The Antidiabetic Hormone Glucagon-Like Peptide-1 Induces Formation of New Elastic Fibers in Human Cardiac Fibroblasts After Cross-Activation of IGF-IR
Bibliographic record
Abstract
Glucagon-like peptide 1 (GLP-1) is a metabolic hormone involved in the stimulation of insulin biosynthesis and secretion. It has been recently reported that GLP-1 also exerts cardioprotective effects and facilitates functional recovery after myocardial infarction through GLP-1 receptormediated signaling in cardiomyocytes. GLP-1 treatment has been also demonstrated to produce sustained improvement in cardiac function in long-term studies, suggesting the involvement of mechanisms beyond the acute metabolic and cytoprotective effects. For example, the possible interaction of GLP-1 with the cardiac fibroblasts, which are responsible for the postinfarct remodeling and extracellular matrix production, has not been previously explored. Here, we report that cultures of human cardiac fibroblasts treated with GLP-1 peptides display a selective up-regulation in elastin gene expression and a consequent increase in elastic fibers production, in the absence of the classic GLP-1 receptor. Importantly, we provide experimental evidence that this GLP-1-induced elastogenesis is triggered through the cross-activation of the IGF-I receptor. Because GLP-1 does not stimulate deposition of collagen I, nor promote the proliferation or apoptosis of cultured cardiac fibroblasts, we speculate that its elastogenic effect may also contribute to the beneficial remodeling of the human heart after myocardial infarction. (Endocrinology 156: 90 -102, 2015) G lucagon-like peptide 1 (GLP-1) is a hormone synthe- sized and processed in the intestinal L-cells in response to food intake (1). It induces numerous effects in different tissues, including the pancreas, intestines, brain, heart, and kidney after its interaction with the widely expressed classic GLP-1 receptor (GLP-1R) (2). GLP-1 upregulates glucose-dependent insulin secretion and inhibits the release of glucagon, thereby decreasing blood glucose levels and contributing to therapeutic benefits for patients with type 2 diabetes mellitus (3). It is well known that diabetic patients are at an increased risk for coronary heart disease and often have a worse prognosis than nondiabetics after acute myocardial infarction (MI) (4, 5). Intriguingly, clinical (6, 7) and animal studies (8 -15) have shown that both the native GLP-1 (7-36 peptide) and its proteolytic cleavage product, the GLP-1 (9-36 peptide), also contribute to the improvement of ventricular ejection fraction and functional recovery after acute ischemic injury of the heart (10).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".