Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Intra- and interpopulation transposition of mobile genetic elements driven by antibiotic selection

Nature Ecology & Evolution volume 6, pages 555–564 (2022)Cite this article

Subjects

Abstract

The spread of genes encoding antibiotic resistance is often mediated by horizontal gene transfer (HGT). Many of these genes are associated with transposons, a type of mobile genetic element that can translocate between the chromosome and plasmids. It is widely accepted that the translocation of antibiotic resistance genes onto plasmids potentiates their spread by HGT. However, it is unclear how this process is modulated by environmental factors, especially antibiotic treatment. To address this issue, we asked whether antibiotic exposure would select for the transposition of resistance genes from chromosomes onto plasmids and, if so, whether antibiotic concentration could tune the distribution of resistance genes between chromosomes and plasmids. We addressed these questions by analysing the transposition dynamics of synthetic and natural transposons that encode resistance to different antibiotics. We found that stronger antibiotic selection leads to a higher fraction of cells carrying the resistance on plasmids because the increased copy number of resistance genes on multicopy plasmids leads to higher expression of those genes and thus higher cell survival when facing antibiotic selection. Once they have transposed to plasmids, antibiotic resistance genes are primed for rapid spread by HGT. Our results provide quantitative evidence for a mechanism by which antibiotic selection accelerates the spread of antibiotic resistance in microbial communities.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A kinetic model illustrates the increase of plasmid-based transposons as antibiotic concentrations increase.
Fig. 2: Antibiotic treatment promotes chromosome-to-plasmid transposition for a model transposon.
Fig. 3: Antibiotic selection increases the copy number of resistance transposons, regardless of promoter, resistance gene or plasmid origin.
Fig. 4: Antibiotic selection promotes chromosome-to-plasmid transposition, regardless of transposon class.
Fig. 5: Intrapopulation transposition enables interpopulation transfer of a transposon in a two-member mixed culture, in response to antibiotic treatment.
Fig. 6: Intrapopulation transposition enables interpopulation transfer of a transposon in a 67-member mixed culture in response to antibiotic treatment.

Similar content being viewed by others

Data availability

The experimental data generated for this manuscript are provided as Source data.

Code availability

Simulation codes are provided as supplementary files.

References

  1. Poirel, L. et al. Tn125-related acquisition of blaNDM-like genes in Acinetobacter baumannii. Antimicrob. Agents Chemother. 56, 1087–1089 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wang, R. et al. The global distribution and spread of the mobilized colistin resistance gene mcr-1. Nat. Commun. 9, 1179 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Clark, N. C., Weigel, L. M., Patel, J. B. & Tenover, F. C. Comparison of Tn1546-like elements in vancomycin-resistant Staphylococcus aureus isolates from Michigan and Pennsylvania. Antimicrob. Agents Chemother. 49, 470–472 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Partridge, S. R., Kwong, S. M., Firth, N. & Jensen, S. O. Mobile genetic elements associated with antimicrobial resistance. Clin. Microbiol. Rev. 31, e00088-17 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Stokes, H. W. & Gillings, M. R. Gene flow, mobile genetic elements and the recruitment of antibiotic resistance genes into Gram-negative pathogens. FEMS Microbiol. Rev. 35, 790–819 (2011).

    Article  CAS  PubMed  Google Scholar 

  6. Ghaly, T. M. & Gillings, M. R. Mobile DNAs as ecologically and evolutionarily independent units of life. Trends Microbiol. 26, 904–912 (2018).

    Article  CAS  PubMed  Google Scholar 

  7. Modi, S. R., Lee, H. H., Spina, C. S. & Collins, J. J. Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome. Nature 499, 219–222 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Brown-Jaque, M., Calero-Cáceres, W. & Muniesa, M. Transfer of antibiotic-resistance genes via phage-related mobile elements. Plasmid https://doi.org/10.1016/j.plasmid.2015.01.001 (2015).

  9. Frantzeskakis, L. et al. Signatures of host specialization and a recent transposable element burst in the dynamic one-speed genome of the fungal barley powdery mildew pathogen. BMC Genomics 19, 381 (2018).

  10. Scott, K. P. The role of conjugative transposons in spreading antibiotic resistance between bacteria that inhabit the gastrointestinal tract. Cell. Mol. Life Sci. 59, 2071–2082 (2002).

