Transforming Growth Factor–β Evokes Ca2+ Waves and Enhances Gene Expression in Human Pulmonary Fibroblasts
Bibliographic record
Abstract
Fibroblasts maintain the structural framework of animal tissue by synthesizing extracellular matrix molecules. Chronic lung diseases are characterized in part by changes in fibroblast numbers, properties, and more. Fibroblasts respond to a variety of growth factors, cytokines, and proinflammatory mediators. However, the signaling mechanisms behind these responses have not been fully explored. We sought to determine the role of Ca(2+) waves in transforming growth factor-β (TGF-β)-mediated gene expression in human pulmonary fibroblasts. Primary human pulmonary fibroblasts were cultured and treated with TGF-β and different blockers under various conditions. Cells were then loaded with the Ca(2+) indicator dye Oregon green, and Ca(2+) waves were monitored by confocal [Ca(2+)](i) fluorimetry. Real-time PCR was used to probe gene expression. TGF-β (1 nM) evoked recurring Ca(2+) waves. A 30-minute pretreatment of SD 208, a TGF-β receptor-1 kinase inhibitor, prevented Ca(2+) waves from being evoked by TGF-β. The removal of external Ca(2+) completely occluded TGF-β-evoked Ca(2+) waves. Cyclopiazonic acid, an inhibitor of the internal Ca(2+) pump, evoked a relatively slowly developing rise in Ca(2+) waves compared with the rapid changes evoked by TGF-β, but the baseline fluorescence was increased. Ryanodine (10(-5) M) also blocked TGF-β-mediated Ca(2+) wave activity. Real-time PCR showed that TGF-β rapidly and dramatically increased the gene expression of collagen A1 and fibronectin. This increase was blocked by ryanodine treatment and cyclopiazonic acid. We conclude that, in human pulmonary fibroblasts, TGF-β acts on ryanodine-sensitive channels, leading to Ca(2+) wave activity, which in turn amplifies extracellular matrix 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 | 0.000 |
| 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".