Methods to Study Fitness and Compensatory Adaptation in Plasmid-Carrying Bacteria

  • Javier DelaFuente
  • Jeronimo Rodriguez-Beltran
  • Alvaro San Millan
Part of the Methods in Molecular Biology book series (MIMB, volume 2075)


Mobile genetic elements such as plasmids mediate horizontal gene transfer in prokaryotes, promoting bacterial adaptation and evolution. Despite the potential advantages conferred by these genetic elements, plasmids can also produce a fitness cost when they arrive to a new host. This initial burden is one of the main limits to the spread of plasmids in bacterial populations. However, plasmid costs can be ameliorated over time through compensatory mutations in the plasmid or the chromosome (compensatory adaptation). Understanding the origin of the cost produced by plasmids and the potential for compensatory adaptation is crucial to predict the spread and evolution of plasmid-mediated traits, such as antibiotic resistance. Here, we describe a simple protocol designed to analyze the fitness effects of a plasmid in a new host bacterium. We also provide a method to examine the potential for compensatory adaptation, using experimental evolution, and to elucidate if compensation originates in the plasmid, the bacterium, or both.

Key words

Evolution Fitness Plasmid Experimental evolution Compensatory adaptation Antibiotic resistance Coevolution 



This work was supported by the Instituto de Salud Carlos III (Plan Estatal de I + D + i 2013–2016)—grants CP15-00012, PI16-00860, and CIBER (CB06/02/0053) actions—and cofinanced by the European Development Regional Fund “A way to achieve Europe” (ERDF) and by the European Research Council under the European Union’s Horizon 2020 research and innovation program (ERC grant agreement no. 757440-PLASREVOLUTION). J.R.B. is a recipient of a Juan de la Cierva Fellowship, Ministerio de Economı́a Industria y Competitividad (FJCI-2016-30019).


