Targeting transcriptional coregulator OCA-B/Pou2af1 blocks activated autoreactive T cells in the pancreas and type-1 diabetes
Bibliographic record
Abstract
Abstract The transcriptional coregulator OCA-B promotes expression of T cell target genes in cases of repeated antigen exposure, a necessary feature of autoimmunity. We hypothesized that T cell-specific OCA-B deletion and pharmacologic OCA-B inhibition would protect mice from autoimmune diabetes. We developed an Ocab conditional allele and backcrossed it onto a diabetes-prone NOD/ShiLtJ strain background. T cell-specific OCA-B loss protected mice from spontaneous disease. Protection was associated with large reductions in islet CD8 + T cell receptor specificities associated with diabetes pathogenesis. CD4 + clones associated with diabetes were present, but associated with anergic phenotypes. The protective effect of OCA-B loss was recapitulated using autoantigen-specific NY8.3 mice, but diminished in monoclonal models specific to artificial or neoantigens. Rationally-designed membrane-penetrating OCA-B peptide inhibitors normalized glucose levels, and reduced T cell infiltration and proinflammatory cytokine expression in newly-diabetic NOD mice. Together, the results indicate that OCA-B is a potent autoimmune regulator and a promising target for pharmacologic inhibition. ~40-word summary statement for the online JEM table of contents and alerts Kim and colleagues show that OCA-B in T cells is essential for the generation of type-1 diabetes. OCA-B loss leaves the pancreatic lymph nodes largely undisturbed, but associates autoreactive CD4 + T cells in the pancreas with anergy while deleting potentially autoreactive CD8 + T cells. Summary Kim et al. show that loss or inhibition of OCA-B in T cells protects mice from type-1 diabetes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".