1887

Abstract

Natural products (NPs), or specialized metabolites, are important for medicine and agriculture alike, and for the fitness of the organisms that produce them. NP genome-mining aims at extracting biosynthetic information from the genomes of microbes presumed to produce these compounds. Typically, canonical enzyme sequences from known biosynthetic systems are identified after sequence similarity searches. Despite this being an efficient process, the likelihood of identifying truly novel systems by this approach is low. To overcome this limitation, we previously introduced EvoMining, a genome-mining approach that incorporates evolutionary principles. Here, we release and use our latest EvoMining version, which includes novel visualization features and customizable databases, to analyse 42 central metabolic enzyme families (EFs) conserved throughout Actinobacteria , Cyanobacteria , Pseudomonas and Archaea. We found that expansion-and-recruitment profiles of these 42 families are lineage specific, opening the metabolic space related to ‘shell’ enzymes. These enzymes, which have been overlooked, are EFs with orthologues present in most of the genomes of a taxonomic group, but not in all. As a case study of canonical shell enzymes, we characterized the expansion and recruitment of glutamate dehydrogenase and acetolactate synthase into scytonemin biosynthesis, and into other central metabolic pathways driving Archaea and Bacteria adaptive evolution. By defining the origin and fate of enzymes, EvoMining complements traditional genome-mining approaches as an unbiased strategy and opens the door to gaining insights into the evolution of NP biosynthesis. We anticipate that EvoMining will be broadly used for evolutionary studies, and for generating predictions of unprecedented chemical scaffolds and new antibiotics. This article contains data hosted by Microreact.

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2019-04-04
2019-10-15
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References

  1. Newman DJ, Cragg GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod 2012;75: 311– 335 [CrossRef] [PubMed]
    [Google Scholar]
  2. 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 [CrossRef] [PubMed]
    [Google Scholar]
  3. Medema MH, Fischbach MA. Computational approaches to natural product discovery. Nat Chem Biol 2015;11: 639– 648 [CrossRef] [PubMed]
    [Google Scholar]
  4. Ziemert N, Alanjary M, Weber T. The evolution of genome mining in microbes - a review. Nat Prod Rep 2016;33: 988– 1005 [CrossRef] [PubMed]
    [Google Scholar]
  5. Chevrette MG, Aicheler F, Kohlbacher O, Currie CR, Medema MH. SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria. Bioinformatics 2017;33: 3202– 3210 [CrossRef] [PubMed]
    [Google Scholar]
  6. Ansari MZ, Yadav G, Gokhale RS, Mohanty D. NRPS-PKS: a knowledge-based resource for analysis of NRPS/PKS megasynthases. Nucleic Acids Res 2004;32: W405– W413 [CrossRef] [PubMed]
    [Google Scholar]
  7. Weber T, Blin K, Duddela S, Krug D, Kim HU et al. antiSMASH 3.0-a comprehensive resource for the genome mining of biosynthetic gene clusters. Nucleic Acids Res 2015;43: W237– W243 [CrossRef] [PubMed]
    [Google Scholar]
  8. Blin K, Pascal Andreu V, de Los Santos ELC, del Carratore F, Lee SY et al. The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters. Nucleic Acids Res 2019;47: D625– D630 [CrossRef] [PubMed]
    [Google Scholar]
  9. Zhang JJ, Moore BS. Digging for biosynthetic dark matter. elife 2015;4: e06453 [CrossRef] [PubMed]
    [Google Scholar]
  10. Garcia-Pichel F, Sherry ND, Castenholz RW. Evidence for an ultraviolet sunscreen role of the extracellular pigment scytonemin in the terrestrial cyanobacterium Chlorogloeopsis sp. Photochem Photobiol 1992;56: 17– 23 [CrossRef] [PubMed]
    [Google Scholar]
  11. Balskus EP, Walsh CT. Investigating the initial steps in the biosynthesis of cyanobacterial sunscreen scytonemin. J Am Chem Soc 2008;130: 15260– 15261 [CrossRef] [PubMed]
