Table_3_Phenotypic Subtyping and Re-analyses of Existing Transcriptomic Data from Autistic Probands in Simplex Families Reveal Differentially Expressed and ASD Trait-Associated Genes.xlsx

Autism spectrum disorder (ASD) describes a collection of neurodevelopmental disorders characterized by core symptoms that include social communication deficits and repetitive, stereotyped behaviors often coupled with restricted interests. Primary challenges to understanding and treating ASD are the genetic and phenotypic heterogeneity of cases that complicates all omics analyses as well as a lack of information on relationships among genes, pathways, and autistic traits. In this study, we re-analyze existing transcriptomic data from simplex families by subtyping individuals with ASD according to multivariate cluster analyses of clinical ADI-R scores that encompass a broad range of behavioral symptoms. We also correlate multiple ASD traits, such as deficits in verbal and non-verbal communication, play and social skills, ritualistic behaviors, and savant skills, with expression profiles using Weighted Gene Correlation Network Analyses (WGCNA). Our results show that subtyping greatly enhances the ability to identify differentially expressed genes involved in specific canonical pathways and biological functions associated with ASD within each phenotypic subgroup. Moreover, using WGCNA, we identify gene modules that correlate significantly with specific ASD traits. Network prediction analyses of the genes in these modules reveal canonical pathways as well as neurological functions and disorders relevant to the pathobiology of ASD. Finally, we compare the WGCNA-derived data on autistic traits in simplex families with analogous data from multiplex families using transcriptomic data from our previous studies. The comparison reveals overlapping trait-associated pathways as well as upstream regulators of the module-associated genes that may serve as useful targets for a precision medicine approach to ASD.

自闭症谱系障碍（Autism spectrum disorder, ASD）是一类以核心症状为特征的神经发育障碍，核心症状涵盖社交沟通缺陷、重复刻板行为与局限兴趣。当前理解与治疗ASD面临的核心挑战包括：病例存在遗传与表型异质性，这为所有组学分析增添了复杂阻碍；同时学界尚未充分明确基因、通路与自闭症特质之间的关联关系。本研究针对涵盖广泛行为症状谱的临床孤独症诊断访谈-修订版（Autism Diagnostic Interview-Revised, ADI-R）得分开展多变量聚类分析，据此对ASD患者进行亚型划分，并基于此重新分析来自单例自闭症谱系障碍家庭（simplex families）的现有转录组数据。此外，本研究借助加权基因共表达网络分析（Weighted Gene Correlation Network Analyses, WGCNA），将言语与非言语沟通缺陷、游戏与社交技能缺陷、仪式化行为以及特殊才能（savant skills）等多种ASD特质与基因表达谱进行关联分析。研究结果表明，亚型划分可显著提升在各表型亚组内识别与ASD相关的特定经典通路及生物学功能相关差异表达基因的能力。通过WGCNA分析，本研究还鉴定出了与特定ASD特质显著相关的基因模块。对上述模块内基因开展的网络预测分析，揭示了与ASD病理生物学相关的经典通路、神经功能及神经疾病。最后，本研究依托既往研究的转录组数据，将单例自闭症谱系障碍家庭中基于WGCNA得到的自闭症特质相关数据，与多例自闭症谱系障碍家庭（multiplex families）的同类数据进行对比。对比结果显示，二者存在重叠的特质相关通路，以及模块关联基因的上游调控因子，这些靶点或可作为ASD精准医学治疗的有效靶标。