CRISPR/Cas9 Technology as a Modern Genetic Manipulation Tool for Recapitulating of Neurodegenerative Disorders in Large Animal Models
Bibliographic record
Abstract
BACKGROUND: Neurodegenerative diseases are often the consequence of alterations in structures and functions of the Central Nervous System (CNS) in patients. Despite obtaining massive genomic information concerning the molecular basis of these diseases and since the neurological disorders are multifactorial, causal connections between pathological pathways at the molecular level and CNS disorders development have remained obscure and need to be elucidated to a great extent. OBJECTIVE: Animal models serve as accessible and valuable tools for understanding and discovering the roles of causative factors in the development of neurodegenerative disorders and finding appropriate treatments. Contrary to rodents and other small animals, large animals, especially non-human primates (NHPs), are remarkably similar to humans; hence, they establish suitable models for recapitulating the main human's neuropathological manifestations that may not be seen in rodent models. In addition, they serve as useful models to discover effective therapeutic targets for neurodegenerative disorders due to their similarity to humans in terms of physiology, evolutionary distance, anatomy, and behavior. METHODS: In this review, we recommend different strategies based on the CRISPR-Cas9 system for generating animal models of human neurodegenerative disorders and explaining in vivo CRISPR-Cas9 delivery procedures that are applied to disease models for therapeutic purposes. RESULTS: With the emergence of CRISPR/Cas9 as a modern specific gene-editing technology in the field of genetic engineering, genetic modification procedures such as gene knock-in and knock-out have become increasingly easier compared to traditional gene targeting techniques. Unlike the old techniques, this versatile technology can efficiently generate transgenic large animal models without the need to complicate lab instruments. Hence, these animals can accurately replicate the signs of neurodegenerative disorders. CONCLUSION: Preclinical applications of CRISPR/Cas9 gene-editing technology supply a unique opportunity to establish animal models of neurodegenerative disorders with high accuracy and facilitate perspectives for breakthroughs in the research on the nervous system disease therapy and drug discovery. Furthermore, the useful outcomes of CRISPR applications in various clinical phases are hopeful for their translation to the clinic in a short time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".