In silico analysis of autism spectrum disorder through the integration of DNA methylation and gene expression data for biomarker search

Abstract

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by apparent communication deficits, repetitious unusual rituals, and social dysfunction. The epigenetic mechanism of the disorder has been researched meticulously yet not fully understood. Furthermore, there is yet no diagnostic marker that is associated with the condition. This study aimed at enhancing the understanding of the epigenetic mechanism of ASD and exploring biomarker candidates for the disorder using an in-silico approach.METHODS: ASD associated blood specific DNA methylation datasets were analyzed and overlapped with gene expression data to determine differentially regulated and methylated genes. Enrichment analysis was performed to identify signaling pathways, gene ontology terms, and disease associations. Additionally, metabolites and metabolic pathways associated with differentially methylated genes were demonstrated.RESULTS: The results revealed 42 differentially regulated and methylated genes, the majority of which were previously not associated with ASD. Particularly downregulated NFATC1 (nuclear factor of activated T cells 1) and upregulated MANBA (mannosidase beta) and ARAP1 (ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 1) may be researched further to assess their biomarker features. The MAPK signaling pathway and gene ontology terms for protein/serine kinase activity and protein kinase binding came into prominence as a result of enrichment analysis. Metabolic alterations in ASD including tyrosine, tryptophan and asparagine were identified. Loss of amino acid homeostasis as well as dysregulation in related metabolic pathways were also observed.CONCLUSIONS: This in-silico study brings a comprehensive outlook on epigenetic alterations complementing governance by DNA methylation through integrating data across omics. DNA methylation, gene expression as well as metabolomics signatures of ASD that may be regarded as biomarker candidates were reported. The results of this study can guide future experimental studies, which will eventually lead to diagnostic tools and therapeutics designed for individuals suffering from the conditions of this complex disorder.