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

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Mullins, Alex J 
Murray, James AH 
Bull, Matthew J 
Jenner, Matthew 
Jones, Cerith 

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.

Animals, Base Sequence, Biological Control Agents, Burkholderia, Burkholderia cepacia complex, DNA, Bacterial, Disease Models, Animal, Genes, Bacterial, Lactones, Mice, Multigene Family, Phylogeny, Plant Diseases, Plasmids, Pythium, Repressor Proteins, Respiratory Tract Infections, Soil Microbiology, Trans-Activators, Virulence
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Nature Microbiology
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Springer Nature
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Biotechnology and Biological Sciences Research Council (BB/L021692/1)
A.J.M. is funded by a Biotechnology and Biological Sciences Research Council (BBSRC) South West doctoral training partnership award (BY1910 7007). E.M., G.L.C., T.R.C. and J.P. acknowledge additional support for genome mining from BBSRC award BB/L021692/1; C.J. and M.J. were funded by this award. M.J. is currently the recipient of a BBSRC Future Leader Fellowship (BB/R01212/1). The Bruker maXis II UHPLC-ESI-Q-TOF-MS system used in this research was funded by the BBSRC (BB/M017982/1). G.W. was supported by awards to E.M. from the Life Sciences Bridging Fund and Wellcome Trust Institutional Strategic Support Fund held at Cardiff University. T.R.C. and M.J.B. acknowledge funding support from the Medical Research Council’s Cloud Infrastructure for Microbial Bioinformatics (MR/L015080/1), which provided the computational resources to undertake the analyses for this work. D.R.N. and A.E.G. acknowledge funding from a Wellcome Trust and Royal Society Sir Henry Dale Fellowship awarded to D.R.N. (grant number 204457/Z/16/Z). G.L.C. is the recipient of a Wolfson Research Merit Award from the Royal Society (WM130033).