CRISPR-Cas9 editing of agricultural crops and medicinal plants: toward a cornucopia of natural products
Bibliographic record
Abstract
Plants have been a part of human health since our very beginnings, and many of our modern pharmaceuticals claim their origins from medicinal plants. The range of specialized metabolites synthesized by plants is highly diverse, and metabolic functions have developed over the millennia to cover roles such as defense, adaptation to environmental stress, and even reproduction. These metabolites subsequently play roles in human health and diseases that are both significant and profound. The importance of plant natural products for the pharmaceutical, cosmetic and nutraceutical industries cannot be overstated. However, the fact that these specialized metabolites may be available only in low quantities from plants that are slow growing, endangered, or from fragile environments due to certain biotic and abiotic stresses makes their commercial use challenging despite the scenario that some stresses can enhance the production of secondary metabolites. Genome editing is a technique or technology that comprises of tools like CRISPR/Cas9, TALEN, ZFN. The following review describes the successful use of CRISPR/Cas9 genome editing in engineering medicinal plants, food crops and commercial crops to modulate metabolic pathways involved in the biosynthesis of valuable compounds to improve natural product identification, development and ultimately, commercial viability.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".