Expression of Putative Defense Responses in Cannabis Primed by Pseudomonas and/or Bacillus Strains and Infected by Botrytis cinerea
Bibliographic record
Abstract
Cannabis ( Cannabis sativa L.) offers many industrial, agricultural, and medicinal applications, but is commonly threatened by the gray mold disease caused by the fungus Botrytis cinerea . With few effective control measures currently available, the use of beneficial rhizobacteria represents a promising biocontrol avenue for cannabis. To counter disease development, plants rely on a complex network of inducible defense pathways, allowing them to respond locally and systemically to pathogens attacks. In this study, we present the first attempt to control gray mold in cannabis using beneficial rhizobacteria, and the first investigation of cannabis defense responses at the molecular level. Four promising Pseudomonas (LBUM223 and WCS417r) and Bacillus strains (LBUM279 and LBUM979) were applied as single or combined root treatments to cannabis seedlings, which were subsequently infected by B. cinerea. Symptoms were recorded and the expression of eight putative defense genes was monitored in leaves by reverse transcription quantitative polymerase chain reaction. The rhizobacteria did not significantly control gray mold and all infected leaves were necrotic after a week, regardless of the treatment. Similarly, no systemic activation of putative cannabis defense genes was reported, neither triggered by the pathogen nor by the rhizobacteria. However, this work identified five putative defense genes ( ERF1 , HEL , PAL , PR1 , and PR2 ) that were strongly and sustainably induced locally at B. cinerea ’s infection sites, as well as two stably expressed reference genes ( TIP41 and APT1 ) in cannabis. These markers will be useful in future researches exploring cannabis defense pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".