Buthionine Sulfoximine, an Inhibitor of Glutathione Biosynthesis, Induces Expression of Soluble Epoxide Hydrolase and Cellular Hypertrophy Markers in a Rat Cardiomyoblast Cell Line: Roles of The NF‐κB And MAPK Signalling Pathways
Bibliographic record
Abstract
Evidence suggests that up‐regulation of soluble epoxide hydrolase (sEH) is associated with the development of cardiac hypertrophy. However, the up‐regulation mechanism is still unknown. In this study, we treated H9C2 cells with buthionine sulfoximine (BSO) to explore whether oxidative stress up‐regulates sEH gene expression and to identify the molecular mechanisms behind this up‐regulatory response. We found that BSO up‐regulated sEH at mRNA levels leading to increase in the cellular hypertrophic markers, atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP). Furthermore, BSO increased the cytosolic phosphorylated IκB‐α and translocation of NF‐κB p50 subunits. This level of translocation was paralleled by an increase in the DNA‐binding activity of NF‐κB P50 subunits. To understand the role of mitogen‐Activated Protein Kinases (MAPKs) pathway in BSO‐mediated induction of sEH mRNA, we examined the role of extracellular signal‐regulated kinase (ERK), c‐JunN‐terminal kinase (JNK) and p38 MAPK. Indeed, treatment with the ERK inhibitor, PD98059, partially blocked the activation of IκB‐α and translocation of NF‐κB p50 subunits induced by BSO. Moreover, pre‐treatment with ERK inhibitor and MEK inhibitors, U0126 and PD0325901, significantly inhibited BSO‐mediated induction of sEH and cellular hypertrophic markers gene expression. These results demonstrated that MAPK/NF‐κB signaling pathways is involved in BSO‐mediated induction of sEH gene expression. Furthermore, our findings provide strong link between sEH‐induced cardiac dysfunction and involvement of NF‐κB in the development of cellular hypertrophy.
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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) | 0.001 | 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".