Donor-recipient mismatch at the <i>SIRPA</i> locus adversely affects kidney allograft outcomes
Bibliographic record
Abstract
Donor-recipient mismatches in histocompatibility antigens recognized by lymphoid cells have been demonstrated to adversely affect allograft outcomes. In contrast, it remains unclear whether mismatches sensed by innate myeloid cells have a similar effect. We investigated the consequences of mismatch in the polymorphic gene encoding signal regulatory protein α (SIRPα) on kidney allograft pathology and survival in mice and humans. We found that SIRPα variants elicit monocyte activation by binding to CD47 and that eliminating SIRPα mismatch or recipient CD47 expression prevented chronic allograft pathology in mice receiving major histocompatibility complex (MHC)–mismatched renal allografts. Human genomic analysis identified two haplotype categories, A and B, encoding SIRPα variants with distinct CD47 binding interfaces. In kidney transplant recipients ( N = 455), SIRPα mismatch was associated with increased acute rejection and graft fibrosis in the first posttransplant year, and A recipients of B kidneys had reduced long-term graft survival (hazard ratio, 3.2; 95% confidence interval, 1.5 to 6.9; P = 0.002), a finding that was confirmed in an independent validation cohort ( N = 258). Moreover, monocytes in these graft recipients had an activated phenotype. The effects of SIRPα mismatch were independent of ancestry, human leukocyte antigen mismatch, donor-specific antibodies, and delayed graft function. Therefore, these data demonstrate that a donor-recipient mismatch that causes innate immune activation is a determinant of kidney transplantation outcomes.
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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".