Coordinated Evolution of Influenza A Surface Proteins

Natural variation in neuraminidase activity influences the evolutionary potential of the seasonal H1N1 lineage hemagglutinin

The antigenic evolution of the influenza A virus hemagglutinin (HA) gene poses a major challenge for the development of vaccines capable of eliciting long-term protection. Prior efforts to understand the mechanisms that govern viral antigenic evolution mainly focus on HA in isolation, ignoring the fact that HA must act in concert with the viral neuraminidase (NA) during replication and spread. Numerous studies have demonstrated that the degree to which the receptor-binding avidity of HA and receptor-cleaving activity of NA are balanced with each other influences overall viral fitness. We recently showed that changes in NA activity can significantly alter the mutational fitness landscape of HA in the context of a lab-adapted virus strain. Here, we test whether natural variation in relative NA activity can influence the evolutionary potential of HA in the context of the seasonal H1N1 lineage (pdmH1N1) that has circulated in humans since the 2009 pandemic. We observed substantial variation in the relative activities of NA proteins encoded by a panel of H1N1 vaccine strains isolated between 2009 and 2019. We comprehensively assessed the effect of NA background on the HA mutational fitness landscape in the circulating pdmH1N1 lineage using deep mutational scanning and observed pronounced epistasis between NA and residues in or near the receptor-binding site of HA. To determine whether NA variation could influence the antigenic evolution of HA, we performed neutralizing antibody selection experiments using a panel of monoclonal antibodies targeting different HA epitopes. We found that the specific antibody escape profiles of HA were highly contingent upon NA background. Overall, our results indicate that natural variation in NA activity plays a significant role in governing the evolutionary potential of HA in the currently circulating pdmH1N1 lineage.

Changing selection on amino acid substitutions in Gag protein between major HIV-1 subtypes

Amino acid preferences at a protein site depend on the role of this site in protein function and structure as well as on external constraints. All these factors can change in the course of evolution, making amino acid propensities of a site time-dependent. When viral subtypes divergently evolve in different host subpopulations, such changes may depend on genetic, medical, and sociocultural differences between these subpopulations. Here, using our previously developed phylogenetic approach, we describe sixty-nine amino acid sites of the Gag protein of human immunodeficiency virus type 1 (HIV-1) where amino acids have different impact on viral fitness in six major subtypes of the type M. These changes in preferences trigger adaptive evolution; indeed, 32 (46 per cent) of these sites experienced strong positive selection at least in one of the subtypes. At some of the sites, changes in amino acid preferences may be associated with differences in immune escape between subtypes. The prevalence of an amino acid in a protein site within a subtype is only a poor predictor for whether this amino acid is preferred in this subtype according to the phylogenetic analysis. Therefore, attempts to identify the factors of viral evolution from comparative genomics data should integrate across multiple sources of information.

Natural variation in neuraminidase activity influences the evolutionary potential of the seasonal H1N1 lineage hemagglutinin

The antigenic evolution of the influenza A virus hemagglutinin (HA) gene poses a major challenge for the development of vaccines capable of eliciting long-term protection. Prior efforts to understand the mechanisms that govern viral antigenic evolution mainly focus on HA in isolation, ignoring the fact that HA must act in concert with the viral neuraminidase (NA) during replication and spread. Numerous studies have demonstrated that the degree to which the receptor binding avidity of HA and receptor cleaving activity of NA are balanced with each other influences overall viral fitness. We recently showed that changes in NA activity can significantly alter the mutational fitness landscape of HA in the context of a lab-adapted virus strain. Here, we test whether natural variation in relative NA activity can influence the evolutionary potential of HA in the context of the seasonal H1N1 lineage (pdmH1N1) that has circulated in humans since the 2009 pandemic. We observed substantial variation in the relative activities of NA proteins encoded by a panel of H1N1 vaccine strains isolated between 2009 and 2019. We comprehensively assessed the effect of NA background on the HA mutational fitness landscape in the circulating pdmH1N1 lineage using deep mutational scanning and observed pronounced epistasis between NA and residues in or near the receptor binding site of HA. To determine whether NA variation could influence the antigenic evolution of HA, we performed neutralizing antibody selection experiments using a panel of monoclonal antibodies targeting different HA epitopes. We found that the specific antibody escape profiles of HA were highly contingent upon NA background. Overall, our results indicate that natural variation in NA activity plays a significant role in governing the evolutionary potential of HA in the currently circulating pdmH1N1 lineage.

Coordinated evolution at amino acid sites of SARS-CoV-2 spike

SARS-CoV-2 has adapted in a stepwise manner, with multiple beneficial mutations accumulating in a rapid succession at origins of VOCs, and the reasons for this are unclear. Here, we searched for coordinated evolution of amino acid sites in the spike protein of SARS-CoV-2. Specifically, we searched for concordantly evolving site pairs (CSPs) for which changes at one site were rapidly followed by changes at the other site in the same lineage. We detected 46 sites which formed 45 CSP. Sites in CSP were closer to each other in the protein structure than random pairs, indicating that concordant evolution has a functional basis. Notably, site pairs carrying lineage defining mutations of the four VOCs that circulated before May 2021 are enriched in CSPs. For the Alpha VOC, the enrichment is detected even if Alpha sequences are removed from analysis, indicating that VOC origin could have been facilitated by positive epistasis. Additionally, we detected nine discordantly evolving pairs of sites where mutations at one site unexpectedly rarely occurred on the background of a specific allele at another site, for example on the background of wild-type D at site 614 (four pairs) or derived Y at site 501 (three pairs). Our findings hint that positive epistasis between accumulating mutations could have delayed the assembly of advantageous combinations of mutations comprising at least some of the VOCs.

RF-Net 2: fast inference of virus reassortment and hybridization networks

MOTIVATION

A phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g. hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens.

RESULTS

Here, we expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers of reticulations without sacrificing accuracy. In addition, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical IAV evolutionary analyses.

