Cross-reactive public TCR sequences undergo positive selection in the human thymic repertoire
Bibliographic record
Abstract
We investigated human T-cell repertoire formation using high throughput TCRβ CDR3 sequencing in immunodeficient mice receiving human hematopoietic stem cells (HSCs) and human thymus grafts. Replicate humanized mice generated diverse and highly divergent repertoires. Repertoire narrowing and increased CDR3β sharing was observed during thymocyte selection. While hydrophobicity analysis implicated self-peptides in positive selection of the overall repertoire, positive selection favored shorter shared sequences that had reduced hydrophobicity at positions 6 and 7 of CDR3βs, suggesting weaker interactions with self-peptides than unshared sequences, possibly allowing escape from negative selection. Sharing was similar between autologous and allogeneic thymi and occurred between different cell subsets. Shared sequences were enriched for allo-crossreactive CDR3βs and for Type 1 diabetes-associated autoreactive CDR3βs. Single-cell TCR-sequencing showed increased sharing of CDR3αs compared to CDR3βs between mice. Our data collectively implicate preferential positive selection for shared human CDR3βs that are highly cross-reactive. While previous studies suggested a role for recombination bias in producing "public" sequences in mice, our study is the first to demonstrate a role for thymic selection. Our results implicate positive selection for promiscuous TCRβ sequences that likely evade negative selection, due to their low affinity for self-ligands, in the abundance of "public" human TCRβ sequences.
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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".