1887

Abstract

Macrolides are broad-spectrum antibiotics used to treat a range of infections. Resistance to macrolides is often conferred by mobile resistance genes encoding Erm methyltransferases or Mph phosphotransferases. New and genes keep being discovered in clinical settings but their origins remain unknown, as is the type of macrolide resistance genes that will appear in the future. In this study, we used optimized hidden Markov models to characterize the macrolide resistome. Over 16 terabases of genomic and metagenomic data, representing a large taxonomic diversity (11 030 species) and diverse environments (1944 metagenomic samples), were searched for the presence of and genes. From this data, we predicted 28 340 macrolide resistance genes encoding 2892 unique protein sequences, which were clustered into 663 gene families (<70 % amino acid identity), of which 619 (94 %) were previously uncharacterized. This included six new resistance gene families, which were located on mobile genetic elements in pathogens. The function of ten predicted new resistance genes were experimentally validated in using a growth assay. Among the ten tested genes, seven conferred increased resistance to erythromycin, with five genes additionally conferring increased resistance to azithromycin, showing that our models can be used to predict new functional resistance genes. Our analysis also showed that macrolide resistance genes have diverse origins and have transferred horizontally over large phylogenetic distances into human pathogens. This study expands the known macrolide resistome more than ten-fold, provides insights into its evolution, and demonstrates how computational screening can identify new resistance genes before they become a significant clinical problem.

Funding
This study was supported by the:
  • Vetenskapsrådet (Award 2018–02835, 2018-05771)
    • Principle Award Recipient: G Joakim LarssonD
  • Vetenskapsrådet (Award 2019–03482)
    • Principle Award Recipient: KristianssonErik
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2022-01-27
2022-05-18
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References

  1. Dinos GP. The macrolide antibiotic renaissance. Br J Pharmacol 2017; 174:2967–2983 [View Article] [PubMed]
    [Google Scholar]
  2. Schönfeld W, Kirst HA. Macrolide Antibiotics Basel: Springer Science & Business Media; 2002 [View Article]
    [Google Scholar]
  3. Aminov R. History of antimicrobial drug discovery: Major classes and health impact. Biochem Pharmacol 2017; 133:4–19 [View Article] [PubMed]
    [Google Scholar]
  4. Schwarz S, Kehrenberg C, Walsh TR. Use of antimicrobial agents in veterinary medicine and food animal production. Int J Antimicrob Agents 2001; 17:431–437 [View Article] [PubMed]
    [Google Scholar]
  5. Kenyon C, Manoharan-Basil SS, Van Dijck C. Is there a resistance threshold for macrolide consumption? positive evidence from an ecological analysis of resistance data from Streptococcus pneumoniae, Treponema pallidum, and Mycoplasma genitalium. Microb Drug Resist 2021; 21:1079–1086 [PubMed]
    [Google Scholar]
  6. Gomes C, Martínez-Puchol S, Palma N, Horna G, Ruiz-Roldán L et al. Macrolide resistance mechanisms in Enterobacteriaceae: Focus on azithromycin. Crit Rev Microbiol 2017; 43:1–30 [View Article] [PubMed]
    [Google Scholar]
  7. Feßler AT, Wang Y, Wu C, Schwarz S. Mobile macrolide resistance genes in staphylococci. Plasmid 2018; 99:2–10 [View Article] [PubMed]
    [Google Scholar]
  8. Vester B, Douthwaite S. Macrolide resistance conferred by base substitutions in 23S rRNA. Antimicrob Agents Chemother 2001; 45:1–12 [View Article] [PubMed]
    [Google Scholar]
  9. Gomes C, Ruiz-Roldán L, Mateu J, Ochoa TJ, Ruiz J. Azithromycin resistance levels and mechanisms in Escherichia coli. Sci Rep 2019; 9:6089 [View Article] [PubMed]
    [Google Scholar]
  10. Schroeder MR, Stephens DS. Macrolide resistance in Streptococcus pneumoniae. Front Cell Infect Microbiol 2016; 6: [View Article] [PubMed]
    [Google Scholar]
  11. Park AK, Kim H, Jin HJ. Phylogenetic analysis of rRNA methyltransferases, Erm and KsgA, as related to antibiotic resistance. FEMS Microbiol Lett 2010; 309:151–162 [View Article]
