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Suppressing evolution of antibiotic resistance through environmental switching

Abstract

Ecology and evolution under changing environments are important in many subfields of biology with implications for medicine. Here, we explore an example: the consequences of fluctuating environments on the emergence of antibiotic resistance, which is an immense and growing problem. Typically, high doses of antibiotics are employed to eliminate the infection quickly and minimize the time under which resistance may emerge. However, this strategy may not be optimal. Since competition can reduce fitness and resistance typically has a reproductive cost, resistant mutants’ fitness can depend on their environment. Here we show conditions under which environmental varying fitness can be exploited to prevent the emergence of resistance. We develop a stochastic Lotka-Volterra model of a microbial system with competing phenotypes: a wild strain susceptible to the antibiotic, and a mutant strain that is resistant. We investigate the impact of various pulsed applications of antibiotics on population suppression. Leveraging competition, we show how a strategy of environmental switching can suppress the infection while avoiding resistant mutants. We discuss limitations of the procedure depending on the microbe and pharmacodynamics and methods to ameliorate them.

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Availability of data and material

Not applicable.

Code availability

The code to run the numerical simulations are available at https://github.com/bmorsky/antibioticresistance.

References

  • Acar N, Cogan NG (2019) Enhanced disinfection of bacterial populations by nutrient and antibiotic challenge timing. Math Biosci 313:12–32

    PubMed  Article  Google Scholar 

  • Andersson DI, Hughes D (2010) Antibiotic resistance and its cost: is it possible to reverse resistance? Nat Rev Microbiol 8(4):260

    CAS  PubMed  Article  Google Scholar 

  • Andersson DI, Hughes D (2011) Persistence of antibiotic resistance in bacterial populations. FEMS Microbiol Rev 35(5):901–911. ISSN 0168-6445. https://doi.org/10.1111/j.1574-6976.2011.00289.x

  • Austin DJ, Anderson RM (1999) Studies of antibiotic resistance within the patient, hospitals and the community using simple mathematical models. Philosophical Transactions of the Royal Society of London B: Biological Sciences 354(1384):721–738

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Baker CM, Ferrari MJ, Shea K (2018) Beyond dose: pulsed antibiotic treatment schedules can maintain individual benefit while reducing resistance. Sci Rep 8(1):5866

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S (2004) Bacterial persistence as a phenotypic switch. Science 305(5690):1622–1625

    CAS  PubMed  Article  Google Scholar 

  • Basra P, Alsaadi A, Bernal-Astrain G, O’Sullivan ML, Hazlett B, Clarke LM, Schoenrock A, Pitre S, Wong A (2018) Fitness tradeoffs of antibiotic resistance in extraintestinal pathogenic Escherichia coli. Genome Biol Evol 10(2):667–679

    PubMed  PubMed Central  Article  Google Scholar 

  • Bhagunde P, Singh R, Ledesma KR, Chang K-T, Nikolaou M, Tam VH (2011) Modelling biphasic killing of fluoroquinolones: guiding optimal dosing regimen design. J Antimicrob Chemother 66(5):1079–1086

    CAS  PubMed  Article  Google Scholar 

  • Bonhoeffer S, Lipsitch M, Levin BR (1997) Evaluating treatment protocols to prevent antibiotic resistance. Proc Natl Acad Sci 94(22):12106–12111

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Borges-Walmsley MI, McKeegan KS, Walmsley AR (2003) Structure and function of efflux pumps that confer resistance to drugs. Biochem J 376(2):313–338

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Bucci V, Nadell CD, Xavier JB (2011) The evolution of bacteriocin production in bacterial biofilms. Am Nat 178(6):E162–E173

    PubMed  Article  Google Scholar 

  • Coates J, Park BR, Le D, Şimşek E, Chaudhry W, Kim M (2018) Antibiotic-induced population fluctuations and stochastic clearance of bacteria. eLife 7:e32976

  • Cogan NG (2006) Effects of persister formation on bacterial response to dosing. J Theor Biol 238(3):694–703

