Volatiles fromSerratia marcescens, S. proteamaculans, andBacillus subtilisInhibit Growth ofRhizopus stoloniferand Other Fungi

Author:

Carter-House Derreck,Chung Joshua,McDonald Skylar,Mauck Kerry,Stajich Jason EORCID

Abstract

abstractThe common soil bacteriaSerratia marcescens, Serratia proteamaculans, andBacillus subtilisproduce small molecular weight volatile compounds that are fungistatic against multiple species, including the zygomycete moldRhizopus stolonifer(Mucoromycota) and the model filamentous moldNeurospora 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 chromatographymass 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 concentration dependent inhibition of each compound and found dimethyl trisulfide and anisole were the most potent with the highest inhibition at the lowest concentration (.7 mg/cm3). This work establishes a pipeline for translating volatile profiles of cultured bacteria into high quality candidate fungistatic compounds which may be useful in combination antifungal control products.importanceBacteria 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 interactions 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.

Publisher

Cold Spring Harbor Laboratory

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