AVAILABILITY AND IMPLEMENTATION

RF-Net 2 is available at https://github.com/flu-crew/rf-net-2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Joint effects of genes underlying a temperature specialization tradeoff in yeast

A central goal of evolutionary genetics is to understand, at the molecular level, how organisms adapt to their environments. For a given trait, the answer often involves the acquisition of variants at unlinked sites across the genome. Genomic methods have achieved landmark successes in pinpointing these adaptive loci. To figure out how a suite of adaptive alleles work together, and to what extent they can reconstitute the phenotype of interest, requires their transfer into an exogenous background. We studied the joint effect of adaptive, gain-of-function thermotolerance alleles at eight unlinked genes from Saccharomyces cerevisiae, when introduced into a thermosensitive sister species, S. paradoxus. Although the loci damped each other’s beneficial impact (that is, they were subject to negative epistasis), most boosted high-temperature growth alone and in combination, and none was deleterious. The complete set of eight genes was sufficient to confer ~15% of the S. cerevisiae thermotolerance phenotype in the S. paradoxus background. The same loci also contributed to a heretofore unknown advantage in cold growth by S. paradoxus. Together, our data establish temperature resistance in yeasts as a model case of a genetically complex evolutionary tradeoff, which can be partly reconstituted from the sequential assembly of unlinked underlying loci.

Organisms adapt to threats in the environment by acquiring DNA sequence variants that tweak traits to improve fitness. Experimental studies of this process have proven to be a particular challenge when they involve manipulation of a suite of genes, all on different chromosomes. We set out to understand how so many loci could work together to confer a trait. We used as a model system eight genes that govern the ability of the unicellular yeast Saccharomyces cerevisiae to grow at high temperature. We introduced these variant loci stepwise into a non-thermotolerant sister species, and found that the more S. cerevisiae alleles we added, the better the phenotype. We saw no evidence for toxic interactions between the genes as they were combined. We also used the eight-fold transgenic to dissect the biological mechanism of thermotolerance. And we discovered a tradeoff: the same alleles that boosted growth at high temperature eroded the organism’s ability to deal with cold conditions. These results serve as a case study of modular construction of a trait from nature, by assembling the genes together in one genome.

Parallel evolution between genomic segments of seasonal human influenza viruses reveals RNA-RNA relationships

The influenza A virus (IAV) genome consists of eight negative-sense viral RNA (vRNA) segments that are selectively assembled into progeny virus particles through RNA-RNA interactions. To explore putative intersegmental RNA-RNA relationships, we quantified similarity between phylogenetic trees comprising each vRNA segment from seasonal human IAV. Intersegmental tree similarity differed between subtype and lineage. While intersegmental relationships were largely conserved over time in H3N2 viruses, they diverged in H1N1 strains isolated before and after the 2009 pandemic. Surprisingly, intersegmental relationships were not driven solely by protein sequence, suggesting that IAV evolution could also be driven by RNA-RNA interactions. Finally, we used confocal microscopy to determine that colocalization of highly coevolved vRNA segments is enriched over other assembly intermediates at the nuclear periphery during productive viral infection. This study illustrates how putative RNA interactions underlying selective assembly of IAV can be interrogated with phylogenetics.

The viruses responsible for influenza evolve rapidly during infection. Changes typically emerge in two key ways: through random mutations in the genetic sequence of the virus, or by reassortment. Reassortment can occur when two or more strains infect the same cell. Once in a cell, viral particles ‘open up’ to release their genetic material so it can make copies of itself using the cell’s machinery. The new copies of the genetic material of the virus are used to make new viral particles, which then envelop the genetic material and are released from the cell to infect other cells. If several strains of a virus infect the same cell, a new viral particle may pick up genetic segments from each of the infecting strains, creating a new strain via reassortment.

Several factors are known to affect the success of the reassortment process. For example, if the new strain acquires a genetic defect that hinders its replication cycle, it is likely to die out quickly. Other times, this trading of genetic information can create a strain that is more resistant to the human immune system, allowing it to sweep across the globe and cause a deadly pandemic. However, a key part of the reassortment process that still remains unclear is how genome segments from two different influenza strains recognize each other before merging together to create hybrid daughter viruses.

To explore this further, Jones et al. used a technique called fluorescence microscopy. They found that genome segments that evolved along similar paths were more likely to cluster in the same area inside infected cells, and therefore, more likely to be reassorted together into a new strain during assembly of daughter viruses. This suggests that assembly may guide the evolutionary path taken by individual genomic segments. Jones et al. also looked at the evolution of different genome segments collected from patients suffering from seasonal influenza, and found that these segments had a distinct evolutionary path to those in pandemic-causing strains.

This research provides new insights into the role of reassortment in the evolution of influenza viruses during infection. In particular, it suggests that how the genome segments interact with one another may have a previously unknown and important role in guiding this evolution. These insights could be used to predict future reassortment events based on evolutionary relationships between influenza virus genomic segments, and may in the future be used as part of risk assessment tools to predict the emergence of new pandemic strains.

Synergistic Effect between 3'-Terminal Noncoding and Adjacent Coding Regions of the Influenza A Virus Hemagglutinin Segment on Template Preference

The influenza A virus genome is comprised of eight single-stranded negative-sense viral RNA (vRNA) segments. Each of the eight vRNA segments contains segment-specific nonconserved noncoding regions (NCRs) of similar sequence and length in different influenza A virus strains. However, in the subtype-determinant segments, encoding hemagglutinin (HA) and neuraminidase (NA), the segment-specific noncoding regions are subtype specific, varying significantly in sequence and length at both the 3' and 5' termini among different subtypes. The significance of these subtype-specific noncoding regions (ssNCR) in the influenza virus replication cycle is not fully understood. In this study, we show that truncations of the 3'-end H1-subtype-specific noncoding region (H1-ssNCR) resulted in recombinant viruses with decreased HA vRNA replication and attenuated growth phenotype, although the vRNA replication was not affected in single-template RNP reconstitution assays. The attenuated viruses were unstable, and point mutations at nucleotide position 76 or 56 in the adjacent coding region of HA vRNA were found after serial passage. The mutations restored the HA vRNA replication and reversed the attenuated virus growth phenotype. We propose that the terminal noncoding and adjacent coding regions act synergistically to ensure optimal levels of HA vRNA replication in a multisegment environment. These results provide novel insights into the role of the 3'-end nonconserved noncoding regions and adjacent coding regions on template preference in multiple-segmented negative-strand RNA viruses. IMPORTANCE While most influenza A virus vRNA segments contain segment-specific nonconserved noncoding regions of similar length and sequence, these regions vary considerably both in length and sequence in the segments encoding HA and NA, the two major antigenic determinants of influenza A viruses. In this study, we investigated the function of the 3'-end H1-ssNCR and observed a synergistic effect between the 3'-end H1-ssNCR nucleotides and adjacent coding nucleotide(s) of the HA segment on template preference in a multisegment environment. The results unravel an additional level of complexity in the regulation of RNA replication in multiple-segmented negative-strand RNA viruses.

