CRISPR/Cas9 Applications in Ganoderma lucidum Breeding for Enhanced Bioactive Compound Production
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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".