    Article  CAS  PubMed  Google Scholar 

  11. Pezzella, C., Ricci, A., DiGiannatale, E., Luzzi, I. & Carattoli, A. Tetracycline and streptomycin resistance genes, transposons, and plasmids in Salmonella enterica isolates from animals in Italy. Antimicrob. Agents Chemother. 48, 903–908 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bengtsson-Palme, J., Boulund, F., Fick, J., Kristiansson, E. & Larsson, D. G. Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Front. Microbiol. 5, 648 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Imchen, M. & Kumavath, R. Shotgun metagenomics reveals a heterogeneous prokaryotic community and a wide array of antibiotic resistance genes in mangrove sediment. FEMS Microbiol. Ecol. 96, fiaa173 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Zhang, T., Zhang, X.-X. & Ye, L. Plasmid metagenome reveals high levels of antibiotic resistance genes and mobile genetic elements in activated sludge. PLoS ONE 6, e26041 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hu, H. et al. Novel plasmid and its variant harboring both a blaNDM-1 gene and type IV secretion system in clinical isolates of Acinetobacter lwoffii. Antimicrob. Agents Chemother. 56, 1698–1702 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Smet, A. et al. Complete nucleotide sequence of CTX-M-15-plasmids from clinical Escherichia coli isolates: insertional events of transposons and insertion sequences. PLoS ONE 5, e11202 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Revilla, C. et al. Different pathways to acquiring resistance genes illustrated by the recent evolution of IncW plasmids. Antimicrob. Agents Chemother. 52, 1472–1480 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Poirel, L., Dortet, L., Bernabeu, S. & Nordmann, P. Genetic features of blaNDM-1-positive Enterobacteriaceae. Antimicrob. Agents Chemother. 55, 5403–5407 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Toleman, M. A., Spencer, J., Jones, L. & Walsh, T. R. blaNDM-1 is a chimera likely constructed in Acinetobacter baumannii. Antimicrob. Agents Chemother. 56, 2773–2776 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bonnin, R. A., Poirel, L. & Nordmann, P. New Delhi metallo-β-lactamase-producing Acinetobacter baumannii: a novel paradigm for spreading antibiotic resistance genes. Future Microbiol. 9, 33–41 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Waterman, P. E. et al. Bacterial peritonitis due to Acinetobacter baumannii sequence type 25 with plasmid-borne New Delhi metallo-β-lactamase in Honduras. Antimicrob. Agents Chemother. 57, 4584–4586 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. McGann, P. et al. Detection of New Delhi metallo-β-lactamase (encoded by blaNDM-1) in Acinetobacter schindleri during routine surveillance. J. Clin. Microbiol. 51, 1942–1944 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Jiang, X. et al. Dissemination of antibiotic resistance genes from antibiotic producers to pathogens. Nat. Commun. 8, 15784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Spanogiannopoulos, P., Waglechner, N., Koteva, K. & Wright, G. D. A rifamycin inactivating phosphotransferase family shared by environmental and pathogenic bacteria. Proc. Natl Acad. Sci. USA 111, 7102–7107 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yang, J. et al. Marine sediment bacteria harbor antibiotic resistance genes highly similar to those found in human pathogens. Microb. Ecol. 65, 975–981 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. D’Costa, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).

    Article  PubMed  CAS  Google Scholar 

  28. Van Goethem, M. W. et al. A reservoir of ‘historical’ antibiotic resistance genes in remote pristine Antarctic soils. Microbiome 6, 40 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mindlin, S., Soina, V. S., Petrova, M. A. & Gorlenko, Zh. M. Isolation of antibiotic resistance bacterial strains from Eastern Siberia permafrost sediments. Genetika 44, 36–44 (2008).

    CAS  PubMed  Google Scholar 

  30. Cohen, S. N. Transposable genetic elements and plasmid evolution. Nature 263, 731–738 (1976).

    Article  CAS  PubMed  Google Scholar 

  31. Wright, G. D. Environmental and clinical antibiotic resistomes, same only different. Curr. Opin. Microbiol. 51, 57–63 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. von Wintersdorff, C. J. et al. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front. Microbiol. 7, 173 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rankin, D. J., Rocha, E. P. C. & Brown, S. P. What traits are carried on mobile genetic elements, and why? Heredity (Edinb) https://doi.org/10.1038/hdy.2010.24 (2011).

