Genome shuffling enhances biocontrol abilities of Streptomyces strains against two potato pathogens
Bibliographic record
Abstract
AIMS: To employ the genome shuffling technique for improving the phenotype of a biocontrol control agent of the genus Streptomyces. METHODS AND RESULTS: Two rounds of genome shuffling (GS) were carried out with Streptomyces melanosporofaciens EF-76, a geldanamycin producer. Six fusants that showed optimized in vitro antagonistic activity against Streptomyces scabies or Phytophthora infestans, two important pathogens of potato crops, were selected. All selected fusants retained the capacity to produce geldanamycin, but none overproduced this antibiotic. The higher antagonism ability appeared to result from a diversification of secreted metabolites. Seven or eight metabolites were detected in the HPLC profiles of parental strains, whereas 12-15 were detected in fusant strains. Biocontrol assays revealed that four of six fusants protected tubers more efficiently than parental strains. CONCLUSIONS: GS emerged as an elegant and rapid tool to optimize the antagonistic ability of Streptomyces strains. Optimization of the in vitro antagonistic activity against plant pathogens appears to be an effective approach to select for improved biocontrol agents. The enhanced phenotype did not depend on an overproduction of a specific antibiotic but rather on the secretion of a wider variety of secondary metabolites. SIGNIFICANCE AND IMPACT OF THE STUDY: Improved capacities of a biocontrol agent compensate for the lack of efficient chemical control of potato scab.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".