The contribution of epistasis to the architecture of fitness in an RNA virus

Structural and Functional Insights into the Evolution of SARS-CoV-2 KP.3.1.1 Spike Protein

The JN.1-sublineage KP.3.1.1 recently emerged as the globally prevalent SARS-CoV-2 variant, demonstrating increased infectivity and antibody escape. We investigated how mutations and a deletion in the KP.3.1.1 spike protein (S) affect ACE2 binding and antibody escape. Mass spectrometry revealed a new glycan site at residue N30 and altered glycoforms at neighboring N61. Cryo-EM structures showed that the N30 glycan and rearrangement of adjacent residues did not significantly change the overall spike structure, up-down ratio of the receptor-binding domains (RBDs), or ACE2 binding. Furthermore, a KP.3.1.1 S structure with hACE2 further confirmed an epistatic effect between F456L and Q493E on ACE2 binding. Our analysis shows SARS-CoV-2 variants that emerged after late 2023 are now incorporating reversions to residues found in other sarbecoviruses, including the N30 glycan, Q493E, and others. Overall, these results inform on the structural and functional consequences of the KP.3.1.1 mutations, the current SARS-CoV-2 evolutionary trajectory, and immune evasion.

Error thresholds in the presence of epistatic interactions

Models for viral populations with high replication error rates (such as RNA viruses) rely on the quasispecies concept, in which mutational pressure beyond the so-called "error threshold" leads to a loss of essential genetic information and population collapse, an effect known as the "error catastrophe." We explain how crossing this threshold, as a result of increasing mutation rates, can be understood as a second-order phase transition, even in the presence of lethal mutations. In particular, we show that, in fitness landscapes with a single peak, this collapse is equivalent to a ferroparamagnetic transition, where the back-mutation rate plays the role of the external magnetic field. We then generalize this framework to rugged fitness landscapes, like the ones that arise from epistatic interactions, and provide numerical evidence that there is a transition from a high average fitness regime to a low average fitness one, similarly to single-peaked landscapes. The onset of the transition is heralded by a sudden change in the susceptibility to variations in the mutation rate. We use insight from replica symmetry breaking mechanisms in spin glasses, in particular by considering the fluctuations of the genotype similarity distribution as the order parameter.

Efficient epistasis inference via higher-order covariance matrix factorization

Epistasis can profoundly influence evolutionary dynamics. Temporal genetic data, consisting of sequences sampled repeatedly from a population over time, provides a unique resource to understand how epistasis shapes evolution. However, detecting epistatic interactions from sequence data is technically challenging. Existing methods for identifying epistasis are computationally demanding, limiting their applicability to real-world data. Here, we present a novel computational method for inferring epistasis that significantly reduces computational costs without sacrificing accuracy. We validated our approach in simulations and applied it to study HIV-1 evolution over multiple years in a data set of 16 individuals. There we observed a strong excess of negative epistatic interactions between beneficial mutations, especially mutations involved in immune escape. Our method is general and could be used to characterize epistasis in other large data sets.

Virology-The next fifty years

Virology has made enormous advances in the last 50 years but has never faced such scrutiny as it does today. Herein, we outline some of the major advances made in virology during this period, particularly in light of the COVID-19 pandemic, and suggest some areas that may be of research importance in the next 50 years. We focus on several linked themes: cataloging the genomic and phenotypic diversity of the virosphere; understanding disease emergence; future directions in viral disease therapies, vaccines, and interventions; host-virus interactions; the role of viruses in chronic diseases; and viruses as tools for cell biology. We highlight the challenges that virology will face moving forward-not just the scientific and technical but also the social and political. Although there are inherent limitations in trying to outline the virology of the future, we hope this article will help inspire the next generation of virologists.

Computational multigene interactions in virus growth and infection spread

Viruses persist in nature owing to their extreme genetic heterogeneity and large population sizes, which enable them to evade host immune defenses, escape antiviral drugs, and adapt to new hosts. The persistence of viruses is challenging to study because mutations affect multiple virus genes, interactions among genes in their impacts on virus growth are seldom known, and measures of viral fitness are yet to be standardized. To address these challenges, we employed a data-driven computational model of cell infection by a virus. The infection model accounted for the kinetics of viral gene expression, functional gene-gene interactions, genome replication, and allocation of host cellular resources to produce progeny of vesicular stomatitis virus, a prototype RNA virus. We used this model to computationally probe how interactions among genes carrying up to eleven deleterious mutations affect different measures of virus fitness: single-cycle growth yields and multicycle rates of infection spread. Individual mutations were implemented by perturbing biophysical parameters associated with individual gene functions of the wild-type model. Our analysis revealed synergistic epistasis among deleterious mutations in their effects on virus yield; so adverse effects of single deleterious mutations were amplified by interaction. For the same mutations, multicycle infection spread indicated weak or negligible epistasis, where single mutations act alone in their effects on infection spread. These results were robust to simulation in high- and low-host resource environments. Our work highlights how different types and magnitudes of epistasis can arise for genetically identical virus variants, depending on the fitness measure. More broadly, gene-gene interactions can differently affect how viruses grow and spread.

A null model for the distribution of fitness effects of mutations

The distribution of fitness effects (DFE) of new mutations is key to our understanding of many evolutionary processes. Theoreticians have developed several models to help understand the patterns seen in empirical DFEs. Many such models reproduce the broad patterns seen in empirical DFEs but these models often rely on structural assumptions that cannot be tested empirically. Here, we investigate how much of the underlying "microscopic" biological processes involved in the mapping of new mutations to fitness can be inferred from "macroscopic" observations of the DFE. We develop a null model by generating random genotype-to-fitness maps and show that the null DFE is that with the largest possible information entropy. We further show that, subject to one simple constraint, this null DFE is a Gompertz distribution. Finally, we illustrate how the predictions of this null DFE match empirically measured DFEs from several datasets, as well as DFEs simulated from Fisher's geometric model. This suggests that a match between models and empirical data is often not a very strong indication of the mechanisms underlying the mapping of mutation to fitness.

Identifying the genetic basis of viral spillover using Lassa virus as a test case

The rate at which zoonotic viruses spill over into the human population varies significantly over space and time. Remarkably, we do not yet know how much of this variation is attributable to genetic variation within viral populations. This gap in understanding arises because we lack methods of genetic analysis that can be easily applied to zoonotic viruses, where the number of available viral sequences is often limited, and opportunistic sampling introduces significant population stratification. Here, we explore the feasibility of using patterns of shared ancestry to correct for population stratification, enabling genome-wide association methods to identify genetic substitutions associated with spillover into the human population. Using a combination of phylogenetically structured simulations and Lassa virus sequences collected from humans and rodents in Sierra Leone, we demonstrate that existing methods do not fully correct for stratification, leading to elevated error rates. We also demonstrate, however, that the Type I error rate can be substantially reduced by confining the analysis to a less-stratified region of the phylogeny, even in an already-small dataset. Using this method, we detect two candidate single-nucleotide polymorphisms associated with spillover in the Lassa virus polymerase gene and provide generalized recommendations for the collection and analysis of zoonotic viruses.

