Background Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is an autoimmune demyelinating disorder of the central nervous system. Methods Extracted proteins from 34 cerebrospinal fluid (CSF) samples [patients with MOGAD (MOG group, n = 12); healthy controls (HC group, n = 12); patients with MOG seronegative and metagenomics next-generation sequencing-negative inflammatory neurological diseases (IND group, n = 10)] were processed and subjected to label-free quantitative proteomics. Supervised partial least squares-discriminant analysis (PLS-DA) and orthogonal PLS-DA (O-PLS-DA) models were also performed based on proteomics data. Functional analysis of differentially expressed proteins (DEPs) was performed using Gene Ontology, InterPro, and Kyoto Encyclopedia Genes and Genomes. An enzyme-linked immunosorbent assay was used to determine the complement levels in serum from patients with MOGAD. Results Four hundred and twenty-nine DEPs (149 upregulated and 280 downregulated proteins) were identified in the MOG group compared to the HC group according to the P value and fold change (FC). Using the O-PLS-DA model, 872 differentially abundant proteins were identified with variable importance projection (VIP) scores > 1. Five proteins (gamma-glutamyl hydrolase, cathepsin F, interalpha-trypsin inhibitor heavy chain 5, latent transforming growth factor beta-binding protein 4 and leukocyte-associated immunoglobulin-like receptor 1) overlapping between the top 30 DEPs with top-ranked P value and FC and top 30 proteins in PLS-DA VIP lists were acquired. Functional analysis revealed that the dysregulated proteins in the MOG group were primarily involved in complement and coagulation cascades, cell adhesion, axon guidance, and glycosphingolipid biosynthesis compared to the HC group. Conclusion The proteomic alterations in CSF samples from children with MOGAD identified in the current study might provide opportunities for developing novel biomarker candidates.

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IntroductionEarly and accurate identification of pathogens is essential for improved outcomes in patients with viral encephalitis (VE) and/or viral meningitis (VM).MethodsIn our research, Metagenomic next-generation sequencing (mNGS) which can identify viral pathogens unbiasedly was performed on RNA and DNA to identify potential pathogens in cerebrospinal fluid (CSF) samples from 50 pediatric patients with suspected VEs and/or VMs. Then we performed proteomics analysis on the 14 HEV-positive CSF samples and another 12 CSF samples from health controls (HCs). A supervised partial least squaresdiscriminant analysis (PLS-DA) and orthogonal PLS-DA (O-PLS-DA) model was performed using proteomics data.ResultsTen viruses in 48% patients were identified and the most common pathogen was human enterovirus (HEV) Echo18. 11 proteins overlapping between the top 20 DEPs in terms of P value and FC and the top 20 proteins in PLS-DA VIP lists were acquired.DiscussionOur result showed mNGS has certain advantages on pathogens identification in VE and VM and our research established a foundation to identify diagnosis biomarker candidates of HEV-positive meningitis based on MS-based proteomics analysis, which could also contribute toward investigating the HEV-specific host response patterns.