Preferential accumulation and export of high molecular weight FGF-2 by rat cardiac non-myocytes
Bibliographic record
Abstract
AIMS: fibroblast growth factor-2 (FGF-2), implicated in paracrine induction of cardiac hypertrophy, is translated as high molecular weight (Hi-FGF-2) and low molecular weight (Lo-FGF-2) isoforms. Paracrine activities are assigned to Lo-FGF-2, whereas Hi-FGF-2 is presumed to have nuclear functions. In this work, we re-examined the latter presumption by asking whether: cardiac non-myocytes (CNMs) accumulate and export Hi-FGF-2 in response to pro-hypertrophic [angiotensin II (Ang II)] stimuli; an unconventional secretory pathway requiring activated caspase-1 affects Hi-FGF2 export; and secreted Hi-FGF-2 is pro-hypertrophic. METHODS AND RESULTS: using neonatal rat heart-derived cultures and immunoblotting, we show that CNMs accumulated over 90% Hi-FGF-2, at levels at least five-fold higher than cardiomyocytes (CMs). Pro-hypertrophic agents (Ang II, endothelin-1, and isoproterenol) up-regulated CNM-associated Hi-FGF-2. The Ang II effect was mediated by Ang II receptor-1 but not Ang II receptor-2 as it was blocked by losartan but not PD123319. CNM-derived Hi-FGF-2 was detected in two extracellular pools: in conditioned medium from Ang II-stimulated CNMs and in association with the cell surface/matrix, eluted with a gentle 2 M NaCl wash of the cell monolayer. Conditioned medium from Ang II-treated CNMs increased neonatal CM size, an effect prevented by anti-FGF-2-neutralizing antibodies. The caspase-1 inhibitor YVAD prevented the Ang II-induced release of Hi-FGF-2 to both extracellular pools. CONCLUSION: CNMs are major producers of Hi-FGF-2, up-regulated by hypertrophic stimuli and exported to the extracellular environment by a mechanism requiring caspase-1 activity, suggesting a link to the innate immune response. Hi-FGF-2 is likely to promote paracrine induction of myocyte hypertrophy in vivo.
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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".