Differential expression of tissue-restricted antigens among mTEC is associated with distinct autoreactive T cell fates
Bibliographic record
Abstract
Abstract Medullary thymic epithelial cells (mTEC) contribute to the development of T cell tolerance by expressing and presenting tissue-restricted antigens (TRA), so that developing T cells can assess the self-reactivity of their antigen receptors prior to leaving the thymus. mTEC are a heterogeneous population of cells that differentially express TRA. Whether mTEC subsets induce distinct autoreactive T cell fates remains unclear. Here, we establish bacterial artificial chromosome (BAC)-transgenic mouse lines with biased mTEClo or mTEChi expression of model antigens. The transgenic lines support negative selection of antigen-specific thymocytes depending on antigen dose. However, model antigen expression predominantly by mTEClo supports TCRαβ+ CD8αα intraepithelial lymphocyte development; meanwhile, mTEChi-restricted expression preferentially induces Treg differentiation of antigen-specific cells in these models to impact control of infectious agents and tumor growth. In summary, our data suggest that mTEC subsets may have a function in directing distinct mechanisms of T cell tolerance.
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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) | 0.001 | 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".