The Molecular Landscape of Antibody-Mediated Kidney Transplant Rejection: Evidence for NK Involvement Through CD16a Fc Receptors
Bibliographic record
Abstract
The recent recognition that antibody-mediated rejection (ABMR) is the major cause of kidney transplant loss creates strong interest in its pathogenesis. We used microarray analysis of kidney transplant biopsies to identify the changes in pure ABMR. We found that the ABMR transcript changes in the initial Discovery Set were strongly conserved in a subsequent Validation Set. In the Combined Set of 703 biopsies, 2603 transcripts were significantly changed (FDR < 0.05) in ABMR versus all other biopsies. In cultured cells, the transcripts strongly associated with ABMR were expressed in endothelial cells, e.g. cadherins CDH5 and CDH13; IFNG-treated endothelial cells, e.g. phospholipase PLA1A and chemokine CXCL11; or NK cells, e.g. cytotoxicity molecules granulysin (GNLY) and FGFBP2. Other ABMR transcripts were expressed in normal kidney but not cell lines, either increased e.g. Duffy chemokine receptor (DARC) or decreased e.g. sclerostin (SOST). Pathway analysis of ABMR transcripts identified angiogenesis, with roles for angiopoietin and vascular endothelial growth factors; leukocyte-endothelial interactions; and NK signaling, including evidence for CD16a Fc receptor signaling elements shared with T cells. These data support a model of ABMR involving injury-repair in the microcirculation induced by cognate recognition involving antibody and CD16a, triggering IFNG release and antibody-dependent NK cell-mediated cytotoxicity.
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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 | 0.001 | 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".