Aldosterone Stimulates Elastogenesis in Cardiac Fibroblasts via Mineralocorticoid Receptor-independent Action Involving the Consecutive Activation of Gα13, c-Src, the Insulin-like Growth Factor-I Receptor, and Phosphatidylinositol 3-Kinase/Akt
Bibliographic record
Abstract
We previously demonstrated that aldosterone, which stimulates collagen production through the mineralocorticoid receptor (MR)-dependent pathway, also induces elastogenesis via a parallel MR-independent mechanism involving insulin-like growth factor-I receptor (IGF-IR) signaling. The present study provides a more detailed explanation of this signaling pathway. Our data demonstrate that small interfering RNA-driven elimination of MR in cardiac fibroblasts does not inhibit aldosterone-induced IGF-IR phosphorylation and subsequent increase in elastin production. These results exclude the involvement of the MR in aldosterone-induced increases in elastin production. Results of further experiments aimed at identifying the upstream signaling component(s) that might be activated by aldosterone also eliminate the putative involvement of pertussis toxin-sensitive Galphai proteins, which have previously been shown to be responsible for some MR-independent effects of aldosterone. Instead, we found that small interfering RNA-dependent elimination of another heterotrimeric G protein, Galpha13, eliminates aldosterone-induced elastogenesis. We further demonstrate that aldosterone first engages Galpha13 and then promotes its transient interaction with c-Src, which constitutes a prerequisite step for aldosterone-dependent activation of the IGF-IR and propagation of consecutive downstream elastogenic signaling involving phosphatidylinositol 3-kinase/Akt. In summary, the data we present reveal new details of an MR-independent cellular signaling pathway through which aldosterone stimulates elastogenesis in human cardiac fibroblasts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".