VGsim: Scalable viral genealogy simulator for global pandemic

Accurate simulation of complex biological processes is an essential component of developing and validating new technologies and inference approaches. As an effort to help contain the COVID-19 pandemic, large numbers of SARS-CoV-2 genomes have been sequenced from most regions in the world. More than 5.5 million viral sequences are publicly available as of November 2021. Many studies estimate viral genealogies from these sequences, as these can provide valuable information about the spread of the pandemic across time and space. Additionally such data are a rich source of information about molecular evolutionary processes including natural selection, for example allowing the identification of new variants with transmissibility and immunity evasion advantages. To our knowledge, there is no framework that is both efficient and flexible enough to simulate the pandemic to approximate world-scale scenarios and generate viral genealogies of millions of samples. Here, we introduce a new fast simulator VGsim which addresses the problem of simulation genealogies under epidemiological models. The simulation process is split into two phases. During the forward run the algorithm generates a chain of population-level events reflecting the dynamics of the pandemic using an hierarchical version of the Gillespie algorithm. During the backward run a coalescent-like approach generates a tree genealogy of samples conditioning on the population-level events chain generated during the forward run. Our software can model complex population structure, epistasis and immunity escape.

We develop a fast and flexible simulation software package VGsim for modeling epidemiological processes and generating genealogies of large pathogen samples. The software takes into account host population structure, pathogen evolution, host immunity and some other epidemiological aspects. The computational efficiency of the package allows to simulate genealogies of tens of millions of samples, which is important, e.g., for SARS-CoV-2 genome studies.

Mutational robustness changes during long-term adaptation in laboratory budding yeast populations

As an adapting population traverses the fitness landscape, its local neighborhood (i.e., the collection of fitness effects of single-step mutations) can change shape because of interactions with mutations acquired during evolution. These changes to the distribution of fitness effects can affect both the rate of adaptation and the accumulation of deleterious mutations. However, while numerous models of fitness landscapes have been proposed in the literature, empirical data on how this distribution changes during evolution remains limited. In this study, we directly measure how the fitness landscape neighborhood changes during laboratory adaptation. Using a barcode-based mutagenesis system, we measure the fitness effects of 91 specific gene disruption mutations in genetic backgrounds spanning 8000–10,000 generations of evolution in two constant environments. We find that the mean of the distribution of fitness effects decreases in one environment, indicating a reduction in mutational robustness, but does not change in the other. We show that these distribution-level patterns result from differences in the relative frequency of certain patterns of epistasis at the level of individual mutations, including fitness-correlated and idiosyncratic epistasis.

Evolutionary history of the SARS-CoV-2 Gamma variant of concern (P.1): a perfect storm

Our goal was to describe in more detail the evolutionary history of Gamma and two derived lineages (P.1.1 and P.1.2), which are part of the arms race that SARS-CoV-2 wages with its host. A total of 4,977 sequences of the Gamma strain of SARS-CoV-2 from Brazil were analyzed. We detected 194 sites under positive selection in 12 genes/ORFs: Spike, N, M, E, ORF1a, ORF1b, ORF3, ORF6, ORF7a, ORF7b, ORF8, and ORF10. Some diagnostic sites for Gamma lacked a signature of positive selection in our study, but these were not fixed, apparently escaping the action of purifying selection. Our network analyses revealed branches leading to expanding haplotypes with sites under selection only detected when P.1.1 and P.1.2 were considered. The P.1.2 exclusive haplotype H_5 originated from a non-synonymous mutational step (H3509Y) in H_1 of ORF1a. The selected allele, 3509Y, represents an adaptive novelty involving ORF1a of P.1. Finally, we discuss how phenomena such as epistasis and antagonistic pleiotropy could limit the emergence of new alleles (and combinations thereof) in SARS-COV-2 lineages, maintaining infectivity in humans, while providing rapid response capabilities to face the arms race triggered by host immuneresponses.

VGsim: scalable viral genealogy simulator for global pandemic

Accurate simulation of complex biological processes is an essential component of developing and validating new technologies and inference approaches. As an effort to help contain the COVID-19 pandemic, large numbers of SARS-CoV-2 genomes have been sequenced from most regions in the world. More than 5.5 million viral sequences are publicly available as of November 2021. Many studies estimate viral genealogies from these sequences, as these can provide valuable information about the spread of the pandemic across time and space. Additionally such data are a rich source of information about molecular evolutionary processes including natural selection, for example allowing the identification of new variants with transmissibility and immunity evasion advantages. To our knowledge, there is no framework that is both efficient and flexible enough to simulate the pandemic to approximate world-scale scenarios and generate viral genealogies of millions of samples. Here, we introduce a new fast simulator VGsim which addresses the problem of simulation genealogies under epidemiological models. The simulation process is split into two phases. During the forward run the algorithm generates a chain of population-level events reflecting the dynamics of the pandemic using an hierarchical version of the Gillespie algorithm. During the backward run a coalescent-like approach generates a tree genealogy of samples conditioning on the population-level events chain generated during the forward run. Our software can model complex population structure, epistasis and immunity escape. The code is freely available at https://github.com/Genomics-HSE/VGsim.

Why are viral genomes so fragile? The bottleneck hypothesis

If they undergo new mutations at each replication cycle, why are RNA viral genomes so fragile, with most mutations being either strongly deleterious or lethal? Here we provide theoretical and numerical evidence for the hypothesis that genetic fragility is partly an evolutionary response to the multiple population bottlenecks experienced by viral populations at various stages of their life cycles. Modelling within-host viral populations as multi-type branching processes, we show that mutational fragility lowers the rate at which Muller’s ratchet clicks and increases the survival probability through multiple bottlenecks. In the context of a susceptible-exposed-infectious-recovered epidemiological model, we find that the attack rate of fragile viral strains can exceed that of more robust strains, particularly at low infectivities and high mutation rates. Our findings highlight the importance of demographic events such as transmission bottlenecks in shaping the genetic architecture of viral pathogens.

Given that most mutations are deleterious, high mutation rates carry a significant evolutionary cost. To reduce this burden, an obvious evolutionary solution would be to reduce the fitness cost of mutations by becoming more robust; this solution is indeed selected in populations of constantly large size. Here, we show that when populations regularly experience bottlenecks, as viruses do upon transmission to a new host, a less obvious solution becomes more viable: namely, to increase the fitness cost of mutations so that unfit mutants are less likely to fix at each passage. This could explain why viruses—especially RNA viruses—do in fact have very fragile genomes.

