Brain proteome‐wide association study linking‐genes in multiple sclerosis pathogenesis

Abstract

Objectives To identify genes that confer MS risk via the alteration of cis‐regulated protein abundance and verify their aberrant expression in human brain. Methods Utilizing a two‐stage proteome‐wide association study (PWAS) design, MS GWAS data ( N = 41,505) was respectively integrated with two distinct human brain proteomes from the dorsolateral prefrontal cortex, including ROSMAP ( N = 376) in the discovery stage and Banner ( N = 152) in the confirmation stage. In the following, Bayesian colocalization analysis was conducted for GWAS and protein quantitative trait loci signals to prioritize candidate genes. Differential expression analysis was then used to verify the dysregulation of risk genes in white matter and gray matter for evidence at the transcription level. Results A total of 51 genes whose protein abundance had association with the MS risk were identified, of which 18 genes overlapped in the discovery and confirmation PWAS. Bayesian colocalization indicated six causal genes with genetic risk variants for the MS risk. The differential expression analysis of SHMT1 ( P FDR = 4.82 × 10 −2 ) , FAM120B ( P FDR = 8.13 × 10 −4 ) in white matter and ICA1L ( P FDR = 3.44 × 10 −2 ) in gray matter confirmed the dysregulation at the transcription level. Further investigation of expression found SHMT1 significantly up‐regulated in white matter lesion, and FAM120B up‐regulated in both white matter lesion and normal appearing white matter . ICA1L was down‐regulated in both gray matter lesion and normal appearing gray matter. Interpretation Dysregulation of SHMT1, FAM120B and ICA1L may confer MS risk. Our findings shed new light on the pathogenesis of MS and prioritized promising targets for future therapy research.

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“…Nowadays, a critical point is the perspective of proteomic analysis suppling the importantly significant proteins no matter in cells or in biofluid ( Jia et al, 2022 ; Riccardi et al, 2022 ; Yuan et al, 2022 ). It plays an important role in biomarker discovery, especially in cancer progression, metastasis, and resistance ( Riccardi et al, 2022 ).…”

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“…Proteome-wide association studies (PWAS) have emerged as a promising approach to identify proteome-phenotype connections using genetically imputed proteomic models and GWAS summary statistics [17][18][19] . PWAS has been applied to understand the pathological mechanisms of multiple complex traits and diseases, including stroke 20 , Alzheimer's disease 19,21 , prostate cancer 22 , depression 23,24 , post-traumatic stress disorder (PTSD) 25,26 , neuropsychiatric and substance use disorders 27,28 , and multiple sclerosis 29 , aiding in the mapping of genetic causal pathways and identifying druggable targets.…”