1887

Abstract

Microbes host a huge variety of biosynthetic gene clusters that produce an immeasurable array of secondary metabolites with many different biological activities such as antimicrobial, anticarcinogenic and antiviral. Despite the complex task of isolating and characterizing novel natural products, microbial genomic strategies can be useful for carrying out these types of studies. However, although genomic-based research on secondary metabolism is on the increase, there is still a lack of reports focusing specifically on the genus . In this work, we aimed (i) to unveil the main biosynthetic systems related to secondary metabolism in type strains (ii) to study the evolutionary processes that drive the diversification of their coding regions and (iii) to select strains showing promising results in the search for useful natural products. We performed a comparative genomic study on 194 species, paying special attention to the evolution and distribution of different classes of biosynthetic gene clusters and the coding features of antimicrobial peptides. Using EvoMining, a bioinformatic approach for studying evolutionary processes related to secondary metabolism, we sought to decipher the protein expansion of enzymes related to the lipid metabolism, which may have evolved toward the biosynthesis of novel secondary metabolites in . The types of metabolites encoded in type strains were predominantly non-ribosomal peptide synthetases, bacteriocins, N-acetylglutaminylglutamine amides and ß-lactones. Also, the evolution of genes related to secondary metabolites was found to coincide with species diversification. Interestingly, only a few species encode polyketide synthases, which are related to the lipid metabolism broadly distributed among bacteria. Thus, our EvoMining-based search may help to discover new types of secondary metabolite gene clusters in which lipid-related enzymes are involved. This work provides information about uncharacterized metabolites produced by type strains, whose gene clusters have evolved in a species-specific way. Our results provide novel insight into the secondary metabolism of and will serve as a basis for the prioritization of the isolated strains. This article contains data hosted by Microreact.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000758
2022-02-23
2024-05-20
Loading full text...

Full text loading...

/deliver/fulltext/mgen/8/2/mgen000758.html?itemId=/content/journal/mgen/10.1099/mgen.0.000758&mimeType=html&fmt=ahah

