The effective rate of influenza reassortment is limited during human infection

Abstract

We characterise the evolutionary dynamics of influenza infection described by viral sequence data collected from two challenge studies conducted in human hosts. Viral sequence data were collected at regular intervals from infected hosts. Changes in the sequence data observed across time show that the within-host evolution of the virus was driven by the reversion of variants acquired during previous passaging of the virus. Treatment of some patients with oseltamivir on the first day of infection did not lead to the emergence of drug resistance variants in patients. Using an evolutionary model, we inferred the effective rate of reassortment between viral segments, measuring the extent to which randomly chosen viruses within the host exchange genetic material. We find strong evidence that the rate of effective reassortment is low, such that genetic associations between polymorphic loci in different segments are preserved during the course of an infection in a manner not compatible with epistasis. Combining our evidence with that of previous studies we suggest that spatial heterogeneity in the viral population may reduce the extent to which reassortment is observed. Our results do not contradict previous findings of high rates of viral reassortment in vitro and in small animal studies, but indicate that in human hosts the effective rate of reassortment may be substantially more limited.

A novel framework for inferring parameters of transmission from viral sequence data

“…A second key assumption in the inference of selection is the existence of regions of the virus separated from each other by recombination or reassortment. This assumption would be preserved in some other viruses, as noted in observations of within-host HIV evolution [71], if not for all influenza populations [72]. Where a viral genome did not exhibit recombination, and only a single transmission event was observed, the neutral version of our method could be applied; in this context our accounting for haplotype structure and sequencing noise in transmission represents an advance over methods which ignore these factors.…”

“…By default we set the growth factor g to be equal to 22. This approach is distinct from the branching process used in another estimate of bottleneck size [72]; our assumption that viruses infect different cells in the host, with competition between viruses occurring after the release of viruses from cells, leads to a Wright-Fisher-type population model, in which the rapid growth of the viral population leads to a smaller amount of genetic drift than inferred in that model. We note that our method can be extended to incorporate multiple rounds of within-host viral growth; a first approximation would be to reduce g to match the effective variance in frequencies induced by repeated rounds of growth.…”

“…Genome sequence data was processed using the SAMFIRE software package, according to default settings [57], calling variant alleles that existed at a frequency of at least 1% at some point during the observed infections. For the calculation of a within-host fitness landscape, the effective depth of sequencing was estimated in a conservative manner, filtering out variants which changed in frequency by more than 5% per day before using the frequencies of remaining variants from different time-points within the same host to estimate the parameter C. For the within-host model, following the approach of previous calculations [52,72], potentially non-neutral variants were identified as those for which a model of frequency change under selection outperformed a neutral model by more than 10 units according to the Bayesian Information Criterion (BIC) [58]. Variants reaching a frequency of at least 5% in at least one sample were then identified before calling multi-locus variant observations from the data; data from all time-points for which within-host data were collected were used in this inference.…”

“…Inference of within-host selection. For the experimental dataset an inference of withinhost selection was conducted according to a method previously described in earlier publications [52,72]. Under the assumption of rapid reassortment in the system [82] different segments of the virus were treated independently.…”

A delay-deterministic model for inferring fitness effects from time-resolved genome sequence data

“…In a recent work, examining a range of potential models for the demographic history of a population, it was concluded that deterministic approximations to evolution under drift can produce accurate estimates of the magnitude of selection 33 . Such models of selection, mutation, and recombination have been used to generate insights into viral adaptation [34][35][36] . Time-resolved sequence data describing pathogenic populations is becoming increasingly available [37][38][39][40] ; in so far as demographic effects can be ignored in such systems, evolutionary inference becomes possible at far-reduced computational cost, making this an important area for methodological development and application.…”