Systematic identification of H274Y compensatory mutations in influenza A virus neuraminidase by high-throughput screening

Deep mutational scanning of influenza A virus neuraminidase facilitates the identification of drug resistance mutations in vivo

IMPORTANCE

NA is a crucial surface antigen and drug target of influenza A virus. A comprehensive understanding of NA's mutational effect and drug resistance profiles in vivo is essential for comprehending the evolutionary constraints and making informed choices regarding drug selection to combat resistance in clinical settings. In the current study, we established an efficient deep mutational screening system in mouse lung tissues and systematically evaluated the fitness effect and drug resistance to three neuraminidase inhibitors of NA single-nucleotide mutations. The fitness of NA mutants is generally correlated with a natural mutation in the database. The fitness of NA mutants is influenced by biophysical factors such as protein stability, complex formation, and the immune response triggered by viral infection. In addition to confirming previously reported drug-resistant mutations, novel mutations were identified. Interestingly, we identified an allosteric drug-resistance mutation that is not located within the drug-binding pocket but potentially affects drug binding by interfering with NA tetramerization. The dual assessments performed in this study provide a more accurate assessment of the evolutionary potential of drug-resistant mutations and offer guidance for the rational selection of antiviral drugs.

In-silico assessment of high-risk non-synonymous SNPs in ADAMTS3 gene associated with Hennekam syndrome and their impact on protein stability and function

Hennekam Lymphangiectasia-Lymphedema Syndrome 3 (HKLLS3) is a rare genetical disorder caused by mutations in a few genes including ADAMTS3. It is characterized by lymphatic dysplasia, intestinal lymphangiectasia, severe lymphedema and distinctive facial appearance. Up till now, no extensive studies have been conducted to elucidate the mechanism of the disease caused by various mutations. As a preliminary investigation of HKLLS3, we sorted out the most deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) that might affect the structure and function of ADAMTS3 protein by using a variety of in silico tools. A total of 919 nsSNPs in the ADAMTS3 gene were identified. 50 nsSNPs were predicted to be deleterious by multiple computational tools. 5 nsSNPs (G298R, C567Y, A370T, C567R and G374S) were found to be the most dangerous and can be associated with the disease as predicted by different bioinformatics tools. Modelling of the protein shows it can be divided into segments 1, 2 and 3, which are connected by short loops. Segment 3 mainly consists of loops without substantial secondary structures. With prediction tools and molecular dynamics simulation, some SNPs were found to significantly destabilize the protein structure and disrupt the secondary structures, especially in segment 2. The deleterious effects of mutations in segment 1 are possibly not from destabilization but from other factors such as the change in phosphorylation as suggested by post-translational modification (PTM) studies. This is the first-ever study of ADAMTS3 gene polymorphism, and the predicted nsSNPs in ADAMST3, some of which have not been reported yet in patients, will serve for diagnostic purposes and further therapeutic implications in Hennekam syndrome, contributing to better diagnosis and treatment.

Predicting Permissive Mutations That Improve the Fitness of A(H1N1)pdm09 Viruses Bearing the H275Y Neuraminidase Substitution

Oseltamivir-resistant influenza viruses arise due to amino acid mutations in key residues of the viral neuraminidase (NA). These changes often come at a fitness cost; however, it is known that permissive mutations in the viral NA can overcome this cost. This result was observed in former seasonal A(H1N1) viruses in 2007 which expressed the H275Y substitution (N1 numbering) with no apparent fitness cost and lead to widespread oseltamivir resistance. Therefore, this study aims to predict permissive mutations that may similarly enable fit H275Y variants to arise in currently circulating A(H1N1)pdm09 viruses. The first approach in this study utilized in silico analyses to predict potentially permissive mutations. The second approach involved the generation of a virus library which encompassed all possible NA mutations while keeping H275Y fixed. Fit variants were then selected by serially passaging the virus library either through ferrets by transmission or passaging once in vitro. The fitness impact of selected substitutions was further evaluated experimentally. The computational approach predicted three candidate permissive NA mutations which, in combination with each other, restored the replicative fitness of an H275Y variant. The second approach identified a stringent bottleneck during transmission between ferrets; however, three further substitutions were identified which may improve transmissibility. A comparison of fit H275Y variants in vitro and in experimentally infected animals showed a statistically significant correlation in the variants that were positively selected. Overall, this study provides valuable tools and insights into potential permissive mutations that may facilitate the emergence of a fit H275Y A(H1N1)pdm09 variant.

IMPORTANCE Oseltamivir (Tamiflu) is the most widely used antiviral for the treatment of influenza infections. Therefore, resistance to oseltamivir is a public health concern. This study is important as it explores the different evolutionary pathways available to current circulating influenza viruses that may lead to widespread oseltamivir resistance. Specifically, this study develops valuable experimental and computational tools to evaluate the fitness landscape of circulating A(H1N1)pmd09 influenza viruses bearing the H275Y mutation. The H275Y substitution is most commonly reported to confer oseltamivir resistance but also leads to loss of virus replication and transmission fitness, which limits its spread. However, it is known from previous influenza seasons that influenza viruses can evolve to overcome this loss of fitness. Therefore, this study aims to prospectively predict how contemporary A(H1N1)pmd09 influenza viruses may evolve to overcome the fitness cost of bearing the H275Y NA substitution, which could result in widespread oseltamivir resistance.

Next-generation T cell-activating vaccination increases influenza virus mutation prevalence

To determine the potential for viral adaptation to T cell responses, we probed the full influenza virus genome by next-generation sequencing directly ex vivo from infected mice, in the context of an experimental T cell-based vaccine, an H5N1-based viral vectored vaccinia vaccine Wyeth/IL-15/5Flu, versus the current standard-of-care, seasonal inactivated influenza vaccine (IIV) and unvaccinated conditions. Wyeth/IL-15/5Flu vaccination was coincident with increased mutation incidence and frequency across the influenza genome; however, mutations were not enriched within T cell epitope regions, but high allele frequency mutations within conserved hemagglutinin stem regions and PB2 mammalian adaptive mutations arose. Depletion of CD4⁺ and CD8⁺ T cell subsets led to reduced frequency of mutants in vaccinated mice; therefore, vaccine-mediated T cell responses were important drivers of virus diversification. Our findings suggest that Wyeth/IL-15/5Flu does not generate T cell escape mutants but increases stochastic events for virus adaptation by stringent bottlenecks.

