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Mechanical feedback promotes bacterial adaptation to antibiotics

Nature Physics volume 17, pages 403–409 (2021)Cite this article

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Abstract

To maximize their fitness, cells must be able to respond effectively to stresses. This demands making trade-offs between processes that conserve resources to promote survival, and processes that use resources to promote growth and division. Understanding the nature of these trade-offs and the physics underlying them remains an outstanding challenge. Here we combine single-cell experiments and theoretical modelling to propose a mechanism for antibiotic adaptation through mechanical feedback between cell growth and morphology. Under long-term exposure to sublethal doses of ribosome-targeting antibiotics, we find that Caulobacter crescentus cells can recover their pre-stimulus growth rates and undergo dramatic changes in cell shape. Upon antibiotic removal, cells recover their original forms over multiple generations. These phenomena are explained by a physical theory of bacterial growth, which demonstrates that an increase in cell width and curvature promotes faster growth under protein synthesis inhibition. Shape changes thereby make bacteria more adaptive to surviving antibiotics.

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Fig. 1: Adaptive growth of C. crescentus under antibiotic stress.
Fig. 2: Mechanics of adaptive growth response.
Fig. 3: Single-cell simulations reproduce experimentally measured growth and cell shape dynamics in response to antibiotic application.
Fig. 4: Adaptation to pulsatory antibiotic stress.

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Data availability

Source data are provided with this paper. All other data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.

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Custom computer codes that were used in this paper are available from the corresponding authors upon reasonable request.

References

  1. Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1102 (2010).

    Article  ADS  Google Scholar 

  2. Jun, S., Si, F., Pugatch, R. & Scott, M. Fundamental principles in bacterial physiology—history, recent progress, and the future with focus on cell size control: a review. Rep. Prog. Phys. 81, 056601 (2018).

    Article  ADS  MathSciNet  Google Scholar 

  3. Willis, L. & Huang, K. C. Sizing up the bacterial cell cycle. Nat. Rev. Microbiol. 15, 606–620 (2017).

    Article  Google Scholar 

  4. Young, K. D. The selective value of bacterial shape. Microbiol. Mol. Biol. Rev. 70, 660–703 (2006).

    Article  Google Scholar 

  5. Yang, D. C., Blair, K. M. & Salama, N. R. Staying in shape: the impact of cell shape on bacterial survival in diverse environments. Microbiol. Mol. Biol. Rev. 80, 187–203 (2016).

    Article  Google Scholar 

  6. Woldemeskel, S. A. & Goley, E. D. Shapeshifting to survive: shape determination and regulation in Caulobacter crescentus. Trends Microbiol. 25, 673–687 (2017).

    Article  Google Scholar 

  7. Deforet, M., van Ditmarsch, D. & Xavier, J. B. Cell-size homeostasis and the incremental rule in a bacterial pathogen. Biophys. J. 109, 521–528 (2015).

    Article  ADS  Google Scholar 

  8. Lock, R. L. & Harry, E. J. Cell-division inhibitors: new insights for future antibiotics. Nat. Rev. Drug Discov. 7, 324–338 (2008).

    Article  Google Scholar 

  9. Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004).

    Article  ADS  Google Scholar 

  10. Kohanski, M. A., DePristo, M. A. & Collins, J. J. Sub-lethal antibiotic treatment leads to multidrug resistance via radical-induced mutagenesis. Mol. Cell 37, 311–320 (2010).

    Article  Google Scholar 

  11. Zhang, Q. et al. Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science 333, 1764–1767 (2011).

    Article  ADS  Google Scholar 

  12. Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 44, 101–105 (2012).

    Article  Google Scholar 

  13. Deris, J. B. et al. The innate growth bistability and fitness landscapes of antibiotic-resistant bacteria. Science 342, 1237435 (2013).

    Article  Google Scholar 

  14. Greulich, P., Scott, M., Evans, M. R. & Allen, R. J. Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics. Mol. Syst. Biol. 11, 796 (2015).

    Article  Google Scholar 

  15. Nonejuie, P., Burkart, M., Pogliano, K. & Pogliano, J. Bacterial cytological profiling rapidly identifies the cellular pathways targeted by antibacterial molecules. Proc. Natl Acad. Sci. USA 110, 16169–16174 (2013).

    Article  ADS  Google Scholar 

  16. Yao, Z., Kahne, D. & Kishony, R. Distinct single-cell morphological dynamics under beta-lactam antibiotics. Mol. Cell 48, 705–712 (2012).

    Article  Google Scholar 

  17. Si, F. et al. Invariance of initiation mass and predictability of cell size in Escherichia coli. Curr. Biol. 27, 1278–1287 (2017).