Cluster of Oseltamivir-Resistant and Hemagglutinin Antigenically Drifted Influenza A(H1N1)pdm09 Viruses, Texas, USA, January 2020

Four cases of oseltamivir-resistant influenza A(H1N1)pdm09 virus infection were detected among inhabitants of a border detention center in Texas, USA. Hemagglutinin of these viruses belongs to 6B.1A5A-156K subclade, which may enable viral escape from preexisting immunity. Our finding highlights the necessity to monitor both drug resistance and antigenic drift of circulating viruses.

An evolution-based high-fidelity method of epistasis measurement: Theory and application to influenza

Linkage effects in a multi-locus population strongly influence its evolution. The models based on the traveling wave approach enable us to predict the average speed of evolution and the statistics of phylogeny. However, predicting statistically the evolution of specific sites and pairs of sites in the multi-locus context remains a mathematical challenge. In particular, the effects of epistasis, the interaction of gene regions contributing to phenotype, is difficult to predict theoretically and detect experimentally in sequence data. A large number of false-positive interactions arises from stochastic linkage effects and indirect interactions, which mask true epistatic interactions. Here we develop a proof-of-principle method to filter out false-positive interactions. We start by demonstrating that the averaging of haplotype frequencies over multiple independent populations is necessary but not sufficient for epistatic detection, because it still leaves high numbers of false-positive interactions. To compensate for the residual stochastic noise, we develop a three-way haplotype method isolating true interactions. The fidelity of the method is confirmed analytically and on simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large sequence database of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure epistatic interactions of any sign from sequence data.

Interactions between genomic sites create a fitness landscape. The knowledge of topology and strength of interactions is vital for predicting the escape of viruses from drugs and immune response and their passing through fitness valleys. Many efforts have been invested into measuring these interactions from DNA sequence sets. Unfortunately, reproducibility of the results remains low due partly to a very small fraction of interaction pairs and partly to stochastic linkage noise masking true interactions. Here we propose a method to separate stochastic linkage and indirect interactions from epistatic interactions and apply it to influenza virus sequence data.

Modeling the selective advantage of new amino acids on the hemagglutinin of H1N1 influenza viruses using their patient age distributions

In 2009, a new strain of H1N1 influenza A virus caused a pandemic, and its descendant strains are causing seasonal epidemics worldwide. Given the high mutation rate of influenza viruses, variant strains having different amino acids on hemagglutinin (HA) continuously emerge. To prepare vaccine strains for the next influenza seasons, it is an urgent task to predict which variants will be selected in the viral population. An analysis of 24,681 pairs of an amino acid sequence of HA of H1N1pdm2009 viruses and its patient age showed that the empirical fixation probability of new amino acids on HA significantly differed depending on their frequencies in the population, patient age distributions, and epitope flags. The selective advantage of a variant strain having a new amino acid was modeled by linear combinations of patients age distributions and epitope flags, and then the fixation probability of the new amino acid was modeled using Kimura’s formula for advantageous selection. The parameters of models were estimated from the sequence data and models were tested with four-fold cross validations. The frequency of new amino acids alone can achieve high sensitivity, specificity, and precision in predicting the fixation of a new amino acid of which frequency is more than 0.11. The estimated parameter suggested that viruses with a new amino acid having a frequency in the population higher than 0.11 have a significantly higher selective advantage compared to viruses with the old amino acid at the same position. The model considering the Z-value of patient age rank-sums of new amino acids predicted amino acid substitutions on HA with a sensitivity of 0.78, specificity of 0.86, and precision of 0.83, showing significant improvement compared to the constant selective advantage model, which used only the frequency of the amino acid. These results suggested that H1N1 viruses tend to be selected in the adult population, and frequency of viruses having new amino acids and their patient ages are useful to predict amino acid substitutions on HA.

Episodic evolution of coadapted sets of amino acid sites in mitochondrial proteins

The rate of evolution differs between protein sites and changes with time. However, the link between these two phenomena remains poorly understood. Here, we design a phylogenetic approach for distinguishing pairs of amino acid sites that evolve concordantly, i.e., such that substitutions at one site trigger subsequent substitutions at the other; and also pairs of sites that evolve discordantly, so that substitutions at one site impede subsequent substitutions at the other. We distinguish groups of amino acid sites that undergo coordinated evolution and evolve discordantly from other such groups. In mitochondrion-encoded proteins of metazoans and fungi, we show that concordantly evolving sites are clustered in protein structures. By analysing the phylogenetic patterns of substitutions at concordantly and discordantly evolving site pairs, we find that concordant evolution has two distinct causes: epistatic interactions between amino acid substitutions and episodes of selection independently affecting substitutions at different sites. The rate of substitutions at concordantly evolving groups of protein sites changes in the course of evolution, indicating episodes of selection limited to some of the lineages. The phylogenetic positions of these changes are consistent between proteins, suggesting common selective forces underlying them.

The mode and rate of evolution of a protein site depends on the effect of its mutations on protein fitness. The fitness effect of a mutation itself can change in the course of evolution for at least two reasons. First, it can be modulated by substitutions occurring at other sites, a phenomenon called epistasis. Second, changes in selection can be non-epistatic, affecting sites independently of one another. Here, we analyse substitutions accumulated by the evolving lineages of the five proteins encoded by the mitochondrial genomes of thousands of species of metazoans and fungi. We show that substitutions at different amino acid sites occur in a coordinated fashion, and this coordination is caused both by epistasis and by episodes of selection affecting groups of sites. We partition each protein into several groups of concordantly evolving sites such that evolution of sites from different groups is discordant, and show that the proteins encoded by the mitochondrial genome consist of coevolving structural blocks. Some of these blocks have a clear functional specialization, e.g. are associated with interfaces between proteins composing respiratory complexes. Together, our results reveal a previously unrecognized complexity in the causes of variation in evolutionary rates between protein sites.

Insights from the comparison of genomic variants from two influenza B viruses grown in the presence of human antibodies in cell culture

Understanding the extent and limitation of viral genome evolution can provide insight about potential drug and vaccine targets. Influenza B Viruses (IBVs) infect humans in a seasonal manner and causes significant morbidity and mortality. IBVs are negative-sense single-stranded RNA viruses with a segmented genome and can be divided into two antigenically distinct lineages. The two lineages have been circulating and further evolving for almost four decades. The immune response to IBV infection can lead to antibodies that target the strain causing the infection. Some antibodies are cross-reactive and are able to bind strains from both lineages but, because of antigenic drift and immunodominance, both lineages continue to evolve and challenge human health. Here we investigate changes in the genomes of an IBVs from each lineage after passage in tissue culture in the presence of human sera containing polyclonal antibodies directed toward antigenically and temporally distinct viruses. Our previous analysis of the fourth segment, which encodes the major surface protein HA, revealed a pattern of change in which signature sequences from one lineage mutated to the signature sequences of the other lineage. Here we analyze genes from the other genomic segments and observe that most of the quasispecies’ heterogeneity occurs at the same loci in each lineage. The nature of the variants at these loci are investigated and possible reasons for this pattern are discussed. This work expands our understanding of the extent and limitations of genomic change in IBV.

