Comprehensive multi-omics analysis reveals the core role of glycerophospholipid metabolism in rheumatoid arthritis development.

OBJECTIVES: Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis and eventually lead to joint deformity. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. METHODS: A total of 113 RA patients and 75 controls were included in our study. Plasma and stool samples were obtained for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing and metabolomics analysis. And PBMCs were obtained for RNA sequencing. We used three models, logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest, respectively, to distinguish NORA from CRA, and finally we validated model performance using an external cohort of 26 subjects. RESULTS: Our 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 29 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. Additionally, we applied three approaches to develop disease classifier models that were based on plasma metabolites and gut microbiota, which effectively distinguished between new-onset and chronic RA patients in both discovery cohort and external validation cohort. CONCLUSIONS: These 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.

Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic, systemic autoimmune inflammatory disease, the pathogenesis of which is not clear. Clinical remission, or decreased disease activity, is the aim of treatment for RA. However, our understanding of disease activity is inadequate, and clinical remission rates for RA are generally poor. In this study, we used multi-omics profiling to study potential alterations in rheumatoid arthritis with different disease activity levels. METHODS: Fecal and plasma samples from 131 rheumatoid arthritis (RA) patients and 50 healthy subjects were collected for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing, and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The PBMCS were also collected for RNA sequencing and whole exome sequencing (WES). The disease groups, based on 28 joints and ESR (DAS28), were divided into DAS28L, DAS28M, and DAS28H groups. Three random forest models were constructed and verified with an external validation cohort of 93 subjects. RESULTS: Our findings revealed significant alterations in plasma metabolites and gut microbiota in RA patients with different disease activities. Moreover, plasma metabolites, especially lipid metabolites, demonstrated a significant correlation with the DAS28 score and also associations with gut bacteria and fungi. KEGG pathway enrichment analysis of plasma metabolites and RNA sequencing data demonstrated alterations in the lipid metabolic pathway in RA progression. Whole exome sequencing (WES) results have shown that non-synonymous single nucleotide variants (nsSNV) of the HLA-DRB1 and HLA-DRB5 gene locus were associated with the disease activity of RA. Furthermore, we developed a disease classifier based on plasma metabolites and gut microbiota that effectively discriminated RA patients with different disease activity in both the discovery cohort and the external validation cohort. CONCLUSION: Overall, our multi-omics analysis confirmed that RA patients with different disease activity were altered in plasma metabolites, gut microbiota composition, transcript levels, and DNA. Our study identified the relationship between gut microbiota and plasma metabolites and RA disease activity, which may provide a novel therapeutic direction for improving the clinical remission rate of RA.

The change of plasma metabolic profile and gut microbiome dysbiosis in patients with rheumatoid arthritis.

Objective: Rheumatoid arthritis (RA) is a chronic inflammatory joint disease, which is associated with progressive disability, systemic complications, and early death. But its etiology and pathogenesis are not fully understood. We aimed to investigate the alterations in plasma metabolite profiles, gut bacteria, and fungi and their role of them in the pathogenesis of RA. Methods: Metabolomics profiling of plasma from 363 participants including RA (n = 244), systemic lupus erythematosus (SLE, n = 50), and healthy control (HC, n = 69) were performed using the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The differentially expressed metabolites were selected among groups and used to explore important metabolic pathways. Gut microbial diversity analysis was performed by 16S rRNA sequencing and ITS sequencing (RA = 195, HC = 269), and the specific microbial floras were identified afterward. The diagnosis models were established based on significant differential metabolites and microbial floras, respectively. Results: There were 63 differential metabolites discovered between RA and HC groups, mainly significantly enriched in the arginine and proline metabolism, glycine, serine, and threonine metabolism, and glycerophospholipid metabolism between RA and HC groups. The core differential metabolites included L-arginine, creatine, D-proline, ornithine, choline, betaine, L-threonine, LysoPC (18:0), phosphorylcholine, and glycerophosphocholine. The L-arginine and phosphorylcholine were increased in the RA group. The AUC of the predictive model was 0.992, based on the combination of the 10 differential metabolites. Compared with the SLE group, 23 metabolites increased and 61 metabolites decreased in the RA group. However, no significant metabolic pathways were enriched between RA and SLE groups. On the genus level, a total of 117 differential bacteria genera and 531 differential fungal genera were identified between RA and HC groups. The results indicated that three bacteria genera (Eubacterium_hallii_group, Escherichia-Shigella, Streptococcus) and two fungal genera (Candida and Debaryomyces) significantly increased in RA patients. The AUC was 0.80 based on a combination of six differential bacterial genera and the AUC was 0.812 based on a combination of seven differential fungal genera. Functional predictive analysis displayed that differential bacterial and differential fungus both were associated with KEGG pathways involving superpathway of L-serine and glycine biosynthesis I, arginine, ornithine, and proline interconversion. Conclusion: The plasma metabolism profile and gut microbe profile changed markedly in RA. The glycine, serine, and threonine metabolism and arginine and proline metabolism played an important role in RA.

Glycerophospholipid metabolism is involved in rheumatoid arthritis pathogenesis by regulating the IL-6/JAK signaling pathway.

To explore the metabolic mechanism of differential plasma interleukin (IL)-6 expression in patients with rheumatoid arthritis (RA). A total of 240 RA patients were enrolled in the non-target metabolomics study cohort and 69 healthy volunteers were included as healthy controls (HCs). Plasma IL-6 levels were detected by electrochemiluminescence assay. Plasma metabolites were detected by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Patients with active RA (n = 20) and remissive RA (n = 20) and 20 HCs were enrolled in the targeted validation cohort. Metabolites identified by non-target metabolomics were quantitatively analyzed by ultra-performance liquid chromatography-triple quadrupole tandem mass spectrometry. Effects of 1-oleoyl-sn-glycero-3-phosphocholine (OGPC) associated with IL-6 on MH7A cells were assessed. After 24-h or 48-h induction by TNF-α, the supernatants were collected for IL-6 quantification by enzyme-linked immunosorbent assay. Furthermore, Western blot was performed to investigate the relative JAK2 and p-JAK2 expressions. With an increasing IL-6 level, OGPC shown to be related to the glycerophospholipid metabolism pathway by Kyoto Encyclopedia of Genes and Genomes analysis displayed a significant decrease. In the validating RA cohort, the OGPC concentrations in remissive RA group and active RA group decreased compared with HC group. OGPC down-regulated IL-6 secretion and p-JAK2 expression in TNF-α-induced MH7A cells in vitro. In conclusion, glycerophospholipid metabolism is the main metabolic pathway associated with the differential IL-6 expression in RA patients. The down-regulated OGPC is a promoting factor for the increased IL-6 plasma level in RA patients, which further affects the downstream JAK signaling pathway.