T cell repertoire analysis suggests a prominent bystander response in human cardiac allograft vasculopathy
Bibliographic record
Abstract
T cells are implicated in the pathogenesis of cardiac allograft vasculopathy (CAV), yet their clonality, specificity, and function are incompletely defined. Here we used T cell receptor β chain (TCRB) sequencing to study the T cell repertoire in the coronary artery, endomyocardium, and peripheral blood at the time of retransplant in four cases of CAV and compared it to the immunoglobulin heavy chain variable region (IGHV) repertoire from the same samples. High-dimensional flow cytometry coupled with single-cell PCR was also used to define the T cell phenotype. Extensive overlap was observed between intragraft and blood TCRBs in all cases, a finding supported by robust quantitative diversity metrics. In contrast, blood and graft IGHV repertoires from the same samples showed minimal overlap. Coronary infiltrates included CD4 + and CD8 + memory T cells expressing inflammatory (IFNγ, TNFα) and profibrotic (TGFβ) cytokines. These were distinguishable from the peripheral blood based on memory, activation, and tissue residency markers (CD45RO, CTLA-4, and CD69). Importantly, high-frequency rearrangements were traced back to endomyocardial biopsies (2–6 years prior). Comparison with four HLA-mismatched blood donors revealed a repertoire of shared TCRBs, including a subset of recently described cross-reactive sequences. These findings provide supportive evidence for an active local intragraft bystander T cell response in late-stage CAV.
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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 | 0.000 | 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".