Senescence and entrenchment in evolution of amino acid sites

Amino acid propensities at a site change in the course of protein evolution. This may happen for two reasons. Changes may be triggered by substitutions at epistatically interacting sites elsewhere in the genome. Alternatively, they may arise due to environmental changes that are external to the genome. Here, we design a framework for distinguishing between these alternatives. Using analytical modelling and simulations, we show that they cause opposite dynamics of the fitness of the allele currently occupying the site: it tends to increase with the time since its origin due to epistasis ("entrenchment"), but to decrease due to random environmental fluctuations ("senescence"). By analysing the genomes of vertebrates and insects, we show that the amino acids originating at negatively selected sites experience strong entrenchment. By contrast, the amino acids originating at positively selected sites experience senescence. We propose that senescence of the current allele is a cause of adaptive evolution.

Deconvolving mutational patterns of poliovirus outbreaks reveals its intrinsic fitness landscape

Vaccination has essentially eradicated poliovirus. Yet, its mutation rate is higher than that of viruses like HIV, for which no effective vaccine exists. To investigate this, we infer a fitness model for the poliovirus viral protein 1 (vp1), which successfully predicts in vitro fitness measurements. This is achieved by first developing a probabilistic model for the prevalence of vp1 sequences that enables us to isolate and remove data that are subject to strong vaccine-derived biases. The intrinsic fitness constraints derived for vp1, a capsid protein subject to antibody responses, are compared with those of analogous HIV proteins. We find that vp1 evolution is subject to tighter constraints, limiting its ability to evade vaccine-induced immune responses. Our analysis also indicates that circulating poliovirus strains in unimmunized populations serve as a reservoir that can seed outbreaks in spatio-temporally localized sub-optimally immunized populations.

Poliovirus has a higher mutation rate than HIV, yet has been almost eradicated by vaccination while an effective vaccine against HIV does not exist. Here, the authors develop a fitness model for poliovirus viral protein 1 to show that it is subject to stringent evolutionary constraints that limit its ability to avoid vaccine-induced immune responses.

Allele-specific nonstationarity in evolution of influenza A virus surface proteins

Influenza A virus (IAV) is a major public health problem and a pandemic threat. Its evolution is largely driven by diversifying positive selection so that relative fitness of different amino acid variants changes with time due to changes in herd immunity or genomic context, and novel amino acid variants attain fitness advantage. Here, we hypothesize that diversifying selection also has another manifestation: the fitness associated with a particular amino acid variant should decline with time since its origin, as the herd immunity adapts to it. By tracing the evolution of antigenic sites at IAV surface proteins, we show that an amino acid variant becomes progressively more likely to become replaced by another variant with time since its origin-a phenomenon we call "senescence." Senescence is particularly pronounced at experimentally validated antigenic sites, implying that it is largely driven by host immunity. By contrast, at internal sites, existing variants become more favorable with time, probably due to arising contingent mutations at other epistatically interacting sites. Our findings reveal a previously undescribed facet of adaptive evolution and suggest approaches for prediction of evolutionary dynamics of pathogens.

Viral Long-Term Evolutionary Strategies Favor Stability over Proliferation

Viruses are known to have some of the highest and most diverse mutation rates found in any biological replicator, with single-stranded (ss) RNA viruses evolving the fastest, and double-stranded (ds) DNA viruses having rates approaching those of bacteria. As mutation rates are tightly and negatively correlated with genome size, selection is a clear driver of viral evolution. However, the role of intragenomic interactions as drivers of viral evolution is still unclear. To understand how these two processes affect the long-term evolution of viruses infecting humans, we comprehensively analyzed ssRNA, ssDNA, dsRNA, and dsDNA viruses, to find which virus types and which functions show evidence for episodic diversifying selection and correlated evolution. We show that selection mostly affects single stranded viruses, that correlated evolution is more prevalent in DNA viruses, and that both processes, taken independently, mostly affect viral replication. However, the genes that are jointly affected by both processes are involved in key aspects of their life cycle, favoring viral stability over proliferation. We further show that both evolutionary processes are intimately linked at the amino acid level, which suggests that it is the joint action of selection and correlated evolution, and not just selection, that shapes the evolutionary trajectories of viruses—and possibly of their epidemiological potential.

Detecting genetic interactions using parallel evolution in experimental populations

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae. Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-loss-of-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84, genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. This article is part of the theme issue 'Convergent evolution in the genomics era: new insights and directions'.

Population dynamics at neuraminidase position 151 of influenza A (H1N1)pdm09 virus in clinical specimens

Influenza A virus mutates rapidly, allowing it to escape natural and vaccine-induced immunity. Neuraminidase (NA) is a surface protein capable of cleaving the glycosidic linkages of neuraminic acids to release newly formed virions from infected cells. Genetic variants within a viral population can influence the emergence of pandemic viruses as well as drug susceptibility and vaccine effectiveness. In the present study, 55 clinical specimens from patients infected with the 2009 pandemic influenza A/H1N1 virus, abbreviated as A(H1N1)pdm09, during the 2015-2016 outbreak season in Taiwan were collected. Whole genomes were obtained through next-generation sequencing. Based on the published sequences from A(H1N1)pdm09 strains worldwide, a mixed population of two distinct variants at NA position 151 was revealed. We initially reasoned that such a mixed population may have emerged during cell culture. However, additional investigations confirmed that these mixed variants were detectable in the specimens of patients. To further investigate the role of the two NA-151 variants in a dynamic population, a reverse genetics system was employed to generate recombinant A(H1N1)pdm09 viruses. It was observed that the mixture of the two distinct variants was characterized by a higher replication rate compared to the recombinant viruses harbouring a single variant. Moreover, an NA inhibition assay revealed that a high frequency of the minor NA-151 variant in A(H1N1)pdm09 was associated with a reduced susceptibility to NA inhibitors. We conclude that two distinct NA-151 variants can be identified in patient specimens and that such variants may increase viral replication and NA activity.

