Unveiling the genomic potential of type strains for discovering new natural products.

Zaki Saati-Santamaría, Nelly Selem-Mojica, Ezequiel Peral-Aranega, Raúl Rivas, Paula García-Fraile
Author Information
  1. Zaki Saati-Santamaría: Microbiology and Genetics Department, University of Salamanca, 37007 Salamanca, Spain.
  2. Nelly Selem-Mojica: Centro de Ciencias Matemáticas UNAM, 58089, Morelia, Michoacán, Mexico.
  3. Ezequiel Peral-Aranega: Microbiology and Genetics Department, University of Salamanca, 37007 Salamanca, Spain.
  4. Raúl Rivas: Microbiology and Genetics Department, University of Salamanca, 37007 Salamanca, Spain.
  5. Paula García-Fraile: Microbiology and Genetics Department, University of Salamanca, 37007 Salamanca, Spain.

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.

Keywords

References

  1. BMC Bioinformatics. 2011 Apr 28;12:124 [PMID: 21526987]
  2. Sci Data. 2019 Aug 13;6(1):148 [PMID: 31409791]
  3. Front Microbiol. 2018 May 08;9:913 [PMID: 29867824]
  4. Nucleic Acids Res. 2020 Jan 8;48(D1):D454-D458 [PMID: 31612915]
  5. Nat Chem Biol. 2017 Apr 10;: [PMID: 28398287]
  6. Nat Commun. 2019 Feb 15;10(1):762 [PMID: 30770834]
  7. Nucleic Acids Res. 2019 Jul 2;47(W1):W256-W259 [PMID: 30931475]
  8. BMC Genomics. 2008 Feb 08;9:75 [PMID: 18261238]
  9. PLoS One. 2013 May 17;8(5):e62946 [PMID: 23690965]
  10. J Microbiol. 2018 Apr;56(4):280-285 [PMID: 29492869]
  11. Microb Ecol. 2021 Feb;81(2):471-482 [PMID: 32901388]
  12. J Agric Food Chem. 2013 Jul 17;61(28):6786-91 [PMID: 23763636]
  13. Nat Chem Biol. 2015 Sep;11(9):625-31 [PMID: 26284661]
  14. Chem Rev. 2019 Dec 26;119(24):12524-12547 [PMID: 31838842]
  15. APMIS. 2020 Mar;128(3):220-231 [PMID: 31709616]
  16. Nat Prod Rep. 2016 Feb;33(2):136-40 [PMID: 26429504]
  17. Nat Chem Biol. 2020 Jan;16(1):60-68 [PMID: 31768033]
  18. Microb Genom. 2019 Dec;5(12): [PMID: 30946645]
  19. BMC Genomics. 2018 Jun 1;19(1):426 [PMID: 29859036]
  20. Genome Biol Evol. 2016 Jul 02;8(6):1906-16 [PMID: 27289100]
  21. Microb Genom. 2021 May;7(5): [PMID: 33979276]
  22. Biology (Basel). 2021 Feb 19;10(2): [PMID: 33669823]
  23. Nucleic Acids Res. 2020 Jan 8;48(D1):D465-D469 [PMID: 31691799]
  24. PLoS Genet. 2012 Jul;8(7):e1002784 [PMID: 22792073]
  25. Insects. 2020 Sep 03;11(9): [PMID: 32899185]
  26. Nat Rev Chem. 2020 Apr;4(4):172-193 [PMID: 37128046]
  27. Front Microbiol. 2017 Jun 30;8:1218 [PMID: 28713346]
  28. Nucleic Acids Res. 2016 Jan 4;44(D1):D1087-93 [PMID: 26602694]
  29. Nat Prod Rep. 2019 Sep 1;36(9):1295-1312 [PMID: 31475269]
  30. J Ind Microbiol Biotechnol. 2017 Feb;44(2):285-293 [PMID: 27885438]
  31. Bioinformatics. 2015 Oct 1;31(19):3210-2 [PMID: 26059717]
  32. Genome Res. 2019 Aug;29(8):1352-1362 [PMID: 31160374]
  33. BMC Bioinformatics. 2007 Nov 22;8:460 [PMID: 18034891]
  34. Nat Commun. 2019 Jan 31;10(1):516 [PMID: 30705269]
  35. Sci Rep. 2019 Feb 4;9(1):1204 [PMID: 30718591]
  36. Bioinformatics. 2020 Aug 1;36(15):4345-4347 [PMID: 32415965]
  37. PeerJ. 2020 Dec 18;8:e10555 [PMID: 33384902]
  38. Bioinformatics. 2013 Apr 15;29(8):1072-5 [PMID: 23422339]
  39. PLoS One. 2015 Jan 30;10(1):e0116457 [PMID: 25635820]
  40. PLoS Biol. 2014 Aug 05;12(8):e1001920 [PMID: 25093819]
  41. J Nat Prod. 2017 Jul 28;80(7):1955-1963 [PMID: 28704049]
  42. Microb Biotechnol. 2016 Sep;9(5):541-8 [PMID: 27470984]
  43. Mar Drugs. 2020 Apr 09;18(4): [PMID: 32283638]
  44. Nat Prod Rep. 2009 Nov;26(11):1408-46 [PMID: 19844639]
  45. mSystems. 2020 Jun 2;5(3): [PMID: 32487740]
  46. Appl Environ Microbiol. 2010 Feb;76(3):910-21 [PMID: 20023108]
  47. Mol Syst Biol. 2019 Feb 22;15(2):e8290 [PMID: 30796087]
  48. Curr Opin Biotechnol. 2018 Feb;49:23-28 [PMID: 28787641]
  49. Biochemistry. 2019 Oct 15;58(41):4169-4182 [PMID: 31553576]
  50. Nat Prod Rep. 2021 Jan 1;38(1):264-278 [PMID: 32856641]
  51. Microorganisms. 2019 Jun 24;7(6): [PMID: 31238501]
  52. Nat Rev Microbiol. 2013 Feb;11(2):95-105 [PMID: 23268227]
  53. Nat Microbiol. 2016 Oct 31;2:16197 [PMID: 27798598]
  54. Gigascience. 2021 Jan 13;10(1): [PMID: 33438731]
  55. Microb Genom. 2016 Nov 30;2(11):e000093 [PMID: 28348833]
  56. Nat Commun. 2018 Feb 23;9(1):803 [PMID: 29476047]
  57. Nucleic Acids Res. 2019 Jan 8;47(D1):D309-D314 [PMID: 30418610]
  58. Nucleic Acids Res. 2019 Jul 2;47(W1):W81-W87 [PMID: 31032519]

MeSH Term

Biological Products
Genomics
Multigene Family
Phylogeny
Pseudomonas

Chemicals

Biological Products

Word Cloud

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