Coevolution of host–plasmid pairs facilitates the emergence of novel multidrug resistance

Abstract

Multidrug resistance (MDR) of pathogens is an ongoing public health crisis exacerbated by the horizontal transfer of antibiotic resistance genes via conjugative plasmids. Factors that stabilize these plasmids in bacterial communities contribute to an even higher incidence of MDR, given the increased likelihood that a host will already contain a plasmid when it acquires another through conjugation. Here, we show one such stabilizing factor is host–plasmid coevolution under antibiotic selection, which facilitated the emergence of MDR via two distinct plasmids in communities consisting of Escherichia coli and Klebsiella pneumoniae once antibiotics were removed. In our system, evolution promoted greater stability of a plasmid in its coevolved host. Further, pleiotropic effects resulted in greater plasmid persistence in both novel host–plasmid combinations and, in some cases, multi-plasmid hosts. This evolved stability favoured the generation of MDR cells and thwarted their loss within communities with multiple plasmids. By selecting for plasmid persistence, the application of antibiotics may promote MDR well after their original period of use.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Predictions of the effects of host–plasmid coevolution on plasmid persistence.
Fig. 2: Predictions of the effects of host–plasmid coevolution on MDR emergence.
Fig. 3: Plasmid persistence in the absence of antibiotics increases after coevolution of plasmids with their hosts.
Fig. 4: The emergence of MDR in mixed-species cocultures increases after coevolution of plasmids with their hosts.
Fig. 5: Maintenance of antibiotic resistance and MDR increases after host–plasmid coevolution in both coevolved pairs and novel combinations of host and plasmids.

Data availability

All sequencing data pertaining to this project have been made available at the National Center for Biotechnology Information (SRA accession number PRJNA552385). All other data that support the findings of this study are available at https://github.com/livkosterlitz/Figures-Jordt-et-al-2020.

Code availability

The StabilityToolkit package used to analyse persistence data via our plasmid population dynamic model and corresponding instructions are available at https://github.com/jmponciano/StabilityToolkit/blob/master/RunningStabToolsPack.zip. The code used for the nonlinear beta-binomial regression model (Supplementary Information, section IX) can be found at https://github.com/jmponciano/JordtEtAl2020. The code used for simulations of our mathematical model is available at https://github.com/evokerr/Jordt_et_al_Gillespe_Code. Code used to generate the figures can be found at https://github.com/livkosterlitz/Figures-Jordt-et-al-2020.

References

  1. 1.

    Norman, A., Hansen, L. H. & Sørensen, S. J. Conjugative plasmids: vessels of the communal gene pool. Phil. Trans. R. Soc. Lond. B 364, 2275–2289 (2009).

    CAS  Article  Google Scholar 

  2. 2.

    San Millan, A. & MacLean, R. C. Fitness costs of plasmids: a limit to plasmid transmission. Microbiol. Spectr. 5, MTBP-0016-2017 (2017).

  3. 3.

    Yano, H. et al. Evolved plasmid–host interactions reduce plasmid interference cost. Mol. Microbiol. 101, 743–756 (2016).

    CAS  Article  Google Scholar 

  4. 4.

    Vogwill, T. & MacLean, R. C. The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evol. Appl. 8, 284–295 (2015).

    Article  Google Scholar 

  5. 5.

    Bergstrom, C. T., Lipsitch, M. & Levin, B. R. Natural selection, infectious transfer and the existence conditions for bacterial plasmids. Genetics 155, 1505–1519 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Harrison, E. & Brockhurst, M. A. Plasmid-mediated horizontal gene transfer is a coevolutionary process. Trends Microbiol. 20, 262–267 (2012).

    CAS  Article  Google Scholar 

  7. 7.

    Lopatkin, A. J. et al. Persistence and reversal of plasmid-mediated antibiotic resistance. Nat. Commun. 8, 1689 (2017).

    Article  Google Scholar 

  8. 8.

