Genomically-selected antifungal Bacillaceae strains improve wheat yield and baking quality
Bibliographic record
Abstract
Soil microbial diversity degradation through agricultural intensification necessitates sustainable alternatives. This study employed genomic and phenotypic approaches to characterize wheat rhizosphere-associated Bacillaceae for agricultural applications. Initial screening of 576 sporulating isolates for antifungal activity against Fusarium graminearum, followed by RAPD analysis, identified 39 distinct genetic profiles, out of which 15 were classified in Bacillus amyloliquefaciens or Priestia megaterium groups by 16S RNA sequence. Whole-genome sequencing of selected strains enabled precise taxonomic classification and comprehensive trait prediction using in silico tools. Genomic mining revealed strain-specific distributions of beneficial traits, including antimicrobial compound production pathways and plant growth-promoting characteristics. Phenotypic validation confirmed key predicted traits while uncovering additional functionalities not detected in silico. Integration of kernel bioassays, pot experiments, and field trials identified Bacillus velezensis ZAV-W70 and P. megaterium ZAV-W64 as promising biofertilizer and biocontrol candidates, demonstrating enhanced yield without fungicides and improved bread-making quality, respectively. These findings highlight the value of combining genomic analysis with traditional screening methods for developing effective agricultural biologicals, contributing to sustainable wheat production practices. KEY POINTS: • Rhizosphere Bacillaceae strains show dual plant growth promotion and biocontrol • B. velezensis ZAV-W70 and P. megaterium ZAV-W64 increase wheat yield • ZAV-W64 increases bread-making quality including total gluten and alveograph W.
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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".