  34. Kottara, A., Hall, J. P., Harrison, E. & Brockhurst, M. A. Variable plasmid fitness effects and mobile genetic element dynamics across Pseudomonas species. FEMS Microbiol. Ecol. 94, fix172 (2018).

    Article  CAS  Google Scholar 

  35. Hall, J. P., Wood, A. J., Harrison, E. & Brockhurst, M. A. Source–sink plasmid transfer dynamics maintain gene mobility in soil bacterial communities. Proc. Natl Acad. Sci. USA 113, 8260–8265 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hall, J. P. J., Williams, D., Paterson, S., Harrison, E. & Brockhurst, M. A. Positive selection inhibits gene mobilisation and transfer in soil bacterial communities. Nat. Ecol. Evol. 1, 1348–1353 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Naumann, T. A. & Reznikoff, W. S. Tn5 transposase with an altered specificity for transposon ends. J. Bacteriol. 184, 233–240 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wang, H. et al. Increased plasmid copy number is essential for Yersinia T3SS function and virulence. Science 353, 492–495 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Sandegren, L. & Andersson, D. I. Bacterial gene amplification: implications for the evolution of antibiotic resistance. Nat. Rev. Microbiol. 7, 578–588 (2009).

    Article  CAS  PubMed  Google Scholar 

  40. Dimitriu, T., Mathews, A. C. & Buckling, A. Increased copy number couples the evolution of plasmid horizontal transmission and plasmid-encoded antibiotic resistance. Proc. Natl Acad. Sci. USA 118, e2107818118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. De Lorenzo, V., Herrero, M., Jakubzik, U. & Timmis, K. N. Mini-Tn5 transposon derivatives for insertion mutagenesis, promoter probing, and chromosomal insertion of cloned DNA in gram-negative eubacteria. J. Bacteriol. 172, 6568–6572 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lichtenstein, C. & Brenner, S. Site-specific properties of Tn7 transposition into the E. coli chromosome. Mol. Gen. Genet. 183, 380–387 (1981).

    Article  CAS  PubMed  Google Scholar 

  43. Bethke, J. H. et al. Environmental and genetic determinants of plasmid mobility in pathogenic Escherichia coli. Sci. Adv. 6, eaax3173 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mahillon, J. & Chandler, M. Insertion sequences. Microbiol. Mol. Biol. Rev. 62, 725–774 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Siguier, P., Perochon, J., Lestrade, L., Mahillon, J. & Chandler, M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res. 34, D32–D36 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Seelke, R. W., Kline, B. C., Trawick, J. D. & Ritts, G. D. Genetic studies of F plasmid maintenance genes involved in copy number control, incompatability, and partitioning. Plasmid 7, 163–179 (1982).

    Article  CAS  PubMed  Google Scholar 

  47. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Watve, M. M., Dahanukar, N. & Watve, M. G. Sociobiological control of plasmid copy number in bacteria. PLoS ONE 5, e9328 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Lehtinen, S. et al. Horizontal gene transfer rate is not the primary determinant of observed antibiotic resistance frequencies in Streptococcus pneumoniae. Sci. Adv. 6, eaaz6137 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ubeda, C. et al. Antibiotic-induced SOS response promotes horizontal dissemination of pathogenicity island-encoded virulence factors in staphylococci. Mol. Microbiol. 56, 836–844 (2005).

    Article  CAS  PubMed  Google Scholar 

  51. Beaber, J. W., Hochhut, B. & Waldor, M. K. SOS response promotes horizontal dissemination of antibiotic resistance genes. Nature 427, 72–74 (2004).

    Article  CAS  PubMed  Google Scholar 

  52. al‐Masaudi, S. B., Day, M. & Russell, A. D. Effect of some antibiotics and biocides on plasmid transfer in Staphylococcus aureus. J. Appl. Bacteriol. 71, 239–243 (1991).