    Turner, P. E., Cooper, V. S. & Lenski, R. E. Tradeoff between horizontal and vertical modes of transmission in bacterial plasmids. Evolution 52, 315 (1998).

    Article  Google Scholar 

  9. 9.

    Li, Y. et al. A post-segregational killing mechanism for maintaining plasmid PMF1 in its Myxococcus fulvus host. Front. Cell. Infect. Microbiol. 8, 274 (2018).

  10. 10.

    Dahlberg, C. & Chao, L. Amelioration of the cost of conjugative plasmid carriage in Eschericha coli K12. Genetics 165, 1641–1649 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Bouma, J. E. & Lenski, R. E. Evolution of a bacteria/plasmid association. Nature 335, 351–352 (1988).

    CAS  Article  Google Scholar 

  12. 12.

    Starikova, I. et al. Fitness costs of various mobile genetic elements in Enterococcus faecium and Enterococcus faecalis. J. Antimicrob. Chemother. 68, 2755–2765 (2013).

    CAS  Article  Google Scholar 

  13. 13.

    Dionisio, F., Conceição, I. C., Marques, A. C. R., Fernandes, L. & Gordo, I. The evolution of a conjugative plasmid and its ability to increase bacterial fitness. Biol. Lett. 1, 250–252 (2005).

    CAS  Article  Google Scholar 

  14. 14.

    Loftie-Eaton, W. et al. Compensatory mutations improve general permissiveness to antibiotic resistance plasmids. Nat. Ecol. Evol. 1, 1354 (2017).

    Article  Google Scholar 

  15. 15.

    Ridenhour, B. J. et al. Persistence of antibiotic resistance plasmids in bacterial biofilms. Evol. Appl. 10, 640–647 (2017).

    CAS  Article  Google Scholar 

  16. 16.

    Harrison, E., Guymer, D., Spiers, A. J., Paterson, S. & Brockhurst, M. A. Parallel compensatory evolution stabilizes plasmids across the parasitism–mutualism continuum. Curr. Biol. 25, 2034–2039 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    Stalder, T. et al. Emerging patterns of plasmid–host coevolution that stabilize antibiotic resistance. Sci. Rep. 7, 4853 (2017).

  18. 18.

    San Millan, A. et al. Positive selection and compensatory adaptation interact to stabilize non-transmissible plasmids. Nat. Commun. 5, 5208 (2014).

  19. 19.

    Porse, A., Schønning, K., Munck, C. & Sommer, M. O. A. Survival and evolution of a large multidrug resistance plasmid in new clinical bacterial hosts. Mol. Biol. Evol. 33, 2860–2873 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    Santos-Lopez, A. et al. Compensatory evolution facilitates the acquisition of multiple plasmids in bacteria. Preprint at bioRxiv https://doi.org/10.1101/187070 (2017).

  21. 21.

    Wein, T., Hülter, N. F., Mizrahi, I. & Dagan, T. Emergence of plasmid stability under non-selective conditions maintains antibiotic resistance. Nat. Commun. 10, 2595 (2019).

  22. 22.

    Exner, M. et al. Antibiotic resistance: what is so special about multidrug-resistant Gram-negative bacteria? GMS Hyg. Infect. Control 12, https://doi.org/10.3205/dgkh000290 (2017).

  23. 23.

    Oliphant, C. M. & Eroschenko, K. Antibiotic resistance, part 2: Gram-negative pathogens. J. Nurse Pract. 11, 79–86 (2015).

    Article  Google Scholar 

  24. 24.

    Prioritization of Pathogens to Guide Discovery, Research and Development of New Antibiotics for Drug Resistant Bacterial Infections, Including Tuberculosis (WHO, 2017); https://go.nature.com/2Jcr0cF

  25. 25.

