Dimerization of the cellular prion protein inhibits propagation of scrapie prions
Bibliographic record
Abstract
A central step in the pathogenesis of prion diseases is the conformational transition of the cellular prion protein (PrP C ) into the scrapie isoform, denoted PrP Sc . Studies in transgenic mice have indicated that this conversion requires a direct interaction between PrP C and PrP Sc ; however, insights into the underlying mechanisms are still missing. Interestingly, only a subfraction of PrP C is converted in scrapie-infected cells, suggesting that not all PrP C species are suitable substrates for the conversion. On the basis of the observation that PrP C can form homodimers under physiological conditions with the internal hydrophobic domain (HD) serving as a putative dimerization domain, we wondered whether PrP dimerization is involved in the formation of neurotoxic and/or infectious PrP conformers. Here, we analyzed the possible impact on dimerization of pathogenic mutations in the HD that induce a spontaneous neurodegenerative disease in transgenic mice. Similarly to wildtype (WT) PrP C , the neurotoxic variant PrP(AV3) formed homodimers as well as heterodimers with WTPrP C . Notably, forced PrP dimerization via an intermolecular disulfide bond did not interfere with its maturation and intracellular trafficking. Covalently linked PrP dimers were complex glycosylated, GPI-anchored, and sorted to the outer leaflet of the plasma membrane. However, forced PrP C dimerization completely blocked its conversion into PrP Sc in chronically scrapie-infected mouse neuroblastoma cells. Moreover, PrP C dimers had a dominant-negative inhibition effect on the conversion of monomeric PrP C . Our findings suggest that PrP C monomers are the major substrates for PrP Sc propagation and that it may be possible to halt prion formation by stabilizing PrP C dimers.
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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".