Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

The physiology and genetics of bacterial responses to antibiotic combinations

Abstract

Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Purchase on Springer Link

Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Interactions in antibiotic mixtures.
Fig. 2: Temporal interactions in sequential antibiotic encounters.
Fig. 3: Parallel SOS induction pathways in Escherichia coli.
Fig. 4: Examples of collateral sensitivity between antibiotics and underlying mechanisms.

Similar content being viewed by others

References

  1. Walsh, C. Antibiotics: Actions, Origins, Resistance (ASM, 2003).

  2. Baquero, F. & Levin, B. R. Proximate and ultimate causes of the bactericidal action of antibiotics. Nat. Rev. Microbiol. 19, 123–132 (2021).

    CAS  PubMed  Google Scholar 

  3. Kohanski, M. A., Dwyer, D. J. & Collins, J. J. How antibiotics kill bacteria: from targets to networks. Nat. Rev. Microbiol. 8, 423–435 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Lopatkin, A. J. et al. Clinically relevant mutations in core metabolic genes confer antibiotic resistance. Science 371, eaba0862 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 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). With clever experiments and derived mathematical relations, this hallmark paper describes how cellular growth and regulation of drug target abundance jointly determine the effects of ribosome-targeting antibiotics on gene expression.

    CAS  PubMed  Google Scholar 

  6. Palmer, A. C. & Kishony, R. Opposing effects of target overexpression reveal drug mechanisms. Nat. Commun. 5, 4296 (2014).

    CAS  PubMed  Google Scholar 

  7. Bloemberg, G. V. et al. Acquired resistance to bedaquiline and delamanid in therapy for tuberculosis. N. Engl. J. Med. 373, 1986–1988 (2015).

    PubMed  PubMed Central  Google Scholar 

  8. Gullberg, E. et al. Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog. 7, e1002158 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Tueffers, L. et al. Pseudomonas aeruginosa populations in the cystic fibrosis lung lose susceptibility to newly applied β-lactams within 3 days. J. Antimicrob. Chemother. 74, 2916–2925 (2019).

    CAS  PubMed  Google Scholar 

  10. Brauner, A., Fridman, O., Gefen, O. & Balaban, N. Q. Distinguishing between resistance, tolerance and persistence to antibiotic treatment. Nat. Rev. Microbiol. 14, 320–330 (2016).

    CAS  PubMed  Google Scholar 

  11. Baym, M., Stone, L. K. & Kishony, R. Multidrug evolutionary strategies to reverse antibiotic resistance. Science 351, aad3292 (2016).

    PubMed  PubMed Central  Google Scholar 

  12. Band, V. I. et al. Antibiotic combinations that exploit heteroresistance to multiple drugs effectively control infection. Nat. Microbiol. 4, 1627–1635 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Tyers, M. & Wright, G. D. Drug combinations: a strategy to extend the life of antibiotics in the 21st century. Nat. Rev. Microbiol. 17, 141 (2019).

    CAS  PubMed  Google Scholar 

  14. Greco, W. R., Bravo, G. & Parsons, J. C. The search for synergy: a critical review from a response surface perspective. Pharmacol. Rev. 47, 331–385 (1995). This paper provides a thorough discussion of additivity models.

    CAS  PubMed  Google Scholar 

  15. Loewe, S. Die quantitativen probleme der pharmakologie [German]. Ergeb. Physiol. 27, 47–187 (1928).

    Google Scholar 

  16. Bliss, C. I. The toxicity of poisons applied jointly. Ann. Appl. Biol. 26, 585–615 (1939).

    CAS  Google Scholar 

  17. Tan, C. et al. The inoculum effect and band-pass bacterial response to periodic antibiotic treatment. Mol. Syst. Biol. 8, 617 (2012).

    PubMed  PubMed Central  Google Scholar 

  18. Rezzoagli, C., Archetti, M., Mignot, I., Baumgartner, M. & Kümmerli, R. Combining antibiotics with antivirulence compounds can have synergistic effects and reverse selection for antibiotic resistance in Pseudomonas aeruginosa. PLoS Biol. 18, e3000805 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Tekin, E., Savage, V. M. & Yeh, P. J. Measuring higher-order drug interactions: a review of recent approaches. Curr. Opin. Syst. Biol. 4, 16–23 (2017).

    Google Scholar 

  20. Brochado, A. R. et al. Species-specific activity of antibacterial drug combinations. Nature 559, 259–263 (2018). This work presents a systematic analysis of pairwise interactions between 79 antibacterial compounds in 3 pathogenic bacteria that showcases species-level differences in synergy.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Harvey, R. J. Interaction of two inhibitors which act on different enzymes of a metabolic pathway. J. Theor. Biol. 74, 411–437 (1978).

    CAS  PubMed  Google Scholar 

  22. Hitchings, G. H. Folate antagonists as antibacterial and antiprotozoal agents. Ann. NY Acad. Sci. 186, 444–451 (1971).

    CAS  PubMed  Google Scholar 

  23. Minato, Y. et al. Mutual potentiation drives synergy between trimethoprim and sulfamethoxazole. Nat. Commun. 9, 1003 (2018).

