De novo variants in genes regulating stress granule assembly associate with neurodevelopmental disorders.
Xiangbin JiaShujie ZhangSenwei TanBing DuMei HeHaisong QinJia ChenXinyu DuanJingsi LuoFei ChenLuping OuyangJian WangGuodong ChenBin YuGe ZhangZimin ZhangYongqing LyuYi HuangJian JiaoJin Yun Helen ChenKathryn J SwobodaEmanuele AgoliniAntonio NovelliChiara LeoniGiuseppe ZampinoGerarda CappuccioNicola Brunetti-PierriBénédicte GérardEmmanuelle GinglingerJulie RicherHugh J McmillanAlexandre White-BrownKendra HoekzemaRaphael A BernierEvangeline C Kurtz-NelsonRachel K EarlClaartje MeddensMarielle AldersMeredith FuchsRoseline CaumesPerrine BrunelleThomas SmolRyan KuehlDebra-Lynn Day-SalvatoreKristin G MonaghanMichelle M MorrowEvan E EichlerZhengmao HuLing YuanJieqiong TanKun XiaYiping ShenHui GuoPublished in: Science advances (2022)
Stress granules (SGs) are cytoplasmic assemblies in response to a variety of stressors. We report a new neurodevelopmental disorder (NDD) with common features of language problems, intellectual disability, and behavioral issues caused by de novo likely gene-disruptive variants in UBAP2L , which encodes an essential regulator of SG assembly. Ubap2l haploinsufficiency in mouse led to social and cognitive impairments accompanied by disrupted neurogenesis and reduced SG formation during early brain development. On the basis of data from 40,853 individuals with NDDs, we report a nominally significant excess of de novo variants within 29 genes that are not implicated in NDDs, including 3 essential genes ( G3BP1 , G3BP2 , and UBAP2L ) in the core SG interaction network. We validated that NDD-related de novo variants in newly implicated and known NDD genes, such as CAPRIN1 , disrupt the interaction of the core SG network and interfere with SG formation. Together, our findings suggest the common SG pathology in NDDs.
Keyphrases
- copy number
- genome wide
- genome wide identification
- intellectual disability
- autism spectrum disorder
- bioinformatics analysis
- dna methylation
- mental health
- transcription factor
- genome wide analysis
- healthcare
- gene expression
- electronic health record
- machine learning
- congenital heart disease
- multiple sclerosis
- data analysis