| Literature DB >> 35967356 |
Vidyanand Anaparti1, Stacy Tanner1, Christine Zhang2, Liam O'Neil1,2,3, Irene Smolik3, Xiaobo Meng1,3, Aaron J Marshall2, Hani El-Gabalawy1,2,3.
Abstract
Background: Despite immune cell dysregulation being an important event preceding the onset of rheumatoid arthritis (RA), the phenotype of T and B cells in preclinical RA is less understood. The aim of this study was to characterize T and B cell populations in RA patients and their autoantibody (aAb) negative and positive first-degree relatives (FDR).Entities:
Keywords: PD-1; TIGIT; first-degree relatives (FDRs); immunophenotyping analysis; multicolor flow cytometry (MFC); rheumatoid arthritis
Mesh:
Substances:
Year: 2022 PMID: 35967356 PMCID: PMC9366176 DOI: 10.3389/fimmu.2022.932627
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 8.786
Baseline characteristics of the study population: All values are reported as either mean (SD) or n (%).
| FDR | RA(n=13) | ||
|---|---|---|---|
| aAb-(n=25) | aAb+(n=10) | ||
|
| 43.6 (10.1) | 47.7 (11.2) | 44.1 (12.5) |
|
| 11 (44) | 4 (40) | 9 (69.23) |
|
| 2.7 (1.9) | 3.7 (3.7) | 15.5 (13.4)$ |
|
| 28.9 (6.5) | 29.3 (5.5) | 28.3 (5.4) |
|
| – | – | 3.9 (1.5) |
|
| 22 (88) | 9 (90) | 10 (77) |
|
| |||
| RF+ only (%) | 0 | 3 (30) | 1(7.7) |
| ACPA+ only (%) | 0 | 3 (30) | 1 (7.7) |
| ACPA+ and RF+ (%) | 0 | 4 (40) | 7 (53.8) |
| ACPA-/RF- (%) | 25 (100) | 0 | 2 (15.4) |
*Autoantibody status is available for two RA patients.
$ = P<0.05 compared to aAb- or aAb+ FDR; analyzed by Mann-Whitney test.
RA, Rheumatoid Arthritis; FDR, first-degree relative; aAb, autoantibody; CRP, C-reactive protein; RF, rheumatoid factor; ACPA, anti-citrullinated protein antibody; BMI, body mass index; DAS28, disease activity score 28.
Figure 1(A) Box-whiskers plot showing the frequency of CD4+ cells positive for TIGIT between aAb-FDR (n=25), aAb+FDR (n=10) and RA patients (n=13). *P<0.05, **P<0.01; Data was analyzed using Kruskal-Wallis method with Dunn’s post-hoc test. (B) Box-whiskers plot showing TIGIT: PD-1 ratio between aAb-FDR (n=25), aAb+FDR (n=10) and RA patients (n=13). *P<0.05, **P<0.01; Data was analyzed using Kruskal-Wallis method with Dunn’s post-hoc test. (C) Figure showing Spearman rank correlation plot between the frequency of TIGIT+ vs PD-1+ CD4 T cells. (D) Plot showing the frequency of naïve, memory, Tph and Tfh cells in the TIGIT+ fraction in all the subjects (n=48). Data analyzed by Wilcoxon matched - pairs signed rank test. ****P<0.0001. ns, non-significant.
Figure 2(A) Plots showing the frequency of TIGIT+ and TIGIT- CD4 T cells expressing various phenotypic markers in the entire study population (i -x; n = 48). Data was analyzed using Wilcoxon matched pairs signed rank test. ***P<0.001, ****P<0.0001 ns, non-significant. (B) Box-whiskers plot showing the frequency of TIGIT- CD4+ cells between aAb-FDR (n=25), aAb+FDR (n=10) and RA patients (n=13). *P<0.05; Data was analyzed using Kruskal-Wallis method with Dunn’s post-hoc test. ns, non-significant.
Figure 3(A) Box-whiskers plot showing the frequency of CD19+CD27- (naïve) and CD19+CD27+ (memory) B cells between aAb-FDR (n=12), aAb+FDR (n=10) and RA patients (n=5). (B) Box-whiskers plot showing the frequency of B-cell subsets between aAb-FDR, aAb+FDR and RA patients. (C) Spearman correlation plots showing the relationship between the frequency of TIGIT+ CD T cells (x-axis) and frequency of PD-1+ or PTEN+ (y-axis) in FDR (n=34). Data was analyzed by Kruskal-Wallis test with Dunn’s post-hoc analysis. *P<0.05, **P<0.01, ns, non-significant.