Loss of small GTPase Rab7 activation in prion infection negatively affects a feedback loop regulating neuronal cholesterol metabolism
Bibliographic record
Abstract
Prion diseases are fatal and infectious neurodegenerative diseases that occur in humans and animals. They are caused by the misfolding of the cellular prion protein PrP c into the infectious isoform PrP Sc . PrP Sc accumulates mostly in endolysosomal vesicles of prion-infected cells, eventually causing neurodegeneration. In response to prion infection, elevated cholesterol levels and a reduction in membrane-attached small GTPase Rab7 have been observed in neuronal cells. Here, we investigated the molecular events causing an impaired Rab7 membrane attachment and the potential mechanistic link with elevated cholesterol levels in prion infection. We demonstrate that prion infection is associated with reduced levels of active Rab7 (Rab7.GTP) in persistently prion-infected neuronal cell lines, primary cerebellar granular neurons, and neurons in the brain of mice with terminal prion disease. In primary cerebellar granular neurons, levels of active Rab7 were increased during the very early stages of the prion infection prior to a significant decrease concomitant with PrP Sc accumulation. The reduced activation of Rab7 in prion-infected neuronal cell lines is also associated with its reduced ubiquitination status, decreased interaction with its effector RILP, and altered lysosomal positioning. Consequently, the Rab7-mediated trafficking of low-density lipoprotein to lysosomes is delayed. This results in an impaired feedback regulation of cholesterol synthesis leading to an increase in cholesterol levels. Notably, transient overexpression of the constitutively active mutant of Rab7 rescues the delay in the low-density lipoprotein trafficking, hence reducing cholesterol levels and attenuating PrP Sc propagation, demonstrating a mechanistic link between the loss of Rab7.GTP and elevated cholesterol 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 | 0.001 |
| 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".