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Volume 169, Issue 8

Quantifying stochastic establishment of mutants in microbial adaptation Open Access

This article is part of the Microbial Evolution collection.

Abstract

Studies of microbial evolution, especially in applied contexts, have focused on the role of selection in shaping predictable, adaptive responses to the environment. However, chance events – the appearance of novel genetic variants and their , i.e. outgrowth from a single cell to a sizeable population – also play critical initiating roles in adaptation. Stochasticity in establishment has received little attention in microbiology, potentially due to lack of awareness as well as practical challenges in quantification. However, methods for high-replicate culturing, mutant labelling and detection, and statistical inference now make it feasible to experimentally quantify the establishment probability of specific adaptive genotypes. I review methods that have emerged over the past decade, including experimental design and mathematical formulas to estimate establishment probability from data. Quantifying establishment in further biological settings and comparing empirical estimates to theoretical predictions represent exciting future directions. More broadly, recognition that adaptive genotypes may be stochastically lost while rare is significant both for interpreting common lab assays and for designing interventions to promote or inhibit microbial evolution.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): demographic stochasticity , establishment probability , experimental evolution , extinction , fixation probability , gene surfing , genetic drift , mutation , population dynamics , predictability , seeding assay and statistical inference
Funding
This study was supported by the:
  • Royal Society (Award URF\R1\191269)
    • Principle Award Recipient: HelenK. Alexander
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2023-08-10
2024-05-17
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References

  1. Alexander HK. Supplementary material for: Quantifying stochastic establishment of mutants in microbial adaptation. FigShare 2023 [View Article]
     [Google Scholar]
  2. Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I et al. Experimental evolution. Trends Ecol Evol 2012; 27:547–560 [View Article] [PubMed]
     [Google Scholar]
  3. McDonald MJ. Microbial experimental evolution - a proving ground for evolutionary theory and a tool for discovery. EMBO Rep 2019; 20:e46992 [View Article] [PubMed]
     [Google Scholar]
  4. Gullberg E, Cao S, Berg OG, Ilbäck C, Sandegren L et al. Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog 2011; 7:e1002158 [View Article] [PubMed]
     [Google Scholar]
  5. Drlica K, Zhao X. Mutant selection window hypothesis updated. Clin Infect Dis 2007; 44:681–688 [View Article] [PubMed]
     [Google Scholar]
  6. zur Wiesch PA, Kouyos R, Engelstädter J, Regoes RR, Bonhoeffer S. Population biological principles of drug-resistance evolution in infectious diseases. Lancet Infect Dis 2011; 11:236–247 [View Article] [PubMed]
     [Google Scholar]
  7. Luria SE, Delbrück M. Mutations of bacteria from virus sensitivity to virus resistance. Genetics 1943; 28:491–511 [View Article] [PubMed]
     [Google Scholar]
  8. Foster P. Methods for determining spontaneous mutation rates. Meth Enzymol 2006; 409:195–213
     [Google Scholar]
  9. Lang G. Measuring mutation rates using the Luria-Delbrueck fluctuation assay. In Muzi-Falconi M, Brown G. eds Springer Protocols, Methods in Molecular Biology 1672: Genome Instability Methods and Protocols Humana Press (Springer Nature); 2018 pp 21–31 [View Article]
     [Google Scholar]
