Identification and genomic characterization of Pseudomonas spp. displaying biocontrol activity against Sclerotinia sclerotiorum in lettuce
Bibliographic record
Abstract
Lettuce is an economically major leafy vegetable that is affected by numerous diseases. One of the most devastating diseases of lettuce is white mold caused by Sclerotinia sclerotiorum. Control methods for this fungus are limited due to the development of genetic resistance to commonly used fungicides, the large number of hosts and the long-term survival of sclerotia in soil. To elaborate a new and more sustainable approach to contain this pathogen, 1,210 Pseudomonas strains previously isolated from agricultural soils in Canada were screened for their antagonistic activity against S. sclerotiorum. Nine Pseudomonas strains showed strong in vitro inhibition in dual-culture confrontational assays. Whole genome sequencing of these strains revealed their affiliation with four phylogenomic subgroups within the Pseudomonas fluorescens group, namely Pseudomonas corrugata, Pseudomonas asplenii, Pseudomonas mandelii, and Pseudomonas protegens. The antagonistic strains harbor several genes and gene clusters involved in the production of secondary metabolites, including mycin-type and peptin-type lipopeptides, and antibiotics such as brabantamide, which may be involved in the inhibitory activity observed against S. sclerotiorum. Three strains also demonstrated significant in planta biocontrol abilities against the pathogen when either inoculated on lettuce leaves or in the growing substrate of lettuce plants grown in pots. They however did not impact S. sclerotiorum populations in the rhizosphere, suggesting that they protect lettuce plants by altering the fitness and the virulence of the pathogen rather than by directly impeding its growth. These results mark a step forward in the development of biocontrol products against S. sclerotiorum.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".