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Abstract

Understanding the dynamics and mechanisms of acquired drug resistance across major classes of antibiotics and bacterial pathogens is of critical importance for the optimization of current anti-infective therapies and the development of novel ones. To systematically address this challenge, we developed a workflow combining experimental evolution in a morbidostat continuous culturing device with deep genomic sequencing of population samples collected in time series. This approach was applied to the experimental evolution of six populations of BW25113 towards acquiring resistance to triclosan (TCS), an antibacterial agent in various consumer products. This study revealed the rapid emergence and expansion (up to 100% in each culture within 4 days) of missense mutations in the gene, encoding enoyl-acyl carrier protein reductase, the known TCS molecular target. A follow-up analysis of isolated clones showed that distinct amino acid substitutions increased the drug IC in a 3–16-fold range, reflecting their proximity to the TCS-binding site. In contrast to other antibiotics, efflux-upregulating mutations occurred only rarely and with low abundance. Mutations in several other genes were detected at an earlier stage of evolution. Most notably, three distinct amino acid substitutions were mapped in the C-terminal periplasmic domain of CadC protein, an acid stress-responsive transcriptional regulator. While these mutations do not confer robust TCS resistance, they appear to play a certain, yet unknown, role in adaptation to relatively low drug pressure. Overall, the observed evolutionary trajectories suggest that the FabI enzyme is the sole target of TCS (at least up to the ~50 µm level), and amino acid substitutions in the TCS-binding site represent the main mechanism of robust TCS resistance in . This model study illustrates the potential utility of the established morbidostat-based approach for uncovering resistance mechanisms and target identification for novel drug candidates with yet unknown mechanisms of action.

Funding
This study was supported by the:
  • F. Hoffmann-La Roche
    • Principle Award Recipient: SemenLeyn
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
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2021-05-04
2024-05-10
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References

  1. Leinonen R, Sugawara H, Shumway M. International nucleotide sequence database C. the sequence read archive. Nucleic Acids Res 2011; 39:D19–21
    [Google Scholar]
  2. Cassini A, Högberg LD, Plachouras D, Quattrocchi A, Hoxha A et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European economic area in 2015: a population-level modelling analysis. Lancet Infect Dis 2019; 19:56–66 [View Article][PubMed]
    [Google Scholar]
  3. Centers for Disease Control and Prevention Antibiotic resistance threats in the United States; 2019
  4. World Health Organization Antibacterial agents in clinical development; 2017
  5. Barrick JE, Lenski RE. Genome dynamics during experimental evolution. Nat Rev Genet 2013; 14:827–839 [View Article][PubMed]
    [Google Scholar]
  6. Gresham D, Dunham MJ. The enduring utility of continuous culturing in experimental evolution. Genomics 2014; 104:399–405 [View Article][PubMed]
    [Google Scholar]
  7. Ekkers DM, Branco Dos Santos F, Mallon CA, Bruggeman F, van Doorn GS. The omnistat: a flexible continuous-culture system for prolonged experimental evolution. Methods Ecol Evol 2020; 11:932–942 [View Article][PubMed]
    [Google Scholar]
  8. Gopalakrishnan V, Krishnan NP, McClure E, Pelesko J, Crozier D et al. A low-cost, open source, self-contained bacterial evolutionary biorEactor (eve). bioRxiv 2019; 729434:
    [Google Scholar]
  9. Heins ZJ, Mancuso CP, Kiriakov S, Wong BG, Bashor CJ et al. Designing automated, high-throughput, continuous cell growth experiments using eVOLVER. J Vis Exp 2019 19 05 2019 [View Article][PubMed]
    [Google Scholar]
  10. Matteau D, Baby V, Pelletier S, Rodrigue S. A small-volume, low-cost, and versatile continuous culture device. PLoS One 2015; 10:e0133384 [View Article][PubMed]
    [Google Scholar]
  11. Toprak E, Veres A, Michel J-B, Chait R, Hartl DL et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat Genet 2011; 44:101–105 [View Article][PubMed]
    [Google Scholar]
  12. Toprak E, Veres A, Yildiz S, Pedraza JM, Chait R et al. Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nat Protoc 2013; 8:555–567 [View Article][PubMed]
    [Google Scholar]
  13. Verhoeven E, Abdellati S, Nys P, Laumen J, De Baetselier I et al. Construction and optimization of a 'NG Morbidostat' - An automated continuous-culture device for studying the pathways towards antibiotic resistance in Neisseria gonorrhoeae . F1000Res 2019; 8:560 [View Article][PubMed]