  10. Krašovec R, Richards H, Gomez G, Gifford DR, Mazoyer A et al. Measuring microbial mutation rates with the fluctuation assay. J Vis Exp 2019; 153:e60406 [View Article] [PubMed]
     [Google Scholar]
  11. Gralka M, Stiewe F, Farrell F, Möbius W, Waclaw B et al. Allele surfing promotes microbial adaptation from standing variation. Ecol Lett 2016; 19:889–898 [View Article] [PubMed]
     [Google Scholar]
  12. Alexander HK, MacLean RC. Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells. Proc Natl Acad Sci U S A 2020; 117:19455–19464 [View Article] [PubMed]
     [Google Scholar]
  13. Yu Q, Gralka M, Duvernoy M-C, Sousa M, Harpak A et al. Mutability of demographic noise in microbial range expansions. ISME J 2021; 15:2643–2654 [View Article] [PubMed]
     [Google Scholar]
  14. Saebelfeld M, Das SG, Brink J, Hagenbeek A, Krug J et al. Antibiotic breakdown by susceptible bacteria enhances the establishment of β-lactam resistant mutants. Front Microbiol 2021; 12:698970 [View Article] [PubMed]
     [Google Scholar]
  15. Saebelfeld M, Das SG, Hagenbeek A, Krug J, de Visser J. Stochastic establishment of β-lactam-resistant Escherichia coli mutants reveals conditions for collective resistance. Proc R Soc B 2022; 289:20212486 [View Article] [PubMed]
     [Google Scholar]
  16. Garoña A, Santer M, Hülter NF, Uecker H, Dagan T. Segregational drift hinders the evolution of antibiotic resistance on polyploid replicons. bioRxiv 2023 [View Article]
     [Google Scholar]
  17. Gifford DR, de Visser J, Wahl LM. Model and test in a fungus of the probability that beneficial mutations survive drift. Biol Lett 2012; 9:20120310 [View Article] [PubMed]
     [Google Scholar]
  18. Gifford DR, MacLean RC. Evolutionary reversals of antibiotic resistance in experimental populations of Pseudomonas aeruginosa. Evolution 2013; 67:2973–2981 [View Article] [PubMed]
     [Google Scholar]
  19. Cvijović I, Nguyen Ba AN, Desai MM. Experimental studies of evolutionary dynamics in microbes. Trends Genet 2018; 34:693–703 [View Article] [PubMed]
     [Google Scholar]
  20. Santos-Lopez A, Marshall CW, Haas AL, Turner C, Rasero J et al. The roles of history, chance, and natural selection in the evolution of antibiotic resistance. eLife 2021; 10:e70676 [View Article] [PubMed]
     [Google Scholar]
  21. Schenk MF, Zwart MP, Hwang S, Ruelens P, Severing E et al. Population size mediates the contribution of high-rate and large-benefit mutations to parallel evolution. Nat Ecol Evol 2022; 6:439–447 [View Article] [PubMed]
     [Google Scholar]
  22. Bell G. Evolutionary rescue. Annu Rev Ecol Evol Syst 2017; 48:605–627 [View Article]
     [Google Scholar]
  23. Patwa Z, Wahl LM. The fixation probability of beneficial mutations. J R Soc Interface 2008; 5:1279–1289 [View Article] [PubMed]
     [Google Scholar]
  24. Haldane JBS. A mathematical theory of natural and artificial selection, part V: selection and mutation. Math Proc Camb Philos Soc 1927; 23:838–844 [View Article]
     [Google Scholar]
  25. Potvin-Trottier L, Luro S, Paulsson J. Microfluidics and single-cell microscopy to study stochastic processes in bacteria. Curr Opin Microbiol 2018; 43:186–192 [View Article] [PubMed]
     [Google Scholar]
  26. Akiyama T, Kim M. Stochastic response of bacterial cells to antibiotics: its mechanisms and implications for population and evolutionary dynamics. Curr Opin Microbiol 2021; 63:104–108 [View Article] [PubMed]
     [Google Scholar]
  27. Schenk MF, Szendro IG, Krug J, de Visser J. Quantifying the adaptive potential of an antibiotic resistance enzyme. PLoS Genet 2012; 8:e1002783 [View Article] [PubMed]
     [Google Scholar]
