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Dense genomic sampling identifies highways of pneumococcal recombination

Nature Genetics volume 46, pages 305–309 (2014)Cite this article

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Abstract

Evasion of clinical interventions by Streptococcus pneumoniae occurs through selection of non-susceptible genomic variants. We report whole-genome sequencing of 3,085 pneumococcal carriage isolates from a 2.4-km2 refugee camp. This sequencing provides unprecedented resolution of the process of recombination and its impact on population evolution. Genomic recombination hotspots show remarkable consistency between lineages, indicating common selective pressures acting at certain loci, particularly those associated with antibiotic resistance. Temporal changes in antibiotic consumption are reflected in changes in recombination trends, demonstrating rapid spread of resistance when selective pressure is high. The highest frequencies of receipt and donation of recombined DNA fragments were observed in non-encapsulated lineages, implying that this largely overlooked pneumococcal group, which is beyond the reach of current vaccines, may have a major role in genetic exchange and the adaptation of the species as a whole. These findings advance understanding of pneumococcal population dynamics and provide information for the design of future intervention strategies.

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Figure 1: Population structure and genetic interactions.
Figure 2: Evolutionary parameters estimated in dominant clusters.
Figure 3: Recombination hotspots in seven prevalent clusters.
Figure 4: Associations between recombining genes and resistant phenotypes.

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Acknowledgements

We thank the microbiology and clinical team in the Shoklo Malaria Research Unit, part of the Mahidol Oxford University Research Unit, Faculty of Tropical Medicine, Mahidol University, Thailand, and the core informatics, library-generating and sequencing teams at the Wellcome Trust Sanger Institute. Attending authors are grateful for the opportunity for discussion at the Permafrost workshop. C.C. was funded by a Royal Thai Government scholarship and a Wellcome Trust PhD studentship. J.C., P.M., A.P. and L.C. were funded by Academy of Finland grant 251170 and European Research Council grant 239784. S.D.B. is partly funded by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. P.T. was funded by Wellcome Trust grant 083735/Z/07Z. This work is sponsored by Wellcome Trust grant 098051.

Author information

Author notes

  1. Paul Turner and Stephen D Bentley: These authors contributed equally to this work.

Authors and Affiliations

  1. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    Claire Chewapreecha, Simon R Harris, Alison E Mather, Andrew J Page, Susannah J Salter, David Harris, Julian Parkhill & Stephen D Bentley

  2. Department of Infectious Disease Epidemiology, Imperial College London, St. Mary's Hospital, London, UK

    Nicholas J Croucher & David M Aanensen

  3. Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Maesot, Thailand

    Claudia Turner, Francois Nosten & Paul Turner

  4. Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia

    Claudia Turner & Paul Turner

  5. Nuffield Department of Medicine, Centre for Tropical Medicine, University of Oxford, Oxford, UK

    Claudia Turner, Francois Nosten & Paul Turner

  6. Department of Information and Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Finland

    Pekka Marttinen

  7. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

    Lu Cheng, Alberto Pessia & Jukka Corander

  8. Immunobiology Unit, Institute of Child Health, University College London, London, UK

    David Goldblatt

  9. Department of Mathematics, Abo Akademi University, Turku, Finland

    Jukka Corander

  10. Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK

    Jukka Corander & Stephen D Bentley

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Contributions

S.D.B., P.T., J.P., D.G. and F.N. conceived the study. P.T. and C.T. collected and provided the samples for the study. J.P., S.D.B., C.C., S.R.H., N.J.C. and J.C. designed the analyses. C.C., S.R.H., P.M., L.C., A.P., D.M.A., A.E.M., A.J.P., S.J.S., D.H. and J.C. performed the analyses. C.C., S.D.B., S.R.H., A.E.M. and N.J.C. wrote the manuscript. All authors read and approved the manuscript.

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Correspondence to Stephen D Bentley.

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Integrated supplementary information

Supplementary Figure 1 Association between recombining pbp genes and resistant phenotypes.

(a) pbp1a gene tree. (b) pbp2b gene tree. (c) pbp2x gene tree. The inner ring is colored according to dominant population clusters (BC1–7), with the rest of the population appearing in white. The outer ring is colored according to resistance to penicillin with black and white showing non-susceptibility and susceptibility, respectively.

Supplementary Figure 2 All possible recombination donors for recombinant fragments detected in one isolate.