Influenza Hemagglutinin and Neuraminidase: Yin⁻Yang Proteins Coevolving to Thwart Immunity

Influenza A virions possess two surface glycoproteins-the hemagglutinin (HA) and neuraminidase (NA)-which exert opposite functions. HA attaches virions to cells by binding to terminal sialic acid residues on glycoproteins/glycolipids to initiate the infectious cycle, while NA cleaves terminal sialic acids, releasing virions to complete the infectious cycle. Antibodies specific for HA or NA can protect experimental animals from IAV pathogenesis and drive antigenic variation in their target epitopes that impairs vaccine effectiveness in humans. Here, we review progress in understanding HA/NA co-evolution as each acquires epistatic mutations to restore viral fitness to mutants selected in the other protein by host innate or adaptive immune pressure. We also discuss recent exciting findings that antibodies to HA can function in vivo by blocking NA enzyme activity to prevent nascent virion release and enhance Fc receptor-based activation of innate immune cells.

Novel reassortants of clade 2.3.4.4 H5N6 highly pathogenic avian influenza viruses possessing genetic heterogeneity in South Korea in late 2017

Novel H5N6 highly pathogenic avian influenza viruses (HPAIVs) were isolated from duck farms and migratory bird habitats in South Korea in November to December 2017. Genetic analysis demonstrated that at least two genotypes of H5N6 were generated through reassortment between clade 2.3.4.4 H5N8 HPAIVs and Eurasian low pathogenic avian influenza virus in migratory birds in late 2017, suggesting frequent reassortment of clade 2.3.4.4 H5 HPAIVs and highlighting the need for systematic surveillance in Eurasian breeding grounds.

Mutations in Influenza A Virus Neuraminidase and Hemagglutinin Confer Resistance against a Broadly Neutralizing Hemagglutinin Stem Antibody

Influenza A virus (IAV), a major cause of human morbidity and mortality, continuously evolves in response to selective pressures. Stem-directed, broadly neutralizing antibodies (sBnAbs) targeting the influenza virus hemagglutinin (HA) are a promising therapeutic strategy, but neutralization escape mutants can develop. We used an integrated approach combining viral passaging, deep sequencing, and protein structural analyses to define escape mutations and mechanisms of neutralization escape in vitro for the F10 sBnAb. IAV was propagated with escalating concentrations of F10 over serial passages in cultured cells to select for escape mutations. Viral sequence analysis revealed three mutations in HA and one in neuraminidase (NA). Introduction of these specific mutations into IAV through reverse genetics confirmed their roles in resistance to F10. Structural analyses revealed that the selected HA mutations (S123G, N460S, and N203V) are away from the F10 epitope but may indirectly impact influenza virus receptor binding, endosomal fusion, or budding. The NA mutation E329K, which was previously identified to be associated with antibody escape, affects the active site of NA, highlighting the importance of the balance between HA and NA function for viral survival. Thus, whole-genome population sequencing enables the identification of viral resistance mutations responding to antibody-induced selective pressure.IMPORTANCE Influenza A virus is a public health threat for which currently available vaccines are not always effective. Broadly neutralizing antibodies that bind to the highly conserved stem region of the influenza virus hemagglutinin (HA) can neutralize many influenza virus strains. To understand how influenza virus can become resistant or escape such antibodies, we propagated influenza A virus in vitro with escalating concentrations of antibody and analyzed viral populations by whole-genome sequencing. We identified HA mutations near and distal to the antibody binding epitope that conferred resistance to antibody neutralization. Additionally, we identified a neuraminidase (NA) mutation that allowed the virus to grow in the presence of high concentrations of the antibody. Virus carrying dual mutations in HA and NA also grew under high antibody concentrations. We show that NA mutations mediate the escape of neutralization by antibodies against HA, highlighting the importance of a balance between HA and NA for optimal virus function.

Dynamic interactions between RNA viruses and human hosts unravelled by a decade of next generation sequencing

BACKGROUND

Next generation sequencing (NGS) methods have significantly contributed to a paradigm shift in genomic research for nearly a decade now. These methods have been useful in studying the dynamic interactions between RNA viruses and human hosts.

SCOPE OF THE REVIEW

In this review, we summarise and discuss key applications of NGS in studying the host - pathogen interactions in RNA viral infections of humans with examples.

MAJOR CONCLUSIONS

Use of NGS to study globally relevant RNA viral infections have revolutionized our understanding of the within host and between host evolution of these viruses. These methods have also been useful in clinical decision-making and in guiding biomedical research on vaccine design.

GENERAL SIGNIFICANCE

NGS has been instrumental in viral genomic studies in resolving within-host viral genomic variants and the distribution of nucleotide polymorphisms along the full-length of viral genomes in a high throughput, cost effective manner. In the future, novel advances such as long read, single molecule sequencing of viral genomes and simultaneous sequencing of host and pathogens may become the standard of practice in research and clinical settings. This will also bring on new challenges in big data analysis.

Mutation and Epistasis in Influenza Virus Evolution

Influenza remains a persistent public health challenge, because the rapid evolution of influenza viruses has led to marginal vaccine efficacy, antiviral resistance, and the annual emergence of novel strains. This evolvability is driven, in part, by the virus's capacity to generate diversity through mutation and reassortment. Because many new traits require multiple mutations and mutations are frequently combined by reassortment, epistatic interactions between mutations play an important role in influenza virus evolution. While mutation and epistasis are fundamental to the adaptability of influenza viruses, they also constrain the evolutionary process in important ways. Here, we review recent work on mutational effects and epistasis in influenza viruses.

Within-Host Evolution of Human Influenza Virus

The rapid global evolution of influenza virus begins with mutations that arise de novo in individual infections, but little is known about how evolution occurs within hosts. We review recent progress in understanding how and why influenza viruses evolve within human hosts. Advances in deep sequencing make it possible to measure within-host genetic diversity in both acute and chronic influenza infections. Factors like antigenic selection, antiviral treatment, tissue specificity, spatial structure, and multiplicity of infection may affect how influenza viruses evolve within human hosts. Studies of within-host evolution can contribute to our understanding of the evolutionary and epidemiological factors that shape influenza virus's global evolution.

