Abstract
Antibiotics typically induce major physiological changes in bacteria. However, their effect on nutrient consumption remains unclear. Here we found that Escherichia coli communities can sustain normal levels of glucose consumption under a broad range of antibiotics. The community-living resulted in a low membrane potential in the bacteria, allowing slow antibiotic accumulation on treatment and better adaptation. Through multi-omics analysis, we identified a prevalent adaptive response characterized by the upregulation of lipid synthesis, which substantially contributes to sustained glucose consumption. The consumption was maintained by the periphery region of the community, thereby restricting glucose penetration into the community interior. The resulting spatial heterogeneity in glucose availability protected the interior from antibiotic accumulation in a membrane potential-dependent manner, ensuring rapid recovery of the community postantibiotic treatment. Our findings unveiled a community-level antibiotic response through spatial regulation of metabolism and suggested new strategies for antibiotic therapies.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All relevant data are included in the article, Extended Data figures, Supplementary Data, Source data and Supplementary Information. Source data are provided with this paper.
References
Lewis, K. The science of antibiotic discovery. Cell 181, 29–45 (2020).
Meylan, S. et al. Carbon sources tune antibiotic susceptibility in Pseudomonas aeruginosa via tricarboxylic acid cycle control. Cell Chem. Biol. 24, 195–206 (2017).
Peng, B. et al. Exogenous alanine and/or glucose plus kanamycin kills antibiotic-resistant bacteria. Cell Metab. 21, 249–262 (2015).
Allison, K. R., Brynildsen, M. P. & Collins, J. J. Metabolite-enabled eradication of bacterial persisters by aminoglycosides. Nature 473, 216–220 (2011).
Dai, X. et al. Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth. Nat. Microbiol. 2, 16231 (2016).
Scott, M. & Hwa, T. Shaping bacterial gene expression by physiological and proteome allocation constraints. Nat. Rev. Microbiol. 21, 327–342 (2023).
Wong, F. et al. Reactive metabolic byproducts contribute to antibiotic lethality under anaerobic conditions. Mol. Cell 82, 3499–3512 (2022).
Zampieri, M. et al. High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. Sci. Transl. Med. 10, eaal3973 (2018).
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).
Liu, Q. et al. Tuberculosis treatment failure associated with evolution of antibiotic resilience. Science 378, 1111–1118 (2022).
Sampaio, N. M. V., Blassick, C. M., Andreani, V., Lugagne, J.-B. & Dunlop, M. J. Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response. Proc. Natl Acad. Sci. USA 119, e2115032119 (2022).
Akiyama, T. & Kim, M. Stochastic response of bacterial cells to antibiotics: its mechanisms and implications for population and evolutionary dynamics. Curr. Opin. Microbiol. 63, 104–108 (2021).
Alexander, H. K. & MacLean, R. C. Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells. Proc. Natl Acad. Sci. USA 117, 19455–19464 (2020).
Sauer, K. et al. The biofilm life cycle: expanding the conceptual model of biofilm formation. Nat. Rev. Microbiol. 20, 608–620 (2022).
Nadell, C. D., Drescher, K. & Foster, K. R. Spatial structure, cooperation and competition in biofilms. Nat. Rev. Microbiol. 14, 589–600 (2016).
Wu, F. et al. Modulation of microbial community dynamics by spatial partitioning. Nat. Chem. Biol. 18, 394–402 (2022).
Arnaouteli, S., Bamford, N. C., Stanley-Wall, N. R. & Kovács, Á. T. Bacillus subtilis biofilm formation and social interactions. Nat. Rev. Microbiol. 19, 600–614 (2021).
Bjarnsholt, T. et al. The importance of understanding the infectious microenvironment. Lancet Infect. Dis. 22, e88–e92 (2022).
Liu, J. et al. Metabolic co-dependence gives rise to collective oscillations within biofilms. Nature 523, 550–554 (2015).
Schreiber, F. et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat. Microbiol. 1, 16055 (2016).
Chou, K.-T. et al. A segmentation clock patterns cellular differentiation in a bacterial biofilm. Cell 185, 145–157 (2022).
Wang, T., Shen, P., He, Y., Zhang, Y. & Liu, J. Spatial transcriptome uncovers rich coordination of metabolism in E. coli K12 biofilm. Nat. Chem. Biol. 19, 940–950 (2023).
Davies, D. Understanding biofilm resistance to antibacterial agents. Nat. Rev. Drug Discov. 2, 114–122 (2003).
Yan, J. & Bassler, B. L. Surviving as a community: antibiotic tolerance and persistence in bacterial biofilms. Cell Host Microbe 26, 15–21 (2019).
Ibberson, C. B., Barraza, J. P., Holmes, A. L., Cao, P. & Whiteley, M. Precise spatial structure impacts antimicrobial susceptibility of S. aureus in polymicrobial wound infections. Proc. Natl Acad. Sci. USA 119, e2212340119 (2022).