    [Google Scholar]
  12. Soule T, Palmer K, Gao Q, Potrafka RM, Stout V et al. A comparative genomics approach to understanding the biosynthesis of the sunscreen scytonemin in cyanobacteria. BMC Genomics 2009;10: 336 [CrossRef] [PubMed]
    [Google Scholar]
  13. Engel PC. Glutamate dehydrogenases: the why and how of coenzyme specificity. Neurochem Res 2014;39: 426– 432 [CrossRef] [PubMed]
    [Google Scholar]
  14. Liu Y, Li Y, Wang X. Acetohydroxyacid synthases: evolution, structure and function. Appl Microbiol Biotechnol 2016;100: 8633– 8649 [CrossRef] [PubMed]
    [Google Scholar]
  15. 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 [CrossRef] [PubMed]
    [Google Scholar]
  16. Barona-Gómez F, Cruz-Morales P, Noda-García L. What can genome-scale metabolic network reconstructions do for prokaryotic systematics?. Antonie van Leeuwenhoek 2012;101: 35– 43 [CrossRef] [PubMed]
    [Google Scholar]
  17. Jordan IK, Makarova KS, Spouge JL, Wolf YI, Koonin EV. Lineage-specific gene expansions in bacterial and archaeal genomes. Genome Res 2001;11: 555– 565 [CrossRef] [PubMed]
    [Google Scholar]
  18. Caetano-Anollés G, Yafremava LS, Gee H, Caetano-Anollés D, Kim HS et al. The origin and evolution of modern metabolism. Int J Biochem Cell Biol 2009;41: 285– 297 [CrossRef] [PubMed]
    [Google Scholar]
  19. Zhao S, Sakai A, Zhang X, Vetting MW, Kumar R et al. Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks. Elife 2014;3: e03275 [CrossRef] [PubMed]
    [Google Scholar]
  20. Schniete JK, Cruz-Morales P, Selem-Mojica N, Fernández-Martínez LT, Hunter IS et al. Expanding primary metabolism helps generate the metabolic robustness to facilitate antibiotic biosynthesis in Streptomyces. MBio 2018;9: e02283-17 [CrossRef] [PubMed]
    [Google Scholar]
  21. Navarro-Muñoz J, Selem-Mojica N, Mullowney M, Kautsar S, Tryon J et al. A computational framework for systematic exploration of biosynthetic diversity from large-scale genomic data. bioRxiv 2018; 445270
    [Google Scholar]
  22. Alanjary M, Kronmiller B, Adamek M, Blin K, Weber T et al. The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery. Nucleic Acids Res 2017;45: W42– W48 [CrossRef] [PubMed]
    [Google Scholar]
  23. Chevrette MG, Currie CR. Emerging evolutionary paradigms in antibiotic discovery. J Ind Microbiol Biotechnol 2019;46: 257– 271 [CrossRef]
    [Google Scholar]
  24. Fischbach MA, Walsh CT, Clardy J. The evolution of gene collectives: how natural selection drives chemical innovation. Proc Natl Acad Sci USA 2008;105: 4601– 4608 [CrossRef] [PubMed]
    [Google Scholar]
  25. Segerman B. The genetic integrity of bacterial species: the core genome and the accessory genome, two different stories. Front Cell Infect Microbiol 2012;2: 116 [CrossRef] [PubMed]
    [Google Scholar]
  26. Verdel-Aranda K, López-Cortina ST, Hodgson DA, Barona-Gómez F. Molecular annotation of ketol-acid reductoisomerases from Streptomyces reveals a novel amino acid biosynthesis interlock mediated by enzyme promiscuity. Microb Biotechnol 2015;8: 239– 252 [CrossRef] [PubMed]
    [Google Scholar]
  27. Borodina I, Siebring J, Zhang J, Smith CP, van Keulen G et al. Antibiotic overproduction in Streptomyces coelicolor A3 2 mediated by phosphofructokinase deletion. J Biol Chem 2008;283: 25186– 25199 [CrossRef] [PubMed]
    [Google Scholar]
  28. Andersson JO, Roger AJ. Evolution of glutamate dehydrogenase genes: evidence for lateral gene transfer within and between prokaryotes and eukaryotes. BMC Evol Biol 2003;3: 14 [CrossRef] [PubMed]
    [Google Scholar]
  29. Koonin EV, Wolf YI. Genomics of bacteria and archaea: the emerging dynamic view of the prokaryotic world. Nucleic Acids Res 2008;36: 6688– 6719 [CrossRef] [PubMed]
    [Google Scholar]
  30. Boettiger C. An introduction to Docker for reproducible research. ACM SIGOPS Operating Systems Review 2015;49: 71– 79 [CrossRef]
    [Google Scholar]
