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Phylogenetic barriers to horizontal transfer of antimicrobial peptide resistance genes in the human gut microbiota

Nature Microbiology volume 4, pages 447–458 (2019)Cite this article

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Abstract

The human gut microbiota has adapted to the presence of antimicrobial peptides (AMPs), which are ancient components of immune defence. Despite its medical importance, it has remained unclear whether AMP resistance genes in the gut microbiome are available for genetic exchange between bacterial species. Here, we show that AMP resistance and antibiotic resistance genes differ in their mobilization patterns and functional compatibilities with new bacterial hosts. First, whereas AMP resistance genes are widespread in the gut microbiome, their rate of horizontal transfer is lower than that of antibiotic resistance genes. Second, gut microbiota culturing and functional metagenomics have revealed that AMP resistance genes originating from phylogenetically distant bacteria have only a limited potential to confer resistance in Escherichia coli, an intrinsically susceptible species. Taken together, functional compatibility with the new bacterial host emerges as a key factor limiting the genetic exchange of AMP resistance genes. Finally, our results suggest that AMPs induce highly specific changes in the composition of the human microbiota, with implications for disease risks.

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Fig. 1: AMP resistance genes are less frequently transferred in the human gut microbiome than antibiotic resistance genes.
Fig. 2: In E. coli, short genomic fragments from the human gut microbiota confer AMP resistance less frequently than antibiotic resistance.
Fig. 3: Culturing reveals that short genomic DNA fragments from the gut microbiota have a limited potential to transfer AMP resistance to E. coli.
Fig. 4: AMP resistance DNA fragments provide host-dependent phenotypic effects.

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Data availability

The GenBank accession nos. for the PacBio sequencing data are MH883365–MH883616. 16S rRNA sequencing reads are available from the Sequence Read Archive (SRA) (BioProject PRJNA494380). All data generated or analysed during this study are included in this article and its Supplementary Information. For each figure, the availability of the analysed data is indicated in the legend. All accession numbers with information on the associated samples are provided as Supplementary Data.

References

  1. Dethlefsen, L., McFall-Ngai, M. & Relman, D. A. An ecological and evolutionary perspective on human–microbe mutualism and disease. Nature 449, 811–818 (2007).

    Article  CAS  Google Scholar 

  2. Nicholson, J. K. et al. Host–gut microbiota metabolic interactions. Science 336, 1262–1267 (2012).

    Article  CAS  Google Scholar 

  3. Littman, D. R. & Pamer, E. G. Role of the commensal microbiota in normal and pathogenic host immune responses. Cell Host Microbe 10, 311–323 (2011).

    Article  CAS  Google Scholar 

  4. Round, J. L. & Mazmanian, S. K. The gut microbiota shapes intestinal immune responses during health and disease. Nat. Rev. Immunol. 9, 600–600 (2009).

    Article  CAS  Google Scholar 

  5. Zasloff, M. Antimicrobial peptides of multicellular organisms. Nature 415, 389–395 (2002).

    Article  CAS  Google Scholar 

  6. Peschel, A. & Sahl, H. G. The co-evolution of host cationic antimicrobial peptides and microbial resistance. Nat. Rev. Microbiol. 4, 529–536 (2006).

    Article  CAS  Google Scholar 

  7. Hancock, R. & Patrzykat, A. Clinical development of cationic antimicrobial peptides: from natural to novel antibiotics. Curr. Drug Target Infect. Disord. 2, 79–83 (2002).

    Article  CAS  Google Scholar 

  8. Hancock, R. E. W. & Sahl, H.-G. Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies. Nat. Biotechnol. 24, 1551–1557 (2006).

    Article  CAS  Google Scholar 

  9. Kubicek-Sutherland, J. Z. et al. Antimicrobial peptide exposure selects for Staphylococcus aureus resistance to human defence peptides. J. Antimicrob. Chemother. 72, 115–127 (2017).

    Article  CAS  Google Scholar 

  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  CAS  Google Scholar 

  11. Sommer, M. O. A., Dantas, G. & Church, G. M. Functional characterization of the antibiotic resistance reservoir in the human microflora. Science 325, 1128–1131 (2009).

    Article  CAS  Google Scholar 

  12. Cullen, T. W. et al. Gut microbiota. Antimicrobial peptide resistance mediates resilience of prominent gut commensals during inflammation. Science 347, 170–175 (2015).