    McGann, P. et al. Escherichia coli harboring mcr-1 and bla CTX-M on a novel IncF plasmid: first report of mcr-1 in the United States. Antimicrob. Agents Chemother. 60, 4420–4421 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    de Man, T. J. B. et al. Genomic analysis of a pan-resistant isolate of Klebsiella pneumoniae, United States 2016. mBio 9, e00440–18 (2018).

  27. 27.

    Navon-Venezia, S., Kondratyeva, K. & Carattoli, A. Klebsiella pneumoniae: a major worldwide source and shuttle for antibiotic resistance. FEMS Microbiol. Rev. 41, 252–275 (2017).

    CAS  Article  Google Scholar 

  28. 28.

    Wyres, K. L. & Holt, K. E. Klebsiella pneumoniae as a key trafficker of drug resistance genes from environmental to clinically important bacteria. Curr. Opin. Microbiol. 45, 131–139 (2018).

    CAS  Article  Google Scholar 

  29. 29.

    Wyres, K. L. & Holt, K. E. Klebsiella pneumoniae population genomics and antimicrobial-resistant clones. Trends Microbiol. 24, 944–956 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Sota, M. et al. Shifts in host range of a promiscuous plasmid through parallel evolution of its replication initiation protein. ISME J. 4, 1568–1580 (2010).

    CAS  Article  Google Scholar 

  31. 31.

    Gelder, L. D. et al. Combining mathematical models and statistical methods to understand and predict the dynamics of antibiotic-sensitive mutants in a population of resistant bacteria during experimental evolution. Genetics 168, 1131–1144 (2004).

    Article  Google Scholar 

  32. 32.

    Ponciano, J. M., Gelder, L. D., Top, E. M. & Joyce, P. The population biology of bacterial plasmids: a hidden Markov model approach. Genetics 176, 957–968 (2007).

    Article  Google Scholar 

  33. 33.

    Loftie-Eaton, W. et al. Evolutionary paths that expand plasmid host-range: implications for spread of antibiotic resistance. Mol. Biol. Evol. 33, 885–897 (2016).

    CAS  Article  Google Scholar 

  34. 34.

    De Gelder, L., Williams, J. J., Ponciano, J. M., Sota, M. & Top, E. M. Adaptive plasmid evolution results in host-range expansion of a broad-host-range plasmid. Genetics 178, 2179–2190 (2008).

    Article  Google Scholar 

  35. 35.

    Tängdén, T. Combination antibiotic therapy for multidrug-resistant Gram-negative bacteria. Ups. J. Med. Sci. 119, 149–153 (2014).

    Article  Google Scholar 

  36. 36.

    Manyi-Loh, C., Mamphweli, S., Meyer, E. & Okoh, A. Antibiotic use in agriculture and its consequential resistance in environmental sources: potential public health implications. Molecules 23, 795 (2018).

  37. 37.

    Vaz‐Moreira, I., Ferreira, C., Nunes, O. C. & Manaia, C. M. in Antibiotic Drug Resistance (eds Capelo‐Martínez, J.-L. & Igrejas, G.) 211–238 (John Wiley & Sons, 2019).

  38. 38.

    Van Boeckel, T. P. et al. Global trends in antimicrobial use in food animals. Proc. Natl Acad. Sci. USA 112, 5649–5654 (2015).

    Article  Google Scholar 

  39. 39.

    Crofts, T. S., Gasparrini, A. J. & Dantas, G. Next-generation approaches to understand and combat the antibiotic resistome. Nat. Rev. Microbiol. 15, 422–434 (2017).

    CAS  Article  Google Scholar 

  40. 40.

    Kaye, K. S., Pogue, J. M., Tran, T. B., Nation, R. L. & Li, J. Agents of last resort: polymyxin resistance. Infect. Dis. Clin. N. Am. 30, 391–414 (2016).

    Article  Google Scholar 

  41. 41.

    Schwarz, S. & Johnson, A. P. Transferable resistance to colistin: a new but old threat. J. Antimicrob. Chemother. 71, 2066–2070 (2016).