  28. Kaushik KS, Ratnayeke N, Katira P, Gordon VD. The spatial profiles and metabolic capabilities of microbial populations impact the growth of antibiotic-resistant mutants. J R Soc Interface 2015; 12:20150018 [View Article] [PubMed]
     [Google Scholar]
  29. Farrell FD, Gralka M, Hallatschek O, Waclaw B. Mechanical interactions in bacterial colonies and the surfing probability of beneficial mutations. J R Soc Interface 2017; 14:20170073 [View Article] [PubMed]
     [Google Scholar]
  30. Coates J, Park BR, Le D, Şimşek E, Chaudhry W et al. Antibiotic-induced population fluctuations and stochastic clearance of bacteria. eLife 2018; 7:e32976 [View Article] [PubMed]
     [Google Scholar]
  31. Kosterlitz O, Muñiz Tirado A, Wate C, Elg C, Bozic I et al. Estimating the transfer rates of bacterial plasmids with an adapted Luria-Delbrück fluctuation analysis. PLoS Biol 2022; 20:e3001732 [View Article] [PubMed]
     [Google Scholar]
  32. Martin G, Aguilée R, Ramsayer J, Kaltz O, Ronce O. The probability of evolutionary rescue: towards a quantitative comparison between theory and evolution experiments. Philos Trans R Soc Lond B Biol Sci 2013; 368:20120088 [View Article] [PubMed]
     [Google Scholar]
  33. Kaul RB, Kramer AM, Dobbs FC, Drake JM. Experimental demonstration of an Allee effect in microbial populations. Biol Lett 2016; 12:20160070 [View Article] [PubMed]
     [Google Scholar]
  34. Koutsoumanis K. A study on the variability in the growth limits of individual cells and its effect on the behavior of microbial populations. Int J Food Microbiol 2008; 128:116–121 [View Article] [PubMed]
     [Google Scholar]
  35. Karisto P, Dora S, Mikaberidze A. Measurement of infection efficiency of a major wheat pathogen using time-resolved imaging of disease progress. Plant Pathol 2019; 68:163–172 [View Article]
     [Google Scholar]
  36. Artemova T, Gerardin Y, Dudley C, Vega NM, Gore J. Isolated cell behavior drives the evolution of antibiotic resistance. Mol Syst Biol 2015; 11:822 [View Article] [PubMed]
     [Google Scholar]
  37. Aif S, Appold N, Kampman L, Hallatschek O, Kayser J. Evolutionary rescue of resistant mutants is governed by a balance between radial expansion and selection in compact populations. Nat Commun 2022; 13:7916 [View Article] [PubMed]
     [Google Scholar]
  38. Schreck CF, Fusco D, Karita Y, Martis S, Kayser J et al. Impact of crowding on the diversity of expanding populations. Proc Natl Acad Sci U S A 2023; 120:e2208361120 [View Article] [PubMed]
     [Google Scholar]
  39. Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N et al. Antibiotic tolerance facilitates the evolution of resistance. Science 2017; 355:826–830 [View Article] [PubMed]
     [Google Scholar]
  40. Scheler O, Makuch K, Debski PR, Horka M, Ruszczak A et al. Droplet-based digital antibiotic susceptibility screen reveals single-cell clonal heteroresistance in an isogenic bacterial population. Sci Rep 2020; 10:3282 [View Article] [PubMed]
     [Google Scholar]
  41. Cochran WG. Estimation of bacterial densities by means of the “most probable number.”. Biometrics 1950; 6:105–116 [PubMed]
     [Google Scholar]
  42. Hurley MA, Roscoe ME. Automated statistical analysis of microbial enumeration by dilution series. J Appl Bacteriol 1983; 55:159–164 [View Article]
     [Google Scholar]
  43. Hall BM, Ma C-X, Liang P, Singh KK. Fluctuation analysis calculator (FALCOR): a web tool for the determination of mutation rate using Luria-Delbrueck fluctuation analysis. Bioinformatics 2009; 25:1564–1565 [View Article] [PubMed]
     [Google Scholar]
  44. Gillet-Markowska A, Louvel G, Fischer G. bz-rates: a web tool to estimate mutation rates from fluctuation analysis. G3 2015; 5:2323–2327 [View Article] [PubMed]
     [Google Scholar]
  45. Mazoyer A, Drouilhet R, Despréaux S, Ycart B. flan: an R package for inference on mutation models. The R J 2017; 9:334–351 [View Article]
