Mutational and fitness landscapes of an RNA virus revealed through population sequencing

Ultra-precise detection of mutations by droplet-based amplification of circularized DNA

Background

NGS (next generation sequencing) has been widely used in studies of biological processes, ranging from microbial evolution to cancer genomics. However, the error rate of NGS (0.1 % ~ 1 %) is still remaining a great challenge for comprehensively investigating the low frequency variations, and the current solution methods have suffered severe amplification bias or low efficiency.

Results

We creatively developed Droplet-CirSeq for relatively efficient, low-bias and ultra-sensitive identification of variations by combining millions of picoliter uniform-sized droplets with Cir-seq. Droplet-CirSeq is entitled with an incredibly low error rate of 3 ~ 5 X 10^-6. To systematically evaluate the performances of amplification uniformity and capability of mutation identification for Droplet-CirSeq, we took the mixtures of two E. coli strains as specific instances to simulate the circumstances of mutations with different frequencies. Compared with Cir-seq, the coefficient of variance of read depth for Droplet-CirSeq was 10 times less (p = 2.6 X 10^-3), and the identified allele frequency presented more concentrated to the authentic frequency of mixtures (p = 4.8 X 10^-3), illustrating a significant improvement of amplification bias and accuracy in allele frequency determination. Additionally, Droplet-CirSeq detected 2.5 times genuine SNPs (p < 0.001), achieved a 2.8 times lower false positive rate (p < 0.05) and a 1.5 times lower false negative rate (p < 0.001), in the case of a 3 pg DNA input. Intriguingly, the false positive sites predominantly represented in two types of base substitutions (G- > A, C- > T). Our findings indicated that 30 pg DNA input accommodated in 5 ~ 10 million droplets resulted in maximal detection of authentic mutations compared to 3 pg (p = 1.2 X 10^-8) and 300 pg input (p = 2.2 X 10^-3).

Conclusions

We developed a method namely Droplet-CirSeq to significantly improve the amplification bias, which presents obvious superiority over the currently prevalent methods in exploitation of ultra-low frequency mutations. Droplet-CirSeq would be promisingly used in the identification of low frequency mutations initiated from extremely low input DNA, such as DNA of uncultured microorganisms, captured DNA of target region, circulation DNA of plasma et al, and its creative conception of rolling circle amplification in droplets would also be used in other low input DNA amplification fields.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2480-1) contains supplementary material, which is available to authorized users.

Conserved rates and patterns of transcription errors across bacterial growth states and lifestyles

Errors that occur during transcription have received much less attention than the mutations that occur in DNA because transcription errors are not heritable and usually result in a very limited number of altered proteins. However, transcription error rates are typically several orders of magnitude higher than the mutation rate. Also, individual transcripts can be translated multiple times, so a single error can have substantial effects on the pool of proteins. Transcription errors can also contribute to cellular noise, thereby influencing cell survival under stressful conditions, such as starvation or antibiotic stress. Implementing a method that captures transcription errors genome-wide, we measured the rates and spectra of transcription errors in Escherichia coli and in endosymbionts for which mutation and/or substitution rates are greatly elevated over those of E. coli Under all tested conditions, across all species, and even for different categories of RNA sequences (mRNA and rRNAs), there were no significant differences in rates of transcription errors, which ranged from 2.3 × 10(-5) per nucleotide in mRNA of the endosymbiont Buchnera aphidicola to 5.2 × 10(-5) per nucleotide in rRNA of the endosymbiont Carsonella ruddii The similarity of transcription error rates in these bacterial endosymbionts to that in E. coli (4.63 × 10(-5) per nucleotide) is all the more surprising given that genomic erosion has resulted in the loss of transcription fidelity factors in both Buchnera and Carsonella.

Haplotype-Phased Synthetic Long Reads from Short-Read Sequencing

Next-generation DNA sequencing has revolutionized the study of biology. However, the short read lengths of the dominant instruments complicate assembly of complex genomes and haplotype phasing of mixtures of similar sequences. Here we demonstrate a method to reconstruct the sequences of individual nucleic acid molecules up to 11.6 kilobases in length from short (150-bp) reads. We show that our method can construct 99.97%-accurate synthetic reads from bacterial, plant, and animal genomic samples, full-length mRNA sequences from human cancer cell lines, and individual HIV env gene variants from a mixture. The preparation of multiple samples can be multiplexed into a single tube, further reducing effort and cost relative to competing approaches. Our approach generates sequencing libraries in three days from less than one microgram of DNA in a single-tube format without custom equipment or specialized expertise.

Virus Population Dynamics Examined with Experimental Model Systems

Experimental evolution permits exploring the effect of controlled environmental variables in virus evolution. Several designs in cell culture and in vivo have established basic concepts that can assist in the interpretation of evolutionary events in the field. Important information has come from cytolytic and persistent infections in cell culture that have unveiled the power of virus-cell coevolution in virus and cell diversification. Equally informative are comparisons of the response of viral populations when subjected to different passage régimes. In particular, plaque-to-plaque transfers in cell culture have revealed unusual genotypes and phenotypes that populate minority layers of viral quasispecies. Some of these viruses display properties that contradict features established in virology textbooks. Several hypotheses and principles of population genetics have found experimental confirmation in experimental designs with viruses. The possibilities of using experimental evolution to understand virus behavior are still largely unexploited.

Genetic Instability of RNA Viruses

Despite having very limited coding capacity, RNA viruses are able to withstand challenge of antiviral drugs, cause epidemics in previously exposed human populations, and, in some cases, infect multiple host species. They are able to achieve this by virtue of their ability to multiply very rapidly, coupled with their extraordinary degree of genetic heterogeneity. RNA viruses exist not as single genotypes, but as a swarm of related variants, and this genomic diversity is an essential feature of their biology. RNA viruses have a variety of mechanisms that act in combination to determine their genetic heterogeneity. These include polymerase fidelity, error-mitigation mechanisms, genomic recombination, and different modes of genome replication. RNA viruses can vary in their ability to tolerate mutations, or “genetic robustness,” and several factors contribute to this. Finally, there is evidence that some RNA viruses exist close to a threshold where polymerase error rate has evolved to maximize the possible sequence space available, while avoiding the accumulation of a lethal load of deleterious mutations. We speculate that different viruses have evolved different error rates to complement the different “life-styles” they possess.

Viral Evolution

Viral infection is a highly dynamic process, which lead to constant evolutionary changes on both sides of the viral–host interface. The high mutation rates of viruses, coupled with short generation times and large population sizes, allow viruses to rapidly adapt to the host environment. However, this high mutation rate also comes at a cost to the viral population, as deleterious mutations are constantly created, leading to a plethora of defective genomes. Here, we will discuss the basic tenets that govern the evolution of viruses: mutation rates, population size, selection, the multiplicity of infection, and how these factors modulate infection as viruses evolve within a host, during transmission to novel susceptible hosts, and as viruses establish infections in new host species.