Identification of a permissive secondary mutation that restores the enzymatic activity of oseltamivir resistance mutation H275Y

Many oseltamivir resistance mutations exhibit fitness defects in the absence of drug pressure that hinders their propagation in hosts. Secondary permissive mutations can rescue fitness defects and facilitate the segregation of resistance mutations in viral populations. Previous studies have identified a panel of permissive or compensatory mutations in neuraminidase (NA) that restore the growth defect of the predominant oseltamivir resistance mutation (H275Y) in H1N1 influenza A. In prior work, we identified a hyperactive mutation (Y276F) that increased NA activity by approximately 70%. While Y276F had not been previously identified as a permissive mutation, we hypothesized that Y276F may counteract the defects caused by H275Y by buffering its reduced NA expression and enzyme activity. In this study we measured the relative fitness, NA activity, and surface expression, as well as sensitivity to oseltamivir, for several oseltamivir resistance mutations including H275Y in the wildtype or Y276F genetic background. Our results demonstrate that Y276F selectively rescues the fitness defect of H275Y by restoring its NA surface expression and enzymatic activity, elucidating the local compensatory structural impacts of Y276F on the adjacent H275Y. Importance The potential for influenza A virus (IAV) to cause pandemics makes understanding evolutionary mechanisms that impact drug resistance critical for developing surveillance and treatment strategies. Oseltamivir is the most widely used therapeutic strategy to treat IAV infections, but mutations in IAV can lead to drug resistance. The main oseltamivir resistance mutation, H275Y, occurs in the neuraminidase (NA) protein of IAV and reduces drug binding as well as NA function. Here, we identify a new helper mutation, Y276F that can rescue the functional defects of H275Y and contribute to the evolution of drug resistance in IAV.

Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing

As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.

Saturation Mutagenesis of the Transmembrane Region of HokC in Escherichia coli Reveals Its High Tolerance to Mutations

Cells adapt to different stress conditions, such as the antibiotics presence. This adaptation sometimes is achieved by changing relevant protein positions, of which the mutability is limited by structural constrains. Understanding the basis of these constrains represent an important challenge for both basic science and potential biotechnological applications. To study these constraints, we performed a systematic saturation mutagenesis of the transmembrane region of HokC, a toxin used by Escherichia coli to control its own population, and observed that 92% of single-point mutations are tolerated and that all the non-tolerated mutations have compensatory mutations that reverse their effect. We provide experimental evidence that HokC accumulates multiple compensatory mutations that are found as correlated mutations in the HokC family multiple sequence alignment. In agreement with these observations, transmembrane proteins show higher probability to present correlated mutations and are less densely packed locally than globular proteins; previous mutagenesis results on transmembrane proteins further support our observations on the high tolerability to mutations of transmembrane regions of proteins. Thus, our experimental results reveal the HokC transmembrane region high tolerance to loss-of-function mutations that is associated with low sequence conservation and high rate of correlated mutations in the HokC family sequences alignment, which are features shared with other transmembrane proteins.

Applications of Deep Mutational Scanning in Virology

Several recently developed high-throughput techniques have changed the field of molecular virology. For example, proteomics studies reveal complete interactomes of a viral protein, genome-wide CRISPR knockout and activation screens probe the importance of every single human gene in aiding or fighting a virus, and ChIP-seq experiments reveal genome-wide epigenetic changes in response to infection. Deep mutational scanning is a relatively novel form of protein science which allows the in-depth functional analysis of every nucleotide within a viral gene or genome, revealing regions of importance, flexibility, and mutational potential. In this review, we discuss the application of this technique to RNA viruses including members of the Flaviviridae family, Influenza A Virus and Severe Acute Respiratory Syndrome Coronavirus 2. We also briefly discuss the reverse genetics systems which allow for analysis of viral replication cycles, next-generation sequencing technologies and the bioinformatics tools that facilitate this research.

Concerns about SARS-CoV-2 evolution should not hold back efforts to expand vaccination

When vaccines are in limited supply, expanding the number of people who receive some vaccine, such as by halving doses or increasing the interval between doses, can reduce disease and mortality compared with concentrating available vaccine doses in a subset of the population. A corollary of such dose-sparing strategies is that the vaccinated individuals may have less protective immunity. Concerns have been raised that expanding the fraction of the population with partial immunity to SARS-CoV-2 could increase selection for vaccine-escape variants, ultimately undermining vaccine effectiveness. We argue that, although this is possible, preliminary evidence instead suggests such strategies should slow the rate of viral escape from vaccine or naturally induced immunity. As long as vaccination provides some protection against escape variants, the corresponding reduction in prevalence and incidence should reduce the rate at which new variants are generated and the speed of adaptation. Because there is little evidence of efficient immune selection of SARS-CoV-2 during typical infections, these population-level effects are likely to dominate vaccine-induced evolution.

Quantifying the Evolutionary Constraints and Potential of Hepatitis C Virus NS5A Protein

RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARS-CoV-2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance).

IMPORTANCE Many viruses adapt rapidly to novel selection pressures, such as antiviral drugs. Understanding how pathogens evolve under drug selection is critical for the success of antiviral therapy against human pathogens. By combining deep sequencing with selection experiments in cell culture, we have quantified the distribution of fitness effects of mutations in hepatitis C virus (HCV) NS5A protein. Our results indicate that the majority of single amino acid substitutions in NS5A protein incur large fitness costs. Simulation of protein stability suggests viral evolution is constrained by the need to maintain protein stability. By subjecting the mutant viruses to selection under an antiviral drug, we find that the adaptive potential of viral proteins in a novel environment is modulated by the level of environmental stress, which can be explained by a pharmacodynamics model. Our comprehensive characterization of the fitness landscapes of NS5A can potentially guide the design of effective strategies to limit viral evolution.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

Circular permutation profiling by deep sequencing libraries created using transposon mutagenesis

