Evaluating the biocontrol potential of Canadian strain Bacillus velezensis 1B-23 via its surfactin production at various pHs and temperatures
Bibliographic record
Abstract
BACKGROUND: Microorganisms, including Bacillus species are used to help control plant pathogens, thereby reducing reliance on synthetic pesticides in agriculture. Bacillus velezensis strain 1B-23 has been shown to reduce symptoms of bacterial disease caused by Clavibacter michiganensis subsp. michiganensis in greenhouse-grown tomatoes, with in vitro studies implicating the lipopeptide surfactin as a key antimicrobial. While surfactin is known to be effective against many bacterial pathogens, it is inhibitory to a smaller proportion of fungi which nonetheless cause the majority of crop diseases. In addition, knowledge of optimal conditions for surfactin production in B. velezensis is lacking. RESULTS: Here, B. velezensis 1B-23 was shown to inhibit in vitro growth of 10 fungal strains including Candida albicans, Cochliobolus carbonum, Cryptococcus neoformans, Cylindrocarpon destructans Fusarium oxysporum, Fusarium solani, Monilinia fructicola, and Rhizoctonia solani, as well as two strains of C. michiganensis michiganensis. Three of the fungal strains (C. carbonum, C. neoformans, and M. fructicola) and the bacterial strains were also inhibited by purified surfactin (surfactin C, or [Leu7] surfactin C15) from B. velezensis 1B-23. Optimal surfactin production occurred in vitro at a relatively low temperature (16 °C) and a slightly acidic pH of 6.0. In addition to surfactin, B. velenzensis also produced macrolactins, cyclic dipeptides and minor amounts of iturins which could be responsible for the bioactivity against fungal strains which were not inhibited by purified surfactin C. CONCLUSIONS: Our study indicates that B. velezensis 1B-23 has potential as a biocontrol agent against both bacterial and fungal pathogens, and may be particularly useful in slightly acidic soils of cooler climates.
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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) | 0.000 | 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".