Necroptosis Is Involved in CD4+ T Cell-Mediated Microvascular Endothelial Cell Death and Chronic Cardiac Allograft Rejection
Bibliographic record
Abstract
BACKGROUND: Despite advances in immunosuppressive therapies, the rate of chronic transplant loss remains substantial. Organ injury involves various forms of cell death including apoptosis and necrosis. We now recognize that early injury of cardiac transplants involves a newly described form of programmed necrotic cell death, termed necroptosis. Because this involves receptor-interacting protein (RIP) kinase 1/3, this study aimed to establish the role of RIP3 in chronic cardiac allograft rejection. METHODS: We used major histocompatibility complex class II mismatched C57BL/6N (H-2; B6) or B6.RIP3 (H-2; RIP3) mice to B6.C-H-2 (H2-Ab1; bm12) mouse cardiac transplantation. Microvascular endothelial cells (MVEC) were developed from B6 and RIP3 cardiac grafts. RESULT: CD4 T cell-mediated cardiac graft rejection is inhibited using RIP3 deficient donor grafts, with reduced cellular infiltration and vasculopathy compared with wild type cardiac grafts. Alloreactive CD4 T cell-mediated MVEC death involves TNFα, Fas ligand (FasL) and granzyme B. Although necroptosis and release of danger molecule high-mobility group box 1 are eliminated by the absence of RIP3, CD4 T cells had attenuated MVEC death through granzyme B and FasL. CONCLUSIONS: CD4 T cell-mediated MVEC death involves in TNFα, FasL and granzyme B. Necroptotic cell death and release of the danger molecule may promote inflammatory responses and transplant rejection. Although loss of RIP3 does not eliminate alloimmune responses, chronic graft injury is reduced. RIP3 is an important therapeutic target but additional granzyme and caspases inhibition is required for sufficiently improving long-term graft survival.
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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) | 0.002 | 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".