    CAS  PubMed  Article  Google Scholar 

  • Cogan NG, Brown Jason, Darres Kyle, Petty Katherine (2012) Optimal control strategies for disinfection of bacterial populations with persister and susceptible dynamics. Antimicrob Agents Chemother 56(9):4816–4826

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Coyte Katharine Z, Schluter Jonas, Foster Kevin R (2015) The ecology of the microbiome: networks, competition, and stability. Science 350(6261):663–666

    CAS  PubMed  Article  Google Scholar 

  • Czock David, Keller Frieder (2007) Mechanism-based pharmacokinetic-pharmacodynamic modeling of antimicrobial drug effects. J Pharmacokinet Pharmacodyn 34(6):727–751

    CAS  PubMed  Article  Google Scholar 

  • Day T, Read AF (2016) Does high-dose antimicrobial chemotherapy prevent the evolution of resistance? PLoS Comput Biol 12(1):e1004689

  • Ender M, McCallum N, Adhikari R, Berger-Bächi B (2004) Fitness cost of SCCMEC and methicillin resistance levels in Staphylococcus aureus. Antimicrob Agents Chemother 48(6):2295–2297

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Enriquez-Navas PM, Wojtkowiak JW, Gatenby RA (2015) Application of evolutionary principles to cancer therapy. Cancer Res 75(22):4675–4680

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Estrela S, Brown SP (2018) Community interactions and spatial structure shape selection on antibiotic resistant lineages. PLoS Comput Biol 14(6):e1006179

  • Fischer A, Vázquez-García I, Mustonen V (2015) The value of monitoring to control evolving populations. Proc Natl Acad Sci 112(4):1007–1012

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Foster KR, Bell T (2012) Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol 22(19):1845–1850

    CAS  PubMed  Article  Google Scholar 

  • Fudenberg D, Nowak MA, Taylor C, Imhof LA (2006) Evolutionary game dynamics in finite populations with strong selection and weak mutation. Theor Popul Biol 70(3):352–363

    PubMed  PubMed Central  Article  Google Scholar 

  • Gagneux S, Long CD, Small PM, Van T, Schoolnik GK, Bohannan BJM (2006) The competitive cost of antibiotic resistance in Mycobacterium tuberculosis. Science 312(5782):1944–1946

    CAS  PubMed  Article  Google Scholar 

  • Gatenby RA, Brown JS (2020) The evolution and ecology of resistance in cancer therapy. Cold Spring Harb Perspect Med 10(11):a040972

  • Gatenby RA, Silva AS, Gillies RJ, Roy Frieden B (2009) Adaptive therapy. Cancer Res 69(11):4894–4903

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Ghoul Melanie, Mitri Sara (2016) The ecology and evolution of microbial competition. Trends Microbiol 24(10):833–845

    CAS  PubMed  Article  Google Scholar 

  • Gloede Julia, Scheerans Christian, Derendorf Hartmut, Kloft Charlotte (2009) In vitro pharmacodynamic models to determine the effect of antibacterial drugs. J Antimicrob Chemother 65(2):186–201

    PubMed  Article  CAS  Google Scholar 

  • Gonze D, Coyte KZ, Lahti L, Faust K (2018) Microbial communities as dynamical systems. Curr Opin Microbiol 44:41–49

    PubMed  Article  Google Scholar 

  • Greulich P, Doležal J, Scott M, Evans MR, Allen RJ (2017) Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics. Phys Biol 14(6):065005

  • Gullberg E, Cao S, Berg OG, Ilbäck C, Sandegren L, Hughes D, Andersson DI (2011) Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog 7(7):e1002158

  • Händel N, Otte S, Jonker M, Brul S, ter Kuile BH (2015) Factors that affect transfer of the inci1 \(\beta\)-lactam resistance plasmid pesbl-283 between E. coli strains. PloS One 10(4):e0123039

  • Hansen E, Karslake J, Woods RJ, Read AF, Wood KB (2020) Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations. Phys Biol 18(5):e3000713

  • Hauert C, Wakano JY, Doebeli M (2008) Ecological public goods games: cooperation and bifurcation. Theor Popul Biol 73(2):257–263

