Evidence for CD16a-Mediated NK Cell Stimulation in Antibody-Mediated Kidney Transplant Rejection
Bibliographic record
Abstract
BACKGROUND: Natural killer (NK) cells localize in the microcirculation in antibody-mediated rejection (AMR) and have been postulated to be activated by donor-specific anti-HLA antibodies triggering their CD16a Fc receptors. However, direct evidence for NK cell CD16a triggering in AMR is lacking. We hypothesized that CD16a-inducible NK cell-selective transcripts would be expressed in human AMR biopsies and would offer evidence for CD16a triggering. METHODS: We stimulated human NK cells through CD16a in vitro, characterized CD16a-inducible transcripts, and studied their expression in human kidney transplant biopsies with AMR and in an extended human cell panel to determine their selectivity. RESULTS: In NK cells, CD16a stimulation induced increased expression of 276 transcripts (FC > 2x, false discovery rate < 0.05), including IFNG, TNF, CSF2, chemokines, such as CCL3, CCL4, and XCL1, and modulators of NK cell effector functions (TNFRSF9, CRTAM, CD160). Examination in an extended human cell panel revealed that CD160 and XCL1 were likely to be selective for NK cells in AMR. In biopsies, 8 of the top 30 CD16a-inducible transcripts were highly associated with AMR (P < 5 × 10): CCL4, CD160, CCL3, XCL1, CRTAM, FCRL3, STARD4, TNFRSF9. Other NK cell transcripts (eg, GNLY) were increased in AMR but not CD16a-inducible, their presence in AMR probably reflecting NK cell localization. CONCLUSIONS: The association of CD16a-inducible NK cell-selective transcripts CD160 and XCL1 with biopsies with AMR provides evidence for NK cell CD16a activation in AMR. This raises the possibility of other CD16a-triggered effects that are not necessarily transcriptional, including NK localization and 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 | 0.000 | 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".