Top panel: nine predicted recombination fragments in SMRU1452 (fragments A–I) are highlighted in different colors and are ordered according to their locations on the genome with their size labeled. Bottom panel: the bar chart presents the possible sources of each recombinant fragment based on the above color scheme. The y axis gives the proportion of hits detected per population of particular lineage. For example, recombination fragment A of 1,236 bp in length was found to have identical matches in 96.29%, 75%, 70.59%, 69.84%, 10% and 8.33% of the population of secondary BAPS clusters of serotype 11A, 15B, 34, 6B, NT and 16F, respectively.

Supplementary Figure 3 Potential donors characterized by isolated and by BAPS primary clusters.

(a) Boxplots represent distribution of donation probability of isolates within each cluster. A red bar represents a mean frequency of donation event of any isolates (2.53 × 10–5), i.e., each isolate has a probability of 1/39,537 to donate DNA in a recombination event. (b) and (c) respectively show positive correlations between potential donor clusters (based on primary BAPS clusters) and outer population size, and separately cluster diversity.

Supplementary Figure 4 Single-nucleotide polymorphism (SNPs)-based phylogeny using method described in Croucher et al.

Each panel represents a major Maela cluster: (a) BC1-19F, (b) BC2-23F, (c) BC3-NT, (d) BC4-6B, (e) BC5-23A/F, (f) BC6-15B/C and (g) BC7-14. Subclades where substitution rates were estimated are highlighted in different colors and labeled accordingly. Please note that substitution rates cannot be confidently estimated from any clades in BC6-15B/C. The scale bar represents the number of SNPs.

Supplementary Figure 5 Demonstration that clocklike signals can be detected from the subclades but not from the whole population.

Each dominant cluster is comprised of more than a single subclade that coevolve together. This plot used BC5-23A/F as an example. The clock signal cannot be detected in the whole cluster as there is confounding from the signals of the subclades.

Supplementary Figure 6 Clocklike signals from Path-O-Gen in the subclades where substitution rates were estimated.

These subclades were highlighted in Supplementary Figure 4. The y axis reports root-to tip divergence while the x axis represents the time scale in days from the first date of collection, which was 12 November 2007. The first date (time = 0) is shown as a vertical dashed line.

Supplementary Figure 7 Recombination per mutation (r/m) of each cluster calculated by linear regression.

Due to the large sample size available in our studies, we alternatively calculated the ratio of recombination events (y axis) over point mutations (x axis) observed on each branch from the slope (r/m) of the linear regression. The number is tabulated in Supplementary Table 4. For comparison of r/m by linear regression, all the data are ranked to accommodate the non-parametric ANCOVA analysis.

Supplementary Figure 8 Comparison of two recombination-detecting methods.

(a) Genome view of recombination fragments predicted by both algorithms. Recombination regions are aligned with taxa on the phylogenetic tree (left). Genome coordinates are labeled on top. Recombination regions exclusively predicted by methods described in Croucher et al. and Marttinen et al. are highlighted in red and blue, respectively. Overlapping regions predicted by both algorthms are highlighted in dark grey. (b) A histogram showing the length of recombination fragments (bp) predicted by two algorithms. (c) Sequence quality of recombination fragments predicted by two algorithms reported as percent “N”. For (b) and (c), fragments predicted by tools described in Croucher et al. and Marttinen et al. are shaded in red and blue, respectively. The values given by (b) and (c) are summarized in (d).

Supplementary Figure 9 Identifying donor blocks from recipient block identity.

Donor blocks are sequences that show identical matching to the recipient blocks. (a) A histogram showing distribution of length of recipient blocks from recombination events detected at the tip of the phylogenies. Shaded in gray are all recipient blocks used as queries for blast searches. In white are recipient blocks where identical hits were detected from the rest of the population. (b) Summarization of the values from distribution in (a). (c) A plot showing association between the length of sequence queries (recipient blocks) and the diversity of detected hits (potential donor blocks classified by secondary BAPS clusters). The data was modeled as exponential decay with the line of best fit (red line) and the 95% confidence interval (dashed red lines).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9, Supplementary Tables 2–9 and Supplementary Note. (PDF 4563 kb)

Supplementary Table 1

Epidemiological data associated with strains and accession codes associated with data deposited in the European Nucleotide Archive. (XLS 543 kb)

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Chewapreecha, C., Harris, S., Croucher, N. et al. Dense genomic sampling identifies highways of pneumococcal recombination. Nat Genet 46, 305–309 (2014). https://doi.org/10.1038/ng.2895

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