Intra-Population Competition during Adaptation to Increased Temperature in an RNA Bacteriophage

Evolution of RNA bacteriophages of the family Leviviridae is governed by the high error rates of their RNA-dependent RNA polymerases. This fact, together with their large population sizes, leads to the generation of highly heterogeneous populations that adapt rapidly to most changes in the environment. Throughout adaptation, the different mutants that make up a viral population compete with each other in a non-trivial process in which their selective values change over time due to the generation of new mutations. In this work we have characterised the intra-population dynamics of a well-studied levivirus, Qβ, when it is propagated at a higher-than-optimal temperature. Our results show that adapting populations experienced rapid changes that involved the ascent of particular genotypes and the loss of some beneficial mutations of early generation. Artificially reconstructed populations, containing a fraction of the diversity present in actual populations, fixed mutations more rapidly, illustrating how population bottlenecks may guide the adaptive pathways. The conclusion is that, when the availability of beneficial mutations under a particular selective condition is elevated, the final outcome of adaptation depends more on the occasional occurrence of population bottlenecks and how mutations combine in genomes than on the selective value of particular mutations.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

Animal and human RNA viruses: genetic variability and ability to overcome vaccines

RNA viruses, in general, exhibit high mutation rates; this is mainly due to the low fidelity displayed by the RNA-dependent polymerases required for their replication that lack the proofreading machinery to correct misincorporated nucleotides and produce high mutation rates. This lack of replication fidelity, together with the fact that RNA viruses can undergo spontaneous mutations, results in genetic variants displaying different viral morphogenesis, as well as variation on their surface glycoproteins that affect viral antigenicity. This diverse viral population, routinely containing a variety of mutants, is known as a viral 'quasispecies'. The mutability of their virions allows for fast evolution of RNA viruses that develop antiviral resistance and overcome vaccines much more rapidly than DNA viruses. This also translates into the fact that pathogenic RNA viruses, that cause many diseases and deaths in humans, represent the major viral group involved in zoonotic disease transmission, and are responsible for worldwide pandemics.

Evolution of linkage and genome expansion in protocells: The origin of chromosomes

Chromosomes are likely to have assembled from unlinked genes in early evolution. Genetic linkage reduces the assortment load and intragenomic conflict in reproducing protocell models to the extent that chromosomes can go to fixation even if chromosomes suffer from a replicative disadvantage, relative to unlinked genes, proportional to their length. Here we numerically show that chromosomes spread within protocells even if recurrent deleterious mutations affecting replicating genes (as ribozymes) are considered. Dosage effect selects for optimal genomic composition within protocells that carries over to the genic composition of emerging chromosomes. Lacking an accurate segregation mechanism, protocells continue to benefit from the stochastic corrector principle (group selection of early replicators), but now at the chromosome level. A remarkable feature of this process is the appearance of multigene families (in optimal genic proportions) on chromosomes. An added benefit of chromosome formation is an increase in the selectively maintainable genome size (number of different genes), primarily due to the marked reduction of the assortment load. The establishment of chromosomes is under strong positive selection in protocells harboring unlinked genes. The error threshold of replication is raised to higher genome size by linkage due to the fact that deleterious mutations affecting protocells metabolism (hence fitness) show antagonistic (diminishing return) epistasis. This result strengthens the established benefit conferred by chromosomes on protocells allowing for the fixation of highly specific and efficient enzymes.

The emergence of chromosomes harboring several genes is a crucial ingredient of the major evolutionary transition from naked replicators to cells. Linkage of replicating genes reduces conflict between them and alleviates the problem of chance loss of genes upon stochastic protocell fission. The emerging organization of protocells maintaining several segregating chromosomes with balanced gene composition also allows for an increase in the number of gene types despite recurrent deleterious mutations. We suggest that this interim genomic organization enabled protocells to evolve specific and efficient enzymes and paved the way toward an accurate mechanism for chromosome segregation later in evolution.

ΦX174 Attenuation by Whole-Genome Codon Deoptimization

Natural selection acting on synonymous mutations in protein-coding genes influences genome composition and evolution. In viruses, introducing synonymous mutations in genes encoding structural proteins can drastically reduce viral growth, providing a means to generate potent, live-attenuated vaccine candidates. However, an improved understanding of what compositional features are under selection and how combinations of synonymous mutations affect viral growth is needed to predictably attenuate viruses and make them resistant to reversion. We systematically recoded all nonoverlapping genes of the bacteriophage ΦX174 with codons rarely used in its Escherichia coli host. The fitness of recombinant viruses decreases as additional deoptimizing mutations are made to the genome, although not always linearly, and not consistently across genes. Combining deoptimizing mutations may reduce viral fitness more or less than expected from the effect size of the constituent mutations and we point out difficulties in untangling correlated compositional features. We test our model by optimizing the same genes and find that the relationship between codon usage and fitness does not hold for optimization, suggesting that wild-type ΦX174 is at a fitness optimum. This work highlights the need to better understand how selection acts on patterns of synonymous codon usage across the genome and provides a convenient system to investigate the genetic determinants of virulence.

Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

Small-Molecule Antiviral β-d-N4-Hydroxycytidine Inhibits a Proofreading-Intact Coronavirus with a High Genetic Barrier to Resistance

Coronaviruses (CoVs) have emerged from animal reservoirs to cause severe and lethal disease in humans, but there are currently no FDA-approved antivirals to treat the infections. One class of antiviral compounds, nucleoside analogues, mimics naturally occurring nucleosides to inhibit viral replication. While these compounds have been successful therapeutics for several viral infections, mutagenic nucleoside analogues, such as ribavirin and 5-fluorouracil, have been ineffective at inhibiting CoVs. This has been attributed to the proofreading activity of the viral 3'-5' exoribonuclease (ExoN). β-d-N4-Hydroxycytidine (NHC) (EIDD-1931; Emory Institute for Drug Development) has recently been reported to inhibit multiple viruses. Here, we demonstrate that NHC inhibits both murine hepatitis virus (MHV) (50% effective concentration [EC50] = 0.17 μM) and Middle East respiratory syndrome CoV (MERS-CoV) (EC50 = 0.56 μM) with minimal cytotoxicity. NHC inhibited MHV lacking ExoN proofreading activity similarly to wild-type (WT) MHV, suggesting an ability to evade or overcome ExoN activity. NHC inhibited MHV only when added early during infection, decreased viral specific infectivity, and increased the number and proportion of G:A and C:U transition mutations present after a single infection. Low-level NHC resistance was difficult to achieve and was associated with multiple transition mutations across the genome in both MHV and MERS-CoV. These results point to a virus-mutagenic mechanism of NHC inhibition in CoVs and indicate a high genetic barrier to NHC resistance. Together, the data support further development of NHC for treatment of CoVs and suggest a novel mechanism of NHC interaction with the CoV replication complex that may shed light on critical aspects of replication.IMPORTANCE The emergence of coronaviruses (CoVs) into human populations from animal reservoirs has demonstrated their epidemic capability, pandemic potential, and ability to cause severe disease. However, no antivirals have been approved to treat these infections. Here, we demonstrate the potent antiviral activity of a broad-spectrum ribonucleoside analogue, β-d-N4-hydroxycytidine (NHC), against two divergent CoVs. Viral proofreading activity does not markedly impact sensitivity to NHC inhibition, suggesting a novel interaction between a nucleoside analogue inhibitor and the CoV replicase. Further, passage in the presence of NHC generates only low-level resistance, likely due to the accumulation of multiple potentially deleterious transition mutations. Together, these data support a mutagenic mechanism of inhibition by NHC and further support the development of NHC for treatment of CoV infections.

