Maintenance of the EBV‐specific CD8<sup>+</sup> TCRαβ repertoire in immunosuppressed lung transplant recipients
Bibliographic record
Abstract
Epstein‐Barr virus (EBV) is one of the most common viruses in humans, capable of causing life‐threatening infections and cancers in immunocompromised individuals. Although CD8+ T cells provide key protection against EBV, the persistence and dynamics of specific T‐cell receptor (TCR) clones during immunosuppression in transplant patients is largely unknown. For the first time, we used a novel single‐cell TCRαβ multiplex‐nested reverse transcriptase PCR to dissect TCRαβ clonal diversity within GLCTLVAML (GLC)‐specific CD8+ T cells in healthy individuals and immunocompromised lung transplant recipients. The GLC peptide presented by HLA‐A*02:01 is one of the most immunogenic T‐cell targets from the EBV proteome. We found that the GLC‐specific TCRαβ repertoire was heavily biased toward TRAV5 and encompassed five classes of public TCRαβs, suggesting that these clonotypes are preferentially utilized following infection. We identified that a common TRAV5 was diversely paired with different TRAJ and TRBV/TRBJ genes, in both immunocompetent and immunocompromised individuals, with an average of 12 different TCRαβ clonotypes/donor. Moreover, pre‐transplant GLC‐specific TCRαβ repertoires were relatively stable over 1 year post transplant under immunosuppression in the absence or presence of EBV reactivation. In addition, we provide the first evidence of early GLC‐specific CD8+ T cells at 87 days post transplant, which preceded clinical EBV detection at 242 days in an EBV‐seronegative patient receiving a lung allograft from an EBV‐seropositive donor. This was associated with a relatively stable TCRαβ repertoire after CD8+ T‐cell expansion. Our findings provide insights into the composition and temporal dynamics of the EBV‐specific TCRαβ repertoire in immunocompromised transplant patients and suggest that the early detection of EBV‐specific T cells might be a predictor of ensuing EBV blood viremia.
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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".