Antibody Immunodominance: The Key to Understanding Influenza Virus Antigenic Drift

Influenza A virus (IAV) imposes a significant socioeconomic burden on humanity. Vaccination is effective in only 60% of individuals, even under optimal circumstances. The difficulty stems from the remarkable ability of IAV to evade existing immunity. IAV's error prone polymerase enables the rapid antigenic evolution of the two virion surface glycoproteins, neuraminidase and hemagglutinin (HA). Since the most potent antibodies (Abs) at neutralizing viral infectivity are directed the head of the HA, amino acid substitutions in this region enable IAV to evade Ab-based immunity. Here, we review recent progress in understanding how immunodominance, the tendency of the immune system to respond to foreign immunogens in a hierarchical manner, shapes IAV evolution.

Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.

Influenza A virus hemagglutinin glycosylation compensates for antibody escape fitness costs

Rapid antigenic evolution enables the persistence of seasonal influenza A and B viruses in human populations despite widespread herd immunity. Understanding viral mechanisms that enable antigenic evolution is critical for designing durable vaccines and therapeutics. Here, we utilize the primerID method of error-correcting viral population sequencing to reveal an unexpected role for hemagglutinin (HA) glycosylation in compensating for fitness defects resulting from escape from anti-HA neutralizing antibodies. Antibody-free propagation following antigenic escape rapidly selected viruses with mutations that modulated receptor binding avidity through the addition of N-linked glycans to the HA globular domain. These findings expand our understanding of the viral mechanisms that maintain fitness during antigenic evolution to include glycan addition, and highlight the immense power of high-definition virus population sequencing to reveal novel viral adaptive mechanisms.

Selection on non-antigenic gene segments of seasonal influenza A virus and its impact on adaptive evolution

Most studies on seasonal influenza A/H3N2 virus adaptation have focused on the main antigenic gene, hemagglutinin. However, there is increasing evidence that the genome-wide genetic background of novel antigenic variants can influence these variants’ emergence probabilities and impact their patterns of dominance in the population. This suggests that non-antigenic genes may be important in shaping the viral evolutionary dynamics. To better understand the role of selection on non-antigenic genes in the adaptive evolution of seasonal influenza viruses, we have developed a simple population genetic model that considers a virus with one antigenic and one non-antigenic gene segment. By simulating this model under different regimes of selection and reassortment, we find that the empirical patterns of lineage turnover for the antigenic and non-antigenic gene segments are best captured when there is both limited viral coinfection and selection operating on both gene segments. In contrast, under a scenario of only neutral evolution in the non-antigenic gene segment, we see persistence of multiple lineages for long periods of time in that segment, which is not compatible with observed molecular evolutionary patterns. Further, we find that reassortment, occurring in coinfected individuals, can increase the speed of viral adaptive evolution by primarily reducing selective interference and genetic linkage effects. Together, these findings suggest that, for influenza, with six internal or non-antigenic gene segments, the evolutionary dynamics of novel antigenic variants are likely to be influenced by the genome-wide genetic background as a result of linked selection among both beneficial and deleterious mutations.

Fitness cost of reassortment in human influenza

Reassortment, which is the exchange of genome sequence between viruses co-infecting a host cell, plays an important role in the evolution of segmented viruses. In the human influenza virus, reassortment happens most frequently between co-existing variants within the same lineage. This process breaks genetic linkage and fitness correlations between viral genome segments, but the resulting net effect on viral fitness has remained unclear. In this paper, we determine rate and average selective effect of reassortment processes in the human influenza lineage A/H3N2. For the surface proteins hemagglutinin and neuraminidase, reassortant variants with a mean distance of at least 3 nucleotides to their parent strains get established at a rate of about 10⁻² in units of the neutral point mutation rate. Our inference is based on a new method to map reassortment events from joint genealogies of multiple genome segments, which is tested by extensive simulations. We show that intra-lineage reassortment processes are, on average, under substantial negative selection that increases in strength with increasing sequence distance between the parent strains. The deleterious effects of reassortment manifest themselves in two ways: there are fewer reassortment events than expected from a null model of neutral reassortment, and reassortant strains have fewer descendants than their non-reassortant counterparts. Our results suggest that influenza evolves under ubiquitous epistasis across proteins, which produces fitness barriers against reassortment even between co-circulating strains within one lineage.

The genome of the human influenza virus consists of 8 disjoint RNA polymer segments. These segments can undergo reassortment: when two viruses co-infect a host cell, they can produce viral offspring with a new combination of segments. In this paper, we show that reassortment within a given influenza lineage induces a fitness cost that increases in strength with increasing genetic distance of the parent viruses. Our finding suggests that evolution continuously produces viral proteins whose fitness depends on each other; reassortment reduces fitness by breaking up successful combinations of proteins. Thus, selection across proteins constrains viral evolution within a given lineage, and it may be an important factor in defining a viral species.

Predicting the Evolutionary Variability of the Influenza A Virus

The influenza A virus remains one of the most common and dangerous human health concerns due to its rapid evolutionary dynamics. Since the evolutionary changes of influenza A viruses can be traced in real time, the last decade has seen a surge in research on influenza A viruses due to an increase in experimental data (selection of escape mutants followed by examination of their phenotypic characteristics and generation of viruses with desired mutations using reverse genetics). Moreover, the advances in our understanding are also attributable to the development of new computational methods based on a phylogenetic analysis of influenza virus strains and mathematical (integro-differential equations, statistical methods, probability-theory-based methods) and simulation modeling. Continuously evolving highly pathogenic influenza A viruses are a serious health concern which necessitates a coupling of theoretical and experimental approaches to predict the evolutionary trends of the influenza A virus, with a focus on the H5 subtype.

Widespread Historical Contingency in Influenza Viruses

In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular "disease-causing" mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.

The Optimisation of Pseudotyped Viruses for the Characterisation of Immune Responses to Equine Influenza Virus

Pseudotyped viruses (PVs) produced by co-transfecting cells with plasmids expressing lentiviral core proteins and viral envelope proteins are potentially powerful tools for studying various aspects of equine influenza virus (EIV) biology. The aim of this study was to optimise production of equine influenza PVs. Co-transfection of the HAT protease to activate the haemagglutinin (HA) yielded a higher titre PV than TMPRSS2 with the HA from A/equine/Richmond/1/2007 (H3N8), whereas for A/equine/Newmarket/79 (H3N8), both proteases resulted in equivalent titres. TMPRSS4 was ineffective with the HA of either strain. There was also an inverse relationship between the amount of protease-expression plasmids and the PV titre obtained.  Interestingly, the PV titre obtained by co-transfection of a plasmid encoding the cognate N8 NA was not as high as that generated by the addition of exogenous neuraminidase (NA) from Clostridium perfringens to allow the release of nascent PV particles. Finally, initial characterisation of the reliability of PV neutralisation tests (PVNTs) demonstrated good intra-laboratory repeatability. In conclusion, we have demonstrated that equine influenza PV production can be readily optimised to provide a flexible tool for studying EIV.