    PubMed  Article  Google Scholar 

  • Huang Weini, Hauert Christoph, Traulsen Arne (2015) Stochastic game dynamics under demographic fluctuations. Proc Natl Acad Sci 112(29):9064–9069

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Imhof LA, Nowak MA (2006) Evolutionary game dynamics in a wright-fisher process. J Math Biol 52(5):667–681

    PubMed  PubMed Central  Article  Google Scholar 

  • Katouli AA, Komarova NL (2011) The worst drug rule revisited: mathematical modeling of cyclic cancer treatments. Bull Math Biol 73(3):549–584

    PubMed  Article  Google Scholar 

  • Kohanski MA, DePristo MA, Collins JJ (2010) Sublethal antibiotic treatment leads to multidrug resistance via radical-induced mutagenesis. Mol Cell 37(3):311–320

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Komarova Natalia (2006) Stochastic modeling of drug resistance in cancer. J Theor Biol 239(3):351–366

    CAS  PubMed  Article  Google Scholar 

  • Kuban W, Jonczyk P, Gawel D, Malanowska K, Schaaper RM, Fijalkowska IJ (2004) Role of Escherichia coli DNA polymerase IV in in vivo replication fidelity. J Bacteriol 186(14):4802–4807

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Kussell E, Kishony R, Balaban NQ, Leibler S (2005) Bacterial persistence: a model of survival in changing environments. Genetics 169(4):1807–1814

    PubMed  PubMed Central  Article  Google Scholar 

  • Laxminarayan R, Duse A, Wattal C, Zaidi AKM, Wertheim HFL, Sumpradit N, Vlieghe E, Hara GL, Gould IM, Goossens H et al (2013) Antibiotic resistance-the need for global solutions. Lancet Infect Dis 13(12):1057–1098

    PubMed  Article  Google Scholar 

  • Letten AD, Hall AR, Levine JM (2021) Using ecological coexistence theory to understand antibiotic resistance and microbial competition. Nature Ecology & Evolution 5(4):431–441

    Article  Google Scholar 

  • Lipsitch M, Levin BR (1997) The population dynamics of antimicrobial chemotherapy. Antimicrob Agents Chemother 41(2):363–373

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Lister PD, Wolter DJ, Hanson ND (2009) Antibacterial-resistant Pseudomonas aeruginosa: clinical impact and complex regulation of chromosomally encoded resistance mechanisms. Clin Microbiol Rev 22(4):582–610

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Lopatkin AJ, Meredith HR, Srimani JK, Pfeiffer C, Durrett R, You L (2017) Persistence and reversal of plasmid-mediated antibiotic resistance. Nat Commun 8(1):1689

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • Marrec L, Bitbol A-F (2020) Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial. PLoS Comput Biol 16(4):e1007798

  • Martinez JL, Baquero F (2000) Mutation frequencies and antibiotic resistance. Antimicrob Agents Chemother 44(7):1771–1777

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Martínez JL, Baquero F (2002) Interactions among strategies associated with bacterial infection: pathogenicity, epidemicity, and antibiotic resistance. Clin Microbiol Rev 15(4):647–679

    PubMed  PubMed Central  Article  Google Scholar 

  • Meka VG, Gold HS, Cooke A, Venkataraman L, Eliopoulos GM, Moellering Jr RC, Jenkins  SG (2004a) Reversion to susceptibility in a linezolid-resistant clinical isolate of Staphylococcus aureus. J Antimicrob Chemother 54(4):818–820

  • Meka VG, Pillai SK, Sakoulas G, Wennersten C, Venkataraman L, DeGirolami PC,  Eliopoulos GM, Moellering Jr RC, Gold HS (2004b) Linezolid resistance in sequential Staphylococcus aureus isolates associated with a t2500a mutation in the 23s RRNA gene and loss of a single copy of RRNA. J Infect Dis 190(2):311–317

  • Melnyk AH, Wong A, Kassen R (2015) The fitness costs of antibiotic resistance mutations. Evol Appl 8(3):273–283

    PubMed  Article  Google Scholar 

  • Moran PAP et al (1962) The statistical processes of evolutionary theory. The statistical processes of evolutionary theory