The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens

Drug resistance is a costly consequence of pathogen evolution and a major concern in public health. In this review, we show how population genetics can be used to study the evolution of drug resistance and also how drug resistance evolution is informative as an evolutionary model system. We highlight five examples from diverse organisms with particular focus on: (i) identifying drug resistance loci in the malaria parasite Plasmodium falciparum using the genomic signatures of selective sweeps, (ii) determining the role of epistasis in drug resistance evolution in influenza, (iii) quantifying the role of standing genetic variation in the evolution of drug resistance in HIV, (iv) using drug resistance mutations to study clonal interference dynamics in tuberculosis and (v) analysing the population structure of the core and accessory genome of Staphylococcus aureus to understand the spread of methicillin resistance. Throughout this review, we discuss the uses of sequence data and population genetic theory in studying the evolution of drug resistance.

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.

E Pluribus Unum: 50 Years of Research, Millions of Viruses, and One Goal--Tailored Acceleration of AAV Evolution

Fifty years ago, a Science paper by Atchison et al. reported a newly discovered virus that would soon become known as adeno-associated virus (AAV) and that would subsequently emerge as one of the most versatile and most auspicious vectors for human gene therapy. A large part of its attraction stems from the ease with which the viral capsid can be engineered for particle retargeting to cell types of choice, evasion from neutralizing antibodies or other desirable properties. Particularly powerful and in the focus of the current review are high-throughput methods aimed at expanding the repertoire of AAV vectors by means of directed molecular evolution, such as random mutagenesis, DNA family shuffling, in silico reconstruction of ancestral capsids, or peptide display. Here, unlike the wealth of prior reviews on this topic, we especially emphasize and critically discuss the practical aspects of the different procedures that affect the ultimate outcome, including diversification protocols, combinatorial library complexity, and selection strategies. Our overall aim is to provide general guidance that should help users at any level, from novice to expert, to safely navigate through the rugged space of directed AAV evolution while avoiding the pitfalls that are associated with these challenging but promising technologies.

On the Causes of Evolutionary Transition:Transversion Bias

A pattern in which nucleotide transitions are favored several fold over transversions is common in molecular evolution. When this pattern occurs among amino acid replacements, explanations often invoke an effect of selection, on the grounds that transitions are more conservative in their effects on proteins. However, the underlying hypothesis of conservative transitions has never been tested directly. Here we assess support for this hypothesis using direct evidence: the fitness effects of mutations in actual proteins measured via individual or paired growth experiments. We assembled data from 8 published studies, ranging in size from 24 to 757 single-nucleotide mutations that change an amino acid. Every study has the statistical power to reveal significant effects of amino acid exchangeability, and most studies have the power to discern a binary conservative-vs-radical distinction. However, only one study suggests that transitions are significantly more conservative than transversions. In the combined set of 1,239 replacements (544 transitions, 695 transversions), the chance that a transition is more conservative than a transversion is 53 % (95 % confidence interval 50 to 56) compared with the null expectation of 50 %. We show that this effect is not large compared with that of most biochemical factors, and is not large enough to explain the several-fold bias observed in evolution. In short, the available data have the power to verify the “conservative transitions” hypothesis if true, but suggest instead that selection on proteins plays at best a minor role in the observed bias.

Comparison of Illumina de novo assembled and Sanger sequenced viral genomes: A case study for RNA viruses recovered from the plant pathogenic fungus Sclerotinia sclerotiorum

The advent of 'next generation sequencing' (NGS) technologies has led to the discovery of many novel mycoviruses, the majority of which are sufficiently different from previously sequenced viruses that there is no appropriate reference sequence on which to base the sequence assembly. Although many new genome sequences are generated by NGS, confirmation of the sequence by Sanger sequencing is still essential for formal classification by the International Committee for the Taxonomy of Viruses (ICTV), although this is currently under review. To empirically test the validity of de novo assembled mycovirus genomes from dsRNA extracts, we compared the results from Illumina sequencing with those from random cloning plus targeted PCR coupled with Sanger sequencing for viruses from five Sclerotinia sclerotiorum isolates. Through Sanger sequencing we detected nine viral genomes while through Illumina sequencing we detected the same nine viruses plus one additional virus from the same samples. Critically, the Illumina derived sequences share >99.3 % identity to those obtained by cloning and Sanger sequencing. Although, there is scope for errors in de novo assembled viral genomes, our results demonstrate that by maximising the proportion of viral sequence in the data and using sufficiently rigorous quality controls, it is possible to generate de novo genome sequences of comparable accuracy from Illumina sequencing to those obtained by Sanger sequencing.

Resistance of Hepatitis C Virus to Inhibitors: Complexity and Clinical Implications

Selection of inhibitor-resistant viral mutants is universal for viruses that display quasi-species dynamics, and hepatitis C virus (HCV) is no exception. Here we review recent results on drug resistance in HCV, with emphasis on resistance to the newly-developed, directly-acting antiviral agents, as they are increasingly employed in the clinic. We put the experimental observations in the context of quasi-species dynamics, in particular what the genetic and phenotypic barriers to resistance mean in terms of exploration of sequence space while HCV replicates in the liver of infected patients or in cell culture. Strategies to diminish the probability of viral breakthrough during treatment are briefly outlined.

Creation of Functional Viruses from Non-Functional cDNA Clones Obtained from an RNA Virus Population by the Use of Ancestral Reconstruction

RNA viruses have the highest known mutation rates. Consequently it is likely that a high proportion of individual RNA virus genomes, isolated from an infected host, will contain lethal mutations and be non-functional. This is problematic if the aim is to clone and investigate high-fitness, functional cDNAs and may also pose problems for sequence-based analysis of viral evolution. To address these challenges we have performed a study of the evolution of classical swine fever virus (CSFV) using deep sequencing and analysis of 84 full-length cDNA clones, each representing individual genomes from a moderately virulent isolate. In addition to here being used as a model for RNA viruses generally, CSFV has high socioeconomic importance and remains a threat to animal welfare and pig production. We find that the majority of the investigated genomes are non-functional and only 12% produced infectious RNA transcripts. Full length sequencing of cDNA clones and deep sequencing of the parental population identified substitutions important for the observed phenotypes. The investigated cDNA clones were furthermore used as the basis for inferring the sequence of functional viruses. Since each unique clone must necessarily be the descendant of a functional ancestor, we hypothesized that it should be possible to produce functional clones by reconstructing ancestral sequences. To test this we used phylogenetic methods to infer two ancestral sequences, which were then reconstructed as cDNA clones. Viruses rescued from the reconstructed cDNAs were tested in cell culture and pigs. Both reconstructed ancestral genomes proved functional, and displayed distinct phenotypes in vitro and in vivo. We suggest that reconstruction of ancestral viruses is a useful tool for experimental and computational investigations of virulence and viral evolution. Importantly, ancestral reconstruction can be done even on the basis of a set of sequences that all correspond to non-functional variants.

Genome scale engineering techniques for metabolic engineering

Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.

