Influence of AIRE-dependent versus -independent tissue restricted antigen expression by medullary thymic epithelial cells on T cell tolerance
Bibliographic record
Abstract
Abstract Thymocytes with high reactivity to self-antigens are, in large part, rendered tolerant through a number of mechanisms including clonal deletion and diversion toward regulatory T cell fates. Medullary thymic epithelial cells (mTECs) contribute to self-tolerance via the expression of tissue restricted antigens (TRAs). AIRE is responsible for the expression of approximately one third of TRAs, whereas AIRE-independent expression of TRAs by mTECs is less well characterized. To identify differences in AIRE-dependent and -independent tolerance, we generated new transgenic mouse models expressing a modified OVA-GFP fusion protein that contains several additional, well-characterized epitopes (2W, LCMV gp33 and LCMV gp66) in an AIRE-dependant or -independent manner. These models have enabled us to characterize distinct modes of tolerance for both MHC class I and II-restricted T cells in response to model peptides directed by AIRE-dependent and -independent TRA expression. Using organotypic culture of thymic slices overlaid with antigen-specific TCR transgenic thymocytes as well as tetramer analysis of the endogenous polyclonal T cell populations, we observed both antigen expression and peptide-specific differences in thymic selection. In addition, these difference lead to distinct levels of response to pathogen challenge and we are currently investigating the cellular interactions in the thymus associated with AIRE-dependent and -independent TRA induced tolerance by two-photon microscopy. These studies will lead to a better understanding of T cell tolerance mechanisms, and, in the longer term, inform the design of improved treatments for autoimmune diseases and cancer immunotherapies.
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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".