Biosynthetic Gene Clusters in Sequenced Genomes of Four Contrasting Rhizobacteria in Phytopathogen Inhibition and Interaction with Capsicum annuum Roots

Through screening of rhizobacteria, species that effectively suppress phytopathogens and/or promote plant growth are found. Genome sequencing is a crucial step in obtaining a complete characterization of microorganisms for biotechnological applications. This study aimed to sequence the genomes of four rhizobacteria that differ in their inhibition of four root pathogens and in their interaction with chili pepper roots to identify the species and analyze differences in the biosynthetic gene clusters (BGCs) for antibiotic metabolites and to determine possible phenotype-genotype correlations. Results from sequencing and genome alignment identified two bacteria as Paenibacillus polymyxa, one as Kocuria polaris, and one that was previously sequenced as Bacillus velezensis. Analysis with antiSMASH and PRISM tools showed that B. velezensis 2A-2B, the strain with the best performance of referred characteristics, had 13 BGCs, including those related to surfactin, fengycin, and macrolactin, not shared with the other bacteria, whereas P. polymyxa 2A-2A and 3A-25AI, with up to 31 BGCs, showed lower pathogen inhibition and plant hostility; K. polaris showed the least antifungal capacity. P. polymyxa and B. velezensis had the highest number of BGCs for nonribosomal peptides and polyketides. In conclusion, the 13 BGCs in the genome of B. velezensis 2A-2B that were not present in the other bacteria could explain its effective antifungal capacity and could also contribute to its friendly interaction with chili pepper roots. The high number of other BGCs for nonribosomal peptides and polyketide shared by the four bacteria contributed much less to phenotypic differences. IMPORTANCE To advance the characterization of a microorganism as a biocontrol agent against phytopathogens, it is highly recommended to analyze the potential of the profile of secondary metabolites as antibiotics that it produces to counteract pathogens. Some specific metabolites have positive impacts in plants. By analyzing sequenced genomes with bioinformatic tools, such as antiSMASH and PRISM, outstanding bacterial strains with high potential to inhibit phytopathogens and/or promote plant growth can be quickly selected to confirm and expand our knowledge of BGCs of great value in phytopathology.