Mechanistic Sharing Between NK Cells in ABMR and Effector T Cells in TCMR
Bibliographic record
Abstract
Human organ allograft rejection depends on effector lymphocytes: NK cells in antibody-mediated rejection (ABMR) and effector T cells in T cell-mediated rejection (TCMR). We hypothesized that NK cell CD16a stimulation and CD8 T cell TCR/CD3 stimulation represent highly similar effector systems, and should lead to shared molecular changes between ABMR and TCMR. We studied similarity between soluble proteins and the transcripts induced in CD16a stimulated NK cells and TCR/CD3-stimulated T cells in vitro. Of 30 soluble mediators tested, CD16a-activated NK cells and CD3/TCR activated T cells produced the same limited set of five mediators-CCL3, CCL4, CSF2, IFNG, and TNF-and failed to produce 25 others. Many transcripts increased in stimulated NK cells were also increased in CD3-stimulated CD8 T cells (FDR < 0.05), including IFNG, CSF2, CCL3, CCL4, and XCL1. We hypothesized that shared transcripts not produced by other cell types should be expressed both in ABMR and TCMR kidney transplant biopsies. CD160, XCL1, TNFRSF9, and IFNG were selective for TCR/CD3-activated T cells and CD16a-NK cells and all were strongly increased in ABMR and TCMR. The molecules such as CD160 and XCL1 shared between NK cells in ABMR and effector T cells in TCMR may hold insights into important rejection mechanisms.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".