A Bayesian approach to infer recombination patterns in coronaviruses.

As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent.

Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2.

The SARS-CoV-2 pandemic has resulted in numerous virus variants, some of which have altered receptor-binding or antigenic phenotypes. Here, we quantify the degree to which adaptive evolution is driving the accumulation of mutations across the genome. We correlate clade growth with mutation accumulation, compare rates of nonsynonymous to synonymous divergence, assess temporal clustering of mutations, and evaluate the evolutionary success of individual mutations. We find that spike S1 is the focus of adaptive evolution but also identify positively selected mutations in other proteins (notably Nsp6) that are sculpting the evolutionary trajectory of SARS-CoV-2. Adaptive changes in S1 accumulated rapidly, resulting in a remarkably high ratio of nonsynonymous to synonymous divergence that is 2.5× greater than that observed in influenza hemagglutinin HA1 at the beginning of the 2009 H1N1 pandemic. These findings uncover a high degree of adaptation in S1 and suggest that SARS-CoV-2 might undergo antigenic drift.

Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2

The SARS-CoV-2 pandemic has resulted in numerous virus variants, some of which have altered receptor-binding or antigenic phenotypes. Here, we quantify the degree to which adaptive evolution is driving the accumulation of mutations across the genome. We correlate clade growth with mutation accumulation, compare rates of nonsynonymous to synonymous divergence, assess temporal clustering of mutations and evaluate the evolutionary success of individual mutations. We find that spike S1 is the focus of adaptive evolution, but also identify positively-selected mutations in other genes (notably Nsp6) that are sculpting the evolutionary trajectory of SARS-CoV-2. Adaptive changes in S1 accumulated rapidly, resulting in a remarkably high ratio of nonsynonymous to synonymous divergence that is 2.5X greater than that observed in influenza hemagglutinin HA1 at the beginning of the 2009 H1N1 pandemic. These findings uncover a high degree of adaptation in S1 and suggest that SARS-CoV-2 may undergo antigenic drift. Graphical