    Article  Google Scholar 

  18. Harris, L. K. & Theriot, J. A. Relative rates of surface and volume synthesis set bacterial cell size. Cell 165, 1479–1492 (2016).

    Article  Google Scholar 

  19. Harris, L. K. & Theriot, J. A. Surface area to volume ratio: a natural variable for bacterial morphogenesis. Trends Microbiol. 26, 815–832 (2018).

    Article  Google Scholar 

  20. Wright, C. S. et al. Intergenerational continuity of cell shape dynamics in Caulobacter crescentus. Sci. Rep. 5, 9155 (2015).

    Article  Google Scholar 

  21. Banerjee, S. et al. Biphasic growth dynamics control cell division in Caulobacter crescentus. Nat. Microbiol. 2, 17116 (2017).

    Article  Google Scholar 

  22. Lin, Y., Crosson, S. & Scherer, N. F. Single-gene tuning of Caulobacter cell cycle period and noise, swarming motility, and surface adhesion. Mol. Syst. Biol. 6, 445 (2010).

    Article  Google Scholar 

  23. Iyer-Biswas, S. et al. Scaling laws governing stochastic growth and division of single bacterial cells. Proc. Natl Acad. Sci. USA 111, 15912–15917 (2014).

    Article  ADS  Google Scholar 

  24. Sliusarenko, O., Cabeen, M. T., Wolgemuth, C. W., Jacobs-Wagner, C. & Emonet, T. Processivity of peptidoglycan synthesis provides a built-in mechanism for the robustness of straight-rod cell morphology. Proc. Natl Acad. Sci. USA 107, 10086–10091 (2010).

    Article  ADS  Google Scholar 

  25. Ursell, T. S. et al. Rod-like bacterial shape is maintained by feedback between cell curvature and cytoskeletal localization. Proc. Natl Acad. Sci. USA 111, E1025–E1034 (2014).

    Article  Google Scholar 

  26. Shi, H. et al. Deep phenotypic mapping of bacterial cytoskeletal mutants reveals physiological robustness to cell size. Curr. Biol. 27, 3419–3429 (2017).

    Article  Google Scholar 

  27. Wong, F. et al. Mechanical strain sensing implicated in cell shape recovery in Escherichia coli. Nat. Microbiol. 2, 17115 (2017).

    Article  Google Scholar 

  28. Ojkic, N., Serbanescu, D. & Banerjee, S. Surface-to-volume scaling and aspect ratio preservation in rod-shaped bacteria. eLife 8, e47033 (2019).

    Article  Google Scholar 

  29. Tu, Y. & Rappel, W.-J. Adaptation in living systems. Annu. Rev. Condens. Matter Phys. 9, 183–205 (2018).

    Article  ADS  Google Scholar 

  30. Jiang, H. & Sun, S. X. Morphology, growth, and size limit of bacterial cells. Phys. Rev. Lett. 105, 028101 (2010).

    Article  ADS  Google Scholar 

  31. Banerjee, S., Scherer, N. F. & Dinner, A. R. Shape dynamics of growing cell walls. Soft Matter 12, 3442–3450 (2016).

    Article  ADS  Google Scholar 

  32. Garner, E. C. et al. Coupled, circumferential motions of the cell wall synthesis machinery and MreB filaments in B. subtilis. Science 333, 222–225 (2011).

    Article  ADS  Google Scholar 

  33. Typas, A., Banzhaf, M., Gross, C. A. & Vollmer, W. From the regulation of peptidoglycan synthesis to bacterial growth and morphology. Nat. Rev. Microbiol. 10, 123–136 (2012).

    Article  Google Scholar 

  34. Pinette, M. F. & Koch, A. L. Turgor pressure responses of a gram-negative bacterium to antibiotic treatment, measured by collapse of gas vesicles. J. Bacteriol. 170, 1129–1136 (1988).

    Article  Google Scholar 

  35. Hocking, J. et al. Osmolality-dependent relocation of penicillin-binding protein PBP2 to the division site in Caulobacter crescentus. J. Bacteriol. 194, 3116–3127 (2012).

    Article  Google Scholar 

  36. Koshland, D. E.Jr., Goldbeter, A. & Stock, J. B. Amplification and adaptation in regulatory and sensory systems. Science 217, 220–225 (1982).

    Article  ADS  Google Scholar 

  37. Barkai, N. & Leibler, S. Robustness in simple biochemical networks. Nature 387, 913–917 (1997).

    Article  ADS  Google Scholar 

  38. Lan, G., Sartori, P., Neumann, S., Sourjik, V. & Tu, Y. The energy–speed–accuracy trade-off in sensory adaptation. Nat. Phys. 8, 422–428 2012).

