01)，应用马尔可夫模型对调控元件进行分类，并用匹配的基因表达数据注释该模型。我们将染色质状态与来自 LA 和左心室的表达数量性状位点、DNA 甲基化和 HiC 染色质相互作用数据进行交叉。最后，我们整合了 AF 和心电图特征的全基因组关联数据，将疾病相关变异与基因联系起来。结果：我们的模型识别了 21 个表观遗传状态，包括启动子、增强子和抑制区域等调控基序。基因受近端染色质状态调节；压抑状态与基因表达显着降低相关( P <2×10 ^-16 )。染色质状态存在差异甲基化，启动子区域的甲基化程度低于抑制区域（ P <2×10 ^-16 ）。我们鉴定了超过 15000 个 LA 特异性增强子，由同源盒家族基序定义，并注释了几个心血管疾病易感位点。 将 AF 和 PR 全基因组关联研究基因座与长程染色质构象数据相交叉，确定了一个由NKX2-5 、 TBX3 、 ZFHX3和SYNPO2L主导的基因相互作用网络。 结论：分析非编码基因组为基因表达和染色质提供了新的见解人类 LA 组织中的调节。这些发现使得能够识别 AF 背后的基因网络；我们的实验和分析方法可以扩展到确定其他心脏病和特征的分子机制。