Sequence specificity for uridylylation of the viral peptide linked to the genome (VPg) of enteroviruses

Enteroviruses (EV) uridylylate a peptide, VPg, as the first step in their replication. VPgpUpU, found free in infected cells, serves as the primer for RNA elongation. The abilities of four polymerases (3D(pol)), from EV-species A-C, to uridylylate VPgs that varied by up to 60% of their residues were compared. Each 3D(pol) was able to uridylylate all five VPgs using polyA RNA as template, while showing specificity for its own genome encoded peptide. All 3D(pol) uridylylated a consensus VPg representing the physical chemical properties of 31 different VPgs. Thus the residues required for uridylylation and the enzymatic mechanism must be similar in diverse EV. As VPg-binding sites differ in co-crystal structures, the reaction is probably done by a second 3D(pol) molecule. The conservation of polymerase residues whose mutation reduces uridylylation but not RNA elongation is compared.

Statistical Mechanics and Thermodynamics of Viral Evolution

This paper uses methods drawn from physics to study the life cycle of viruses. The paper analyzes a model of viral infection and evolution using the "grand canonical ensemble" and formalisms from statistical mechanics and thermodynamics. Using this approach we enumerate all possible genetic states of a model virus and host as a function of two independent pressures-immune response and system temperature. We prove the system has a real thermodynamic temperature, and discover a new phase transition between a positive temperature regime of normal replication and a negative temperature "disordered" phase of the virus. We distinguish this from previous observations of a phase transition that arises as a function of mutation rate. From an evolutionary biology point of view, at steady state the viruses naturally evolve to distinct quasispecies. This paper also reveals a universal relationship that relates the order parameter (as a measure of mutational robustness) to evolvability in agreement with recent experimental and theoretical work. Given that real viruses have finite length RNA segments that encode proteins which determine virus fitness, the approach used here could be refined to apply to real biological systems, perhaps providing insight into immune escape, the emergence of novel pathogens and other results of viral evolution.

CoVaMa: Co-Variation Mapper for disequilibrium analysis of mutant loci in viral populations using next-generation sequence data

Next-Generation Sequencing (NGS) has transformed our understanding of the dynamics and diversity of virus populations for human pathogens and model systems alike. Due to the sensitivity and depth of coverage in NGS, it is possible to measure the frequency of mutations that may be present even at vanishingly low frequencies within the viral population. Here, we describe a simple bioinformatic pipeline called CoVaMa (Co-Variation Mapper) scripted in Python that detects correlated patterns of mutations in a viral sample. Our algorithm takes NGS alignment data and populates large matrices of contingency tables that correspond to every possible pairwise interaction of nucleotides in the viral genome or amino acids in the chosen open reading frame. These tables are then analysed using classical linkage disequilibrium to detect and report evidence of epistasis. We test our analysis with simulated data and then apply the approach to find epistatically linked loci in Flock House Virus genomic RNA grown under controlled cell culture conditions. We also reanalyze NGS data from a large cohort of HIV infected patients and find correlated amino acid substitution events in the protease gene that have arisen in response to anti-viral therapy. This both confirms previous findings and suggests new pairs of interactions within HIV protease. The script is publically available at http://sourceforge.net/projects/covama.

High definition viral vaccine strain identity and stability testing using full-genome population data--The next generation of vaccine quality control

BACKGROUND

Vaccines are the most effective prophylactic public health tools. With the help of vaccines, prevention of infectious disease spread and, in concert with other measures, even eradication has become possible. Until now, licensing and quality control require the determination of consensus genome sequences of replication competent infectious agents contained in vaccines. Recent improvements in sequencing technologies now enable the sequencing of complete genomes and the genetic analysis of populations with high reliability and resolution. The latter is particularly important for RNA viruses, which consist of fluctuating heterogeneous populations rather than genetically stable entities. This information now has to be integrated into the existing regulatory framework, challenging both licensing authorities and vaccine producers to develop new quality control criteria.

METHODS

Commercially available modified-live oral rabies vaccines and their precursor strains were deep-sequenced to assess strain identity and relations between strains based on population diversity. Strain relations were inferred based on the Manhattan distances calculated between the compositions of the viral populations of the strains.

RESULTS

We provide a novel approach to assess viral strain relations with high resolution and reliability by deep sequencing with subsequent analysis of the overall genetic diversity within the viral populations. A comparison of our novel approach of inferring strain relations based on population data with consensus sequence analysis clearly shows that consensus sequence analysis of diverse viral populations can be misleading. Therefore, for quality control of viral vaccines deep sequencing analysis is to be preferred over consensus sequence analysis.

CONCLUSIONS

The presented methodology allows for routine integration of deep sequencing data in vaccine quality control and licensing for highly reliable assessment of strain identity and stability.

Analysis of Dengue Virus Genetic Diversity during Human and Mosquito Infection Reveals Genetic Constraints

Dengue viruses (DENV) cause debilitating and potentially life-threatening acute disease throughout the tropical world. While drug development efforts are underway, there are concerns that resistant strains will emerge rapidly. Indeed, antiviral drugs that target even conserved regions in other RNA viruses lose efficacy over time as the virus mutates. Here, we sought to determine if there are regions in the DENV genome that are not only evolutionarily conserved but genetically constrained in their ability to mutate and could hence serve as better antiviral targets. High-throughput sequencing of DENV-1 genome directly from twelve, paired dengue patients’ sera and then passaging these sera into the two primary mosquito vectors showed consistent and distinct sequence changes during infection. In particular, two residues in the NS5 protein coding sequence appear to be specifically acquired during infection in Ae. aegypti but not Ae. albopictus. Importantly, we identified a region within the NS3 protein coding sequence that is refractory to mutation during human and mosquito infection. Collectively, these findings provide fresh insights into antiviral targets and could serve as an approach to defining evolutionarily constrained regions for therapeutic targeting in other RNA viruses.

Dengue viruses cause debilitating and potentially life-threatening acute disease throughout the tropical world. While drug development efforts are underway, there are concerns that drug-resistant strains will emerge rapidly. Indeed, many antiviral drugs for other RNA viruses lose efficacy over time as the virus mutates. Here, we sought to determine if there are regions in the dengue virus genome that are constrained in their ability to mutate and could therefore serve as better targets for antiviral drugs. Deep sequencing of the dengue virus 1 genome directly from the blood of twelve dengue patients and from mosquitoes given this blood showed consistent and distinct mutation patterns during infection. Importantly, we identified regions within the viral genome that are resistant to mutation during human and mosquito infection. Collectively, these findings provide fresh insights into potential antiviral targets and could serve as an approach to defining better regions for therapeutic targeting in other RNA viruses.

Fitness Landscapes of Functional RNAs

The notion of fitness landscapes, a map between genotype and fitness, was proposed more than 80 years ago. For most of this time data was only available for a few alleles, and thus we had only a restricted view of the whole fitness landscape. Recently, advances in genetics and molecular biology allow a more detailed view of them. Here we review experimental and theoretical studies of fitness landscapes of functional RNAs, especially aptamers and ribozymes. We find that RNA structures can be divided into critical structures, connecting structures, neutral structures and forbidden structures. Such characterisation, coupled with theoretical sequence-to-structure predictions, allows us to construct the whole fitness landscape. Fitness landscapes then can be used to study evolution, and in our case the development of the RNA world.