Deep mutational scanning has been used to create high-resolution DNA sequence maps that illustrate the functional consequences of large numbers of point mutations. However, this approach has not yet been applied to libraries of genes created by random circular permutation, an engineering strategy that is used to create open reading frames that express proteins with altered contact order. We describe a new method, termed circular permutation profiling with DNA sequencing (CPP-seq), which combines a one-step transposon mutagenesis protocol for creating libraries with a functional selection, deep sequencing and computational analysis to obtain unbiased insight into a protein's tolerance to circular permutation. Application of this method to an adenylate kinase revealed that CPP-seq creates two types of vectors encoding each circularly permuted gene, which differ in their ability to express proteins. Functional selection of this library revealed that >65% of the sampled vectors that express proteins are enriched relative to those that cannot translate proteins. Mapping enriched sequences onto structure revealed that the mobile AMP binding and rigid core domains display greater tolerance to backbone fragmentation than the mobile lid domain, illustrating how CPP-seq can be used to relate a protein's biophysical characteristics to the retention of activity upon permutation.

Diagnostic challenges in influenza

In 2018 there are still microbiology laboratories that do not subtype or detect influenza viruses, one of the main agents of community-acquired pneumonia. A major challenge is to introduce multiplex-type technologies into most clinical virological diagnostic laboratories, increasing the feasibility of timely etiological diagnosis of influenza and other respiratory viruses whenever required and thus limiting antibiotic treatments. Other diagnostic tools such as markers of severity and the detection of resistance are pending challenges to complete and expand. Viral culture, an essential tool in the epidemiological surveillance of viruses, has been relegated by more sensitive and affordable molecular techniques. Sequencing of the influenza virus together with the antigenic characterisation and detection techniques of antibodies against hemagglutinin and neuraminidase will, in future, be used in tandem with other techniques to detect antibodies against other structural proteins, helping to elucidate the complicated epidemiology of these viruses and the production of new vaccines and their evaluation. Supplement information: This article is part of a supplement entitled «SEIMC External Quality Control Programme. Year 2016», which is sponsored by Roche, Vircell Microbiologists, Abbott Molecular and Francisco Soria Melguizo, S.A. © 2019 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosasy Microbiología Clínica. All rights reserved.

Application of deep sequencing methods for inferring viral population diversity

The first deep sequencing method was announced in 2005. Due to an increasing number of sequencing data and a reduction in the costs of each sequencing dataset, this innovative technique was soon applied to genetic investigations of viral genome diversity in various viruses, particularly RNA viruses. These deep sequencing findings documented viral epidemiology and evolution and provided high-resolution data on the genetic changes in viral populations. Here, we review deep sequencing platforms that have been applied in viral quasispecies studies. Further, we discuss recent deep sequencing studies on viral inter- and intrahost evolution, drug resistance, and humoral immune selection, especially in emerging and re-emerging viruses. Deep sequencing methods are becoming the standard for providing comprehensive results of viral population diversity, and their applications are discussed.

Novel antiviral drug discovery strategies to tackle drug-resistant mutants of influenza virus strains

INTRODUCTION

The emergence of drug-resistant influenza virus strains highlights the need for new antiviral therapeutics to combat future pandemic outbreaks as well as continuing seasonal cycles of influenza. Areas covered: This review summarizes the mechanisms of current FDA-approved anti-influenza drugs and patterns of resistance to those drugs. It also discusses potential novel targets for broad-spectrum antiviral drugs and recent progress in novel drug design to overcome drug resistance in influenza. Expert opinion: Using the available structural information about drug-binding pockets, research is currently underway to identify molecular interactions that can be exploited to generate new antiviral drugs. Despite continued efforts, antivirals targeting viral surface proteins like HA, NA, and M2, are all susceptible to developing resistance. Structural information on the internal viral polymerase complex (PB1, PB2, and PA) provides a new avenue for influenza drug discovery. Host factors, either at the initial step of viral infection or at the later step of nuclear trafficking of viral RNP complex, are being actively pursued to generate novel drugs with new modes of action, without resulting in drug resistance.

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.

Rapid and simple detection of Tamiflu-resistant influenza virus: Development of oseltamivir derivative-based lateral flow biosensor for point-of-care (POC) diagnostics

We have developed a novel oseltamivir derivative (oseltamivir hexylthiol; OHT) that exhibits a higher binding affinity for Tamiflu-resistant virus (Tamiflu resistance) than for the wild-type virus (Tamiflu-susceptible virus; WT) as an antibody. First, OHT-modified gold nanoparticles (OHT-GNPs) are used in a simple colorimetric assay as nanoprobes for the Tamiflu-resistant virus. In the presence of Tamiflu-resistant virus, they show a colorimetric change from deep red to purple because of the OHT-GNP aggregation driven by strong interactions between OHT and neuraminidase (NA) on the surface of the Tamiflu-resistance. Moreover, the color gradually turns purple as the concentration of the Tamiflu-resistant virus increases, allowing the determination of the presence of the virus with the naked eye. Furthermore, an OHT-based lateral flow assay (LFA) has been developed as a rapid and easy detection device for Tamiflu resistance. It shows detection specificity for various virus concentrations of Tamiflu-resistant virus even for the mixture of WT and Tamiflu-resistant viruses, where the limit of detection (LOD) is 5 × 10² ~ 10³ PFU per test (=1 × 10⁴ PFU/mL). It has been confirmed that this platform can provide accurate information on whether a virus exhibits Tamiflu resistance, thus supporting the selection of appropriate treatments using point-of-care (POC) diagnostics.

Intracellular interleukin-32γ mediates antiviral activity of cytokines against hepatitis B virus

Cytokines are involved in early host defense against pathogen infections. In particular, tumor necrosis factor (TNF) and interferon-gamma (IFN-γ) have critical functions in non-cytopathic elimination of hepatitis B virus (HBV) in hepatocytes. However, the molecular mechanisms and mediator molecules are largely unknown. Here we show that interleukin-32 (IL-32) is induced by TNF and IFN-γ in hepatocytes, and inhibits the replication of HBV by acting intracellularly to suppress HBV transcription and replication. The gamma isoform of IL-32 (IL-32γ) inhibits viral enhancer activities by downregulating liver-enriched transcription factors. Our data are validated in both an in vivo HBV mouse model and primary human hepatocytes. This study thus suggests that IL-32γ functions as intracellular effector in hepatocytes for suppressing HBV replication to implicate a possible mechanism of non-cytopathic viral clearance.

