Regulation of TNF-Related Apoptosis-Inducing Ligand Signaling by Glycosylation
Bibliographic record
Abstract
Tumor necrosis-factor related apoptosis-inducing ligand, also known as TRAIL or APO2L (Apo-2 ligand), is a cytokine of the TNF superfamily acknowledged for its ability to trigger selective apoptosis in tumor cells while being relatively safe towards normal cells. Its binding to its cognate agonist receptors, namely death receptor 4 (DR4) and/or DR5, can induce the formation of a membrane-bound macromolecular complex, coined DISC (death-signaling inducing complex), necessary and sufficient to engage the apoptotic machinery. At the very proximal level, TRAIL DISC formation and activation of apoptosis is regulated both by antagonist receptors and by glycosylation. Remarkably, though, despite the fact that all membrane-bound TRAIL receptors harbor putative glycosylation sites, only pro-apoptotic signaling through DR4 and DR5 has, so far, been found to be regulated by N- and O-glycosylation, respectively. Because putative N-glycosylation sequons and O-glycosylation sites are also found and conserved in all these receptors throughout all animal species (in which these receptors have been identified), glycosylation is likely to play a more prominent role than anticipated in regulating receptor/receptor interactions or trafficking, ultimately defining cell fate through TRAIL stimulation. This review aims to present and discuss these emerging concepts, the comprehension of which is likely to lead to innovative anticancer therapies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".