Phenotype and Functional Plasticity of Airway Smooth Muscle: Role of Caveolae and Caveolins
Bibliographic record
Abstract
Airway smooth muscle (ASM) cells exhibit phenotype plasticity that is under control of external stimuli such as growth factors and the extracellular matrix, and is regulated by a network of intracellular signaling cascades that control transcription and protein translation of phenotype-specific genes. Phenotype plasticity underpins the ability of airway myocytes to contribute both to acute bronchospasm, and to the features of airway remodeling in chronic asthma. A feature of mature, contractile ASM cells is the presence of abundant caveolae, omega-shaped plasma membrane invaginations that develop from the association of lipid rafts with caveolin-1, a unique protein with structural and functional properties. Caveolae and caveolin-1 modulate signaling from receptors for growth factors and contractile agonists, and thus may modulate functional diversity of myocytes. Caveolin-1 appears to play a suppressive role in ASM cell proliferation, and orchestrates receptor-mediated signal transduction that regulates phenotype expression of ASM cells. Interestingly, in contractile myocytes caveolae are organized in close proximity to intracellular Ca2+-handling organelles, and are partitioned into discrete linear domains aligned with beta-dystroglycan, a subunit of the actin-tethered dystrophin glycoprotein complex (DGC). Despite development of transgenic models to investigate caveolin biology, only superficial understanding of the role of these proteins in ASM phenotype expression and modulation of the functional responses of myocytes of a particular phenotype is available. This review summarizes mechanisms regulating ASM cell phenotype plasticity, and the role of caveolae as determinants of the functional diversity of ASM cells of a particular phenotypic state.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".