Antibiotic-resistant bacteria show widespread collateral sensitivity to antimicrobial peptides

Abstract

Antimicrobial peptides are promising alternative antimicrobial agents. However, little is known about whether resistance to small-molecule antibiotics leads to cross-resistance (decreased sensitivity) or collateral sensitivity (increased sensitivity) to antimicrobial peptides. We systematically addressed this question by studying the susceptibilities of a comprehensive set of 60 antibiotic-resistant Escherichia coli strains towards 24 antimicrobial peptides. Strikingly, antibiotic-resistant bacteria show a high frequency of collateral sensitivity to antimicrobial peptides, whereas cross-resistance is relatively rare. We identify clinically relevant multidrug-resistance mutations that increase bacterial sensitivity to antimicrobial peptides. Collateral sensitivity in multidrug-resistant bacteria arises partly through regulatory changes shaping the lipopolysaccharide composition of the bacterial outer membrane. These advances allow the identification of antimicrobial peptide–antibiotic combinations that enhance antibiotic activity against multidrug-resistant bacteria and slow down de novo evolution of resistance. In particular, when co-administered as an adjuvant, the antimicrobial peptide glycine-leucine-amide caused up to 30-fold decrease in the antibiotic resistance level of resistant bacteria. Our work provides guidelines for the development of efficient peptide-based therapies of antibiotic-resistant infections.

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Fig. 1: Susceptibility profiles of 60 laboratory-evolved antibiotic-resistant E. coli strains.
Fig. 2: Survival of collateral-sensitive antibiotic-resistant strains under lethal antimicrobial peptide stress.
Fig. 3: Altered membrane composition in antibiotic-resistant bacteria contributes to increased sensitivity to antimicrobial peptides.
Fig. 4: A putative mechanism underlying collateral sensitivity of antibiotic-resistant bacteria to cationic antimicrobial peptides.
Fig. 5: Interaction of PGLA and antibiotics when applied in combination.
Fig. 6: MICs of laboratory-evolved lines adapted to antibiotics in the absence and the presence of subinhibitory dosage of antimicrobial peptides.

References

  1. 1.

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

    Article  PubMed  CAS  Google Scholar 

  2. 2.

    Lázár, V. et al. Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network. Nat. Commun. 5, 4352 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. 3.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. 4.

    Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 44, 101–105 (2012).

    Article  CAS  Google Scholar 

  5. 5.

    Palmer, A. C. & Kishony, R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat. Rev. Genet. 14, 243–248 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. 6.

    Pál, C., Papp, B. & Lázár, V. Collateral sensitivity of antibiotic-resistant microbes. Trends Microbiol. 23, 401–407 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. 7.

    Jenssen, H., Hamill, P. & Hancock, R. E. W. Peptide antimicrobial agents. Clin. Microbiol. Rev. 19, 491–511 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. 8.

    Fjell, C. D., Hiss, J. A., Hancock, R. E. & Schneider, G. Designing antimicrobial peptides: form follows function. Nat. Rev. Drug Discov. 11, 37–51 (2012).

    Article  CAS  Google Scholar 

  9. 9.

    Mahlapuu, M., Håkansson, J., Ringstad, L. & Björn, C. Antimicrobial peptides: an emerging category of therapeutic agents. Front. Cell. Infect. Microbiol. 6, 194 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. 10.

    Andersson, D. I., Hughes, D. & Kubicek-Sutherland, J. Z. Mechanisms and consequences of bacterial resistance to antimicrobial peptides. Drug Resist. Updat. 26, 43–57 (2016).

    Article  PubMed  CAS  Google Scholar 

  11. 11.

    Melo, M. N., Ferre, R. & Castanho, M. A. R. B. Antimicrobial peptides: linking partition, activity and high membrane-bound concentrations. Nat. Rev. Microbiol. 7, 245 (2009).

    Article  PubMed  CAS  Google Scholar 

  12. 12.

    Alves, C. S. et al. Escherichia coli cell surface perturbation and disruption induced by antimicrobial peptides BP100 and pepR. J. Biol. Chem. 285, 27536–27544 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. 13.

    Avitabile, C., D’Andrea, L. D. & Romanelli, A. Circular dichroism studies on the interactions of antimicrobial peptides with bacterial cells. Sci. Rep. 4, 4293 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. 14.

    Soriano, F., Ponte, C., Santamaria, M. & Jimenez-Arriero, M. Relevance of the inoculum effect of antibiotics in the outcome of experimental infections caused by Escherichia coli. J. Antimicrob. Chemother. 25, 621–627 (1990).

