<i>Agrobacterium</i>-mediated transformation of THC-containing <i>Cannabis sativa</i> L. yields a high frequency of transgenic calli expressing bialaphos resistance and non-expressor of PR1 (<i>NPR1)</i> genes
Bibliographic record
Abstract
We established transformation technologies using Agrobacterium tumefaciens (Smith & Townsend) Conn to insert foreign genes into high THC-containing cannabis ( Cannabis sativa L.). The Arabidopsis non-expressor of pathogenesis-related protein 1 ( AtNPR1) gene was selected as a potentially useful agronomic gene, which was linked to the bar gene from Streptomyces that encodes herbicide resistance. We investigated how Agrobacterium strains (EHA105 and GV3101), glufosinate concentrations, explant source, and light intensity affected transformation frequency (TF). Transformation was confirmed by RT-PCR with primers for the NPR1 or bar genes. Glufosinate at 0.5–1 mg/L inhibited growth of non-transformed calli within 8 weeks following A. tumefaciens infection. Strain EHA105 yielded a higher TF compared to strain GV3101. Whole leaflets yielded a higher TF compared to sectioned leaf explants with strain GV3101. However, this effect was not seen with EHA105. Petiole segments showed a higher TF than leaf sections with strain EHA105. Placing explants under light or dark conditions did not affect TF, which ranged from 5% to 95% in different experiments. This is the first report of successful transformation of two high THC-containing C. sativa genotypes with two foreign genes simultaneously— AtNPR1 and bar. The recovery of plantlets from transgenic calli was not attempted and awaits further research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".