TRAIL regulatory receptors constrain human hepatic stellate cell apoptosis
Bibliographic record
Abstract
The TRAIL pathway can mediate apoptosis of hepatic stellate cells to promote the resolution of liver fibrosis. However, TRAIL has the capacity to bind to regulatory receptors in addition to death-inducing receptors; their differential roles in liver fibrosis have not been investigated. Here we have dissected the contribution of regulatory TRAIL receptors to apoptosis resistance in primary human hepatic stellate cells (hHSC). hHSC isolated from healthy margins of liver resections from different donors expressed variable levels of TRAIL-R2/3/4 (but negligible TRAIL-R1) ex vivo and after activation. The apoptotic potential of TRAIL-R2 on hHSC was confirmed by lentiviral-mediated knockdown. A functional inhibitory role for TRAIL-R3/4 was revealed by shRNA knockdown and mAb blockade, showing that these regulatory receptors limit apoptosis of hHSC in response to both oligomerised TRAIL and NK cells. A close inverse ex vivo correlation between hHSC TRAIL-R4 expression and susceptibility to apoptosis underscored its central regulatory role. Our data provide the first demonstration of non-redundant functional roles for the regulatory TRAIL receptors (TRAIL-R3/4) in a physiological setting. The potential for these inhibitory TRAIL receptors to protect hHSC from apoptosis opens new avenues for prognostic and therapeutic approaches to the management of liver fibrosis.
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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) | 0.003 | 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".