    Article  PubMed  CAS  Google Scholar 

  15. 15.

    Bechinger, B., Zasloff, M. & Opella, S. J. Structure and dynamics of the antibiotic peptide PGLa in membranes by solution and solid-state nuclear magnetic resonance spectroscopy. Biophys. J. 74, 981–987 (1998).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. 16.

    Ostorhazi, E., Nemes-Nikodem, E., Knappe, D. & Hoffmann, R. In vivo activity of optimized apidaecin and oncocin peptides against a multiresistant, KPC-producing Klebsiella pneumoniae strain.Prot. Pept. Lett. 21, 368–373 (2014).

    Article  CAS  Google Scholar 

  17. 17.

    Proctor, R. A. et al. Small colony variants: a pathogenic form of bacteria that facilitates persistent and recurrent infections. Nat. Rev. Micro. 4, 295–305 (2006).

    Article  CAS  Google Scholar 

  18. 18.

    Munck, C., Gumpert, H. K., Wallin, A. I., Wang, H. H. & Sommer, M. O. Prediction of resistance development against drug combinations by collateral responses to component drugs. Sci. Transl. Med. 6, 262ra156 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. 19.

    Mattiuzzo, M. et al. Role of the Escherichia coli SbmA in the antimicrobial activity of proline-rich peptides. Mol. Microbiol. 66, 151–163 (2007).

    Article  PubMed  CAS  Google Scholar 

  20. 20.

    Pages, J.-M., James, C. E. & Winterhalter, M. The porin and the permeating antibiotic: a selective diffusion barrier in Gram-negative bacteria. Nat. Rev. Micro. 6, 893–903 (2008).

    Article  CAS  Google Scholar 

  21. 21.

    Fernández, L. & Hancock, R. E. W. Adaptive and mutational resistance: role of porins and efflux pumps in drug resistance. Clin. Microbiol. Rev. 25, 661–681 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. 22.

    Seoane, A. S. & Levy, S. B. Characterization of MarR, the repressor of the multiple antibiotic resistance (mar) operon in Escherichia coli. J. Bacteriol. 177, 3414–3419 (1995).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. 23.

    Davin-Regli, A. et al. Membrane permeability and regulation of drug “influx and efflux” in enterobacterial pathogens. Curr. Drug Targets 9, 750–759 (2008).

    Article  PubMed  CAS  Google Scholar 

  24. 24.

    Gutsmann, T., Fix, M., Larrick, J. W. & Wiese, A. Mechanisms of action of rabbit CAP18 on monolayers and liposomes made from endotoxins of phospholipids. J. Membr. Biol. 176, 223–236 (2000).

    Article  PubMed  CAS  Google Scholar 

  25. 25.

    Kitagawa, M. et al. Complete set of ORF clones of Escherichia coli ASKA library (A complete Set of E. coli K-12 ORF Archive): unique resources for biological research. DNA Res. 12, 291–299 (2006).

    Article  CAS  Google Scholar 

  26. 26.

    Delcour, A. H. Outer membrane permeability and antibiotic resistance. BBA Proteins Proteom. 1794, 808–816 (2009).

    Article  CAS  Google Scholar 

  27. 27.

    Bociek, K. et al. Lipopolysaccharide phosphorylation by the WaaY kinase affects the susceptibility of Escherichia coli to the human antimicrobial peptide LL-37. J. Biol. Chem. 290, 19933–19941 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. 28.

    Lee, J.-H., Lee, K.-L., Yeo, W.-S., Park, S.-J. & Roe, J.-H. SoxRS-mediated lipopolysaccharide modification enhances resistance against multiple drugs in Escherichia coli. J. Bacteriol. 191, 4441–4450 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. 29.

    Gonzales, P. R. et al. Synergistic, collaterally sensitive β-lactam combinations suppress resistance in MRSA. Nat. Chem. Biol. 11, 855 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. 30.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. 31.

    Oz, T. et al. Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol. Biol. Evol. 31, 2387–2401 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. 32.

    Horinouchi, T. et al. Prediction of cross-resistance and collateral sensitivity by gene expression profiles and genomic mutations. Sci. Rep. 7, 14009 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. 33.

    Pena-Miller, R. et al. When the most potent combination of antibiotics selects for the greatest bacterial load: The smile-frown transition. PLoS Biol. 11, e1001540 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. 34.

    Avan, I., Hall, C. D. & Katritzky, A. R. Peptidomimetics via modifications of amino acids and peptide bonds. Chem. Soc. Rev. 43, 3575–3594 (2014).