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.

Comparative mapping of host-pathogen protein-protein interactions

Pathogens usurp a variety of host pathways via protein-protein interactions to ensure efficient pathogen replication. Despite the existence of an impressive toolkit of systematic and unbiased approaches, we still lack a comprehensive list of these PPIs and an understanding of their functional implications. Here, we highlight the importance of harnessing genetic diversity of hosts and pathogens for uncovering the biochemical basis of pathogen restriction, virulence, fitness, and pathogenesis. We further suggest that integrating physical interaction data with orthogonal types of data will allow researchers to draw meaningful conclusions both for basic and translational science.

Fitness Inference from Short-Read Data: Within-Host Evolution of a Reassortant H5N1 Influenza Virus

We present a method to infer the role of selection acting during the within-host evolution of the influenza virus from short-read genome sequence data. Linkage disequilibrium between loci is accounted for by treating short-read sequences as noisy multilocus emissions from an underlying model of haplotype evolution. A hierarchical model-selection procedure is used to infer the underlying fitness landscape of the virus insofar as that landscape is explored by the viral population. In a first application of our method, we analyze data from an evolutionary experiment describing the growth of a reassortant H5N1 virus in ferrets. Across two sets of replica experiments we infer multiple alleles to be under selection, including variants associated with receptor binding specificity, glycosylation, and with the increased transmissibility of the virus. We identify epistasis as an important component of the within-host fitness landscape, and show that adaptation can proceed through multiple genetic pathways.

Viral evolution: beyond drift and shift

Technological advances have allowed aspects of viral evolution to be explored at unprecedented scales. As a consequence, new quantitative approaches are needed to investigate features of viral evolution that fall outside traditional areas of study, such as antigenic evolution. We examine three areas of viral evolution where tools from disciplines such as statistical physics, topology, and information theory have been used recently as quantitative frameworks for large-scale studies and, in some cases, suggest a novel theoretical approach to a problem. Ongoing interaction among these disciplines with biology is necessary so that experimental researchers can determine which quantitative tools are right for them and quantitative researchers can learn which aspects of viral evolution can be understood and advanced with their approaches.

BAsE-Seq: a method for obtaining long viral haplotypes from short sequence reads

We present a method for obtaining long haplotypes, of over 3 kb in length, using a short-read sequencer, Barcode-directed Assembly for Extra-long Sequences (BAsE-Seq). BAsE-Seq relies on transposing a template-specific barcode onto random segments of the template molecule and assembling the barcoded short reads into complete haplotypes. We applied BAsE-Seq on mixed clones of hepatitis B virus and accurately identified haplotypes occurring at frequencies greater than or equal to 0.4%, with >99.9% specificity. Applying BAsE-Seq to a clinical sample, we obtained over 9,000 viral haplotypes, which provided an unprecedented view of hepatitis B virus population structure during chronic infection. BAsE-Seq is readily applicable for monitoring quasispecies evolution in viral diseases.

BAsE-Seq: a method for obtaining long viral haplotypes from short sequence reads

We present a method for obtaining long haplotypes, of over 3 kb in length, using a short-read sequencer, Barcode-directed Assembly for Extra-long Sequences (BAsE-Seq). BAsE-Seq relies on transposing a template-specific barcode onto random segments of the template molecule and assembling the barcoded short reads into complete haplotypes. We applied BAsE-Seq on mixed clones of hepatitis B virus and accurately identified haplotypes occurring at frequencies greater than or equal to 0.4%, with >99.9% specificity. Applying BAsE-Seq to a clinical sample, we obtained over 9,000 viral haplotypes, which provided an unprecedented view of hepatitis B virus population structure during chronic infection. BAsE-Seq is readily applicable for monitoring quasispecies evolution in viral diseases.

Enzymological description of multitemplate PCR-Shrinking amplification bias by optimizing the polymerase-template ratio

Multitemplate polymerase chain reaction (PCR) is used for preparative and analytical applications in diagnostics and research. Classical PCR and qPCR are two basic setups with many possible experimental modifications. Classical PCR is a method of choice to obtain enough material for subsequent sophisticated applications such as construction of libraries for next-generation sequencing or high-throughput screening. Sequencing and Single Nucleotide Primer Extension (SNuPE) employ one-strand synthesis and represent a distinct variant of analytical DNA synthesis. In all these applications, maintaining the initial ratio of templates and avoiding underestimation of minority templates is desired. Here, we demonstrate that different templates can amplify independently at low template concentrations (typical in qPCR setups, in which the polymerase concentration is usually several orders of magnitude higher than the template concentration). However, rare templates can be diluted in an effort to keep DNA amplification in the exponential phase, or template concentration can be biased by differences in amplification efficiency. Moreover, amplification of templates present in low concentrations is more vulnerable to stochastic events that lead to proportional changes in the product ratio, as well as by incomplete amplification leading to chimera formation. These undesired effects can be compensated for by using highly processive polymerases with high and equal affinity to different primer-template complexes. Novel enhanced polymerases are desired. With increasing concentration of a primer-template of interest, the system becomes more deterministic. Nevertheless, marked deviation from independent exponential amplification occurs when the total template concentration starts to approach the polymerase concentration. The primer-template complexes compete for enzyme molecules, and the amount of products grows arithmetically-the system starts to obey Michaelis-Menten kinetics. Synthesis of rare products in a multitemplate mixture can run more easily under the detection limit in such conditions, although it would be unequivocally detectable in a single template assay. When fishing out rare template variants, the best processive polymerases should be used to decrease both amplification and detection limits. The possibility of stochastic events, should be taken into account to correctly interpret the obtained data.

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.

Sequence-Specific Fidelity Alterations Associated with West Nile Virus Attenuation in Mosquitoes

High rates of error-prone replication result in the rapid accumulation of genetic diversity of RNA viruses. Recent studies suggest that mutation rates are selected for optimal viral fitness and that modest variations in replicase fidelity may be associated with viral attenuation. Arthropod-borne viruses (arboviruses) are unique in their requirement for host cycling and may necessitate substantial genetic and phenotypic plasticity. In order to more thoroughly investigate the correlates, mechanisms and consequences of arbovirus fidelity, we selected fidelity variants of West Nile virus (WNV; Flaviviridae, Flavivirus) utilizing selection in the presence of a mutagen. We identified two mutations in the WNV RNA-dependent RNA polymerase associated with increased fidelity, V793I and G806R, and a single mutation in the WNV methyltransferase, T248I, associated with decreased fidelity. Both deep-sequencing and in vitro biochemical assays confirmed strain-specific differences in both fidelity and mutational bias. WNV fidelity variants demonstrated host-specific alterations to replicative fitness in vitro, with modest attenuation in mosquito but not vertebrate cell culture. Experimental infections of colonized and field populations of Cx. quinquefaciatus demonstrated that WNV fidelity alterations are associated with a significantly impaired capacity to establish viable infections in mosquitoes. Taken together, these studies (i) demonstrate the importance of allosteric interactions in regulating mutation rates, (ii) establish that mutational spectra can be both sequence and strain-dependent, and (iii) display the profound phenotypic consequences associated with altered replication complex function of flaviviruses.

