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Frequency-dependent selection in vaccine-associated pneumococcal population dynamics

Nature Ecology & Evolutionvolume 1pages19501960 (2017) | Download Citation

Abstract

Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often cocirculate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, because the changes in population structure associated with the recent introduction of partial-coverage vaccines have substantially reduced pneumococcal disease. Here we show that pneumococcal lineages from multiple populations each have a distinct combination of intermediate-frequency genes. Functional analysis suggested that these loci may be subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Bayesian Computation generated reproducible estimates of the influence of NFDS on pneumococcal evolution, the strength of which varied between loci. Simulations replicated the stable frequency of lineages unperturbed by vaccination, patterns of serotype switching and clonal replacement. This framework highlights how bacterial ecology affects the impact of clinical interventions.

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Acknowledgements

We thank R. Gladstone, J. Jefferies, S. Faust and S. Clarke for sharing epidemiological data on the Southampton isolates. N.J.C. was funded by a Sir Henry Dale fellowship, and jointly funded by the Wellcome Trust and Royal Society (Grant Number 104169/Z/14/Z). J.C. was funded by the COIN Centre of Excellence. M.L. was funded by NIH grant R01 AI048935 and W.P.H. by NIH grant R01 AI106786.

Author information

Affiliations

  1. Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, 00014, Helsinki, Finland

    • Jukka Corander
  2. Department of Biostatistics, University of Oslo, 0317, Oslo, Norway

    • Jukka Corander
  3. Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK

    • Jukka Corander
    •  & Stephen D. Bentley
  4. Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LF, UK

    • Christophe Fraser
  5. School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK

    • Michael U. Gutmann
  6. Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA

    • Brian Arnold
    • , William P. Hanage
    •  & Marc Lipsitch
  7. Departments of Epidemiology and Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA

    • Marc Lipsitch
  8. MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK

    • Nicholas J. Croucher

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Contributions

J.C., C.F., B.A., W.P.H., M.L. and N.J.C. designed the model; J.C., M.U.G. and N.J.C. fitted the model; W.P.H., S.D.B. and N.J.C. analysed the genomic data; J.C. and N.J.C. initially drafted the manuscript, with all authors contributing to the final version.

Competing interests

M.L. has consulted for Pfizer, Affinivax and Merck and has received grant support not related to this paper from Pfizer and PATH Vaccine Solutions. W.P.H., M.L. and N.J.C. have consulted for Antigen Discovery Inc.

Corresponding author

Correspondence to Nicholas J. Croucher.

Electronic supplementary material

  1. Supplementary Information

    Supplementary Figures 1–10; Supplementary Table 1; legends for Supplementary Datasets 1–3

  2. Supplementary Dataset 1

    Annotation of the intermediate frequency genes in the Massachusetts pneumococcal population

  3. Supplementary Dataset 2

    Annotation of the core genes in the Massachusetts pneumococcal population

  4. Supplementary Dataset 3

    Samples used in the analyses, associated epidemiological characteristics, and accession codes

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DOI

https://doi.org/10.1038/s41559-017-0337-x

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