    Article  Google Scholar 

  42. 42.

    Novick, R. P. Plasmid incompatibility. Microbiol. Mol. Biol. Rev. 51, 381–395 (1987).

    CAS  Google Scholar 

  43. 43.

    Bottery, M. J., Wood, A. J. & Brockhurst, M. A. Adaptive modulation of antibiotic resistance through intragenomic coevolution. Nat. Ecol. Evol. 1, 1364–1369 (2017).

    Article  Google Scholar 

  44. 44.

    Lam, M. M. C. et al. Convergence of virulence and MDR in a single plasmid vector in MDR Klebsiella pneumoniae ST15. J. Antimicrob. Chemother. 74, 1218–1222 (2019).

    CAS  Article  Google Scholar 

  45. 45.

    Harrison, E., Hall, J. P. J. & Brockhurst, M. A. Migration promotes plasmid stability under spatially heterogeneous positive selection. Proc. R. Soc. B 285, 20180324 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Institute of Allergy and Infectious Diseases Extramural Activities grant no. R01 AI084918 of the National Institutes of Health and through the National Science Foundation (NSF) under Cooperative Agreement no. DBI-0939454. H.J. was supported in part by Public Health Service, National Research Service Award grant no. T32GM007270, from the National Institute of General Medical Sciences. O.K. was supported in part by the NSF Graduate Research Fellowship grant no. DGE-1762114. B.K. was supported in part by the NSF Career Award grant no. DEB-0952825. We thank W. Loftie-Eaton for aiding in initial training of experimental techniques and the Kerr and Top laboratories for useful suggestions on the manuscript.

Author information

Affiliations

Authors

Contributions

H.J., B.K. and E.M.T. designed the study. H.J. performed experiments. O.K. facilitated part of the experiments. H.J., B.K. and J.M.P. performed the statistical analyses. T.S. processed samples for sequencing. T.S. and O.K. performed the genomic analyses. B.K. developed the mathematical models. H.J., B.K., E.M.T., O.K., T.S. and J.M.P. wrote the manuscript.

Corresponding author

Correspondence to Benjamin Kerr.

Ethics declarations

Competing interests

The authors declare no competing interests.

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 Model simulation of plasmid persistence.

a, The Gillespie algorithm was used to simulate population dynamics with μ*=0.7, c=0.2, KR=0.004, λ=0.05, τ=0.00001, and 1/ψ=0.000002, and the dynamic variables were initialized with R0=0.02, A0=0, and Aa,0=200. Many of the parameter values used here are similar to those from a previous parameterized model2. The average plasmid-bearing proportion of the simulated population over 10 replicates is shown as points, with each point located at the time closest to the end of the relevant 8-hour transfer period, after which a 60-fold dilution and resource replenishment occurred. The dashed line indicates that ancestral populations are being tracked. b, Evolution is integrated by a reduction in the cost of plasmid carriage. Here we set p=0.99, which makes the effective cost of the plasmid two orders of magnitude lower. Consequently, the loss of the plasmid from the evolved population is slower compared to its ancestor in part a. The solid line indicates that evolved populations are being tracked.

Extended Data Fig. 2 Model simulation of MDR emergence.

a, The Gillespie algorithm was used to simulate community dynamics with parameters as in Extended Data Fig. 1, and initial non-zero variables set to Aa,0=200, Bb,0=200, and R0=0.02. The average cell densities for the simulated population over 10 replicates are shown as points, with each point located at the time closest to the end of the relevant transfer period. The dashed lines indicate that ancestral populations are being tracked. b, Evolution is incorporated by a reduction in the plasmid cost (p=0.99) for both plasmids in their native hosts. Consequently, the density of MDR cells is higher compared to the ancestral community in part a. The solid lines indicate that evolved populations are being tracked.

Extended Data Fig. 3 Plasmid persistence in the absence of antibiotics increases after coevolution of plasmids with their hosts.

