Abstract
Background Myelin oligodendrocyte glycoprotein(MOG)antibody-associated disease(MOGAD)is an autoimmune demy-elinating 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.
基金项目
Key Research and Development Plan of Zhejiang Province(2020C03038)
国家自然科学基金青年基金(81901679)
浙江省自然科学基金(LGF19H090020)
NETL
NSTL
万方数据