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Record W4281739725 · doi:10.1002/pld3.412

Delineating genetic regulation of cannabinoid biosynthesis during female flower development in <scp> <i>Cannabis sativa</i> </scp>

2022· article· en· W4281739725 on OpenAlexfundno aff
Peter V. Apicella, Lauren B. Sands, Yi Ma, Gerald A. Berkowitz

Bibliographic record

VenuePlant Direct · 2022
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersAlberta Hatching Egg Producers
KeywordsCannabis sativaCannabinoidCannabisBiologyBiosynthesisGeneGeneticsBotanyMedicineReceptorPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.240
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2022
Admission routes1
Has abstractyes

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