    PubMed  PubMed Central  Google Scholar 

  24. Nichols, R. J. et al. Phenotypic landscape of a bacterial cell. Cell 144, 143–156 (2011).

    CAS  PubMed  Google Scholar 

  25. Yonath, A. Antibiotics targeting ribosomes: resistance, selectivity, synergism and cellular regulation. Annu. Rev. Biochem. 74, 649–679 (2005).

    CAS  PubMed  Google Scholar 

  26. Belousoff, M. J. et al. Crystal structure of the synergistic antibiotic pair, lankamycin and lankacidin, in complex with the large ribosomal subunit. Proc. Natl Acad. Sci. USA 108, 2717–2722 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Kavčič, B., Tkačik, G. & Bollenbach, T. Mechanisms of drug interactions between translation-inhibiting antibiotics. Nat. Commun. 11, 4013 (2020). By combining experiments and mathematical modelling, this paper significantly advances the mechanistic understanding of drug interactions between ribosome-targeting antibiotics.

    PubMed  PubMed Central  Google Scholar 

  28. Chait, R., Craney, A. & Kishony, R. Antibiotic interactions that select against resistance. Nature 446, 668–671 (2007).

    CAS  PubMed  Google Scholar 

  29. Yeh, P., Tschumi, A. I. & Kishony, R. Functional classification of drugs by properties of their pairwise interactions. Nat. Genet. 38, 489–494 (2006).

    CAS  PubMed  Google Scholar 

  30. Kavcˇicˇ, B., Tkacˇik, G. & Bollenbach, T. Minimal biophysical model of combined antibiotic action. PLoS Comput. Biol. 17, e1008529 (2021).

    PubMed  PubMed Central  Google Scholar 

  31. Jawetz, E., Gunnison, J. B. & Speck, R. S. Antibiotic synergism and antagonism. N. Engl. J. Med. 245, 966–968 (1951).

    CAS  PubMed  Google Scholar 

  32. Moellering, R. C. & Weinberg, A. N. Studies on antibiotic synergism against enterococci. II. Effect of various antibiotics on the uptake of 14C-labeled streptomycin by enterococci. J. Clin. Invest. 50, 2580–2584 (1971).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Plotz, P. H. & Davis, B. D. Synergism between streptomycin and penicillin: a proposed mechanism. Science 135, 1067–1068 (1962).

    CAS  PubMed  Google Scholar 

  34. Lewis, K. The science of antibiotic discovery. Cell 181, 29–45 (2020).

    CAS  PubMed  Google Scholar 

  35. Klobucar, K. & Brown, E. D. New potentiators of ineffective antibiotics: targeting the Gram-negative outer membrane to overcome intrinsic resistance. Curr. Opin. Chem. Biol. 66, 102099 (2021).

    PubMed  Google Scholar 

  36. Cokol, M. et al. Systematic exploration of synergistic drug pairs. Mol. Syst. Biol. 7, 544 (2011).

    PubMed  PubMed Central  Google Scholar 

  37. Liu, A. et al. Antibiotic sensitivity profiles determined with an Escherichia coli gene knockout collection: generating an antibiotic bar code. Antimicrob. Agents Chemother. 54, 1393–1403 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Chevereau, G. & Bollenbach, T. Systematic discovery of drug interaction mechanisms. Mol. Syst. Biol. 11, 807 (2015).

    PubMed  PubMed Central  Google Scholar 

  39. Falconer, S. B., Czarny, T. L. & Brown, E. D. Antibiotics as probes of biological complexity. Nat. Chem. Biol. 7, 415–423 (2011).

    CAS  PubMed  Google Scholar 

  40. Lehár, J. et al. Chemical combination effects predict connectivity in biological systems. Mol. Syst. Biol. 3, 80 (2007).

    PubMed  PubMed Central  Google Scholar 

  41. Maier, L. et al. Unravelling the collateral damage of antibiotics on gut bacteria. Nature 599, 120–124 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Eng, R. H., Padberg, F. T., Smith, S. M., Tan, E. N. & Cherubin, C. E. Bactericidal effects of antibiotics on slowly growing and nongrowing bacteria. Antimicrob. Agents Chemother. 35, 1824–1828 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Tuomanen, E., Cozens, R., Tosch, W., Zak, O. & Tomasz, A. The rate of killing of Escherichia coli by β-lactam antibiotics is strictly proportional to the rate of bacterial growth. J. Gen. Microbiol. 132, 1297–1304 (1986).

    CAS  PubMed  Google Scholar 

  44. Ocampo, P. S. et al. Antagonism between bacteriostatic and bactericidal antibiotics is prevalent. Antimicrob. Agents Chemother. 58, 4573–4582 (2014).

    PubMed  PubMed Central  Google Scholar 

  45. Bollenbach, T., Quan, S., Chait, R. & Kishony, R. Nonoptimal microbial response to antibiotics underlies suppressive drug interactions. Cell 139, 707–718 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Xavier, J. B. & Sander, C. Principle of system balance for drug interactions. N. Engl. J. Med. 362, 1339–1340 (2010).

