MHC Polymorphism Can Enrich the T Cell Repertoire of the Species by Shifts in Intrathymic Selection
Bibliographic record
Abstract
The murine class I molecule H-2Kb and its natural gene conversion variant, H-2Kbm8, which differs from H-2Kb solely at 4 aa at the bottom of the peptide-binding B pocket, are expressed in coisogenic mouse strains C57BL/6 (B6) and B6.C-H-2bm8 (bm8). These two strains provide an excellent opportunity to study the effects of Mhc class I polymorphism on the T cell repertoire. We recently discovered a gain in the antiviral CTL repertoire in bm8 mice as a consequence of the emergence of the Mhc class I allele H-2Kbm8. In this report we sought to determine the mechanism behind the generation of this increased CTL diversity. Our results demonstrate that repertoire diversification occurred by a gain in intrathymic positive selection. As previously shown, the emergence of the same Mhc allele also caused a loss in positive selection of T cell repertoire specific for another Ag, OVA-8. This indicates that a reciprocal loss-and-gain pattern of intrathymic selection exists between H-2Kb and H-2Kbm8. Therefore, in the thymus of an individual, a new Mhc allele can select new T cell specificities, while abandoning some T cell specificities selected by the wild-type allele. A byproduct of this repertoire shift is a net gain of T cell repertoire of the species, which is likely to improve its survival fitness.
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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".