Molecular and biochemical detection of fengycin- and bacillomycin D-producing <i>Bacillus</i> spp., antagonistic to fungal pathogens of canola and wheat
Bibliographic record
Abstract
Bacillus species are well known for their ability to control plant diseases through various mechanisms, including the production of secondary metabolites. Bacillus subtilis DFH08, an antagonist of Fusarium graminearum, and other Bacillus spp. that are antagonists of common fungal pathogens of canola were screened for peptide synthetase biosynthetic genes of fengycin and bacillomycin D. Specific polymerase chain reaction (PCR) primers identified B. subtilis strains DFH08 and 49 for the presence of the fenD gene of the fengycin operon. Bacillus cereus DFE4, Bacillus amyloliquefaciens strains DFE16 and BS6, and B. subtilis 49 were identified for the presence of the bamC gene of the bacillomycin D synthetase biosynthetic operon. Both fengycin and bacillomycin D were detected in the culture extract of strain Bs49, characterized through MALDI-TOF-MS (matrix-assisted laser desorption ionization - time of flight - mass spectrometry), and their antifungal activities demonstrated against F. graminearum and Sclerotinia sclerotiorum. This study designed and used specific PCR primers for the detection of potential fengycin- and bacillomycin D-producing bacterial antagonists and confirmed the molecular detection with the biochemical detection of the corresponding antibiotic produced. This is also the first report of a B. cereus strain (DFE4) to have bacillomycin D biosynthetic genes. Bacteria that synthesize these lipopeptides could act as natural genetic sources for genetic engineering of the peptide synthetases for production of novel peptides.
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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".