Differential contributions for Bim and Nur77 in negative selection against ubiquitous and tissue-restricted self-antigens (BA2P.122)
Bibliographic record
Abstract
Abstract The thymic cortex and medulla are both sites of negative selection, where high affinity encounter with ubiquitous (UbA) and tissue-restricted (TRA) self-antigens, respectively, is thought to result in clonal deletion. Our group has shown that Bim is not required for UbA-mediated deletion but is required for TRA-mediated deletion. Nur77 is another protein implicated in thymocyte apoptosis, though its mechanism of action and regulation remain unclear. Using a physiological TCR transgenic model, HYcd4, we found that Nur77 was also dispensable for clonal deletion against UbA. Though Nur77 and its family member Nor-1 were induced during negative selection in HYcd4 Bim-/- mice, caspase-3 activation remained abrogated. Concurrent expression of a TCR transgene with a Nur77 transgene significantly inhibited Nur77-mediated thymocyte apoptosis, suggesting that TCR signalling can impair the pro-apoptotic function of Nur77. In contrast to a recent study using the OT-II Rip-mOva system, we found that Nur77 deficiency only modestly inhibited TRA-mediated clonal deletion in OT-I Rip-mOva chimeras. Furthermore, in polyclonal Bim-/- or Nur77-/- mice, we observed an increase in anergic phenotype CD4+ T cells, which may indicate an increase in self-specificities due to a block in clonal deletion. Collectively, these studies highlight potential differences in the molecular mechanisms of negative selection against UbA versus TRA, as well as of MHC class I versus MHC class II restricted thymocytes.
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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) | 0.002 | 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".