Dual Regulation of Myocardin Expression by Tumor Necrosis Factor-α in Vascular Smooth Muscle Cells
Bibliographic record
Abstract
De-differentiation of vascular smooth muscle cells (VSMCs) plays a critical role in the development of atherosclerosis, a chronic inflammatory disease involving various cytokines such as tumor necrosis factor-α (TNFα). Myocardin is a co-factor of serum response factor (SRF) and is considered to be the master regulator of VSMC differentiation. It binds to SRF and regulates the expression of contractile proteins in VSMCs. Myocardin is also known to inhibit VSMC proliferation by inhibiting the NF-κB pathway, whereas TNFα is known to activate the NF-κB pathway in VSMCs. NF-κB activation has also been shown to inhibit myocardin expression and smooth muscle contractile marker genes. However, it is not definitively known whether TNFα regulates the expression and activity of myocardin in VSMCs. The current study aimed to investigate the role of TNFα in regulating myocardin and VSMC function. Our studies showed that TNFα down-regulated myocardin expression and activity in cultured VSMCs by activating the NF-κB pathway, resulting in decreased VSMC contractility and increased VSMC proliferation. Surprisingly, we also found that TNFα prevented myocardin mRNA degradation, and resulted in a further significant increase in myocardin expression and activity in differentiated VSMCs. Both the NF-κB and p44/42 MAPK pathways were involved in TNFα regulation of myocardin, which further increased the contractility of VSMCs. These differential effects of TNFα on myocardin seemingly depended on whether VSMCs were in a differentiated or de-differentiated state. Taken together, our results demonstrate that TNFα differentially regulates myocardin expression and activity, which may play a key role in regulating VSMC functions.
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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) | 0.001 | 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 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".