GPCR agonist-induced transactivation of the EGFR upregulates MLC II expression and promotes hypertension in insulin-resistant rats
Bibliographic record
Abstract
AIMS: The presence of metabolic abnormalities such as insulin resistance and elevated levels of various vasoconstrictor G-protein-coupled receptor (GPCR) agonists contributes to the development of hypertension. Recent studies have suggested a link between disease progression and activation of growth factor receptor signalling pathways such as the epidermal growth factor receptor (EGFR) by matrix metalloproteinases (MMPs). We hypothesized that excessive stimulation of GPCRs such as alpha(1)-adrenergic receptors activates MMP-dependent EGFR transactivation and contributes to the development of hypertension by promoting increased synthesis of contractile proteins in vascular smooth muscle (VSM). METHODS AND RESULTS: We tested this concept in experiments using insulin-resistant VSM cells (VSMCs) and fructose hypertensive rats (FHRs), a model of acquired systolic hypertension and insulin resistance. We found that insulin resistance and agonist stimulation increased the expression and activity of MMPs (MMP-2 and MMP-7), the EGFR, contractile proteins such as myosin light chain kinase and MLC II, and their transcriptional activators including P90 ribosomal kinase (P90RSK) and serum response factor, possibly via the activation of extracellular signal-regulated kinase (ERK1/2) in VSMCs. Further, in insulin-resistant VSMCs and arteries from FHRs, disruption of MMP-EGFR signalling either by a pharmacological or small interfering RNA approach normalized the increased expression and activity of contractile proteins and their transcriptional activators and prevented the development of hypertension in FHRs. CONCLUSION: Our data suggest that the MMP-EGFR pathway could be a potential target in the treatment of hypertension in insulin resistance and/or hyperglycaemic conditions such as type 2 diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".