Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive Factor
In rheumatoid arthritis (RA), only a subset of patients develop irreversible bone destruction. Our aim was to identify a microRNA (miR)‐based osteoclast‐related signature predictive of erosiveness in RA. Seventy‐six adults with erosive (E) or nonerosive (NE) seropositive RA and 43 sex‐ and age‐match...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | JBMR Plus |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/jbm4.10776 |
| _version_ | 1797739495956676608 |
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| author | Nguyen Hoang Dong Lortie Audrey Mbous Nguimbus Leopold Marrugo Javier Allard‐Chamard Hugues Bouchard Luigi Boire Gilles Michelle S Scott Roux Sophie |
| author_facet | Nguyen Hoang Dong Lortie Audrey Mbous Nguimbus Leopold Marrugo Javier Allard‐Chamard Hugues Bouchard Luigi Boire Gilles Michelle S Scott Roux Sophie |
| author_sort | Nguyen Hoang Dong |
| collection | DOAJ |
| description | In rheumatoid arthritis (RA), only a subset of patients develop irreversible bone destruction. Our aim was to identify a microRNA (miR)‐based osteoclast‐related signature predictive of erosiveness in RA. Seventy‐six adults with erosive (E) or nonerosive (NE) seropositive RA and 43 sex‐ and age‐matched healthy controls were recruited. Twenty‐five miRs from peripheral blood mononuclear cell (PBMC)‐derived osteoclasts selected from RNA‐Seq (discovery cohort) were assessed by qPCR (replication cohort), as were 33 target genes (direct targets or associated with regulated pathways). The top five miRs found differentially expressed in RA osteoclasts were either decreased (hsa‐miR‐34a‐3p, 365b‐3p, 374a‐3p, and 511‐3p [E versus NE]) or increased (hsa‐miR‐193b‐3p [E versus controls]). In vitro, inhibition of miR‐34a‐3p had an impact on osteoclast bone resorption. An integrative network analysis of miRs and their targets highlighted correlations between mRNA and miR expression, both negative (CD38, CD80, SIRT1) and positive (MITF), and differential gene expression between NE versus E (GXYLT1, MITF) or versus controls (CD38, KLF4). Machine‐learning models were used to evaluate the value of miRs and target genes, in combination with clinical data, to predict erosion. One model, including a set of miRs (predominantly 365b‐3p) combined with rheumatoid factor titer, provided 70% accuracy (area under the curve [AUC] 0.66). Adding genes directly targeted or belonging to related pathways improved the predictive power of the model for the erosive phenotype (78% accuracy, AUC 0.85). This proof‐of‐concept study indicates that identification of RA subjects at risk of erosions may be improved by studying miR expression in PBMC‐derived osteoclasts, suggesting novel approaches toward personalized treatment. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research. |
| first_indexed | 2024-03-12T13:57:57Z |
| format | Article |
| id | doaj.art-96fe34f780f742cca90f8dd0ca620288 |
| institution | Directory Open Access Journal |
| issn | 2473-4039 |
| language | English |
| last_indexed | 2024-03-12T13:57:57Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | JBMR Plus |
| spelling | doaj.art-96fe34f780f742cca90f8dd0ca6202882023-08-22T11:14:27ZengWileyJBMR Plus2473-40392023-08-0178n/an/a10.1002/jbm4.10776Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive FactorNguyen Hoang Dong0Lortie Audrey1Mbous Nguimbus Leopold2Marrugo Javier3Allard‐Chamard Hugues4Bouchard Luigi5Boire Gilles6Michelle S Scott7Roux Sophie8Department of Biochemistry and Functional Genomics University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaDivision of Rheumatology, Department of Medicine, Faculty of Medicine and Health Sciences University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaDivision of Rheumatology, Department of Medicine, Faculty of Medicine and Health Sciences University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaDivision of Rheumatology, Department of Medicine, Faculty of Medicine and Health Sciences University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaDivision of Rheumatology, Department of Medicine, Faculty of Medicine and Health Sciences University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaDepartment of Biochemistry and Functional Genomics University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaDivision of Rheumatology, Department of Medicine, Faculty of Medicine and Health Sciences University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaDepartment of Biochemistry and Functional Genomics University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaDivision of Rheumatology, Department of Medicine, Faculty of Medicine and Health Sciences University of Sherbrooke and Research Centre of the Centre Intégré Universitaire de Santé et Services Sociaux de l'Estrie – Centre Hospitalier Universitaire de Sherbrooke (CIUSSSE‐CHUS) Sherbrooke CanadaIn rheumatoid arthritis (RA), only a subset of patients develop irreversible bone destruction. Our aim was to identify a microRNA (miR)‐based osteoclast‐related signature predictive of erosiveness in RA. Seventy‐six adults with erosive (E) or nonerosive (NE) seropositive RA and 43 sex‐ and age‐matched healthy controls were recruited. Twenty‐five miRs from peripheral blood mononuclear cell (PBMC)‐derived osteoclasts selected from RNA‐Seq (discovery cohort) were assessed by qPCR (replication cohort), as were 33 target genes (direct targets or associated with regulated pathways). The top five miRs found differentially expressed in RA osteoclasts were either decreased (hsa‐miR‐34a‐3p, 365b‐3p, 374a‐3p, and 511‐3p [E versus NE]) or increased (hsa‐miR‐193b‐3p [E versus controls]). In vitro, inhibition of miR‐34a‐3p had an impact on osteoclast bone resorption. An integrative network analysis of miRs and their targets highlighted correlations between mRNA and miR expression, both negative (CD38, CD80, SIRT1) and positive (MITF), and differential gene expression between NE versus E (GXYLT1, MITF) or versus controls (CD38, KLF4). Machine‐learning models were used to evaluate the value of miRs and target genes, in combination with clinical data, to predict erosion. One model, including a set of miRs (predominantly 365b‐3p) combined with rheumatoid factor titer, provided 70% accuracy (area under the curve [AUC] 0.66). Adding genes directly targeted or belonging to related pathways improved the predictive power of the model for the erosive phenotype (78% accuracy, AUC 0.85). This proof‐of‐concept study indicates that identification of RA subjects at risk of erosions may be improved by studying miR expression in PBMC‐derived osteoclasts, suggesting novel approaches toward personalized treatment. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.https://doi.org/10.1002/jbm4.10776MACHINE LEARNINGMICRORNASOSTEOCLASTPREDICTIVE MODELRHEUMATOID ARTHRITIS |
| spellingShingle | Nguyen Hoang Dong Lortie Audrey Mbous Nguimbus Leopold Marrugo Javier Allard‐Chamard Hugues Bouchard Luigi Boire Gilles Michelle S Scott Roux Sophie Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive Factor JBMR Plus MACHINE LEARNING MICRORNAS OSTEOCLAST PREDICTIVE MODEL RHEUMATOID ARTHRITIS |
| title | Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive Factor |
| title_full | Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive Factor |
| title_fullStr | Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive Factor |
| title_full_unstemmed | Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive Factor |
| title_short | Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive Factor |
| title_sort | osteoclast microrna profiling in rheumatoid arthritis to capture the erosive factor |
| topic | MACHINE LEARNING MICRORNAS OSTEOCLAST PREDICTIVE MODEL RHEUMATOID ARTHRITIS |
| url | https://doi.org/10.1002/jbm4.10776 |
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