Crabbé, A., Jensen, P. Ø., Bjarnsholt, T. & Coenye, T. Antimicrobial tolerance and metabolic adaptations in microbial biofilms. Trends Microbiol. 27, 850–863 (2019).
Lobritz, M. A. et al. Antibiotic efficacy is linked to bacterial cellular respiration. Proc. Natl Acad. Sci. USA 112, 8173–8180 (2015).
Pu, Y. et al. ATP-dependent dynamic protein aggregation regulates bacterial dormancy depth critical for antibiotic tolerance. Mol. Cell 73, 143–156 (2019).
Ciofu, O., Moser, C., Jensen, P. Ø. & Høiby, N. Tolerance and resistance of microbial biofilms. Nat. Rev. Microbiol. 20, 621–635 (2022).
Díaz-Pascual, F. et al. Spatial alanine metabolism determines local growth dynamics of Escherichia coli colonies. eLife 10, e70794 (2021).
Zhang, Y. et al. A microfluidic approach for quantitative study of spatial heterogeneity in bacterial biofilms. Small Sci. 2, 2200047 (2022).
Stewart, P. S. & Franklin, M. J. Physiological heterogeneity in biofilms. Nat. Rev. Microbiol. 6, 199–210 (2008).
Jo, J., Price-Whelan, A. & Dietrich, L. E. P. Gradients and consequences of heterogeneity in biofilms. Nat. Rev. Microbiol. 20, 593–607 (2022).
Yoshioka, K. et al. Intracellular fate of 2-NBDG, a fluorescent probe for glucose uptake activity, in Escherichia coli cells. Biosci. Biotechnol. Biochem. 60, 1899–1901 (1996).
Dal Co, A., Ackermann, M. & van Vliet, S. Metabolic activity affects the response of single cells to a nutrient switch in structured populations. J. R. Soc. Interface 16, 20190182 (2019).
Carlotti, B., Fuoco, D. & Elisei, F. Fast and ultrafast spectroscopic investigation of tetracycline derivatives in organic and aqueous media. Phys. Chem. Chem. Phys. 12, 15580–15591 (2010).
Jin, X. et al. Sensitive bacterial Vm sensors revealed the excitability of bacterial Vm and its role in antibiotic tolerance. Proc. Natl Acad. Sci. USA 120, e2208348120 (2023).
Kohanski, M. A., Dwyer, D. J., Hayete, B., Lawrence, C. A. & Collins, J. J. A common mechanism of cellular death induced by bactericidal antibiotics. Cell 130, 797–810 (2007).
Wong, F. et al. Cytoplasmic condensation induced by membrane damage is associated with antibiotic lethality. Nat. Commun. 12, 2321 (2021).
Rasmussen, B. et al. Molecular basis of tetracycline action: identification of analogs whose primary target is not the bacterial ribosome. Antimicrob. Agents Chemother. 35, 2306–2311 (1991).
Kohanski, M. A., Dwyer, D. J., Wierzbowski, J., Cottarel, G. & Collins, J. J. Mistranslation of membrane proteins and two-component system activation trigger antibiotic-mediated cell death. Cell 135, 679–690 (2008).
Bryan, L. E. & Kwan, S. Roles of ribosomal binding, membrane potential, and electron transport in bacterial uptake of streptomycin and gentamicin. Antimicrob. Agents Chemother. 23, 835–845 (1983).
Argast, M. & Beck, C. F. Tetracycline uptake by susceptible Escherichia coli cells. Arch. Microbiol. 141, 260–265 (1985).
Dobson, P. D. & Kell, D. B. Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule? Nat. Rev. Drug Discov. 7, 205–220 (2008).
Schmidt, A. et al. The quantitative and condition-dependent Escherichia coli proteome. Nat. Biotechnol. 34, 104–110 (2016).
Zhang, Y.-M. & Rock, C. O. Membrane lipid homeostasis in bacteria. Nat. Rev. Microbiol. 6, 222–233 (2008).
Tsay, J. T., Oh, W., Larson, T. J., Jackowski, S. & Rock, C. O. Isolation and characterization of the β-ketoacyl-acyl carrier protein synthase III gene (fabH) from Escherichia coli K-12. J. Biol. Chem. 267, 6807–6814 (1992).
Yao, Z., Davis, R. M., Kishony, R., Kahne, D. & Ruiz, N. Regulation of cell size in response to nutrient availability by fatty acid biosynthesis in Escherichia coli. Proc. Natl Acad. Sci. USA 109, E2561–E2568 (2012).
Yang, C.-Y. et al. Encoding membrane-potential-based memory within a microbial community. Cell Syst. 10, 417–423 (2020).
