Volatiles from <i>Serratia marcescens, S. proteamaculans</i> , and <i>Bacillus subtilis</i> Inhibit Growth of <i>Rhizopus stolonifer</i> and Other Fungi
Bibliographic record
Abstract
ABSTRACT The common soil bacteria Serratia marcescens, Serratia proteamaculans , and Bacillus subtilis produce small molecular weight volatile compounds that are fungi-static against multiple species, including the zygomycete mold Rhizopus stolonifer (Mucoromycota) and the model filamentous mold Neurospora crassa (Ascomycota). The compounds or the bacteria can be exploited in development of biological controls to prevent establishment of fungi on food and surfaces. Here, we quantified and identified bacteria-produced volatiles using headspace sampling and gas chromatography-mass spectrometry. We found that each bacterial species in culture has a unique volatile profile consisting of dozens of compounds. Using multivariate statistical approaches, we identified compounds in common or unique to each species. Our analysis suggested that three compounds, dimethyl trisulfide, anisole, and 2-undecanone, are characteristic of the volatiles emitted by these antagonistic bacteria. We developed bioassays for testing inhibition of each compound and found dimethyl trisulfide and anisole were the most potent. This work establishes a pipeline for translating volatile profiles of cultured bacteria into high quality candidate fungistatic compounds which may be useful in combination as antifungal control products. IMPORTANCE Bacteria may benefit by producing fungistatic volatiles that limit fungal growth providing a mechanism to exclude competitors for resources. Volatile production is potentially mediating long distance biological control and competitive in-teractions among microbes, but the specific bioactive compounds are poorly characterized. This work provides evidence that fungistatic compounds in complex blends can be identified using machine-learning and multivariate approaches. This is the first step in identifying pathways responsible for fungistatic volatile production in order to phenotype and select natural strains for biocontrol ability, or engineer bacteria with relevant pathways.
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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".