Blood biomarker discovery for autism spectrum disorder: A proteomic analysis

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. Given the lack of specific pharmacological therapy for ASD and the clinical heterogeneity of the disorder, current biomarker research efforts are geared mainly toward identifying markers for determining ASD risk or for assisting with a diagnosis. A wide range of putative biological markers for ASD is currently being investigated. Proteomic analyses indicate that the levels of many proteins in plasma/serum are altered in ASD, suggesting that a panel of proteins may provide a blood biomarker for ASD. Serum samples from 76 boys with ASD and 78 typically developing (TD) boys, 18 months-8 years of age, were analyzed to identify possible early biological markers for ASD. Proteomic analysis of serum was performed using SomaLogic’s SOMAScanTM assay 1.3K platform. A total of 1,125 proteins were analyzed. There were 86 downregulated proteins and 52 upregulated proteins in ASD (FDR < 0.05). Combining three different algorithms, we found a panel of 9 proteins that identified ASD with an area under the curve (AUC) = 0.8599±0.0640, with specificity and sensitivity of 0.8217±0.1178 and 0.835±0.1176, respectively. All 9 proteins were significantly different in ASD compared with TD boys, and were significantly correlated with ASD severity as measured by ADOS total scores. Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys. Further verification of the protein biomarker panel with independent test sets is warranted.

The Metallome as a Link Between the “Omes” in Autism Spectrum Disorders

“…Pathway analysis using proteomics/transcriptomics data has also pointed to synaptic processes with significant alterations in glutamate (NMDA) receptor signaling identified in various brain regions. In addition, pathways involved in redox mechanisms and inflammatory processes were in the focus ( Voineagu et al, 2011 ; Feng et al, 2017 ; Gandal et al, 2018 ; Schwede et al, 2018 ; Abraham et al, 2019a ; Gordon et al, 2021 ; Hewitson et al, 2021 ). However, there is a lack of overlap between the genes/proteins identified due to the heterogeneity of ASD and different cohorts of varying ages and severity of symptoms used in human proteomic/transcriptomics studies ( Wetie et al, 2015b ; Cortelazzo et al, 2016 ).…”

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

“…Suvarna et al (2021) identified protein classifiers of patients with non-severe and severe COVID-19, by using SVMs models (Suvarna et al, 2021). Hewitson et al (2021) used Random Forest and logistic regression models to classify proteins in blood as potential biomarkers in autism spectrum disorder (Hewitson et al, 2021).…”

Convergent Canonical Pathways in Autism Spectrum Disorder from Proteomic, Transcriptomic and DNA Methylation Data

“…Overall, data were collated from 19 studies and meta-analyses from ASD cohorts published between 2017 and 2021 ( Table 1 ). The publications included six proteomic datasets [ 11 , 14 , 15 , 16 , 17 , 18 ], eight transcriptomic studies [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], and five DNAm screens [ 26 , 27 , 28 , 29 , 30 ]. The data collated from each study were the published list of genes or proteins found to be significantly associated with ASD after data quality control, normalisation, processing, statistical analyses and peer review ( Supplementary Table S1 ).…”