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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. [ABSTRACT FROM AUTHOR]

Autism is one of the most common neurological developmental disorder associated with social isolation and restricted interests in children. The etiology of this disorder is still unknown. There is neither any confirmed laboratory test nor any effective therapeutic strategy to diagnose or cure it. To search for biomarkers for early detection and exploration of the disease mechanisms, here, we investigated the protein expression signatures of peripheral blood mononuclear cells (PBMCs) in autistic children compared with healthy controls by using isobaric tags for relative and absolute quantitation (iTRAQ) proteomics approach. The results showed a total of 41 proteins as differentially expressed in autistic group as compared to control. These proteins are found associated with metabolic pathways, endoplasmic reticulum (ER) stress and protein folding, endocytosis, immune and inflammatory response, plasma lipoprotein particle organization, and cell adhesion. Among these, 17 proteins (13 up-regulated and four down-regulated) are found to be linked with mitochondria. Eight proteins including three already reported proteins in our previous studies were selected to be verified. Five already reported autism associated pro-inflammatory cytokines [interferon-γ (IFN-γ), interleukin-1β (IL-1β), IL-6, IL-12, and tumor necrosis factor-α (TNF-α)] were detected in plasma by enzyme-linked immunosorbent assay (ELISA) analysis. The results were consistent with proteomic results and reports from previous literature. These results proposed that PBMCs from autistic children might be activated, and ER stress, unfolded protein response (UPR), acute-phase response (APR), inflammatory response, and endocytosis may be involved in autism occurrence. These reported proteins may serve as potential biomarkers for early diagnosis of autism. More specifically, simultaneous detection of three proteins [complement C3 (C3), calreticulin (CALR), and SERPINA1] in the plasma and PBMCs could increase the authenticity of detection. [ABSTRACT FROM AUTHOR]