Genome mining identifies cepacin as a plant-protective metabolite of the biopesticidal bacterium Burkholderia ambifaria

Alex J. Mullins, James A. H. Murray, Matthew J. Bull, Matthew Jenner, Cerith Jones, Gordon Webster, Angharad E. Green, Daniel R. Neill, Thomas R. Connor, Julian Parkhill, Gregory L. Challis, Eshwar Mahenthiralingam

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    Beneficial microorganisms are widely used in agriculture for control of plant pathogens, but a lack of efficacy and safety information has limited the exploitation of multiple promising biopesticides. We applied phylogeny-led genome mining, metabolite analyses and biological control assays to define the efficacy of Burkholderia ambifaria, a naturally beneficial bacterium with proven biocontrol properties but potential pathogenic risk. A panel of 64 B. ambifaria strains demonstrated significant antimicrobial activity against priority plant pathogens. Genome sequencing, specialized metabolite biosynthetic gene cluster mining and metabolite analysis revealed an armoury of known and unknown pathways within B. ambifaria. The biosynthetic gene cluster responsible for the production of the metabolite cepacin was identified and directly shown to mediate protection of germinating crops against Pythium damping-off disease. B. ambifaria maintained biopesticidal protection and overall fitness in the soil after deletion of its third replicon, a non-essential plasmid associated with virulence in Burkholderia cepacia complex bacteria. Removal of the third replicon reduced B. ambifaria persistence in a murine respiratory infection model. Here, we show that by using interdisciplinary phylogenomic, metabolomic and functional approaches, the mode of action of natural biological control agents related to pathogens can be systematically established to facilitate their future exploitation.
    Original languageEnglish
    Pages (from-to)996–1005
    JournalNature Microbiology
    Publication statusPublished - 4 Mar 2019


    • antimicrobials
    • bacterial genomics
    • biotechnology
    • chemical biology
    • computational biology and bioinformatics


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