11-Hydroxyeicosatetraenoics induces cellular hypertrophy in an enantioselective manner
Bibliographic record
Abstract
Background: R/S enantiomers of 11-hydroxyeicosatertraenoic acid (11-HETE) are formed from arachidonic acid by enzymatic and non-enzymatic pathways. 11-HETE is predominately formed by the cytochrome P450 1B1 (CYP1B1). The role of CYP1B1 in the development of cardiovascular diseases is well established. Objectives: This study aimed to assess the cellular hypertrophic effect of 11-HETE enantiomers in human RL-14 cardiomyocyte cell line and to examine their association with CYP1B1 levels. Methods: Human fetal ventricular cardiomyocyte, RL-14 cells, were treated with 20 µM (R) or (S) 11-HETE for 24 h. Thereafter, cellular hypertrophic markers and cell size were then determined using real-time polymerase chain reaction (RT-PCR) and phase-contrast imaging, respectively. The mRNA and protein levels of selected CYPs were determined using RT-PCR and Western blot, respectively. In addition, we examined the effect of (R) and (S) 11-HETE on CYP1B1 catalytic activity using human recombinant CYP1B1 and human liver microsomes. Results: Both (R) and (S) 11-HETE induced cellular hypertrophic markers and cell surface area in RL-14 cells. Both enantiomers significantly upregulated CYP1B1, CYP1A1, CYP4F2, and CYP4A11 at the mRNA and protein levels, however, the effect of the S-enantiomer was more pronounced. Furthermore, 11(S)-HETE increased the mRNA and protein levels of CYP2J and CYP4F2, whereas 11(R)-HETE increased only CYP4F2. Only 11(S)-HETE significantly increased the catalytic activity of CYP1B1 in recombinant human CYP1B1, suggesting allosteric activation in an enantioselective manner. Conclusion: Our study provides the first evidence that 11-HETE can induce cellular hypertrophy in RL-14 cells via the increase in CYP1B1 mRNA, protein, and activity levels.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".