Genome Mining and Characterization of Biosynthetic Gene Clusters in Two Cave Strains of Paenibacillus sp.

Abstract

The genome sequencing and mining of microorganisms from unexplored and extreme environments has become important in the process of identifying novel biosynthetic pathways. In the present study, the biosynthetic potential of Paenibacillus sp. strains 23TSA30-6 and 28ISP30-2 was investigated. Both strains were isolated from the deep oligotrophic Krubera-Voronja Cave and were found to be highly active against both Gram-positive and Gram-negative bacteria. Genome mining revealed a high number of biosynthetic gene clusters in the cave strains: 21 for strain 23TSA30-6 and 19 for strain 28ISP30-2. Single clusters encoding the biosynthesis of phosphonate, terpene, and siderophore, as well as a single trans-AT polyketide synthase/non-ribosomal peptide synthetase, were identified in both genomes. The most numerous clusters were assigned to the biosynthetic pathways of non-ribosomal peptides and ribosomally synthesized and post-translationally modified peptides. Although four non-ribosomal peptide synthetase gene clusters were predicted to be involved in the biosynthesis of known compounds (fusaricidin, polymyxin B, colistin A, and tridecaptin) of the genus Paenibacillus, discrepancies in the structural organization of the clusters, as well as in the substrate specificity of some adenylation domains, were detected between the reference pathways and the clusters in our study. Among the clusters involved in the biosynthesis of ribosomally synthesized peptides, only one was predicted to be involved in the biosynthesis of a known compound: paenicidin B. Most biosynthetic gene clusters in the genomes of the cave strains showed a low similarity with the reference pathways and were predicted to represent novel biosynthetic pathways. In addition, the cave strains differed in their potential to encode the biosynthesis of a few unique, previously unknown compounds (class II lanthipeptides and three non-ribosomal peptides). The phenotypic characterization of proteinaceous and volatile compounds produced by strains 23TSA30-6 and 28ISP30-2 was also performed, and the results were compared with those of genome mining.

Cave Microbes as a Potential Source of Drugs Development in the Modern Era

“…[ 13 ], diazepinomicin of Streptomyces sp. [ 58 ], lanthipeptides, polymyxin B, paenicidin B, fusaricidin, tridecaptin, and colistin A of Paenibacillus [ 66 , 67 ]. The discovery of novel bioactive compounds emphasizes the significance of caves and other pristine environments in the development of novel antibiotics.…”

“…[ 13 ], diazepinomicin of Streptomyces sp. [ 58 ], lanthipeptides, polymyxin B, paenicidin B, fusaricidin, tridecaptin, and colistin A of Paenibacillus [ 66 , 67 ].…”

Genome-guided purification and characterization of polymyxin A1 from Paenibacillus thiaminolyticus SY20: A rarely explored member of polymyxins

“…Paenibacillus genus is known for its potential to produce a wide range of structurally diverse antimicrobial compounds, including bacteriocins, non-ribosomal lipopeptides, polyketides, peptide–polyketide hybrids, and even a wide range of volatile organic compounds ( Grady et al, 2016 ; Lebedeva et al, 2020 ). Therefore, the supernatant of P. thiaminolyticus SY20 was used to determine the physical and chemical properties, which were regarded as an essential task for characterizing the antimicrobial agents and contributed to the subsequent purification.…”

“…Forty biosynthetic gene clusters were characterized in two cave strains of Paenibacillus sp. by genome mining ( Lebedeva et al, 2020 ). Therefore, through biosynthetic gene clusters analysis, the molecular weight predicted by mass spectrometry combined with physicochemical assessment of the supernatant, we could deduce roughly what the active agent was.…”

“…This study aimed to screen a novel microbial strain with the potency of producing antimicrobial agents against foodborne pathogens or human pathogens. Since the purification and identification of an antimicrobial agent require laborious procedures if followed the classical methods ( Lebedeva et al, 2020 ), genome mining approaches that explore both DNA and peptide databases have provided a rationale for targeted isolation of active agent from complex protein mixtures ( Baindara et al, 2020 ). In addition, mass spectrometry also facilitates the identification and quantification of novel antimicrobial agents.…”