West Nile virus (WNV) is the most geographically widespread arthropod-borne virus (arbovirus) in the world. Like most arboviruses, WNV is a RNA virus which is highly mutable and exists in nature as genetically diverse mutant swarms. Although many recent studies have investigated the relationship between virus mutation rate and viral fitness, this had not previously been determined for WNV or other flaviviruses. We identified WNV mutations associated with variation in mutation rate using cell culture passage in the presence of a mutagen and engineered these mutations into an infectious WNV clone in order to investigate the causes and consequences of altered fidelity. Our results demonstrate that interactions among proteins which comprise the WNV replication complex can significantly alter both the extent and types of mutations that occur. In addition, we show that both increasing and decreasing WNV fidelity has host-specific effects on replication in cell culture and is associated with nearly complete ablation of WNV infection in mosquito vectors. These results have significant implications for our understanding of arbovirus evolution, replication complex function and arboviral fitness in mosquitoes, and identify important targets to study the determinants and mechanisms of vector competence and arbovirus fidelity.

Viewing protein fitness landscapes through a next-gen lens

High-throughput sequencing has enabled many powerful approaches in biological research. Here, we review sequencing approaches to measure frequency changes within engineered mutational libraries subject to selection. These analyses can provide direct estimates of biochemical and fitness effects for all individual mutations across entire genes (and likely compact genomes in the near future) in genetically tractable systems such as microbes, viruses, and mammalian cells. The effects of mutations on experimental fitness can be assessed using sequencing to monitor time-dependent changes in mutant frequency during bulk competitions. The impact of mutations on biochemical functions can be determined using reporters or other means of separating variants based on individual activities (e.g., binding affinity for a partner molecule can be interrogated using surface display of libraries of mutant proteins and isolation of bound and unbound populations). The comprehensive investigation of mutant effects on both biochemical function and experimental fitness provide promising new avenues to investigate the connections between biochemistry, cell physiology, and evolution. We summarize recent findings from systematic mutational analyses; describe how they relate to a field rich in both theory and experimentation; and highlight how they may contribute to ongoing and future research into protein structure-function relationships, systems-level descriptions of cell physiology, and population-genetic inferences on the relative contributions of selection and drift.

Tipping points and early warning signals in the genomic composition of populations induced by environmental changes

We live in an ever changing biosphere that faces continuous and often stressing environmental challenges. From this perspective, much effort is currently devoted to understanding how natural populations succeed or fail in adapting to evolving conditions. In a different context, many complex dynamical systems experience critical transitions where their dynamical behaviour or internal structure changes suddenly. Here we connect both approaches and show that in rough and correlated fitness landscapes, population dynamics shows flickering under small stochastic environmental changes, alerting of the existence of tipping points. Our analytical and numerical results demonstrate that transitions at the genomic level preceded by early-warning signals are a generic phenomenon in constant and slowly driven landscapes affected by even slight stochasticity. As these genomic shifts are approached, the time to reach mutation-selection equilibrium dramatically increases, leading to the appearance of hysteresis in the composition of the population. Eventually, environmental changes significantly faster than the typical adaptation time may result in population extinction. Our work points out several indicators that are at reach with current technologies to anticipate these sudden and largely unavoidable transitions.

Group Selection and Contribution of Minority Variants during Virus Adaptation Determines Virus Fitness and Phenotype

Understanding how a pathogen colonizes and adapts to a new host environment is a primary aim in studying emerging infectious diseases. Adaptive mutations arise among the thousands of variants generated during RNA virus infection, and identifying these variants will shed light onto how changes in tropism and species jumps can occur. Here, we adapted Coxsackie virus B3 to a highly permissive and less permissive environment. Using deep sequencing and bioinformatics, we identified a multi-step adaptive process to adaptation involving residues in the receptor footprints that correlated with receptor availability and with increase in virus fitness in an environment-specific manner. We show that adaptation occurs by selection of a dominant mutation followed by group selection of minority variants that together, confer the fitness increase observed in the population, rather than selection of a single dominant genotype.

The relationship between dN/dS and scaled selection coefficients

Numerous computational methods exist to assess the mode and strength of natural selection in protein-coding sequences, yet how distinct methods relate to one another remains largely unknown. Here, we elucidate the relationship between two widely used phylogenetic modeling frameworks: dN/dS models and mutation-selection (MutSel) models. We derive a mathematical relationship between dN/dS and scaled selection coefficients, the focal parameters of MutSel models, and use this relationship to gain deeper insight into the behaviors, limitations, and applicabilities of these two modeling frameworks. We prove that, if all synonymous changes are neutral, standard MutSel models correspond to dN/dS ≤ 1. However, if synonymous codons differ in fitness, dN/dS can take on arbitrarily high values even if all selection is purifying. Thus, the MutSel modeling framework cannot necessarily accommodate positive, diversifying selection, while dN/dS cannot distinguish between purifying selection on synonymous codons and positive selection on amino acids. We further propose a new benchmarking strategy of dN/dS inferences against MutSel simulations and demonstrate that the widely used Goldman-Yang-style dN/dS models yield substantially biased dN/dS estimates on realistic sequence data. In contrast, the less frequently used Muse-Gaut-style models display much less bias. Strikingly, the least-biased and most precise dN/dS estimates are never found in the models with the best fit to the data, measured through both AIC and BIC scores. Thus, selecting models based on goodness-of-fit criteria can yield poor parameter estimates if the models considered do not precisely correspond to the underlying mechanism that generated the data. In conclusion, establishing mathematical links among modeling frameworks represents a novel, powerful strategy to pinpoint previously unrecognized model limitations and strengths.

Viral quasispecies

New generation sequencing is greatly expanding the capacity to examine the composition of mutant spectra of viral quasispecies in infected cells and host organisms. Here we review recent progress in the understanding of quasispecies dynamics, notably the occurrence of intra-mutant spectrum interactions, and implications of fitness landscapes for virus adaptation and de-adaptation. Complementation or interference can be established among components of the same mutant spectrum, dependent on the mutational status of the ensemble. Replicative fitness relates to an optimal mutant spectrum that provides the molecular basis for phenotypic flexibility, with implications for antiviral therapy. The biological impact of viral fitness renders particularly relevant the capacity of new generation sequencing to establish viral fitness landscapes. Progress with experimental model systems is becoming an important asset to understand virus behavior in the more complex environments faced during natural infections.

