Intracellular pH Regulates TRAIL-Induced Apoptosis and Necroptosis in Endothelial Cells
Bibliographic record
Abstract
During ischemia or inflammation of organs, intracellular pH can decrease if acid production exceeds buffering capacity. Thus, the microenvironment can expose parenchymal cells to a reduced extracellular pH which can alter pH-dependent intracellular functions. We have previously shown that while silencing caspase-8 in an in vivo ischemia reperfusion injury (IRI) model results in improved organ function and survival, removal of caspase-8 function in a donor organ can paradoxically result in enhanced receptor-interacting protein kinase 1/3- (RIPK1/3-) regulated necroptosis and accelerated graft loss following transplantation. In our current study, TRAIL- (TNF-related apoptosis-inducing ligand-) induced cell death in vitro at neutral pH and caspase-8 inhibition-enhanced RIPK1-dependent necroptotic death were confirmed. In contrast, both caspase-8 inhibition and RIPK1 inhibition attenuated cell death at a cell pH of 6.7. Cell death was attenuated with mixed lineage kinase domain-like (MLKL) silencing, indicating that MLKL membrane rupture, a distinctive feature of necroptosis, occurs regardless of pH. In summary, there is a distinct regulatory control of apoptosis and necroptosis in endothelial cells at different intracellular pH. These results highlight the complexity of modulating cell death and therapeutic strategies that may need to consider different consequences on cell death dependent on the model.
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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) | 0.001 | 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".