Ca<sup>2+</sup>/calmodulin-dependent protein kinase IIβ and IIδ mediate TGFβ-induced transduction of fibronectin and collagen in human pulmonary fibroblasts
Bibliographic record
Abstract
It is now clear that in addition to activating several complex kinase pathways (Smad, MAP kinase, PI3 kinase), TGFβ also acts by elevating cytosolic Ca 2+ concentration within human pulmonary fibroblasts. Ca 2+ /calmodulin-dependent protein kinase II (CamK II) is also known to regulate gene expression in fibroblasts. In this study, we examined the interactions between calcium signaling, activation of CamK and other kinases, and extracellular matrix (ECM) gene expression. Human pulmonary fibroblasts were cultured and stimulated with artificially generated Ca 2+ pulses in the absence of TGFβ, or with TGFβ (1 nM) or vehicle in the presence of various blockers of Ca 2+ signaling. PCR and Western blotting were used to measure gene expression and protein levels, respectively. We found that Ca 2+ pulses in the absence of TGFβ increased ECM gene expression in a pulse frequency-dependent manner, and that blocking Ca 2+ signaling and the CamK II pathway significantly reduced TGFβ-mediated ECM gene expression, without having any effects on other kinase pathways (Smad, PI3 kinase, or MAP kinase). We also found that TGFβ elevated the expression of CamK IIβ and CamK IIδ, while siRNA silencing of those two subtypes significantly reduced TGFβ-mediated expression of collagen A1 and fibronectin 1. Our data suggest that TGFβ induces the expression of CamK IIβ and CamK IIδ, which in turn are activated by TGFβ-evoked Ca 2+ waves in a frequency-dependent manner, leading to increased expression of ECM proteins.
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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".