    Article  PubMed  CAS  Google Scholar 

  35. 35.

    Jiao, Y. J., Baym, M., Veres, A. & Kishony, R. Population diversity jeopardizes the efficacy of antibiotic cycling. Preprint at https://www.biorxiv.org/content/early/2016/10/20/082107 (2016).

  36. 36.

    Fleitas, O. & Franco, O. L. Induced bacterial cross-resistance toward host antimicrobial peptides: a worrying phenomenon. Front. Microbiol. 7, 381 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Kubicek-Sutherland, J. Z. et al. Antimicrobial peptide exposure selects for Staphylococcus aureus resistance to human defence peptides. J. Antimicrob. Chemother. 72, 115–127 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Nyerges, Á. et al. A highly precise and portable genome engineering method allows comparison of mutational effects across bacterial species. Proc. Natl Acad. Sci. 113, 2502–2507 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. 39.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Tenaillon, O. et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature 536, 165 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. 41.

    Malouin, F., Chamberland, S., Brochu, N. & Parr, T. R. Influence of growth media on Escherichia coli cell composition and ceftazidime susceptibility. Antimicrob. Agents Chemother. 35, 477–483 (1991).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. 42.

    Yeaman, M. R. & Yount, N. Y. Mechanisms of antimicrobial peptide action and resistance. Pharmacol. Rev. 55, 27–55 (2003).

    Article  PubMed  CAS  Google Scholar 

  43. 43.

    Wiegand, I., Hilpert, K. & Hancock, R. E. W. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat. Protoc. 3, 163–175 (2008).

    Article  PubMed  CAS  Google Scholar 

  44. 44.

    Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).

  45. 45.

    Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).

    Article  PubMed  Google Scholar 

  46. 46.

    Méhi, O. et al. Perturbation of iron homeostasis promotes the evolution of antibiotic resistance. Mol. Biol. Evol. 31, 2793–2804 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. 47.

    Karcagi, I. et al. Indispensability of horizontally transferred genes and its impact on bacterial genome streamlining. Mol. Biol. Evol. 33, 1257–1269 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. 48.

    Zhou, J. & Rudd, K. E. EcoGene 3.0. Nucleic Acids Res. 41, D613–D624 (2013).

    Article  PubMed  CAS  Google Scholar 

  49. 49.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Viena, Austria, 2014).

  50. 50.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  PubMed  CAS  Google Scholar 

  51. 51.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. 52.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. 53.

    Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. 54.

    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).

    Article  PubMed  CAS  Google Scholar 

  55. 55.

    Tjaden, B. et al. Transcriptome analysis of Escherichia coli using high‐density oligonucleotide probe arrays. Nucleic Acids Res. 30, 3732–3738 (2002).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. 56.

    Dong, T. & Schellhorn, H. E. Control of RpoS in global gene expression of Escherichia coli in minimal media. Mol. Genet. Genom. 281, 19–33 (2009).

    Article  CAS  Google Scholar 

  57. 57.

    Notebaart, R. A. et al. Network-level architecture and the evolutionary potential of underground metabolism. Proc. Natl Acad. Sci. 111, 11762–11767 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. 58.

    Pierce, S. E., Davis, R. W., Nislow, C. & Giaever, G. Genome-wide analysis of barcoded Saccharomyces cerevisiae gene-deletion mutants in pooled cultures. Nat. Protoc. 2, 2958–2974 (2007).

    Article  PubMed  CAS  Google Scholar 

  59. 59.

    Robinson, D. G., Chen, W., Storey, J. D. & Gresham, D. Design and analysis of Bar-seq experiments. G3 4, 11–18 (2014).

    Article  PubMed  CAS  Google Scholar 

  60. 60.

    Rocke, D. M. & Durbin, B. Approximate variance-stabilizing transformations for gene-expression microarray data. Bioinformatics 19, 966–972 (2003).

    Article  PubMed  CAS  Google Scholar 

  61. 61.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

    Google Scholar 

  62. 62.

    Zhou, K. et al. Novel reference genes for quantifying transcriptional responses of Escherichia coli to protein overexpression by quantitative PCR. BMC Mol. Biol. 12, 18 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. 63.

    Loewe, S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung 3, 285–290 (1953).

    PubMed  CAS  Google Scholar 

  64. 64.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. 65.

    Suzuki, S., Horinouchi, T. & Furusawa, C. Prediction of antibiotic resistance by gene expression profiles. Nat. Commun. 5, 5792 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. 66.