    CAS  PubMed  Google Scholar 

  47. Batra, A. et al. High potency of sequential therapy with only β-lactam antibiotics. eLife 10, e68876 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Roemhild, R. et al. Cellular hysteresis as a principle to maximize the efficacy of antibiotic therapy. Proc. Natl Acad. Sci. USA 115, 9767–9772 (2018). This paper demonstrates that negative hysteresis can significantly delay the evolution of resistance in sequential treatments with three bactericidal antibiotics.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. VanBogelen, R. A. & Neidhardt, F. C. Ribosomes as sensors of heat and cold shock in Escherichia coli. Proc. Natl Acad. Sci. USA 87, 5589–5593 (1990). This classic paper demonstrates that ribosome-targeting antibiotics cause changes to the proteome that are identical to those after temperature shock.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Mitosch, K., Rieckh, G. & Bollenbach, T. Temporal order and precision of complex stress responses in individual bacteria. Mol. Syst. Biol. 15, e8470 (2019).

    PubMed  PubMed Central  Google Scholar 

  51. Gellert, M., Mizuuchi, K., O’Dea, M. H., Itoh, T. & Tomizawa, J.-I. Nalidixic acid resistance: a second genetic character involved in DNA gyrase activity. Proc. Natl Acad. Sci. USA 74, 4772–4776 (1977).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Storz, G. & Hengge, R. Bacterial Stress Responses (ASM, 2010). This book provides an excellent overview of bacterial stress-response systems.

  53. Dörr, T., Lewis, K. & Vulić, M. SOS response induces persistence to fluoroquinolones in Escherichia coli. PLoS Genet. 5, e1000760 (2009).

    PubMed  PubMed Central  Google Scholar 

  54. Theodore, A., Lewis, K. & Vulić, M. Tolerance of Escherichia coli to fluoroquinolone antibiotics depends on specific components of the SOS response pathway. Genetics 195, 1265–1276 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Epshtein, V. et al. UvrD facilitates DNA repair by pulling RNA polymerase backwards. Nature 505, 372–377 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Miller, C. et al. SOS response induction by β-lactams and bacterial defense against antibiotic lethality. Science 305, 1629–1631 (2004).

    CAS  PubMed  Google Scholar 

  57. Miller, C., Ingmer, H., Thomsen, L. E., Skarstad, K. & Cohen, S. N. DpiA binding to the replication origin of Escherichia coli plasmids and chromosomes destabilizes plasmid inheritance and induces the bacterial SOS response. J. Bacteriol. 185, 6025–6031 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Bi, E. & Lutkenhaus, J. Cell division inhibitors SulA and MinCD prevent formation of the FtsZ ring. J. Bacteriol. 175, 1118–1125 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Jeannot, K., Sobel, M. L., Garch, F. E., Poole, K. & Plésiat, P. Induction of the MexXY efflux pump in Pseudomonas aeruginosa is dependent on drug–ribosome interaction. J. Bacteriol. 187, 5341–5346 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Lobritz, M. A. et al. Antibiotic efficacy is linked to bacterial cellular respiration. Proc. Natl Acad. Sci. USA 112, 8173–8180 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Kudrin, P. et al. Subinhibitory concentrations of bacteriostatic antibiotics induce relA-dependent and relA-independent tolerance to β-lactams. Antimicrob. Agents Chemother. 61, e02173-16 (2017).

    PubMed  PubMed Central  Google Scholar 

  63. Johnson, P. J. T. & Levin, B. R. Pharmacodynamics, population dynamics, and the evolution of persistence in Staphylococcus aureus. PLoS Genet. 9, e1003123 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Harpaz, D., Marks, R. S., Kushmaro, A. & Eltzov, E. Environmental pollutants induce noninherited antibiotic resistance to polymyxin B in Escherichia coli. Future Microbiol. 15, 1631–1643 (2020).

    CAS  PubMed  Google Scholar 

  65. Masi, M., Pinet, E. & Pagès, J.-M. Complex response of the CpxAR two-component system to β-Lactams on antibiotic resistance and envelope homeostasis in Enterobacteriaceae. Antimicrob. Agents Chemother. 64, e00291-20 (2020).

    PubMed  PubMed Central  Google Scholar 

  66. Mitosch, K., Rieckh, G. & Bollenbach, T. Noisy response to antibiotic stress predicts subsequent single-cell survival in an acidic environment. Cell Syst. 4, 393–403.e5 (2017).

    CAS  PubMed  Google Scholar 

  67. Hong, Y., Zeng, J., Wang, X., Drlica, K. & Zhao, X. Post-stress bacterial cell death mediated by reactive oxygen species. Proc. Natl Acad. Sci. USA 116, 10064–10071 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Ni, M. et al. Pre-disposition and epigenetics govern variation in bacterial survival upon stress. PLoS Genet. 8, e1003148 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. MacKenzie, F. M. & Gould, I. M. The post-antibiotic effect. J. Antimicrob. Chemother. 32, 519–537 (1993).

    CAS  PubMed  Google Scholar 

  70. Srimani, J. K., Huang, S., Lopatkin, A. J. & You, L. Drug detoxification dynamics explain the postantibiotic effect. Mol. Syst. Biol. 13, 948 (2017).

