Protein Biomarkers of Autism Spectrum Disorder Identified by Computational and Experimental Methods

Overview

Journal Front Psychiatry

Specialty Psychiatry

Date 2021 Mar 15

PMID 33716802

Citations 5

Authors

Fang Yao

Kaoyuan Zhang

Chengyun Feng

Yan Gao

Liming Shen

Xukun Liu

Jiazuan Ni

Affiliations

Soon will be listed here.

Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects millions of people worldwide. However, there are currently no reliable biomarkers for ASD diagnosis. The strategy of computational prediction combined with experimental verification was used to identify blood protein biomarkers for ASD. First, brain tissue-based transcriptome data of ASD were collected from Gene Expression Omnibus database and analyzed to find ASD-related genes by bioinformatics method of significance analysis of microarrays. Then, a prediction program of blood-secretory proteins was applied on these genes to predict ASD-related proteins in blood. Furthermore, ELISA was used to verify these proteins in plasma samples of ASD patients. A total of 364 genes were identified differentially expressed in brain tissue of ASD, among which 59 genes were predicted to encode ASD-related blood-secretory proteins. After functional analysis and literature survey, six proteins were chosen for experimental verification and five were successfully validated. Receiver operating characteristic curve analyses showed that the area under the curve of SLC25A12, LIMK1, and RARS was larger than 0.85, indicating that they are more powerful in discriminating ASD cases from controls. SLC25A12, LIMK1, and RARS might serve as new potential blood protein biomarkers for ASD. Our findings provide new insights into the pathogenesis and diagnosis of ASD.

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