References

  1. Hug JJ, Krug D, Müller R. Bacteria as genetically programmable producers of bioactive natural products. Nat Rev Chem 2020; 4:172–193 [View Article]
    [Google Scholar]
  2. Albarano L, Esposito R, Ruocco N, Costantini M. Genome Mining as New Challenge in Natural Products Discovery. Mar Drugs 2020; 18:199 [View Article] [PubMed]
    [Google Scholar]
  3. Wohlleben W, Mast Y, Stegmann E, Ziemert N. Antibiotic drug discovery. Microb Biotechnol 2016; 9:541–548 [View Article] [PubMed]
    [Google Scholar]
  4. Blin K, Shaw S, Steinke K, Villebro R, Ziemert N et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res 2019; 47:W81–W87 [View Article] [PubMed]
    [Google Scholar]
  5. Meunier L, Tocquin P, Cornet L, Sirjacobs D, Leclère V et al. Palantir: a springboard for the analysis of secondary metabolite gene clusters in large-scale genome mining projects. Bioinformatics 2020; 36:4345–4347 [View Article] [PubMed]
    [Google Scholar]
  6. Navarro-Muñoz JC, Selem-Mojica N, Mullowney MW, Kautsar SA, Tryon JH et al. A computational framework to explore large-scale biosynthetic diversity. Nat Chem Biol 2020; 16:60–68 [View Article] [PubMed]
    [Google Scholar]
  7. Ziemert N, Weber T, Medema MH. Genome mining approaches to bacterial natural product discovery. Chem Biol 2020; 6:19–33
    [Google Scholar]
  8. Kang HS. Phylogeny-guided (meta)genome mining approach for the targeted discovery of new microbial natural products. J Ind Microbiol Biotechnol 2017; 44:285–293 [View Article] [PubMed]
    [Google Scholar]
  9. Beedessee G, Hisata K, Roy MC, Van Dolah FM, Satoh N et al. Diversified secondary metabolite biosynthesis gene repertoire revealed in symbiotic dinoflagellates. Sci Rep 2019; 9:1–12 [View Article] [PubMed]
    [Google Scholar]
  10. van Santen JA, Kautsar SA, Medema MH, Linington RG. Microbial natural product databases: moving forward in the multi-omics era. Nat Prod Rep 2021; 38:264–278 [View Article] [PubMed]
    [Google Scholar]
  11. Sélem-Mojica N, Aguilar C, Gutiérrez-García K, Martínez-Guerrero CE, Barona-Gómez F. EvoMining reveals the origin and fate of natural product biosynthetic enzymes. Microb Genom 2019; 5:e000260 [View Article] [PubMed]
    [Google Scholar]
  12. Cruz-Morales P, Kopp JF, Martínez-Guerrero C, Yáñez-Guerra LA, Selem-Mojica N et al. Phylogenomic analysis of natural products biosynthetic gene clusters allows discovery of arseno-organic metabolites in model Streptomycetes . Genome Biol Evol 2016; 8:1906–1916 [View Article] [PubMed]
    [Google Scholar]
  13. Creamer KE, Kudo Y, Moore BS, Jensen PR. Phylogenetic analysis of the salinipostin γ-butyrolactone gene cluster uncovers new potential for bacterial signalling-molecule diversity. Microb Genom 2021; 7:000568 [View Article] [PubMed]
    [Google Scholar]
  14. Chevrette MG, Carlson CM, Ortega HE, Thomas C, Ananiev GE et al. The antimicrobial potential of Streptomyces from insect microbiomes. Nat Commun 2019; 10:1–11 [View Article] [PubMed]
    [Google Scholar]
  15. Adamek M, Alanjary M, Ziemert N. Applied evolution: phylogeny-based approaches in natural products research. Nat Prod Rep 2019; 36:1295–1312 [View Article] [PubMed]
    [Google Scholar]
  16. Zhang MM, Wong FT, Wang Y, Luo S, Lim YH et al. CRISPR-Cas9 strategy for activation of silent Streptomyces biosynthetic gene clusters. Nat Chem Biol 2017; 13:607–609 [View Article] [PubMed]
    [Google Scholar]
  17. Loper JE, Hassan KA, Mavrodi DV, Davis EW, Lim CK et al. Comparative genomics of plant-associated Pseudomonas spp.: insights into diversity and inheritance of traits involved in multitrophic interactions. PLoS Genet 2012; 8:e1002784 [View Article] [PubMed]
    [Google Scholar]
  18. Crone S, Vives-Flórez M, Kvich L, Saunders AM, Malone M et al. The environmental occurrence of Pseudomonas aeruginosa . APMIS 2020; 128:220–231 [View Article] [PubMed]
    [Google Scholar]
  19. Jiménez-Gómez A, Saati-Santamaría Z, Kostovcik M, Rivas R, Velázquez E et al. Selection of the root endophyte Pseudomonas brassicacearum CDVBN10 as plant growth promoter for Brassica napus L. Crops. Agronomy 2020; 10:1788 [View Article]
    [Google Scholar]
  20. Saati-Santamaría Z, Rivas R, Kolařik M, García-Fraile P. A new perspective of Pseudomonas-host interactions: distribution and potential ecological functions of the genus Pseudomonas within the bark beetle holobiont. Biology (Basel) 2021; 10:164 [View Article] [PubMed]
    [Google Scholar]
  21. Peral-Aranega E, Saati-Santamaría Z, Kolařik M, Rivas R, García-Fraile P. Bacteria belonging to Pseudomonas typographi sp. nov. from the bark beetle ips typographus have genomic potential to aid in the host ecology. Insects 2020; 11:E593 [View Article] [PubMed]
