Optimizing <i>ex-vitro</i> one-step RUBY-equipped hairy root transformation in drug- and hemp-type Cannabis
Bibliographic record
Abstract
Abstract Using synthetic biology techniques to engineer secondary metabolic pathways through hairy root transformation is one of the most advanced approaches used in research. In this study, we optimized an ex-vitro one-step hairy root transformation of the RUBY system in both drug- and hemp-type cannabis, shedding light on its potential applications in secondary metabolite production. Three different strains of A. rhizogenes including (A4, ARqual, and K599) were used. Significant variation in HR induction and transformation efficiency (TE) was observed based on A. rhizogenes strains and seed types. Drug-type seedlings exhibited the highest hairy root induction, increasing by 58.8% compared to hemp-type seedlings. Also, the A4 strain consistently demonstrated the highest transformation efficiency (75%) irrespective of genotype, while the ARqual strain yielded the lowest one (8.33%). In conclusion, our study is the first to present an ex-vitro one-step transformation of both hemp- and drug-type cannabis. In comparison to the in vitro method, our ex-vitro method is simpler, faster, and has a lower risk of contamination, making it an excellent choice for the efficient production of secondary metabolites in cannabis using the CRISPR/Cas system.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".