  1. 1.
    Gogarten JP, Townsend JP (2005) Horizontal gene transfer, genome innovation and evolution. Nat Rev Microbiol 3(9):679–687. doi:nrmicro1204. Scholar
  2. 2.
    O’Neill J (2016) Tackling drug-resistant infections globally: final report and recommendations. Review on antimicrobal resistanceGoogle Scholar
  3. 3.
    Carattoli A (2013) Plasmids and the spread of resistance. Int J Med Microbiol 303(6–7):298–304. Scholar
  4. 4.
    Vogwill T, MacLean RC (2015) The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evol Appl 8(3):284–295. Scholar
  5. 5.
    San Millan A, MacLean RC (2017) Fitness costs of plasmids: a limit to plasmid transmission. Microbiol Spectr 5(5).
  6. 6.
    Baltrus DA (2013) Exploring the costs of horizontal gene transfer. Trends Ecol Evol 28(8):489–495. Scholar
  7. 7.
    Bouma JE, Lenski RE (1988) Evolution of a bacteria/plasmid association. Nature 335(6188):351–352. Scholar
  8. 8.
    Harrison E, Brockhurst MA (2012) Plasmid-mediated horizontal gene transfer is a coevolutionary process. Trends Microbiol 20(6):262–267. doi:S0966-842X(12)00066-2. Scholar
  9. 9.
    San Millan A, Peña-Miller R, Toll-Riera M, Halbert ZV, McLean AR, Cooper BS, MacLean RC (2014) Positive selection and compensatory adaptation interact to stabilize non-transmissible plasmids. Nat Commun 5:5208. Scholar
  10. 10.
    Harrison E, Guymer D, Spiers AJ, Paterson S, Brockhurst MA (2015) Parallel compensatory evolution stabilizes plasmids across the parasitism-mutualism continuum. Curr Biol 25(15):2034–2039. Scholar
  11. 11.
    San Millan A, Toll-Riera M, Qi Q, MacLean RC (2015) Interactions between horizontally acquired genes create a fitness cost in Pseudomonas aeruginosa. Nat Commun 6:6845. Scholar
  12. 12.
    Loftie-Eaton W, Yano H, Burleigh S, Simmons RS, Hughes JM, Rogers LM, Hunter SS, Settles ML, Forney LJ, Ponciano JM, Top EM (2016) Evolutionary paths that expand plasmid host-range: implications for spread of antibiotic resistance. Mol Biol Evol 33(4):885–897. Scholar
  13. 13.
    Porse A, Schonning K, Munck C, Sommer MO (2016) Survival and evolution of a large multidrug resistance plasmid in new clinical bacterial hosts. Mol Biol Evol. Scholar
  14. 14.
    Loftie-Eaton W, Bashford K, Quinn H, Dong K, Millstein J, Hunter S, Thomason MK, Merrikh H, Ponciano JM, Top EM (2017) Compensatory mutations improve general permissiveness to antibiotic resistance plasmids. Nat Ecol Evol 1(9):1354–1363. Scholar
  15. 15.
    Bottery MJ, Wood AJ, Brockhurst MA (2017) Adaptive modulation of antibiotic resistance through intragenomic coevolution. Nat Ecol Evol 1(9):1364–1369. Scholar
  16. 16.
    San Millan A (2018) Evolution of plasmid-mediated antibiotic resistance in the clinical context. Trends Microbiol. Scholar
  17. 17.
    San Millan A, Toll-Riera M, Qi Q, Betts A, Hopkinson RJ, McCullagh J, MacLean RC (2018) Integrative analysis of fitness and metabolic effects of plasmids in Pseudomonas aeruginosa PAO1. ISME J. Scholar
  18. 18.
    Lenski RE, Rose MR, Simpson SC, Tadler SC (1991) Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2,000 generations. Am Nat 138(6):1315–1341. Scholar
  19. 19.
    Shintani M, Takahashi Y, Tokumaru H, Kadota K, Hara H, Miyakoshi M, Naito K, Yamane H, Nishida H, Nojiri H (2010) Response of the Pseudomonas host chromosomal transcriptome to carriage of the IncP-7 plasmid pCAR1. Environ Microbiol 12(6):1413–1426. doi:EMI2110. Scholar
  20. 20.
    Spengler G, Molnar A, Schelz Z, Amaral L, Sharples D, Molnar J (2006) The mechanism of plasmid curing in bacteria. Curr Drug Targets 7(7):823–841CrossRefPubMedGoogle Scholar
  21. 21.
    Hale L, Lazos O, Haines A, Thomas C (2010) An efficient stress-free strategy to displace stable bacterial plasmids. BioTechniques 48(3):223–228. Scholar
  22. 22.
    Bikard D, Euler CW, Jiang W, Nussenzweig PM, Goldberg GW, Duportet X, Fischetti VA, Marraffini LA (2014) Exploiting CRISPR-Cas nucleases to produce sequence-specific antimicrobials. Nat Biotechnol 32(11):1146–1150. Scholar
  23. 23.
    Hall BG, Acar H, Nandipati A, Barlow M (2014) Growth rates made easy. Mol Biol Evol 31(1):232–238. Scholar
  24. 24.
    Sprouffske K, Wagner A (2016) Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinformatics 17:172. Scholar
  25. 25.
    Ram Y, Dellus-Gur E, Bibi M, Karkare K, Obolski U, Feldman MW, Cooper TF, Berman J, Hadany L (2019) Predicting microbial growth in a mixed culture from growth curve data. Proc Natl Acad Sci U S A 116 (29):14698–14707. Scholar
  26. 26.
    Miller JF (1994) Bacterial transformation by electroporation. Methods Enzymol 235:375–385CrossRefPubMedGoogle Scholar
  27. 27.
    Woodall CA (2003) DNA transfer by bacterial conjugation. In: Casali N, Preston A (eds) E. coli plasmid vectors: methods and applications. Humana Press, Totowa, NJ, pp 61–65. Scholar
  28. 28.
    Charlesworth B (2009) Fundamental concepts in genetics: effective population size and patterns of molecular evolution and variation. Nat Rev Genet 10(3):195–205. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Javier DelaFuente
    • 1
    • 2
  • Jeronimo Rodriguez-Beltran
    • 1
    • 2
  • Alvaro San Millan
    • 1
    • 2
  1. 1.Department of MicrobiologyHospital Universitario Ramon y Cajal (IRYCIS)MadridSpain
  2. 2.Network Research Centre for Epidemiology and Public Health (CIBERESP)MadridSpain

Personalised recommendations