Receptor for Advanced Glycation End-Products Signaling Interferes with the Vascular Smooth Muscle Cell Contractile Phenotype and Function
Bibliographic record
Abstract
Increased blood glucose concentrations promote reactions between glucose and proteins to form advanced glycation end-products (AGE). Circulating AGE in the blood plasma can activate the receptor for advanced end-products (RAGE), which is present on both endothelial and vascular smooth muscle cells (VSMC). RAGE exhibits a complex signaling that involves small G-proteins and mitogen activated protein kinases (MAPK), which lead to increased nuclear factor kappa B (NF-κB) activity. While RAGE signaling has been previously addressed in endothelial cells, little is known regarding its impact on the function of VSMC. Therefore, we hypothesized that RAGE signaling leads to alterations in the mechanical and functional properties of VSMC, which could contribute to complications associated with diabetes. We demonstrated that RAGE is expressed and functional in the A7r5 VSMC model, and its activation by AGE significantly increased NF-κB activity, which is known to interfere with the contractile phenotype of VSMC. The protein levels of the contraction-related transcription factor myocardin were also decreased by RAGE activation with a concomitant decrease in the mRNA and protein levels of transgelin (SM-22α), a regulator of VSMC contraction. Interestingly, we demonstrated that RAGE activation increased the overall cell rigidity, an effect that can be related to an increase in myosin activity. Finally, although RAGE stimulation amplified calcium signaling and slightly myosin activity in VSMC challenged with vasopressin, their contractile capacity was negatively affected. Overall, RAGE activation in VSMC could represent a keystone in the development of vascular diseases associated with diabetes by interfering with the contractile phenotype of VSMC through the modification of their mechanical and functional properties.
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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) | 0.000 | 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 | 0.001 |
| 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".