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Antibiotic-resistant bacteria show widespread collateral sensitivity to antimicrobial peptides

Nature Microbiology volume 3, pages 718–731 (2018)Cite this article

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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.

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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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  1. Viktória Lázár

    Present address: Faculty of Biology, Technion – Israel Institute of Technology, Haifa, Israel

  2. These authors contributed equally to this work: Viktória Lázár and Ana Martins.

Authors and Affiliations

  1. Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

    Viktória Lázár, Ana Martins, Réka Spohn, Lejla Daruka, Gábor Grézal, Gergely Fekete, Mónika Számel, Pramod K Jangir, Bálint Kintses, Bálint Csörgő, Ákos Nyerges, Ádám Györkei, Orsolya Méhi, Balázs Papp & Csaba Pál

  2. Biomolecular Electronics Research Group, Bionanoscience Unit, Institute of Biophysics, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

    András Kincses & András Dér

  3. Biological Barriers Research Group, Institute of Biophysics, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

    Fruzsina R Walter & Mária A Deli

  4. Institute of Clinical Microbiology, Albert Szent-Györgyi Medical and Pharmaceutical Center, Faculty of Medicine, University of Szeged, Szeged, Hungary

    Edit Urbán

  5. Institute of Pharmaceutical Analysis, University of Szeged, Szeged, Hungary

    Zsófia Hegedűs, Gábor Olajos & Tamás A Martinek

  6. SeqOmics Biotechnology Ltd, Mórahalom, Hungary

    Balázs Bálint & István Nagy

  7. Sequencing Platform, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary

    István Nagy

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Correspondence to Balázs Papp or Csaba Pál.

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Competing interests

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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