    Article  CAS  Google Scholar 

  13. Liu, Y.-Y. et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. Lancet Infect. Dis. 16, 161–168 (2016).

    Article  Google Scholar 

  14. Chen, L. et al. Newly identified colistin resistance genes, mcr-4 and mcr-5, from upper and lower alimentary tract of pigs and poultry in China. PLoS ONE 13, e0193957 (2018).

    Article  Google Scholar 

  15. Brito, I. L. et al. Mobile genes in the human microbiome are structured from global to individual scales. Nature 535, 435–439 (2016).

    Article  CAS  Google Scholar 

  16. Jain, R., Rivera, M. C. & Lake, J. A. Horizontal gene transfer among genomes: the complexity hypothesis. Proc. Natl Acad. Sci. USA 96, 3801–3806 (1999).

    Article  CAS  Google Scholar 

  17. Cohen, O., Gophna, U. & Pupko, T. The complexity hypothesis revisited: connectivity rather than function constitutes a barrier to horizontal gene transfer. Mol. Biol. Evol. 28, 1481–1489 (2011).

    Article  CAS  Google Scholar 

  18. Forsberg, K. J. et al. Bacterial phylogeny structures soil resistomes across habitats. Nature 509, 612–616 (2014).

    Article  CAS  Google Scholar 

  19. van der Helm, E. et al. Rapid resistome mapping using nanopore sequencing. Nucleic Acids Res. 45, e61 (2017).

    Article  Google Scholar 

  20. Pehrsson, E. C. et al. Interconnected microbiomes and resistomes in low-income human habitats. Nature 533, 212–216 (2016).

    Article  CAS  Google Scholar 

  21. Kaye, K. S., Pogue, J. M., Tran, T. B., Nation, R. L. & Li, J. Agents of last resort. Infect. Dis. Clin. North Am. 30, 391–414 (2016).

    Article  Google Scholar 

  22. Zhi, C., Lv, L., Yu, L.-F., Doi, Y. & Liu, J.-H. Dissemination of the mcr-1 colistin resistance gene. Lancet Infect. Dis. 16, 292–293 (2016).

    Article  Google Scholar 

  23. Dürr, U. H. N., Sudheendra, U. S. & Ramamoorthy, A. LL-37, the only human member of the cathelicidin family of antimicrobial peptides. Biochim. Biophys. Acta 1758, 1408–1425 (2006).

    Article  Google Scholar 

  24. Methé, B. A. et al. A framework for human microbiome research. Nature 486, 215–221 (2012).

    Article  Google Scholar 

  25. Rettedal, E. A., Gumpert, H. & Sommer, M. O. A. Cultivation-based multiplex phenotyping of human gut microbiota allows targeted recovery of previously uncultured bacteria. Nat. Commun. 5, 4714 (2014).

    Article  CAS  Google Scholar 

  26. Rowan, F., Docherty, N. & Murphy, M. Desulfovibrio bacterial species are increased in ulcerative colitis. Dis. Colon 53, 1530–1536 (2010).

    Article  Google Scholar 

  27. Wehkamp, J. et al. Inducible and constitutive β-defensins are differentially expressed in Crohn’s disease and ulcerative colitis. Inflamm. Bowel Dis. 9, 215–223 (2003).

    Article  Google Scholar 

  28. Lopetuso, L. R., Scaldaferri, F., Petito, V. & Gasbarrini, A. Commensal Clostridia: leading players in the maintenance of gut homeostasis. Gut Pathog. 5, 23 (2013).

    Article  Google Scholar 

  29. Coats, S. R., To, T. T., Jain, S., Braham, P. H. & Darveau, R. P. Porphyromonas gingivalis resistance to polymyxin B is determined by the lipid A 4′-phosphatase, PGN_0524. Int. J. Oral Sci. 1, 126–135 (2009).

    Article  Google Scholar 

  30. Ingram, B. O., Masoudi, A. & Raetz, C. R. H. Escherichia coli mutants that synthesize dephosphorylated lipid A molecules. Biochemistry 49, 8325–8337 (2010).

    Article  CAS  Google Scholar 

  31. Porse, A., Schou, T. S., Munck, C., Ellabaan, M. M. H. & Sommer, M. O. A. Biochemical mechanisms determine the functional compatibility of heterologous genes. Nat. Commun. 9, 522 (2018).

