Recombination patterns in coronaviruses

Abstract

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.

Figure 1:. Evolutionary history of SARS-like viruses.

A Maximum clade credibility network of SARS-like viruses. Blue dots denote samples and green dots recombination events. B Common ancestor times of Wuhan-Hu1 (SARS-CoV-2) with different SARS-like viruses on different positions of the genome. The y-axis denotes common ancestor times in log scale. C Most recent time anywhere on the genome that Wuhan-Hu1 shared a common ancestor with different SARS-like viruses

Figure 2:. Recombination networks and rates for coronaviruses MERS, 229E, OC43 and NL63.

Recombination networks for MERS (A) and seasonal human coronaviruses 229E (B), OC43 (C) and NL63 (D). E Recombination rates (per lineage and year) for the different coronaviruses compared to reassortment rates in seasonal human influenza A/H3N2 and influenza B viruses as estimated in Müller et al. (2020). For OC43 and NL63, the parts of the recombination networks that stretch beyond 1950 are not shown to increase readability of more recent parts of the networks.

Figure 3:. Comparison of recombination rates with rates of adaptation on different parts of the genomes of seasonal human coronaviruses 229E, OC43 and NL63.

Relationship between estimated relative recombination rate (x-axis) and relative adaptation rate (y-axis) for three different seasonal human coronaviruses: 229E, OC43 and NL63. These estimates are shown for different parts of the genome, indicated by the different colors. These result from two different types of analysis: one using spike only (subunit 1 over subunit 2, shown in yellow) and one using the full genome (shown in orange, blue and green). The rate ratios denote the rate on a part of the genome divided by the average rate on the two other parts of the genome.

Figure 4:. Example recombination network.

Events that can occur on a recombination network as considered here. We consider events to occur from present backwards in time to the past (as is the norm when looking at coalescent processes). Lineages can be added upon sampling events, which occur at predefined points in time and are conditioned on. Recombination events split the path of a lineage in two, with everything on one side of a recombination breakpoint going in one direction and everything on the other side of a breakpoint going in the other direction.