Abstract
Currently, the treatment and prevention of multiple sclerosis (MS) continue to encounter significant challenges. Mendelian randomization (MR) analysis has emerged as a crucial research method in the pursuit of new therapeutic strategies. Accordingly, we hypothesize that there exists a causal association between genetic variants of specific plasma proteins and MS through MR mechanisms, and that key therapeutic targets can be precisely identified by integrating multi-omics analytical approaches. In this study, we developed a comprehensive analytical framework aimed at identifying and validating potential therapeutic targets for MS. The framework commenced with a two-sample Mendelian randomization (MR) study utilizing two large plasma protein quantitative trait locus (pQTL) datasets. Building on this foundation, we performed Bayesian co-localization analysis of coding genes, followed by a full phenotype-wide association study (PheWAS) on the co-positive genes identified through both analytical methods. This approach allowed us to explore the functions of key genes and the mechanisms of co-morbidity associated with the disease. Subsequently, we integrated protein-protein interaction (PPI) network analysis, gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to facilitate drug prediction and molecular docking studies. This study conducted a systematic analysis between two large plasma pQTLs datasets and MS. In the MR analysis, the MR analysis of Icelandic plasma pQTLs and MS identified 88 positive plasma proteins, while the MR analysis of the UK Biobank database pQTLs and MS identified 122 positive plasma proteins. By comparison, uroporphyrinogen III synthase (UROS) and glutathione S-transferase theta 2B (GSTT2B) were found to be the positive proteins shared by the two datasets. After false discovery rate (FDR) correction, signal transducer and activator of transcription 3 (STAT3) was a significantly positive protein in the analysis of Icelandic plasma pQTLs. In the analysis of the UK Biobank database pQTLs, advanced glycosylation end product-specific receptor (AGER), allograft inflammatory factor 1 (AIF1), butyrophilin subfamily 1 member A1 (BTN1A1), cluster of differentiation 58 (CD58), desmoglein 4 (DSG4), ecotropic viral integration site 5 (EVI5), tumor necrosis factor (TNF), and tumor necrosis factor receptor superfamily member 14 (TNFRSF14) were significantly positive proteins. After Bonferroni correction, AGER, CD58, EVI5, and TNF remained significantly positive proteins in the analysis of the UK Biobank database pQTLs. In the Bayesian colocalization analysis, EVI5 (PPH4 = 0.9800), O-GlcNAcase (OGA) (PPH4 = 0.8569), and TNFRSF14 (PPH4 = 0.8904) were the common positive genes in the two analysis methods. In conclusion, EVI5, OGA, and TNFRSF14 may be potential therapeutic targets for MS. Through the comprehensive application of MR analysis and Bayesian colocalization analysis, we have successfully identified that EVI5, OGA, and TNFRSF14 may be key therapeutic targets for MS. These findings may provide a scientific basis for the development of novel immunotherapies, combination treatment regimens, or targeted intervention strategies.
figure 1
Flow chart of the analysis process in this article. MR: Mendelian randomization analysis; PheWAS: Phenome-wide association study; PPI: Protein-protein interaction; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; DSigDB: Drug Signatures Database.
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figure 2
Volcano plot of the results of the MR analysis between plasma pQTLs and MS.
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figure 3
Circular heat map of the positive results of the MR analysis between plasma pQTLs and MS.
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figure 4
Bayesian co localization results of EVI5 and multiple sclerosis.
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figure 5
Bayesian co localization results of OGA and multiple sclerosis.
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figure 6
Bayesian co localization results of TNFRSF14 and multiple sclerosis.
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figure 7
Manhattan plot of significant positive gene PheWAS in multiple sclerosis.
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figure 11
STRING database protein interaction network diagram.
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figure 13
Histogram of the results of the GO enrichment analysis.
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figure 14
Bubble chart of the results of the GO enrichment analysis.
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figure 15
Histogram of the results of the KEGG enrichment analysis.
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figure 16
Bubble chart of the results of the KEGG enrichment analysis.
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figure 17
Molecular docking between EVI5 and Adehl.
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figure 18
Molecular docking between EVI5 and flunixin.
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figure 19
Molecular docking between EVI5 and imidurea.
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figure 20
Molecular docking between EVI5 and Sanguinarine.
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figure 21
Molecular docking between TNFRSF14 and DMBA.
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figure 22
Molecular docking between TNFRSF14 and EXEEMESTANE.
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figure 23
Molecular docking of TNFRSF14 with folic acid.
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