Intra- and Interhost Evolutionary Dynamics of Equine Influenza Virus

Abstract

Determining the evolutionary basis of cross-species transmission and immune evasion is key to understanding the mechanisms that control the emergence of either new viruses or novel antigenic variants with pandemic potential. The hemagglutinin glycoprotein of influenza A viruses is a critical host range determinant and a major target of neutralizing antibodies. Equine influenza virus (EIV) is a significant pathogen of the horse that causes periodical outbreaks of disease even in populations with high vaccination coverage. EIV has also jumped the species barrier and emerged as a novel respiratory pathogen in dogs, canine influenza virus. We studied the dynamics of equine influenza virus evolution in horses at the intrahost level and how this evolutionary process is affected by interhost transmission in a natural setting. To this end, we performed clonal sequencing of the hemagglutinin 1 gene derived from individual animals at different times postinfection. Our results show that despite the population consensus sequence remaining invariant, genetically distinct subpopulations persist during the course of infection and are also transmitted, with some variants likely to change antigenicity. We also detected a natural case of mixed infection in an animal infected during an outbreak of equine influenza, raising the possibility of reassortment between different strains of virus. In sum, our data suggest that transmission bottlenecks may not be as narrow as originally perceived and that the genetic diversity required to adapt to new host species may be partially present in the donor host and potentially transmitted to the recipient host.

Genome plasticity of triple-reassortant H1N1 influenza A virus during infection of vaccinated pigs

“…In IAVs, this research has focused on HA Murcia et al, 2010Murcia et al, , 2013. To the best of our knowledge, our study is the first to evaluate the intra-host diversity of the complete genome of IAV during infection of pigs using NGS, and our results are comparable with a recent study in children (Bourret et al, 2015).…”

“…However, our results also showed that nucleotide substitutions can occur in the signal peptide and the HA2 region of the HA, which were not evaluated by Murcia et al (2012). In addition, other factors in our study, such as the group housing conditions, which facilitated greater interaction between pigs compared with previous studies where pairs of individuals were used to measure intra-host diversity of IAV (Hensley et al, 2009;Murcia et al, 2010Murcia et al, , 2012, may have had an effect on the increased overall genetic diversity.…”

Reassortment Complements Spontaneous Mutation in Influenza A Virus NP and M1 Genes To Accelerate Adaptation to a New Host

“…The transmission of multiple variants, observed in this study and in other transmission models (35)(36)(37)(38), suggests that the transmission bottleneck in IAV may not be so severe in many circumstances, which would enhance the diversity on which selection can act in transmission recipients. The mode of transmission likely impacts the size of the bottleneck.…”

“…In either case, the transmission of diversity may allow variants with moderate fitness advantages to emerge and grow out over the course of multiple transmission events. Preexisting immunity in guinea pigs has been shown to impact virus evolution during the course of a single infection (39), which raises the possibility of modeling in GPs viral evolution during transmission to animals with various states of preexisting immunity, as has been demonstrated in swine, horses, and dogs (36,37,40).…”

The path of least resistance: aggressive or moderate treatment?

“…The key missing elements for understanding how treatment strength shapes the emergence and spread of resistance are (i) experimental data on both the shape of the conceptual curve (discussed above) and the location of the clinically neutral range of treatments on the drug pressure axis; (ii) a more thorough understanding of the cross-scale dynamics of anti-microbial resistance (see, e.g. [51]); and (iii) consideration of the different pathogen ecologies that determine how much competition between resistant and sensitive pathogens is likely to occur within a host. Quantitative predictions of resistance evolution, including the outcome of greatest public health interest, i.e.…”