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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.

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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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