    PubMed  PubMed Central  Google Scholar 

  71. Mateus, A., Matsson, P. & Artursson, P. Rapid measurement of intracellular unbound drug concentrations. Mol. Pharm. 10, 2467–2478 (2013).

    CAS  PubMed  Google Scholar 

  72. Bergmiller, T. et al. Biased partitioning of the multidrug efflux pump AcrAB–TolC underlies long-lived phenotypic heterogeneity. Science 356, 311–315 (2017).

    CAS  PubMed  Google Scholar 

  73. Mathis, R. & Ackermann, M. Asymmetric cellular memory in bacteria exposed to antibiotics. BMC Evol. Biol. 17, 73 (2017).

    PubMed  PubMed Central  Google Scholar 

  74. Govers, S. K., Mortier, J., Adam, A. & Aertsen, A. Protein aggregates encode epigenetic memory of stressful encounters in individual Escherichia coli cells. PLoS Biol. 16, e2003853 (2018). This paper shows that protein aggregates confer a transient cellular memory of sublethal stress that is epigenetically inherited and provides cross-stress protection.

    PubMed  PubMed Central  Google Scholar 

  75. Lambert, G. & Kussell, E. Memory and fitness optimization of bacteria under fluctuating environments. PLoS Genet. 10, e1004556 (2014). This work presents a groundbreaking experimental analysis of memory in bacterial utilization of lactose.

    PubMed  PubMed Central  Google Scholar 

  76. Ozbudak, E. M., Thattai, M., Lim, H. N., Shraiman, B. I. & van Oudenaarden, A. Multistability in the lactose utilization network of Escherichia coli. Nature 427, 737–740 (2004).

    CAS  PubMed  Google Scholar 

  77. Williams, K., Savageau, M. A. & Blumenthal, R. M. A bistable hysteretic switch in an activator–repressor regulated restriction–modification system. Nucleic Acids Res. 41, 6045–6057 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Blair, J. M. A., Webber, M. A., Baylay, A. J., Ogbolu, D. O. & Piddock, L. J. V. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13, 42–51 (2015).

    CAS  PubMed  Google Scholar 

  79. Durão, P., Balbontín, R. & Gordo, I. Evolutionary mechanisms shaping the maintenance of antibiotic resistance. Trends Microbiol. 26, 677–691 (2018).

    PubMed  Google Scholar 

  80. Levin-Reisman, I., Brauner, A., Ronin, I. & Balaban, N. Q. Epistasis between antibiotic tolerance, persistence, and resistance mutations. Proc. Natl Acad. Sci. USA 116, 14734–14739 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Porse, A., Jahn, L. J., Ellabaan, M. M. H. & Sommer, M. O. A. Dominant resistance and negative epistasis can limit the co-selection of de novo resistance mutations and antibiotic resistance genes. Nat. Commun. 11, 1199 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Barbosa, C., Römhild, R., Rosenstiel, P. & Schulenburg, H. Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa. eLife 8, e51481 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. De Angelis, G., Del Giacomo, P., Posteraro, B., Sanguinetti, M. & Tumbarello, M. Molecular mechanisms, epidemiology, and clinical importance of β-lactam resistance in Enterobacteriaceae. Int. J. Mol. Sci. 21, 5090 (2020).

    PubMed Central  Google Scholar 

  84. Serio, A. W., Keepers, T., Andrews, L. & Krause, K. M. Aminoglycoside revival: review of a historically important class of antimicrobials undergoing rejuvenation. EcoSal Plus https://doi.org/10.1128/ecosalplus.ESP-0002-2018 (2018).

    Article  PubMed  Google Scholar 

  85. Fyfe, C., Grossman, T. H., Kerstein, K. & Sutcliffe, J. Resistance to macrolide antibiotics in public health pathogens. Cold Spring Harb. Perspect. Med. 6, a025395 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. Prajapati, J. D., Kleinekathöfer, U. & Winterhalter, M. How to enter a bacterium: bacterial porins and the permeation of antibiotics. Chem. Rev. 121, 5158–5192 (2021).

    CAS  PubMed  Google Scholar 

  87. Du, D. et al. Multidrug efflux pumps: structure, function and regulation. Nat. Rev. Microbiol. 16, 523–539 (2018).

    CAS  PubMed  Google Scholar 

  88. Goossens, S. N., Sampson, S. L. & Rie, A. V. Mechanisms of drug-induced tolerance in Mycobacterium tuberculosis. Clin. Microbiol. Rev. 34, e00141-20 (2020).

    PubMed  PubMed Central  Google Scholar 

  89. Roemhild, R., Linkevicius, M. & Andersson, D. I. Molecular mechanisms of collateral sensitivity to the antibiotic nitrofurantoin. PLoS Biol. 18, e3000612 (2020). This work characterizes several molecular mechanisms that explain collateral sensitivity to a clinically relevant antibiotic.

    PubMed  PubMed Central  Google Scholar 

  90. Apjok, G. et al. Limited evolutionary conservation of the phenotypic effects of antibiotic resistance mutations. Mol. Biol. Evol. 36, 1601–1611 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Lázár, V. et al. Bacterial evolution of antibiotic hypersensitivity. Mol. Syst. Biol. 9, 700 (2013).

