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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The data that support the findings of this study are available from the corresponding author on reasonable request.
Zampieri, M., Zimmermann, M., Claassen, M. & Sauer, U. Nontargeted metabolomics reveals the multilevel response to antibiotic perturbations. Cell Rep. 19, 1214–1228 (2017).
Yang, J. H., Bening, S. C. & Collins, J. J. Antibiotic efficacy—context matters. Curr. Opin. Microbiol. 39, 73–80 (2017).
Lee, A. J. et al. Robust, linear correlations between growth rates and β-lactam–mediated lysis rates. Proc. Natl Acad. Sci. USA 115, 4069–4074 (2018).
Lipson, D. A. The complex relationship between microbial growth rate and yield and its implications for ecosystem processes. Front. Microbiol. 6, 615 (2015).
Brown, M. R., Collier, P. J. & Gilbert, P. Influence of growth rate on susceptibility to antimicrobial agents: modification of the cell envelope and batch and continuous culture studies. Antimicrob. Agents Chemother. 34, 1623–1628 (1990).
Russell, J. B. The energy spilling reactions of bacteria and other organisms. J. Mol. Microbiol. Biotechnol. 13, 1–11 (2007).
Greulich, P., Scott, M., Evans, M. R. & Allen, R. J. Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics. Mol. Syst. Biol. 11, 796 (2015).
Haugan, M. S., Løbner-Olesen, A. & Frimodt-Møller, N. Comparative activity of ceftriaxone, ciprofloxacin, and gentamicin as a function of bacterial growth rate probed by Escherichia coli chromosome replication in the mouse peritonitis model. Antimicrob. Agents Chemother. 63, e02133-18 (2019).
Fisher, J. F. & Mobashery, S. Endless resistance. Endless antibiotics? MedChemComm 7, 37–49 (2016).
Low, E. W. & Chase, H. A. Reducing production of excess biomass during wastewater treatment. Water Res. 33, 1119–1132 (1999).
Russell, J. B. & Cook, G. M. Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol. Rev. 59, 48–62 (1995).
Molenaar, D., van Berlo, R., de Ridder, D. & Teusink, B. Shifts in growth strategies reflect tradeoffs in cellular economics. Mol. Syst. Biol. 5, 323 (2009).
Erickson, D. W. et al. A global resource allocation strategy governs growth transition kinetics of Escherichia coli. Nature 551, 119–123 (2017).
Manzoni, S., Taylor, P., Richter, A., Porporato, A. & Ågren, G. I. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 196, 79–91 (2012).
Lahtvee, P.-J. et al. Multi-omics approach to study the growth efficiency and amino acid metabolism in Lactococcus lactis at various specific growth rates. Microb. Cell Fact. 10, 12 (2011).
Basan, M. et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528, 99–104 (2015).
Waschina, S., Souza, G. D., Kost, C. & Kaleta, C. Metabolic network architecture and carbon source determine metabolite production costs. FEBS J. 283, 2149–2163 (2016).
Akashi, H. & Gojobori, T. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc. Natl Acad. Sci. USA 99, 3695–3700 (2002).
Gschaedler, A. & Boudrant, J. Amino acid utilization during batch and continuous cultures of Escherichia coli on a semi-synthetic medium. J. Biotechnol. 37, 235–251 (1994).
Orth, J. D. et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism. Mol. Syst. Biol. 7, 535 (2011).
Holms, H. Flux analysis and control of the central metabolic pathways in Escherichia coli. FEMS Microbiol. Rev. 19, 85–116 (1996).
Kempes, C. P. et al. Drivers of bacterial maintenance and minimal energy requirements. Front. Microbiol. 8, 31 (2017).
Shan, Y. et al. ATP-dependent persister formation in Escherichia coli. mBio 8, 02267-16 (2017).
Chang, D.-E., Smalley, D. J. & Conway, T. Gene expression profiling of Escherichia coli growth transitions: an expanded stringent response model. Mol. Microbiol. 45, 289–306 (2002).
Rampersad, S. N. Multiple applications of Alamar Blue as an indicator of metabolic function and cellular health in cell viability bioassays. Sensors 12, 12347–12360 (2012).
Yang, J. H. et al. A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell 177, 1649–1661 (2019).
Lobritz, M. A. et al. Antibiotic efficacy is linked to bacterial cellular respiration. Proc. Natl Acad. Sci. USA 112, 8173–8180 (2015).
Dwyer, D. J. et al. Antibiotics induce redox-related physiological alterations as part of their lethality. Proc. Natl Acad. Sci. USA 111, E2100–E2109 (2014).
Wright, N. E. & Gilbert, P. Influence of specific growth rate and nutrient limitation upon the sensitivity of Escherichia coli towards chlorhexidine diacetate. J. Appl. Bacteriol. 62, 309–314 (1987).
Lopatkin, A. J. et al. Antibiotics as a selective driver for conjugation dynamics. Nat. Microbiol. 1, 16044 (2016).
Lopatkin, A. J. et al. Persistence and reversal of plasmid-mediated antibiotic resistance. Nat. Commun. 8, 1689 (2017).
Schellenberger, J. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6, 1290–1307 (2011).
Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004).
Theodosiou, E., Frick, O., Bühler, B. & Schmid, A. Metabolic network capacity of Escherichia coli for Krebs cycle-dependent proline hydroxylation. Microb. Cell Fact. 14, 108 (2015).
Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).
Gottschalk, G. Bacterial Metabolism (Springer, 1979).
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.
J.J.C. is scientific co-founder and Scientific Advisory Board chair of EnBiotix, an antibiotic drug discovery company.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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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