T Cell Repertoire Evolution after Allogeneic Bone Marrow Transplantation: An Organizational Perspective
Bibliographic record
Abstract
High-throughput sequencing (HTS) of human T cell receptors has revealed a high level of complexity in the T cell repertoire, which makes it difficult to correlate T cell reconstitution with clinical outcomes. The associations identified thus far are of a broadly statistical nature, precluding precise modeling of outcomes based on T cell repertoire development following bone marrow transplantation (BMT). Previous work has demonstrated an inherent, mathematically definable order observed in the T cells from a diverse group of donors, which is perturbed in recipients following BMT. In this study, T cell receptor (TCR)-β sequences from HLA-matched related donor and recipient pairs are analyzed to further develop this methodology. TCR-β sequencing from unsorted and sorted T cell subsets isolated from the peripheral blood samples of BMT donors and recipients show conservation and symmetry of VJ segment usage in the clonal frequencies, linked to the organization of the gene segments along the TCR locus. This TCR-β VJ segment translational symmetry is preserved post-transplantation and even in cases of acute graft-versus-host disease (aGVHD), suggesting that GVHD occurrence represents a polyclonal donor T cell response to recipient antigens. The complexity of the repertoire is significantly diminished after BMT, and the T cell clonal hierarchy is altered post-transplantation. Low-frequency donor clones tended to take on a higher rank in the recipients following BMT, especially in patients with aGVHD. Over time, the repertoire evolves to a more donor-like state in the recipients who did not develop GVHD as opposed to those who did. The results presented here support new methods of quantifying and characterizing post-transplantation T cell repertoire reconstitution.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".