    PubMed  PubMed Central  Google Scholar 

  92. Bryant, D. W. & McCalla, D. R. Nitrofuran induced mutagenesis and error prone repair in Escherichia coli. Chem. Biol. Interact. 31, 151–166 (1980).

    CAS  PubMed  Google Scholar 

  93. Mizusawa, S. & Gottesman, S. Protein degradation in Escherichia coli: the lon gene controls the stability of sulA protein. Proc. Natl Acad. Sci. USA 80, 358–362 (1983).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Chong, Y., Shimoda, S. & Shimono, N. Current epidemiology, genetic evolution and clinical impact of extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae. Infect. Genet. Evol. 61, 185–188 (2018).

    PubMed  Google Scholar 

  95. Rosenkilde, C. E. H. et al. Collateral sensitivity constrains resistance evolution of the CTX-M-15 β-lactamase. Nat. Commun. 10, 618 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Lutz, R. & Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Res. 25, 1203–1210 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

    PubMed  PubMed Central  Google Scholar 

  100. Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).

    CAS  PubMed  Google Scholar 

  101. Hillenmeyer, M. E. et al. The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320, 362–365 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Brochado, A. R. & Typas, A. High-throughput approaches to understanding gene function and mapping network architecture in bacteria. Curr. Opin. Microbiol. 16, 199–206 (2013).

    CAS  PubMed  Google Scholar 

  103. Cacace, E., Kritikos, G. & Typas, A. Chemical genetics in drug discovery. Curr. Opin. Syst. Biol. 4, 35–42 (2017).

    PubMed  PubMed Central  Google Scholar 

  104. Podnecky, N. L. et al. Conserved collateral antibiotic susceptibility networks in diverse clinical strains of Escherichia coli. Nat. Commun. 9, 3673 (2018).

    PubMed  PubMed Central  Google Scholar 

  105. Imamovic, L. & Sommer, M. O. A. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci. Transl Med. 5, 204ra132 (2013).

    PubMed  Google Scholar 

  106. Hernando-Amado, S., Sanz-García, F. & Martínez, J. L. Rapid and robust evolution of collateral sensitivity in Pseudomonas aeruginosa antibiotic-resistant mutants. Sci. Adv. 6, eaba5493 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Fass, R. J. Comparative in vitro activities of β-lactam–tobramycin combinations against Pseudomonas aeruginosa and multidrug-resistant Gram-negative enteric bacilli. Antimicrob. Agents Chemother. 21, 1003–1006 (1982).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Fatsis-Kavalopoulos, N., Roemhild, R., Tang, P.-C., Kreuger, J. & Andersson, D. I. CombiANT: antibiotic interaction testing made easy. PLoS Biol. 18, e3000856 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  111. Pinheiro, F., Warsi, O., Andersson, D. I. & Lässig, M. Metabolic fitness landscapes predict the evolution of antibiotic resistance. Nat. Ecol. Evol. 5, 677–687 (2021).

    PubMed  Google Scholar 

  112. Wistrand-Yuen, E. et al. Evolution of high-level resistance during low-level antibiotic exposure. Nat. Commun. 9, 1599 (2018).

    PubMed  PubMed Central  Google Scholar 

  113. Knöppel, A., Näsvall, J. & Andersson, D. I. Evolution of antibiotic resistance without antibiotic exposure. Antimicrob. Agents Chemother. 61, e01495-17 (2017).

    PubMed  PubMed Central  Google Scholar 

  114. Drlica, K. & Zhao, X. DNA gyrase, topoisomerase IV, and the 4-quinolones. Microbiol. Mol. Biol. Rev. 61, 377–392 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Shaw, K. J. et al. Comparison of the changes in global gene expression of Escherichia coli induced by four bactericidal agents. J. Mol. Microbiol. Biotechnol. 5, 105–122 (2003).

    CAS  PubMed  Google Scholar 

  116. Lewin, C. S. & Amyes, S. G. B. The role of the SOS response in bacteria exposed to zidovudine or trimethoprim. J. Med. Microbiol. 34, 329–332 (1991).

    CAS  PubMed  Google Scholar 

  117. Baharoglu, Z., Krin, E. & Mazel, D. RpoS plays a central role in the SOS induction by sub-lethal aminoglycoside concentrations in Vibrio cholerae. PLoS Genet. 9, e1003421 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Boshoff, H. I. M. et al. The transcriptional responses of Mycobacterium tuberculosis to inhibitors of metabolism: novel insights into drug mechanisms of action. J. Biol. Chem. 279, 40174–40184 (2004).

    CAS  PubMed  Google Scholar 

  119. Blázquez, J. et al. PBP3 inhibition elicits adaptive responses in Pseudomonas aeruginosa. Mol. Microbiol. 62, 84–99 (2006).

    PubMed  Google Scholar 

  120. Mesak, L. R., Miao, V. & Davies, J. Effects of subinhibitory concentrations of antibiotics on SOS and DNA repair gene expression in Staphylococcus aureus. Antimicrob. Agents Chemother. 52, 3394–3397 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Baharoglu, Z. & Mazel, D. Vibrio cholerae triggers SOS and mutagenesis in response to a wide range of antibiotics: a route towards multiresistance. Antimicrob. Agents Chemother. 55, 2438–2441 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Wood, L. F., Leech, A. J. & Ohman, D. E. Cell wall-inhibitory antibiotics activate the alginate biosynthesis operon in Pseudomonas aeruginosa: roles of σ22 (AlgT) and the AlgW and Prc proteases. Mol. Microbiol. 62, 412–426 (2006).

