Transforming growth factor-beta 1 (TGF-β1) induces the differentiation of rainbow trout (<i>Oncorhynchus mykiss</i>) cardiac fibroblasts into myofibroblasts
Bibliographic record
Abstract
The collagen content of the rainbow trout heart increases in response to cold acclimation, and decreases with warm acclimation. This ability to remodel the myocardial extracellular matrix (ECM) makes these fish useful models to study the cellular pathways involved in collagen regulation in the vertebrate heart. Remodeling of the ECM in the mammalian heart is regulated, in part, by myofibroblasts which arise from pre-existing fibroblasts in response to transforming growth factor-beta 1 (TGF-β1). We have previously demonstrated that treatment of cultured rainbow trout cardiac fibroblasts with human TGF-β1 causes an increase in collagen production. Here we show that repetitive treatment of rainbow trout cardiac fibroblasts with a physiologically relevant concentration of human recombinant TGF-β1 results in a ∼29-fold increase in phosphorylated small mothers against decapentaplegic 2 (pSmad2); a 2.9-fold increase in vinculin protein, a 1.2-fold increase in cellular size and a 3-fold increase in filamentous actin (F-actin). These are common markers of the transition of fibroblasts to myofibroblasts. Cells treated with TGF-β1 also had highly organized cytoskeletal alpha-smooth muscle actin, as well as increased transcript abundances of mmp-9, timp-2, and col1a1. Furthermore, using gelatin zymography, we demonstrate that TGF-β1 treatment causes a 5.3-fold increase in gelatinase activity. Together, these results demonstrate that trout cardiac fibroblasts have the capacity to differentiate into myofibroblasts and that this cell type can increase extracellular collagen turnover via gelatinase activity. Cardiac myofibroblasts are, therefore, likely involved in the remodeling of the cardiac ECM in the trout heart during thermal acclimation.
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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".