RT Journal Article
SR Electronic
T1 Predicting evolution using frequency-dependent selection in bacterial populations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 420315
DO 10.1101/420315
A1 Taj Azarian
A1 Pamela P Martinez
A1 Brian J Arnold
A1 Lindsay R Grant
A1 Jukka Corander
A1 Christophe Fraser
A1 Nicholas J Croucher
A1 Laura L Hammitt
A1 Raymond Reid
A1 Mathuram Santosham
A1 Robert C Weatherholtz
A1 Stephen D Bentley
A1 Katherine L O’Brien
A1 Marc Lipsitch
A1 William P Hanage
YR 2020
UL http://biorxiv.org/content/early/2020/02/25/420315.abstract
AB 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.