    CAS  PubMed  Google Scholar 

  123. Audrain, B. et al. Induction of the Cpx envelope stress pathway contributes to Escherichia coli tolerance to antimicrobial peptides. Appl. Environ. Microbiol. 79, 7770–7779 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Delhaye, A., Collet, J.-F. & Laloux, G. Fine-tuning of the Cpx envelope stress response is required for cell wall homeostasis in Escherichia coli. mBio 7, e00047-16 (2016).

    PubMed  PubMed Central  Google Scholar 

  125. Jing, W., Liu, J., Wu, S., Li, X. & Liu, Y. Role of cpxA mutations in the resistance to aminoglycosides and β-lactams in Salmonella enterica serovar Typhimurium. Front. Microbiol. 12, 106 (2021).

    Google Scholar 

  126. Kaldalu, N., Mei, R. & Lewis, K. Killing by ampicillin and ofloxacin induces overlapping changes in Escherichia coli transcription profile. Antimicrob. Agents Chemother. 48, 890–896 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Laubacher, M. E. & Ades, S. E. The Rcs phosphorelay is a cell envelope stress response activated by peptidoglycan stress and contributes to intrinsic antibiotic resistance. J. Bacteriol. 190, 2065–2074 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Lee, S. et al. Targeting a bacterial stress response to enhance antibiotic action. Proc. Natl Acad. Sci. USA 106, 14570–14575 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Fernández, L. et al. Adaptive resistance to the “last hope” antibiotics polymyxin B and colistin in Pseudomonas aeruginosa is mediated by the novel two-component regulatory system ParR–ParS. Antimicrob. Agents Chemother. 54, 3372–3382 (2010).

    PubMed  PubMed Central  Google Scholar 

  130. Dörr, T. et al. A cell wall damage response mediated by a sensor kinase/response regulator pair enables β-lactam tolerance. Proc. Natl Acad. Sci. USA 113, 404–409 (2016).

    PubMed  Google Scholar 

  131. Cao, M., Wang, T., Ye, R. & Helmann, J. D. Antibiotics that inhibit cell wall biosynthesis induce expression of the Bacillus subtilis σW and σM regulons. Mol. Microbiol. 45, 1267–1276 (2002).

    CAS  PubMed  Google Scholar 

  132. Thackray, P. D. & Moir, A. SigM, an extracytoplasmic function σ factor of Bacillus subtilis, is activated in response to cell wall antibiotics, ethanol, heat, acid, and superoxide stress. J. Bacteriol. 185, 3491–3498 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Mascher, T., Zimmer, S. L., Smith, T.-A. & Helmann, J. D. Antibiotic-inducible promoter regulated by the cell envelope stress-sensing two-component system LiaRS of Bacillus subtilis. Antimicrob. Agents Chemother. 48, 2888–2896 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Dubrac, S., Bisicchia, P., Devine, K. M. & Msadek, T. A matter of life and death: cell wall homeostasis and the WalKR (YycGF) essential signal transduction pathway. Mol. Microbiol. 70, 1307–1322 (2008).

    CAS  PubMed  Google Scholar 

  135. Staron´, A., Finkeisen, D. E. & Mascher, T. Peptide antibiotic sensing and detoxification modules of Bacillus subtilis. Antimicrob. Agents Chemother. 55, 515–525 (2011).

    PubMed  Google Scholar 

  136. Kallipolitis, B. H., Ingmer, H., Gahan, C. G., Hill, C. & Søgaard-Andersen, L. CesRK, a two-component signal transduction system in Listeria monocytogenes, responds to the presence of cell wall-acting antibiotics and affects β-lactam resistance. Antimicrob. Agents Chemother. 47, 3421–3429 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Suntharalingam, P., Senadheera, M. D., Mair, R. W., Lévesque, C. M. & Cvitkovitch, D. G. The LiaFSR system regulates the cell envelope stress response in Streptococcus mutans. J. Bacteriol. 191, 2973–2984 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  138. Yin, S., Daum, R. S. & Boyle-Vavra, S. VraSR two-component regulatory system and its role in induction of pbp2 and vraSR expression by cell wall antimicrobials in Staphylococcus aureus. Antimicrob. Agents Chemother. 50, 336–343 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Balibar, C. J. et al. cwrA, a gene that specifically responds to cell wall damage in Staphylococcus aureus. Microbiol. Read. Engl. 156, 1372–1383 (2010).