     [Google Scholar]
  46. Zheng Q. rSalvador: an R package for the fluctuation experiment. G3 2017; 7:3849–3856 [View Article] [PubMed]
     [Google Scholar]
  47. Levy SF, Blundell JR, Venkataram S, Petrov DA, Fisher DS et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 2015; 519:181–186 [View Article] [PubMed]
     [Google Scholar]
  48. Nguyen Ba AN, Cvijović I, Rojas Echenique JI, Lawrence KR, Rego-Costa A et al. High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast. Nature 2019; 575:494–499 [View Article] [PubMed]
     [Google Scholar]
  49. Druett HA. Bacterial invasion. Nature 1952; 170:288 [View Article] [PubMed]
     [Google Scholar]
  50. Lunn TJ, Restif O, Peel AJ, Munster VJ, de Wit E et al. Dose-response and transmission: the nexus between reservoir hosts, environment and recipient hosts. Phil Trans R Soc B 2019; 374:20190016 [View Article] [PubMed]
     [Google Scholar]
  51. Sheets MB, Tague N, Dunlop MJ. An optogenetic toolkit for light-inducible antibiotic resistance. Nat Commun 2023; 14:1034 [View Article] [PubMed]
     [Google Scholar]
  52. Taylor D, Verdon N, Lomax P, Allen RJ, Titmuss S. Tracking the stochastic growth of bacterial populations in microfluidic droplets. Phys Biol 2022; 19:026003 [View Article] [PubMed]
     [Google Scholar]
  53. Choudhary D, Lagage V, Foster KR, Uphoff S. Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions. Cell Rep 2023; 42:112168 [View Article] [PubMed]
     [Google Scholar]
  54. European Committee for Antimicrobial Susceptibility Testing (EUCAST) Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution. Clin Microbiol Infect 2003; 9:1–7 [View Article]
     [Google Scholar]
  55. Udekwu KI, Parrish N, Ankomah P, Baquero F, Levin BR. Functional relationship between bacterial cell density and the efficacy of antibiotics. J Antimicrob Chemother 2009; 63:745–757 [View Article] [PubMed]
     [Google Scholar]
  56. Salas JR, Jaberi-Douraki M, Wen X, Volkova VV. Mathematical modeling of the “inoculum effect”: six applicable models and the MIC advancement point concept. FEMS Microbiol Lett 2020; 367:fnaa012 [View Article] [PubMed]
     [Google Scholar]
  57. Zhao X, Drlica K. Restricting the selection of antibiotic-resistant mutants: a general strategy derived from fluoroquinolone studies. Clin Infect Dis 2001; 33:S147–S156 [View Article] [PubMed]
     [Google Scholar]
  58. Olofsson SK, Cars O. Optimizing drug exposure to minimize selection of antibiotic resistance. Clin Infect Dis 2007; 45:S129–S136 [View Article] [PubMed]
     [Google Scholar]
  59. Lipsitch M, Levin BR. The population dynamics of antimicrobial chemotherapy. Antimicrob Agents Chemother 1997; 41:363–373 [View Article] [PubMed]
     [Google Scholar]
  60. Radlinski L, Conlon BP. Antibiotic efficacy in the complex infection environment. Curr Opin Microbiol 2018; 42:19–24 [View Article] [PubMed]
     [Google Scholar]
  61. Luber P, Bartelt E, Genschow E, Wagner J, Hahn H. Comparison of broth microdilution, E test, and agar dilution methods for antibiotic susceptibility testing of Campylobacter jejuni and Campylobacter coli. J Clin Microbiol 2003; 41:1062–1068 [View Article] [PubMed]
     [Google Scholar]
  62. Watson C, Hush P, Williams J, Dawson A, Ojkic N et al. Reduced adhesion between cells and substrate confers selective advantage in bacterial colonies. EPL 2018; 123:68001 [View Article]
     [Google Scholar]
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Quantifying stochastic establishment of mutants in microbial adaptation
Microbiology 169, 001365 (2023); https://doi.org/10.1099/mic.0.001365
/content/journal/micro/10.1099/mic.0.001365
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