A Systematic Investigation of Complement and Coagulation-Related Protein in Autism Spectrum Disorder Using Multiple Reaction Monitoring Technology

 Xueshan Cao1,2 · Xiaoxiao Tang1 · Chengyun Feng3 · Jing Lin1 · Huajie Zhang1 · Qiong Liu1,2 · Qihong Zheng1 · Hongbin Zhuang1 · Xukun Liu1 · Haiying Li4 · Naseer Ullah Khan1 · Liming Shen1,5,6
1 College of Life Science and Oceanography, Shenzhen University, Shenzhen 518060, China
2 College of Physics and Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
3 Maternal and Child Health Hospital of Baoan, Shenzhen 518100, China
4 Department of Endocrinology, Guiyang First People’s Hospital, Guiyang 550002, China
5 Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
6 Shenzhen Key Laboratory of Marine Biotechnology and Ecology, Shenzhen 518060, China

Autism spectrum disorder (ASD) is one of the common neurodevelopmental disorders in children. Its etiology and pathogenesis are poorly understood. Previous studies have suggested potential changes in the complement and coagulation pathways in individuals with ASD. In this study, using multiple reactions monitoring proteomic technology, 16 of the 33 proteins involved in this pathway were identified as differentially-expressed proteins in plasma between children with ASD and controls. Among them, CFHR3, C4BPB, C4BPA, CFH, C9, SERPIND1, C8A, F9, and F11 were found to be altered in the plasma of children with ASD for the first time. SERPIND1 expression was positively correlated with the CARS score. Using the machine learning method, we obtained a panel composed of 12 differentially-expressed proteins with diagnostic potential for ASD. We also reviewed the proteins changed in this pathway in the brain and blood of patients with ASD. The complement and coagulation pathways may be activated in the peripheral blood of children with ASD and play a key role in the pathogenesis of ASD.

Autism spectrum disorder; Biomarker; Complement and coagulation cascade; Complement system; Machine learning; Multiple reaction monitoring