Antiviral susceptibility profile of influenza A viruses; keep an eye on immunocompromised patients under prolonged treatment

There was an increase in severe and fatal influenza cases in Greece during the 2011-2015 post-pandemic period. To investigate causality, we determined neuraminidase (NA) inhibitor susceptibility and resistance-conferring NA and hemagglutinin (HA) mutations in circulating influenza type A viruses during the pandemic (2009-2010) and post-pandemic periods in Greece. One hundred thirty-four influenza A(H1N1)pdm09 and 95 influenza A(H3N2) viruses submitted to the National Influenza Reference Laboratory of Southern Greece were tested for susceptibility to oseltamivir and zanamivir. Antiviral resistance was assessed by neuraminidase sequence analysis, as well as the fluorescence-based 50 % inhibitory concentration (IC₅₀) method. Five influenza A(H1N1)pdm09 viruses (2.2 %) showed significantly reduced inhibition by oseltamivir (average IC₅₀ 300.60nM vs. 1.19nM) by Gaussian kernel density plot analysis. These viruses were isolated from immunocompromised patients and harbored the H275Y oseltamivir resistance-conferring NA substitution. All A(H1N1)pdm09 viruses were zanamivir-susceptible, and all A(H3N2) viruses were susceptible to both drugs. Oseltamivir-resistant viruses did not form a distinct cluster by phylogenetic analysis. Permissive mutations were detected in immunogenic and non immunogenic NA regions of both oseltamivir- resistant and susceptible viruses in the post-pandemic seasons. Several amino acid substitutions in the HA1 domain of the HA gene of post-pandemic viruses were identified. This study indicated low resistance to NAIs among tested influenza viruses. Antiviral resistance emerged only in immunocompromised patients under long-term oseltamivir treatment. Sequential sample testing in this vulnerable group of patients is recommended to characterise resistance or reinfection and viral evolution.

A Mutational Hotspot and Strong Selection Contribute to the Order of Mutations Selected for during Escherichia coli Adaptation to the Gut

The relative role of drift versus selection underlying the evolution of bacterial species within the gut microbiota remains poorly understood. The large sizes of bacterial populations in this environment suggest that even adaptive mutations with weak effects, thought to be the most frequently occurring, could substantially contribute to a rapid pace of evolutionary change in the gut. We followed the emergence of intra-species diversity in a commensal Escherichia coli strain that previously acquired an adaptive mutation with strong effect during one week of colonization of the mouse gut. Following this first step, which consisted of inactivating a metabolic operon, one third of the subsequent adaptive mutations were found to have a selective effect as high as the first. Nevertheless, the order of the adaptive steps was strongly affected by a mutational hotspot with an exceptionally high mutation rate of 10-5. The pattern of polymorphism emerging in the populations evolving within different hosts was characterized by periodic selection, which reduced diversity, but also frequency-dependent selection, actively maintaining genetic diversity. Furthermore, the continuous emergence of similar phenotypes due to distinct mutations, known as clonal interference, was pervasive. Evolutionary change within the gut is therefore highly repeatable within and across hosts, with adaptive mutations of selection coefficients as strong as 12% accumulating without strong constraints on genetic background. In vivo competitive assays showed that one of the second steps (focA) exhibited positive epistasis with the first, while another (dcuB) exhibited negative epistasis. The data shows that strong effect adaptive mutations continuously recur in gut commensal bacterial species.

Changing preferences: deformation of single position amino acid fitness landscapes and evolution of proteins

The fitness landscape-the function that relates genotypes to fitness-and its role in directing evolution are a central object of evolutionary biology. However, its huge dimensionality precludes understanding of even the basic aspects of its shape. One way to approach it is to ask a simpler question: what are the properties of a function that assigns fitness to each possible variant at just one particular site-a single position fitness landscape-and how does it change in the course of evolution? Analyses of genomic data from multiple species and multiple individuals within a species have proved beyond reasonable doubt that fitness functions of positions throughout the genome do themselves change with time, thus shaping protein evolution. Here, I will briefly review the literature that addresses these dynamics, focusing on recent genome-scale analyses of fitness functions of amino acid sites, i.e. vectors of fitnesses of 20 individual amino acid variants at a given position of a protein. The set of amino acids that confer high fitness at a particular position changes with time, and the rate of this change is comparable with the rate at which a position evolves, implying that this process plays a major role in evolutionary dynamics. However, the causes of these changes remain largely unclear.

Positive Selection Drives Evolution at the Host-Filovirus Interaction Surface

Filovirus infection is mediated by engagement of the surface-exposed glycoprotein (GP) by its cellular receptor, NPC1 (Niemann-Pick C1). Two loops in the C domain of NPC1 (NPC1-C) bind filovirus GP. Herein, we show that filovirus GP and NPC1-C evolve under mutual selective pressure. Analysis of a large mammalian phylogeny indicated that strong functional/structural constraints limit the NPC1 sequence space available for adaptive change and most sites at the contact interface with GP are under negative selection. These constraints notwithstanding, we detected positive selection at NPC1-C in all mammalian orders, from Primates to Xenarthra. Different codons evolved adaptively in distinct mammals, and most selected sites are located within the two NPC1-C loops that engage GP, or at their anchor points. In Homininae, NPC1-C was a preferential selection target, and the T419I variant possibly represents a human-specific adaptation to filovirus infection. On the other side of the arms-race, GP evolved adaptively during filovirus speciation. One of the selected sites (S142Q) establishes several atom-to-atom contacts with NPC1-C. Additional selected sites are located within epitopes recognized by neutralizing antibodies, including the 14G7 epitope, where sites selected during the recent EBOV epidemic also map. Finally, pairs of co-evolving sites in Marburgviruses and Ebolaviruses were found to involve antigenic determinants. These findings suggest that the host humoral immune response was a major selective pressure during filovirus speciation. The S142Q variant may contribute to determine Ebolavirus host range in the wild. If this were the case, EBOV/BDBV (S142) and SUDV (Q142) may not share the same reservoir(s).

