Ischemia-reperfusion induces death receptor-independent necroptosis via calpain-STAT3 activation in a lung transplant setting
Bibliographic record
Abstract
Ischemia-reperfusion (I/R)-induced lung injury undermines lung transplantation (LTx) outcomes by predisposing lung grafts to primary graft dysfunction (PGD). Necrosis is a feature of I/R lung injury. However, regulated necrosis (RN) with specific signaling pathways has not been explored in an LTx setting. In this study, we investigated the role of RN in I/R-induced lung injury. To study I/R-induced cell death, we simulated an LTx procedure using our cell culture model with human lung epithelial (BEAS-2B) cells. After 18 h of cold ischemic time (CIT) followed by reperfusion, caspase-independent cell death, mitochondrial reactive oxygen species production, and mitochondrial membrane permeability were significantly increased. N-acetyl-Leu-Leu-norleucinal (ALLN) (calpain inhibitor) or necrostatin-1 (Nec-1) [receptor interacting serine/threonine kinase 1 (RIPK1) inhibitor] reduced these changes. ALLN altered RIPK1/RIPK3 expression and mixed lineage kinase domain-like (MLKL) phosphorylation, whereas Nec-1 did not change calpain/calpastatin expression. Furthermore, signal transducer and activator of transcription 3 (STAT3) was demonstrated to be downstream of calpain and regulate RIPK3 expression and MLKL phosphorylation during I/R. This calpain-STAT3-RIPK axis induces endoplasmic reticulum stress and mitochondrial calcium dysregulation. LTx patients' samples demonstrate that RIPK1, MLKL, and STAT3 mRNA expression increased from CIT to reperfusion. Moreover, the expressions of the key proteins are higher in PGD samples than in non-PGD samples. Cell death associated with prolonged lung preservation is mediated by the calpain-STAT3-RIPK axis. Inhibition of RIPK and/or calpain pathways could be an effective therapy in LTx.
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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 | 0.001 |
| 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".