bioRxivImmunology
Comprehensive Multi-omics Analysis Reveals the Core Role of Glycerophospholipid Metabolism in Rheumatoid Arthritis Development
C. Jian, L. Wei, T. Wu, S. Li, T. Wang, J. Chen, S. Chang, J. Zhang, B. He, J. Wu, J. Su, J. Zhu, M. Wu, Y. Zhang, F. Zeng
Abstract
ObjectivesRheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. MethodsA total of 162 subjects were included in our study, 121 subjects were used for 16S rRNA, ITS sequencing and metabolomics analysis and 41 subjects were used for transcriptomics analysis. Enrichment analysis was based on significant difference metabolites and genes. Protein-protein interaction network and correlation analyses were performed to interpret the interactions among intestinal flora, metabolites and genes. We applied three models to distinguish between NORA and CRA using computational OR values, LASSO and random forest, respectively. ResultsOur results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 28 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. ConclusionsThese findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.
- organism: human
- assay: metagenomics
- assay: metabolomics
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- approach: machine learning (classical)
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