Gene-Edited Cell Models to Study Chronic Wasting Disease
Bibliographic record
Abstract
, and currently no options exist to prevent or cure prion diseases. Chronic wasting disease (CWD) in deer, elk and other cervids is considered the most contagious prion disease, with extensive shedding of infectivity into the environment. Cell culture models provide a versatile platform for convenient quantification of prions, for studying the molecular and cellular biology of prions, and for performing high-throughput screening of potential therapeutic compounds. Unfortunately, only a very limited number of cell lines are available that facilitate robust and persistent propagation of CWD prions. Gene-editing using programmable nucleases (e.g., CRISPR-Cas9 (CC9)) has proven to be a valuable tool for high precision site-specific gene modification, including gene deletion, insertion, and replacement. CC9-based gene editing was used recently for replacing the PrP gene in mouse and cell culture models, as efficient prion propagation usually requires matching sequence homology between infecting prions and prion protein in the recipient host. As expected, such gene-editing proved to be useful for developing CWD models. Several transgenic mouse models were available that propagate CWD prions effectively, however, mostly fail to reproduce CWD pathogenesis as found in the cervid host, including CWD prion shedding. This is different for the few currently available knock-in mouse models that seem to do so. In this review, we discuss the available in vitro and in vivo models of CWD, and the impact of gene-editing strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".