Dashed lines (left graphs) indicate ancestral strains. Solid lines (right graphs) indicate evolved strains. a, The proportion of cells containing plasmid p2 decreased in an ancestral E(p2) population in the absence of the relevant antibiotic (chloramphenicol). However, coevolution of this host and plasmid in six replicate populations with chloramphenicol led to (b) greater plasmid persistence in the absence of antibiotic for nearly all of the E2(p2E) populations. c, The proportion of cells containing p1 rapidly decreased in an ancestral K(p1) population in the absence of the relevant antibiotic (tetracycline). Host–plasmid coevolution in six replicate populations with tetracycline resulted in (d) greater plasmid persistence in the absence of antibiotic for all K1(p1K) populations. In these graphs, every point is the mean of the three replicate persistence assays conducted for each isolate, with upward-pointing triangles used to represent points for p1-containing populations, and downward-pointing triangles used to represent points for p2-containing populations. Bars indicate the s.e.m. of replicate cultures. E. coli and K. pneumoniae icons are represented as rectangles and rods, respectively. The two “” symbols denote the persistence profiles for the evolved strains selected to be used for all further assays.

Extended Data Fig. 4 The emergence of MDR in mixed-species cocultures increases after coevolution of plasmids with their hosts.

a, Three replicate cocultures of ancestral strains E(p2) and K(p1) were propagated over 4 transfers in the absence of antibiotics. Within each coculture, E. coli (upper graph) and K. pneumoniae (lower graph) were tracked via daily selective plating for each host–plasmid combination. b, Three replicate cocultures of evolved strains E2(p2E) and K1(p1K) were propagated and tracked in an identical manner. In all plots, blue lines indicate a host containing only a p1-type plasmid (p1 or p1K); red lines indicate a host containing only a p2-type plasmid (p2 or p2E); purple lines indicate an MDR host containing both plasmid types ((p1, p2) or (p1K, p2E)); and grey lines indicate plasmid-free cells. Bars indicate the s.e.m. of three replicate cocultures. Dashed and solid lines indicate ancestral and evolved cells, respectively. Note that the point outlined in black in the plasmid-free cell trajectory indicates that the point was interpolated using the previous and following points on the trajectory, due to missing data (see Supplementary Information VI, section a). The key comparison here is between the solid purple and dashed purple trajectories. Cumulatively, there are significantly more MDR K. pneumoniae in evolved than ancestral cocultures (t = −19.565, 95% CI = [−44541787, −28484560], df = 2, P = 0.0026), by a difference of 3.65 ×107 cells. MDR E. coli exhibits a similar trend, although the mean difference of 5.83 ×102 cells is not significant (t = −2.6576, 95% CI = [−1527.7, 361.1], df = 2, P = 0.1172).

Extended Data Fig. 5 The emergence of MDR in E. coli cocultures increases after coevolution of plasmids with their hosts.

a, Three replicate cocultures of ancestral strains E(p1) and E(p2) were propagated over 4 transfers in the absence of antibiotics. Within each coculture, E. coli cells were tracked via daily selective plating for each host–plasmid combination. b, Three replicate cocultures of evolved strains E1(p1E) and E2(p2E) were propagated and tracked in an identical manner. The asterisk notation (E*) indicates either host E1 or E2. In all plots, blue lines indicate a host containing only a p1-type plasmid (p1 or p1E); red lines indicate a host containing only a p2-type plasmid (p2 or p2E); purple lines indicate an MDR host containing both plasmid types ((p1, p2) or (p1E, p2E)); and grey lines indicate plasmid-free cells. Bars indicate the s.e.m. of three replicate cocultures. Dashed and solid lines indicate ancestral and evolved cells, respectively. Note that the point outlined in black in the double-plasmid-containing cell trajectory indicates that the point was interpolated using the previous and following points on the trajectory, due to missing data (see Supplementary Information VI, section a). The key comparison here is between the solid purple and dashed purple trajectories. Cumulatively there are significantly more MDR E. coli in evolved than ancestral cocultures by a difference of 1.53 ×106 cells (t = −27.465, 95% CI = [-1767743, -1288990], df = 2.0005, P = 0.001321).

