HLA-DRB1 Shared Epitope Alleles and Disease Activity Are Correlated with Reduced T Cell Receptor Repertoire Diversity in CD4+ T Cells in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Shared epitope (SE) alleles are the most significant genetic susceptibility locus in rheumatoid arthritis (RA); however, their target populations in CD4+ T cells are not well elucidated. We analyzed the association between SE alleles and the T cell receptor (TCR) repertoire diversity of naive and memory CD4+ T cells using next-generation sequencing (NGS). METHODS: The TCR beta chains in naive and memory CD4+ T cells from the peripheral blood of 22 patients with RA and 18 age- and sex-matched healthy donors (HD) were analyzed by NGS. The Renyi entropy was used to evaluate TCR repertoire diversity and its correlations with SE alleles and other variables were examined. Serum cytokine levels were measured by multiplex ELISA. RESULTS: The TCR repertoire diversity in memory CD4+ T cells was reduced in SE allele-positive patients with RA compared with HD, and showed a significant negative correlation with the SE allele dosage in RA. The TCR repertoire diversity of naive and memory T cells was also negatively correlated with disease activity, and the SE allele dosage and disease activity were independently associated with reduced TCR repertoire diversity. TCR repertoire diversity showed a significant positive correlation with the serum interleukin 2 levels. CONCLUSION: SE alleles and disease activity were negatively correlated with the TCR repertoire diversity of CD4+ T cells in RA. Considering the pivotal role of CD4+ T cells in RA, restoring the altered TCR repertoire diversity will provide a potential RA therapeutic target.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".