    [Google Scholar]
  12. Fyfe C, Grossman TH, Kerstein K, Sutcliffe J. Resistance to macrolide antibiotics in public health pathogens. Cold Spring Harb Perspect Med 2016; 6:10 [View Article]
    [Google Scholar]
  13. Wang C, Sui Z, Leclercq SO, Zhang G, Zhao M et al. Functional characterization and phylogenetic analysis of acquired and intrinsic macrolide phosphotransferases in the Bacillus cereus group. Environ Microbiol 2015; 17:1560–1573 [View Article]
    [Google Scholar]
  14. Pawlowski AC, Stogios PJ, Koteva K, Skarina T, Evdokimova E et al. The evolution of substrate discrimination in macrolide antibiotic resistance enzymes. Nat Commun 2018; 9:112 [View Article]
    [Google Scholar]
  15. Hon WC, McKay GA, Thompson PR, Sweet RM, Yang DS et al. Structure of an enzyme required for aminoglycoside antibiotic resistance reveals homology to eukaryotic protein kinases. Cell 1997; 89:887–895 [View Article]
    [Google Scholar]
  16. Roberts M. Tetracycline and MLS nomenclature; 2019 https://faculty.washington.edu/marilynr
  17. Roberts MC. Update on macrolide-lincosamide-streptogramin, ketolide, and oxazolidinone resistance genes. FEMS Microbiol Lett 2008; 282:147–159 [View Article] [PubMed]
    [Google Scholar]
  18. Bengtsson-Palme J, Kristiansson E, Larsson DGJ. Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiol Rev 2018; 42: [View Article] [PubMed]
    [Google Scholar]
  19. Berglund F, Marathe NP, Österlund T, Bengtsson-Palme J, Kotsakis S et al. Identification of 76 novel B1 metallo-β-lactamases through large-scale screening of genomic and metagenomic data. Microbiome 2017; 5:134 [View Article] [PubMed]
    [Google Scholar]
  20. Berglund F, Böhm M-E, Martinsson A, Ebmeyer S, Österlund T et al. Comprehensive screening of genomic and metagenomic data reveals a large diversity of tetracycline resistance genes. Microb Genom 2020; 6:11 [View Article] [PubMed]
    [Google Scholar]
  21. Berglund F, Johnning A, Larsson DGJ, Kristiansson E. An updated phylogeny of the metallo-β-lactamases. J Antimicrob Chemother 2021; 76:117–123 [View Article] [PubMed]
    [Google Scholar]
  22. Greninger AL, Addetia A, Starr K, Cybulski RJ, Stewart MK et al. International spread of multidrug-resistant Campylobacter coli in men who have sex with men in Washington State and Québec, 2015-2018. Clin Infect Dis 2020; 71:1896–1904 [View Article] [PubMed]
    [Google Scholar]
  23. Wendlandt S, Heß S, Li J, Feßler AT, Wang Y et al. Detection of the macrolide-lincosamide-streptogramin B resistance gene erm(44) and a novel erm(44) variant in staphylococci from aquatic environments. FEMS Microbiol Ecol 2015; 91:fiv090 [View Article] [PubMed]
    [Google Scholar]
  24. Pawlowski AC, Wang W, Koteva K, Barton HA, McArthur AG et al. A diverse intrinsic antibiotic resistome from a cave bacterium. Nat Commun 2016; 7:13803 [View Article] [PubMed]
    [Google Scholar]
  25. Martínez N, Luque R, Milani C, Ventura M, Bañuelos O et al. A gene homologous to rRNA methylase genes confers erythromycin and clindamycin resistance in Bifidobacterium breve. Appl Environ Microbiol 2018; 84:10 [View Article] [PubMed]
    [Google Scholar]
  26. Berglund F, Österlund T, Boulund F, Marathe NP, Larsson DGJ et al. Identification and reconstruction of novel antibiotic resistance genes from metagenomes. Microbiome 2019; 7:52 [View Article]
    [Google Scholar]
  27. NCBI Resource Coordinators Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2018; 46:D8–D13 [View Article]
    [Google Scholar]
  28. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010; 26:2460–2461 [View Article]
    [Google Scholar]
  29. Madeira F, Park YM, Lee J, Buso N, Gur T et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res 2019; 47:W636–W641 [View Article]
    [Google Scholar]
  30. Huang W, Li L, Myers JR, Marth GT. ART: a next-generation sequencing read simulator. Bioinformatics 2012; 28:593–594 [View Article]
    [Google Scholar]
  31. Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 2002; 30:3059–3066 [View Article] [PubMed]
    [Google Scholar]
  32. Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One 2010; 5:e9490 [View Article] [PubMed]