Intramolecular circularization increases efficiency of RNA sequencing and enables CLIP-Seq of nuclear RNA from human cells

RNA sequencing (RNA-Seq) is a powerful tool for analyzing the identity of cellular RNAs but is often limited by the amount of material available for analysis. In spite of extensive efforts employing existing protocols, we observed that it was not possible to obtain useful sequencing libraries from nuclear RNA derived from cultured human cells after crosslinking and immunoprecipitation (CLIP). Here, we report a method for obtaining strand-specific small RNA libraries for RNA sequencing that requires picograms of RNA. We employ an intramolecular circularization step that increases the efficiency of library preparation and avoids the need for intermolecular ligations of adaptor sequences. Other key features include random priming for full-length cDNA synthesis and gel-free library purification. Using our method, we generated CLIP-Seq libraries from nuclear RNA that had been UV-crosslinked and immunoprecipitated with anti-Argonaute 2 (Ago2) antibody. Computational protocols were developed to enable analysis of raw sequencing data and we observe substantial differences between recognition by Ago2 of RNA species in the nucleus relative to the cytoplasm. This RNA self-circularization approach to RNA sequencing (RC-Seq) allows data to be obtained using small amounts of input RNA that cannot be sequenced by standard methods.

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

BACKGROUND

RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial.

RESULTS

We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site's coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone.

CONCLUSIONS

These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Deconvoluting the composition of low-frequency hepatitis C viral quasispecies: comparison of genotypes and NS3 resistance-associated variants between HCV/HIV coinfected hemophiliacs and HCV monoinfected patients in Japan

Pre-existing low-frequency resistance-associated variants (RAVs) may jeopardize successful sustained virological responses (SVR) to HCV treatment with direct-acting antivirals (DAAs). However, the potential impact of low-frequency (∼0.1%) mutations, concatenated mutations (haplotypes), and their association with genotypes (Gts) on the treatment outcome has not yet been elucidated, most probably owing to the difficulty in detecting pre-existing minor haplotypes with sufficient length and accuracy. Herein, we characterize a methodological framework based on Illumina MiSeq next-generation sequencing (NGS) coupled with bioinformatics of quasispecies reconstruction (QSR) to realize highly accurate variant calling and genotype-haplotype detection. The core-to-NS3 protease coding sequences in 10 HCV monoinfected patients, 5 of whom had a history of blood transfusion, and 11 HCV/HIV coinfected patients with hemophilia, were studied. Simulation experiments showed that, for minor variants constituting more than 1%, our framework achieved a positive predictive value (PPV) of 100% and sensitivities of 91.7–100% for genotyping and 80.6% for RAV screening. Genotyping analysis indicated the prevalence of dominant Gt1a infection in coinfected patients (6/11 vs 0/10, p = 0.01). For clinical samples, minor genotype overlapping infection was prevalent in HCV/HIV coinfected hemophiliacs (10/11) and patients who experienced whole-blood transfusion (4/5) but none in patients without exposure to blood (0/5). As for RAV screening, the Q80K/R and S122K/R variants were particularly prevalent among minor RAVs observed, detected in 12/21 and 6/21 cases, respectively. Q80K was detected only in coinfected patients, whereas Q80R was predominantly detected in monoinfected patients (1/11 vs 7/10, p < 0.01). Multivariate interdependence analysis revealed the previously unrecognized prevalence of Gt1b-Q80K, in HCV/HIV coinfected hemophiliacs [Odds ratio = 13.4 (3.48–51.9), p < 0.01]. Our study revealed the distinct characteristics of viral quasispecies between the subgroups specified above and the feasibility of NGS and QSR-based genetic deconvolution of pre-existing minor Gts, RAVs, and their interrelationships.

Viruses roll the dice: the stochastic behavior of viral genome molecules accelerates viral adaptation at the cell and tissue levels

Recent studies on evolutionarily distant viral groups have shown that the number of viral genomes that establish cell infection after cell-to-cell transmission is unexpectedly small (1-20 genomes). This aspect of viral infection appears to be important for the adaptation and survival of viruses. To clarify how the number of viral genomes that establish cell infection is determined, we developed a simulation model of cell infection for tomato mosaic virus (ToMV), a positive-strand RNA virus. The model showed that stochastic processes that govern the replication or degradation of individual genomes result in the infection by a small number of genomes, while a large number of infectious genomes are introduced in the cell. It also predicted two interesting characteristics regarding cell infection patterns: stochastic variation among cells in the number of viral genomes that establish infection and stochastic inequality in the accumulation of their progenies in each cell. Both characteristics were validated experimentally by inoculating tobacco cells with a library of nucleotide sequence-tagged ToMV and analyzing the viral genomes that accumulated in each cell using a high-throughput sequencer. An additional simulation model revealed that these two characteristics enhance selection during tissue infection. The cell infection model also predicted a mechanism that enhances selection at the cellular level: a small difference in the replication abilities of coinfected variants results in a large difference in individual accumulation via the multiple-round formation of the replication complex (i.e., the replication machinery). Importantly, this predicted effect was observed in vivo. The cell infection model was robust to changes in the parameter values, suggesting that other viruses could adopt similar adaptation mechanisms. Taken together, these data reveal a comprehensive picture of viral infection processes including replication, cell-to-cell transmission, and evolution, which are based on the stochastic behavior of the viral genome molecules in each cell.

Deep sequencing analysis of viral infection and evolution allows rapid and detailed characterization of viral mutant spectrum

Motivation: The study of RNA virus populations is a challenging task. Each population of RNA virus is composed of a collection of different, yet related genomes often referred to as mutant spectra or quasispecies. Virologists using deep sequencing technologies face major obstacles when studying virus population dynamics, both experimentally and in natural settings due to the relatively high error rates of these technologies and the lack of high performance pipelines. In order to overcome these hurdles we developed a computational pipeline, termed ViVan (Viral Variance Analysis). ViVan is a complete pipeline facilitating the identification, characterization and comparison of sequence variance in deep sequenced virus populations.

Results: Applying ViVan on deep sequenced data obtained from samples that were previously characterized by more classical approaches, we uncovered novel and potentially crucial aspects of virus populations. With our experimental work, we illustrate how ViVan can be used for studies ranging from the more practical, detection of resistant mutations and effects of antiviral treatments, to the more theoretical temporal characterization of the population in evolutionary studies.

Availability and implementation: Freely available on the web at http://www.vivanbioinfo.org

Contact: nshomron@post.tau.ac.il

Supplementary information:Supplementary data are available at Bioinformatics online.

CHOPER filters enable rare mutation detection in complex mutagenesis populations by next-generation sequencing

Next-generation sequencing (NGS) has revolutionized genetics and enabled the accurate identification of many genetic variants across many genomes. However, detection of biologically important low-frequency variants within genetically heterogeneous populations remains challenging, because they are difficult to distinguish from intrinsic NGS sequencing error rates. Approaches to overcome these limitations are essential to detect rare mutations in large cohorts, virus or microbial populations, mitochondria heteroplasmy, and other heterogeneous mixtures such as tumors. Modifications in library preparation can overcome some of these limitations, but are experimentally challenging and restricted to skilled biologists. This paper describes a novel quality filtering and base pruning pipeline, called Complex Heterogeneous Overlapped Paired-End Reads (CHOPER), designed to detect sequence variants in a complex population with high sequence similarity derived from All-Codon-Scanning (ACS) mutagenesis. A novel fast alignment algorithm, designed for the specified application, has O(n) time complexity. CHOPER was applied to a p53 cancer mutant reactivation study derived from ACS mutagenesis. Relative to error filtering based on Phred quality scores, CHOPER improved accuracy by about 13% while discarding only half as many bases. These results are a step toward extending the power of NGS to the analysis of genetically heterogeneous populations.

