Exploring the roles and potential therapeutic strategies of inflammation and metabolism in the pathogenesis of vitiligo: a mendelian randomization and bioinformatics-based investigation

Abstract

Introduction: Vitiligo, a common autoimmune acquired pigmentary skin disorder, poses challenges due to its unclear pathogenesis. Evidence suggests inflammation and metabolism's pivotal roles in its onset and progression. This study aims to elucidate the causal relationships between vitiligo and inflammatory proteins, immune cells, and metabolites, exploring bidirectional associations and potential drug targets.

Methods: Mendelian Randomization (MR) analysis encompassed 4,907 plasma proteins, 91 inflammatory proteins, 731 immune cell features, and 1400 metabolites. Bioinformatics analysis included Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Subnetwork discovery and hub protein identification utilized the Molecular Complex Detection (MCODE) plugin. Colocalization analysis and drug target exploration, including molecular docking validation, were performed.

Results: MR analysis identified 49 proteins, 39 immune cell features, and 59 metabolites causally related to vitiligo. Bioinformatics analysis revealed significant involvement in PPI, GO enrichment, and KEGG pathways. Subnetwork analysis identified six central proteins, with Interferon Regulatory Factor 3 (IRF3) exhibiting strong colocalization evidence. Molecular docking validated Piceatannol's binding to IRF3, indicating a stable interaction.

Conclusion: This study comprehensively elucidates inflammation, immune response, and metabolism's intricate involvement in vitiligo pathogenesis. Identified proteins and pathways offer potential therapeutic targets, with IRF3 emerging as a promising candidate. These findings deepen our understanding of vitiligo's etiology, informing future research and drug development endeavors.

Keywords: bioinformatics; inflammation; mendelian randomization; metabolism; therapeutic strategies; vitiligo.

FIGURE 1

Study design. GO, gene ontology, IVs instrumental variables, KEGG, kyoto encyclopedia of genes and genomes, MR, mendelian randomization, PPI, protein–protein interaction.

FIGURE 2

MR analysis illustrates the causal relationships between inflammatory-immune-related proteins, immune cells, metabolites, and metabolite ratios with vitiligo. (A) The volcano plot displays the causal relationships between 49 inflammatory-immune-related proteins and vitiligo. However, certain protein names, including TNFRSF11B, TNFSF12, SELL, TLR3 (all with odds ratios less than 1), were not displayed due to overlapping positions, represented by gray circles; (B) The volcano plot displays the causal relationships between 39 immune cell features and vitiligo; (C) The forest plot presents the causal relationships between 19 metabolite ratios and vitiligo; (D) The forest plot presents the causal relationships between 40 metabolites and vitiligo.

FIGURE 3

The forest plot illustrates the results of the reverse Mendelian Randomization analysis when vitiligo is considered as the exposure.

FIGURE 4

(A) The PPI graph created using the STRING website; (B) Further processing of the 19 proteins using Cytoscape software; (C,D) Identification of two subnetworks comprising six hub proteins using the MCODE plugin.

FIGURE 5

(A) Results of GO enrichment analysis, sorted by FDR values, with only the top 10 BP pathways displayed; (B) Results of KEGG pathway analysis. BP, biological processes; CC, cellular components, MF, molecular functions.

FIGURE 6

Displays the colocalization analysis results between IRF3 and vitiligo. IRF3, interferon regulatory factor 3.

FIGURE 7

Binding mode of screened drugs to their targets by molecular docking. (A) Cartoon representation, overlay of the crystal structures of small molecule compounds and their targets were illustrated by Molecule of the Month feature; (B) The PyMOL software displays the three-dimensional structure of the binding pocket along with the linkage between the compound and its target; (C) The 3D structure of IRF3; (D) The 3D structure of Piceatannol.