Work

Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae

Year: 2020

Type: article

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 us 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.

Source: PLoS Biology

DOI: 10.1371/journal.pbio.3000878

Authors: Taj Azarian, Pamela P. Martinez, Brian J. Arnold, Xueting Qiu, 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

Institutions: University of Central Florida, Harvard University, Johns Hopkins University, University of Helsinki, University of Oslo, Wellcome Sanger Institute, Helsinki Institute for Information Technology, University of Oxford, Imperial College London, World Health Organization

Language: English

Topic: Pneumonia and Respiratory Infections

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,000

Funders: GAVI Alliance, Pfizer, Sanofi, World Health Organization, Wellcome Trust, National Institutes of Health, Sanofi Pasteur, Medical Research Council, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, H2020 European Research Council

Awards: G1683598284, G17091616, MIDAS Center for Communicable Disease Dynamics, Evolutionary dynamics underlying pneumococcal genomic diversity., G3120089969, Evolutionary dynamics underlying pneumococcal genomic diversity., Mechanisms and Population Genomics of Pneumococcal Antigenic Diversity, Scalable inference algorithms for Bayesian evolutionary epidemiology, G6520098237, G709560905, G8229061963, G9000008914, G9000087992, G9000195442, Epidemiology of Infectious Diseases

OpenAlex ID: W3094143455

FWCI: 2.608

Cites: 63

Cited by: 39

Related to: 10