Cholesterol synthesis inhibition promotes axonal regeneration in the injured central nervous system
Bibliographic record
Abstract
Neuronal regeneration in the injured central nervous system is hampered by multiple extracellular proteins. These proteins exert their inhibitory action through interactions with receptors that are located in cholesterol rich compartments of the membrane termed lipid rafts. Here we show that cholesterol-synthesis inhibition prevents the association of the Neogenin receptor with lipid rafts. Furthermore, we show that cholesterol-synthesis inhibition enhances axonal growth both on inhibitory -myelin and -RGMa substrates. Following optic nerve injury, lowering cholesterol synthesis with both drugs and siRNA-strategies allows for robust axonal regeneration and promotes neuronal survival. Cholesterol inhibition also enhanced photoreceptor survival in a model of Retinitis Pigmentosa. Our data reveal that Lovastatin leads to several opposing effects on regenerating axons: cholesterol synthesis inhibition promotes regeneration whereas altered prenylation impairs regeneration. We also show that the lactone prodrug form of lovastatin has differing effects on regeneration when compared to the ring-open hydroxy-acid form. Thus the association of cell surface receptors with lipid rafts contributes to axonal regeneration inhibition, and blocking cholesterol synthesis provides a potential therapeutic approach to promote neuronal regeneration and survival in the diseased Central Nervous System. SIGNIFICANCE STATEMENT: Statins have been intensively used to treat high levels of cholesterol in humans. However, the effect of cholesterol inhibition in both the healthy and the diseased brain remains controversial. In particular, it is unclear whether cholesterol inhibition with statins can promote regeneration and survival following injuries. Here we show that late stage cholesterol inhibition promotes robust axonal regeneration following optic nerve injury. We identified distinct mechanisms of action for activated vs non-activated Lovastatin that may account for discrepancies found in the literature. We show that late stage cholesterol synthesis inhibition alters Neogenin association with lipid rafts, thereby i) neutralizing the inhibitory function of its ligand and ii) offering a novel opportunity to promote CNS regeneration and survival following injuries.
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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 | 0.001 |
| 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".