    Article  Google Scholar 

  32. Kong, Q. et al. Phosphate groups of lipid A are essential for Salmonella enterica serovar Typhimurium virulence and affect innate and adaptive immunity. Infect. Immun. 80, 3215–3224 (2012).

    Article  CAS  Google Scholar 

  33. Wang, R. et al. The global distribution and spread of the mobilized colistin resistance gene mcr-1. N at. Commu n. 9, 1179 (2018).

    Google Scholar 

  34. Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: Reconstruction, analysis and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).

    Article  CAS  Google Scholar 

  35. McArthur, A. G. et al. The comprehensive antibiotic resistance database. Antimicrob. Agents Chemother. 57, 3348–3357 (2013).

    Article  CAS  Google Scholar 

  36. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  Google Scholar 

  37. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article  CAS  Google Scholar 

  38. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article  CAS  Google Scholar 

  39. Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).

    Article  CAS  Google Scholar 

  40. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  41. O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).

    Article  Google Scholar 

  42. Orlek, A. et al. A curated dataset of complete Enterobacteriaceae plasmids compiled from the NCBI nucleotide database. Data Brief 12, 423–426 (2017).

    Article  Google Scholar 

  43. Smillie, C. S. et al. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241–244 (2011).

    Article  CAS  Google Scholar 

  44. Jiang, X. et al. Comprehensive analysis of mobile genetic elements in the gut microbiome reveals a phylum-level niche-adaptive gene pool. Preprint at https://www.biorxiv.org/content/early/2017/11/06/214213 (2017).

  45. Szybalski, W. Genetic studies on microbial cross resistance to toxic agents. IV. Cross resistance of Bacillus megaterium to forty-four antimicrobial drugs. Appl. Microbiol. 2, 57–63 (1954).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 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  CAS  Google Scholar 

  47. Rhoads, A. & Au, K. F. PacBio sequencing and its applications. Genomics Proteomics Bioinformatics 13, 278–289 (2015).

    Article  Google Scholar 

  48. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Article  CAS  Google Scholar 

  49. Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    Article  CAS  Google Scholar 

  50. Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).

    Article  CAS  Google Scholar 

  51. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

    Article  CAS  Google Scholar 

  52. Oksanen, J. et al. vegan: community ecology package (2017); https://cran.r-project.org/web/packages/vegan/index.html

  53. Chakravorty, S., Helb, D., Burday, M., Connell, N. & Alland, D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 69, 330–339 (2007).

    Article  CAS  Google Scholar 

  54. Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42 (1943).

    Article  Google Scholar 

  55. Simpson, E. H. Measurement of diversity. Nature 163, 688 (1949).

    Article  Google Scholar 

  56. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article  CAS  Google Scholar 

  57. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA–Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  Google Scholar 

  58. Jonsson, V., Österlund, T., Nerman, O. & Kristiansson, E. Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics. BMC Genomics 17, 78 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  61. Sambrook, J. & Russell, D. W. Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 2001).

    Google Scholar 

  62. Wang, X., McGrath, S. C., Cotter, R. J. & Raetz, C. R. H. Expression cloning and periplasmic orientation of the Francisella novicida lipid A 4′-phosphatase LpxF. J. Biol. Chem. 281, 9321–9330 (2006).

    Article  CAS  Google Scholar 

  63. Rossetti, F. F. et al. Interaction of poly(l-lysine)-g-poly(ethylene glycol) with supported phospholipid bilayers. Biophys. J. 87, 1711–1721 (2004).

    Article  CAS  Google Scholar 

  64. Bacic, M. K. & Smith, C. J. Laboratory maintenance and cultication of Bacteroides species. Curr. Protoc. Microbiol. 13, 13C.1 (2008).

    Google Scholar 

  65. Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 73, 1576–1585 (2007).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank D. Módos, D. Fazekas, K. Kovács, A. Tooming-Klunderud, J. Sóki and E. Urbán for technical support. This work was supported by the ‘Lendület’ programme of the Hungarian Academy of Sciences (B.P. and C.P.), the Wellcome Trust (B.P.), The European Research Council H2020-ERC-2014-CoG 648364 Resistance Evolution (C.P.), GINOP 2.3.2-15-2016-00014 (EVOMER, C.P. and B.P.), GINOP-2.3.2-15-2016-00020 (MolMedEx TUMORDNS, C.P.), GINOP-2.3.2-15-2016-00026 (iChamber, B.P.), the National Research, Development and Innovation Office, Hungary (NKFIH grant K120220, B.K.), NKFIH grants FK124254 (O.M.) and KH125616 (B.P.), and a PhD fellowship from the Boehringer Ingelheim Fonds (A.N.). B.K. holds a Bolyai Janos Scholarship. The Pacific Biosciences sequencing service was provided by the Norwegian Sequencing Centre, a national technology platform hosted by the University of Oslo and supported by the ‘Functional Genomics’ and ‘Infrastructure’ programs of the Research Council of Norway and the Southeastern Regional Health Authorities.