  • Nagaev I, Björkman J, Andersson DI, Hughes D (2001) Biological cost and compensatory evolution in Fusidic acid-resistant Staphylococcus aureus. Mol Microbiol 40(2):433–439

    CAS  PubMed  Article  Google Scholar 

  • Nielsen EI, Friberg LE (2013) Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs. Pharmacol Rev 65(3):1053–1090

    PubMed  Article  CAS  Google Scholar 

  • Nielsen EI, Cars O, Friberg LE (2011) Pk/Pd indices of antibiotics predicted by a semi-mechanistic PKPD model–a step towards model-based dose optimization. Antimicrob Agents Chemother AAC–00182

  • Nilsson AI, Zorzet A, Kanth A, Dahlström S, Berg OG, Andersson DI (2006) Reducing the fitness cost of antibiotic resistance by amplification of initiator TRNA genes. Proc Natl Acad Sci 103(18):6976–6981

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Novozhilov AS, Karev GP, Koonin EV (2006) Biological applications of the theory of birth-and-death processes. Brief Bioinform 7(1):70–85

    PubMed  Article  Google Scholar 

  • Olivares Pacheco J, Alvarez-Ortega C, Rico MA, Martínez JL (2017) Metabolic compensation of fitness costs is a general outcome for antibiotic-resistant Pseudomonas aeruginosa mutants overexpressing efflux pumps. mBio, 8(4):e00500–17

  • Opatowski L, Guillemot D, Boelle P-Y, Temime L (2011) Contribution of mathematical modeling to the fight against bacterial antibiotic resistance. Curr Opin Infect Dis 24(3):279–287

    PubMed  Article  Google Scholar 

  • Perron GG, Gonzalez A, Buckling A (2007) Source-sink dynamics shape the evolution of antibiotic resistance and its pleiotropic fitness cost. Proceedings of the Royal Society of London B: Biological Sciences 274(1623):2351–2356

    Google Scholar 

  • Poole K (2002) Mechanisms of bacterial biocide and antibiotic resistance. J Appl Microbiol 92:55S-64S

    PubMed  Article  Google Scholar 

  • Rackauckas C, Nie Q (2017) Differentialequations. jl–a performant and feature-rich ecosystem for solving differential equations in Julia. Journal of Open Research Software 5(1). https://doi.org/10.5334/jors.151

  • Roca CP, Cuesta JA, Sánchez A (2009) Effect of spatial structure on the evolution of cooperation. Phys Rev E 80(4):046106

  • Rundell EA, Commodore N, Goodman AL, Kazmierczak BI, Brun YV (2020) A screen for antibiotic resistance determinants reveals a fitness cost of the Flagellum in Pseudomonas aeruginosa. J Bacteriol 202(6):e00682–19. https://doi.org/10.1128/JB.00682-19

  • Sandegren L, Lindqvist A, Kahlmeter G, Andersson DI (2008) Nitrofurantoin resistance mechanism and fitness cost in Escherichia coli. J Antimicrob Chemother 62(3):495–503

    CAS  PubMed  Article  Google Scholar 

  • Schmidt S, Sabarinath SN, Barbour A, Abbanat D, Manitpisitkul P, Sha S, Derendorf H (2009) Pharmacokinetic-pharmacodynamic modeling of the in vitro activities of oxazolidinone antimicrobial agents against methicillin-resistant Staphylococcus aureus. Antimicrob Agents Chemother 53(12):5039–5045

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Scire Jérémie, Hozé Nathanaël, Uecker Hildegard (2019) Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels. PLoS Comput Biol 15(8):e1007223

  • Sharma B, Brown AV, Matluck NE, Hu LT, Lewis K (2015) Borrelia burgdorferi, the causative agent of Lyme disease, forms drug-tolerant Persister cells. Antimicrob Agents Chemother 59(8):4616–4624

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Shojaee AliAbadi F, Lees P (2000) Antibiotic treatment for animals: effect on bacterial population and dosage regimen optimisation. Int J Antimicrob Agents 14(4):307–313

