Toll-like receptor 3 is an endogenous sensor of cell death and a potential target for induction of long-term cardiac transplant survival
Bibliographic record
Abstract
Inflammation posttransplant is directly linked to cell death programs including apoptosis and necrosis. Cell death leads to the release of cellular contents which can promote inflammation. Targeting of these pathways should be an effective strategy to prevent transplant rejection. Toll-like receptor 3 (TLR3) is emerging as a major endogenous sensor of inflammation. In this study, we assessed the role of TLR3 on cell death and transplant rejection. We showed that TLR3 is highly expressed on mouse microvascular endothelial cell (ECs) and the endothelium of cardiac grafts. We demonstrated that TLR3 interacting with dsRNA or self-RNA triggered apoptosis and necroptosis in ECs. Interestingly, TLR3-induced necroptosis led mitochondrial damage. Inhibition of the mitochondrial membrane permeability molecule Cyclophilin D prevented necroptosis in ECs. In vivo, endothelium damage and activities of caspase-3 and mixed lineage kinase domain-like protein were inhibited in TLR3−/− cardiac grafts compared with C57BL/6 grafts posttransplant (n = 5, p < .001). Importantly, TLR3−/− cardiac grafts had prolonged survival in allogeneic BALB/c mice (mean survival = 121 ± 67 vs. 31 ± 6 days of C57BL/6 grafts, n = 7, p = .002). In summary, our study suggests that TLR3 is an important cell death inducer in ECs and cardiac grafts and thus a potential therapeutic target in preventing cardiac transplant rejection.
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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 | 0.001 |
| 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".