    Article  Google Scholar 

  39. Rojas, E. R., Huang, K. C. & Theriot, J. A. Homeostatic cell growth is accomplished mechanically through membrane tension inhibition of cell-wall synthesis. Cell Syst. 5, 578–590 (2017).

    Article  Google Scholar 

  40. Campos, M. et al. A constant size extension drives bacterial cell size homeostasis. Cell 159, 1433–1446 (2014).

    Article  Google Scholar 

  41. Heinrich, K., Leslie, D. J., Morlock, M., Bertilsson, S. & Jonas, K. Molecular basis and ecological relevance of Caulobacter cell filamentation in freshwater habitats. mBio 10, e01557-19 (2019).

    Article  Google Scholar 

  42. Harris, L. K., Dye, N. A. & Theriot, J. A. A Caulobacter MreB mutant with irregular cell shape exhibits compensatory widening to maintain a preferred surface area to volume ratio. Mol. Microbiol. 94, 988–1005 (2014).

    Article  Google Scholar 

  43. Schaechter, M., Maaløe, O. & Kjeldgaard, N. O. Dependency on medium and temperature of cell size and chemical composition during balanced growth of Salmonella typhimurium. J. Gen. Microbiol. 19, 592–606 (1958).

    Article  Google Scholar 

  44. Basan, M. et al. Inflating bacterial cells by increased protein synthesis. Mol. Syst. Biol. 11, 836 (2015).

    Article  Google Scholar 

  45. Ducret, A., Quardokus, E. M. & Brun, Y. V. MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis. Nat. Microbiol. 1, 16077 (2016).

    Article  Google Scholar 

  46. Deng, Y., Sun, M. & Shaevitz, J. W. Direct measurement of cell wall stress stiffening and turgor pressure in live bacterial cells. Phys. Rev. Lett. 107, 158101 (2011).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We thank L. Harris for providing additional data for C. crescentus under chloramphenicol treatment. We thank C. Wright and S. Iyer-Biswas for experimental data from C. crescentus single-cell measurements and C. Nikas for assistance with data analysis. S.B. acknowledges support from the Engineering and Physical Sciences Research Council of the United Kingdom (grant no. EP/R029822/1), Royal Society University Research Fellowship (URF/R1/180187), and Royal Society Fellows Enhancement Award (grant no. RGF/EA/181044). A.R.D. and N.F.S. acknowledge funding from the National Science Foundation Physics of Living Systems Program (NSF PHY-1305542) and from the National Science Foundation Materials Research Science and Engineering Center at the University of Chicago (NSF DMR-1420709 and NSF DMR-2011854). A.R.D. also acknowledges support from National Science Foundation award MCB-1953402.

Author information

Authors and Affiliations

  1. Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA

    Shiladitya Banerjee

  2. Department of Physics and Astronomy, University College London, London, UK

    Shiladitya Banerjee, Nikola Ojkic & Roisin Stephens

  3. James Franck Institute, The University of Chicago, Chicago, IL, USA

    Klevin Lo, Norbert F. Scherer & Aaron R. Dinner

  4. Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA

    Klevin Lo, Norbert F. Scherer & Aaron R. Dinner

  5. Mathematical Institute, University of Oxford, Oxford, UK

    Roisin Stephens

  6. Department of Chemistry, The University of Chicago, Chicago, IL, USA

    Norbert F. Scherer & Aaron R. Dinner

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  1. Shiladitya Banerjee
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Contributions

S.B., N.F.S. and A.R.D. designed the study. A.R.D. and N.F.S. designed the experiments. S.B. developed the theory. K.L. performed experiments. S.B., N.O. and R.S. performed model simulations. S.B., K.L., N.O. and R.S. analysed the data. S.B., N.F.S. and A.R.D. wrote the manuscript.

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Correspondence to Shiladitya Banerjee, Norbert F. Scherer or Aaron R. Dinner.

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Extended data

Extended Data Fig. 1 Cell shape, size control and growth dynamics during antibiotic adaptation, shown in real time.

a, Cell elongation rate, κ, as a function of absolute time for CHL concentrations: 0.1 μg/ml (blue, Number of cells n=40, Total number of generations g=941) and 0.5 μg/ml (red, n=135, g=986). Error bars indicate ± 1 SEM. b, Interdivision time, τ, as a function of absolute time. c, Cell length at birth, L(0), as a function of absolute time. d, Correlation between cell length at division, L(τ), and cell length at birth, L(0), is best described by a mixer model: L(τ)=1.1 L(0) +0.75 μm. e, Spatiotemporally averaged cell diameter (width), w, as a function of absolute time. f, Cell-cycle averaged cell curvature, R−1, as a function of absolute time.

Source data

Extended Data Fig. 2 Dynamics of cell shape and growth rate in response to mechano-chemical perturbations.