Elongation-Competent Pauses Govern the Fidelity of a Viral RNA-Dependent RNA Polymerase

RNA viruses have specific mutation rates that balance the conflicting needs of an evolutionary response to host antiviral defenses and avoidance of the error catastrophe. While most mutations are known to originate in replication errors, difficulties of capturing the underlying dynamics have left the mechanochemical basis of viral mutagenesis unresolved. Here, we use multiplexed magnetic tweezers to investigate error incorporation by the bacteriophage Φ6 RNA-dependent RNA polymerase. We extract large datasets fingerprinting real-time polymerase dynamics over four magnitudes in time, in the presence of nucleotide analogs, and under varying NTP and divalent cation concentrations and fork stability. Quantitative analysis reveals a new pause state that modulates polymerase fidelity and so ties viral polymerase pausing to the biological function of optimizing virulence. Adjusting the frequency of such pauses offers a target for therapeutics and may also reflect an evolutionary strategy for virus populations to track the gradual evolution of their hosts.

Cell-specific establishment of poliovirus resistance to an inhibitor targeting a cellular protein

It is hypothesized that targeting stable cellular factors involved in viral replication instead of virus-specific proteins may raise the barrier for development of resistant mutants, which is especially important for highly adaptable small (+)RNA viruses. However, contrary to this assumption, the accumulated evidence shows that these viruses easily generate mutants resistant to the inhibitors of cellular proteins at least in some systems. We investigated here the development of poliovirus resistance to brefeldin A (BFA), an inhibitor of the cellular protein GBF1, a guanine nucleotide exchange factor for the small cellular GTPase Arf1. We found that while resistant viruses can be easily selected in HeLa cells, they do not emerge in Vero cells, in spite that in the absence of the drug both cultures support robust virus replication. Our data show that the viral replication is much more resilient to BFA than functioning of the cellular secretory pathway, suggesting that the role of GBF1 in the viral replication is independent of its Arf activating function. We demonstrate that the level of recruitment of GBF1 to the replication complexes limits the establishment and expression of a BFA resistance phenotype in both HeLa and Vero cells. Moreover, the BFA resistance phenotype of poliovirus mutants is also cell type dependent in different cells of human origin and results in a fitness loss in the form of reduced efficiency of RNA replication in the absence of the drug. Thus, a rational approach to the development of host-targeting antivirals may overcome the superior adaptability of (+)RNA viruses.

IMPORTANCE

Compared to the number of viral diseases, the number of available vaccines is miniscule. For some viruses vaccine development has not been successful after multiple attempts, and for many others vaccination is not a viable option. Antiviral drugs are needed for clinical practice and public health emergencies. However, viruses are highly adaptable and can easily generate mutants resistant to practically any compounds targeting viral proteins. An alternative approach is to target stable cellular factors recruited for the virus-specific functions. In the present study, we analyzed the factors permitting and restricting the establishment of the resistance of poliovirus, a small (+)RNA virus, to brefeldin A (BFA), a drug targeting a cellular component of the viral replication complex. We found that the emergence and replication potential of resistant mutants is cell type dependent and that BFA resistance reduces virus fitness. Our data provide a rational approach to the development of antiviral therapeutics targeting host factors.

Experimentally guided models reveal replication principles that shape the mutation distribution of RNA viruses

Life history theory posits that the sequence and timing of events in an organism's lifespan are fine-tuned by evolution to maximize the production of viable offspring. In a virus, a life history strategy is largely manifested in its replication mode. Here, we develop a stochastic mathematical model to infer the replication mode shaping the structure and mutation distribution of a poliovirus population in an intact single infected cell. We measure production of RNA and poliovirus particles through the infection cycle, and use these data to infer the parameters of our model. We find that on average the viral progeny produced from each cell are approximately five generations removed from the infecting virus. Multiple generations within a single cell infection provide opportunities for significant accumulation of mutations per viral genome and for intracellular selection.

DOI:http://dx.doi.org/10.7554/eLife.03753.001

Viruses with genetic information made up of molecules of RNA can multiply quickly, but not very accurately. This means that many errors, or mutations, occur when the RNA is copied to create new viruses. The advantage of this rapid, but mistake-filled, RNA replication process is that some of the mutations will be beneficial to the virus. This allows viruses to rapidly evolve, for example, to develop resistance against drugs.

The poliovirus is an RNA virus that can cause paralysis and death in humans. To prevent such infections, scientists have extensively studied the poliovirus and have developed effective vaccines against it that have eliminated the virus from all but a few countries. Because so much is known about the poliovirus and because it has a very simple structure, scientists continue to use the poliovirus as a model to study virus behavior.

One unknown aspect of the poliovirus' behavior is how it replicates after invading a cell. Are all of its RNA copies made from the original viral RNA that first infected the cell, in what is known as a ‘stamping machine’ model? Or do the new copies of the RNA also get copied themselves in a ‘geometric replication mode’ that increases the likelihood of mutations and enables the virus to evolve more rapidly?

Viral RNA molecules are copied by one of the virus's own proteins and so before the viral RNA can be replicated, it must first be translated to form viral proteins. When and where replication begins depends on the concentration of translated proteins around the RNA and so replication tends to begin in particular areas of the cell at different times. Schulte, Draghi et al. used mathematical modeling to create computer simulations of the number of polioviruses in a cell that take into account these time and space constraints. By including random elements in the model, the simulated behavior more accurately follows experimentally recorded data than previously used models.

The results of the model led Schulte, Draghi et al. to conclude that the poliovirus replicates by the ‘geometric mode’; as new copies of the poliovirus RNA are made, each copy goes on to make more copies. This means that in a single infected cell there are multiple generations of RNA, and each generation may undergo distinct mutations that are passed on to the next set of RNA copies. In fact, Schulte, Draghi et al. found that the average virus released from an infected cell is the great-great-great-granddaughter of the original virus that infected the cell. With so many different generations of virus coexisting in a cell, there are a lot of opportunities for new genetic combinations to occur and for viruses to evolve new abilities.

DOI:http://dx.doi.org/10.7554/eLife.03753.002

Lost in transcription: transient errors in information transfer

Errors in information transfer from DNA to RNA to protein are inevitable. Here, we focus on errors that occur in nascent transcripts during transcription, epimutations. Recent approaches using novel cDNA library preparation and next-generation sequencing begin to directly determine the rate of epimutation and allow analysis of the epimutational spectrum of transcription errors, the type and sequence context of the errors produced in a transcript by an RNA polymerase. The phenotypic consequences of transcription errors have been assessed using both forward and reverse epimutation systems. These studies reveal that transient transcription errors can produce a modification of cell phenotype, partial phenotypic suppression of a mutant allele, and a heritable change in cell phenotype, epigenetic switching in a bistable gene network.