Cytokines such as TNF and IFN-γ are important for immunity against hepatitis B virus (HBV). Here the authors show that interleukin-32 gamma (IL-32γ) acts downstream of TNF and IFN-γ as an intracellular effector, and that IL-32γ negatively regulates host factors contributing to HBV transcription to promote HBV clearance.

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.

Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas

Given the constantly improving cost and speed of genome sequencing, it is reasonable to expect that personal genomes will soon be known for many millions of humans. This stands in stark contrast with our limited ability to interpret the sequence variants which we find. Although it is, perhaps, easiest to interpret variants in coding regions, knowledge of functional impact is unknown for the vast majority of missense variants. While many computational approaches can predict the impact of coding variants, they are given a little weight in the current guidelines for interpreting clinical variants. Laboratory assays produce comparatively more trustworthy results, but until recently did not scale to the space of all possible mutations. The development of deep mutational scanning and other multiplexed assays of variant effect has now brought feasibility of this endeavour within view. Here, we review progress in this field over the last decade, break down the different approaches into their components, and compare methodological differences.

Characterization of influenza virus variants induced by treatment with the endonuclease inhibitor baloxavir marboxil

Baloxavir acid (BXA), derived from the prodrug baloxavir marboxil (BXM), potently and selectively inhibits the cap-dependent endonuclease within the polymerase PA subunit of influenza A and B viruses. In clinical trials, single doses of BXM profoundly decrease viral titers as well as alleviating influenza symptoms. Here, we characterize the impact on BXA susceptibility and replicative capacity of variant viruses detected in the post-treatment monitoring of the clinical studies. We find that the PA I38T substitution is a major pathway for reduced susceptibility to BXA, with 30- to 50-fold and 7-fold EC₅₀ changes in A and B viruses, respectively. The viruses harboring the I38T substitution show severely impaired replicative fitness in cells, and correspondingly reduced endonuclease activity in vitro. Co-crystal structures of wild-type and I38T influenza A and B endonucleases bound to BXA show that the mutation reduces van der Waals contacts with the inhibitor. A reduced affinity to the I38T mutant is supported by the lower stability of the BXA-bound endonuclease. These mechanistic insights provide markers for future surveillance of treated populations.

Hyphenated 3D-QSAR statistical model-drug repurposing analysis for the identification of potent neuraminidase inhibitor

The Influenza A virus is one of the principle causes of respiratory illness in human. The surface glycoprotein of the influenza virus, neuraminidase (NA), has a vital role in the release of new viral particle and spreads infection in the respiratory tract. It has been long recognized as a valid drug target for influenza A virus infection. Oseltamivir is used as a standard drug of choice for the treatment of influenza. However, the emergence of mutants with novel mutations has increased the resistance to potent NA inhibitor. In the present investigation, we have employed computer-assisted combinatorial techniques in the screening of 8621 molecules from Drug Bank to find potent NA inhibitors. A three-dimensional pharmacophore model was generated from the previously reported 28 carbocylic influenza NA inhibitors along with oseltamivir using PHASE module of Schrödinger Suite. The model generated consists of one hydrogen bond acceptor (A), one hydrogen bond donors (D), one hydrophobic group (H), and one positively charged group (P), ADHP. The hypothesis was further validated for its integrity and significance using enrichment analysis. Subsequently, an atom-based 3D-QSAR model was built using the common pharmacophore hypothesis (CPH). The developed 3D-QSAR model was found to be statistically significant with R² value of 0.9866 and Q² value of 0.7629. Further screening was accomplished using three-stage docking process using the Glide algorithm. The resultant lead molecules were examined for its drug-like properties using the Qikprop algorithm. Finally, the calculated pIC₅₀ values of the lead compounds were validated by the AutoQSAR algorithm. Overall, the results from our analysis highlights that lisinopril (DB00722) is predicted to bind better with NA than currently approved drug. In addition, it has the best match in binding geometry conformations with the existing NA inhibitor. Note that the antiviral activity of lisinopril is reported in the literature. However, our paper is the first report on lisinopril activity against influenza A virus infection. These results are envisioned to help design the novel NA inhibitors with an increased antiviral efficacy.

Mapping the Evolutionary Potential of RNA Viruses

The deterministic force of natural selection and stochastic influence of drift shape RNA virus evolution. New deep-sequencing and microfluidics technologies allow us to quantify the effect of mutations and trace the evolution of viral populations with single-genome and single-nucleotide resolution. Such experiments can reveal the topography of the genotype-fitness landscapes that shape the path of viral evolution. By combining historical analyses, like phylogenetic approaches, with high-throughput and high-resolution evolutionary experiments, we can observe parallel patterns of evolution that drive important phenotypic transitions. These developments provide a framework for quantifying and anticipating potential evolutionary events. Here, we examine emerging technologies that can map the selective landscapes of viruses, focusing on their application to pathogenic viruses. We identify areas where these technologies can bolster our ability to study the evolution of viruses and to anticipate and possibly intervene in evolutionary events and prevent viral disease.

Understanding the complex evolution of rapidly mutating viruses with deep sequencing: Beyond the analysis of viral diversity

With the advent of affordable deep sequencing technologies, detection of low frequency variants within genetically diverse viral populations can now be achieved with unprecedented depth and efficiency. The high-resolution data provided by next generation sequencing technologies is currently recognised as the gold standard in estimation of viral diversity. In the analysis of rapidly mutating viruses, longitudinal deep sequencing datasets from viral genomes during individual infection episodes, as well as at the epidemiological level during outbreaks, now allow for more sophisticated analyses such as statistical estimates of the impact of complex mutation patterns on the evolution of the viral populations both within and between hosts. These analyses are revealing more accurate descriptions of the evolutionary dynamics that underpin the rapid adaptation of these viruses to the host response, and to drug therapies. This review assesses recent developments in methods and provide informative research examples using deep sequencing data generated from rapidly mutating viruses infecting humans, particularly hepatitis C virus (HCV), human immunodeficiency virus (HIV), Ebola virus and influenza virus, to understand the evolution of viral genomes and to explore the relationship between viral mutations and the host adaptive immune response. Finally, we discuss limitations in current technologies, and future directions that take advantage of publically available large deep sequencing datasets.

