Serum Response Factor Expression in Excess Permits a Dual Contractile–Proliferative Phenotype of Airway Smooth Muscle
Bibliographic record
Abstract
The transcription factors (TFs) MyoCD (myocardin) and Elk-1 (ETS Like-1 protein) competitively bind to SRF (serum response factor) and control myogenic- and mitogenic-related gene expression in smooth muscle, respectively. Their functions are therefore mutually inhibitory, which results in a contractile-versus-proliferative phenotype dichotomy. Airway smooth muscle cell (ASMC) phenotype alterations occur in various inflammatory airway diseases, promoting pathological remodeling and contributing to airflow obstruction. We characterized MyoCD and Elk-1 interactions and their roles in phenotype determination in human ASMCs. MyoCD overexpression in ASMCs increased smooth muscle gene expression, force generation, and partially restored the loss of smooth muscle protein associated with prolonged culturing while inhibiting Elk-1 transcriptional activities and proliferation induced by EGF (epidermal growth factor). However, MyoCD overexpression failed to suppress these responses induced by FBS, as FBS also upregulated SRF expression to a degree that allowed unopposed function of both TFs. Inhibition of the RhoA pathway reversed said SRF changes, allowing inhibition of Elk-1 by MyoCD overexpression and suppressing FBS-mediated contractile protein gene upregulation. Our study confirmed that MyoCD in increased abundance can competitively inhibit Elk-1 function. However, SRF upregulation permits a dual contractile-proliferative ASMC phenotype that is anticipated to exacerbate pathological alterations, whereas therapies targeting SRF may inhibit pathological ASMC proliferation and contractile protein gene expression.
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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".