Rupel, K. et al. Blue laser light inhibits biofilm formation in vitro and in vivo by inducing oxidative stress. npj Biofilms Microbiomes 5, 29 (2019).
Chalabaev, S. et al. Biofilms formed by Gram-negative bacteria undergo increased lipid a palmitoylation, enhancing in vivo survival. mBio 5, e01116-14 (2014).
Erental, A., Sharon, I. & Engelberg-Kulka, H. Two programmed cell death systems in Escherichia coli: an apoptotic-like death is inhibited by the mazEF-mediated death pathway. PLoS Biol. 10, e1001281 (2012).
Xu, J.-Y. et al. Integrative proteomic characterization of human lung adenocarcinoma. Cell 182, 245–261 (2020).
Cappelletti, V. et al. Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ. Cell 184, 545–559 (2021).
Piazza, I. et al. A map of protein–metabolite interactions reveals principles of chemical communication. Cell 172, 358–372 (2018).
Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41 (2003).
Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res. 43, D261–D269 (2015).
Reichl, B. et al. Evaluation and optimization of common lipid extraction methods in cerebrospinal fluid samples. J. Chromatogr. B 1153, 122271 (2020).
Lennen, R. M. & Pfleger, B. F. Modulating membrane composition alters free fatty acid tolerance in Escherichia coli. PLoS ONE 8, e54031 (2013).
Guder, J. C., Schramm, T., Sander, T. & Link, H. Time-optimized isotope ratio LC–MS/MS for high-throughput quantification of primary metabolites. Anal. Chem. 89, 1624–1631 (2017).
Acknowledgements
We thank S. Hu and Z. Wang for assistance in data analysis, the Protein Chemistry and Proteomics Facility at Tsinghua University for assistance with proteomics assays and the Tsinghua University Center of Pharmaceutical Technology for assistance with metabolomics assays. J.L. was supported by the National Key R&D Program of China (2023YFC2306300), the National Natural Science Foundation of China (32170099), the Tsinghua University Dushi Program (20221080020 and 20231080040) and the Tsinghua–Peking Center for Life Sciences. Y.Z. was supported by the National Nature Science Foundation of China (21908129), Chinese Postdoctoral Science Foundation (2018M631481) and the Tsinghua–Peking Center for Life Sciences.
Author information
Authors and Affiliations
Contributions
J.L. conceived the project. Y.Z., Y.C. and Q.W. performed the experiments for the initial submission. Y.C. performed the experiments for the revision. X.J. and F.B. contributed to the measurement of membrane potential. Y.Z., Y.C. and J.L. designed the research, analyzed the data and wrote the paper draft. Y.C. and J.L. analyzed the data and revised the paper to its final form.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Chemical Biology thanks the anonymous reviewers for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Bacterial response to antibiotics.
a, Growth curves of planktonic culture under various antibiotic treatments (initiated at time zero). Data are presented as mean values +/− SD, 3 biological replicates. b, Colony-forming units (CFU) of planktonic culture under various antibiotic treatments (initiated at time zero). Two biological replicates. c, Snapshots of the microfluidic chamber. To visualize the flow of the growth medium, we switched from the regular medium to a medium containing the fluorescent dye rhodamine B (0.1 mg ml−1). The arrow denotes the direction of flow, and the dashed line denotes the edge of the community. Scale bar, 500 μm. d, Snapshots of the region surrounding the community. Scale bar, 200 μm. The fluorescence front swept across the field of view (2,400 μm in width) in 5.6 s, implying a flow rate of ~430 μm s−1. Results in c and d are representative of at least 3 biological replicates.
Extended Data Fig. 2 Snapshots of the bacterial community following antibiotic treatment.
a, Control; b, tetracycline; c, kanamycin; d, ciprofloxacin; e, ceftazidime; f, trimethoprim; g, chloramphenicol; h, rifampicin. i,j, Polymyxin B, 100 μg ml−1. Images are composites of phase contrast and fluorescence. Red and green represent propidium iodide (PI) and 2-NBDG fluorescence, respectively. All snapshots are displayed with the same contrast settings. The images are representative of at least 3 biological replicates. Scale bars, 100 μm. k, Kymograph of 2-NBDG fluorescence within the community in response to polymyxin B treatment. l–n, Snapshots of DCFH-DA fluorescence following 20 h of treatment with ciprofloxacin, kanamycin, and ceftazidime. Scale bar, 100 μm. o, Depth of the 2-NBDG ring, membrane potential transition, and antibiotic transition within the community, under three different glucose concentrations: 1× (22 mM), 0.5×, and 2×. Two biological replicates.
Extended Data Fig. 3 Distinct responses of community and planktonic bacteria.
a,b, Temporal changes in PtetR-mCherry fluorescence at the community periphery (a) and in planktonic bacteria (b). aTc, 1 μg ml−1 anhydrotetracycline. a is representative of >3 biological replicates. c,d, Temporal changes in DiBAC4(5) fluorescence at the community periphery (c) and in planktonic bacteria (d). For the planktonic data, each curve represents a single bacterium.