    CAS  Google Scholar 

  140. Campbell, J. et al. An antibiotic that inhibits a late step in wall teichoic acid biosynthesis induces the cell wall stress stimulon in Staphylococcus aureus. Antimicrob. Agents Chemother. 56, 1810–1820 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Dukan, S. et al. Protein oxidation in response to increased transcriptional or translational errors. Proc. Natl Acad. Sci. USA 97, 5746–5749 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Lin, J. T., Connelly, M. B., Amolo, C., Otani, S. & Yaver, D. S. Global transcriptional response of Bacillus subtilis to treatment with subinhibitory concentrations of antibiotics that inhibit protein synthesis. Antimicrob. Agents Chemother. 49, 1915–1926 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Wu, X. et al. Dynamic proteome response of Pseudomonas aeruginosa to tobramycin antibiotic treatment. Mol. Cell. Proteom. 14, 2126–2137 (2015).

    CAS  Google Scholar 

  144. Tran, T. D.-H. et al. Decrease in penicillin susceptibility due to heat shock protein ClpL in Streptococcus pneumoniae. Antimicrob. Agents Chemother. 55, 2714–2728 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Reiß, S. et al. Global analysis of the Staphylococcus aureus response to mupirocin. Antimicrob. Agents Chemother. 56, 787–804 (2012).

    PubMed  PubMed Central  Google Scholar 

  146. Mathieu, A. et al. Discovery and function of a general core hormetic stress response in E. coli induced by sublethal concentrations of antibiotics. Cell Rep. 17, 46–57 (2016).

    CAS  PubMed  Google Scholar 

  147. Gutierrez, A. et al. β-Lactam antibiotics promote bacterial mutagenesis via an RpoS-mediated reduction in replication fidelity. Nat. Commun. 4, 1610 (2013). This paper demonstrates that cell wall-targeting drugs induce error-prone replication of DNA as part of the antibiotic-induced general stress response.

    CAS  PubMed  Google Scholar 

  148. Jacoby, G. A. AmpC β-lactamases. Clin. Microbiol. Rev. 22, 161–182 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Li, L. et al. Sensor histidine kinase is a β-lactam receptor and induces resistance to β-lactam antibiotics. Proc. Natl Acad. Sci. USA 113, 1648–1653 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Muller, C., Plésiat, P. & Jeannot, K. A two-component regulatory system interconnects resistance to polymyxins, aminoglycosides, fluoroquinolones, and β-lactams in Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 55, 1211–1221 (2011).

    CAS  PubMed  Google Scholar 

  151. Beck, C. F., Mutzel, R., Barbé, J. & Müller, W. A multifunctional gene (tetR) controls Tn10-encoded tetracycline resistance. J. Bacteriol. 150, 633–642 (1982).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Kehrenberg, C. & Schwarz, S. fexA, a novel Staphylococcus lentus gene encoding resistance to florfenicol and chloramphenicol. Antimicrob. Agents Chemother. 48, 615–618 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. George, A. M. & Hall, R. M. Efflux of chloramphenicol by the CmlA1 protein. FEMS Microbiol. Lett. 209, 209–213 (2002).

    CAS  PubMed  Google Scholar 

  154. Terán, W. et al. Antibiotic-dependent induction of Pseudomonas putida DOT-T1E TtgABC efflux pump is mediated by the drug binding repressor TtgR. Antimicrob. Agents Chemother. 47, 3067–3072 (2003).

    PubMed  PubMed Central  Google Scholar 

  155. Brogden, K. A., Guthmiller, J. M. & Taylor, C. E. Human polymicrobial infections. Lancet 365, 253–255 (2005).

    PubMed  PubMed Central  Google Scholar 

  156. Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8, 15–25 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Vos de, M. G. J., Zagorski, M., McNally, A. & Bollenbach, T. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. Proc. Natl Acad. Sci. USA 114, 10666–10671 (2017).

    Google Scholar 

  158. Aranda-Díaz, A. et al. Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance. eLife 9, e51493 (2020).

    PubMed  PubMed Central  Google Scholar 

  159. Hoffman, L. R. et al. Selection for Staphylococcus aureus small-colony variants due to growth in the presence of Pseudomonas aeruginosa. Proc. Natl Acad. Sci. USA 103, 19890–19895 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Radlinski, L. et al. Pseudomonas aeruginosa exoproducts determine antibiotic efficacy against Staphylococcus aureus. PLoS Biol. 15, e2003981 (2017).

    PubMed  PubMed Central  Google Scholar 

  161. Nicoloff, H. & Andersson, D. I. Indirect resistance to several classes of antibiotics in cocultures with resistant bacteria expressing antibiotic-modifying or -degrading enzymes. J. Antimicrob. Chemother. 71, 100–110 (2016).

    CAS  PubMed  Google Scholar 

  162. Sorg, R. A. et al. Collective resistance in microbial communities by intracellular antibiotic deactivation. PLoS Biol. 14, e2000631 (2016).

    PubMed  PubMed Central  Google Scholar 

  163. Maddocks, J. L. & May, J. R. ‘Indirect pathogenicity’ of penicillinase-producing Enterobacteria in chronic bronchial infections. Lancet 293, 793–795 (1969).

    Google Scholar 

  164. Adamowicz, E. M. & Harcombe, W. R. Weakest-link dynamics predict apparent antibiotic interactions in a model cross-feeding community. Antimicrob. Agents Chemother. 64, e00465-20 (2020).