    Article  PubMed  Google Scholar 

  53. Nichols, B. P. & Guay, G. G. Gene amplification contributes to sulfonamide resistance in Escherichia coli. Antimicrob. Agents Chemother. 33, 2042–2048 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Normark, S., Edlund, T., Grundström, T., Bergström, S. & Wolf-Watz, H. Escherichia coli K-12 mutants hyperproducing chromosomal beta-lactamase by gene repetitions. J. Bacteriol. 132, 912–922 (1977).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zienkiewicz, M., Kern-Zdanowicz, I., Carattoli, A., Gniadkowski, M. & Cegłowski, P. Tandem multiplication of the IS 26-flanked amplicon with the blaSHV-5 gene within plasmid p1658/97. FEMS Microbiol. Lett. 341, 27–36 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. Matthews, P. R. & Stewart, P. R. Amplification of a section of chromosomal DNA in methicillin-resistant Staphylococcus aureus following growth in high concentrations of methicillin. J. Gen. Microbiol. 134, 1455–1464 (1988).

    CAS  PubMed  Google Scholar 

  57. Sun, S., Berg, O. G., Roth, J. R. & Andersson, D. I. Contribution of gene amplification to evolution of increased antibiotic resistance in Salmonella typhimurium. Genetics 182, 1183–1195 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Andersson, D. I. & Hughes, D. Gene amplification and adaptive evolution in bacteria. Annu. Rev. Genet. 43, 167–195 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Nicoloff, H., Perreten, V. & Levy, S. B. Increased genome instability in Escherichia coli lon mutants: relation to emergence of multiple-antibiotic-resistant (Mar) mutants caused by insertion sequence elements and large tandem genomic amplifications. Antimicrob. Agents Chemother. 51, 1293–1303 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Bertini, A. et al. Multicopy blaOXA-58 gene as a source of high-level resistance to carbapenems in Acinetobacter baumannii. Antimicrob. Agents Chemother. 51, 2324–2328 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Knapp, C. W. et al. Indirect evidence of transposon-mediated selection of antibiotic resistance genes in aquatic systems at low-level oxytetracycline exposures. Environ. Sci. Technol. 42, 5348–5353 (2008).

    Article  CAS  PubMed  Google Scholar 

  62. San Millan, A., Escudero, J. A., Gifford, D. R., Mazel, D. & MacLean, R. C. Multicopy plasmids potentiate the evolution of antibiotic resistance in bacteria. Nat. Ecol. Evol. 1, 10 (2016).

    Article  PubMed  Google Scholar 

  63. Rodriguez-Beltran, J. et al. Multicopy plasmids allow bacteria to escape from fitness trade-offs during evolutionary innovation. Nat. Ecol. Evol. 2, 873–881 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Rodríguez-Beltrán, J., DelaFuente, J., León-Sampedro, R., MacLean, R. C. & San Millán, Á. Beyond horizontal gene transfer: the role of plasmids in bacterial evolution. Nat. Rev. Microbiol. 19, 347–359 (2021).

    Article  PubMed  CAS  Google Scholar 

  65. Frost, L. S., Leplae, R., Summers, A. O. & Toussaint, A. Mobile genetic elements: the agents of open source evolution. Nat. Rev. Microbiol. 3, 722–732 (2005).

    Article  CAS  PubMed  Google Scholar 

  66. You, L., Hoonlor, A. & Yin, J. Modeling biological systems using Dynetica—a simulator of dynamic networks. Bioinformatics 19, 435–436 (2003).

    Article  CAS  PubMed  Google Scholar 

  67. Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wingett, S. W. & Andrews, S. FastQ Screen: a tool for multi-genome mapping and quality control. F1000Res. 7, 1338 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Blankenberg, D. et al. Manipulation of FASTQ data with Galaxy. Bioinformatics 26, 1783–1785 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  73. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Institutes of Health (grant nos. R01A1125604, R01GM110494 and R01EB029466 to L.Y.), the National Science Foundation (no. MCB-1937259 to L.Y.) and David and Lucile Packard Foundation (L.Y.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Yi Yao, Rohan Maddamsetti, Andrea Weiss, Yuanchi Ha, Teng Wang, Shangying Wang & Lingchong You

  2. Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA

    Lingchong You

  3. Center for Genomic and Computational Biology, Duke University, Durham, NC, USA

    Lingchong You

Authors
  1. Yi Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rohan Maddamsetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Weiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanchi Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Teng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shangying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lingchong You
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.Y. and L.Y. conceived the project and designed the research. Y.Y., Y.H. and A.W. conducted the experiments. Y.Y., S.W., T.W. and Y.H. conducted the modelling analysis. L.Y. assisted with data analysis and interpretation. Y.Y., R.M. and L.Y. wrote the manuscript with input from S.W., T.W., A.W. and Y.H. All authors approved the manuscript.