    [Google Scholar]
  14. Dößelmann B, Willmann M, Steglich M, Bunk B, Nübel U et al. Rapid and consistent evolution of colistin resistance in extensively drug-resistant Pseudomonas aeruginosa during Morbidostat culture. Antimicrob Agents Chemother 2017; 61: 24 08 2017 [View Article][PubMed]
    [Google Scholar]
  15. Jones RD, Jampani HB, Newman JL, Lee AS. Triclosan: a review of effectiveness and safety in health care settings. Am J Infect Control 2000; 28:184–196 [View Article][PubMed]
    [Google Scholar]
  16. Carey DE, McNamara PJ. The impact of triclosan on the spread of antibiotic resistance in the environment. Front Microbiol 2014; 5:780 [View Article][PubMed]
    [Google Scholar]
  17. McNamara PJ, Levy SB. Triclosan: an instructive tale. Antimicrob Agents Chemother 2016; 60:AAC.02105-16–6 [View Article][PubMed]
    [Google Scholar]
  18. Westfall C, Flores-Mireles AL, Robinson JI, Lynch AJL, Hultgren S et al. The widely used antimicrobial triclosan induces high levels of antibiotic tolerance in vitro and reduces antibiotic efficacy up to 100-fold in vivo . Antimicrob Agents Chemother 2019; 63: 25 04 2019 [View Article][PubMed]
    [Google Scholar]
  19. McMurry LM, Oethinger M, Levy SB. Triclosan targets lipid synthesis. Nature 1998; 394:531–532 [View Article][PubMed]
    [Google Scholar]
  20. Heath RJ, Yu YT, Shapiro MA, Olson E, Rock CO. Broad spectrum antimicrobial biocides target the FabI component of fatty acid synthesis. J Biol Chem 1998; 273:30316–30320 [View Article][PubMed]
    [Google Scholar]
  21. Heath RJ, Rubin JR, Holland DR, Zhang E, Snow ME et al. Mechanism of triclosan inhibition of bacterial fatty acid synthesis. J Biol Chem 1999; 274:11110–11114 [View Article][PubMed]
    [Google Scholar]
  22. Roujeinikova A, Levy CW, Rowsell S, Sedelnikova S, Baker PJ et al. Crystallographic analysis of triclosan bound to enoyl reductase. J Mol Biol 1999; 294:527–535 [View Article][PubMed]
    [Google Scholar]
  23. Stewart MJ, Parikh S, Xiao G, Tonge PJ, Kisker C. Structural basis and mechanism of enoyl reductase inhibition by triclosan. J Mol Biol 1999; 290:859–865 [View Article][PubMed]
    [Google Scholar]
  24. Khan R, Kong HG, Jung Y-H, Choi J, Baek K-Y et al. Triclosan resistome from metagenome reveals diverse enoyl acyl carrier protein reductases and selective enrichment of triclosan resistance genes. Sci Rep 2016; 6:32322 [View Article][PubMed]
    [Google Scholar]
  25. Grandgirard D, Furi L, Ciusa ML, Baldassarri L, Knight DR et al. Mutations upstream of fabI in triclosan resistant Staphylococcus aureus strains are associated with elevated fabI gene expression. BMC Genomics 2015; 16:345 [View Article][PubMed]
    [Google Scholar]
  26. McMurry LM, Oethinger M, Levy SB. Overexpression of marA, soxS, or acrAB produces resistance to triclosan in laboratory and clinical strains of Escherichia coli. FEMS Microbiol Lett 1998; 166:305–309 [View Article][PubMed]
    [Google Scholar]
  27. Villalaín J, Mateo CR, Aranda FJ, Shapiro S, Micol V. Membranotropic effects of the antibacterial agent triclosan. Arch Biochem Biophys 2001; 390:128–136 [View Article][PubMed]
    [Google Scholar]
  28. Escalada MG, Russell AD, Maillard J-Y, Ochs D. Triclosan-bacteria interactions: single or multiple target sites?. Lett Appl Microbiol 2005; 41:476–481 [View Article][PubMed]
    [Google Scholar]
  29. Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol 2006; 2:0008 [View Article][PubMed]
    [Google Scholar]
  30. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article][PubMed]
    [Google Scholar]
  31. Wattam AR, Davis JJ, Assaf R, Boisvert S, Brettin T et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 2017; 45:D535–D542 [View Article][PubMed]
    [Google Scholar]
  32. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25:1754–1760 [View Article][PubMed]
    [Google Scholar]
  33. Wilm A, Aw PPK, Bertrand D, Yeo GHT, Ong SH et al. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res 2012; 40:11189–11201 [View Article][PubMed]
    [Google Scholar]
  34. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010; 20:1297–1303 [View Article][PubMed]
    [Google Scholar]
  35. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M et al. Versatile and open software for comparing large genomes. Genome Biol 2004; 5:R12 [View Article][PubMed]
    [Google Scholar]
  36. Deatherage DE, Traverse CC, Wolf LN, Barrick JE. Detecting rare structural variation in evolving microbial populations from new sequence junctions using breseq. Front Genet 2014; 5:468 [View Article][PubMed]
    [Google Scholar]
  37. Leyn SA. iJump: a fast tool for tracking bacterial mobile elements rearrangements in course of adaptive laboratory evolution [version 1; not peer reviewed]. DOI: https://doi.org/10.7490/f1000research.1118098.1. F1000Research, ISCB Comm J. 2020;9:799 (poster).
