Summary: A newly discovered cell-specific molecular network associated with ASD could lay the groundwork for finding an effective treatment for those with autism.
Professor Kim Min-sik’s team of the Department of New Biology, DGIST (President: Kuk Yang), succeeded in identifying the cell-specific molecular network of autism spectrum disorder. It is expected to lay the foundation for treating autism spectrum disorder.
Autism spectrum disorder is known to occur from early childhood and is a neuro-developmental disorder characterized by continuous impairment of social communication and interaction-related behaviors leading to limited ranges of behavioral patterns, interests, and activities, and repetitive behaviors.
Most autism spectrum disorder patients have behavioral disorders, sometimes accompanied by other developmental disabilities. Currently, since there is no accurate molecular diagnosis method, early diagnosis of autism spectrum disorder is made at a fairly late period, and there is no appropriate treatment.
Professor Kim Min-sik’s team utilized the Cntnap2 defect model, a spectral disorder mouse model established by Professor Lee Yong-Seok’s team at Seoul National University College of Medicine to extract prefrontal cortex tissue and performed mass spectrometry-based integrated quantitative proteomic and metabolomic analysis.
In addition, by comparing and analyzing this with previously reported big data of autism spectrum disorder patients, the team confirmed that problems occur in networks such as metabolism and synapses in excitable neurons.
Professor Kim Min-sik of the Department of New Biology said, “The multi-omics integrated analysis technology developed through this study has advanced the pathological understanding of autism spectrum disorder and made it possible to discover an integrated network ranging from molecular-level cell differentiation induced by a specific autism gene to biometric information,“ and added,
“We are trying to find the core network of autism spectrum disorder and discover treatment targets by conducting an integrated analysis of various models.”
Meanwhile, the results of this research were published in ‘Molecular Psychiatry’ on October 17, 2022, and this research was carried out with support from the Brain Science Source Technology Development Project of the Ministry of Science and ICT.
About this autism research news
Author: Kwanghoon CHOI Source: DGIST Contact: Kwanghoon CHOI – DGIST Image: The image is in the public domain
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.