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

Abstract

Background: 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. Materials and Methods: 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. Results: 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. Conclusion: 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.

Keywords: autism spectrum disorder; biomarker; blood; computational; experimental; protein.

Figure 1

An overview of the work flow used in this study.

Figure 2

The top 10 enriched GO terms of biological processes, cellular components, and molecular functions. (A) Biological processes (BP) enriched by the differentially expressed genes of ASD. (B) Cellular components (CC) enriched by the differentially expressed genes of ASD. (C) Molecular functions (MF) enriched by the differentially expressed genes of ASD. The number of proteins associated with each category is presented at the end of each bar. (D) The pathways enriched by the differentially expressed genes of ASD. The number of proteins enriched in each pathway is at the end of each bar.

Figure 3

Functional interaction network analysis of the differentially expressed genes. (A) The differentially expressed genes were mapped to the GO categories, including biological processes and cellular components by using ClueGO cytoscape plugin. (B) The differentially expressed genes were mapped to the KEGG pathway, REACTOME pathway, and Wiki pathway by using ClueGO cytoscape plugin.

Figure 4

Database survey and protein–protein interaction network analysis on the 59 blood-secretory proteins. (A) Compared with plasma protein database. (B) Compared with ASD-related database (AutismKB). *The AutismKB database contains 1,379 genes, 5,420 copy number variants (CNVs)/structural variations (SVs), 11,669 single-nucleotide variations (SNVs)/insertions and deletions (InDels), and 172 linkage regions associated with ASD. (C) The blood-secretory proteins overlapped in plasma protein database and AutismKB database. (D) The protein–protein interaction network of these 59 blood-secretory proteins. The predicted blood-secretory proteins are shown as red nodes and autism pathology-related proteins are shown as blue nodes. (E) UniProt keywords were enriched by using String database. Gene with a node color of purplish red, whose Uniprot keyword is alternative splicing.

Figure 5

Verification of the potential blood protein biomarkers for ASD by ELISA. **p < 0.005; ***p < 0.0005.

Figure 6

ROC curve analyses on five differentially expressed proteins in plasma samples of ASD patients. The blue line represents protein ACTL6B, the red line is LIMK1, the green line is PRKAA1, the black line is RARS, and the orange line is SLC25A12.