Estimating Fitness of Viral Quasispecies from Next-Generation Sequencing Data

The quasispecies model is ubiquitous in the study of viruses. While having lead to a number of insights that have stood the test of time, the quasispecies model has mostly been discussed in a theoretical fashion with little support of data. With next-generation sequencing (NGS), this situation is changing and a wealth of data can now be produced in a time- and cost-efficient manner. NGS can, after removal of technical errors, yield an exceedingly detailed picture of the viral population structure. The widespread availability of cross-sectional data can be used to study fitness landscapes of viral populations in the quasispecies model. This chapter highlights methods that estimate the strength of selection in selective sweeps, assesses marginal fitness effects of quasispecies, and finally infers the fitness landscape of a viral quasispecies, all on the basis of NGS data.

Evolution of RNA-Based Networks

RNA molecules have served for decades as a paradigmatic example of molecular evolution that is tractable both in in vitro experiments and in detailed computer simulation. The adaptation of RNA sequences to external selection pressures is well studied and well understood. The de novo innovation or optimization of RNA aptamers and riboswitches in SELEX experiments serves as a case in point. Likewise, fitness landscapes building upon the efficiently computable RNA secondary structures have been a key toward understanding realistic fitness landscapes. Much less is known, however, on models in which multiple RNAs interact with each other, thus actively influencing the selection pressures acting on them. From a computational perspective, RNA-RNA interactions can be dealt with by same basic methods as the folding of a single RNA molecule, although many details become more complicated. RNA-RNA interactions are frequently employed in cellular regulation networks, e.g., as miRNA bases mRNA silencing or in the modulation of bacterial mRNAs by small, often highly structured sRNAs. In this chapter, we summarize the key features of networks of replicators. We highlight the differences between quasispecies-like models describing templates copied by an external replicase and hypercycle similar to autocatalytic replicators. Two aspects are of importance: the dynamics of selection within a population, usually described by conventional dynamical systems, and the evolution of replicating species in the space of chemical types. Product inhibition plays a key role in modulating selection dynamics from survival of the fittest to extinction of unfittest. The sequence evolution of replicators is rather well understood as approximate optimization in a fitness landscape for templates that is shaped by the sequence-structure map of RNA. Some of the properties of this map, in particular shape space covering and extensive neutral networks, give rise to evolutionary patterns such as drift-like motion in sequence space, akin to the behavior of RNA quasispecies. In contrast, very little is known about the influence of sequence-structure maps on autocatalytic replication systems.

Structure-function relationships underlying the replication fidelity of viral RNA-dependent RNA polymerases

Viral RNA-dependent RNA polymerases are considered to be low-fidelity enzymes, providing high mutation rates that allow for the rapid adaptation of RNA viruses to different host cell environments. Fidelity is tuned to provide the proper balance of virus replication rates, pathogenesis, and tissue tropism needed for virus growth. Using our structures of picornaviral polymerase-RNA elongation complexes, we have previously engineered more than a dozen coxsackievirus B3 polymerase mutations that significantly altered virus replication rates and in vivo fidelity and also provided a set of secondary adaptation mutations after tissue culture passage. Here we report a biochemical analysis of these mutations based on rapid stopped-flow kinetics to determine elongation rates and nucleotide discrimination factors. The data show a spatial separation of fidelity and replication rate effects within the polymerase structure. Mutations in the palm domain have the greatest effects on in vitro nucleotide discrimination, and these effects are strongly correlated with elongation rates and in vivo mutation frequencies, with faster polymerases having lower fidelity. Mutations located at the top of the finger domain, on the other hand, primarily affect elongation rates and have relatively minor effects on fidelity. Similar modulation effects are seen in poliovirus polymerase, an inherently lower-fidelity enzyme where analogous mutations increase nucleotide discrimination. These findings further our understanding of viral RNA-dependent RNA polymerase structure-function relationships and suggest that positive-strand RNA viruses retain a unique palm domain-based active-site closure mechanism to fine-tune replication fidelity.

IMPORTANCE

Positive-strand RNA viruses represent a major class of human and animal pathogens with significant health and economic impacts. These viruses replicate by using a virally encoded RNA-dependent RNA polymerase enzyme that has low fidelity, generating many mutations that allow the rapid adaptation of these viruses to different tissue types and host cells. In this work, we use a structure-based approach to engineer mutations in viral polymerases and study their effects on in vitro nucleotide discrimination as well as virus growth and genome replication fidelity. These results show that mutation rates can be drastically increased or decreased as a result of single mutations at several key residues in the polymerase palm domain, and this can significantly attenuate virus growth in vivo. These findings provide a pathway for developing live attenuated virus vaccines based on engineering the polymerase to reduce virus fitness.

Getting to Know Viral Evolutionary Strategies: Towards the Next Generation of Quasispecies Models

Viral populations are formed by complex ensembles of genomes with broad phenotypic diversity. The adaptive strategies deployed by these ensembles are multiple and often cannot be predicted a priori. Our understanding of viral dynamics is mostly based on two kinds of empirical approaches: one directed towards characterizing molecular changes underlying fitness changes and another focused on population-level responses. Simultaneously, theoretical efforts are directed towards developing a formal picture of viral evolution by means of more realistic fitness landscapes and reliable population dynamics models. New technologies, chiefly the use of next-generation sequencing and related tools, are opening avenues connecting the molecular and the population levels. In the near future, we hope to be witnesses of an integration of these still decoupled approaches, leading into more accurate and realistic quasispecies models able to capture robust generalities and endowed with a satisfactory predictive power.

Evolution of the division of labor between genes and enzymes in the RNA world

The RNA world is a very likely interim stage of the evolution after the first replicators and before the advent of the genetic code and translated proteins. Ribozymes are known to be able to catalyze many reaction types, including cofactor-aided metabolic transformations. In a metabolically complex RNA world, early division of labor between genes and enzymes could have evolved, where the ribozymes would have been transcribed from the genes more often than the other way round, benefiting the encapsulating cells through this dosage effect. Here we show, by computer simulations of protocells harboring unlinked RNA replicators, that the origin of replicational asymmetry producing more ribozymes from a gene template than gene strands from a ribozyme template is feasible and robust. Enzymatic activities of the two modeled ribozymes are in trade-off with their replication rates, and the relative replication rates compared to those of complementary strands are evolvable traits of the ribozymes. The degree of trade-off is shown to have the strongest effect in favor of the division of labor. Although some asymmetry between gene and enzymatic strands could have evolved even in earlier, surface-bound systems, the shown mechanism in protocells seems inevitable and under strong positive selection. This could have preadapted the genetic system for transcription after the subsequent origin of chromosomes and DNA.