Donor human leukocyte antigen-G single nucleotide polymorphisms are associated with post-lung transplant mortality
Bibliographic record
Abstract
Human leukocyte antigen (HLA)-G is a non-classical HLA that inhibits immune responses. Its expression is modified by single nucleotide polymorphisms (SNPs), which are associated with transplant outcomes. Our aim was to investigate the association of donor and recipient HLA-G SNPs with chronic lung allograft dysfunction (CLAD) and mortality after lung transplantation. In this single-centre study, we examined 11 HLA-G SNPs in 345 consecutive recipients and 297 donors of a first bilateral lung transplant. A multivariable Cox proportional hazards model assessed associations of SNPs with death and CLAD. Transbronchial biopsies (TBBx) and bronchoalveolar lavage (BAL) samples were examined using quantitative PCR, ELISA and immunofluorescence. Over a median of 4.75 years, 142 patients (41%) developed CLAD; 170 (49%) died. Multivariable analysis revealed donor SNP +3142 (GG+CG versus CC) was associated with increased mortality (hazard ratio 1.78, 95% CI 1.12–2.84; p=0.015). In contrast, five donor SNPs, -201(CC), -716(TT), -56(CC), G*01:03(AA) and 14 bp INDEL, conferred reduced mortality risk. Specific donor–recipient SNP pairings reduced CLAD risk. Predominantly epithelial HLA-G expression was observed on TBBx without rejection. Soluble HLA-G was present in higher concentrations in the BAL samples of patients who later developed CLAD. Specific donor SNPs were associated with mortality risk after lung transplantation, while certain donor–recipient SNP pairings modulated CLAD risk. TBBx demonstrated predominantly epithelial, and therefore presumably donor-derived, HLA-G expression in keeping with these observations. This study is the first to demonstrate an effect of donor HLA-G SNPs on lung transplantation outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".