A Statistical Guide to the Design of Deep Mutational Scanning Experiments

The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deep-sequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, and the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately compared with increasing the sequencing depth or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increase experimental power and allow for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates.

Coupling high-throughput genetics with phylogenetic information reveals an epistatic interaction on the influenza A virus M segment

Background

Epistasis is one of the central themes in viral evolution due to its importance in drug resistance, immune escape, and interspecies transmission. However, there is a lack of experimental approach to systematically probe for epistatic residues.

Results

By utilizing the information from natural occurring sequences and high-throughput genetics, this study established a novel strategy to identify epistatic residues. The rationale is that a substitution that is deleterious in one strain may be prevalent in nature due to the presence of a naturally occurring compensatory substitution. Here, high-throughput genetics was applied to influenza A virus M segment to systematically identify deleterious substitutions. Comparison with natural sequence variation showed that a deleterious substitution M1 Q214H was prevalent in circulating strains. A coevolution analysis was then performed and indicated that M1 residues 121, 207, 209, and 214 naturally coevolved as a group. Subsequently, we experimentally validated that M1 A209T was a compensatory substitution for M1 Q214H.

Conclusions

This work provided a proof-of-concept to identify epistatic residues by coupling high-throughput genetics with phylogenetic information. In particular, we were able to identify an epistatic interaction between M1 substitutions A209T and Q214H. This analytic strategy can potentially be adapted to study any protein of interest, provided that the information on natural sequence variants is available.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2358-7) contains supplementary material, which is available to authorized users.

A Balance between Inhibitor Binding and Substrate Processing Confers Influenza Drug Resistance

The therapeutic benefits of the neuraminidase (NA) inhibitor oseltamivir are dampened by the emergence of drug resistance mutations in influenza A virus (IAV). To investigate the mechanistic features that underlie resistance, we developed an approach to quantify the effects of all possible single-nucleotide substitutions introduced into important regions of NA. We determined the experimental fitness effects of 450 nucleotide mutations encoding positions both surrounding the active site and at more distant sites in an N1 strain of IAV in the presence and absence of oseltamivir. NA mutations previously known to confer oseltamivir resistance in N1 strains, including H275Y and N295S, were adaptive in the presence of drug, indicating that our experimental system captured salient features of real-world selection pressures acting on NA. We identified mutations, including several at position 223, that reduce the apparent affinity for oseltamivir in vitro. Position 223 of NA is located adjacent to a hydrophobic portion of oseltamivir that is chemically distinct from the substrate, making it a hotspot for substitutions that preferentially impact drug binding relative to substrate processing. Furthermore, two NA mutations, K221N and Y276F, each reduce susceptibility to oseltamivir by increasing NA activity without altering drug binding. These results indicate that competitive expansion of IAV in the face of drug pressure is mediated by a balance between inhibitor binding and substrate processing.

Deep sequencing reveals the viral adaptation process of environment-derived H10N8 in mice

The H10N8 virus was isolated from the water of Dongting Lake, China. Mice were infected while showing no obvious symptoms and replication was restricted to the lungs. When the wild-type virus was serially passaged in the lungs of mice, the resulting viruses became lethal and capable of replication in many other organs. This offered an applicable model for the exploration of viral genome gradual mutation during adaptation in mice. The different passage viruses from mice lung lavage were named P1, P3, P5, and P7, respectively. We sequenced the four viruses using next-generation sequencing (NGS) to analyze the dynamics of the H10N8 viral genome, polymorphism, and amino acid mutation of related proteins. We aimed to demonstrate how a mutant strain of low pathogenicity could become lethal to mice. Using Illumina high-throughput data, we detected the gradual mutations of F277S, C278Q, F611S and L653P in the polymerase acidic (PA) protein, and of L207V and E627K in the PB2 protein during adaptation. Interestingly, many amino acid sites mutated quickly; the others did so more slowly and remained in a heterozygous state for several generations. The PA amino acids S277 and Q278 have previously been found in clinical wild-type strains, including the human-H10N8 isolate in 2013. This demonstrates that the wild-type H10N8 virus had mutated to adapt to mammalian hosts. These data provide important reference information for influenza virus research.

Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations

Systematic mappings of the effects of protein mutations are becoming increasingly popular. Unexpectedly, these experiments often find that proteins are tolerant to most amino acid substitutions, including substitutions in positions that are highly conserved in nature. To obtain a more realistic distribution of the effects of protein mutations, we applied a laboratory drift comprising 17 rounds of random mutagenesis and selection of M.HaeIII, a DNA methyltransferase. During this drift, multiple mutations gradually accumulated. Deep sequencing of the drifted gene ensembles allowed determination of the relative effects of all possible single nucleotide mutations. Despite being averaged across many different genetic backgrounds, about 67% of all nonsynonymous, missense mutations were evidently deleterious, and an additional 16% were likely to be deleterious. In the early generations, the frequency of most deleterious mutations remained high. However, by the 17th generation, their frequency was consistently reduced, and those remaining were accepted alongside compensatory mutations. The tolerance to mutations measured in this laboratory drift correlated with sequence exchanges seen in M.HaeIII’s natural orthologs. The biophysical constraints dictating purging in nature and in this laboratory drift also seemed to overlap. Our experiment therefore provides an improved method for measuring the effects of protein mutations that more closely replicates the natural evolutionary forces, and thereby a more realistic view of the mutational space of proteins.

