Cntnap2-dependent molecular networks in autism spectrum disorder revealed through an integrative multi-omics analysis.
Wooyoung Eric JangJi Hwan ParkGaeun ParkGeul BangChan Hyun NaJin Young KimKwang-Youl KimKwang Pyo KimChan Young ShinJoon-Yong AnYong-Seok LeeMin-Sik KimPublished in: Molecular psychiatry (2022)
Autism spectrum disorder (ASD) is a major neurodevelopmental disorder in which patients present with core symptoms of social communication impairment, restricted interest, and repetitive behaviors. Although various studies have been performed to identify ASD-related mechanisms, ASD pathology is still poorly understood. CNTNAP2 genetic variants have been found that represent ASD genetic risk factors, and disruption of Cntnap2 expression has been associated with ASD phenotypes in mice. In this study, we performed an integrative multi-omics analysis by combining quantitative proteometabolomic data obtained with Cntnap2 knockout (KO) mice with multi-omics data obtained from ASD patients and forebrain organoids to elucidate Cntnap2-dependent molecular networks in ASD. To this end, a mass spectrometry-based proteometabolomic analysis of the medial prefrontal cortex in Cntnap2 KO mice led to the identification of Cntnap2-associated molecular features, and these features were assessed in combination with multi-omics data obtained on the prefrontal cortex in ASD patients to identify bona fide ASD cellular processes. Furthermore, a reanalysis of single-cell RNA sequencing data obtained from forebrain organoids derived from patients with CNTNAP2-associated ASD revealed that the aforementioned identified ASD processes were mainly linked to excitatory neurons. On the basis of these data, we constructed Cntnap2-associated ASD network models showing mitochondrial dysfunction, axonal impairment, and synaptic activity. Our results may shed light on the Cntnap2-dependent molecular networks in ASD.
Keyphrases
- autism spectrum disorder
- single cell
- attention deficit hyperactivity disorder
- intellectual disability
- end stage renal disease
- prefrontal cortex
- ejection fraction
- newly diagnosed
- risk factors
- chronic kidney disease
- mass spectrometry
- electronic health record
- rna seq
- prognostic factors
- peritoneal dialysis
- type diabetes
- patient reported outcomes
- big data
- gene expression
- high resolution
- machine learning
- high throughput
- spinal cord injury
- high frequency
- poor prognosis
- artificial intelligence
- binding protein
- skeletal muscle
- network analysis
- simultaneous determination
- wild type