Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease affecting multiple organ systems, whose pathogenesis is influenced by genetic factors, immune dysregulation, and environmental stimuli. With the rise of multi-omics research, integrating transcriptomic, metabolomic, and microbiomic data has become an important strategy to elucidate the fundamental mechanisms of the disease [1,2]. However, multi-omics integration studies of SLE remain relatively limited, especially lacking systematic analyses based on large-scale public databases.

Objectives: This study, using real data obtained from multiple authoritative public databases, aims to reveal the molecular mechanisms and key regulatory networks of SLE from a systemic perspective.

Methods: In this study, SLE peripheral blood transcriptomic data were obtained from the GEO (Gene Expression Omnibus) database, including three datasets, totaling 212 samples (132 SLE cases and 80 controls). Metabolomic data were obtained from the gutMGene database, including 178 human gut microbiota-derived metabolites and their regulatory relationships with host genes. Microbiome data were obtained from the NCBI Sequence Read Archive (SRA), including two BioProjects, totaling 146 gut samples (92 SLE cases and 54 controls). Transcriptomic data were analyzed for differential expression and functional enrichment using R. Microbiome data were processed with QIIME2 for ASV construction, diversity analysis, and differential abundance assessment. Metabolomic data were integrated with metabolic pathway analysis and metabolite–gene associations. Finally, systematic integration of the three omics datasets was achieved through correlation analysis and multi-layer network construction.

Results: Transcriptomic analysis identified 368 differentially expressed genes, mainly enriched in type I interferon signaling, TLR7/TLR9-mediated innate immune responses, and B cell activation-related processes. At the metabolomic level, SLE patients exhibited significant disruptions in the tryptophan–kynurenine pathway, increased arachidonic acid-related lipid oxidation, and purine metabolism abnormalities, among which 21 microbiota-derived metabolites were positively correlated with disease activity. Microbiome analysis revealed that SLE patients had a marked reduction in short-chain fatty acid (SCFA)-producing bacteria and a significant increase in conditional pathogenic bacteria such as Escherichia-Shigella , with significant differences in β-diversity (PERMANOVA, P < 0.01). Cross-omics integration indicated that microbial dysbiosis could influence key metabolite levels, further driving upregulation of interferon signaling and B cell activation genes, forming a “microbial structure alteration–metabolic remodeling–immune dysregulation” functional axis.

Conclusions: Based on real public database data from GEO, gutMGene, and NCBI SRA, this study systematically constructed a multi-omics regulatory map of SLE, revealing that microbial imbalance and metabolic reprogramming synergistically amplify innate and adaptive immune dysregulation. The results highlight the critical role of the “microbiota–metabolite–immune” axis in SLE pathogenesis, provide new evidence for understanding its complex mechanisms, and offer potential targets for future precise therapeutic strategies based on metabolic intervention or microbial modulation.

REFERENCES: [1] Zhang L, Qing P, Yang H, Wu Y, Liu Y, Luo Y. Gut Microbiome and Metabolites in Systemic Lupus Erythematosus: Link, Mechanisms and Intervention. Front Immunol. 2021 Jul 15;12:686501.

[2] Wang H, Zhang J, Yang M, Chen J, Yang X, Yang N, Zhao B. Causal relationship between gut microbiome, immune cell, and systemic lupus erythematosus: A Mendelian randomization analysis. Medicine (Baltimore). 2025 Aug 1;104(31):e43703.

Acknowledgments: NIL.

Disclosure of Interests: None declared.