Simplified and efficient <i>Agrobacterium</i> -mediated genetic transformation of <i>Botrytis cinerea</i> using mycelia
Bibliographic record
Abstract
Botrytis cinerea is a necrotrophic fungal pathogen responsible for grey mold disease, causing significant crop losses globally. Effective genetic manipulation of this pathogen is crucial for understanding its biology and developing better disease management strategies. However, current transformation methods typically use conidia spores or protoplasts, processes that are labor-intensive, inefficient, and often yield inconsistent results. We developed a novel Agrobacterium-mediated transformation protocol that utilizes B. cinerea mycelia, eliminating the need for sporulation or protoplast generation and simplifying genetic manipulation. Using a newly constructed binary expression vector encoding green fluorescent protein and hygromycin resistance, we transformed four different B. cinerea strains. All transformations resulted in stable integration and robust green fluorescent protein expression, confirmed by quantitative polymerase chain reaction and confocal microscopy. Although transformants exhibited altered colony morphology compared to wild-type strains, they remained viable and stably expressed the integrated transgene, supporting the method’s utility for genetic studies in B. cinerea. This streamlined method provides a reliable, efficient, and scalable approach for genetic studies in B. cinerea, significantly enhancing fungal functional genomics research and plant pathology investigations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".