Rapid Intrahost Evolution of Human Cytomegalovirus Is Shaped by Demography and Positive Selection

Populations of human cytomegalovirus (HCMV), a large DNA virus, are highly polymorphic in patient samples, which may allow for rapid evolution within human hosts. To understand HCMV evolution, longitudinally sampled genomic populations from the urine and plasma of 5 infants with symptomatic congenital HCMV infection were analyzed. Temporal and compartmental variability of viral populations were quantified using high throughput sequencing and population genetics approaches. HCMV populations were generally stable over time, with similar to 88% of SNPs displaying similar frequencies. However, samples collected from plasma and urine of the same patient at the same time were highly differentiated with approximately 1700 consensus sequence SNPs (1.2% of the genome) identified between compartments. This inter-compartment differentiation was comparable to the differentiation observed in unrelated hosts. Models of demography (i.e., changes in population size and structure) and positive selection were evaluated to explain the observed patterns of variation. Evidence for strong bottlenecks (>90% reduction in viral population size) was consistent among all patients. From the timing of the bottlenecks, we conclude that fetal infection occurred between 13-18 weeks gestational age in patients analyzed, while colonization of the urine compartment followed roughly 2 months later. The timing of these bottlenecks is consistent with the clinical histories of congenital HCMV infections. We next inferred that positive selection plays a small but measurable role in viral evolution within a single compartment. However, positive selection appears to be a strong and pervasive driver of evolution associated with compartmentalization, affecting >= 34 of the 167 open reading frames (similar to 20%) of the genome. This work offers the most detailed map of HCMV in vivo evolution to date and provides evidence that viral populations can be stable or rapidly differentiate, depending on host environment. The application of population genetic methods to these data provides clinically useful information, such as the timing of infection and compartment colonization.

Characterizing human cytomegalovirus reinfection in congenitally infected infants: an evolutionary perspective

Kristen Kay Irwin, Jeffrey David Jensen, Susanne Petra Pfeifer, Cornelia Pokalyuk

Given the strong selective pressures often faced by populations when colonizing a novel habitat, the level of variation present on which selection may act is an important indicator of adaptive potential. While often discussed in an ecological context, this notion is also highly relevant in our clinical understanding of viral infection, in which the novel habitat is a new host. Thus, quantifying the factors determining levels of variation is of considerable importance for the design of improved treatment strategies. Here, we focus on such a quantification of human cytomegalovirus (HCMV) - a virus which can be transmitted across the placenta, resulting in foetal infection that can potentially cause severe disease in multiple organs. Recent studies using genomewide sequencing data have demonstrated that viral populations in some congenitally infected infants diverge rapidly over time and between tissue compartments within individuals, while in other infants, the populations remain highly stable. Here, we investigate the underlying causes of these extreme differences in observed intrahost levels of variation by estimating the underlying demographic histories of infection. Importantly, reinfection (i.e. population admixture) appears to be an important, and previously unappreciated, player. We highlight illustrative examples likely to represent a single-population transmission from a mother during pregnancy and multiple-population transmissions during pregnancy and after birth.

Wiley2017

Evolution of the Influenza A Virus Genome during Development of Oseltamivir Resistance In Vitro

Jeffrey David Jensen

Influenza A virus (IAV) is a major cause of morbidity and mortality throughout the world. Current antiviral therapies include oseltamivir, a neuraminidase inhibitor that prevents the release of nascent viral particles from infected cells. However, the IAV genome can evolve rapidly, and oseltamivir resistance mutations have been detected in numerous clinical samples. Using an in vitro evolution platform and whole-genome population sequencing, we investigated the population genomics of IAV during the development of oseltamivir resistance. Strain A/Brisbane/59/2007 (H1N1) was grown in Madin-Darby canine kidney cells with or without escalating concentrations of oseltamivir over serial passages. Following drug treatment, the H274Y resistance mutation fixed reproducibly within the population. The presence of the H274Y mutation in the viral population, at either a low or a high frequency, led to measurable changes in the neuraminidase inhibition assay. Surprisingly, fixation of the resistance mutation was not accompanied by alterations of viral population diversity or differentiation, and oseltamivir did not alter the selective environment. While the neighboring K248E mutation was also a target of positive selection prior to H274Y fixation, H274Y was the primary beneficial mutation in the population. In addition, once evolved, the H274Y mutation persisted after the withdrawal of the drug, even when not fixed in viral populations. We conclude that only selection of H274Y is required for oseltamivir resistance and that H274Y is not deleterious in the absence of the drug. These collective results could offer an explanation for the recent reproducible rise in oseltamivir resistance in seasonal H1N1 IAV strains in humans.

Amer Soc Microbiology2014

Influenza Virus Drug Resistance: A Time-Sampled Population Genetics Perspective

Claudia Bank, Gregory Bruce Ewing, Anna Ferrer Admetlla, Matthieu Foll, Jeffrey David Jensen, Hyunjin Shim

Author Summary In recent years, considerable attention has been given to the evolution of drug resistance in the influenza A H1N1 strain. As a major annual cause of morbidity and mortality, combined with the rapid global spread of drug resistance, influenza remains as one of the most important global health concerns. Our work here focuses on a novel multi-faceted population-genetic approach utilizing unique whole-genome multi-time point experimental datasets in both the presence and absence of drug treatment. In addition, we present novel theoretical results and two newly developed and widely applicable statistical methodologies for utilizing time-sampled data - with a focus on distinguishing the relative contribution of genetic drift from that of positive and purifying selection. Results illustrate the available mutational paths to drug resistance, and offer important insights in to the mode and tempo of adaptation in a viral population. The challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of demography and selection from time course data. Utilizing these novel statistical and computational tools, we evaluate whole-genome datasets of an influenza A H1N1 strain in the presence and absence of oseltamivir (an inhibitor of neuraminidase) collected at thirteen time points. Results reveal a striking consistency amongst the three estimation procedures developed, showing strongly increased selection pressure in the presence of drug treatment. Importantly, these approaches re-identify the known oseltamivir resistance site, successfully validating the approaches used. Enticingly, a number of previously unknown variants have also been identified as being positively selected. Results are interpreted in the light of Fisher's Geometric Model, allowing for a quantification of the increased distance to optimum exerted by the presence of drug, and theoretical predictions regarding the distribution of beneficial fitness effects of contending mutations are empirically tested. Further, given the fit to expectations of the Geometric Model, results suggest the ability to predict certain aspects of viral evolution in response to changing host environments and novel selective pressures.

Public Library of Science2014

Influenza Virus Drug Resistance: A Time-Sampled Population Genetics Perspective

Claudia Bank, Gregory Bruce Ewing, Anna Ferrer Admetlla, Matthieu Foll, Jeffrey David Jensen, Hyunjin Shim

Public Library of Science2014