Key genetic biomarkers and therapeutic potential in multiple sclerosis: Insights from integrated bioinformatics and mendelian randomization.

Background

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Objectives

Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system characterized by inflammation, demyelination, and neurodegeneration. This study aimed to identify key genetic and immune-related biomarkers involved in MS pathogenesis and to explore structurally plausible therapeutic targets.

Materials And Methods

Publicly available gene-expression datasets were analyzed using an integrated computational framework. This framework comprised differential expression analysis, Mendelian randomization, immune-cell infiltration profiling, functional enrichment analysis, and drug-gene interaction screening. Candidate STAT6-ligand complexes were then examined by CB-Dock2 molecular docking, followed by 100 ns molecular dynamics (MD) simulation, MM-PBSA decomposition, per-residue energy analysis, and PCA-based free-energy landscape (FEL) analysis.

Results

Three MS-associated genes were prioritized: MS4A3, STAT6, and UBE2O. MS4A3 and STAT6 showed positive associations with MS risk, whereas UBE2O showed a protective association. Enrichment analyses implicated immune-regulatory and inflammatory pathways, including Th17 differentiation, Th1/Th2 differentiation, and ubiquitin-mediated proteolysis. Drug-gene interaction screening and CB-Dock2 docking identified STAT6-ligand interactions, particularly with methylprednisolone and Doxorubicin hydrochloride. Subsequent 100 ns MD simulations supported continued ligand association with the predicted STAT6 pocket. MM-PBSA and PCA-based free-energy analyses indicated favorable non-bonded interactions and distinct low-energy conformational ensembles, with methylprednisolone providing the more clinically interpretable STAT6-associated result.

Conclusions

This study integrates transcriptomic, genetic-causal, immunological, and structural analyses to prioritize STAT6 as a potentially druggable MS-associated target. The additional MD, MM-PBSA, and PCA/FEL analyses strengthen the computational plausibility of STAT6-ligand interactions, especially for the clinically relevant STAT6-methylprednisolone complex. Nevertheless, all structural findings remain computational predictions and require experimental validation before clinical translation.