Osteoclast microRNA Profiling in Rheumatoid Arthritis to Capture the Erosive Factor

Author:

Hoang Dong Nguyen1,Audrey Lortie2,Leopold Mbous Nguimbus2,Javier Marrugo2,Hugues Allard‐Chamard2,Luigi Bouchard13,Gilles Boire2,Scott Michelle S1,Sophie Roux2ORCID

Affiliation:

1. Department 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 Canada

2. Division 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 Canada

3. Department of Medical Biology CIUSS du Saguenay‐Lac‐Saint‐Jean Hôpital Universitaire de Chicoutimi Saguenay Canada

Abstract

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.

Funder

Canadian Institutes of Health Research

FRQS

Publisher

Oxford University Press (OUP)

Subject

Orthopedics and Sports Medicine,Endocrinology, Diabetes and Metabolism

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