Extended Data Fig. 4 Impact of CCCP on tetracycline accumulation and glucose consumption within the community.
a, Kymograph of ViBac2 signal within the community. b, Temporal changes in ViBac2 signal at the community periphery. c, Kymograph of tetracycline fluorescence within the community. d, Temporal changes in tetracycline fluorescence at the community periphery. e, Kymograph of 2-NBDG fluorescence within the community. These data are representative of >3 biological replicates.
Extended Data Fig. 5 Metabolites secreted by the community, with or without tetracycline treatment (20 h).
We collected the waste medium flown out of the microfluidic chamber, and measured the metabolites in the waste medium using HPLC–MS. Normalized to the protein concentration of each community sample in the microfluidic chip. Two biological replicates each. n.s., not significant. Error bars indicate s.d. Statistical significance was determined by two-sided Student’s t-tests. ns, not significant.
Extended Data Fig. 6 Effect of 2-NBDG on planktonic bacteria.
a, Clusters of orthologous genes (COGs) analysis. Proteins detected in our proteomic experiment were grouped according to their COG categories, and then the percentage of protein mass in each category was calculated. Average of 2 biological replicates. b, Differential expression analysis of the COG categories shown in a. P-values were calculated using two-sided Student’s t-tests. c, Fold changes in the levels of proteins related to central carbon metabolism induced by 2-NBDG. The results indicate no significant effect. d, Growth curves of planktonic bacteria with or without 2-NBDG. Data are presented as mean values +/− SD, 3 biological replicates. The results indicate that 2-NBDG did not significantly affect bacterial growth. This lack of effect can be attributed to the low 2-NBDG concentration in our experiments (33 μM), which is substantially lower than the glucose concentration (22 mM). Additionally, the uptake of 2-NBDG by the bacteria is competitively inhibited by glucose (Supplementary Note). UDP-GlcNAc, uridine diphosphate N-acetylglucosamine; PEP, phosphoenolpyruvic acid; Ac-CoA, acetyl coenzyme A; OAA, oxaloacetic acid; α-KG, α-ketoglutarate; LPS, lipopolysaccharide; TCA, tricarboxylic acid.
Extended Data Fig. 7 Differential expression analysis of protein levels following antibiotic treatment.
a, Response of the community periphery (left panel) and interior (right panel) to tetracycline treatment respectively. Two biological replicates. P-values were calculated using two-sided Student’s t-tests. Refer to Supplementary Fig. 5 for analysis of other antibiotics. b, Venn diagrams depicting the proteins significantly upregulated in the community following antibiotic treatment, as indicated by the differential expression analysis in a. Blue represents the community periphery and red represents the interior. The numbers indicate the number of genes identified in each group.
Extended Data Fig. 8 Impact of fabH deletion on bacterial growth and glucose consumption.
a, Growth of planktonic culture. Data are presented as mean values +/− SD, 3 biological replicates. b, Growth rate of the bacterial community. Data are presented as mean values +/− SD. Biological replicates: 6 for wild type, 8 for fabH deletion. c,d, Snapshots of wild type and fabH deletion communities respectively. Green represents 2-NBDG fluorescence. Scale bar, 100 μm. e, Depth of the 2-NBDG ring within the community. Data are presented as mean values +/− SD. Eight biological replicates for each strain. Statistical significance was determined using two-sided Student’s t-tests: n.s., not significant.
Supplementary information
Supplementary Information
Supplementary Note, Figs. 1–7 and Table 1.
Supplementary Data 1
The proteomics dataset and COG classification results shown in Fig. 4.
Supplementary Data 2
The lipidome data shown in Fig. 4.
Source data
Source Data Fig. 1
Statistical source data and unprocessed image data.
Source Data Fig. 2
Statistical source data and unprocessed image data.
Source Data Fig. 3
Statistical source data.
Source Data Fig. 4
Statistical source data.
Extended Data Fig. 1
Statistical source data and unprocessed image data.
Extended Data Fig. 2
Statistical source data and unprocessed image data.
Extended Data Fig. 3
Statistical source data.
Extended Data Fig. 4
Statistical source data.
Extended Data Fig. 5
Statistical source data.
Extended Data Fig. 6
Statistical source data.
Extended Data Fig. 7
Statistical source data.
Extended Data Fig. 8
Statistical source data and unprocessed image data.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, Y., Cai, Y., Jin, X. et al. Persistent glucose consumption under antibiotic treatment protects bacterial community. Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01708-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41589-024-01708-z
This article is cited by
-
Spatially structured exchange of metabolites enhances bacterial survival and resilience in biofilms
Nature Communications (2024)
