Overexpression of quality control proteins reduces prion conversion in prion-infected cells
Bibliographic record
Abstract
Prion diseases are fatal infectious neurodegenerative disorders in humans and other animals and are caused by misfolding of the cellular prion protein (PrP C ) into the pathological isoform PrP Sc . These diseases have the potential to transmit within or between species, including zoonotic transmission to humans. Elucidating the molecular and cellular mechanisms underlying prion propagation and transmission is therefore critical for developing molecular strategies for disease intervention. We have shown previously that impaired quality control mechanisms directly influence prion propagation. In this study, we manipulated cellular quality control pathways in vitro by stably and transiently overexpressing selected quality control folding (ERp57) and cargo (VIP36) proteins and investigated the effects of this overexpression on prion propagation. We found that ERp57 or VIP36 overexpression in persistently prion-infected neuroblastoma cells significantly reduces the amount of PrP Sc in immunoblots and prion-seeding activity in the real-time quaking-induced conversion (RT-QuIC) assay. Using different cell lines infected with various prion strains confirmed that this effect is not cell type– or prion strain–specific. Moreover, de novo prion infection revealed that the overexpression significantly reduced newly formed PrP Sc in acutely infected cells. ERp57-overexpressing cells significantly overcame endoplasmic reticulum stress, as revealed by expression of lower levels of the stress markers BiP and CHOP, accompanied by a decrease in PrP aggregates. Furthermore, application of ERp57-expressing lentiviruses prolonged the survival of prion-infected mice. Taken together, improved cellular quality control via ERp57 or VIP36 overexpression impairs prion propagation and could be utilized as a potential therapeutic strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".