Deletion of the Natural Killer Cell Receptor NKG2C Encoding KLR2C Gene and Kidney Transplant Outcome
Bibliographic record
Abstract
Natural killer (NK) cells may contribute to antibody-mediated rejection (ABMR) of renal allografts. The role of distinct NK cell subsets in this specific context, such as NK cells expressing the activating receptor NKG2C, is unknown. Our aim was to investigate whether KLRC2 gene deletion variants which determine NKG2C expression affect the pathogenicity of donor-specific antibodies (DSA) and, if so, influence long-term graft survival. We genotyped the KLRC2wt/del variants for two distinct kidney transplant cohorts, (i) a cross-sectional cohort of 86 recipients who, on the basis of a positive post-transplant DSA result, all underwent allograft biopsies, and (ii) 1,860 recipients of a deceased donor renal allograft randomly selected from the Collaborative Transplant Study (CTS) database. In the DSA+ patient cohort, KLRC2wt/wt (80%) was associated with antibody-mediated rejection (ABMR; 65% versus 29% among KLRC2wt/del subjects; P=0.012), microvascular inflammation [MVI; median g+ptc score: 2 (interquartile range: 0-4) versus 0 (0-1), P=0.002], a molecular classifier of ABMR [0.41 (0.14-0.72) versus 0.10 (0.07-0.27), P=0.001], and elevated NK cell-related transcripts (P=0.017). In combined analyses of KLRC2 variants and a functional polymorphism in the Fc gamma receptor IIIA gene (FCGR3A-V/F158), ABMR rates and activity gradually increased with the number of risk genotypes. In DSA+ and CTS cohorts, however, the KLRC2wt/wt variant did not impact long-term death-censored graft survival, also when combined with the FCGR3A-V158 risk variant. KLRC2wt/wt may be associated with DSA-triggered MVI and ABMR-associated gene expression patterns, but the findings observed in a highly selected cohort of DSA+ patients did not translate into meaningful graft survival differences in a large multicenter kidney transplant cohort not selected for HLA sensitization.
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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 | 0.001 | 0.001 |
| 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".