ROCK Controls Matrix Synthesis in Vascular Smooth Muscle Cells
Bibliographic record
Abstract
Tenascin-C (TN-C) is an extracellular matrix (ECM) protein expressed within remodeling systemic and pulmonary arteries (PAs), where it supports vascular smooth muscle cell (SMC) proliferation. Previously, we showed that A10 SMCs cultivated on native type I collagen possess a spindle-shaped morphology and do not express TN-C, whereas those on denatured collagen possess a well-defined F-actin stress fiber network, a spread morphology, and they do express TN-C. To determine whether changes in cytoskeletal architecture control TN-C, SMCs on denatured collagen were treated with cytochalasin D, which decreased SMC spreading and activation of extracellular signal-regulated kinase 1/2 (ERK1/2), signaling effectors required for TN-C transcription. Next, to determine whether cell shape, dictated by the F-actin cytoskeleton, regulates TN-C, different geometries of SMCs (ranging from spread to round) were engineered on denatured collagen: as SMCs progressively rounded, ERK1/2 activity and TN-C transcription declined. Because RhoA and Rho kinase (ROCK) regulate cell morphology by controlling cytoskeletal architecture, we reasoned that these factors might also regulate TN-C. Indeed, SMCs on denatured collagen possessed higher levels of RhoA activity than those on native collagen, and blocking RhoA or ROCK activities attenuated SMC spreading, ERK1/2 activity, and TN-C expression in SMCs on denatured collagen. Thus, ROCK controls the configuration of the F-actin cytoskeleton and SMC shape in a manner that is permissive for ERK1/2-dependent production of TN-C. Finally, we showed that inhibition of ROCK activity suppresses SMC TN-C expression and disease progression in hypertensive rat PAs. Thus, in addition to its role in regulating vasoconstriction, ROCK also controls matrix production.
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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) | 0.001 | 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".