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Predicting evolution using frequency-dependent selection in bacterial populations

View ORCID ProfileTaj Azarian, View ORCID ProfilePamela P Martinez, Brian J Arnold, Lindsay R Grant, Jukka Corander, View ORCID ProfileChristophe Fraser, View ORCID ProfileNicholas J Croucher, Laura L Hammitt, Raymond Reid, Mathuram Santosham, Robert C Weatherholtz, Stephen D Bentley, Katherine L O’Brien, View ORCID ProfileMarc Lipsitch, View ORCID ProfileWilliam P Hanage
doi: https://doi.org/10.1101/420315
Taj Azarian
1Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL
2Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston MA
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  • For correspondence: taj.azarian@ucf.edu
Pamela P Martinez
2Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston MA
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Brian J Arnold
2Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston MA
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Lindsay R Grant
3Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Jukka Corander
4Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
5Department of Biostatistics, University of Oslo, 0317 Oslo, Norway
6Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
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Christophe Fraser
7Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK
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Nicholas J Croucher
8MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
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Laura L Hammitt
3Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Raymond Reid
3Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Mathuram Santosham
3Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Robert C Weatherholtz
3Center for American Indian Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Stephen D Bentley
6Infection Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
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Katherine L O’Brien
9World Health Organization, Geneva Switzerland
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Marc Lipsitch
2Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston MA
10Department of Immunology and Infectious Diseases, T.H. Chan School of Public Health, Harvard University, Boston MA
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William P Hanage
2Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston MA
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Abstract

Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain estimated by an NFDS-based model at the time the vaccine is introduced enables to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.

Footnotes

  • ↵† Co-senior authors

  • Pamela P Martinez pmartinez{at}hsph.harvard.edu, Brian J Arnold brianjohnarnold{at}gmail.com, Lindsay R Grant lgrant10{at}jhu.edu, Jukka Corander jukka.corander{at}medisin.uio.no, Christophe Fraser christophe.fraser{at}bdi.ox.ac.uk, Nicholas J Croucher n.croucher{at}imperial.ac.uk, Laura Hammitt lhammitt{at}jhu.edu, Raymond Reid rreid2{at}jhu.edu, Mathuram Santosham msantosham{at}jhu.edu, Robert R Weatherholtz rweathe1{at}jhu.edu, Stephen D Bentley sdb{at}sanger.ac.uk, Katherine L O’Brien obrienk{at}who.int, Marc Lipsitch mlipsitc{at}hsph.harvard.edu, William P Hanage whanage{at}hsph.harvard.edu

  • Revised figures and analysis, additional data set added, revised supplemental material

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 25, 2020.
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Predicting evolution using frequency-dependent selection in bacterial populations
Taj Azarian, Pamela P Martinez, Brian J Arnold, Lindsay R Grant, Jukka Corander, Christophe Fraser, Nicholas J Croucher, Laura L Hammitt, Raymond Reid, Mathuram Santosham, Robert C Weatherholtz, Stephen D Bentley, Katherine L O’Brien, Marc Lipsitch, William P Hanage
bioRxiv 420315; doi: https://doi.org/10.1101/420315
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Predicting evolution using frequency-dependent selection in bacterial populations
Taj Azarian, Pamela P Martinez, Brian J Arnold, Lindsay R Grant, Jukka Corander, Christophe Fraser, Nicholas J Croucher, Laura L Hammitt, Raymond Reid, Mathuram Santosham, Robert C Weatherholtz, Stephen D Bentley, Katherine L O’Brien, Marc Lipsitch, William P Hanage
bioRxiv 420315; doi: https://doi.org/10.1101/420315

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