Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA.
Nat Commun. 2024 Sep 27;15(1):8268. doi: 10.1038/s41467-024-52463-7.
Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generate single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. We evaluate enhancer activity for 59 elements using an in vivo transgenic assay and validate 44 (75%), demonstrating that single cell accessibility can be a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieve significant reduction in our variant search space and nominate candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work delivers non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.
未解决的孟德尔病例通常缺乏明显的致病编码变异,这表明可能存在非编码病因。在这里,我们提出了一个单细胞多组学框架,该框架整合了胚胎小鼠染色质可及性、组蛋白修饰和基因表达分析,以发现颅运动神经元 (cMN) 的顺式调控元件,并随后提名先天性颅神经发育障碍 (CCDDs) 的候选非编码变异,这是一组改变 cMN 发育的孟德尔疾病。我们生成了约 86000 个 cMN 和相关细胞类型的单细胞表观基因组图谱,确定了约 250000 个可及的调控元件,这些元件与约 145000 个潜在增强子的同源基因预测相关。我们使用体内转基因测定评估了 59 个元件的增强子活性,并验证了 44 个(75%),表明单细胞可及性可以是增强子活性的有力预测因子。将我们的 cMN 图谱应用于 270 个遗传上未解决的 CCDD 家系的 899 个全基因组序列,我们显著减少了我们的变异搜索空间,并提名候选变异,这些变异被预测可调节已知的 CCDD 疾病基因 MAFB、PHOX2A、CHN1 和 EBF3,以及在频繁突变的增强子中通过峰和基因中心等位基因聚集预测的候选基因。这项工作为 CCDD 提供了与非编码变异相关的发现,并为提名其他孟德尔疾病中具有潜在高功能影响的非编码变异提供了一个可推广的框架。