    [Google Scholar]
  22. Saati-Santamaría Z, López-Mondéjar R, Jiménez-Gómez A, Díez-Méndez A, Větrovský T et al. Discovery of phloeophagus beetles as a source of Pseudomonas strains that produce potentially new bioactive substances and description of Pseudomonas bohemica sp. nov.. Front Microbiol 2018; 9:913 [View Article] [PubMed]
    [Google Scholar]
  23. Gross H, Loper JE. Genomics of secondary metabolite production by Pseudomonas spp. Nat Prod Rep 2009; 26:1408–1446 [View Article] [PubMed]
    [Google Scholar]
  24. Nguyen DD, Melnik AV, Koyama N, Lu X, Schorn M et al. Indexing the Pseudomonas specialized metabolome enabled the discovery of poaeamide B and the bananamides. Nat Microbiol 2016; 2:16197 [View Article] [PubMed]
    [Google Scholar]
  25. Li W, Rokni-Zadeh H, De Vleeschouwer M, Ghequire MGK, Sinnaeve D et al. The antimicrobial compound xantholysin defines a new group of Pseudomonas cyclic lipopeptides. PLoS One 2013; 8:e62946 [View Article] [PubMed]
    [Google Scholar]
  26. Kautsar SA, Blin K, Shaw S, Navarro-Muñoz JC, Terlouw BR et al. MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res 2020; 48:D454–D458 [View Article] [PubMed]
    [Google Scholar]
  27. Gregory K, Salvador LA, Akbar S, Adaikpoh BI, Stevens DC. Survey of biosynthetic gene clusters from sequenced myxobacteria reveals unexplored biosynthetic potential. Microorganisms 2019; 7:181 [View Article] [PubMed]
    [Google Scholar]
  28. González-Dominici LI, Saati-Santamaría Z, García-Fraile P. Genome analysis and genomic comparison of the novel species Arthrobacter ipsi reveal its potential protective role in its bark beetle host. Microb Ecol 2021; 81:471–482 [View Article] [PubMed]
    [Google Scholar]
  29. Männle D, McKinnie SMK, Mantri SS, Steinke K, Lu Z et al. Comparative genomics and metabolomics in the genus Nocardia . mSystems 2020; 5:e00125-20 [View Article] [PubMed]
    [Google Scholar]
  30. Gutiérrez-García K, Neira-González A, Pérez-Gutiérrez RM, Granados-Ramírez G, Zarraga R et al. Phylogenomics of 2,4-diacetylphloroglucinol-producing Pseudomonas and novel antiglycation endophytes from Piper auritum . J Nat Prod 2017; 80:1955–1963 [View Article] [PubMed]
    [Google Scholar]
  31. Lapage SP, Sneath PHA, Lessel EF, Skerman VBD, Seeliger HPR et al. International Code of Nomenclature of Bacteria Washington, D.C: ASM Press; 1990
    [Google Scholar]
  32. Kyrpides NC, Hugenholtz P, Eisen JA, Woyke T, Göker M et al. Genomic encyclopedia of bacteria and archaea: sequencing a myriad of type strains. PLoS Biol 2014; 12:e1001920 [View Article] [PubMed]
    [Google Scholar]
  33. Nivina A, Yuet KP, Hsu J, Khosla C. Evolution and diversity of assembly-line polyketide synthases. Chem Rev 2019; 119:12524–12547 [View Article] [PubMed]
    [Google Scholar]
  34. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics 2008; 9:1–15 [View Article] [PubMed]
    [Google Scholar]
  35. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 2013; 29:1072–1075 [View Article] [PubMed]
    [Google Scholar]
  36. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 2015; 31:3210–3212 [View Article] [PubMed]
    [Google Scholar]
  37. Medema MH, Kottmann R, Yilmaz P, Cummings M, Biggins JB et al. Minimum information about a biosynthetic gene cluster. Nat Chem Biol 2015; 11:625–631 [View Article] [PubMed]
    [Google Scholar]
  38. Wickham H. Ggplot2. WIREs Comp Stat 2011; 3:180–185 [View Article]
    [Google Scholar]
  39. Lechner M, Findeiss S, Steiner L, Marz M, Stadler PF et al. Proteinortho: detection of (co-)orthologs in large-scale analysis. BMC Bioinformatics 2011; 12:1–9 [View Article] [PubMed]
    [Google Scholar]
  40. Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 2019; 47:D309–D314 [View Article] [PubMed]
    [Google Scholar]
  41. Argimón S, Abudahab K, Goater RJE, Fedosejev A, Bhai J et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom 2016; 2:e000093 [View Article] [PubMed]
    [Google Scholar]
  42. Na SI, Kim YO, Yoon SH, Ha SM, Baek I et al. UBCG: Up-to-date bacterial core gene set and pipeline for phylogenomic tree reconstruction. J Microbiol 2018; 56:280–285 [View Article] [PubMed]
    [Google Scholar]
  43. Letunic I, Bork P. Interactive tree of life (iTOL) v4: recent updates and new developments. Nucleic Acids Res 2019; 47:W256–W259 [View Article] [PubMed]
    [Google Scholar]
  44. Huson DH, Richter DC, Rausch C, Dezulian T, Franz M et al. Dendroscope: an interactive viewer for large phylogenetic trees. BMC Bioinformatics 2007; 8:460 [View Article] [PubMed]