    Article  Google Scholar 

  • Stein RR, Bucci V, Toussaint NC, Buffie CG, Rätsch G, Pamer EG, Sander C, Xavier JB (2013) Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput Biol 9(12):e1003388

  • Sun Jingjing, Deng Ziqing, Yan Aixin (2014) Bacterial multidrug efflux pumps: mechanisms, physiology and pharmacological exploitations. Biochem Biophys Res Commun 453(2):254–267

    CAS  PubMed  Article  Google Scholar 

  • Tam VH, Louie A, Deziel MR, Liu W, Leary R, Drusano GL (2005) Bacterial-population responses to drug-selective pressure: examination of garenoxacin’s effect on Pseudomonas aeruginosa. J Infect Dis 192(3):420–428

    PubMed  Article  Google Scholar 

  • Tam VH, Schilling AN, Poole K, Nikolaou M (2007) Mathematical modelling response of Pseudomonas aeruginosa to meropenem. J Antimicrob Chemother 60(6):1302–1309

    CAS  PubMed  Article  Google Scholar 

  • Tam VH, Ledesma KR, Vo G, Kabbara S, Lim T-P, Nikolaou M (2008) Pharmacodynamic modeling of aminoglycosides against Pseudomonas aeruginosa and Acinetobacter baumannii: identifying dosing regimens to suppress resistance development. Antimicrob Agents Chemother 52(11):3987–3993

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Taylor C, Fudenberg D, Sasaki A, Nowak MA (2004) Evolutionary game dynamics in finite populations. Bull Math Biol 66(6):1621–1644

    PubMed  Article  Google Scholar 

  • Van Bambeke F, Balzi E, Tulkens PM (2000) Antibiotic efflux pumps. Biochem Pharmacol 60(4):457–470

    PubMed  Article  Google Scholar 

  • Wade MJ, Harmand J, Benyahia B, Bouchez T, Chaillou S, Cloez B, Godon J-J, Boudjemaa BM, Rapaport A, Sari T et al (2016) Perspectives in mathematical modelling for microbial ecology. Ecol Model 321:64–74

    Article  Google Scholar 

  • Wang-Kan X, Blair JMA, Chirullo B, Betts J, La Ragione RM, Ivens A, Ricci V, Opperman TJ, Piddock LJV (2017) Lack of ACRB efflux function confers loss of virulence on Salmonella enterica serovar Typhimurium. mBio 8(4):e00968–17

  • Webber MA, Piddock LJV (2003) The importance of efflux pumps in bacterial antibiotic resistance. J Antimicrob Chemother 51(1):9–11

    CAS  PubMed  Article  Google Scholar 

  • West Jeffrey, You Li, Zhang Jingsong, Gatenby RA, Brown JS, Newton PK, Anderson ARA (2020) Towards multidrug adaptive therapy. Cancer Res 80(7):1578–1589

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Wilkinson DJ (2011) Stochastic modelling for systems biology. CRC press

  • Woodford N, Ellington MJ (2007) The emergence of antibiotic resistance by mutation. Clin Microbiol Infect 13(1):5–18

    CAS  PubMed  Article  Google Scholar 

  • Zhang Y, Yew WW, Barer MR (2012) Targeting persisters for tuberculosis control. Antimicrob Agents Chemother 56(5):2223–2230

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

We would like to thank Plamen Kamenov and Giuseppe Forte for assistance with earlier versions of this project.

Funding

This material is based upon work supported by the Defense Advanced Research Projects Agency under Contract No. HR0011-16-C-0062, and the University of Pennsylvania.

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Both authors contributed to the conception of the study and the final manuscript. B.M. developed the code for and analyzed the numerical simulations, and wrote the first draft.

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Correspondence to Bryce Morsky.

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Morsky, B., Vural, D.C. Suppressing evolution of antibiotic resistance through environmental switching. Theor Ecol 15, 115–127 (2022). https://doi.org/10.1007/s12080-022-00530-4

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Keywords

  • Antibiotic resistance
  • Changing environments
  • Competition
  • Lotka-Volterra
  • Microbial ecology
  • Pulsed antibiotic treatment