Delineating genetic regulation of cannabinoid biosynthesis during female flower development in <scp> <i>Cannabis sativa</i> </scp>
Bibliographic record
Abstract
Abstract Cannabinoids are predominantly produced in the glandular trichomes on cannabis female flowers. There is little known on how cannabinoid biosynthesis is regulated during female flower development. We aim to understand the rate‐limiting step(s) in the cannabinoid biosynthetic pathway. We investigated the transcript levels of cannabinoid biosynthetic genes together with cannabinoid contents during 7 weeks of female flower development. We demonstrated that the enzymatic steps for producing cannabigerol (CBG), which involve genes GPPS , PT , TKS , and OAC , could rate limit cannabinoid biosynthesis. Our findings further suggest that upregulation of cannabinoid synthases, CBDAS and THCAS in a commercial hemp and medical marijuana variety, respectively, is not critical for cannabinoid biosynthesis. The cannabinoid biosynthetic genes are generally upregulated during flower maturation; increased expression occurs coincident with glandular trichome development and cannabinoid production in the maturing flower. The results also suggest that different cannabis varieties may experience discrete transcriptional regulation of cannabinoid biosynthetic genes. In addition, we showed that methyl jasmonate (MeJA) can potentially increase cannabinoid production. We propose that biweekly applications of 100 μM MeJA starting from flower initiation would be efficacious for promoting cannabinoid biosynthesis. Our findings provide important genetic information for cannabis breeding to generate new varieties with favorable traits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".