Inhibition of Rab prenylation by statins induces cellular glycosphingolipid remodeling
Bibliographic record
Abstract
Statins, which specifically inhibit HMG Co-A reductase, the rate-limiting step of cholesterol biosynthesis, are widely prescribed to reduce serum cholesterol and cardiac risk, but many other effects are seen. We now show an effect of these drugs to induce profound changes in the step-wise synthesis of glycosphingolipids (GSLs) in the Golgi. Glucosylceramide (GlcCer) was increased several-fold in all cell lines tested, demonstrating a widespread effect. Additionally, de novo or elevated lactotriaosylceramide (Lc3Cer; GlcNAcβ1-3Galβ1-4GlcCer) synthesis was observed in 70%. Western blot showed that GlcCer synthase (GCS) was elevated by statins, and GCS and Lc3Cer synthase (Lc3S) activities were increased; however, transcript was elevated for Lc3S only. Supplementation with the isoprenoid precursor, geranylgeranyl pyrophosphate (GGPP), a downstream product of HMG Co-A reductase, reversed statin-induced glycosyltransferase and GSL elevation. The Rab geranylgeranyl transferase inhibitor 3-PEHPC, but not specific inhibitors of farnesyl transferase, or geranylgeranyl transferase I, was sufficient to replicate statin-induced GlcCer and Lc3Cer synthesis, supporting a Rab prenylation-dependent mechanism. While total cholesterol was unaffected, the trans-Golgi network (TGN) cholesterol pool was dissipated and medial Golgi GCS partially relocated by statins. GSL-dependent vesicular retrograde transport of Verotoxin and cholera toxin to the Golgi/endoplasmic reticulum were blocked after statin or 3-PEHPC treatment, suggesting aberrant, prenylation-dependent vesicular traffic as a basis of glycosyltransferase increase and GSL remodeling. These in vitro studies indicate a previously unreported link between Rab prenylation and regulation of GCS activity and GlcCer metabolism.
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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) | 0.001 | 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 | 0.001 |
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".