    [Google Scholar]
  33. 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]
  34. Yu GC, Smith DK, Zhu HC, Guan Y, Lam TTY. GGTREE: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 2017; 8:28–36 [View Article]
    [Google Scholar]
  35. Ebmeyer S, Coertze RD, Berglund F, Kristiansson E, Larsson DGJ. GEnView: a gene-centric, phylogeny-based comparative genomics pipeline for bacterial genomes and plasmids. Bioinformatics 2021btab855 [View Article] [PubMed]
    [Google Scholar]
  36. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res 2006; 34:D32–6 [View Article] [PubMed]
    [Google Scholar]
  37. Madden T. The BLAST sequence analysis tool. The NCBI Handbook, 2nd edition. National Center for Biotechnology Information (US; 2013
    [Google Scholar]
  38. Abby SS, Cury J, Guglielmini J, Néron B, Touchon M et al. Identification of protein secretion systems in bacterial genomes. Sci Rep 2016; 6:1–14 [View Article] [PubMed]
    [Google Scholar]
  39. Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol 2011; 7:10 [View Article] [PubMed]
    [Google Scholar]
  40. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 2020; 75:3491–3500 [View Article] [PubMed]
    [Google Scholar]
  41. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother 2014; 58:3895–3903 [View Article] [PubMed]
    [Google Scholar]
  42. Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH et al. Structure, function and diversity of the healthy human microbiome. Nature 2012; 486:207–214 [View Article] [PubMed]
    [Google Scholar]
  43. Qin J, Li Y, Cai Z, Li S, Zhu J et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490:55–60 [View Article] [PubMed]
    [Google Scholar]
  44. Bedarf JR, Hildebrand F, Coelho LP, Sunagawa S, Bahram M et al. Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson’s disease patients. Genome Med 2017; 9:1–13 [View Article] [PubMed]
    [Google Scholar]
  45. Xiao L, Estellé J, Kiilerich P, Ramayo-Caldas Y, Xia Z et al. A reference gene catalogue of the pig gut microbiome. Nat Microbiol 2016; 1:16161 [View Article] [PubMed]
    [Google Scholar]
  46. Bengtsson-Palme J, Hammarén R, Pal C, Östman M, Björlenius B et al. Elucidating selection processes for antibiotic resistance in sewage treatment plants using metagenomics. Sci Total Environ 2016; 572:697–712 [View Article]
    [Google Scholar]
  47. Marathe NP, Pal C, Gaikwad SS, Jonsson V, Kristiansson E et al. Untreated urban waste contaminates Indian river sediments with resistance genes to last resort antibiotics. Water Res 2017; 124:388–397 [View Article]
    [Google Scholar]
  48. Ma B, Zhao K, Lv X, Su W, Dai Z et al. Genetic correlation network prediction of forest soil microbial functional organization. ISME J 2018; 12:2492–2505 [View Article]
    [Google Scholar]
  49. Colby GA, Ruuskanen MO, Pierre KAS, Louis VLS, Poulain AJ et al. Climate change lowers diversity and functional potential of microbes in Canada’s high arctic. bioRxiv 2019705178 [View Article]
    [Google Scholar]
  50. van Schaik W. The human gut resistome. Philos Trans R Soc Lond B Biol Sci 2015; 370:20140087 [View Article]
    [Google Scholar]
  51. Rasmussen JL, Odelson DA, Macrina FL. Complete nucleotide sequence and transcription of ermF, a macrolide-lincosamide-streptogramin B resistance determinant from Bacteroides fragilis. J Bacteriol 1986; 168:523–533 [View Article] [PubMed]
    [Google Scholar]
  52. Luo H, Liu M, Wang L, Zhou W, Wang M et al. Identification of ribosomal RNA methyltransferase gene ermF in Riemerella anatipestifer. Avian Pathol 2015; 44:162–168 [View Article] [PubMed]
    [Google Scholar]
  53. Leclercq R, Courvalin P. Intrinsic and unusual resistance to macrolide, lincosamide, and streptogramin antibiotics in bacteria. Antimicrob Agents Chemother 1991; 35:1273–1276 [View Article] [PubMed]
    [Google Scholar]
  54. Smillie C, Garcillán-Barcia MP, Francia MV, Rocha EPC, de la Cruz F. Mobility of plasmids. Microbiol Mol Biol Rev 2010; 74:434–452 [View Article] [PubMed]
    [Google Scholar]
  55. Toleman MA, Bennett PM, Walsh TR. ISCR elements: novel gene-capturing systems of the 21st century?. Microbiol Mol Biol Rev 2006; 70:296–316 [View Article] [PubMed]
    [Google Scholar]
