Molecular Landscape of T Cell–Mediated Rejection in Human Kidney Transplants: Prominence of CTLA4 and PD Ligands
Bibliographic record
Abstract
We used expression microarrays to characterize the changes most specific for pure T cell-mediated rejection (TCMR) compared to other diseases including antibody-mediated rejection in 703 human kidney transplant biopsies, using a Discovery Set-Validation Set approach. The expression of thousands of transcripts--fold change and association strength--changed in a pattern that was highly conserved between the Discovery and Validation sets, reflecting a hierarchy of T cell signaling, costimulation, antigen-presenting cell (APC) activation and interferon-gamma (IFNG) expression and effects, with weaker associations for inflammasome activation, innate immunity, cytotoxic molecules and parenchymal injury. In cell lines, the transcripts most specific for TCMR were expressed most strongly in effector T cells (e.g. CTLA4, CD28, IFNG), macrophages (e.g. PDL1, CD86, SLAMF8, ADAMDEC1), B cells (e.g. CD72, BTLA) and IFNG-treated macrophages (e.g. ANKRD22, AIM2). In pathway analysis, the top pathways included T cell receptor signaling and CTLA4 costimulation. These results suggest a model in which TCMR creates an inflammatory compartment with a rigorous hierarchy dominated by the proximal aspects of cognate engagement of effector T cell receptor and costimulator triggering by APCs. The prominence of inhibitors like CTLA4 and PDL1 raises the possibility of active negative controls within the rejecting tissue.
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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 | 0.001 | 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) | 0.001 | 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".