Extended Data Fig. 6 The emergence of MDR in K. pneumoniae cocultures increases after coevolution of plasmids with their hosts.

a, Three replicate cocultures of ancestral strains K(p1) and K(p2) were propagated over 4 transfers in the absence of antibiotics. Within each coculture, K. pneumoniae cells were tracked via daily selective plating for each host–plasmid combination. b, Three replicate cocultures of evolved strains K1(p1K) and K2(p2K) were propagated and tracked in an identical manner. The asterisk notation (K*) indicates either host K1 or K2. In all plots, blue lines indicate a host containing only a p1-type plasmid (p1 or p1K); red lines indicate a host containing only a p2-type plasmid (p2 or p2K); purple lines indicate an MDR host containing both plasmid types ((p1, p2) or (p1K, p2K)); and grey lines indicate plasmid-free cells. Bars indicate the s.e.m. of three replicate cocultures. Dashed and solid lines indicate ancestral and evolved cells, respectively. Note that the open diamonds in the ancestral double-plasmid-containing cell trajectories indicate that the colony counts fell below our false positive threshold (see Supplementary Information VI, section a). The key comparison here is between the solid purple and dashed purple trajectories. Cumulatively there are significantly more MDR K. pneumoniae in evolved than ancestral cocultures by a difference of 1.08 ×105 cells (t = −7.174, 95% CI = [−172891.2, −43255.5], df = 2, P = 0.01888).

Extended Data Fig. 7 Model simulation of MDR emergence with pleiotropic effects.

a, The Gillespie algorithm was used to simulate community dynamics with parameters and initial variable settings as in Extended Data Figs. 1 and 2. The average cell densities for the simulated population over 10 replicates are shown as points, with each point located at the time closest to the end of the relevant transfer period. Here we assume that evolutionary changes have led to a reduction in the plasmid cost (p=0.5) for both plasmids in their native hosts, but note for any non-native configuration there is no cost reduction (q=0). b, Here we allow for pleiotropic effects—namely a reduction of plasmid cost in non-native configurations (q=0.5). Although the difference is not substantial, the density of MDR cells is greater at every transfer. c, More generally, we show a (literal) density plot for different p -q combinations. The grey level for each square is the log of the cumulative density of MDR cells at the end of 8 transfers for each combination of direct (p) and pleiotropic (q) effects of compensatory mutations. Greater reductions of plasmid costs in either native or non-native contexts correspond to higher incidence of MDR.

Extended Data Fig. 8 Hosts evolved with one plasmid became more permissive towards a novel plasmid.

Longer dashed lines indicate a host that was coevolved with the alternate plasmid. Shorter dashed lines represent ancestral host–plasmid pairs and are identical to those seen in Fig. 3 and Extended Data Fig. 3. a, Persistence of the ancestral plasmid p1 was greater in K2(p1) than it was in K(p1) (ΔBIC = −151.7), indicating that changes in the host due to evolution with alternate plasmid type p2 have allowed it to better retain novel plasmid p1 (that is, permissiveness is observed). b, Persistence of the ancestral plasmid p2 was greater in K1(p2) than it was in K(p2) (ΔBIC = −765.7), again indicating permissiveness. Each point is the mean of three replicate persistence assays. Bars indicate the s.e.m.