Model predictions for the response of (a) growth rate κ, (b) curvature R−1, and (c) width w, to perturbations in parameters: {ε,kc} (blue), ε (green), {ε,kL} (purple), {ε, kc, kL} (red), and {ε, kc,P} (black). Perturbation to a particular parameter μ is of the form μ → μ/(1+ϕ) for t > ta, where μ ∈ {ε, kc, kL,P}. The translation inhibitors used experimentally for Figure 1 likely affect parameters ε and kc.

Extended Data Fig. 3 Effect of turgor pressure on cellular response to chloramphenicol.

Intergenerational dynamics of (a) growth rate κ, (b) average cell width w, (c) average curvature R−1 and (d) length at birth L(0) in response to a step pulse of 0.1 μg/ml CHL applied at t=450 min for three different cases: turgor pressure remains unchanged (blue solid circles), turgor pressure is reduced by 25% by CHL (red solid circles), and turgor pressure is increased by 25% by CHL (green data points). Turgor pressure reduction leads to a decrease in cell diameter, inconsistent with experimental data. Moderate increase in turgor pressure is consistent with experimental data.

Extended Data Fig. 4 Cell width modulation alone is not sufficient to achieve growth rate adaptation.

Intergenerational dynamics of (a) growth rate κ, (b) average cell width w, and (c) average curvature R−1 in response to a step pulse of 0.1 μg/ml CHL applied at t=450 min for two different cases: Cell curvature is variable and adapts to CHL-induced growth inhibition (blue data points) and curvature is constant and not affected by CHL (red data points). In the absence of curvature modulation, adaptive response is much weaker.

Extended Data Fig. 5 Coupling the physical model for bacterial growth with a biochemical model for chloramphenicol-ribosome interactions.

a, Schematic of the biochemical pathway of ribosome-CHL interaction. CHL with extracellular concentration aex enters the cell with net flux proportional to (Pin aex- Poutain)A/V, where Pin and Pout are the inward and outward permeabilities of the cell envelope. CHL binds to ribosomes at a rate kon and unbinds with a rate koff. Growth rate is linearly proportional to the fraction of unbound ribosomes. Ribosomes upregulate their synthesis when a fraction of them are bound to CHL. Model A: No mechanical feedback between cell shape and growth rate. Model B: Cell elongation promotes an increase in surface stress σ which in turn inhibits growth rate. b-f, Intergenerational dynamics of (b) growth rate κ, (c) intracellular CHL concentration ain, (d) concentration of active ribosomes, (e) average cell width w, and (f) average curvature R−1 in response to a step pulse of 0.1 μg/ml CHL applied at t=450 min for Model A (blue) and Model B (red). g, Cell shape evolution simulated using Model B (time progression: left-to-right and top-to-bottom), shows antibiotic dilution. Color coding indicates the intracellular concentration of CHL.

Extended Data Fig. 6 Speed-accuracy trade-off in antibiotic adaptation.

a, Adaptation error (post-stimulus recovery error %) for κ, R, w and L as a function of antibiotic stress, ϕ. b, Rate of adaptation (in units of generation-1) as a function of ϕ. c, Trade-off between adaptation speed (defined as the rate of recovery) and adaptation accuracy (defined as 100-Error%).

Extended Data Fig. 7 Quantitative comparisons between single-cell simulations and experimental data for pulsatory chloramphenicol dose.

a-b, Cell growth rate κ (a) and interdivision time τ (b) upon application of a step dose of 0.1 μg/ml chloramphenicol. Blue: experimental data, Orange: Simulation data with ϕ=0.8. c-d, Cell growth rate (c) and interdivision time (d) for a pulsatile antibiotic dose of 0.5 μg/ml. Blue: experimental data, Orange: Simulation data with ϕ=3.0. Error bars indicate ± 1 standard deviation.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, Figs. 1–3 and Tables 1–3.

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Source data

Source Data Fig. 1

Experimental time course data for Caulobacter cell shape and growth rate under chloramphenicol treatment.

Source Data Fig. 2

Correlation data between cell growth rate and curvature.

Source Data Fig. 3

Simulated data for cell shape and growth rate under chloramphenicol treatment.

Source Data Fig. 4

Experimental and simulated time course data for Caulobacter cell shape and growth rate under pulsatile chloramphenicol stress.

Source Data Extended Data Fig. 1

Experimental data for Caulobacter cell shape and growth rate under chloramphenicol treatment, plotted in real time.

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Banerjee, S., Lo, K., Ojkic, N. et al. Mechanical feedback promotes bacterial adaptation to antibiotics. Nat. Phys. 17, 403–409 (2021). https://doi.org/10.1038/s41567-020-01079-x

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