Understanding and predicting the effects of single nucleotide polymorphisms (SNPs) is of fundamental importance in many fields. Systematic experimental mappings of the effects of such mutations within a given gene/protein comprise an essential experimental tool for determining protein function and for refining models of protein evolution, as well as an important resource for improving prediction algorithms. Here, we present the results of a laboratory system that mimics the manner by which protein sequences diverge in nature: a prolonged process of gradually accumulating random mutations that retain the protein’s structure and function. The change in frequencies of mutations over generations, as obtained by deep sequencing, enabled us to assess the relative effects of all possible SNPs at the background of an accumulating number of mutations. Compared to previous reports, we found that > 80% of all possible amino acid exchanges have potential deleterious effects, with 67% being clearly deleterious. Tolerance vs. purging of mutations in our prolonged drift also showed better correlation with natural diversity. Overall, our experimental setup provides a better understanding of how protein sequences diverge in nature, plus a new basis for improving the prediction accuracy of the effects of protein mutations, and specifically of SNPs.

Predicting the combined effect of multiple genetic variants

Background

Many genetic variants have been identified in the human genome. The functional effects of a single variant have been intensively studied. However, the joint effects of multiple variants in the same genes have been largely ignored due to their complexity or lack of data. This paper uses HMMvar, a hidden Markov model based approach, to investigate the combined effect of multiple variants from the 1000 Genomes Project. Two tumor suppressor genes, TP53 and phosphatase and tensin homolog (PTEN), are also studied for the joint effect of compensatory indel variants.

Results

Results show that there are cases where the joint effect of having multiple variants in the same genes is significantly different from that of a single variant. The deleterious effect of a single indel variant can be alleviated by their compensatory indels in TP53 and PTEN. Compound mutations in two genes, β-MHC and MyBP-C, leading to severer cardiovascular disease compared to single mutations, are also validated.

Conclusions

This paper extends the functionality of HMMvar, a tool for assigning a quantitative score to a variant, to measure not only the deleterious effect of a single variant but also the joint effect of multiple variants. HMMvar is the first tool that can predict the functional effects of both single and general multiple variations on proteins. The precomputed scores for multiple variants from the 1000 Genomes Project and the HMMvar package are available at https://bioinformatics.cs.vt.edu/zhanglab/HMMvar/

Functional Constraint Profiling of a Viral Protein Reveals Discordance of Evolutionary Conservation and Functionality

Viruses often encode proteins with multiple functions due to their compact genomes. Existing approaches to identify functional residues largely rely on sequence conservation analysis. Inferring functional residues from sequence conservation can produce false positives, in which the conserved residues are functionally silent, or false negatives, where functional residues are not identified since they are species-specific and therefore non-conserved. Furthermore, the tedious process of constructing and analyzing individual mutations limits the number of residues that can be examined in a single study. Here, we developed a systematic approach to identify the functional residues of a viral protein by coupling experimental fitness profiling with protein stability prediction using the influenza virus polymerase PA subunit as the target protein. We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types. Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone. More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.

The analysis of sequence conservation is a common approach to identify functional residues within a protein. However, not all functional residues are conserved as natural evolution and species diversification permit continuous innovation of protein functionality through the retention of advantageous mutations. Non-conserved functional residues, which are often species-specific, may not be identified by conventional analysis of sequence conservation despite being biologically important. Here we described a novel approach to identify functional residues within a protein by coupling a high-throughput experimental fitness profiling approach with computational protein modeling. Our methodology is independent of sequence conservation and is applicable to any protein where structural information is available. In this study, we systematically mapped the functional residues on the influenza A PA protein and revealed that non-conserved functional residues are prevalent. Our results not only have significant implication on how functionality evolves during natural evolution, but also highlight the caveats when applying conservation-based approaches to identify functional residues within a protein.

A review of neuraminidase inhibitor susceptibility in influenza strains

Influenza human infections are considered as a persistent global public health issue. Whereas vaccination is important for prevention, given its limitations, antiviral therapy is at the forefront of treatment, while it also plays a significant role in prevention. Currently, two classes of drugs, adamantanes (M2 blockers) and neuraminidase inhibitors (NAIs), are available for treatment and chemoprophylaxis of influenza infections. Given the resistance patterns of circulating influenza strains, adamantanes are not currently recommended. The current review mainly focuses on the development of resistance to NAIs among A and B subtypes of influenza virus strains over the last 5 years. 'Permissive' drift mutations and reassortment of viral gene segments have resulted in NAI oseltamivir-resistant A/(H1N1) variants that rapidly became predominant worldwide in the period 2007-2009. However, the prevalence of antiviral resistance to NAI zanamivir remains relatively low. In addition, the recently developed NAIs, peramivir and laninamivir, while licensed in certain countries, are still under evaluation and only a few reports have described resistance to peramivir. Although in 2014, the majority of circulating human influenza viruses remains susceptible to all NAIs, the emergence of oseltamivir-resistant influenza variants that could retain viral transmissibility, highlights the necessity for enhanced epidemiological and microbiological surveillance and clinical assessment of antiviral resistance.

Dissecting enzyme function with microfluidic-based deep mutational scanning

Natural enzymes are incredibly proficient catalysts, but engineering them to have new or improved functions is challenging due to the complexity of how an enzyme's sequence relates to its biochemical properties. Here, we present an ultrahigh-throughput method for mapping enzyme sequence-function relationships that combines droplet microfluidic screening with next-generation DNA sequencing. We apply our method to map the activity of millions of glycosidase sequence variants. Microfluidic-based deep mutational scanning provides a comprehensive and unbiased view of the enzyme function landscape. The mapping displays expected patterns of mutational tolerance and a strong correspondence to sequence variation within the enzyme family, but also reveals previously unreported sites that are crucial for glycosidase function. We modified the screening protocol to include a high-temperature incubation step, and the resulting thermotolerance landscape allowed the discovery of mutations that enhance enzyme thermostability. Droplet microfluidics provides a general platform for enzyme screening that, when combined with DNA-sequencing technologies, enables high-throughput mapping of enzyme sequence space.