  56. Xu Y, Wang C, Zhang G, Tian J, Liu Y et al. ISCR2 is associated with the dissemination of multiple resistance genes among Vibrio spp. and Pseudoalteromonas spp. isolated from farmed fish. Arch Microbiol 2017; 199:891–896 [View Article]
    [Google Scholar]
  57. Ebmeyer S, Kristiansson E, Larsson DGJ. A framework for identifying the recent origins of mobile antibiotic resistance genes. Commun Biol 2021; 4:8 [View Article] [PubMed]
    [Google Scholar]
  58. Sekirov I, Russell SL, Antunes LCM, Finlay BB. Gut microbiota in health and disease. Physiol Rev 2010; 90:859–904 [View Article] [PubMed]
    [Google Scholar]
  59. Lagier J-C, Khelaifia S, Azhar EI, Croce O, Bibi F et al. Genome sequence of Oceanobacillus picturae strain S1, an halophilic bacterium first isolated in human gut. Stand Genomic Sci 2015; 10:91 [View Article] [PubMed]
    [Google Scholar]
  60. Roux V, Million M, Robert C, Magne A, Raoult D. Non-contiguous finished genome sequence and description of Oceanobacillus massiliensis sp. nov. Stand Genomic Sci 2013; 9:370–384 [View Article] [PubMed]
    [Google Scholar]
  61. Rozwandowicz M, Brouwer MSM, Fischer J, Wagenaar JA, Gonzalez-Zorn B et al. Plasmids carrying antimicrobial resistance genes in Enterobacteriaceae. J Antimicrob Chemother 2018; 73:1121–1137 [View Article] [PubMed]
    [Google Scholar]
  62. Chesneau O, Tsvetkova K, Courvalin P. Resistance phenotypes conferred by macrolide phosphotransferases. FEMS Microbiol Lett 2007; 269:317–322 [View Article] [PubMed]
    [Google Scholar]
  63. Weisblum B. Erythromycin resistance by ribosome modification. Antimicrob Agents Chemother 1995; 39:577–585 [View Article] [PubMed]
    [Google Scholar]
  64. Aminov RI, Mackie RI. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol Lett 2007; 271:147–161 [View Article] [PubMed]
    [Google Scholar]
  65. Kim H-J, Han C-Y, Park J-S, Oh S-H, Kang S-H et al. Nystatin-like Pseudonocardia polyene B1, a novel disaccharide-containing antifungal heptaene antibiotic. Sci Rep 2018; 8:13584 [View Article] [PubMed]
    [Google Scholar]
  66. Schwartz E, Henne A, Cramm R, Eitinger T, Friedrich B et al. Complete nucleotide sequence of pHG1: a Ralstonia eutropha H16 megaplasmid encoding key enzymes of H(2)-based ithoautotrophy and anaerobiosis. J Mol Biol 2003; 332:369–383 [View Article] [PubMed]
    [Google Scholar]
  67. Mac Aogáin M, Lau KJX, Cai Z, Kumar Narayana J, Purbojati RW et al. Metagenomics Reveals a Core Macrolide Resistome Related to Microbiota in Chronic Respiratory Disease. Am J Respir Crit Care Med 2020; 202:433–447 [View Article] [PubMed]
    [Google Scholar]
  68. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 2016; 44:D733–45 [View Article] [PubMed]
    [Google Scholar]
  69. Kitts PA, Church DM, Thibaud-Nissen F, Choi J, Hem V et al. Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res 2016; 44:D73–80 [View Article] [PubMed]
    [Google Scholar]
  70. Tung J, Barreiro LB, Burns MB, Grenier J-C, Lynch J et al. Social networks predict gut microbiome composition in wild baboons. Elife 2015; 4: [View Article] [PubMed]
    [Google Scholar]
  71. Gibson KM, Nguyen BN, Neumann LM, Miller M, Buss P et al. Gut microbiome differences between wild and captive black rhinoceros - implications for rhino health. Sci Rep 2019; 9:7570 [View Article]
    [Google Scholar]
  72. Karsenti E, Acinas SG, Bork P, Bowler C, De Vargas C et al. A holistic approach to marine eco-systems biology. PLoS Biol 2011; 9:10 [View Article]
    [Google Scholar]
  73. Ji M, Greening C, Vanwonterghem I, Carere CR, Bay SK et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature 2017; 552:400–403 [View Article]
    [Google Scholar]
  74. Mason OU, Hazen TC, Borglin S, Chain PSG, Dubinsky EA et al. Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to Deepwater Horizon oil spill. ISME J 2012; 6:1715–1727 [View Article]
    [Google Scholar]
  75. Tessler M, Neumann JS, Afshinnekoo E, Pineda M, Hersch R et al. Large-scale differences in microbial biodiversity discovery between 16S amplicon and shotgun sequencing. Sci Rep 2017; 7:6589 [View Article]
    [Google Scholar]
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