  38. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res 2006; 34:D32–D36 [View Article][PubMed]
    [Google Scholar]
  39. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J et al. The sequence Alignment/Map format and SAMtools. Bioinformatics 2009; 25:2078–2079 [View Article][PubMed]
    [Google Scholar]
  40. Milne I, Stephen G, Bayer M, Cock PJA, Pritchard L et al. Using tablet for visual exploration of second-generation sequencing data. Brief Bioinform 2013; 14:193–202 [View Article][PubMed]
    [Google Scholar]
  41. Farahpour F, Saeedghalati M, Hoffmann D. MullerPlot: generates Muller Plot from Population/Abundance/Frequency dynamics data. R package version 012. 2016.
  42. Sprouffske K, Wagner A. Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinformatics 2016; 17:172 [View Article][PubMed]
    [Google Scholar]
  43. Ritz C, Baty F, Streibig JC, Gerhard D. Dose-Response analysis using R. PLoS One 2015; 10:e0146021 [View Article][PubMed]
    [Google Scholar]
  44. Abraham JM, Freitag CS, Clements JR, Eisenstein BI. An invertible element of DNA controls phase variation of type 1 fimbriae of Escherichia coli . Proc Natl Acad Sci U S A 1985; 82:5724–5727 [View Article][PubMed]
    [Google Scholar]
  45. Ma D, Alberti M, Lynch C, Nikaido H, Hearst JE. The local repressor AcrR plays a modulating role in the regulation of acrAB genes of Escherichia coli by global stress signals. Mol Microbiol 1996; 19:101–112 [View Article][PubMed]
    [Google Scholar]
  46. Haneburger I, Fritz G, Jurkschat N, Tetsch L, Eichinger A et al. Deactivation of the E. coli pH stress sensor CadC by cadaverine. J Mol Biol 2012; 424:15–27 [View Article][PubMed]
    [Google Scholar]
  47. Barker CS, Prüss BM, Matsumura P. Increased motility of Escherichia coli by insertion sequence element integration into the regulatory region of the flhD operon. J Bacteriol 2004; 186:7529–7537 [View Article][PubMed]
    [Google Scholar]
  48. Ko M, Park C. H-NS-Dependent regulation of flagellar synthesis is mediated by a LysR family protein. J Bacteriol 2000; 182:4670–4672 [View Article][PubMed]
    [Google Scholar]
  49. Kuper C, Jung K. CadC-mediated activation of the cadBA promoter in Escherichia coli . J Mol Microbiol Biotechnol 2005; 10:26–39 [View Article][PubMed]
    [Google Scholar]
  50. Tetsch L, Koller C, Haneburger I, Jung K. The membrane-integrated transcriptional activator CadC of Escherichia coli senses lysine indirectly via the interaction with the lysine permease LysP. Mol Microbiol 2008; 67:570–583 [View Article][PubMed]
    [Google Scholar]
  51. Webber MA, Whitehead RN, Mount M, Loman NJ, Pallen MJ et al. Parallel evolutionary pathways to antibiotic resistance selected by biocide exposure. J Antimicrob Chemother 2015; 70:2241–2248 [View Article][PubMed]
    [Google Scholar]
  52. Al-Mnaser AA, Woodward MJ. Sub-lethal concentrations of phytochemicals (Carvacrol and Oregano) select for reduced susceptibility mutants of Escherichia coli O23:H52. Pol J Microbiol 2020; 69:121–125 [View Article][PubMed]
    [Google Scholar]
  53. Lenahan M, Sheridan Áine, Morris D, Duffy G, Fanning S et al. Transcriptomic analysis of triclosan-susceptible and -tolerant Escherichia coli O157:H19 in response to triclosan exposure. Microb Drug Resist 2014; 20:91–103 [View Article][PubMed]
    [Google Scholar]
  54. Bailey AM, Constantinidou C, Ivens A, Garvey MI, Webber MA et al. Exposure of Escherichia coli and Salmonella enterica serovar Typhimurium to triclosan induces a species-specific response, including drug detoxification. J Antimicrob Chemother 2009; 64:973–985 [View Article][PubMed]
    [Google Scholar]
  55. Yasir M, Turner AK, Bastkowski S, Baker D, Page AJ et al. TraDIS-Xpress: a high-resolution whole-genome assay identifies novel mechanisms of triclosan action and resistance. Genome Res 2020; 30:239–249 [View Article][PubMed]
    [Google Scholar]
  56. Lehnen D, Blumer C, Polen T, Wackwitz B, Wendisch VF et al. LrhA as a new transcriptional key regulator of flagella, motility and chemotaxis genes in Escherichia coli . Mol Microbiol 2002; 45:521–532 [View Article][PubMed]
    [Google Scholar]
  57. Khan R, Roy N, Choi K, Lee S-W. Distribution of triclosan-resistant genes in major pathogenic microorganisms revealed by metagenome and genome-wide analysis. PLoS One 2018; 13:e0192277 [View Article][PubMed]
    [Google Scholar]
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