The distribution of epistasis on simple fitness landscapes

Fitness interactions between mutations can influence a population's evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA (small nucleolar RNA). Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.

Higher-fitness yeast genotypes are less robust to deleterious mutations

Natural selection drives populations toward higher fitness, but second-order selection for adaptability and mutational robustness can also influence evolution. In many microbial systems, diminishing-returns epistasis contributes to a tendency for more-fit genotypes to be less adaptable, but no analogous patterns for robustness are known. To understand how robustness varies across genotypes, we measure the fitness effects of hundreds of individual insertion mutations in a panel of yeast strains. We find that more-fit strains are less robust: They have distributions of fitness effects with lower mean and higher variance. These differences arise because many mutations have more strongly deleterious effects in faster-growing strains. This negative correlation between fitness and robustness implies that second-order selection for robustness will tend to conflict with first-order selection for fitness.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Contrasting selective patterns across the segmented genome of bluetongue virus in a global reassortment hotspot

For segmented viruses, rapid genomic and phenotypic changes can occur through the process of reassortment, whereby co-infecting strains exchange entire segments creating novel progeny virus genotypes. However, for many viruses with segmented genomes, this process and its effect on transmission dynamics remain poorly understood. Here, we assessed the consequences of reassortment for selection on viral diversity through time using bluetongue virus (BTV), a segmented arbovirus that is the causative agent of a major disease of ruminants. We analysed ninety-two BTV genomes isolated across four decades from India, where BTV diversity, and thus opportunities for reassortment, are among the highest in the world. Our results point to frequent reassortment and segment turnover, some of which appear to be driven by selective sweeps and serial hitchhiking. Particularly, we found evidence for a recent selective sweep affecting segment 5 and its encoded NS1 protein that has allowed a single variant to essentially invade the full range of BTV genomic backgrounds and serotypes currently circulating in India. In contrast, diversifying selection was found to play an important role in maintaining genetic diversity in genes encoding outer surface proteins involved in virus interactions (VP2 and VP5, encoded by segments 2 and 6, respectively). Our results support the role of reassortment in driving rapid phenotypic change in segmented viruses and generate testable hypotheses for in vitro experiments aiming at understanding the specific mechanisms underlying differences in fitness and selection across viral genomes.

Simulations reveal challenges to artificial community selection and possible strategies for success

Multispecies microbial communities often display "community functions" arising from interactions of member species. Interactions are often difficult to decipher, making it challenging to design communities with desired functions. Alternatively, similar to artificial selection for individuals in agriculture and industry, one could repeatedly choose communities with the highest community functions to reproduce by randomly partitioning each into multiple "Newborn" communities for the next cycle. However, previous efforts in selecting complex communities have generated mixed outcomes that are difficult to interpret. To understand how to effectively enact community selection, we simulated community selection to improve a community function that requires 2 species and imposes a fitness cost on one or both species. Our simulations predict that improvement could be easily stalled unless various aspects of selection are carefully considered. These aspects include promoting species coexistence, suppressing noncontributors, choosing additional communities besides the highest functioning ones to reproduce, and reducing stochastic fluctuations in the biomass of each member species in Newborn communities. These considerations can be addressed experimentally. When executed effectively, community selection is predicted to improve costly community function, and may even force species to evolve slow growth to achieve species coexistence. Our conclusions hold under various alternative model assumptions and are therefore applicable to a variety of communities.

Existing Host Range Mutations Constrain Further Emergence of RNA Viruses

RNA viruses are capable of rapid host shifting, typically due to a point mutation that confers expanded host range. As additional point mutations are necessary for further expansions, epistasis among host range mutations can potentially affect the mutational neighborhood and frequency of niche expansion. We mapped the mutational neighborhood of host range expansion using three genotypes of the double-stranded RNA (dsRNA) bacteriophage φ6 (wild type and two isogenic host range mutants) on the novel host Pseudomonas syringae pv. atrofaciens. Both Sanger sequencing of 50 P. syringae pv. atrofaciens mutant clones for each genotype and population Illumina sequencing revealed the same high-frequency mutations allowing infection of P. syringae pv. atrofaciens. Wild-type φ6 had at least nine different ways of mutating to enter the novel host, eight of which are in p3 (host attachment protein gene), and 13/50 clones had unchanged p3 genes. However, the two isogenic mutants had dramatically restricted neighborhoods: only one or two mutations, all in p3. Deep sequencing revealed that wild-type clones without mutations in p3 likely had changes in p12 (morphogenic protein), a region that was not polymorphic for the two isogenic host range mutants. Sanger sequencing confirmed that 10/13 of the wild-type φ6 clones had nonsynonymous mutations in p12, and 2 others had point mutations in p9 and p5. None of these genes had previously been associated with host range expansion in φ6. We demonstrate, for the first time, epistatic constraint in an RNA virus due to host range mutations themselves, which has implications for models of serial host range expansion.IMPORTANCE RNA viruses mutate rapidly and frequently expand their host ranges to infect novel hosts, leading to serial host shifts. Using an RNA bacteriophage model system (Pseudomonas phage φ6), we studied the impact of preexisting host range mutations on another host range expansion. Results from both clonal Sanger and Illumina sequencing show that extant host range mutations dramatically narrow the neighborhood of potential host range mutations compared to that of wild-type φ6. This research suggests that serial host-shifting viruses may follow a small number of molecular paths to enter additional novel hosts. We also identified new genes involved in φ6 host range expansion, expanding our knowledge of this important model system in experimental evolution.

Feature Learning of Virus Genome Evolution With the Nucleotide Skip-Gram Neural Network

Recent studies reveal that even the smallest genomes such as viruses evolve through complex and stochastic processes, and the assumption of independent alleles is not valid in most applications. Advances in sequencing technologies produce multiple time-point whole-genome data, which enable potential interactions between these alleles to be investigated empirically. To investigate these interactions, we represent alleles as distributed vectors that encode for relationships with other alleles in the course of evolution and apply artificial neural networks to time-sampled whole-genome datasets for feature learning. We build this platform using methods and algorithms derived from natural language processing (NLP), and we denote it as the nucleotide skip-gram neural network. We learn distributed vectors of alleles using the changes in allele frequency of echovirus 11 in the presence or absence of the disinfectant (ClO₂) from the experimental evolution data. Results from the training using a new open-source software TensorFlow show that the learned distributed vectors can be clustered using principal component analysis and hierarchical clustering to reveal a list of non-synonymous mutations that arise on the structural protein VP1 in connection to the candidate mutation for ClO₂ adaptation. Furthermore, this method can account for recombination rates by setting the extent of interactions as a biological hyper-parameter, and the results show that the most realistic scenario of mid-range interactions across the genome is most consistent with the previous studies.

Analysis of Fitness Trade-Offs in the Host Range Expansion of an RNA Virus, Tobacco Mild Green Mosaic Virus

The acquisition of new hosts provides a virus with more opportunities for transmission and survival but may be limited by across-host fitness trade-offs. Major causes of across-host trade-offs are antagonistic pleiotropy, that is, host differential phenotypic effects of mutations, a Genotype x Environment interaction, and epistasis, a Genotype x Genotype interaction. Here, we analyze if there are trade-offs, and what are the causes, associated with the acquisition by tobacco mild green mosaic virus (TMGMV) of a new host. For this, the multiplication of sympatric field isolates of TMGMV from its wild reservoir host Nicotiana glauca and from pepper crops was quantified in the original and the heterologous hosts. TMGMV isolates from N. glauca were adapted to their host, but pepper isolates were not adapted to pepper, and the acquisition of this new host was associated with a fitness penalty in the original host. Analyses of the collection of field isolates and of mutant genotypes derived from biologically active cDNA clones showed a role of mutations in the coat protein and the 3' untranslated region in determining within-host virus fitness. Fitness depended on host-specific effects of these mutations, on the genetic background in which they occurred, and on higher-order interactions of the type Genotype x Genotype x Environment. These types of effects had been reported to generate across-host fitness trade-offs under experimental evolution. Our results show they may also operate in heterogeneous natural environments and could explain why pepper isolates were not adapted to pepper and their lower fitness in N. glaucaIMPORTANCE The acquisition of new hosts conditions virus epidemiology and emergence; hence it is important to understand the mechanisms behind host range expansion. Experimental evolution studies have identified antagonistic pleiotropy and epistasis as genetic mechanisms that limit host range expansion, but studies from virus field populations are few. Here, we compare the performance of isolates of tobacco mild green mosaic virus from its reservoir host, Nicotiana glauca, and its new host, pepper, showing that acquisition of a new host was not followed by adaptation to it but was associated with a fitness loss in the original host. Analysis of mutations determining host-specific virus multiplication identified antagonistic pleiotropy, epistasis, and host-specific epistasis as mechanisms generating across-host fitness trade-offs that may prevent adaptation to pepper and cause a loss of fitness in N. glauca Thus, mechanisms determining trade-offs, identified under experimental evolution, could also operate in the heterogeneous environment in which natural plant virus populations occur.

Evolutionary Determinants of Host and Vector Manipulation by Plant Viruses

Plant viruses possess adaptations for facilitating acquisition, retention, and inoculation by vectors. Until recently, it was hypothesized that these adaptations are limited to virus proteins that enable virions to bind to vector mouthparts or invade their internal tissues. However, increasing evidence suggests that viruses can also manipulate host plant phenotypes and vector behaviors in ways that enhance their own transmission. Manipulation of vector-host interactions occurs through virus effects on host cues that mediate vector orientation, feeding, and dispersal behaviors, and thereby, the probability of virus transmission. Effects on host phenotypes vary by pathosystem but show a remarkable degree of convergence among unrelated viruses whose transmission is favored by the same vector behaviors. Convergence based on transmission mechanism, rather than phylogeny, supports the hypothesis that virus effects are adaptive and not just by-products of infection. Based on this, it has been proposed that viruses manipulate hosts through multifunctional proteins that facilitate exploitation of host resources and elicitation of specific changes in host phenotypes. But this proposition is rarely discussed in the context of the numerous constraints on virus evolution imposed by molecular and environmental factors, which figure prominently in research on virus-host interactions not dealing with host manipulation. To explore the implications of this oversight, we synthesized available literature to identify patterns in virus effects among pathogens with shared transmission mechanisms and discussed the results of this synthesis in the context of molecular and environmental constraints on virus evolution, limitations of existing studies, and prospects for future research.

Identification of epistatic mutations and insights into the evolution of the influenza virus using a mass-based protein phylogenetic approach

A mass-based protein phylogenetic approach developed in this laboratory has been applied to study mutation trends and identify consecutive or near-consecutive mutations typically associated with positive epistasis. While epistasis is thought to occur commonly during the evolution of viruses, the extent of epistasis in influenza, and its role in the evolution of immune escape and drug resistant mutants, remains to be systematically investigated. Here putative epistatic mutations within H3 hemagglutinin in type A influenza are identified where leading parent mutations were found to predominate within reported antigenic sites of the protein. Frequent subsequent mutations resided exclusively in different antigenic regions, providing the virus with a possible immune escape mechanism, or at other remote sites that drive beneficial protein structural and functional change. The results also enable a "small steps" evolutionary model to be proposed where the more frequent consecutive, or near-consecutive, non-conservative mutations exhibited less structural, and thus functional, change. This favours the evolutionary survival of the virus over mutations associated with more substantive change that may cause or risk its own extinction.

Key issues review: evolution on rugged adaptive landscapes

Adaptive landscapes represent a mapping between genotype and fitness. Rugged adaptive landscapes contain two or more adaptive peaks: allele combinations with higher fitness than any of their neighbors in the genetic space. How do populations evolve on such rugged landscapes? Evolutionary biologists have struggled with this question since it was first introduced in the 1930s by Sewall Wright. Discoveries in the fields of genetics and biochemistry inspired various mathematical models of adaptive landscapes. The development of landscape models led to numerous theoretical studies analyzing evolution on rugged landscapes under different biological conditions. The large body of theoretical work suggests that adaptive landscapes are major determinants of the progress and outcome of evolutionary processes. Recent technological advances in molecular biology and microbiology allow experimenters to measure adaptive values of large sets of allele combinations and construct empirical adaptive landscapes for the first time. Such empirical landscapes have already been generated in bacteria, yeast, viruses, and fungi, and are contributing to new insights about evolution on adaptive landscapes. In this Key Issues Review we will: (i) introduce the concept of adaptive landscapes; (ii) review the major theoretical studies of evolution on rugged landscapes; (iii) review some of the recently obtained empirical adaptive landscapes; (iv) discuss recent mathematical and statistical analyses motivated by empirical adaptive landscapes, as well as provide the reader with instructions and source code to implement simulations of evolution on adaptive landscapes; and (v) discuss possible future directions for this exciting field.

Diminishing-returns epistasis among random beneficial mutations in a multicellular fungus

Adaptive evolution ultimately is fuelled by mutations generating novel genetic variation. Non-additivity of fitness effects of mutations (called epistasis) may affect the dynamics and repeatability of adaptation. However, understanding the importance and implications of epistasis is hampered by the observation of substantial variation in patterns of epistasis across empirical studies. Interestingly, some recent studies report increasingly smaller benefits of beneficial mutations once genotypes become better adapted (called diminishing-returns epistasis) in unicellular microbes and single genes. Here, we use Fisher's geometric model (FGM) to generate analytical predictions about the relationship between the effect size of mutations and the extent of epistasis. We then test these predictions using the multicellular fungus Aspergillus nidulans by generating a collection of 108 strains in either a poor or a rich nutrient environment that each carry a beneficial mutation and constructing pairwise combinations using sexual crosses. Our results support the predictions from FGM and indicate negative epistasis among beneficial mutations in both environments, which scale with mutational effect size. Hence, our findings show the importance of diminishing-returns epistasis among beneficial mutations also for a multicellular organism, and suggest that this pattern reflects a generic constraint operating at diverse levels of biological organization.

Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations

Predicting viral evolution has proven to be a particularly difficult task, mainly owing to our incomplete knowledge of some of the fundamental principles that drive it. Recently, valuable information has been provided about mutation and recombination rates, the role of genetic drift and the distribution of mutational, epistatic and pleiotropic fitness effects. However, information about the topography of virus' adaptive landscapes is still scarce, and to our knowledge no data has been reported so far on how its ruggedness may condition virus' evolvability. Here, we show that populations of an RNA virus move efficiently on a rugged landscape and scape from the basin of attraction of a local optimum. We have evolved a set of Tobacco etch virus genotypes located at increasing distances from a local adaptive optimum in a highly rugged fitness landscape, and we observed that few evolved lineages remained trapped in the local optimum, while many others explored distant regions of the landscape. Most of the diversification in fitness among the evolved lineages was explained by adaptation, while historical contingency and chance events contribution was less important. Our results demonstrate that the ruggedness of adaptive landscapes is not an impediment for RNA viruses to efficiently explore remote parts of it.

Selecting among three basic fitness landscape models: Additive, multiplicative and stickbreaking

Fitness landscapes map genotypes to organismal fitness. Their topographies depend on how mutational effects interact - epistasis - andare important for understanding evolutionary processes such as speciation, the rate of adaptation, the advantage of recombination, and the predictability versus stochasticity of evolution. The growing amount of data has made it possible to better test landscape models empirically. We argue that this endeavor will benefit from the development and use of meaningful basic models against which to compare more complex models. Here we develop statistical and computational methods for fitting fitness data from mutation combinatorial networks to three simple models: additive, multiplicative and stickbreaking. We employ a Bayesian framework for doing model selection. Using simulations, we demonstrate that our methods work and we explore their statistical performance: bias, error, and the power to discriminate among models. We then illustrate our approach and its flexibility by analyzing several previously published datasets. An R-package that implements our methods is available in the CRAN repository under the name Stickbreaker.

A Selective Bottleneck Shapes the Evolutionary Mutant Spectra of Enterovirus A71 during Viral Dissemination in Humans

RNA viruses accumulate mutations to rapidly adapt to environmental changes. Enterovirus A71 (EV-A71) causes various clinical manifestations with occasional severe neurological complications. However, the mechanism by which EV-A71 evolves within the human body is unclear. Utilizing deep sequencing and haplotype analyses of viruses from various tissues of an autopsy patient, we sought to define the evolutionary pathway by which enterovirus A71 evolves fitness for invading the central nervous system in humans. Broad mutant spectra with divergent mutations were observed at the initial infection sites in the respiratory and digestive systems. After viral invasion, we identified a haplotype switch and dominant haplotype, with glycine at VP1 residue 31 (VP1-31G) in viral particles disseminated into the integumentary and central nervous systems. In vitro viral growth and fitness analyses indicated that VP1-31G conferred growth and a fitness advantage in human neuronal cells, whereas VP1-31D conferred enhanced replication in human colorectal cells. A higher proportion of VP1-31G was also found among fatal cases, suggesting that it may facilitate central nervous system infection in humans. Our data provide the first glimpse of EV-A71 quasispecies from oral tissues to the central nervous system within humans, showing broad implications for the surveillance and pathogenesis of this reemerging viral pathogen.IMPORTANCE EV-A71 continues to be a worldwide burden to public health. Although EV-A71 is the major etiological agent of hand, foot, and mouth disease, it can also cause neurological pulmonary edema, encephalitis, and even death, especially in children. Understanding selection processes enabling dissemination and accurately estimating EV-A71 diversity during invasion in humans are critical for applications in viral pathogenesis and vaccine studies. Here, we define a selection bottleneck appearing in respiratory and digestive tissues. Glycine substitution at VP1 residue 31 helps viruses break through the bottleneck and invade the central nervous system. This substitution is also advantageous for replication in neuronal cells in vitro Considering that fatal cases contain enhanced glycine substitution at VP1-31, we suggest that the increased prevalence of VP1-31G may alter viral tropism and aid central nervous system invasion. Our findings provide new insights into a dynamic mutant spectral switch active during acute viral infection with emerging viral pathogens.

Additive Phenotypes Underlie Epistasis of Fitness Effects

Gene interactions, or epistasis, play a large role in determining evolutionary outcomes. The ruggedness of fitness landscapes, and thus the predictability of evolution and the accessibility of high-fitness genotypes, is determined largely by the pervasiveness of epistasis and the degree of correlation between similar genotypes. We created all possible pairings of three sets of five beneficial first-step mutations fixed during adaptive walks under three different regimes: selection on growth rate alone, on growth rate and thermal stability, and on growth rate and pH stability. All 30 double-mutants displayed negative, antagonistic epistasis with regard to growth rate and fitness, but positive epistasis and additivity were common for the stability phenotypes. This suggested that biophysically simple phenotypes, such as capsid stability, may on average behave more additively than complex phenotypes like viral growth rate. Growth rate epistasis was also smaller in magnitude when the individual effects of single mutations were smaller. Significant sign epistasis, such that the effect of a mutation that is beneficial in the wild-type background is deleterious in combination with a second mutation, emerged more frequently in intragenic mutational pairings than in intergenic pairs, and was evident in nearly half of the double-mutants, indicating that the fitness landscape is moderately uncorrelated and of intermediate ruggedness. Together, our results indicated that mutations may interact additively with regard to phenotype when considered at a basic, biophysical level, but that epistasis arises as a result of pleiotropic interactions between the individual components of complex phenotypes and diminishing returns arising from intermediate phenotypic optima.

Mosaics often outperform pyramids: insights from a model comparing strategies for the deployment of plant resistance genes against viruses in agricultural landscapes

The breakdown of plant virus resistance genes is a major issue in agriculture. We investigated whether a set of resistance genes would last longer when stacked into a single plant cultivar (pyramiding) or when deployed individually in regional mosaics (mosaic strategy). We modeled the genetic and epidemiological processes shaping the demogenetic dynamics of viruses under a multilocus gene-for-gene system, from the plant to landscape scales. The landscape consisted of many fields, was subject to seasonality, and of a reservoir hosting viruses year-round. Strategy performance depended principally on the fitness costs of adaptive mutations, epidemic intensity before resistance deployment and landscape connectivity. Mosaics were at least as good as pyramiding strategies in most production situations tested. Pyramiding strategies performed better only with slowly changing virus reservoir dynamics. Mosaics are more versatile than pyramiding strategies, and we found that deploying a mosaic of three to five resistance genes generally provided effective disease control, unless the epidemics were driven mostly by within-field infections. We considered the epidemiological and evolutionary mechanisms underlying the greater versatility of mosaics in our case study, with a view to providing breeders and growers with guidance as to the most appropriate deployment strategy.

Strain-dependent mutational effects for Pepino mosaic virus in a natural host

Background

Pepino mosaic virus (PepMV) is an emerging plant pathogen that infects tomatoes worldwide. Understanding the factors that influence its evolutionary success is essential for developing new control strategies that may be more robust against the evolution of new viral strains. One of these evolutionary factors is the distribution of mutational fitness effect (DMFE), that is, the fraction of mutations that are lethal, deleterious, neutral, and beneficial on a given viral strain and host species. The goal of this study was to characterize the DMFE of introduced nonsynonymous mutations on a mild isolate of PepMV from the Chilean 2 strain (PepMV-P22). Additionally, we also explored whether the fitness effect of a given mutation depends on the gene where it appears or on epistatic interactions with the genetic background. To address this latter possibility, a subset of mutations were also introduced in a mild isolate of the European strain (PepMV-P11) and the fitness of the resulting clones measured.

Results

A collection of 25 PepMV clones each containing a single nucleotide nonsynonymous substitution was created by site-directed mutagenesis and the fitness of each mutant was determined. PepMV-P22 genome showed a high degree of robustness against point mutations, with 80% of mutations being either neutral or even beneficial and only 20% being deleterious or lethal. We found that the effect of mutations strongly depended on the gene in which they were introduced. Mutations with the largest average beneficial effects were those affecting the RdRp gene, in contrast to mutations affecting TGB1 and CP genes, for which the average effects were deleterious. Moreover, significant epistatic interactions were observed between nonsynonymous mutations and the genetic background, meaning that the effect of a given nucleotide substitution on a particular genomic context cannot be predicted by knowing its effect in a different one.

Conclusions

Our results indicated that PepMV genome has a surprisingly high robustness against mutations. We also found that fitness consequences of a given mutation differ between the two strains analyzed. This discovery suggests that the strength of selection, and thus the rates of evolution, vary among PepMV strains.

Dynamics of molecular evolution in RNA virus populations depend on sudden versus gradual environmental change

Understanding the dynamics of molecular adaptation is a fundamental goal of evolutionary biology. While adaptation to constant environments has been well characterized, the effects of environmental complexity remain seldom studied. One simple but understudied factor is the rate of environmental change. Here we used experimental evolution with RNA viruses to investigate whether evolutionary dynamics varied based on the rate of environmental turnover. We used whole-genome next-generation sequencing to characterize evolutionary dynamics in virus populations adapting to a sudden versus gradual shift onto a novel host cell type. In support of theoretical models, we found that when populations evolved in response to a sudden environmental change, mutations of large beneficial effect tended to fix early, followed by mutations of smaller beneficial effect; as predicted, this pattern broke down in response to a gradual environmental change. Early mutational steps were highly parallel across replicate populations in both treatments. The fixation of single mutations was less common than sweeps of associated "cohorts" of mutations, and this pattern intensified when the environment changed gradually. Additionally, clonal interference appeared stronger in response to a gradual change. Our results suggest that the rate of environmental change is an important determinant of evolutionary dynamics in asexual populations.

Evolutionary ecology of virus emergence

The cross-species transmission of viruses into new host populations, termed virus emergence, is a significant issue in public health, agriculture, wildlife management, and related fields. Virus emergence requires overlap between host populations, alterations in virus genetics to permit infection of new hosts, and adaptation to novel hosts such that between-host transmission is sustainable, all of which are the purview of the fields of ecology and evolution. A firm understanding of the ecology of viruses and how they evolve is required for understanding how and why viruses emerge. In this paper, I address the evolutionary mechanisms of virus emergence and how they relate to virus ecology. I argue that, while virus acquisition of the ability to infect new hosts is not difficult, limited evolutionary trajectories to sustained virus between-host transmission and the combined effects of mutational meltdown, bottlenecking, demographic stochasticity, density dependence, and genetic erosion in ecological sinks limit most emergence events to dead-end spillover infections. Despite the relative rarity of pandemic emerging viruses, the potential of viruses to search evolutionary space and find means to spread epidemically and the consequences of pandemic viruses that do emerge necessitate sustained attention to virus research, surveillance, prophylaxis, and treatment.

Synonymous Co-Variation across the E1/E2 Gene Junction of Hepatitis C Virus Defines Virion Fitness

Hepatitis C virus is a positive-sense single-stranded RNA virus. The gene junction partitioning the viral glycoproteins E1 and E2 displays concurrent sequence evolution with the 3'-end of E1 highly conserved and the 5'-end of E2 highly heterogeneous. This gene junction is also believed to contain structured RNA elements, with a growing body of evidence suggesting that such structures can act as an additional level of viral replication and transcriptional control. We have previously used ultradeep pyrosequencing to analyze an amplicon library spanning the E1/E2 gene junction from a treatment naïve patient where samples were collected over 10 years of chronic HCV infection. During this timeframe maintenance of an in-frame insertion, recombination and humoral immune targeting of discrete virus sub-populations was reported. In the current study, we present evidence of epistatic evolution across the E1/E2 gene junction and observe the development of co-varying networks of codons set against a background of a complex virome with periodic shifts in population dominance. Overtime, the number of codons actively mutating decreases for all virus groupings. We identify strong synonymous co-variation between codon sites in a group of sequences harbouring a 3 bp in-frame insertion and propose that synonymous mutation acts to stabilize the RNA structural backbone.

The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is shaped by strong epistatic interactions

Colistin is an antimicrobial peptide that has become the only remaining alternative for the treatment of multidrug-resistant Gram-negative bacterial infections, but little is known of how clinical levels of colistin resistance evolve. We use in vitro experimental evolution and whole-genome sequencing of colistin-resistant Pseudomonas aeruginosa isolates from cystic fibrosis patients to reconstruct the molecular evolutionary pathways open for high-level colistin resistance. We show that the evolution of resistance is a complex, multistep process that requires mutation in at least five independent loci that synergistically create the phenotype. Strong intergenic epistasis limits the number of possible evolutionary pathways to resistance. Mutations in transcriptional regulators are essential for resistance evolution and function as nodes that potentiate further evolution towards higher resistance by functionalizing and increasing the effect of the other mutations. These results add to our understanding of clinical antimicrobial peptide resistance and the prediction of resistance evolution.

Colistin is an antibiotic used in the treatment of Pseudomonas aeruginosa infections in cystic fibrosis patients. Here, Jochumsen et al. reconstruct the pathways for the molecular evolution of colistin resistance in P. aeruginosa and show that the number of pathways is highly constrained by interactions among genes.

Impact of increased mutagenesis on adaptation to high temperature in bacteriophage Qβ

RNA viruses replicate with very high error rates, which makes them more sensitive to additional increases in this parameter. This fact has inspired an antiviral strategy named lethal mutagenesis, which is based on the artificial increase of the error rate above a threshold incompatible with virus infectivity. A relevant issue concerning lethal mutagenesis is whether incomplete treatments might enhance the adaptive possibilities of viruses. We have addressed this question by subjecting an RNA virus, the bacteriophage Qβ, to different transmission regimes in the presence or the absence of sublethal concentrations of the mutagenic nucleoside analogue 5-azacytidine (AZC). Populations obtained were subsequently exposed to a non-optimal temperature and analyzed to determine their consensus sequences. Our results show that previously mutagenized populations rapidly fixed a specific set of mutations upon propagation at the new temperature, suggesting that the expansion of the mutant spectrum caused by AZC has an influence on later evolutionary behavior.

Epistasis and the Structure of Fitness Landscapes: Are Experimental Fitness Landscapes Compatible with Fisher's Geometric Model?

The fitness landscape defines the relationship between genotypes and fitness in a given environment and underlies fundamental quantities such as the distribution of selection coefficient and the magnitude and type of epistasis. A better understanding of variation in landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigate the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher's model) to 26 empirical landscapes representing nine diverse biological systems. Despite uncertainty owing to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness-of-fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible in only three of nine biological systems. More precisely, although Fisher's model was able to explain several statistical properties of the landscapes-including the mean and SD of selection and epistasis coefficients-it was often unable to explain the full structure of fitness landscapes.

How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.

Involvement of a joker mutation in a polymerase-independent lethal mutagenesis escape mechanism

We previously characterized a foot-and-mouth disease virus (FMDV) with three amino acid replacements in its polymerase (3D) that conferred resistance to the mutagenic nucleoside analogue ribavirin. Here we show that passage of this mutant in the presence of high ribavirin concentrations resulted in selection of viruses with the additional replacement I248T in 2C. This 2C substitution alone (even in the absence of replacements in 3D) increased FMDV fitness mainly in the presence of ribavirin, prevented an incorporation bias in favor of A and U associated with ribavirin mutagenesis, and conferred the ATPase activity of 2C decreased sensitivity to ribavirin-triphosphate. Since in previous studies we described that 2C with I248T was selected under different selective pressures, this replacement qualifies as a joker substitution in FMDV evolution. The results have identified a role of 2C in nucleotide incorporation, and have unveiled a new polymerase-independent mechanism of virus escape to lethal mutagenesis.

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A replacement in FMDV protein 2C confers reduced sensitivity to the mutagen ribavirin.

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The effect of the replacement is to prevent a mutational bias evoked by ribavirin.

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2C has an effect in nucleotide incorporation by the FMDV polymerase.

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We describe a new molecular mechanism of escape to ribavirin-mediated extinction.

Genetic variation in fitness within a clonal population of a plant RNA virus

A long-standing observation in evolutionary virology is that RNA virus populations are highly polymorphic, composed by a mixture of genotypes whose abundances in the population depend on complex interaction between fitness differences, mutational coupling and genetic drift. It was shown long ago, though in cell cultures, that most of these genotypes had lower fitness than the population they belong, an observation that explained why single-virion passages turned on Muller’s ratchet while very large population passages resulted in fitness increases in novel environments. Here we report the results of an experiment specifically designed to evaluate in vivo the fitness differences among the subclonal components of a clonal population of the plant RNA virus tobacco etch potyvirus (TEV). Over 100 individual biological subclones from a TEV clonal population well adapted to the natural tobacco host were obtained by infectivity assays on a local lesion host. The replicative fitness of these subclones was then evaluated during infection of tobacco relative to the fitness of large random samples taken from the starting clonal population. Fitness was evaluated at increasing number of days post-inoculation. We found that at early days, the average fitness of subclones was significantly lower than the fitness of the clonal population, thus confirming previous observations that most subclones contained deleterious mutations. However, as the number of days of viral replication increases, population size expands exponentially, more beneficial and compensatory mutations are produced, and selection becomes more effective in optimizing fitness, the differences between subclones and the population disappeared.

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

Background

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

Results

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

Conclusions

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

Electronic supplementary material

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

Distribution of mutational fitness effects and of epistasis in the 5' untranslated region of a plant RNA virus

BACKGROUND

Understanding the causes and consequences of phenotypic variability is a central topic of evolutionary biology. Mutations within non-coding cis-regulatory regions are thought to be of major effect since they affect the expression of downstream genes. To address the evolutionary potential of mutations affecting such regions in RNA viruses, we explored the fitness properties of mutations affecting the 5'-untranslated region (UTR) of a prototypical member of the picorna-like superfamily, Tobacco etch virus (TEV). This 5' UTR acts as an internal ribosomal entry site (IRES) and is essential for expression of all viral genes.

RESULTS

We determined in vitro the folding of 5' UTR using the selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) technique. Then, we created a collection of single-nucleotide substitutions on this region and evaluated the statistical properties of their fitness effects in vivo. We found that, compared to random mutations affecting coding sequences, mutations at the 5' UTR were of weaker effect. We also created double mutants by combining pairs of these single mutations and found variation in the magnitude and sign of epistatic interactions, with an enrichment of cases of positive epistasis. A correlation exists between the magnitude of fitness effects and the size of the perturbation made in the RNA folding structure, suggesting that the larger the departure from the predicted fold, the more negative impact in viral fitness.

CONCLUSIONS

Evidence that mutational fitness effects on the short 5' UTR regulatory sequence of TEV are weaker than those affecting its coding sequences have been found. Epistasis among pairs of mutations on the 5' UTR ranged between the extreme cases of synthetic lethal and compensatory. A plausible hypothesis to explain all these observations is that the interaction between the 5' UTR and the host translational machinery was shaped by natural selection to be robust to mutations, thus ensuring the homeostatic expression of viral genes even at high mutation rates.