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

Abstract

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.

Figure 1. Core-gene phylogeny of 64 B. ambifaria strains (a) aligned with presence/absence grid of known antimicrobial specialized metabolite BGCs (b) and antimicrobial activity heatmap (c).

(a) The phylogenetic tree was constructed based on 3784 core genes identified and aligned using the software Roary. The root was determined using a secondary tree containing an outgroup species, Burkholderia vietnamiensis G4 (Supplementary Figure 10). Six clades were defined in the phylogeny, however, strains BCC1066 and MEX-5 branched outside these clades. Strains subject to further LC-MS analysis are highlighted in bold; strains with historical biocontrol usage are indicated with an asterisk. RAxML was used to construct the maximum-likelihood phylogeny using the generalised time reversible (GTR) model with a GAMMA substitution (100 bootstraps). Nodes with bootstrap values <70% are indicated with black circles. The evolutionary distance scale bar represents the number of base substitutions per site. (b) The presence of the eight characterised anti-fungal and antibiotic gene clusters: pyrrolnitrin, burkholdine, AFC-BC11, hydroxyquinolines, cepacin A, bactobolins, phenazine and enacyloxin IIa in the 64 B. ambifaria strains are ordered by phylogenetic position. Matrix generated using Phandango. (c) The antimicrobial activity of 62 B. ambifaria strains were defined by measuring the diameter of the zones of inhibition (mm); n = 2 overlays of each B. ambifaria strain against each susceptibility organism. Heatmap shows mean zone of inhibition. Strains MEX-5 and IOP40-10 were not available for the antimicrobial production assay.

Figure 2. Specialized metabolite BGC network analysis of 64 B. ambifaria strains.

A total of 1,272 BGCs were detected across the 64 strains, and dereplication indicated these represented 38 distinct BGCs (38 distinct network clusters). Nucleotide sequences were clustered using Mash and visualized with Cytoscape. This network analysis was used to provide a visual summary of the breadth of B. ambifaria BGCs including their biosynthetic diversity, strain distribution, and core or accessory nature within the species. Each node represents a specialized metabolite BGC extracted from a single B. ambifaria strain. Node colours represent specialized metabolite classes, and numbers correspond to the number BGC examples (nodes) of each distinct BGC (network cluster). Core BGCs were defined as BGCs that occurred in >98% of B. ambifaria strains. Characterised BGCs known in the literature are labelled. BGCs responsible for pyrrolnitrin, AFC-BC11 and hydroxyquinolines biosynthesis are classified as Other (O) by antiSMASH but represent different metabolite classes not recognised by antiSMASH.

Figure 3. Unrooted phylogeny of LuxR protein homologues extracted from 64 B. ambifaria strains.

Branches were labelled with characterised quorum sensing systems or putative/confirmed LuxR regulatory functions based on the literature and annotated flanking genes starting within 5 kbp upstream and/or downstream of the luxR gene. The number of strains encoding distinct LuxR homologues is indicated in brackets. A total of 356 homologues were identified across the 64 strains, representing 14 distinct LuxR protein clades. FastTree was used to construct the approximate-maximum-likelihood phylogeny using the generalised time reversible substitution model. The evolutionary distance scale bar represents the number of base substitutions per site.

Figure 4. Organization of the cepacin A biosynthetic gene cluster, LC-MS analysis of cepacin A production and antimicrobial screening of B. ambifaria BCC0191 wild-type (WT) and cepacin A deficient derivative (::ccnJ).

(a) Organisation and putative function of genes within the cepacin A BGC; further annotation details are provided in Supplementary Figure 9. The insertion site of the vector used during mutagenesis is highlighted by the inverted yellow triangle. (b) Zones of inhibition against S. aureus NCTC 12981, P. carotovorum LMG 2464 and P. ultimum Trow var. ultimum MUCL 16164 by BCC0191 WT and BCC0191::ccnJ. Scale bar represents 20 mm. n = 3 biological replicates. Images were converted to greyscale, brightness decreased by 20%, and contrast increased by 20%. (c) Extracted ion chromatograms at m/z = 293.08 ± 0.02, corresponding to [M + Na]⁺ for cepacin A, from LC-MS analyses of crude extracts from agar-grown cultures of BCC0191 WT (top) and the BCC0191::ccnJ mutant (bottom); n = 3 independent LC-MS analyses of WT and mutant cultures. (d) Structure of cepacin A, the identity of which was confirmed by comparison to an authentic standard from a known producer (Supplementary Figure 3).

Figure 5. Biological control of Pythium damping-off disease is mediated by B. ambifaria cepacin.

(a) Pea germination (14 days) in P. ultimum infested soil observed for groups of 10 seeds coated with 10⁷, 10⁶ and 10⁵ cfu, respectively, of BCC0191 wild-type (WT) and BCC0191::ccnJ. The overall percentage survival of germinating peas treated with the WT and BCC0191::ccnJ B. ambifaria strains is shown on the right of panel A. Survival was assessed as plants that had stems >30 mm in height after 14 days. Plant survival was significantly different at every inoculum level between BCC0191 WT and BCC0191::ccnJ, as indicated by two-sided t-test or Welch’s two-sided t-test (* = p < 0.05; ** = p < 0.01); significant difference (left to right) p = 0.002, p = 0.03, p = 0.002 with 95% confidence interval. n = 10 seeds per condition (seed coat) and dosage (cfu/seed), repeated in triplicate. Centre bar represents mean, and error bars represent standard error. (b) Pea germination (14 days) in P. ultimum infested soil observed for groups of 10 seeds coated with 10⁷, 10⁶ and 10⁵ cfu, respectively, of BCC0191 WT and BCC0191Δc3. The overall percentage survival of germinating peas treated with BCC0191 WT and BCC0191Δc3 is shown on the right of panel B. No significant difference (left to right: p = 0.22, p = 0.22, p = 0.16), as determined by two-sided t-test with 95% confidence interval, in plant survival between BCC0191 WT and BCC0191Δc3 at all inoculum levels. n = 10 seeds per condition (seed coat) and dosage (cfu/seed), repeated in triplicate. Centre bar represents mean, and error bars represent standard error.