Genomic DNA Methylation Signatures Enable Concurrent Diagnosis and Clinical Genetic Variant Classification in Neurodevelopmental Syndromes
Genomic DNA Methylation Signatures Enable Concurrent Diagnosis and Clinical Genetic Variant Classification in Neurodevelopmental Syndromes
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DOI：
10.1016/j.ajhg.2017.12.008
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发表时间：
2018-01-04
影响因子：
9.8
通讯作者：
Sadikovic, Bekim
中科院分区：
文献类型：
--
作者：
Aref-Eshghi, Erfan;Rodenhiser, David I.;Sadikovic, Bekim
儿童发育综合征具有系统性、复杂性和经常重叠的临床特征，这些特征通常是涉及DNA甲基化、组蛋白修饰的建立和染色质重塑（“表观遗传机制”）的基因突变的孟德尔遗传的结果。组蛋白修饰和DNA甲基化之间的机制相互作用表明，这些综合征可能显示出特定的DNA甲基化特征，这些特征反映了与染色质失调相关的主要错误。鉴于这些染色质调节蛋白的相关功能，我们试图鉴定DNA甲基化外显特征，这些特征可以提供综合征特异性生物标志物，以补充标准临床诊断。在本研究中，我们检测了来自14个孟德尔疾病个体的外周血样本，这些个体在编码表观遗传机制蛋白质的基因中表现出突变。我们证明了特定但部分重叠的DNA甲基化特征与许多这些条件有关。这些外显特征之间的重叠程度很小，进一步表明，与初始事件一致，每种综合征的下游变化都是独特的。此外，通过结合这些epi-signature，我们已经证明了机器学习工具可以用于同时筛选具有高灵敏度和特异性的多种综合征，并且我们强调了该工具在解决具有未知意义变体的模糊病例受试者中的实用性。随着它的能力产生准确的预测对象呈现与表观遗传机制的破坏相关的重叠临床和分子特征。
Pediatric developmental syndromes present with systemic, complex, and often overlapping clinical features that are not infrequently a consequence of Mendelian inheritance of mutations in genes involved in DNA methylation, establishment of histone modifications, and chromatin remodeling (the "epigenetic machinery''). The mechanistic cross-talk between histone modification and DNA methylation suggests that these syndromes might be expected to display specific DNA methylation signatures that are a reflection of those primary errors associated with chromatin dysregulation. Given the interrelated functions of these chromatin regulatory proteins, we sought to identify DNA methylation epi-signatures that could provide syndrome-specific biomarkers to complement standard clinical diagnostics. In the present study, we examined peripheral blood samples from a large cohort of individuals encompassing 14 Mendelian disorders displaying mutations in the genes encoding proteins of the epigenetic machinery. We demonstrated that specific but partially overlapping DNA methylation signatures are associated with many of these conditions. The degree of overlap among these epi-signatures is minimal, further suggesting that, consistent with the initial event, the downstream changes are unique to every syndrome. In addition, by combining these epi-signatures, we have demonstrated that a machine learning tool can be built to concurrently screen for multiple syndromes with high sensitivity and specificity, and we highlight the utility of this tool in solving ambiguous case subjects presenting with variants of unknown significance, along with its ability to generate accurate predictions for subjects presenting with the overlapping clinical and molecular features associated with the disruption of the epigenetic machinery.