| Autor: |
Inamo J, Keegan J, Griffith A, Ghosh T, Horisberger A, Howard K, Pulford J, Murzin E, Hancock B, Jonsson AH, Seifert J, Feser ML, Norris JM, Cao Y, Apruzzese W, Louis Bridges S, Bykerk V, Goodman S, Donlin L, Firestein GS, Perlman H, Bathon JM, Hughes LB, Tabechian D, Filer A, Pitzalis C, Anolik JH, Moreland L, Guthridge JM, James JA, Brenner MB, Raychaudhuri S, Sparks JA, Michael Holers V, Deane KD, Lederer JA, Rao DA, Zhang F |
| Jazyk: |
angličtina |
| Zdroj: |
BioRxiv : the preprint server for biology [bioRxiv] 2023 Jul 04. Date of Electronic Publication: 2023 Jul 04. |
| DOI: |
10.1101/2023.07.03.547507 |
| Abstrakt: |
Rheumatoid arthritis (RA) is a systemic autoimmune disease with currently no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in 'At-Risk' populations prior to clinical disease onset is crucial to establishing effective prevention strategies. Here, we applied mass cytometry to deeply characterize the immunophenotypes in blood from At-Risk individuals identified through the presence of serum antibodies to citrullinated protein antigens (ACPA) and/or first-degree relative (FDR) status (n=52), as compared to established RA (n=67), and healthy controls (n=48). We identified significant cell expansions in At-Risk individuals compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5 low naïve B cell population was expanded in ACPA-positive FDRs. Further, we developed an "RA immunophenotype score" classification method based on the degree of enrichment of cell states relevant to established RA patients. This score significantly distinguished At-Risk individuals from controls. In all, we systematically identified activated lymphocyte phenotypes in At-Risk individuals, along with immunophenotypic differences among both ACPA+ and ACPA-FDR At-Risk subpopulations. Our classification model provides a promising approach for understanding RA pathogenesis with the goal to further improve prevention strategies and identify novel therapeutic targets. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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