Corresponding author

Correspondence to Lingchong You.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Alvaro San Millan, Thibault Stalder and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Transposition dynamics, in response to antibiotic selection, is robust to variation in transposition rates (ηT), half-maximal effective concentration (Ap) and growth rate (μp) of Sp.

All simulations here ensure that the core assumptions of our mechanism are satisfied: Sc grows faster than Sp in the absence of the antibiotic, but the latter grows faster at a sufficiently high antibiotic concentration. All parameters are kept the same as in Fig. 1, unless noted otherwise. a, Transposition dynamics for varying ηT: 0.01 (blue), 10−4 (green), and 10−6 (black). b, Transposition dynamics for varying Ap: 20 (blue), 10 (green), and 5 (black). c, Transposition dynamics for varying μp: 0.5 (blue), 0.4 (green), and 0.3 (black).

Extended Data Fig. 2 Transposition dynamics depends on how Sc and Sp are individually affected by the antibiotic treatment.

In Fig. 1, we considered the case where (1) Sc grows faster than Sp in the absence of an antibiotic and (2) the growth rate of Sc decreases fasters than does the growth rate of Sp with increasing antibiotic concentrations. These antibiotic-dose responses are described by a set of Hill terms (equation (3) in Methods section). The general trend described in Fig. 1 captured the dynamics for the experimental systems analyzed. In general, the responses of the two strains to antibiotics can be diverse. Here, numerical simulations are performed with 3 different sets of nc and np values, while keeping other parameters the same as in Fig. 1. a, The growth rates of the subpopulation with chromosomal transposons (Sc) and the subpopulation with plasmid-based transposons (Sp) under different antibiotic concentrations. b, The relative growth rates (△μ) of the subpopulation with chromosomal transposons Sc) and the subpopulation with plasmid-based transposons (Sp) under different antibiotic concentrations. Here, different parameter sets generate three different trends of △μ with increasing antibiotic concentrations: (Left) △μ increases and then decreases, (Middle) △μ increases, decreases and then increases; (Right) △μ decreases, increases, and then decreases. c, Simulated dependence of the fraction of Sp on the antibiotic concentration. Overall, Sp is the dominant subpopulation in the culture above a threshold antibiotic concentration. Under some conditions (middle column), there may exist additional thresholds where the cost of carrying the transposon on the plasmid eventually outweighs its benefit.

Extended Data Fig. 3 Measurement of the transposon on plasmid fraction before and after antibiotic treatment by plasmid extraction and transformation.

Plasmids were extracted from the inoculation culture (a) and cultures after different antibiotic concentration treatment (b), and the fraction containing the resistance transposon was measured by qPCR (result shown in Fig. 2c) and then plasmids were transformed into E. coli DH5α. Next, qPCR was performed on the transformed culture to re-estimate the fraction of plasmids containing the resistant transposon. This result confirms the qPCR results shown in Fig. 2c. (mean ± S.D., n = 4). For the boxplot (a), the top whisker represents the maximum value, the top of the box represents the 75 percentile, the center line represents the median; the bottom of the box represents the 25th percentile, and the bottom whisker represents the minimum value.

Source data

Extended Data Fig. 4 Experiments with K-12 MG1655 confirmed the generality of the transposition to plasmid mechanism across E. coli strains.

(a) qPCR and (b) DNA electrophoresis demonstrate that the fraction of cells with the transposon on the plasmid, Sp, increases as antibiotic concentrations increase (data shown as mean ± S.D., n = 3). The MG1655 strain is used as the genetic background, and the same genetic constructs that were used in DH5α for previous experiments (See Fig. 2), were inserted into the homologous genomic region (strain S127). The top arrow in the panel (b) indicates the plasmid with the transposon insertion, and the bottom arrow indicates the empty plasmid. Two independent repeated experiments were performed for the DNA electrophoresis experiments (b).

Source data

Extended Data Fig. 5 Extended data demonstrating the increase of the copy number of resistance transposons by antibiotic selection, regardless of promoter, resistance gene, or plasmid origin.

