Transcriptional and posttranscriptional inhibition of HMGCR and PC biosynthesis by geraniol in 2 Hep-G2 cell proliferation linked pathways
Bibliographic record
Abstract
Geraniol, present in the essential oils of many aromatic plants, has in vitro and in vivo antitumor activity against several cell lines. We investigated the effects of geraniol on lipid metabolic pathways involved in Hep-G2 cell proliferation and found that geraniol inhibits the mevalonate pathway, phosphatidylcholine biosynthesis, cell growth, and cell cycle progression (with an arrest occurring at the G0/G1 interphase) and increases apoptosis. The expression of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), the rate-limiting step in cholesterol synthesis, was inhibited at the transcriptional and posttranscriptional levels, as assessed by real-time RT-PCR, Western blots, and [(14)C]HMG-CoA-conversion radioactivity assays. That geraniol decreased cholesterogenesis but increased the incorporation of [(14)C]acetate into other nonsaponifiable metabolites indicated the existence of a second control point between squalene and cholesterol involved in redirecting the flow of cholesterol-derived carbon toward other metabolites of the mevalonate pathway. That exogenous mevalonate failed to restore growth in geraniol-inhibited cells suggests that, in addition to the inhibition of HMGCR, other dose-dependent actions exist through which geraniol can impact the mevalonate pathway and consequently inhibit cell proliferation. These results suggest that geraniol, a nontoxic compound found in many fruits and herbs, exhibits notable potential as a natural agent for combatting cancer and (or) cardiovascular diseases.
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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) | 0.001 | 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".