Collagenase‐resistant collagen promotes mouse aging and vascular cell senescence
Why this work is in the frame
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Bibliographic record
Abstract
Collagen fibrils become resistant to cleavage over time. We hypothesized that resistance to type I collagen proteolysis not only marks biological aging but also drives it. To test this, we followed mice with a targeted mutation (Col1a1(r/r) ) that yields collagenase-resistant type I collagen. Compared with wild-type littermates, Col1a1(r/r) mice had a shortened lifespan and developed features of premature aging including kyphosis, weight loss, decreased bone mineral density, and hypertension. We also found that vascular smooth muscle cells (SMCs) in the aortic wall of Col1a1(r/r) mice were susceptible to stress-induced senescence, displaying senescence-associated ß-galactosidase (SA-ßGal) activity and upregulated p16(INK4A) in response to angiotensin II infusion. To elucidate the basis of this pro-aging effect, vascular SMCs from twelve patients undergoing coronary artery bypass surgery were cultured on collagen derived from Col1a1(r/r) or wild-type mice. This revealed that mutant collagen directly reduced replicative lifespan and increased stress-induced SA-ßGal activity, p16(INK4A) expression, and p21(CIP1) expression. The pro-senescence effect of mutant collagen was blocked by vitronectin, a ligand for αvß3 integrin that is presented by denatured but not native collagen. Moreover, inhibition of αvß3 with echistatin or with αvß3-blocking antibody increased senescence of SMCs on wild-type collagen. These findings reveal a novel aging cascade whereby resistance to collagen cleavage accelerates cellular aging. This interplay between extracellular and cellular compartments could hasten mammalian aging and the progression of aging-related diseases.
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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 it