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Author notes

  1. These authors contributed equally: Bálint Kintses, Orsolya Méhi, Eszter Ari.

Authors and Affiliations

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

    Bálint Kintses, Orsolya Méhi, Eszter Ari, Mónika Számel, Ádám Györkei, Pramod K. Jangir, Ferenc Pál, Gergely Fekete, Roland Tengölics, Ákos Nyerges, Balázs Papp & Csaba Pál

  2. Department of Genetics, Eötvös Loránd University, Budapest, Hungary

    Eszter Ari & Misshelle Bustamante

  3. Doctoral School in Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary

    Mónika Számel, Pramod K. Jangir & Ákos Nyerges

  4. SeqOmics Biotechnology Ltd, Mórahalom, Hungary

    István Nagy, Balázs Bálint & Bálint Márk Vásárhelyi

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

    István Nagy

  6. Hereditary Endocrine Tumors Research Group, Hungarian Academy of Sciences and Semmelweis University, Budapest, Hungary

    István Likó

  7. 1st Department of Internal Medicine, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary

    Anita Bálint & Tamás Molnár

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Contributions

B.K. and C.P. conceived of the project. B.K., O.M., E.A., A.N., B.P. and C.P. planned the experiments and data analyses. O.M., M.S., P.K.J. and R.T. performed the experiments. I.N. performed Illumina sequencing. A.B. and T.M. were responsible for faecal sample collection. B.K., O.M., E.A., A.G., F.P., G.F., I.L., B.B., B.M.V and M.B. analysed the experimental data and carried out bioinformatic analyses. B.K., O.M., B.P. and C.P. wrote the manuscript.

Corresponding authors

Correspondence to Bálint Kintses, Balázs Papp or Csaba Pál.

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Competing interests

B.M.V., 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 authors’ adherence to all the Nature policies on sharing data and materials. The other authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figures 1–13, Supplementary Table 5, Supplementary Table 8, Supplementary Table 9 and Supplementary Table 11.

Reporting Summary

Supplementary Data

Accession numbers of contigs from PacBio sequencing and 16S rRNA sequences.

Supplementary Table 1

A comprehensive catalogue of previously reported AMP resistance genes, compiled based on literature mining, and antibiotic resistance genes obtained from the CARD database.

Supplementary Table 2

Identification of AMP- and antibiotic-resistance genes in the bacterial genome sequences from which the mobile gene pool was derived.

Supplementary Table 3

Characteristics of the transferred AMP- and antibiotic-resistance genes in the mobile gene pool.

Supplementary Table 4

Identification of AMP- and antibiotic-resistance genes associated with naturally occurring plasmids and ICEs in the human microbiota.

Supplementary Table 6

List of resistance contigs identified from the functional metagenomic selections of the uncultured microbiota with 12 AMPs and 11 small-molecule antibiotics (Supplementary Table 5).

Supplementary Table 7

Bacterial abundances in the untreated and AMP- and antibiotic-resistant microbiota at family and order levels, respectively. Abundances at order and family levels are presented on separate Excel sheets.

Supplementary Table 10

List of resistance contigs identified from the functional selection of the cultured microbiota with 5 AMPs and 5 small-molecule antibiotics (Supplementary Table 5).

Supplementary Table 12

Resistance gains in Escherichia coli and Salmonella enterica provided by a representative set of plasmids carrying AMP- and antibiotic-resistance contigs that were isolated in metagenomic screens.

Supplementary Table 13

List of primers used in this study.

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Kintses, B., Méhi, O., Ari, E. et al. Phylogenetic barriers to horizontal transfer of antimicrobial peptide resistance genes in the human gut microbiota. Nat Microbiol 4, 447–458 (2019). https://doi.org/10.1038/s41564-018-0313-5

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