Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses

Abstract

Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. We here introduce a novel coalescent based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. Together, these provide the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.

Whole-genome analysis to determine the rate and patterns of intra-subtype reassortment among influenza type-A viruses in Africa

“…Interestingly, our GiRaF-based event frequencies were comparable with the coalescent-based reassortment rates among the Ugandan 0.2668 (95 per cent HPD, 0.1237–0.4255) and African H1N1pdm09 viruses 0.1788 (95 per cent HPD, 0.1227–0.2366 events/lineage/year). Overall, reassortment rates among Africa H1N1pdm09 viruses (0.1237–0.4255) were comparable to the 0 · 15–0 · 8 events/lineage/year estimated among global pandemic H1N1pdm09 viruses ( Müller et al. 2020 ).…”

“…The coalescent-based analysis ( Müller et al. 2020 ) estimated the Uganda H1N1pdm09 and H3N2 viruses evolved at mean rates of 2 · 76 × 10 −3 [95 per cent HPD, 2 · 54–2 · 99 × 10 −3 ] and 2 · 73 × 10 −4 [95 per cent HPD, 1 · 938–3 · 53 × 10 −4 ] mutations per site per year across all eight genes and reassorted at mean rates of 0.2668 [95 per cent HPD, 0.1237–0.4255] and 0.0216 [95 per cent HPD, 0 · 00912–0.0355] events per lineage per year, respectively.…”

“…The rate of reassortment (the number of reassortments events per lineage per year) for the Uganda (D1) and Africa datasets (D2) were estimated using a coalescent reassortant constant population model (CoalRe) ( Müller et al. 2020 ) in BEAST2 v.2.6.6 ( https://www.beast2.org/ ) with parameters: GTR + I + G4 substitution model, strict clock, prior for reassortment rate = exponential with mean 0.25, chain length of 500 million, and 10 per cent burn-in.…”

“…used a network-based coalescent reassortant constant population model on viral sequences sampled from Europe, America, and Asia. The model estimated high reassortment rates among the 1995–2019 H3N2 and 2009 H1N1pdm09 viruses at 0 · 35–0 · 65 and 0 · 15–0 · 8 events/lineage/year, respectively ( Müller et al. 2020 ).…”

“…We aimed to identify reassortment events and reassortant viruses involved in these events among Africa H1N1pdm09 and H3N2 viruses and characterize the temporal and spatial distribution of reassortants from 1994 to 2020 in Africa. Our approach enabled us to estimate the frequency of reassortment among Africa IAVs, which we compared to the coalescent-based reassortment rate ( Müller et al. 2020 ) and identified African hubs for influenza reassortment.…”