Multi-omic data integration improves the resolution of the molecular etiology of autism in a mouse model.
TL;DR
RNA-sequencing, 3-dimensional protein-centric chromatin conformation, and whole genome DNA methylation sequencing approaches are used to investigate hippocampal tissue from an ASD mouse model to determine if multi-omic data integration improves the resolution of key molecular pathways contributing to the complex ASD phenotype.
Abstract
Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder with complex molecular etiology. Since genetic causes account for less than 50% of ASD cases, novel approaches are required to overcome the challenges of characterizing genes and molecular pathways linking risk alleles to phenotype. Here we use RNA-sequencing, 3-dimensional protein-centric chromatin conformation (Hi-ChIP), and whole genome DNA methylation sequencing approaches to investigate hippocampal tissue from an ASD mouse model (Cntnap2 knockout [Cntnap2 KO]) to determine if multi-omic data integration improves the resolution of key molecular pathways contributing to the complex ASD phenotype. Each -omic dataset individually identified disruptions in numerous genes and pathways, providing broad ASD-related insights, such as 1) 699 downregulated genes with an enrichment of neuronal ontological terms; 2) unique chromatin interactions in Cntnap2 KO mice; and 3) that most differentially methylated genes (1220/1659) have a neuronal function. The multi-omic data integration reduced the heterogeneity and refined the data to 43 genes with links to ASD (e.g., Zbtb18, Cttnbp2, Gabbr2). A pathways analysis of these 43 genes identified one gene ontological term: regulation of neuronal synaptic plasticity. These findings are consistent with large scale gene expression studies of human ASD postmortem brain tissue, suggesting that multi-omic data integration can be used to achieve a greater resolution of the heterogeneous ASD molecular etiology.