Dating the Emergence of Human Endemic Coronaviruses.

Four endemic coronaviruses infect humans and cause mild symptoms. Because previous analyses were based on a limited number of sequences and did not control for effects that affect molecular dating, we re-assessed the timing of endemic coronavirus emergence. After controlling for recombination, selective pressure, and molecular clock model, we obtained similar tMRCA (time to the most recent common ancestor) estimates for the four coronaviruses, ranging from 72 (HCoV-229E) to 54 (HCoV-NL63) years ago. The split times of HCoV-229E and HCoV-OC43 from camel alphacoronavirus and bovine coronavirus were dated ~268 and ~99 years ago. The split times of HCoV-HKU1 and HCoV-NL63 could not be calculated, as their zoonoticic sources are unknown. To compare the timing of coronavirus emergence to that of another respiratory virus, we recorded the occurrence of influenza pandemics since 1500. Although there is no clear relationship between pandemic occurrence and human population size, the frequency of influenza pandemics seems to intensify starting around 1700, which corresponds with the initial phase of exponential increase of human population and to the emergence of HCoV-229E. The frequency of flu pandemics in the 19th century also suggests that the concurrence of HCoV-OC43 emergence and the Russian flu pandemic may be due to chance.

Dating the Emergence of Human Endemic Coronaviruses

Four endemic coronaviruses infect humans and cause mild symptoms. Because previous analyses were based on a limited number of sequences and did not control for effects that affect molecular dating, we re-assessed the timing of endemic coronavirus emergence. After controlling for recombination, selective pressure, and molecular clock model, we obtained similar tMRCA (time to the most recent common ancestor) estimates for the four coronaviruses, ranging from 72 (HCoV-229E) to 54 (HCoV-NL63) years ago. The split times of HCoV-229E and HCoV-OC43 from camel alphacoronavirus and bovine coronavirus were dated ~268 and ~99 years ago. The split times of HCoV-HKU1 and HCoV-NL63 could not be calculated, as their zoonoticic sources are unknown. To compare the timing of coronavirus emergence to that of another respiratory virus, we recorded the occurrence of influenza pandemics since 1500. Although there is no clear relationship between pandemic occurrence and human population size, the frequency of influenza pandemics seems to intensify starting around 1700, which corresponds with the initial phase of exponential increase of human population and to the emergence of HCoV-229E. The frequency of flu pandemics in the 19th century also suggests that the concurrence of HCoV-OC43 emergence and the Russian flu pandemic may be due to chance.

Homology-based classification of accessory proteins in coronavirus genomes uncovers extremely dynamic evolution of gene content.

Coronaviruses (CoVs) have complex genomes that encode a fixed array of structural and nonstructural components, as well as a variety of accessory proteins that differ even among closely related viruses. Accessory proteins often play a role in the suppression of immune responses and may represent virulence factors. Despite their relevance for CoV phenotypic variability, information on accessory proteins is fragmentary. We applied a systematic approach based on homology detection to create a comprehensive catalogue of accessory proteins encoded by CoVs. Our analyses grouped accessory proteins into 379 orthogroups and 12 super-groups. No orthogroup was shared by the four CoV genera and very few were present in all or most viruses in the same genus, reflecting the dynamic evolution of CoV genomes. We observed differences in the distribution of accessory proteins in CoV genera. Alphacoronaviruses harboured the largest diversity of accessory open reading frames (ORFs), deltacoronaviruses the smallest. However, the average number of accessory proteins per genome was highest in betacoronaviruses. Analysis of the evolutionary history of some orthogroups indicated that the different CoV genera adopted similar evolutionary strategies. Thus, alphacoronaviruses and betacoronaviruses acquired phosphodiesterases and spike-like accessory proteins independently, whereas horizontal gene transfer from reoviruses endowed betacoronaviruses and deltacoronaviruses with fusion-associated small transmembrane (FAST) proteins. Finally, analysis of accessory ORFs in annotated CoV genomes indicated ambiguity in their naming. This complicates cross-communication among researchers and hinders automated searches of large data sets (e.g., PubMed, GenBank). We suggest that orthogroup membership is used together with a naming system to provide information on protein function.

Adaptation of the endemic coronaviruses HCoV-OC43 and HCoV-229E to the human host.

Four coronaviruses (HCoV-OC43, HCoV-HKU1, HCoV-NL63, and HCoV-229E) are endemic in human populations. All these viruses are seasonal and generate short-term immunity. Like the highly pathogenic coronaviruses, the endemic coronaviruses have zoonotic origins. Thus, understanding the evolutionary dynamics of these human viruses might provide insight into the future trajectories of SARS-CoV-2 evolution. Because the zoonotic sources of HCoV-OC43 and HCoV-229E are known, we applied a population genetics-phylogenetic approach to investigate which selective events accompanied the divergence of these viruses from the animal ones. Results indicated that positive selection drove the evolution of some accessory proteins, as well as of the membrane proteins. However, the spike proteins of both viruses and the hemagglutinin-esterase (HE) of HCoV-OC43 represented the major selection targets. Specifically, for both viruses, most positively selected sites map to the receptor-binding domains (RBDs) and are polymorphic. Molecular dating for the HCoV-229E spike protein indicated that RBD Classes I, II, III, and IV emerged 3-9 years apart. However, since the appearance of Class V (with much higher binding affinity), around 25 years ago, limited genetic diversity accumulated in the RBD. These different time intervals are not fully consistent with the hypothesis that HCoV-229E spike evolution was driven by antigenic drift. An alternative, not mutually exclusive possibility is that strains with higher affinity for the cellular receptor have out-competed strains with lower affinity. The evolution of the HCoV-OC43 spike protein was also suggested to undergo antigenic drift. However, we also found abundant signals of positive selection in HE. Whereas such signals might result from antigenic drift, as well, previous data showing co-evolution of the spike protein with HE suggest that optimization for human cell infection also drove the evolution of this virus. These data provide insight into the possible trajectories of SARS-CoV-2 evolution, especially in case the virus should become endemic.