The use of data independent acquisition based proteomic analysis and machine learning to reveal potential biomarkers for autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurological developmental disorder in children, and is associated with social isolation and restricted interests. 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. We performed data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis of plasma from children with ASD and controls. The result showed that 45 differentially expressed proteins (DEPs) were identified between autistic subjects and controls. Among these, only one DEP was down-regulated in ASD; other DEPs were up-regulated in ASD children's plasma. These proteins are found associated with complement and coagulation cascades, vitamin digestion and absorption, cholesterol metabolism, platelet degranulation, selenium micronutrient network, extracellular matrix organization and inflammatory pathway, which have been reported to be related to ASD. After MRM verification, five key proteins in complement pathway (PLG, SERPINC1, and A2M) and inflammatory pathway (CD5L, ATRN, SERPINC1, and A2M) were confirmed to be significantly up-regulated in ASD group. Through the screening of machine learning model and MRM verification, we found that two proteins (biotinidase and carbonic anhydrase 1) can be used as early diagnostic markers of ASD (AUC = 0.8, p = 0.0001). SIGNIFICANCE: ASD is the fastest growing neurodevelopmental disorder in the world and has become a major public health problem worldwide. Its prevalence has been steadily increasing, with a global prevalence rate of 1%. Early diagnosis and intervention can achieve better prognosis. In this study, data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis was applied to analyze the plasma proteome of ASD patients (31 (±5) months old), and 378 proteins were quantified. 45 differentially expressed proteins (DEPs) were identified between the ASD group and the control group. They mainly were associated with platelet degranulation, ECM proteoglycar, complement and coagulation cascades, selenium micronutrient network, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), cholesterol metabolism, vitamin metabolism, and inflammatory pathway. Through the integrated machine learning methods and the MRM verification of independent samples, it is considered that biotinidase and carbon anhydrase 1 have the potential to become biomarkers for the early diagnosis of ASD. These results complement proteomics database of the ASD patients, broaden our understanding of ASD, and provide a panel of biomarkers for the early diagnosis of ASD.

Trace Element Changes in the Plasma of Autism Spectrum Disorder Children and the Positive Correlation Between Chromium and Vanadium.

Existing data demonstrate a significant correlation between autism spectrum disorder (ASD) and the status of biologically essential and toxic trace elements. However, there is still a lack of data on the steady state of trace elements in ASD. We performed a case-control study to explore the association between the risk of ASD and 23 trace elements in plasma. The results showed that children with ASD had considerably decreased lithium (Li), manganese (Mn), selenium (Se), barium (Ba), mercury (Hg), and tin (Sn) levels when compared to their age- and sex-matched controls. Meanwhile, children with ASD had considerably increased plasma chromium (Cr) and vanadium (V) concentrations. We also divided each group into subgroups based on age and gender and created element-related networks for each subgroup. We detected significant element correlations within or between subgroups, as well as changes in correlations that included all elements examined. Finally, more element correlations were observed among males, which may open a new avenue for understanding the complicated process behind the sex ratio of children with ASD. Overall, our data revealed a novel relationship between elements and ASD, which may extend current understanding about ASD.