Acute mercury toxicity modulates cytochrome P450, soluble epoxide hydrolase and their associated arachidonic acid metabolites in C57BL/6 mouse heart (652.5)
Bibliographic record
Abstract
Mercury exposure is associated with increased risk of cardiovascular disease and profound cardiotoxicity. However, the correlation between Hg2+‐mediated toxicity and alteration in cardiac cytochrome P450s (Cyp) and their dependent arachidonic acid metabolites has never been investigated. Therefore, we investigated the effect of acute mercury toxicity on the expression of Cyp‐epoxygenases and Cyp‐ω‐hydroxylases; and their associated arachidonic acid metabolites in mice hearts. In addition, we examined the expression and activity of soluble epoxide hydrolase (sEH) as a key player in arachidonic acid metabolism pathway. Mercury toxicity was induced by a single intraperitoneal injection (IP) of 2.5 mg/kg of mercuric chloride (HgCl2). Our results showed that mercury treatment caused a significant induction of the cardiac hypertrophy markers, atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP); in addition to Cyp1a1, Cyp1b1, Cyp2b9, Cyp2b10, Cyp2b19, Cyp2c29, Cyp2c38, Cyp4a10, Cyp4a12, Cyp4a14, Cyp4f13, Cyp4f15, Cyp4f16 and Cyp4f18 gene expression. Moreover, Hg2+ significantly increased sEH protein expression and activity, in hearts of mercury‐treated mice, with a consequent decrease in 14,15‐, and 11,12‐epoxyeicosatrienoic acids (EETs) formation. Whereas the formation of 14,15‐, 11,12‐, 8,9‐dihydroxyeicosatrienoic acids (DHETs) was significantly increased. In conclusion, acute Hg2+ toxicity modulates the expression of several Cyp and sEH enzymes with a consequent decrease in the cardioprotective EETs which could represent a novel mechanism by which mercury causes progressive cardiotoxicity. Furthermore, inhibiting sEH might represent a novel therapeutic approach to prevent Hg2+‐induced hypertrophy. Grant Funding Source : Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN 250139
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".