NK Cell Transcripts and NK Cells in Kidney Biopsies from Patients with Donor-Specific Antibodies: Evidence for NK Cell Involvement in Antibody-Mediated Rejection
Bibliographic record
Abstract
To explore the mechanisms of antibody-mediated rejection (ABMR) in kidney transplants, we studied the transcripts expressed in clinically indicated biopsies from patients with donor-specific antibody (DSA). Comparison of biopsies from DSA-positive versus DSA-negative patients revealed 132 differentially expressed transcripts: all were associated with class II DSA but none with class I DSA. Many transcripts were expressed in DSA-positive ABMR but were also expressed in T-cell-mediated rejection (TCMR), reflecting shared molecular features. Removal of shared transcripts created 23 DSA selective transcripts (DSASTs). Some DSASTs (6/23) showed selective high expression in NK cells, whereas others (8/23) were expressed in endothelium or in endothelium plus other cell types (7/23). Of 145 biopsies ranked by DSAST expression, the 25 with highest DSAST expression primarily consisted of ABMR (22/25, 88%), either C4d-positive or C4d-negative. By immunostaining, CD56+ and CD68+ cells in peritubular capillaries, but not CD3+ cells, were increased in ABMR compared to TCMR, compatible with a role for NK cells, as well as macrophages, as effectors in endothelial injury during ABMR. Thus, the strategy of using DSASTs in the biopsy to identify mechanism-related transcripts in biopsies from patients with clinical phenotypes indicates the selective involvement of NK cells in ABMR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".