OCA-B promotes pathogenic maturation of stem-like CD4 <sup>+</sup> T cells and autoimmune demyelination
Bibliographic record
Abstract
Abstract Stem-like T cells selectively contribute to autoimmunity, but the activities that promote their pathogenicity are incompletely understood. Here, we identify the transcription coregulator OCA-B as a driver of the pathogenic maturation of stem-like CD4 + T cell to promote autoimmune demyelination. Using two human multiple sclerosis (MS) datasets, we show that POU2AF1 , the gene encoding OCA-B, is elevated in CD4 + T cells from MS patients. We show that T cell-intrinsic OCA-B loss protects mice from experimental autoimmune encephalomyelitis (EAE) while preserving responses to viral CNS infection. In EAE models driven by antigen reencounter, OCA-B deletion nearly eliminates CNS infiltration, proinflammatory cytokine production and clinical disease. OCA-B-expressing CD4 + T cells of mice primed with autoantigen express an encephalitogenic gene program and preferentially confer disease. In a relapsing-remitting EAE model, OCA-B loss protects mice specifically at relapse. During remission, OCA-B promotes the expression of Tcf7 , Slamf6 , and Sell in proliferating CNS T cell populations. At relapse timepoints, OCA-B loss results in both the accumulation of an immunomodulatory CD4 + T cell population expressing Ccr9 and Bach2 , and loss of pro-inflammatory gene expression from Th17 cells. These results identify OCA-B as a driver of pathogenic CD4 + T cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".