    PubMed  PubMed Central  Google Scholar 

  165. Adamowicz, E. M., Flynn, J., Hunter, R. C. & Harcombe, W. R. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J. 12, 2723–2735 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. Guggenbichler, J. P., Allerberger, F., Dierich, M. P., Schmitzberger, R. & Semenitz, E. Spaced administration of antibiotic combinations to eliminate Pseudomonas from sputum in cystic fibrosis. Lancet 2, 749–750 (1988). This small clinical study suggests that staggered application of β-lactam and aminoglycoside improves treatment of chronic lung infections compared with combination treatment.

    CAS  PubMed  Google Scholar 

  167. Imamovic, L. et al. Drug-driven phenotypic convergence supports rational treatment strategies of chronic infections. Cell 172, 121–134.e14 (2018). This paper shows that phenotypic changes in a bacterial lung infection mirror those predicted from collateral sensitivity in evolution experiments.

    CAS  PubMed  PubMed Central  Google Scholar 

  168. Medical Research Council. Streptomycin treatment of pulmonary tuberculosis. Br. Med. J. 2, 769–782 (1948).

    Google Scholar 

  169. Kerantzas, C. A. & Jacobs, W. R. Origins of combination therapy for tuberculosis: lessons for future antimicrobial development and application. mBio 8, e01586-16 (2017).

    PubMed  PubMed Central  Google Scholar 

  170. Richman, D. D. HIV chemotherapy. Nature 410, 995–1001 (2001).

    CAS  PubMed  Google Scholar 

  171. Martin, J. K. et al. A dual-mechanism antibiotic kills Gram-negative bacteria and avoids drug resistance. Cell 181, 1518–1532.e14 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. Tamma, P. D., Cosgrove, S. E. & Maragakis, L. L. Combination therapy for treatment of infections with Gram-negative bacteria. Clin. Microbiol. Rev. 25, 450–470 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  173. Tängdén, T. Combination antibiotic therapy for multidrug-resistant Gram-negative bacteria. Ups. J. Med. Sci. 119, 149–153 (2014).

    PubMed  PubMed Central  Google Scholar 

  174. Ersoy, S. C. et al. Correcting a fundamental flaw in the paradigm for antimicrobial susceptibility testing. EBioMedicine 20, 173–181 (2017).

    PubMed  PubMed Central  Google Scholar 

  175. Levin, B. R. & Rozen, D. E. Non-inherited antibiotic resistance. Nat. Rev. Microbiol. 4, 556–562 (2006).

    CAS  PubMed  Google Scholar 

  176. Allen, R. C., Pfrunder-Cardozo, K. R. & Hall, A. R. Collateral sensitivity interactions between antibiotics depend on local abiotic conditions. mSystems 6, e0105521 (2021).

    PubMed  Google Scholar 

  177. Larkins-Ford, J. et al. Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis. Cell Syst. 12, 1046–1063.e7 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank B. Kavčič and H. Schulenburg for constructive feedback on the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Dan I. Andersson.

Ethics declarations

Competing interests

R.R. and D.I.A. are involved in patent application SE 2050304-1 relating to the CombiANT method. T.B. declares no competing interests.

Peer review

Peer review information

Nature Reviews Microbiology thanks J. Arjan G.M. de Visser and Athanasios Typas 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.

Supplementary information

Glossary

Tolerance

The capacity of a cell population to endure stressful exposure to, for example, drugs.

Loewe additivity

A null model of drug interaction that assumes that antibiotics cannot interact with themselves so that inhibitory doses are additive and form straight lines of equal inhibition on the response surface.

Bliss independence

A null model of drug interaction that assumes that antibiotics have independent modes of action so that their individual effects can be multiplied.

Cross-feeding

Increased tolerance of a bacterial strain to a drug that is caused by proximity to other strains.

Collective resistance

Interaction between bacterial strains whereby molecules produced by one strain are consumed by the other.

Cellular hysteresis

The long-lasting physiological effect of pretreatment on the tolerance of a cell population to a later treatment.

Cellular memory

A biological process that maintains information of the past.

SOS response

The bacterial response to DNA damage that involves RecA and LexA, and involves growth arrest and DNA repair.

Persister

A cell that survives an inhibitory dose of antibiotic due to phenotypic heterogeneity.

Pleiotropy

The production by a single gene or mutation of multiple effects.

Epistasis

The combined effect of two genetic entities is quantitatively different from that expected by additive interaction of the individual genetic effects.

Collateral sensitivity

Decreased tolerance to a drug that is caused by a mutation or gene conferring resistance to a different drug.

Chemical genomics

The study of effects of drugs and other chemicals on genome-wide genetic variation.

Clinical testing

A prospective or retrospective research study that tests how well a medical approach works in people by comparison with an included control group.

Pharmacodynamics

The study of the molecular action of a drug on the target organism, including binding, dose–response relations and interactions with other molecules.

Pharmacokinetics

The study of the processes in the human body that govern resorption, distribution, metabolization and excretion of a drug.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roemhild, R., Bollenbach, T. & Andersson, D.I. The physiology and genetics of bacterial responses to antibiotic combinations. Nat Rev Microbiol 20, 478–490 (2022). https://doi.org/10.1038/s41579-022-00700-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41579-022-00700-5

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing