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

Point mutations at specific sites of the nsp12-nsp8 interface dramatically affect the RNA polymerization activity of SARS-CoV-2

In a recent characterization of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variability present in 30 diagnostic samples from patients of the first COVID-19 pandemic wave, 41 amino acid substitutions were documented in the RNA-dependent RNA polymerase (RdRp) nsp12. Eight substitutions were selected in this work to determine whether they had an impact on the RdRp activity of the SARS-CoV-2 nsp12-nsp8-nsp7 replication complex. Three of these substitutions were found around the polymerase central cavity, in the template entry channel (D499G and M668V), and within the motif B (V560A), and they showed polymerization rates similar to the wild type RdRp. The remaining five mutations (P323L, L372F, L372P, V373A, and L527H) were placed near the nsp12-nsp8F contact surface; residues L372, V373, and L527 participated in a large hydrophobic cluster involving contacts between two helices in the nsp12 fingers and the long α-helix of nsp8F. The presence of any of these five amino acid substitutions resulted in important alterations in the RNA polymerization activity. Comparative primer elongation assays showed different behavior depending on the hydrophobicity of their side chains. The substitution of L by the bulkier F side chain at position 372 slightly promoted RdRp activity. However, this activity was dramatically reduced with the L372P, and L527H mutations, and to a lesser extent with V373A, all of which weaken the hydrophobic interactions within the cluster. Additional mutations, specifically designed to disrupt the nsp12-nsp8F interactions (nsp12-V330S, nsp12-V341S, and nsp8-R111A/D112A), also resulted in an impaired RdRp activity, further illustrating the importance of this contact interface in the regulation of RNA synthesis.

Structure and dynamics of enterovirus genotype networks

Like all biological populations, viral populations exist as networks of genotypes connected through mutation. Mapping the topology of these networks and quantifying population dynamics across them is crucial to understanding how populations adapt to changes in their selective environment. The influence of mutational networks is especially profound in viral populations that rapidly explore their mutational neighborhoods via high mutation rates. Using a single-cell sequencing method, scRNA-seq-enabled acquisition of mRNA and consensus haplotypes linking individual genotypes and host transcriptomes (SEARCHLIGHT), we captured and assembled viral haplotypes from hundreds of individual infected cells, revealing the complexity of viral population structures. We obtained these genotypes in parallel with host cell transcriptome information, enabling us to link host cell transcriptional phenotypes to the genetic structures underlying virus adaptation. Our examination of these structures reveals the common evolutionary dynamics of enterovirus populations and illustrates how viral populations reach through mutational "tunnels" to span evolutionary landscapes and maintain connection with multiple adaptive genotypes simultaneously.

Targeted accurate RNA consensus sequencing (tARC-seq) reveals mechanisms of replication error affecting SARS-CoV-2 divergence

RNA viruses, like SARS-CoV-2, depend on their RNA-dependent RNA polymerases (RdRp) for replication, which is error prone. Monitoring replication errors is crucial for understanding the virus's evolution. Current methods lack the precision to detect rare de novo RNA mutations, particularly in low-input samples such as those from patients. Here we introduce a targeted accurate RNA consensus sequencing method (tARC-seq) to accurately determine the mutation frequency and types in SARS-CoV-2, both in cell culture and clinical samples. Our findings show an average of 2.68 × 10-5 de novo errors per cycle with a C > T bias that cannot be solely attributed to APOBEC editing. We identified hotspots and cold spots throughout the genome, correlating with high or low GC content, and pinpointed transcription regulatory sites as regions more susceptible to errors. tARC-seq captured template switching events including insertions, deletions and complex mutations. These insights shed light on the genetic diversity generation and evolutionary dynamics of SARS-CoV-2.

Uncovering Structural Plasticity of Enterovirus A through Deep Insertional and Deletional Scanning

Insertions and deletions (InDels) are essential sources of novelty in protein evolution. In RNA viruses, InDels cause dramatic phenotypic changes contributing to the emergence of viruses with altered immune profiles and host engagement. This work aimed to expand our current understanding of viral evolution and explore the mutational tolerance of RNA viruses to InDels, focusing on Enterovirus A71 (EV-A71) as a prototype for Enterovirus A species (EV-A). Using newly described deep InDel scanning approaches, we engineered approximately 45,000 insertions and 6,000 deletions at every site across the viral proteome, quantifying their effects on viral fitness. As a general trend, most InDels were lethal to the virus. However, our screen reproducibly identified a set of InDel-tolerant regions, demonstrating our ability to comprehensively map tolerance to these mutations. Tolerant sites highlighted structurally flexible and mutationally plastic regions of viral proteins that avoid core structural and functional elements. Phylogenetic analysis on EV-A species infecting diverse mammalian hosts revealed that the experimentally-identified hotspots overlapped with sites of InDels across the EV-A species, suggesting structural plasticity at these sites is an important function for InDels in EV speciation. Our work reveals the fitness effects of InDels across EV-A71, identifying regions of evolutionary capacity that require further monitoring, which could guide the development of Enterovirus vaccines.

Transient RNA structures underlie highly pathogenic avian influenza virus genesis

Highly pathogenic avian influenza viruses (HPAIVs) cause severe disease and high fatality in poultry1. They emerge exclusively from H5 and H7 low pathogenic avian influenza viruses (LPAIVs)2. Although insertion of a furin-cleavable multibasic cleavage site (MBCS) in the hemagglutinin gene was identified decades ago as the genetic basis for LPAIV-to-HPAIV transition3,4, the exact mechanisms underlying said insertion have remained unknown. Here we used an innovative combination of bioinformatic models to predict RNA structures forming around the influenza virus RNA polymerase during replication, and circular sequencing5 to reliably detect nucleotide insertions. We show that transient H5 hemagglutinin RNA structures predicted to trap the polymerase on purine-rich sequences drive nucleotide insertions characteristic of MBCSs, providing the first strong empirical evidence of RNA structure involvement in MBCS acquisition. Insertion frequencies at the H5 cleavage site were strongly affected by substitutions in flanking genomic regions altering predicted transient RNA structures. Introduction of H5-like cleavage site sequences and structures into an H6 hemagglutinin resulted in MBCS-yielding insertions never observed before in H6 viruses. Our results demonstrate that nucleotide insertions that underlie H5 HPAIV emergence result from a previously unknown RNA-structure-driven diversity-generating mechanism, which could be shared with other RNA viruses.

Parallel evolution and enhanced virulence upon in vivo passage of an RNA virus in Drosophila melanogaster

Virus evolution is strongly affected by antagonistic co-evolution of virus and host. Host immunity positively selects for viruses that evade the immune response, which in turn may drive counter-adaptations in host immune genes. We investigated how host immune pressure shapes virus populations, using the fruit fly Drosophila melanogaster and its natural pathogen Drosophila C virus (DCV), as a model. We performed an experimental evolution study in which DCV was serially passaged for ten generations in three fly genotypes differing in their antiviral RNAi response: wild-type flies and flies in which the endonuclease gene Dicer-2 was either overexpressed or inactivated. All evolved virus populations replicated more efficiently in vivo and were more virulent than the parental stock. The number of polymorphisms increased in all three host genotypes with passage number, which was most pronounced in Dicer-2 knockout flies. Mutational analysis showed strong parallel evolution, as mutations accumulated in a specific region of the VP3 capsid protein in every lineage in a host genotype-independent manner. The parental tyrosine at position ninety-five of VP3 was substituted with either one of five different amino acids in fourteen out of fifteen lineages. However, no consistent amino acid changes were observed in the viral RNAi suppressor gene 1A, nor elsewhere in the genome in any of the host backgrounds. Our study indicates that the RNAi response restricts the sequence space that can be explored by viral populations. Moreover, our study illustrates how evolution towards higher virulence can be a highly reproducible, yet unpredictable process.

A tradeoff between enterovirus A71 particle stability and cell entry

A central role of viral capsids is to protect the viral genome from the harsh extracellular environment while facilitating initiation of infection when the virus encounters a target cell. Viruses are thought to have evolved an optimal equilibrium between particle stability and efficiency of cell entry. In this study, we genetically perturb this equilibrium in a non-enveloped virus, enterovirus A71 to determine its structural basis. We isolate a single-point mutation variant with increased particle thermotolerance and decreased efficiency of cell entry. Using cryo-electron microscopy and molecular dynamics simulations, we determine that the thermostable native particles have acquired an expanded conformation that results in a significant increase in protein dynamics. Examining the intermediate states of the thermostable variant reveals a potential pathway for uncoating. We propose a sequential release of the lipid pocket factor, followed by internal VP4 and ultimately the viral RNA.

Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates

AbstractThe joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the "nominal" distribution of possible changes, prior to mutation or selection; (2) the "de novo" distribution of realized mutations; and (3) the "fixed" distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong-selection/weak-mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

Engineered bacteria launch and control an oncolytic virus

The ability of bacteria and viruses to selectively replicate in tumors has led to synthetic engineering of new microbial therapies. Here we design a cooperative strategy whereby S. typhimurium bacteria transcribe and deliver the Senecavirus A RNA genome inside host cells, launching a potent oncolytic viral infection. Then, we engineer the virus to require a bacterially delivered protease in order to achieve virion maturation, demonstrating bacterial control over the virus. This work extends bacterially delivered therapeutics to viral genomes, and the governing of a viral population through engineered microbial interactions.

One-Sentence Summary

Bacteria are engineered to act as a synthetic "capsid" delivering Senecavirus A genome and controlling its spread.

Fitness effects of mutations to SARS-CoV-2 proteins

Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here, we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.

High-resolution mapping reveals the mechanism and contribution of genome insertions and deletions to RNA virus evolution

RNA viruses rapidly adapt to selective conditions due to the high intrinsic mutation rates of their RNA-dependent RNA polymerases (RdRps). Insertions and deletions (indels) in viral genomes are major contributors to both deleterious mutational load and evolutionary novelty, but remain understudied. To characterize the mechanistic details of their formation and evolutionary dynamics during infection, we developed a hybrid experimental-bioinformatic approach. This approach, called MultiMatch, extracts insertions and deletions from ultradeep sequencing experiments, including those occurring at extremely low frequencies, allowing us to map their genomic distribution and quantify the rates at which they occur. Mapping indel mutations in adapting poliovirus and dengue virus populations, we determine the rates of indel generation and identify mechanistic and functional constraints shaping indel diversity. Using poliovirus RdRp variants of distinct fidelity and genome recombination rates, we demonstrate tradeoffs between fidelity and Indel generation. Additionally, we show that maintaining translation frame and viral RNA structures constrain the Indel landscape and that, due to these significant fitness effects, Indels exert a significant deleterious load on adapting viral populations. Conversely, we uncover positively selected Indels that modulate RNA structure, generate protein variants, and produce defective interfering genomes in viral populations. Together, our analyses establish the kinetic and mechanistic tradeoffs between misincorporation, recombination, and Indel rates and reveal functional principles defining the central role of Indels in virus evolution, emergence, and the regulation of viral infection.

Single-cell mutation rate of turnip crinkle virus (-)-strand replication intermediates

Viruses with single-stranded, positive-sense (+) RNA genomes incur high numbers of errors during replication, thereby creating diversified genome populations from which new, better adapted viral variants can emerge. However, a definitive error rate is known for a relatively few (+) RNA plant viruses, due to challenges to account for perturbations caused by natural selection and/or experimental set-ups. To address these challenges, we developed a new approach that exclusively profiled errors in the (-)-strand replication intermediates of turnip crinkle virus (TCV), in singly infected cells. A series of controls and safeguards were devised to ensure errors inherent to the experimental process were accounted for. This approach permitted the estimation of a TCV error rate of 8.47 X 10-5 substitution per nucleotide site per cell infection. Importantly, the characteristic error distribution pattern among the 50 copies of 2,363-base-pair cDNA fragments predicted that nearly all TCV (-) strands were products of one replication cycle per cell. Furthermore, some of the errors probably elevated error frequencies by lowering the fidelity of TCV RNA-dependent RNA polymerase, and/or permitting occasional re-replication of progeny genomes. In summary, by profiling errors in TCV (-)-strand intermediates incurred during replication in single cells, this study provided strong support for a stamping machine mode of replication employed by a (+) RNA virus.

Amphioxus adenosine-to-inosine tRNA-editing enzyme that can perform C-to-U and A-to-I deamination of DNA

Adenosine-to-inosine tRNA-editing enzyme has been identified for more than two decades, but the study on its DNA editing activity is rather scarce. We show that amphioxus (Branchiostoma japonicum) ADAT2 (BjADAT2) contains the active site 'HxE-PCxxC' and the key residues for target-base-binding, and amphioxus ADAT3 (BjADAT3) harbors both the N-terminal positively charged region and the C-terminal pseudo-catalytic domain important for recognition of substrates. The sequencing of BjADAT2-transformed Escherichia coli genome suggests that BjADAT2 has the potential to target E. coli DNA and can deaminate at TCG and GAA sites in the E. coli genome. Biochemical analyses further demonstrate that BjADAT2, in complex with BjADAT3, can perform A-to-I editing of tRNA and convert C-to-U and A-to-I deamination of DNA. We also show that BjADAT2 preferentially deaminates adenosines and cytidines in the loop of DNA hairpin structures of substrates, and BjADAT3 also affects the type of DNA substrate targeted by BjADAT2. Finally, we find that C89, N113, C148 and Y156 play critical roles in the DNA editing activity of BjADAT2. Collectively, our study indicates that BjADAT2/3 is the sole naturally occurring deaminase with both tRNA and DNA editing capacity identified so far in Metazoa.

Fitness effects of mutations to SARS-CoV-2 proteins

Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino-acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.

Mutation bias and the predictability of evolution

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Mutation rate, selection, and epistasis inferred from RNA virus haplotypes via neural posterior estimation

RNA viruses are particularly notorious for their high levels of genetic diversity, which is generated through the forces of mutation and natural selection. However, disentangling these two forces is a considerable challenge, and this may lead to widely divergent estimates of viral mutation rates, as well as difficulties in inferring the fitness effects of mutations. Here, we develop, test, and apply an approach aimed at inferring the mutation rate and key parameters that govern natural selection, from haplotype sequences covering full-length genomes of an evolving virus population. Our approach employs neural posterior estimation, a computational technique that applies simulation-based inference with neural networks to jointly infer multiple model parameters. We first tested our approach on synthetic data simulated using different mutation rates and selection parameters while accounting for sequencing errors. Reassuringly, the inferred parameter estimates were accurate and unbiased. We then applied our approach to haplotype sequencing data from a serial passaging experiment with the MS2 bacteriophage, a virus that parasites Escherichia coli. We estimated that the mutation rate of this phage is around 0.2 mutations per genome per replication cycle (95% highest density interval: 0.051-0.56). We validated this finding with two different approaches based on single-locus models that gave similar estimates but with much broader posterior distributions. Furthermore, we found evidence for reciprocal sign epistasis between four strongly beneficial mutations that all reside in an RNA stem loop that controls the expression of the viral lysis protein, responsible for lysing host cells and viral egress. We surmise that there is a fine balance between over- and underexpression of lysis that leads to this pattern of epistasis. To recap, we have developed an approach for joint inference of the mutation rate and selection parameters from full haplotype data with sequencing errors and used it to reveal features governing MS2 evolution.

Genetic Diversity among Rose Rosette Virus Isolates: A Roadmap towards Studies of Gene Function and Pathogenicity

The phylogenetic relationships of ninety-five rose rosette virus (RRV) isolates with full-length genomic sequences were analyzed. These isolates were recovered mostly from commercial roses that are vegetatively propagated rather than grown from seed. First, the genome segments were concatenated, and the maximum likelihood (ML) tree shows that the branches arrange independent of their geographic origination. There were six major groups of isolates, with 54 isolates in group 6 and distributed in two subgroups. An analysis of nucleotide diversity across the concatenated isolates showed lower genetic differences among RNAs encoding the core proteins required for encapsidation than the latter genome segments. Recombination breakpoints were identified near the junctions of several genome segments, suggesting that the genetic exchange of segments contributes to differences among isolates. The ML analysis of individual RNA segments revealed different relationship patterns among isolates, which supports the notion of genome reassortment. We tracked the branch positions of two newly sequenced isolates to highlight how genome segments relate to segments of other isolates. RNA6 has an interesting pattern of single-nucleotide mutations that appear to influence amino acid changes in the protein products derived from ORF6a and ORF6b. The P6a proteins were typically 61 residues, although three isolates encoded P6a proteins truncated to 29 residues, and four proteins extended 76-94 residues. Homologous P5 and P7 proteins appear to be evolving independently. These results suggest greater diversity among RRV isolates than previously recognized.

Genotype-phenotype landscapes for immune-pathogen coevolution

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

An RNA-based system to study hepatitis B virus replication and evaluate antivirals

Hepatitis B virus (HBV) chronically infects an estimated 300 million people, and standard treatments are rarely curative. Infection increases the risk of liver cirrhosis and hepatocellular carcinoma, and consequently, nearly 1 million people die each year from chronic hepatitis B. Tools and approaches that bring insights into HBV biology and facilitate the discovery and evaluation of antiviral drugs are in demand. Here, we describe a method to initiate the replication of HBV, a DNA virus, using synthetic RNA. This approach eliminates contaminating background signals from input virus or plasmid DNA that plagues existing systems and can be used to study multiple stages of HBV replication. We further demonstrate that this method can be uniquely applied to identify sequence variants that confer resistance to antiviral drugs.

Viral intra-host evolutionary dynamics revealed via serial passage of Japanese encephalitis virus in vitro

Analyses of viral inter- and intra-host mutations could better guide the prevention and control of infectious diseases. For a long time, studies on viral evolution have focused on viral inter-host variations. Next-generation sequencing has accelerated the investigations of viral intra-host diversity. However, the theoretical basis and dynamic characteristics of viral intra-host mutations remain unknown. Here, using serial passages of the SA14-14-2 vaccine strain of Japanese encephalitis virus (JEV) as the in vitro model, the distribution characteristics of 1,788 detected intra-host single-nucleotide variations (iSNVs) and their mutated frequencies from 477 deep-sequenced samples were analyzed. Our results revealed that in adaptive (baby hamster kidney (BHK)) cells, JEV is under a nearly neutral selection pressure, and both non-synonymous and synonymous mutations represent an S-shaped growth trend over time. A higher positive selection pressure was observed in the nonadaptive (C6/36) cells, and logarithmic growth in non-synonymous iSNVs and linear growth in synonymous iSNVs were observed over time. Moreover, the mutation rates of the NS4B protein and the untranslated region (UTR) of the JEV are significantly different between BHK and C6/36 cells, suggesting that viral selection pressure is regulated by different cellular environments. In addition, no significant difference was detected in the distribution of mutated frequencies of iSNVs between BHK and C6/36 cells.

Evolutionary conservation of the fidelity of transcription

Accurate transcription is required for the faithful expression of genetic information. However, relatively little is known about the molecular mechanisms that control the fidelity of transcription, or the conservation of these mechanisms across the tree of life. To address these issues, we measured the error rate of transcription in five organisms of increasing complexity and found that the error rate of RNA polymerase II ranges from 2.9 × 10-6 ± 1.9 × 10-7/bp in yeast to 4.0 × 10-6 ± 5.2 × 10-7/bp in worms, 5.69 × 10-6 ± 8.2 × 10-7/bp in flies, 4.9 × 10-6 ± 3.6 × 10-7/bp in mouse cells and 4.7 × 10-6 ± 9.9 × 10-8/bp in human cells. These error rates were modified by various factors including aging, mutagen treatment and gene modifications. For example, the deletion or modification of several related genes increased the error rate substantially in both yeast and human cells. This research highlights the evolutionary conservation of factors that control the fidelity of transcription. Additionally, these experiments provide a reasonable estimate of the error rate of transcription in human cells and identify disease alleles in a subunit of RNA polymerase II that display error-prone transcription. Finally, we provide evidence suggesting that the error rate and spectrum of transcription co-evolved with our genetic code.

Kill or corrupt: Mechanisms of action and drug-resistance of nucleotide analogues against SARS-CoV-2

Nucleoside/tide analogues (NAs) have long been used in the fight against viral diseases, and now present a promising option for the treatment of COVID-19. Once activated to the 5'-triphosphate state, NAs act by targeting the viral RNA-dependent RNA-polymerase for incorporation into the viral RNA genome. Incorporated analogues can either 'kill' (terminate) synthesis, or 'corrupt' (genetically or chemically) the RNA. Against coronaviruses, the use of NAs has been further complicated by the presence of a virally encoded exonuclease domain (nsp14) with proofreading and repair capacities. Here, we describe the mechanism of action of four promising anti-COVID-19 NAs; remdesivir, molnupiravir, favipiravir and bemnifosbuvir. Their distinct mechanisms of action best exemplify the concept of 'killers' and 'corruptors'. We review available data regarding their ability to be incorporated and excised, and discuss the specific structural features that dictate their overall potency, toxicity, and mutagenic potential. This should guide the synthesis of novel analogues, lend insight into the potential for resistance mutations, and provide a rational basis for upcoming combinations therapies.

The fidelity of transcription in human cells

To determine the error rate of transcription in human cells, we analyzed the transcriptome of H1 human embryonic stem cells with a circle-sequencing approach that allows for high-fidelity sequencing of the transcriptome. These experiments identified approximately 100,000 errors distributed over every major RNA species in human cells. Our results indicate that different RNA species display different error rates, suggesting that human cells prioritize the fidelity of some RNAs over others. Cross-referencing the errors that we detected with various genetic and epigenetic features of the human genome revealed that the in vivo error rate in human cells changes along the length of a transcript and is further modified by genetic context, repetitive elements, epigenetic markers, and the speed of transcription. Our experiments further suggest that BRCA1, a DNA repair protein implicated in breast cancer, has a previously unknown role in the suppression of transcription errors. Finally, we analyzed the distribution of transcription errors in multiple tissues of a new mouse model and found that they occur preferentially in neurons, compared to other cell types. These observations lend additional weight to the idea that transcription errors play a key role in the progression of various neurological disorders, including Alzheimer's disease.

Viral Fitness Landscapes Based on Self-organizing Maps

The creation of fitness maps from viral populations especially in the case of RNA viruses, with high mutation rates producing quasispecies, is complex since the mutant spectrum is in a very high-dimensional space. In this work, a new approach is presented using a class of neural networks, Self-Organized Maps (SOM), to represent realistic fitness landscapes in two RNA viruses: Human Immunodeficiency Virus type 1 (HIV-1) and Hepatitis C Virus (HCV). This methodology has proven to be very effective in the classification of viral quasispecies, using as criterium the mutant sequences in the population. With HIV-1, the fitness landscapes are constructed by representing the experimentally determined fitness on the sequence map. This approach permitted the depiction of the evolutionary paths of the variants subjected to processes of fitness loss and gain in cell culture. In the case of HCV, the efficiency was measured as a function of the frequency of each haplotype in the population by ultra-deep sequencing. The fitness landscapes obtained provided information on the efficiency of each variant in the quasispecies environment, that is, in relation to the entire spectrum of mutants. With the SOM maps, it is possible to determine the evolutionary dynamics of the different haplotypes.

Viral Fitness, Population Complexity, Host Interactions, and Resistance to Antiviral Agents

Fitness of viruses has become a standard parameter to quantify their adaptation to a biological environment. Fitness determinations for RNA viruses (and some highly variable DNA viruses) meet with several uncertainties. Of particular interest are those that arise from mutant spectrum complexity, absence of population equilibrium, and internal interactions among components of a mutant spectrum. Here, concepts, fitness measurements, limitations, and current views on experimental viral fitness landscapes are discussed. The effect of viral fitness on resistance to antiviral agents is covered in some detail since it constitutes a widespread problem in antiviral pharmacology, and a challenge for the design of effective antiviral treatments. Recent evidence with hepatitis C virus suggests the operation of mechanisms of antiviral resistance additional to the standard selection of drug-escape mutants. The possibility that high replicative fitness may be the driver of such alternative mechanisms is considered. New broad-spectrum antiviral designs that target viral fitness may curtail the impact of drug-escape mutants in treatment failures. We consider to what extent fitness-related concepts apply to coronaviruses and how they may affect strategies for COVID-19 prevention and treatment.

Virus Evolution on Fitness Landscapes

The landscape paradigm is revisited in the light of evolution in simple systems. A brief overview of different classes of fitness landscapes is followed by a more detailed discussion of the RNA model, which is currently the only evolutionary model that allows for a comprehensive molecular analysis of a fitness landscape. Neutral networks of genotypes are indispensable for the success of evolution. Important insights into the evolutionary mechanism are gained by considering the topology of sequence and shape spaces. The dynamic concept of molecular quasispecies is viewed in the light of the landscape paradigm. The distribution of fitness values in state space is mirrored by the population structures of mutant distributions. Two classes of thresholds for replication error or mutations are important: (i) the-conventional-genotypic error threshold, which separates ordered replication from random drift on neutral networks, and (ii) a phenotypic error threshold above which the molecular phenotype is lost. Empirical landscapes are reviewed and finally, the implications of the landscape concept for virus evolution are discussed.

Small-Sample Estimation of the Mutational Support and Distribution of SARS-CoV-2

We consider the problem of determining the mutational support and distribution of the SARS-CoV-2 viral genome in the small-sample regime. The mutational support refers to the unknown number of sites that may eventually mutate in the SARS-CoV-2 genome while mutational distribution refers to the distribution of point mutations in the viral genome across a population. The mutational support may be used to assess the virulence of the virus and guide primer selection for real-time RT-PCR testing. Estimating the distribution of mutations in the genome of different subpopulations while accounting for the unseen may also aid in discovering new variants. To estimate the mutational support in the small-sample regime, we use GISAID sequencing data and our state-of-the-art polynomial estimation techniques based on new weighted and regularized Chebyshev approximation methods. For distribution estimation, we adapt the well-known Good-Turing estimator. Our analysis reveals several findings: First, the mutational supports exhibit significant differences in the ORF6 and ORF7a regions (older versus younger patients), ORF1b and ORF10 regions (females versus males) and in almost all ORFs (Asia/Europe/North America). Second, even though the N region of SARS-CoV-2 has a predicted 10% mutational support, mutations fall outside of the primer regions recommended by the CDC.

How do emerging long-read sequencing technologies function in transforming the plant pathology research landscape?

Long-read sequencing technologies are revolutionizing the sequencing and analysis of plant and pathogen genomes and transcriptomes, as well as contributing to emerging areas of interest in plant-pathogen interactions, disease management techniques, and the introduction of new plant varieties or cultivars. Long-read sequencing (LRS) technologies are progressively being implemented to study plants and pathogens of agricultural importance, which have substantial economic effects. The variability and complexity of the genome and transcriptome affect plant growth, development and pathogen responses. Overcoming the limitations of second-generation sequencing, LRS technology has significantly increased the length of a single contiguous read from a few hundred to millions of base pairs. Because of the longer read lengths, new analysis methods and tools have been developed for plant and pathogen genomics and transcriptomics. LRS technologies enable faster, more efficient, and high-throughput ultralong reads, allowing direct sequencing of genomes that would be impossible or difficult to investigate using short-read sequencing approaches. These benefits include genome assembly in repetitive areas, creating more comprehensive and exact genome determinations, assembling full-length transcripts, and detecting DNA and RNA alterations. Furthermore, these technologies allow for the identification of transcriptome diversity, significant structural variation analysis, and direct epigenetic mark detection in plant and pathogen genomic regions. LRS in plant pathology is found efficient for identifying and characterization of effectors in plants as well as known and unknown plant pathogens. In this review, we investigate how these technologies are transforming the landscape of determination and characterization of plant and pathogen genomes and transcriptomes efficiently and accurately. Moreover, we highlight potential areas of interest offered by LRS technologies for future study into plant-pathogen interactions, disease control strategies, and the development of new plant varieties or cultivars.

Viruses in astrobiology

Viruses are the most abundant biological entities on Earth, and yet, they have not received enough consideration in astrobiology. Viruses are also extraordinarily diverse, which is evident in the types of relationships they establish with their host, their strategies to store and replicate their genetic information and the enormous diversity of genes they contain. A viral population, especially if it corresponds to a virus with an RNA genome, can contain an array of sequence variants that greatly exceeds what is present in most cell populations. The fact that viruses always need cellular resources to multiply means that they establish very close interactions with cells. Although in the short term these relationships may appear to be negative for life, it is evident that they can be beneficial in the long term. Viruses are one of the most powerful selective pressures that exist, accelerating the evolution of defense mechanisms in the cellular world. They can also exchange genetic material with the host during the infection process, providing organisms with capacities that favor the colonization of new ecological niches or confer an advantage over competitors, just to cite a few examples. In addition, viruses have a relevant participation in the biogeochemical cycles of our planet, contributing to the recycling of the matter necessary for the maintenance of life. Therefore, although viruses have traditionally been excluded from the tree of life, the structure of this tree is largely the result of the interactions that have been established throughout the intertwined history of the cellular and the viral worlds. We do not know how other possible biospheres outside our planet could be, but it is clear that viruses play an essential role in the terrestrial one. Therefore, they must be taken into account both to improve our understanding of life that we know, and to understand other possible lives that might exist in the cosmos.

Adaptation to host cell environment during experimental evolution of Zika virus

Zika virus (ZIKV) infection can cause important developmental and neurological defects in Humans. Type I/III interferon responses control ZIKV infection and pathological processes, yet the virus has evolved various mechanisms to defeat these host responses. Here, we established a pipeline to delineate at high-resolution the genetic evolution of ZIKV in a controlled host cell environment. We uncovered that serially passaged ZIKV acquired increased infectivity and simultaneously developed a resistance to TLR3-induced restriction. We built a mathematical model that suggests that the increased infectivity is due to a reduced time-lag between infection and viral replication. We found that this adaptation is cell-type specific, suggesting that different cell environments may drive viral evolution along different routes. Deep-sequencing of ZIKV populations pinpointed mutations whose increased frequencies temporally coincide with the acquisition of the adapted phenotype. We functionally validated S455L, a substitution in ZIKV envelope (E) protein, recapitulating the adapted phenotype. Its positioning on the E structure suggests a putative function in protein refolding/stability. Taken together, our results uncovered ZIKV adaptations to the cellular environment leading to accelerated replication onset coupled with resistance to TLR3-induced antiviral response. Our work provides insights into Zika virus adaptation to host cells and immune escape mechanisms.

In vitro analyses and computational modelling indicate that Zika virus adapts to the cellular environment of its host over time

Pathogen evolution during vaccination campaigns

Following the initiation of the unprecedented global vaccination campaign against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), attention has now turned to the potential impact of this large-scale intervention on the evolution of the virus. In this Essay, we summarize what is currently known about pathogen evolution in the context of immune priming (including vaccination) from research on other pathogen species, with an eye towards the future evolution of SARS-CoV-2.

Phenotypic mutations contribute to protein diversity and shape protein evolution

Errors in DNA replication generate genetic mutations, while errors in transcription and translation lead to phenotypic mutations. Phenotypic mutations are orders of magnitude more frequent than genetic ones, yet they are less understood. Here, we review the types of phenotypic mutations, their quantifications, and their role in protein evolution and disease. The diversity generated by phenotypic mutation can facilitate adaptive evolution. Indeed, phenotypic mutations, such as ribosomal frameshift and stop codon readthrough, sometimes serve to regulate protein expression and function. Phenotypic mutations have often been linked to fitness decrease and diseases. Thus, understanding the protein heterogeneity and phenotypic diversity caused by phenotypic mutations will advance our understanding of protein evolution and have implications on human health and diseases.

From Clinical Specimen to Whole Genome Sequencing of A(H3N2) Influenza Viruses: A Fast and Reliable High-Throughput Protocol

(1) Background: Over the last few years, there has been growing interest in the whole genome sequencing (WGS) of rapidly mutating pathogens, such as influenza viruses (IVs), which has led us to carry out in-depth studies on viral evolution in both research and diagnostic settings. We aimed at describing and determining the validity of a WGS protocol that can obtain the complete genome sequence of A(H3N2) IVs directly from clinical specimens. (2) Methods: RNA was extracted from 80 A(H3N2)-positive respiratory specimens. A one-step RT-PCR assay, based on the use of a single set of specific primers, was used to retro-transcribe and amplify the entire IV type A genome in a single reaction, thus avoiding additional enrichment approaches and host genome removal treatments. Purified DNA was quantified; genomic libraries were prepared and sequenced by using Illumina MiSeq platform. The obtained reads were evaluated for sequence quality and read-pair length. (3) Results: All of the study specimens were successfully amplified, and the purified DNA concentration proved to be suitable for NGS (at least 0.2 ng/µL). An acceptable coverage depth for all eight genes of influenza A(H3N2) virus was obtained for 90% (72/80) of the clinical samples with viral loads >105 genome copies/mL. The mean depth of sequencing ranged from 105 to 200 reads per position, with the majority of the mean depth values being above 103 reads per position. The total turnaround time per set of 20 samples was four working days, including sequence analysis. (4) Conclusions: This fast and reliable high-throughput sequencing protocol should be used for influenza surveillance and outbreak investigation.

RNA polymerase inaccuracy underlies SARS-CoV-2 variants and vaccine heterogeneity

Both the SARS-CoV-2 virus and its mRNA vaccines depend on RNA polymerases (RNAP)1,2; however, these enzymes are inherently error-prone and can introduce variants into the RNA3. To understand SARS-CoV-2 evolution and vaccine efficacy, it is critical to identify the extent and distribution of errors introduced by the RNAPs involved in each process. Current methods lack the sensitivity and specificity to measure de novo RNA variants in low input samples like viral isolates3. Here, we determine the frequency and nature of RNA errors in both SARS-CoV-2 and its vaccine using a targeted Accurate RNA Consensus sequencing method (tARC-seq). We found that the viral RNA-dependent RNAP (RdRp) makes ~1 error every 10,000 nucleotides - higher than previous estimates4. We also observed that RNA variants are not randomly distributed across the genome but are associated with certain genomic features and genes, such as S (Spike). tARC-seq captured a number of large insertions, deletions and complex mutations that can be modeled through non-programmed RdRp template switching. This template switching feature of RdRp explains many key genetic changes observed during the evolution of different lineages worldwide, including Omicron. Further sequencing of the Pfizer-BioNTech COVID-19 vaccine revealed an RNA variant frequency of ~1 in 5,000, meaning most of the vaccine transcripts produced in vitro by T7 phage RNAP harbor a variant. These results demonstrate the extraordinary genetic diversity of viral populations and the heterogeneous nature of an mRNA vaccine fueled by RNAP inaccuracy. Along with functional studies and pandemic data, tARC-seq variant spectra can inform models to predict how SARS-CoV-2 may evolve. Finally, our results may help improve future vaccine development and study design as mRNA therapies continue to gain traction.

Plasticity in structure and assembly of SARS-CoV-2 nucleocapsid protein

Worldwide SARS-CoV-2 sequencing efforts track emerging mutations in its spike protein, as well as characteristic mutations in other viral proteins. Besides their epidemiological importance, the observed SARS-CoV-2 sequences present an ensemble of viable protein variants, and thereby a source of information on viral protein structure and function. Charting the mutational landscape of the nucleocapsid (N) protein that facilitates viral assembly, we observe variability exceeding that of the spike protein, with more than 86% of residues that can be substituted, on average by three to four different amino acids. However, mutations exhibit an uneven distribution that tracks known structural features but also reveals highly protected stretches of unknown function. One of these conserved regions is in the central disordered linker proximal to the N-G215C mutation that has become dominant in the Delta variant, outcompeting G215 variants without further spike or N-protein substitutions. Structural models suggest that the G215C mutation stabilizes conserved transient helices in the disordered linker serving as protein-protein interaction interfaces. Comparing Delta variant N-protein to its ancestral version in biophysical experiments, we find a significantly more compact and less disordered structure. N-G215C exhibits substantially stronger self-association, shifting the unliganded protein from a dimeric to a tetrameric oligomeric state, which leads to enhanced coassembly with nucleic acids. This suggests that the sequence variability of N-protein is mirrored by high plasticity of N-protein biophysical properties, which we hypothesize can be exploited by SARS-CoV-2 to achieve greater efficiency of viral assembly, and thereby enhanced infectivity.

Mutational spectrum of hepatitis C virus in patients with chronic hepatitis C determined by single molecule real-time sequencing

The emergence of hepatitis C virus (HCV) with resistance-associated substitution (RAS), produced by mutations in the HCV genome, is a major problem in direct acting antivirals (DAA) treatment. This study aimed to clarify the mutational spectrum in HCV-RNA and the substitution pattern for the emergence of RASs in patients with chronic HCV infection. HCV-RNA from two HCV replicon cell lines and the serum HCV-RNA of four non-liver transplant and four post-liver transplant patients with unsuccessful DAA treatment were analyzed using high-accuracy single-molecule real-time long-read sequencing. Transition substitutions, especially A>G and U>C, occurred prominently under DAAs in both non-transplant and post-transplant patients, with a mutational bias identical to that occurring in HCV replicon cell lines during 10-year culturing. These mutational biases were reproduced in natural courses after DAA treatment. RASs emerged via both transition and transversion substitutions. NS3-D168 and NS5A-L31 RASs resulted from transversion mutations, while NS5A-Y93 RASs was caused by transition substitutions. The fidelity of the RNA-dependent RNA polymerase, HCV-NS5B, produces mutational bias in the HCV genome, characterized by dominant transition mutations, notably A>G and U>C substitutions. However, RASs are acquired by both transition and transversion substitutions, and the RASs-positive HCV clones are selected and proliferated under DAA treatment pressure.

SARS-CoV-2 mutations acquired during serial passage in human cell lines are consistent with several of those found in recent natural SARS-CoV-2 variants

Since the outbreak of coronavirus disease (COVID-19) in 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into diverse variants. Here, an early isolate of SARS-CoV-2 was serially passaged in multiple cell lines of human origin in triplicate, and selected mutations were compared to those found in natural SARS-CoV-2 variants. In the spike protein, Q493R and Q498R substitutions from passaged viruses were consistent with those in the B.1.1.529 (Omicron) variant. Y144del and H655Y substitutions from passaged viruses were also reported in B.1.1.7 (Alpha), P.1 (Gamma), and B.1.1.529 (Omicron) variants. Several single nucleotide polymorphisms (SNPs) found in first-passaged viruses have also been identified as selected mutation sites in serially passaged viruses. Considering the consistent mutations found between serially passaged SARS-CoV-2 and natural variants, there may be host-specific selective mutation patterns of viral evolution in humans. Additional studies on the selective mutations in SARS-CoV-2 experiencing diverse host environments will help elucidate the direction of SARS-CoV-2 evolution.

Importance

SARS-CoV-2 isolate (SARS-CoV-2/human/KOR/KCDC03-NCCP43326/2020) was serially passaged in A549, CaCO2, and HRT-18 cells in triplicate. After 12 times of serial passages in each cell lines, several consistent selected mutations were found on spike protein between the serially passaged SARS-CoV-2 in human cell lines and recent natural variants of SARS-CoV-2 like omicron. On the non-spike protein genes, selected mutations were more frequent in viruses passaged in Caco-2 and HRT-18 cells (Colon epithelial-like) than in those passaged in A549 cells (Lung epithelial-like). In addition, several SNPs identified after one round of passaging were consistently identified as the selected mutation sites in serially passaged viruses. Thus, mutation patterns of SARS-CoV-2 in certain host environments may provide researchers information to understand and predict future SARS-CoV-2 variants.

Bluetongue Research at a Crossroads: Modern Genomics Tools Can Pave the Way to New Insights

Bluetongue virus (BTV) is an arthropod-borne, segmented double-stranded RNA virus that can cause severe disease in both wild and domestic ruminants. BTV evolves via several key mechanisms, including the accumulation of mutations over time and the reassortment of genome segments.Additionally, BTV must maintain fitness in two disparate hosts, the insect vector and the ruminant. The specific features of viral adaptation in each host that permit host-switching are poorly characterized. Limited field studies and experimental work have alluded to the presence of these phenomena at work, but our understanding of the factors that drive or constrain BTV's genetic diversification remains incomplete. Current research leveraging novel approaches and whole genome sequencing applications promises to improve our understanding of BTV's evolution, ultimately contributing to the development of better predictive models and management strategies to reduce future impacts of bluetongue epizootics.

Assessment of the Risk of Severe Dengue Using Intrahost Viral Population in Dengue Virus Serotype 2 Patients via Machine Learning

Dengue virus, a positive-sense single-stranded RNA virus, continuously threatens human health. Although several criteria for evaluation of severe dengue have been recently established, the ability to prognose the risk of severe outcomes for dengue patients remains limited. Mutant spectra of RNA viruses, including single nucleotide variants (SNVs) and defective virus genomes (DVGs), contribute to viral virulence and growth. Here, we determine the potency of intrahost viral population in dengue patients with primary infection that progresses into severe dengue. A total of 65 dengue virus serotype 2 infected patients in primary infection including 17 severe cases were enrolled. We utilized deep sequencing to directly define the frequency of SNVs and detection times of DVGs in sera of dengue patients and analyzed their associations with severe dengue. Among the detected SNVs and DVGs, the frequencies of 9 SNVs and the detection time of 1 DVG exhibited statistically significant differences between patients with dengue fever and those with severe dengue. By utilizing the detected frequencies/times of the selected SNVs/DVG as features, the machine learning model showed high average with a value of area under the receiver operating characteristic curve (AUROC, 0.966 ± 0.064). The elevation of the frequency of SNVs at E (nucleotide position 995 and 2216), NS2A (nucleotide position 4105), NS3 (nucleotide position 4536, 4606), and NS5 protein (nucleotide position 7643 and 10067) and the detection times of the selected DVG that had a deletion junction in the E protein region (nucleotide positions of the junction: between 969 and 1022) increased the possibility of dengue patients for severe dengue. In summary, we demonstrated the detected frequencies/times of SNVs/DVG in dengue patients associated with severe disease and successfully utilized them to discriminate severe patients using machine learning algorithm. The identified SNVs and DVGs that are associated with severe dengue will expand our understanding of intrahost viral population in dengue pathogenesis.

Plasticity in structure and assembly of SARS-CoV-2 nucleocapsid protein

Worldwide SARS-CoV-2 sequencing efforts track emerging mutations in its spike protein, as well as characteristic mutations in other viral proteins. Besides their epidemiological importance, the observed SARS-CoV-2 sequences present an ensemble of viable protein variants, and thereby a source of information on viral protein structure and function. Charting the mutational landscape of the nucleocapsid (N) protein that facilitates viral assembly, we observe variability exceeding that of the spike protein, with more than 86% of residues that can be substituted, on average by 3-4 different amino acids. However, mutations exhibit an uneven distribution that tracks known structural features but also reveals highly protected stretches of unknown function. One of these conserved regions is in the central disordered linker proximal to the N-G215C mutation that has become dominant in the Delta variant, outcompeting G215 variants without further spike or N-protein substitutions. Structural models suggest that the G215C mutation stabilizes conserved transient helices in the disordered linker serving as protein-protein interaction interfaces. Comparing Delta variant N-protein to its ancestral version in biophysical experiments, we find a significantly more compact and less disordered structure. N-G215C exhibits substantially stronger self-association, shifting the unliganded protein from a dimeric to a tetrameric oligomeric state, which leads to enhanced co-assembly with nucleic acids. This suggests that the sequence variability of N-protein is mirrored by high plasticity of N-protein biophysical properties, which we hypothesize can be exploited by SARS-CoV-2 to achieve greater efficiency of viral assembly, and thereby enhanced infectivity.

Inferring the distribution of fitness effects in patient-sampled and experimental virus populations: two case studies

We here propose an analysis pipeline for inferring the distribution of fitness effects (DFE) from either patient-sampled or experimentally-evolved viral populations, that explicitly accounts for non-Wright-Fisher and non-equilibrium population dynamics inherent to pathogens. We examine the performance of this approach via extensive power and performance analyses, and highlight two illustrative applications - one from an experimentally-passaged RNA virus, and the other from a clinically-sampled DNA virus. Finally, we discuss how such DFE inference may shed light on major research questions in virus evolution, ranging from a quantification of the population genetic processes governing genome size, to the role of Hill-Robertson interference in dictating adaptive outcomes, to the potential design of novel therapeutic approaches to eradicate within-patient viral populations via induced mutational meltdown.

In vivo infection dynamics and human adaptive changes of SIVsm-derived viral siblings SIVmac239, SIVB670 and SIVhu in humanized mice as a paralog of HIV-2 genesis

Simian immunodeficiency virus native to sooty mangabeys (SIVsm) is believed to have given rise to HIV-2 through cross-species transmission and evolution in the human. SIVmac239 and SIVB670, pathogenic to macaques, and SIVhu, isolated from an accidental human infection, also have origins in SIVsm. With their common ancestral lineage as that of HIV-2 from the progenitor SIVsm, but with different passage history in different hosts, they provide a unique opportunity to evaluate cross-species transmission to a new host and their adaptation/evolution both in terms of potential genetic and phenotypic changes. Using humanized mice with a transplanted human system, we evaluated in vivo replication kinetics, CD4+ T cell dynamics and genetic adaptive changes during serial passage with a goal to understand their evolution under human selective immune pressure. All the three viruses readily infected hu-mice causing chronic viremia. While SIVmac and SIVB670 caused CD4+ T cell depletion during sequential passaging, SIVhu with a deletion in nef gene was found to be less pathogenic. Deep sequencing of the genomes of these viruses isolated at different times revealed numerous adaptive mutations of significance that increased in frequency during sequential passages. The ability of these viruses to infect and replicate in humanized mice provides a new small animal model to study SIVs in vivo in addition to more expensive macaques. Since SIVmac and related viruses have been indispensable in many areas of HIV pathogenesis, therapeutics and cure research, availability of this small animal hu-mouse model that is susceptible to both SIV and HIV viruses is likely to open novel avenues of investigation for comparative studies using the same host.

A Two-Level, Intramutant Spectrum Haplotype Profile of Hepatitis C Virus Revealed by Self-Organized Maps

RNA viruses replicate as complex mutant spectra termed viral quasispecies. The frequency of each individual genome in a mutant spectrum depends on its rate of generation and its relative fitness in the replicating population ensemble. The advent of deep sequencing methodologies allows for the first-time quantification of haplotype abundances within mutant spectra. There is no information on the haplotype profile of the resident genomes and how the landscape evolves when a virus replicates in a controlled cell culture environment. Here, we report the construction of intramutant spectrum haplotype landscapes of three amplicons of the NS5A-NS5B coding region of hepatitis C virus (HCV). Two-dimensional (2D) neural networks were constructed for 44 related HCV populations derived from a common clonal ancestor that was passaged up to 210 times in human hepatoma Huh-7.5 cells in the absence of external selective pressures. The haplotype profiles consisted of an extended dense basal platform, from which a lower number of protruding higher peaks emerged. As HCV increased its adaptation to the cells, the number of haplotype peaks within each mutant spectrum expanded, and their distribution shifted in the 2D network. The results show that extensive HCV replication in a monotonous cell culture environment does not limit HCV exploration of sequence space through haplotype peak movements. The landscapes reflect dynamic variation in the intramutant spectrum haplotype profile and may serve as a reference to interpret the modifications produced by external selective pressures or to compare with the landscapes of mutant spectra in complex in vivo environments. IMPORTANCE The study provides for the first time the haplotype profile and its variation in the course of virus adaptation to a cell culture environment in the absence of external selective constraints. The deep sequencing-based self-organized maps document a two-layer haplotype distribution with an ample basal platform and a lower number of protruding peaks. The results suggest an inferred intramutant spectrum fitness landscape structure that offers potential benefits for virus resilience to mutational inputs.

An experimental strategy for preparing circular ssDNA virus genomes for next-generation sequencing

The ability of begomoviruses to evolve rapidly threatens many crops and underscores the importance of detecting these viruses quickly and to understand their genome diversity. This study presents an improved protocol for the enhanced amplification and enrichment of begomovirus DNA for use in next generation sequencing of the viral genomes. An enhanced rolling circle amplification (RCA) method using EquiPhi29 polymerase was combined with size selection to generate a cost-effective, short-read sequencing method. This improved short-read sequencing produced at least 50 % of the reads mapping to the target viral reference genomes, African cassava mosaic virus and East African cassava mosaic virus. This study provided other insights into common misconceptions about RCA and lessons that could be learned from the sequencing of single-stranded DNA virus genomes. This protocol can be used to examine the viral DNA as it moves from host to vector, thus producing valuable information for viral DNA population studies, and would likely work well with other circular Rep-encoding ssDNA viruses (CRESS) DNA viruses.

Accurate detection of circulating tumor DNA using nanopore consensus sequencing

Levels of circulating tumor DNA (ctDNA) in liquid biopsies may serve as a sensitive biomarker for real-time, minimally-invasive tumor diagnostics and monitoring. However, detecting ctDNA is challenging, as much fewer than 5% of the cell-free DNA in the blood typically originates from the tumor. To detect lowly abundant ctDNA molecules based on somatic variants, extremely sensitive sequencing methods are required. Here, we describe a new technique, CyclomicsSeq, which is based on Oxford Nanopore sequencing of concatenated copies of a single DNA molecule. Consensus calling of the DNA copies increased the base-calling accuracy ~60×, enabling accurate detection of TP53 mutations at frequencies down to 0.02%. We demonstrate that a TP53-specific CyclomicsSeq assay can be successfully used to monitor tumor burden during treatment for head-and-neck cancer patients. CyclomicsSeq can be applied to any genomic locus and offers an accurate diagnostic liquid biopsy approach that can be implemented in clinical workflows.

Nucleotide amplification and sequencing of the GC-rich region between matrix and fusion protein genes of peste des petits ruminants virus

Peste des petits ruminants virus (PPRV) causes a highly devastating disease of sheep and goats, that threatens the conservation of small wild ruminants. The development of PPRV vaccines, diagnostics and therapeutics, greatly depends on in-depth genomic data. Yet, high guanine-cytosine (GC) content between matrix (M) and fusion (F) genes of PPRV poses difficulty for both primer design and nucleotide amplification. In turn, this has led into absence or low nucleotide sequence coverage in this region. This poses a risk of missing important part of the genome that could help to infer viral evolution. Here, an overlapping long-read primer-based amplification strategy was developed to amplify the GC-rich fragments between M-F gene junction using nexus gradient polymerase chain reaction (PCR). The resulting amplicons were sequenced by dideoxynucleotide cycle sequencing and compared with other PPRV nucleotide sequences available at GenBank. Our findings indicate clear PCR amplification products with expected size of the GC-rich fragments on agarose gel electrophoresis. The sequencing results of these fragments indicate 99.5 % nucleotide identity with PPRV strain KY628761. An extremely difficult PCR target of 67.4 % GC contents was successfully amplified and sequenced using this long-read primer approach. The long-read primer set may be used in tiling multiplex PCR for complete genome sequencing of PPRV.

Host-specific asymmetric accumulation of mutation types reveals that the origin of SARS-CoV-2 is consistent with a natural process

The capacity of RNA viruses to adapt to new hosts and rapidly escape the host immune system is largely attributable to de novo genetic diversity that emerges through mutations in RNA. Although the molecular spectrum of de novo mutations—the relative rates at which various base substitutions occur—are widely recognized as informative toward understanding the evolution of a viral genome, little attention has been paid to the possibility of using molecular spectra to infer the host origins of a virus. Here, we characterize the molecular spectrum of de novo mutations for SARS-CoV-2 from transcriptomic data obtained from virus-infected cell lines, enabled by the use of sporadic junctions formed during discontinuous transcription as molecular barcodes. We find that de novo mutations are generated in a replication-independent manner, typically on the genomic strand, and highly dependent on mutagenic mechanisms specific to the host cellular environment. De novo mutations will then strongly influence the types of base substitutions accumulated during SARS-CoV-2 evolution, in an asymmetric manner favoring specific mutation types. Consequently, similarities between the mutation spectra of SARS-CoV-2 and the bat coronavirus RaTG13, which have accumulated since their divergence strongly suggest that SARS-CoV-2 evolved in a host cellular environment highly similar to that of bats before its zoonotic transfer into humans. Collectively, our findings provide data-driven support for the natural origin of SARS-CoV-2.

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The asymmetric de novo mutations in SARS-CoV-2 are induced by mutagenic mechanisms in the host cellular environment

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De novo mutations determine the molecular spectrum of accumulated mutations during SARS-CoV-2 evolution

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Molecular spectra of accumulated mutations in betacoronaviruses cluster according to the host species instead of the phylogenetic relationship

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The mutations accumulated in SARS-CoV-2 prior to its transmission to humans are consistent with an evolutionary process in a bat host

Historical Perspective on the Discovery of the Quasispecies Concept

Viral quasispecies are dynamic distributions of nonidentical but closely related mutant and recombinant viral genomes subjected to a continuous process of genetic variation, competition, and selection that may act as a unit of selection. The quasispecies concept owes its theoretical origins to a model for the origin of life as a collection of mutant RNA replicators. Independently, experimental evidence for the quasispecies concept was obtained from sampling of bacteriophage clones, which revealed that the viral populations consisted of many mutant genomes whose frequency varied with time of replication. Similar findings were made in animal and plant RNA viruses. Quasispecies became a theoretical framework to understand viral population dynamics and adaptability. The evidence came at a time when mutations were considered rare events in genetics, a perception that was to change dramatically in subsequent decades. Indeed, viral quasispecies was the conceptual forefront of a remarkable degree of biological diversity, now evident for cell populations and organisms, not only for viruses. Quasispecies dynamics unveiled complexities in the behavior of viral populations,with consequences for disease mechanisms and control strategies. This review addresses the origin of the quasispecies concept, its major implications on both viral evolution and antiviral strategies, and current and future prospects.

Experimental and mathematical insights on the interactions between poliovirus and a defective interfering genome

During replication, RNA viruses accumulate genome alterations, such as mutations and deletions. The interactions between individual variants can determine the fitness of the virus population and, thus, the outcome of infection. To investigate the effects of defective interfering genomes (DI) on wild-type (WT) poliovirus replication, we developed an ordinary differential equation model, which enables exploring the parameter space of the WT and DI competition. We also experimentally examined virus and DI replication kinetics during co-infection, and used these data to infer model parameters. Our model identifies, and our experimental measurements confirm, that the efficiencies of DI genome replication and encapsidation are two most critical parameters determining the outcome of WT replication. However, an equilibrium can be established which enables WT to replicate, albeit to reduced levels.

Metagenomic Studies of Viruses in Weeds and Wild Plants: A Powerful Approach to Characterise Variable Virus Communities

High throughput sequencing (HTS) has revolutionised virus detection and discovery, allowing for the untargeted characterisation of whole viromes. Viral metagenomics studies have demonstrated the ubiquity of virus infection - often in the absence of disease symptoms - and tend to discover many novel viruses, highlighting the small fraction of virus biodiversity described to date. The majority of the studies using high-throughput sequencing to characterise plant viromes have focused on economically important crops, and only a small number of studies have considered weeds and wild plants. Characterising the viromes of wild plants is highly relevant, as these plants can affect disease dynamics in crops, often by acting as viral reservoirs. Moreover, the viruses in unmanaged systems may also have important effects on wild plant populations and communities. Here, we review metagenomic studies on weeds and wild plants to show the benefits and limitations of this approach and identify knowledge gaps. We consider key genomics developments that are likely to benefit the field in the near future. Although only a small number of HTS studies have been performed on weeds and wild plants, these studies have already discovered many novel viruses, demonstrated unexpected trends in virus distributions, and highlighted the potential of metagenomics as an approach.