The qPCR results in Fig. 3 show increasing Sp fractions with increasing antibiotic selection at different promoters, resistance genes, and plasmid origins. Here, we picked several samples from Fig. 3, and performed extended verification by DNA electrophoresis (a-c) or plasmid extraction and transformation (d) results (mean ± S.D., n = 4). The top arrow on the gel panels (a-c) represents the plasmid with transposon insertion, while the bottom arrow represents the empty plasmid. Two independent repeated experiments were performed for the DNA electrophoresis experiments (a-c).

Source data

Extended Data Fig. 6 Experiments with K-12 MG1655 demonstrated the generality of the transposition to plasmid mechanism, across E. coli strains, and under the control of different promoters.

qPCR showed transposon copy numbers increased with increasing antibiotic concentrations for synthetic transposons with different basal expression levels of the tetA resistance gene (mean ± S.D., n = 3). qPCR was performed on MG1655 strains containing the same high-copy-number plasmid, but with chromosomal-based transposons under the control of different promoters: a weak promoter in strain S128 (a) or a medium promoter in strain S129 (b).

Source data

Extended Data Fig. 7 Differences in growth rates between Sc and Sp strains with different plasmids.

Measurement of the growth rates of the strains with a stable tetA resistance gene on the chromosome or on different plasmids (strain S04, S05 S130-135) without antibiotic selection. In general, Sp grew slower than Sc without selection, indicating a higher burden caused by transposons on the plasmids. (n = 4 for PUC origin, n=6 for PBR322 origin, n=8 for other origins, p value calculated by two-tailed Student’s t-test). For each boxplot, the top whisker represents the maximum value, the top of the box represents the 75 percentile, the center line represents the median; the bottom of the box represents the 25th percentile, and the bottom whisker represents the minimum value.

Source data

Extended Data Fig. 8 The serial inoculation protocol in experiments with native transposons.

As native-derived transposons may not be as active as the synthetic miniTn5-derived transposons, we used serial inoculation experiments to select for higher transposon copy numbers. Cultures were inoculated into deep well plates with increasing tetracycline concentrations over time. The protocol enriched cells with plasmid-borne transposons, which were hard to detect using the protocol designed for miniTn5-transposons.

Extended Data Fig. 9 Tn10 carrying the resistance gene transposed from the F plasmid to high-copy number pUC plasmids in response to antibiotic treatment.

a, Schematic of experimental design. F plasmid has a copy number of ~1-2; pUC plasmid has a copy number of ~200. b, qPCR showed that distribution of native Tn10 transposons shifted from the F plasmid to a high-copy plasmid (mean ± S.D., n = 3) at high antibiotic concentrations (strain S136).

Source data

Extended Data Fig. 10 Schematic of the method used to determine the Sp fraction by plasmid extraction and transformation, and of the method used to determine the copy number of transposons and plasmids in qPCR.

a, We used the Tet+ transposon and Kan+ plasmid as an example (Extended Data Fig. 3b) to show how we calculate the fraction of transposons on the plasmids. Three kinds of plasmids were extracted from the culture: originally empty plasmids (Kan+), plasmids with transposon insertions outside the kanR gene region (Kan+Tet+), and plasmids with transposons insertion inside the kanR gene region (Tet+). After transformation, plates with different antibiotic combinations were used to determine the total number of different plasmids from the original mixture. The detailed calculation process is in the method section. b, We used the Tet+ transposon and Kan+ plasmid as an example (Fig. 2c) to show how we calculate the copy of transposons or plasmids. During the chromosome integration process, a cmR gene was inserted into the chromosome together with but outside the transposon, so the probes for cmR gene can be used to represent the copy of chromosome. The transposon and the plasmid were marked with tetA and kanR gene respectively. We also constructed a DNA fragment with all three markers at 1:1:1 ratio, so this ca be used as a control to calculate the relative copies of different genes. For details of these calculations, see the Methods section.

Supplementary information

Supplementary Information

Supplementary Tables 1–6.

Reporting Summary

Peer Review File

Supplementary Software 1

Dynetica simulation code for the single-strain model (Fig. 1 and Extended Data Figs. 1 and 2).

Supplementary Software 2

Dynetica simulation code for the two-strain model (Fig. 5).

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, Y., Maddamsetti, R., Weiss, A. et al. Intra- and interpopulation transposition of mobile genetic elements driven by antibiotic selection. Nat Ecol Evol 6, 555–564 (2022). https://doi.org/10.1038/s41559-022-01705-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-022-01705-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing