Synaptic Vesicle Cycling Disorders: Cross-Sectional Phenotyping Study of a Gene Functional Network

Abstract

Background and objectivesGenes associated with neurodevelopmental disorders (NDDs) can be grouped into networks according to their molecular and cellular functions. However, the links between gene functional networks and neurodevelopmental phenotypes are not well understood. Synaptic vesicle cycling (SVC) is one gene functional network in which rare, high penetrance variants are known to cause NDDs. SVC genes regulate neurotransmitter release and recycling, thus essential for synaptic transmission and plasticity. We investigated whether SVC disorders are associated with a different neurodevelopmental spectrum from other monogenic NDDs, and whether phenotypic variation within SVC disorders can be predicted. MethodsWe included 199 children, young people, and adults with a genetic NDD diagnosis (n=109 with SVC disorders, n=90 with non-SVC disorders). Families were recruited via clinical genetics and neurology services, and support organisations. Parents or carers completed quantitative questionnaire measures previously validated in populations with NDDs. We implemented a PCA-derived K-prototype clustering analysis to assess associations between gene functional network and neurodevelopmental variation. ResultsCohort-wide K-prototype clustering identified four phenotypic similarity clusters, each having membership from both SVC and non-SVC participants. The clusters differed in phenotype enrichments as follows: 1) behavioural difficulties and sleep problems, 2) less severe neurodevelopmental problems overall, 3) epilepsies, sleep problems, severe adaptive impairments and visual awareness difficulties, 4) sensory-motor problems. The SVC group was over-represented within cluster 3. Cluster memberships within the SVC group cannot be predicted by age, sex, variant type, gene or SVC sub-process. DiscussionThe K-prototype method can be used to describe multi-dimensional phenotypic structure within the heterogeneous genetic NDD population. We found that SVC functional network membership contributes to the likelihood of phenotype cluster memberships, but does not specify a distinct or homogeneous neurodevelopmental profile. Investigating convergent disease mechanisms arising from SVC dysfunction is central to understanding the observed neurodevelopmental spectrum, and may ultimately guide evidence-based prognostication and mechanism-informed management.