Natural Killer (NK) Cells’ Response to Epstein - Barr virus Infections and its Influence on the Risk of Developing Post-transplant Lymphoproliferative Disease (PTLD) after Allogeneic Hematopoietic Cell Transplantation: Effect of Donor Killer Ig-like Receptor Genes and Motifs Copy Numbers
Bibliographic record
Abstract
Abstract BACKGROUND Uncontrolled reactivation of Epstein-Barr virus (EBV) leading to post-transplant lymphoproliferative disorder (PTLD) is one of the major complications after T-cell depleted HCT. Recovering within weeks after HCT, natural killer (NK) cells are deemed important in the immunopathogenesis of EBV complications. Their role however remains elusive. NK cell responses are regulated by a series of activating and inhibitory cell surface receptors, central to which are the Killer Ig-like Receptors (KIR). Here we hypothesized and tested whether diverse NK cell receptor repertoires can titrate NK cell functional responses to EBV and can potentially modify the risk of developing PTLD. METHODS KIR genotypes, centromeric and telomeric motifs and their variants were determined for 356 allo-HCT donors through next generation sequencing of KIR locus. PBMNCs from KIR typed healthy volunteers were co-cultured with EBV-transformed cells and degranulation and IFNγ producing NK cells were enumerated using multi-parameter flow cytometry. Effect of donor KIR profile on PTLD was tested using competing risks regression statistics. Segregation of NK cell response to EBV across various KIR repertoires was tested by Mann-Whitney U statistics RESULTS At least one copy of Donor tA01 motifs was required for a strong protection against PTLD (p=0.0001, SHR=0.17). The number of EBV induced NK cells increased with increasing tA01 motifs. There was no influence of recipients’ KIR repertoire on the risk of developing PTLD. CONCLUSIONS KIR-regulated NK cells have a profound effect on the risk of PTLD. KIR gene profile based identification of HCT recipients at high risk of PTLD will enable closer monitoring of EBV DNAemia and facilitate prompt therapy.
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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) | 0.002 | 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".