机构：[1]Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.[2]Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China.[3]Department of Rheumatology and Immunology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.四川省人民医院[4]Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.四川大学华西医院[5]School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.[6]Department of Rheumatology and Immunology, Dazhou Central Hospital, Dazhou, Sichuan, China.[7]Sichuan Province Orthopaedic Hospital, Chengdu, Sichuan, China.[8]Shantou University Medical College, Shantou University, Guangdong, China.[9]Clinical Institute of Inflammation and Immunology (CIII), Frontiers Science Center for Disease‑Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.四川大学华西医院[10]The National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.[11]Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100871, China.
BackgroundRheumatoid 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.MethodsFecal 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.ResultsOur 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.ConclusionOverall, 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.
基金：
This study received funding in part from the Scientific Research Fund of
Sichuan Health and Health Committee (21PJ085, 20PJ311), The Key Projects
fund of Science & Technology Department of Sichuan Province (2021YFS0165,
2022YFS0250, 22MZGC0090), and Innovative Scientific Research Project of
Medical in Sichuan Province (S20001).
第一作者机构：[1]Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.[2]Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China.
共同第一作者：
通讯作者：
通讯机构：[1]Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.[2]Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China.[4]Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.[9]Clinical Institute of Inflammation and Immunology (CIII), Frontiers Science Center for Disease‑Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.[11]Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100871, China.
推荐引用方式(GB/T 7714)：
Chen Jianghua,Li Shilin,Zhu Jing,et al.Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels[J].ARTHRITIS RESEARCH & THERAPY.2023,25(1):doi:10.1186/s13075-023-03049-z.
APA：
Chen, Jianghua,Li, Shilin,Zhu, Jing,Su, Wei,Jian, Congcong...&Zeng, Fanxin.(2023).Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels.ARTHRITIS RESEARCH & THERAPY,25,(1)
MLA：
Chen, Jianghua,et al."Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels".ARTHRITIS RESEARCH & THERAPY 25..1(2023)
医学