Secondary Metabolites of Bacillus zhangzhouensis from Zygophyllum oxianum and Their Antifungal and Plant Growth-Regulating Properties
Bibliographic record
Abstract
Plant species harbor diverse rhizospheric bacteria within their resilient root zones, serving as a valuable reservoir of bioactive microorganisms with strong potential for natural antifungal and plant growth-promoting applications. This study aimed to investigate the antagonistic potential of Bacillus zhangzhouensis, isolated from Zygophyllum oxianum in the Aral Sea region, Uzbekistan, against the fungal pathogen Cytospora mali. Due to its strong antifungal activity, B. zhangzhouensis was selected for bioactive compound profiling. Methanolic extracts were fractionated via silica and Sephadex gel chromatography, followed by antifungal screening using the agar diffusion method. A highly active fraction (dichloromethane/methanol, 9:1) underwent further purification, yielding twelve antifungal sub-fractions. Mass spectrometry analysis across positive and negative ion modes identified 2475 metabolites, with polar solvents—particularly methanol—enhancing compound recovery. Refinement using Bacillus-specific references identified six known antibiotics. Four pure compounds were isolated and structurally characterized using NMR: oleanolic acid, ursolic acid, cyclo-(Pro-Ser), and uracil. Their growth regulatory activity was assessed on Amaranthus retroflexus, Nicotiana benthamiana, triticale, and Triticum aestivum at concentrations of 5, 20, 100, and 500 mg L−1. All compounds negatively affected root growth in a concentration-dependent manner, especially in monocots. Interestingly, some treatments enhanced stem growth, particularly in N. benthamiana. These findings indicate that B. zhangzhouensis produces diverse bioactive compounds with dual antifungal and plant growth-modulatory effects, highlighting its potential as a biocontrol agent and a source of natural bioactive compounds.
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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".