Enhanced production of select phytocannabinoids in medical Cannabis cultivars using microbial consortia
Bibliographic record
Abstract
The root microbiome of medical cannabis plants has been largely unexplored due to past legal restrictions in many countries. Microbes that live on and within the tissue of Cannabis sativa L. similar to other plants, provide advantages such as stimulating plant growth, helping it absorb minerals, providing protection against pathogen attacks, and influencing the production of secondary metabolites. To gain insight into the microbial communities of C. sativa cultivars with different tetrahydrocannabinol (THC) and cannabidiol (CBD) profiles, a greenhouse trial was carried out with and without inoculants added to the growth substrate. Illumina MiSeq metabarcoding was used to analyze the root and rhizosphere microbiomes of the five cultivars. Plant biomass production showed higher levels in three of five cultivars inoculated with the arbuscular mycorrhizal fungus Rhizophagus irregularis and microbial suspension. The blossom dry weight of the cultivar THE was greater when inoculated with R. irregularis and microbial suspension than with no inoculation. Increasing plant biomass and blossom dry weight are two important parameters for producing cannabis for medical applications. In mature Cannabis , 12 phytocannabinoid compounds varied among cultivars and were affected by inoculants. Significant differences ( p ≤ 0.01) in concentrations of cannabidivarinic acid (CBDVA), cannabidivarin (CBDV), cannabigerol (CBG), cannabidiol (CBD), and cannabigerolic acid (CBGA) were observed in all Cannabis cultivars when amended with F, K1, and K2 inoculants. We found microbes that were shared among cultivars. For example, Terrimicrobium sp., Actinoplanes sp., and Trichoderma reesei were shared by the cultivars ECC-EUS-THE, CCL-ECC, and EUS-THE, respectively. Actinoplanes sp. is a known species that produces phosphatase enzymes, while Trichoderma reesei is a fungal train that produces cellulase and contributes to organic matter mineralization. However, the role of Terrimicrobium sp. as an anaerobic bacterium remains unknown. This study demonstrated that the use of inoculants had an impact on the production of phytocannabinoids in five Cannabis cultivars. These inoculants could have useful applications for optimizing cannabis cultivation practices and increasing the production of phytocannabinoids.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".