Influenza A Virus NS1 Protein Inhibits the NLRP3 Inflammasome

The inflammasome is a molecular platform that stimulates the activation of caspase-1 and the processing of pro-interleukin (IL)-1β and pro-IL-18 for secretion. The NOD-like receptor family, pyrin domain containing 3 (NLRP3) protein is activated by diverse molecules and pathogens, leading to the formation of the NLRP3 inflammasome. Recent studies showed that the NLRP3 inflammasome mediates innate immunity against influenza A virus (IAV) infection. In this study, we investigated the function of the IAV non-structural protein 1 (NS1) in the modulation of NLRP3 inflammasome. We found that NS1 proteins derived from both highly pathogenic and low pathogenic strains efficiently decreased secretion of IL-1β and IL-18 from THP-1 cells treated with LPS and ATP. NS1 overexpression significantly impaired the transcription of proinflammatory cytokines by inhibiting transactivation of the nuclear factor-κB (NF-κB), a major transcription activator. Furthermore, NS1 physically interacted with endogenous NLRP3 and activation of the NLRP3 inflammasome was abrogated in NS1-expressing THP-1 cells. These findings suggest that NS1 downregulates NLRP3 inflammasome activation by targeting NLRP3 as well as NF-κB, leading to a reduction in the levels of inflammatory cytokines as a viral immune evasion strategy.

Error-prone pcr-based mutagenesis strategy for rapidly generating high-yield influenza vaccine candidates

Vaccination is the primary strategy for the prevention and control of influenza outbreaks. However, the manufacture of influenza vaccine requires a high-yield seed strain, and the conventional methods for generating such strains are time consuming. In this study, we developed a novel method to rapidly generate high-yield candidate vaccine strains by integrating error-prone PCR, site-directed mutagenesis strategies, and reverse genetics. We used this method to generate seed strains for the influenza A(H1N1)pdm09 virus and produced six high-yield candidate strains. We used a mouse model to assess the efficacy of two of the six candidate strains as a vaccine seed virus: both strains provided complete protection in mice against lethal challenge, thus validating our method. Results confirmed that the efficacy of these candidate vaccine seed strains was not affected by the yield-optimization procedure.

High-throughput profiling of point mutations across the HIV-1 genome

Background

The HIV-1 pandemic is not the result of a static pathogen but a large genetically diverse and dynamic viral population. The virus is characterized by a highly mutable genome rendering efforts to design a universal vaccine a significant challenge and drives the emergence of drug resistant variants upon antiviral pressure. Gaining a comprehensive understanding of the mutational tolerance of each HIV-1 genomic position is therefore of critical importance.

Results

Here we combine high-density mutagenesis with the power of next-generation sequencing to gauge the replication capacity and therefore mutational tolerability of single point mutations across the entire HIV-1 genome. We were able to achieve the evaluation of point mutational effects on viral replicative capacity for 5,553 individual HIV-1 nucleotide positions – representing 57% of the viral genome. Replicative capacity was assessed at 3,943 nucleotide positions for a single alternate base change, 1,459 nucleotide positions for two alternate base changes, and 151 nucleotide positions for all three possible alternate base changes. This resulted in the study of how a total of 7,314 individual point mutations impact HIV-1 replication on a single experimental platform. We further utilize the dataset for a focused structural analysis on a capsid inhibitor binding pocket.

Conclusion

The approach presented here can be applied to any pathogen that can be genetically manipulated in a laboratory setting. Furthermore, the methodology can be utilized under externally applied selection conditions, such as drug or immune pressure, to identify genetic elements that contribute to drug or host interactions, and therefore mutational routes of pathogen resistance and escape.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-014-0124-6) contains supplementary material, which is available to authorized users.

Epistatic interactions between neuraminidase mutations facilitated the emergence of the oseltamivir-resistant H1N1 influenza viruses

Oseltamivir-resistant H1N1 influenza viruses carrying the H275Y neuraminidase mutation predominated worldwide during the 2007–2009 seasons. While several neuraminidase substitutions were found to be necessary to counteract the adverse effects of H275Y, the order and impact of evolutionary events involved remain elusive. Here, we reconstruct H1N1 neuraminidase phylogeny during 1999–2009, estimate the timing and order of crucial amino acid changes, and evaluate their impact on the biological outcome of the H275Y mutation. Of the twelve neuraminidase substitutions that occurred during 1999–2009, five (chronologically, V234M, R222Q, K329E, D344N, H275Y, and D354G) are necessary for maintaining full neuraminidase function in the presence of the H275Y mutation by altering protein accumulation or enzyme affinity/activity. The sequential emergence and cumulative effects of these mutations clearly illustrate a role for epistasis in shaping the emergence and subsequent evolution of a drug-resistant virus population, which can be useful in understanding emergence of novel viral phenotypes of influenza.

Deep mutational scanning: a new style of protein science

Mutagenesis provides insight into proteins, but only recently have assays that couple genotype to phenotype been used to assess the activities of as many as 1 million mutant versions of a protein in a single experiment. This approach-'deep mutational scanning'-yields large-scale data sets that can reveal intrinsic protein properties, protein behavior within cells and the consequences of human genetic variation. Deep mutational scanning is transforming the study of proteins, but many challenges must be tackled for it to fulfill its promise.

Measuring the activity of protein variants on a large scale using deep mutational scanning

Deep mutational scanning marries selection for protein function to high-throughput DNA sequencing in order to quantify the activity of variants of a protein on a massive scale. First, an appropriate selection system for the protein function of interest is identified and validated. Second, a library of variants is created, introduced into the selection system and subjected to selection. Third, library DNA is recovered throughout the selection and deep-sequenced. Finally, a functional score for each variant is calculated on the basis of the change in the frequency of the variant during the selection. This protocol describes the steps that must be carried out to generate a large-scale mutagenesis data set consisting of functional scores for up to hundreds of thousands of variants of a protein of interest. Establishing an assay, generating a library of variants and carrying out a selection and its accompanying sequencing takes on the order of 4-6 weeks; the initial data analysis can be completed in 1 week.

Next-generation sequencing in veterinary medicine: how can the massive amount of information arising from high-throughput technologies improve diagnosis, control, and management of infectious diseases?

