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BAF 相关疾病的 DNA 甲基化表观遗传特征可用于诊断 Coffin-Siris 和 Nicolaides-Baraitser 综合征,并显示出这两种病症之间存在功能连续性。

BAFopathies' DNA methylation epi-signatures demonstrate diagnostic utility and functional continuum of Coffin-Siris and Nicolaides-Baraitser syndromes.

机构信息

Department of Pathology and Laboratory Medicine, Western University, London, N6A 5W9, ON, Canada.

Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, N6A 5W9, ON, Canada.

出版信息

Nat Commun. 2018 Nov 20;9(1):4885. doi: 10.1038/s41467-018-07193-y.

DOI:10.1038/s41467-018-07193-y
PMID:30459321
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6244416/
Abstract

Coffin-Siris and Nicolaides-Baraitser syndromes (CSS and NCBRS) are Mendelian disorders caused by mutations in subunits of the BAF chromatin remodeling complex. We report overlapping peripheral blood DNA methylation epi-signatures in individuals with various subtypes of CSS (ARID1B, SMARCB1, and SMARCA4) and NCBRS (SMARCA2). We demonstrate that the degree of similarity in the epi-signatures of some CSS subtypes and NCBRS can be greater than that within CSS, indicating a link in the functional basis of the two syndromes. We show that chromosome 6q25 microdeletion syndrome, harboring ARID1B deletions, exhibits a similar CSS/NCBRS methylation profile. Specificity of this epi-signature was confirmed across a wide range of neurodevelopmental conditions including other chromatin remodeling and epigenetic machinery disorders. We demonstrate that a machine-learning model trained on this DNA methylation profile can resolve ambiguous clinical cases, reclassify those with variants of unknown significance, and identify previously undiagnosed subjects through targeted population screening.

摘要

Coffin-Siris 和 Nicolaides-Baraitser 综合征(CSS 和 NCBRS)是由 BAF 染色质重塑复合物亚基突变引起的孟德尔疾病。我们报告了具有不同 CSS 亚型(ARID1B、SMARCB1 和 SMARCA4)和 NCBRS(SMARCA2)的个体外周血 DNA 甲基化 epi 特征的重叠。我们证明,一些 CSS 亚型和 NCBRS 的 epi 特征的相似程度可以大于 CSS 内的相似程度,表明这两个综合征在功能基础上存在联系。我们表明,携带 ARID1B 缺失的 6q25 微缺失综合征表现出类似的 CSS/NCBRS 甲基化谱。该 epi 特征的特异性已在包括其他染色质重塑和表观遗传机制障碍在内的广泛神经发育疾病中得到证实。我们证明,基于该 DNA 甲基化谱训练的机器学习模型可以解决模棱两可的临床病例,重新分类那些具有未知意义的变异,并通过靶向人群筛查识别以前未诊断的患者。

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