Exo-metabolome profiling of soybean endophytes: a road map of antagonism against <i>Fusarium oxysporum</i>
Bibliographic record
Abstract
ABSTRACT Soybean is one of the world’s most valuable agricultural crops. The phytopathogen Fusarium oxysporum compromises the growth of soybean and decreases its yield. Controlling this pathogen with fungicides is environmentally harmful. As with other species, soybean plants are associated with beneficial bacterial communities that contribute to plant health. Based on 16S rRNA sequencing, the present study identifies three important bacterial strains from this community: Bacillus -HT1, Bacillus -HT2, and Bacillus -HT3. Screening of these bacteria against F. oxysporum of soybean showed that Bacillus- HT1 and Bacillus- HT2 are antagonistic against this pathogen. The overall changes in fungal structure caused by biocontrol bacteria were visualized using scanning electron microscopy. An untargeted metabolomics approach based on liquid chromatography–tandem mass spectrometry was employed to uncover the bacterial secondary metabolites underlying this antagonistic activity. Based on MS2 fragmentation analysis and annotation, Bacillus- HT1 and Bacillus -HT2 potentially produce significant amounts of putative antifungal compounds, whereas Bacillus -HT3 lacks these. This absence of relevant metabolites correlates with this strain’s inability to inhibit F. oxysporum fungal growth. Among the putative antifungal metabolites produced by Bacillus- HT1 and Bacillus -HT2 are bacillibactin and surfactin C. Bacillus -HT1 also produces significant amounts of fusidic acid and kynurenine, while Bacillus- HT2 produces significant amounts of putative antifungal fatty acids. These findings suggest that Bacillus -HT1 and Bacillus- HT2 have the potential to be developed into effective biocontrol agents, reducing chemical fungicides in crop cultivation, improving plant health and yield, thus contributing to the protection of biodiversity and soil health in the long term. IMPORTANCE Modern agricultural practices depend heavily on synthetic fertilizers and pesticides, which are major contributors to greenhouse gas emissions, groundwater pollution, and disruptions in agroecosystem dynamics. These challenges underscore the pressing need for sustainable alternatives that maintain crop productivity while minimizing environmental impact. Here, we investigate the use of antifungal-producing biocontrol agents as a microbial-based strategy to suppress pathogenic fungi in soybean cultivation. By harnessing the metabolic capabilities of beneficial microbes, this approach offers a promising path toward environmentally responsible crop protection, with implications for future food security and sustainable agricultural systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".