Effects of Sialic Acid Modifications on Virus Binding and Infection

Sialic acids (Sias) are abundantly displayed on the surfaces of vertebrate cells, and particularly on all mucosal surfaces. Sias interact with microbes of many types, and are the targets of specific recognition by many different viruses. They may mediate virus binding and infection of cells, or alternatively can act as decoy receptors that bind virions and block virus infection. These nine-carbon backbone monosaccharides naturally occur in many different modified forms, and are attached to underlying glycans through varied linkages, creating significant diversity in the pathogen receptor forms. Here we review the current knowledge regarding the distribution of modified Sias in different vertebrate hosts, tissues, and cells, their effects on viral pathogens where those have been examined, and outline unresolved questions.

Sialic acids (Sias) are components of cell-surface glycoproteins and glycolipids, as well as secreted glycoproteins and milk oligosaccharides. Sias play important roles in cell signaling, development, and host–pathogen interactions. Cellular enzymes can modify Sias, yet how modifications vary between tissues and hosts has not been fully elucidated.

Many viruses use Sias as receptors, with different modifications aiding or inhibiting virus infection. How modified Sias influence viral protein evolution and determine host/tissue tropism are poorly understood, and are important areas of research.

New advances in molecular glycobiology using pathogen proteins to detect varied forms allows for improved study of modified Sias that have otherwise proven difficult to isolate. This opens new avenues of inquiry for virology, as well as host interactions with bacterial and eukaryotic pathogens.

In-Depth Analysis of HA and NS1 Genes in A(H1N1)pdm09 Infected Patients

In March/April 2009, a new pandemic influenza A virus (A(H1N1)pdm09) emerged and spread rapidly via human-to-human transmission, giving rise to the first pandemic of the 21th century. Influenza virus may be present in the infected host as a mixture of variants, referred to as quasi-species, on which natural and immune-driven selection operates. Since hemagglutinin (HA) and non-structural 1 (NS1) proteins are relevant in respect of adaptive and innate immune responses, the present study was aimed at establishing the intra-host genetic heterogeneity of HA and NS1 genes, applying ultra-deep pyrosequencing (UDPS) to nasopharyngeal swabs (NPS) from patients with confirmed influenza A(H1N1)pdm09 infection. The intra-patient nucleotide diversity of HA was significantly higher than that of NS1 (median (IQR): 37.9 (32.8–42.3) X 10⁻⁴ vs 30.6 (27.4–33.6) X 10⁻⁴ substitutions/site, p = 0.024); no significant correlation for nucleotide diversity of NS1 and HA was observed (r = 0.319, p = 0.29). Furthermore, a strong inverse correlation between nucleotide diversity of NS1 and viral load was observed (r = - 0.74, p = 0.004). For both HA and NS1, the variants appeared scattered along the genes, thus indicating no privileged mutation site. Known polymorphisms, S203T (HA) and I123V (NS1), were observed as dominant variants (>98%) in almost all patients; three HA and two NS1 further variants were observed at frequency >40%; a number of additional variants were detected at frequency <6% (minority variants), of which three HA and four NS1 variants were novel. In few patients multiple variants were observed at HA residues 203 and 222. According to the FLUSURVER tool, some of these variants may affect immune recognition and host range; however, these inferences are based on H5N1, and their extension to A(H1N1)pdm09 requires caution. More studies are necessary to address the significance of the composite nature of influenza virus quasi-species within infected patients.

Cooperation between distinct viral variants promotes growth of H3N2 influenza in cell culture

RNA viruses rapidly diversify into quasispecies of related genotypes. This genetic diversity has long been known to facilitate adaptation, but recent studies have suggested that cooperation between variants might also increase population fitness. Here, we demonstrate strong cooperation between two H3N2 influenza variants that differ by a single mutation at residue 151 in neuraminidase, which normally mediates viral exit from host cells. Residue 151 is often annotated as an ambiguous amino acid in sequenced isolates, indicating mixed viral populations. We show that mixed populations grow better than either variant alone in cell culture. Pure populations of either variant generate the other through mutation and then stably maintain a mix of the two genotypes. We suggest that cooperation arises because mixed populations combine one variant's proficiency at cell entry with the other's proficiency at cell exit. Our work demonstrates a specific cooperative interaction between defined variants in a viral quasispecies.

Extensive Positive Selection Drives the Evolution of Nonstructural Proteins in Lineage C Betacoronaviruses

Middle East respiratory syndrome-related coronavirus (MERS-CoV) spreads to humans via zoonotic transmission from camels. MERS-CoV belongs to lineage C of betacoronaviruses (betaCoVs), which also includes viruses isolated from bats and hedgehogs. A large portion of the betaCoV genome consists of two open reading frames (ORF1a and ORF1b) that are translated into polyproteins. These are cleaved by viral proteases to generate 16 nonstructural proteins (nsp1 to nsp16) which compose the viral replication-transcription complex. We investigated the evolution of ORF1a and ORF1b in lineage C betaCoVs. Results indicated widespread positive selection, acting mostly on ORF1a. The proportion of positively selected sites in ORF1a was much higher than that previously reported for the surface-exposed spike protein. Selected sites were unevenly distributed, with nsp3 representing the preferential target. Several pairs of coevolving sites were also detected, possibly indicating epistatic interactions; most of these were located in nsp3. Adaptive evolution at nsp3 is ongoing in MERS-CoV strains, and two selected sites (G720 and R911) were detected in the protease domain. While position 720 is variable in camel-derived viruses, suggesting that the selective event does not represent a specific adaptation to humans, the R911C substitution was observed only in human-derived MERS-CoV isolates, including the viral strain responsible for the recent South Korean outbreak. It will be extremely important to assess whether these changes affect host range or other viral phenotypes. More generally, data herein indicate that CoV nsp3 represents a major selection target and that nsp3 sequencing should be envisaged in monitoring programs and field surveys.

IMPORTANCE

Both severe acute respiratory syndrome coronavirus (SARS-CoV) and MERS-CoV originated in bats and spread to humans via an intermediate host. This clearly highlights the potential for coronavirus host shifting and the relevance of understanding the molecular events underlying the adaptation to new host species. We investigated the evolution of ORF1a and ORF1b in lineage C betaCoVs and in 87 sequenced MERS-CoV isolates. Results indicated widespread positive selection, stronger in ORF1a than in ORF1b. Several selected sites were found to be located in functionally relevant protein regions, and some of them corresponded to functional mutations in other coronaviruses. The proportion of selected sites we identified in ORF1a is much higher than that for the surface-exposed spike protein. This observation suggests that adaptive evolution in ORF1a might contribute to host shifts or immune evasion. Data herein also indicate that genetic diversity at nonstructural proteins should be taken into account when antiviral compounds are developed.