Extended Data Fig. 9 The dynamics of antibiotic resistance and MDR persistence after host–plasmid coevolution, in both coevolved pairs and novel combinations of host and plasmids.

a, A lineage can lose MDR by first losing either one of the two plasmid types and then losing the remaining plasmid. Here we compare the rates of loss of a focal plasmid between ancestral and evolved strains for all single- and double-plasmid-containing cells. b, and c, represent the transition from double-plasmid-containing cells to single-plasmid-containing cells, whereas d, and e, show the transition from single-plasmid-containing cells to plasmid-free cells, with conclusions from (d) having already been drawn from a previous assay (Extended Data Fig. 3c, d). All persistence assays were done with K. pneumoniae as the host in an ancestral context (K(p1), K(p2) or K(p1,p2)) or an evolved context (K1(p1K), K1(p2E) or K1(p1K,p2E)). In all the evolved contexts, p1K is the coevolved plasmid and p2E is recently introduced. Furthermore, for all trajectories in panels (b)-(e), the left cell in the displayed “two-cell transition icon” possesses the focal plasmid; thus, the proportion being tracked refers to this left cell (whereas the right cell refers to a host without the focal plasmid). (b), Persistence of the focal plasmid in a double-plasmid-containing population under selection for the alternate plasmid is initially higher and ultimately different in the evolved context compared to the ancestral context for the p1-type plasmid (ΔBIC = −27.7, Supplementary Table 7) and (c), higher but not meaningfully different compared to the ancestral context for the p2-type plasmid (ΔBIC = 11.7, Supplementary Table 7). (d), Plasmid persistence in a single-plasmid-containing population is higher in the evolved context than the ancestral context for the p1-type plasmid (ΔBIC = −552.3, Supplementary Table 7) and (e) p2-type plasmid (ΔBIC = −893.7, Supplementary Table 7). We also note an interesting result regarding the effect of plasmid coresidency on plasmid persistence in the ancestral strains: the dashed lines in (b) and (d) are tracking the loss of the same plasmid but in the context of the plasmid either on its own or with a coresiding plasmid (see Supplementary Information XI). Graph background shading and line colours are identical to Fig. 5. Dashed and solid lines indicate ancestral and evolved contexts, respectively. Bars indicate s.e.m.

Extended Data Fig. 10 The dynamics of MDR persistence in the absence of antibiotics.

a, A K. pneumoniae cell lineage loses MDR by first losing either one of the two plasmid types and then losing whatever plasmid type remains. The diagrams in this figure are repeated from Fig. 5a and Extended Data Fig. 9a. Here we compare the rates of plasmid loss between ancestral and evolved strains for all double-plasmid-containing cells as their lineages transition to further plasmid loss. White cell backgrounds indicate ancestral hosts and plasmids. Grey cell backgrounds indicate evolved hosts and plasmids. Plasmid persistence is measured as the transition from double-plasmid-containing cells to either single-plasmid-containing cells or plasmid-free cells. Plasmid persistence assays were done with K. pneumoniae as the host in an ancestral context (K(p1,p2); dashed line) or in an evolved context (K1(p1K,p2E); solid line). While there appears to be greater MDR persistence in the evolved context, there is no meaningful difference between the two persistence curves (ΔBIC = 4.5) according to a nonlinear Beta-binomial regression model (see Supplementary Information IX). b, We compare the same ancestral persistence curve (dashed line) to the alternate evolved context (K2(p1E,p2K); solid line), in which case there is a meaningful difference between the curves (ΔBIC = −2.1; Supplementary Information IX). No antibiotics were present during the assay. Purple lines indicate MDR hosts containing both plasmid types. Bars indicate s.e.m.

Supplementary information

Supplementary Information

Supplementary text, Figs. 1–12, Tables 1–8, discussion and refs. 1–25.

Reporting Summary

Supplementary Data

Supplementary Data 1 and 2.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jordt, H., Stalder, T., Kosterlitz, O. et al. Coevolution of host–plasmid pairs facilitates the emergence of novel multidrug resistance. Nat Ecol Evol 4, 863–869 (2020). https://doi.org/10.1038/s41559-020-1170-1

Download citation

Further reading

Search

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