    Bódi, Z. et al. Phenotypic heterogeneity promotes adaptive evolution. PLoS Biol. 15, e2000644 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. 67.

    Murtagh, F. & Legendre, P. Ward’s hierarchical agglomerative clustering method: Which algorithms implement Ward’s criterion? J. Classif. 31, 274–295 (2014).

    Article  Google Scholar 

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Acknowledgements

The authors thank É. Kondorosi for providing the cationic antimicrobial peptide NCR335, and M.O.A. Sommer for the clinical isolates. The authors also acknowledge the following financial support: the Hungarian Academy of Sciences Postdoctoral Fellowship Programme (V.L.), the Hungarian Scientific Research Fund NKFI PD 116222 (A.M.), NKFI 120220 (B.K.), OTKA PD 109572 (B.C.) and NKFI FK 124254 (O.M.), the ‘Lendület’ Programme of the Hungarian Academy of Sciences and The Wellcome Trust (B.P. and C.P.), the European Research Council H2020-ERC-2014-CoG 648364 - Resistance Evolution (C.P.), GINOP-2.3.2-15-2016-00014 (EVOMER), GINOP-2.3.2-15-2016-00020 (MolMedEx TUMORDNS) and GINOP-2.3.3-15-2016-00001. I.N. and B.K. were supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and Á.N. by the Ph.D. fellowship of the Boehringer Ingelheim Fonds.

Author contributions

C.P. and B.P. conceived and supervised the project. V.L. and A.M. designed the experiments and developed data analysis procedures. C.P., B.P., V.L., A.M. and R.S. wrote the paper. R.S., A.K., A.D., F.W. and M.D. performed the zeta potential measurements. G.O., Z.H. and T.A.M. synthetized the peptides magainin 2 and anginex. R.S. and O.M. purified RNA for transcriptomic analysis. B.B. and I.N. performed RNA-Seq experiments. P.K.J., G.F., M.S. and B.K. generated and analysed the chemogenomic data. E.U. isolated and identified E. coli clinical isolates. B.C. and A.N. prepared the mutant strains. V.L., L.D., A.M., R.S. and B.C. contributed to all other experiments. V.L., A.M., G.G., G.F. and A.G. analysed and interpreted the data.

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Correspondence to Balázs Papp or Csaba Pál.

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The authors declare no competing interests. B.B. and I.N. had consulting positions at SeqOmics Biotechnology Ltd. at the time the study was conceived. SeqOmics Biotechnology Ltd. was not directly involved in the design and execution of the experiments or in the writing of the manuscript. This does not alter the author’s adherence to all the Nature policies on sharing data and materials.

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Supplementary information

Supplementary Information

Supplementary Text 1–4, Supplementary Methods, Supplementary Table 1, Supplementary Table 5, Supplementary Table 11, Supplementary Table 12, Supplementary Figures 1–18, Supplementary References

Reporting Summary

Supplementary Table 2

The list of antimicrobial peptides employed in this study and the available information about them based on literature mining.

Supplementary Table 3

Dataset of collateral sensitivity and cross-resistance interactions identified at the level of antibiotic-resistant strains.

Supplementary Table 4

Relative changes in the minimum inhibitory concentrations of the antimicrobial peptides towards antibiotic-resistant strains.

Supplementary Table 6

List of the main chemical and physical properties of the antimicrobial peptides employed in this study.

Supplementary Table 7

Susceptibility profiles of antibiotic-resistant E. coli clinical isolates across antimicrobial peptides.

Supplementary Table 8

Differential expression analysis of RNA-Seq data of 24 antibiotic-resistant strains.

Supplementary Table 9

Bile acid sensitivity of the antibiotic-resistant strains and list of genes involved in phospholipid and LPS synthesis.

Supplementary Table 10

List of genes sensitizing towards CAP18 and CP1 in the chemogenomic study but not to control peptide CP1.

Supplementary Table 13

Combination index (CI) values of PGLA–antibiotic (AB) combinations on E. coli clinical isolates and respective antibiotic-resistant strains.

Supplementary Table 14

Mutation-incorporating pORTMAGE oligonucleotides, allele-specific colony-PCR, HRM PCR and sequencing primers.

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Lázár, V., Martins, A., Spohn, R. et al. Antibiotic-resistant bacteria show widespread collateral sensitivity to antimicrobial peptides. Nat Microbiol 3, 718–731 (2018). https://doi.org/10.1038/s41564-018-0164-0

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