The development of high-throughput molecular technologies and associated bioinformatics has dramatically changed the capacities of scientists to produce, handle, and analyze large amounts of genomic, transcriptomic, and proteomic data. A clear example of this step-change is represented by the amount of DNA sequence data that can be now produced using next-generation sequencing (NGS) platforms. Similarly, recent improvements in protein and peptide separation efficiencies and highly accurate mass spectrometry have promoted the identification and quantification of proteins in a given sample. These advancements in biotechnology have increasingly been applied to the study of animal infectious diseases and are beginning to revolutionize the way that biological and evolutionary processes can be studied at the molecular level. Studies have demonstrated the value of NGS technologies for molecular characterization, ranging from metagenomic characterization of unknown pathogens or microbial communities to molecular epidemiology and evolution of viral quasispecies. Moreover, high-throughput technologies now allow detailed studies of host-pathogen interactions at the level of their genomes (genomics), transcriptomes (transcriptomics), or proteomes (proteomics). Ultimately, the interaction between pathogen and host biological networks can be questioned by analytically integrating these levels (integrative OMICS and systems biology). The application of high-throughput biotechnology platforms in these fields and their typical low-cost per information content has revolutionized the resolution with which these processes can now be studied. The aim of this chapter is to provide a current and prospective view on the opportunities and challenges associated with the application of massive parallel sequencing technologies to veterinary medicine, with particular focus on applications that have a potential impact on disease control and management.

High-throughput identification of loss-of-function mutations for anti-interferon activity in the influenza A virus NS segment

Viral proteins often display several functions which require multiple assays to dissect their genetic basis. Here, we describe a systematic approach to screen for loss-of-function mutations that confer a fitness disadvantage under a specified growth condition. Our methodology was achieved by genetically monitoring a mutant library under two growth conditions, with and without interferon, by deep sequencing. We employed a molecular tagging technique to distinguish true mutations from sequencing error. This approach enabled us to identify mutations that were negatively selected against, in addition to those that were positively selected for. Using this technique, we identified loss-of-function mutations in the influenza A virus NS segment that were sensitive to type I interferon in a high-throughput fashion. Mechanistic characterization further showed that a single substitution, D92Y, resulted in the inability of NS to inhibit RIG-I ubiquitination. The approach described in this study can be applied under any specified condition for any virus that can be genetically manipulated.

IMPORTANCE

Traditional genetics focuses on a single genotype-phenotype relationship, whereas high-throughput genetics permits phenotypic characterization of numerous mutants in parallel. High-throughput genetics often involves monitoring of a mutant library with deep sequencing. However, deep sequencing suffers from a high error rate (∼0.1 to 1%), which is usually higher than the occurrence frequency for individual point mutations within a mutant library. Therefore, only mutations that confer a fitness advantage can be identified with confidence due to an enrichment in the occurrence frequency. In contrast, it is impossible to identify deleterious mutations using most next-generation sequencing techniques. In this study, we have applied a molecular tagging technique to distinguish true mutations from sequencing errors. It enabled us to identify mutations that underwent negative selection, in addition to mutations that experienced positive selection. This study provides a proof of concept by screening for loss-of-function mutations on the influenza A virus NS segment that are involved in its anti-interferon activity.

Deep sequencing: becoming a critical tool in clinical virology

Population (Sanger) sequencing has been the standard method in basic and clinical DNA sequencing for almost 40 years; however, next-generation (deep) sequencing methodologies are now revolutionizing the field of genomics, and clinical virology is no exception. Deep sequencing is highly efficient, producing an enormous amount of information at low cost in a relatively short period of time. High-throughput sequencing techniques have enabled significant contributions to multiples areas in virology, including virus discovery and metagenomics (viromes), molecular epidemiology, pathogenesis, and studies of how viruses to escape the host immune system and antiviral pressures. In addition, new and more affordable deep sequencing-based assays are now being implemented in clinical laboratories. Here, we review the use of the current deep sequencing platforms in virology, focusing on three of the most studied viruses: human immunodeficiency virus (HIV), hepatitis C virus (HCV), and influenza virus.

Exploring the functional residues in a flavin-binding fluorescent protein using deep mutational scanning

Flavin mononucleotide (FMN)-based fluorescent proteins are versatile reporters that can monitor various cellular processes in both aerobic and anaerobic conditions. However, the understanding of the role of individual amino acid residues on the protein function has been limited and has restricted the development of better functional variants. Here we examine the functional amino acid residues of Escherichia coli flavin mononucleotide binding fluorescent protein (EcFbFP) using the application of high-throughput sequencing of functional variants, termed deep mutational scanning. The variants were classified into 329 function-retained (FR) and 259 function-loss (FL) mutations, and further the mutational enrichment in each amino acid residues was weighed to find the functionally important residues of EcFbFP. We show that the crucial amino acid residues of EcFbFP lie among the FMN-binding pocket, turns and loops of the protein where conformation changes occur, and spatially clustered residues near the E56-K97 salt bridges. In addition, the mutational sensitivity of the critical residues was confirmed by site-directed mutagenesis. The deep mutational scanning of EcFbFP has demonstrated important implications for constructing better functioning protein variants.

HIV-1 quasispecies delineation by tag linkage deep sequencing

Trade-offs between throughput, read length, and error rates in high-throughput sequencing limit certain applications such as monitoring viral quasispecies. Here, we describe a molecular-based tag linkage method that allows assemblage of short sequence reads into long DNA fragments. It enables haplotype phasing with high accuracy and sensitivity to interrogate individual viral sequences in a quasispecies. This approach is demonstrated to deduce ∼2000 unique 1.3 kb viral sequences from HIV-1 quasispecies in vivo and after passaging ex vivo with a detection limit of ∼0.005% to ∼0.001%. Reproducibility of the method is validated quantitatively and qualitatively by a technical replicate. This approach can improve monitoring of the genetic architecture and evolution dynamics in any quasispecies population.

High-throughput profiling of influenza A virus hemagglutinin gene at single-nucleotide resolution

Genetic research on influenza virus biology has been informed in large part by nucleotide variants present in seasonal or pandemic samples, or individual mutants generated in the laboratory, leaving a substantial part of the genome uncharacterized. Here, we have developed a single-nucleotide resolution genetic approach to interrogate the fitness effect of point mutations in 98% of the amino acid positions in the influenza A virus hemagglutinin (HA) gene. Our HA fitness map provides a reference to identify indispensable regions to aid in drug and vaccine design as targeting these regions will increase the genetic barrier for the emergence of escape mutations. This study offers a new platform for studying genome dynamics, structure-function relationships, virus-host interactions, and can further rational drug and vaccine design. Our approach can also be applied to any virus that can be genetically manipulated.