Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels
Abstract 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...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | Arthritis Research & Therapy |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13075-023-03049-z |
| _version_ | 1797831996010921984 |
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| author | Jianghua Chen Shilin Li Jing Zhu Wei Su Congcong Jian Jie Zhang Jianhong Wu Tingting Wang Weihua Zhang Fanwei Zeng Shengjia Chang Lihua Jia Jiang Su Yi Zhao Jing Wang Fanxin Zeng |
| author_facet | Jianghua Chen Shilin Li Jing Zhu Wei Su Congcong Jian Jie Zhang Jianhong Wu Tingting Wang Weihua Zhang Fanwei Zeng Shengjia Chang Lihua Jia Jiang Su Yi Zhao Jing Wang Fanxin Zeng |
| author_sort | Jianghua Chen |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T14:01:45Z |
| format | Article |
| id | doaj.art-fd33c516934c43958fbbac3a471cfe48 |
| institution | Directory Open Access Journal |
| issn | 1478-6362 |
| language | English |
| last_indexed | 2024-04-09T14:01:45Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Arthritis Research & Therapy |
| spelling | doaj.art-fd33c516934c43958fbbac3a471cfe482023-05-07T11:19:36ZengBMCArthritis Research & Therapy1478-63622023-05-0125111610.1186/s13075-023-03049-zMulti-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levelsJianghua Chen0Shilin Li1Jing Zhu2Wei Su3Congcong Jian4Jie Zhang5Jianhong Wu6Tingting Wang7Weihua Zhang8Fanwei Zeng9Shengjia Chang10Lihua Jia11Jiang Su12Yi Zhao13Jing Wang14Fanxin Zeng15Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical CollegeDepartment of Clinical Research Center, Dazhou Central HospitalDepartment of Rheumatology and Immunology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Rheumatology and Immunology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaSchool of Basic Medical Sciences, Chengdu University of Traditional Chinese MedicineDepartment of Clinical Research Center, Dazhou Central HospitalDepartment of Rheumatology and Immunology, Dazhou Central HospitalDepartment of Rheumatology and Immunology, Dazhou Central HospitalDepartment of Rheumatology and Immunology, Dazhou Central HospitalSichuan Province Orthopaedic HospitalShantou University Medical College, Shantou UniversityInstitute of Basic Medicine and Forensic Medicine, North Sichuan Medical CollegeDepartment of Rheumatology and Immunology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Rheumatology and Immunology, West China Hospital, Sichuan UniversityThe National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityInstitute of Basic Medicine and Forensic Medicine, North Sichuan Medical CollegeAbstract 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.https://doi.org/10.1186/s13075-023-03049-zRheumatoid arthritisDAS28-ESRMulti-omicsLipid metabolismWhole exome sequencingRandom forest |
| spellingShingle | Jianghua Chen Shilin Li Jing Zhu Wei Su Congcong Jian Jie Zhang Jianhong Wu Tingting Wang Weihua Zhang Fanwei Zeng Shengjia Chang Lihua Jia Jiang Su Yi Zhao Jing Wang Fanxin Zeng Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels Arthritis Research & Therapy Rheumatoid arthritis DAS28-ESR Multi-omics Lipid metabolism Whole exome sequencing Random forest |
| title | Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels |
| title_full | Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels |
| title_fullStr | Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels |
| title_full_unstemmed | Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels |
| title_short | Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels |
| title_sort | multi omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels |
| topic | Rheumatoid arthritis DAS28-ESR Multi-omics Lipid metabolism Whole exome sequencing Random forest |
| url | https://doi.org/10.1186/s13075-023-03049-z |
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