Novel Function of PERK as a Mediator of Force-induced Apoptosis
Bibliographic record
Abstract
Induction of apoptosis by tensile forces is an important determinant of connective tissue destruction in osteoarthritis and periodontal diseases. We examined the role of molecular components of the unfolded protein response in force-induced apoptosis. Magnetic fields were used to apply tensile force through integrins to cultured fibroblasts bound with collagen-coated magnetite beads. Tensile force induced caspase 3 cleavage, DNA fragmentation, depolarization of mitochondria, and induction of CHOP10, all indicative of activation of apoptosis. Immunoblotting, immunocytochemistry, and release of Ca(2+) from the endoplasmic reticulum showed evidence for both physical and functional associations between bound beads and the endoplasmic reticulum. Force-induced apoptosis was not detected in PERK null cells, but reconstitution of wild-type PERK in PERK null cells restored the apoptotic response. Force-induced apoptosis did not require PKR, GCN2, eIF2alpha, or CHOP10. Furthermore, force more than 24 h did not activate other initiators of the unfolded protein response including IRE-1 and ATF6. However, force-induced activation of caspase 3 was dependent on caspase 9 but was independent of mitochondria. We conclude that force-induced apoptosis depends on a novel function of PERK that occurs in addition to its canonical role in the unfolded protein response.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".