Role of the Lipid Peroxidation Product, 4-Hydroxynonenal, in the Development of Nitrate Tolerance
Bibliographic record
Abstract
Tolerance to nitrates such as nitroglycerin (GTN) is associated with oxidative stress, inactivation of aldehyde dehydrogenase 2 (ALDH2), and decreased GTN-induced cGMP accumulation and vasodilation. We hypothesized that GTN-induced inactivation of ALDH2 results in increased 4-hydroxy-2-nonenal (HNE) adduct formation of key proteins involved in GTN bioactivation, and, consequently, an attenuated vasodilator response to GTN (i.e., tolerance). We used an in vivo GTN tolerance model, a cell culture model of nitrate action, and Aldh2(-/-) mice to assess whether GTN exposure resulted in HNE adduct formation, and whether exogenous HNE affected GTN-induced relaxation and cGMP accumulation. Immunoblot analysis indicated a marked increase in HNE adduct formation in GTN-tolerant porcine kidney epithelial cells (PK1) and in aortae from GTN-tolerant rats and untreated Aldh2(-/-) mice. Preincubation of PK1 cells with HNE resulted in a dose-dependent decrease in GTN-induced cGMP accumulation, and pretreatment of isolated rat aorta with HNE resulted in dose-dependent decreases in the vasodilator response to GTN, thus mimicking GTN-tolerance. Pretreatment of aortae from Aldh2(-/-) mice with 10 μM HNE resulted in a desensitized vasodilator response to GTN. In the in vivo rat tolerance model, changes in HNE adduct formation correlated well with the onset of GTN tolerance and tolerance reversal. Furthermore, coadministration of an HNE scavenger during the tolerance induction protocol completely prevented HNE adduct formation and GTN tolerance but did not prevent the inactivation of ALDH2. The data are consistent with a novel mechanism of GTN tolerance suggesting a primary role of HNE adduct formation in the development of GTN tolerance.
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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 | 0.001 | 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".