Alterations in the Thymic Selection Threshold Skew the Self-Reactivity of the TCR Repertoire in Neonates
Bibliographic record
Abstract
Neonatal and adult T cells differ in their effector functions. Although it is known that cell-intrinsic differences in mature T cells contribute to this phenomenon, the factors involved remain unclear. Given emerging evidence that the binding strength of a TCR for self-peptide presented by MHC (self-pMHC) impacts T cell function, we sought to determine whether altered thymic selection influences the self-reactivity of the TCR repertoire during ontogeny. We found that conventional and regulatory T cell subsets in the thymus of neonates and young mice expressed higher levels of cell surface CD5, a surrogate marker for TCR avidity for self-pMHC, as compared with their adult counterparts, and this difference in self-reactivity was independent of the germline bias of the neonatal TCR repertoire. The increased binding strength of the TCR repertoire for self-pMHC in neonates was not solely due to reported defects in clonal deletion. Rather, our data suggest that thymic selection is altered in young mice such that thymocytes bearing TCRs with low affinity for self-peptide are not efficiently selected into the neonatal repertoire, and stronger TCR signals accompany both conventional and regulatory T cell selection. Importantly, the distinct levels of T cell self-reactivity reflect physiologically relevant differences based on the preferential expansion of T cells from young mice to fill a lymphopenic environment. Therefore, differences in thymic selection in young versus adult mice skew the TCR repertoire, and the relatively higher self-reactivity of the T cell pool may contribute to the distinct immune responses observed in neonates.
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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 | 0.001 |
| 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) | 0.001 | 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".