CRISPR/Cas9 Applications in Ganoderma lucidum Breeding for Enhanced Bioactive Compound Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study sets out to explore the potential of CRISPR/Cas9 gene-editing technology in improving Ganoderma lucidum , with a particular focus on whether it can truly help boost the yield of active ingredients. The article begins with a brief overview of the fungus’s main bioactive compounds-polysaccharides and triterpenoids-and their medicinal value. But here’s the catch: traditional breeding methods, while useful in the past, appear to have hit a bottleneck when it comes to further improving the efficiency of these compounds’ synthesis. Against this backdrop, attention has naturally shifted to CRISPR/Cas9. The paper explains the system’s basic principles and advantages, then illustrates them with practical examples from fungal genetic studies. Notably, the technology has already delivered promising results in editing key genes (such as cyp5150l8 and cyp505d13) and in optimizing metabolic pathways. At the same time, the authors stress that if homologous recombination efficiency could be improved-or if newer methods like ribonucleoprotein (RNP) complex delivery were applied-the accuracy and overall efficiency of gene editing could be pushed even further. Finally, the article steps back to consider the bigger picture: CRISPR/Cas9 is not just another piece of lab equipment. It may well become a powerful tool for targeted breeding of active ingredients in Ganoderma lucidum , while also fueling the development of new medicines and functional foods. Looking ahead, it even holds the promise of playing a pivotal role in the broader industrialization of fungal biotechnology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it