Caspase-6 Is a Non-Apoptotic Effector of Shear-Induced Morphological Adaptation in Pulmonary Artery Endothelial Cells In Vitro
Bibliographic record
Abstract
Caspases are known for their roles in cell death and inflammation. However, emerging evidence suggests they also mediate non-lethal processes, governed by a finely tuned balance of localization, activity, kinetics, and substrate availability. Given that many caspase substrates are implicated in mechanoadaptive processes, we investigated if caspases contribute to morphological adaptation of human pulmonary artery endothelial cells to fluid shear stress and other morphology-altering stimuli in vitro. Using selective inhibitors, we screened all major caspases for a role in endothelial cell adaptation to unidirectional laminar shear stress (15 dyn/cm2, 72 h). Selective inhibition of caspase-6, but not other caspases, impaired morphological shear adaptation. Only 5.5% of caspase-6-inhibited cells shear-adapted vs. 75.2% of vector controls. Live-cell FRET imaging revealed progressive caspase-6 activation starting at 18 h of shear stress, coinciding with the onset of morphological remodeling. The active caspase-6 localized predominantly perinuclearly, while caspase-3 remained inactive throughout shear exposure. Caspase-6 inhibition did not affect elongation in response to alternative biomechanical or biochemical stimuli, including uniaxial cyclic stretch (5%, 1 Hz), spatial confinement on narrow micropatterned RGD-lines, or TNF-α stimulation, nor did it impair cell adhesion, directed migration, wound healing, or barrier recovery after wounding. Our study uncovers a previously unidentified role of caspase-6 as a non-apoptotic, mechanosensitive effector specifically required for shear-induced morphological adaptation of pulmonary artery endothelial cells, highlighting a novel regulatory axis in vascular mechanoadaptation.
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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 | 0.001 |
| 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".