    [Google Scholar]
  45. Santos-Júnior CD, Pan S, Zhao X-M, Coelho LP. Macrel: antimicrobial peptide screening in genomes and metagenomes. PeerJ 2020; 8:e10555 [View Article] [PubMed]
    [Google Scholar]
  46. Zallot R, Oberg N, Gerlt JA. The EFI web resource for genomic enzymology tools: leveraging protein, genome, and metagenome databases to discover novel enzymes and metabolic pathways. Biochemistry 2019; 58:4169–4182 [View Article] [PubMed]
    [Google Scholar]
  47. Kang X, Dong F, Shi C, Liu S, Sun J et al. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci Data 2019; 6:148 [View Article] [PubMed]
    [Google Scholar]
  48. Wang G, Li X, Wang Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res 2016; 44:D1087–93 [View Article] [PubMed]
    [Google Scholar]
  49. Meleshko D, Mohimani H, Tracanna V, Hajirasouliha I, Medema MH et al. BiosyntheticSPAdes: reconstructing biosynthetic gene clusters from assembly graphs. Genome Res 2019; 29:1352–1362 [View Article] [PubMed]
    [Google Scholar]
  50. Seipke RF. Strain-level diversity of secondary metabolism in Streptomyces albus . PLoS One 2015; 10:e0116457 [View Article] [PubMed]
    [Google Scholar]
  51. Flissi A, Ricart E, Campart C, Chevalier M, Dufresne Y et al. Norine: update of the nonribosomal peptide resource. Nucleic Acids Res 2020; 48:D465–D469 [View Article] [PubMed]
    [Google Scholar]
  52. Chikindas ML, Weeks R, Drider D, Chistyakov VA, Dicks LM. Functions and emerging applications of bacteriocins. Curr Opin Biotechnol 2018; 49:23–28 [View Article] [PubMed]
    [Google Scholar]
  53. Cotter PD, Ross RP, Hill C. Bacteriocins - a viable alternative to antibiotics?. Nat Rev Microbiol 2013; 11:95–105 [View Article] [PubMed]
    [Google Scholar]
  54. Hoffmann T, Krug D, Bozkurt N, Duddela S, Jansen R et al. Correlating chemical diversity with taxonomic distance for discovery of natural products in myxobacteria. Nat Commun 2018; 9:1–10 [View Article] [PubMed]
    [Google Scholar]
  55. Adamek M, Alanjary M, Sales-Ortells H, Goodfellow M, Bull AT et al. Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species. BMC Genomics 2018; 19:1–15 [View Article] [PubMed]
    [Google Scholar]
  56. Kautsar SA, van der Hooft JJJ, de Ridder D, Medema MH. BiG-SLiCE: a highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters. Gigascience 2021; 10:giaa154 [View Article] [PubMed]
    [Google Scholar]
  57. Marahiel MA. A structural model for multimodular NRPS assembly lines. Nat Prod Rep 2016; 33:136–140 [View Article] [PubMed]
    [Google Scholar]
  58. Miravet-Verde S, Ferrar T, Espadas-García G, Mazzolini R, Gharrab A et al. Unraveling the hidden universe of small proteins in bacterial genomes. Mol Syst Biol 2019; 15:e8290 [View Article] [PubMed]
    [Google Scholar]
  59. Jang JY, Yang SY, Kim YC, Lee CW, Park MS et al. Identification of orfamide A as an insecticidal metabolite produced by Pseudomonas protegens F6. J Agric Food Chem 2013; 61:6786–6791 [View Article] [PubMed]
    [Google Scholar]
  60. Almario J, Bruto M, Vacheron J, Prigent-Combaret C, Moënne-Loccoz Y et al. Distribution of 2,4-diacetylphloroglucinol biosynthetic genes among the Pseudomonas spp. reveals unexpected polyphyletism. Front Microbiol 2017; 8:1218 [View Article] [PubMed]
    [Google Scholar]
  61. Thacharodi A, Priyadharshini R, Karthikeyan G, Jeganathan C, Reghu A et al. Extraction, purification and characterization of phenazine from Pseudomonas aeruginosa isolate of wastewater sources: a panacea towards clinical pathogens. Appl Nanosci 20211–14 [View Article]
    [Google Scholar]
  62. Schiessl KT, Hu F, Jo J, Nazia SZ, Wang B et al. Phenazine production promotes antibiotic tolerance and metabolic heterogeneity in Pseudomonas aeruginosa biofilms. Nat Commun 2019; 10:1–10 [View Article]
    [Google Scholar]
  63. Vallet-Gely I, Novikov A, Augusto L, Liehl P, Bolbach G et al. Association of hemolytic activity of Pseudomonas entomophila, a versatile soil bacterium, with cyclic lipopeptide production. Appl Environ Microbiol 2010; 76:910–921 [View Article]
    [Google Scholar]
  64. Wäspi U, Blanc D, Winkler T, Rüedi P, Dudler R. Syringolin, a novel peptide elicitor from Pseudomonas syringae pv. syringae that induces resistance to Pyricularia oryzae in rice. MPMI 1998; 11:727–733 [View Article]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000758
Loading
/content/journal/mgen/10.1099/mgen.0.000758
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Supplementary material 2

EXCEL
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error