N-glycosylation of mouse TRAIL-R restrains TRAIL-induced apoptosis
Bibliographic record
Abstract
The sensitivity of cells to death receptor-induced apoptosis is commonly controlled by multiple checkpoints in order to limit induction of excessive or unnecessary death. Although cytotoxic in various cancer cells, tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) does not trigger apoptosis in most non-transformed cells. The molecular nature of the checkpoints that normally protect the cells from TRAIL-induced death are not fully understood. Endoplasmic reticulum (ER) stress has been reported to switch the sensitivity of human cells to the cytotoxic effect of TRAIL, suggesting that this cellular state perturbs some of these protective mechanisms. We found that tunicamycin (TU), but no other ER stress inducers, sensitized mouse fibroblasts and hippocampal neuronal cells to TRAIL-induced apoptosis. Importantly, the sensitization was specific to TRAIL and not caused by differences in ER stress induction. Instead, it relied on the inhibition of N-glycosylation of the mouse TRAIL receptor (mTRAIL-R). Inhibition of N-glycosylation did not alter cell surface expression of mTRAIL-R but enhanced its ability to bind TRAIL, and facilitated mTRAIL-R oligomerization, which resulted in enhanced death-inducing signaling complex (DISC) formation and caspase-8 activation. Remarkably, reconstitution of mTRAIL-R-deficient cells with a version of mTRAIL-R mutated for the three N-glycosylation sites identified in its ectodomain confirmed higher sensitivity to TRAIL-induced apoptosis. Together, our results demonstrate that inhibition of N-glycosylation of mTRAIL-R, and not ER stress induction, sensitizes mouse cells to TRAIL-induced apoptosis. We therefore reveal a new mechanism restraining TRAIL cytotoxicity in mouse cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".