NADPH oxidase activation by hyperglycaemia in cardiomyocytes is independent of glucose metabolism but requires SGLT1
Bibliographic record
Abstract
AIMS: Exposure to high glucose (HG) stimulates reactive oxygen species (ROS) production by NADPH oxidase in cardiomyocytes, but the underlying mechanism remains elusive. In this study, we have dissected the link between glucose transport and metabolism and NADPH oxidase activation under hyperglycaemic conditions. METHODS AND RESULTS: Primary cultures of adult rat cardiomyocytes were exposed to HG concentration (HG, 21 mM) and compared with the normal glucose level (LG, 5 mM). HG exposure activated Rac1GTP and induced p47phox translocation to the plasma membrane, resulting in NADPH oxidase (NOX2) activation, increased ROS production, insulin resistance, and eventually cell death. Comparison of the level of O-linked N-acetylglucosamine (O-GlcNAc) residues in LG- and HG-treated cells did not reveal any significant difference. Inhibition of the pentose phosphate pathway (PPP) by 6-aminonicotinamide counteracted ROS production in response to HG but did not prevent Rac-1 upregulation and p47phox translocation leading to NOX2 activation. Modulation of glucose uptake barely affected oxidative stress and toxicity induced by HG. More interestingly, non-metabolizable glucose analogues (i.e. 3-O-methyl-D-glucopyranoside and α-methyl-D-glucopyranoside) reproduced the toxic effect of HG. Inhibition of the sodium/glucose cotransporter SGLT1 by phlorizin counteracted HG-induced NOX2 activation and ROS production. CONCLUSION: Increased glucose metabolism by itself does not trigger NADPH oxidase activation, although PPP is required to provide NOX2 with NADPH and to produce ROS. NOX2 activation results from glucose transport through SGLT1, suggesting that an extracellular metabolic signal transduces into an intracellular ionic signal.
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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 | 0.001 |
| 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".