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Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate

Nature Microbiology volume 4, pages 2109–2117 (2019)Cite this article

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Abstract

Growth rate and metabolic state of bacteria have been separately shown to affect antibiotic efficacy1,2,3. However, the two are interrelated as bacterial growth inherently imposes a metabolic burden4; thus, determining individual contributions from each is challenging5,6. Indeed, faster growth is often correlated with increased antibiotic efficacy7,8; however, the concurrent role of metabolism in that relationship has not been well characterized. As a result, a clear understanding of the interdependence between growth and metabolism, and their implications for antibiotic efficacy, are lacking9. Here, we measured growth and metabolism in parallel across a broad range of coupled and uncoupled conditions to determine their relative contribution to antibiotic lethality. We show that when growth and metabolism are uncoupled, antibiotic lethality uniformly depends on the bacterial metabolic state at the time of treatment, rather than growth rate. We further reveal a critical metabolic threshold below which antibiotic lethality is negligible. These findings were general for a wide range of conditions, including nine representative bactericidal drugs and a diverse range of Gram-positive and Gram-negative species (Escherichia coli, Acinetobacter baumannii and Staphylococcus aureus). This study provides a cohesive metabolic-dependent basis for antibiotic-mediated cell death, with implications for current treatment strategies and future drug development.

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Fig. 1: Uncoupling growth from metabolism
Fig. 2: Metabolic state correlates with antibiotic lethality for both coupled and uncoupled conditions
Fig. 3: Antibiotic lethality depends on cellular metabolism during exponential growth over a wider parameter space
Fig. 4: Metabolic-dependent threshold for lethality and generality

Data availability

The data that support the findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank D. Hung from the Broad Institute for providing the bacterial pathogens used in this study; J. K. Srimani, R. P. Smith and S. Bening for input on data interpretation and manuscript editing. This work was supported by the Defence Threat Reduction Agency (grant number HDTRA1-15-1-0051), the National Institutes of Health (grant number K99GM118907), the Banting Postdoctoral Fellowship Program (to J.M.S.; grant number 393360), the Broad Institute of MIT and Harvard, and a generous gift from A. Bekenstein and J. Bekenstein.

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Authors and Affiliations

  1. Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Allison J. Lopatkin, Jonathan M. Stokes, Jason H. Yang, Melissa K. Takahashi & James J. Collins

  2. Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Allison J. Lopatkin, Jonathan M. Stokes, Erica J. Zheng, Jason H. Yang & James J. Collins

  3. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Allison J. Lopatkin & James J. Collins

  4. Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jonathan M. Stokes

  5. Program in Chemical Biology, Harvard University, Cambridge, MA, USA

    Erica J. Zheng

  6. Department of Biology, California State University Northridge, Northridge, CA, USA

    Melissa K. Takahashi

  7. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Lingchong You

  8. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    James J. Collins

  9. Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA

    James J. Collins

  10. Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA

    James J. Collins

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  1. Allison J. Lopatkin
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Contributions

A.J.L. conceived the research, designed and carried out experiments and data analysis, developed the simplified model, interpreted data and wrote the manuscript. J.M.S. assisted with data acquisition and interpretation and manuscript editing. E.J.Z. assisted with data acquisition and manuscript editing. J.H.Y. assisted with metabolic modelling, data interpretation and manuscript editing. M.K.T. assisted with data acquisition and interpretation and manuscript editing. L.Y. assisted with data interpretation, model development and manuscript editing. J.J.C. conceived the research and assisted with data interpretation and manuscript editing.

Corresponding author

Correspondence to James J. Collins.

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Competing interests

J.J.C. is scientific co-founder and Scientific Advisory Board chair of EnBiotix, an antibiotic drug discovery company.

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Supplementary Information

Supplementary Text, Supplementary Figs. 1–11, Supplementary Tables 1–8 and Supplementary References.

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Lopatkin, A.J., Stokes, J.M., Zheng, E.J. et al. Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate. Nat Microbiol 4, 2109–2117 (2019). https://doi.org/10.1038/s41564-019-0536-0

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