  31. Gruening B, Sallou O, Moreno P, da Veiga Leprevost F, Ménager H et al. Recommendations for the packaging and containerizing of bioinformatics software. F1000Res 2018;7: 742 [CrossRef]
    [Google Scholar]
  32. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 2004;32: 1792– 1797 [CrossRef] [PubMed]
    [Google Scholar]
  33. Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol 2000;17: 540– 552 [CrossRef] [PubMed]
    [Google Scholar]
  34. Price MN, Dehal PS, Arkin AP. FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS One 2010;5: e9490 [CrossRef] [PubMed]
    [Google Scholar]
  35. Junier T, Zdobnov EM. The Newick utilities: high-throughput phylogenetic tree processing in the UNIX shell. Bioinformatics 2010;26: 1669– 1670 [CrossRef] [PubMed]
    [Google Scholar]
  36. Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ et al. The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res 2014;42: D206– D214 [CrossRef] [PubMed]
    [Google Scholar]
  37. Argimón S, Abudahab K, Goater RJ, Fedosejev A, Bhai J et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom 2016;2: e000093 [CrossRef] [PubMed]
    [Google Scholar]
  38. van der Veen BE, Harris HM, O'Toole PW, Claesson MJ. Metaphor: finding bi-directional best hit homology relationships in (meta)genomic datasets. Genomics 2014;104: 459– 463 [CrossRef] [PubMed]
    [Google Scholar]
  39. Ronquist F, Huelsenbeck JP. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 2003;19: 1572– 1574 [CrossRef] [PubMed]
    [Google Scholar]
  40. Charlesworth JC, Burns BP. Untapped resources: biotechnological potential of peptides and secondary metabolites in archaea. Archaea 2015;2015: 1– 7 [CrossRef] [PubMed]
    [Google Scholar]
  41. Kudo F, Kasama Y, Hirayama T, Eguchi T. Cloning of the pactamycin biosynthetic gene cluster and characterization of a crucial glycosyltransferase prior to a unique cyclopentane ring formation. J Antibiot 2007;60: 492– 503 [CrossRef] [PubMed]
    [Google Scholar]
  42. Lilley KS, Baker PJ, Britton KL, Stillman TJ, Brown PE et al. The partial amino acid sequence of the NAD(+)-dependent glutamate dehydrogenase of Clostridium symbiosum: implications for the evolution and structural basis of coenzyme specificity. Biochim Biophys Acta 1991;1080: 191– 197 [CrossRef] [PubMed]
    [Google Scholar]
  43. Consalvi V, Chiaraluce R, Politi L, Gambacorta A, de Rosa M et al. Glutamate dehydrogenase from the thermoacidophilic archaebacterium Sulfolobus solfataricus. Eur J Biochem 1991;196: 459– 467 [CrossRef] [PubMed]
    [Google Scholar]
  44. Ferrer J, Pérez-Pomares F, Bonete MJ. NADP-glutamate dehydrogenase from the halophilic archaeon Haloferax mediterranei: enzyme purification, N-terminal sequence and stability. FEMS Microbiol Lett 1996;141: 59– 63 [CrossRef] [PubMed]
    [Google Scholar]
  45. Tholl D. Terpene synthases and the regulation, diversity and biological roles of terpene metabolism. Curr Opin Plant Biol 2006;9: 297– 304 [CrossRef] [PubMed]
    [Google Scholar]
  46. Balskus EP, Walsh CT. The genetic and molecular basis for sunscreen biosynthesis in cyanobacteria. Science 2010;329: 1653– 1656 [CrossRef] [PubMed]
    [Google Scholar]
  47. Hillwig ML, Fuhrman HA, Ittiamornkul K, Sevco TJ, Kwak DH et al. Identification and characterization of a welwitindolinone alkaloid biosynthetic gene cluster in the stigonematalean Cyanobacterium Hapalosiphon welwitschii. Chembiochem 2014;15: 665– 669 [CrossRef] [PubMed]
    [Google Scholar]
  48. Li S, Lowell AN, Yu F, Raveh A, Newmister SA et al. Hapalindole/Ambiguine biogenesis is mediated by a cope rearrangement, C-C bond-forming cascade. J Am Chem Soc 2015;137: 15366– 15369 [CrossRef] [PubMed]
    [Google Scholar]
  49. Li S, Lowell AN, Newmister SA, Yu F, Williams RM et al. Decoding cyclase-dependent assembly of hapalindole and fischerindole alkaloids. Nat Chem Biol 2017;13: 467– 469 [CrossRef] [PubMed]
    [Google Scholar]
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