A Phylogenetic Method for Detecting Positive Epistasis in Gene Sequences and Its Application to RNA Virus Evolution

A positive selection at binding site 501 in the B.1 lineage might have triggered the highly infectious sub-lineages of SARS-CoV-2

The descendants of the B lineage are the most predominant variants among the SARS-CoV-2 virus due to the incorporation of new mutations augmenting the infectivity of the virus. There is a substantial increase in the transition transversion bias, nucleotide diversity and purifying selection on the spike protein in the descendants of the B lineage of the SARS-CoV-2 virus on a temporal scale. A strong bias for C-to-U substitutions is found in the genes encoding spike protein in this lineage. The positive selection has operated on the spike gene of B lineages and its sub-lineages. The B.1 lineage has undergone positive selection on site 501 of the receptor binding domain ultimately reflected in a key substitution N501Y in its three descendant lineages namely B.1.1.7, B.1.351 and P.1. The intensity of purifying selection on the multiple sites of the spike gene has increased substantially in the sub-lineages of B.1 in a timescale. The binding site 501 on the spike protein in B lineage is found to coevolve with other amino acid sites. This study sheds light on the evolutionary trajectory of the B lineage into highly infectious descendants in the recent past under the influence of positive and purifying selection exerted by natural immunity and vaccination of the host.

Intragenic compensation through the lens of deep mutational scanning

A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01005-w.

Robustness of phylogenetic inference to model misspecification caused by pairwise epistasis

Likelihood-based phylogenetic inference posits a probabilistic model of character state change along branches of a phylogenetic tree. These models typically assume statistical independence of sites in the sequence alignment. This is a restrictive assumption that facilitates computational tractability, but ignores how epistasis, the effect of genetic background on mutational effects, influences the evolution of functional sequences. We consider the effect of using a misspecified site-independent model on the accuracy of Bayesian phylogenetic inference in the setting of pairwise-site epistasis. Previous work has shown that as alignment length increases, tree reconstruction accuracy also increases. Here, we present a simulation study demonstrating that accuracy increases with alignment size even if the additional sites are epistatically coupled. We introduce an alignment-based test statistic that is a diagnostic for pairwise epistasis and can be used in posterior predictive checks.

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

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

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

Recombination Events and Conserved Nature of Receptor Binding Motifs in Coxsackievirus A9 Isolates

Coxsackievirus A9 (CVA9) is an enterically transmitted enterovirus and one of the most pathogenic type among human enteroviruses. CVA9 isolates use a distinctive RGD (Arg-Gly-Asp) motif within VP1 capsid protein that defines its ability to bind to integrin receptor(s) for cellular entry. To investigate CVA9 evolution and pathogenicity, genetic relationships and recombination events were analyzed between 54 novel clinical isolates of CVA9, as well as 21 previously published full length CVA9 sequences from GenBank. Samples were investigated by partial sequencing of the novel VP1 and 3Dpol genes, as well as including the corresponding areas from GenBank sequences. Phylogenetic analyses were combined with clinical data in a further attempt to analyze whether sequence evolution reflects CVA9 pathogenicity in the phylogenies. Furthermore, VP1 gene was also analyzed for receptor binding sites including the RGD motif and the putative heparan sulfate (HS) site. Analysis of the 559-nucleotide-long VP1 sequences identified six clades. Although most of the strains within each clade showed geographical clustering, the grouping pattern of the isolates in the analysis of the VP1 gene was strikingly different from grouping of 3Dpol, which suggests that recombination events may have occurred in the region encoding the nonstructural proteins. Inclusion of clinical data did not provide any evidence of symptom based phylogenetic clustering of CVA9 isolates. Amino acid sequence analysis of the VP1 polypeptide demonstrated that the RGD motif was fully conserved among the isolates while the putative HS binding site was only found in one isolate. These data suggest that integrin binding is essential for virus tropism, but do not explain the symptom repertoire.

Detecting genetic interactions using parallel evolution in experimental populations

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

Mutation and Epistasis in Influenza Virus Evolution

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

Widespread Historical Contingency in Influenza Viruses

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

Plastid-Nuclear Interaction and Accelerated Coevolution in Plastid Ribosomal Genes in Geraniaceae

Plastids and mitochondria have many protein complexes that include subunits encoded by organelle and nuclear genomes. In animal cells, compensatory evolution between mitochondrial and nuclear-encoded subunits was identified and the high mitochondrial mutation rates were hypothesized to drive compensatory evolution in nuclear genomes. In plant cells, compensatory evolution between plastid and nucleus has rarely been investigated in a phylogenetic framework. To investigate plastid–nuclear coevolution, we focused on plastid ribosomal protein genes that are encoded by plastid and nuclear genomes from 27 Geraniales species. Substitution rates were compared for five sets of genes representing plastid- and nuclear-encoded ribosomal subunit proteins targeted to the cytosol or the plastid as well as nonribosomal protein controls. We found that nonsynonymous substitution rates (dN) and the ratios of nonsynonymous to synonymous substitution rates (ω) were accelerated in both plastid- (CpRP) and nuclear-encoded subunits (NuCpRP) of the plastid ribosome relative to control sequences. Our analyses revealed strong signals of cytonuclear coevolution between plastid- and nuclear-encoded subunits, in which nonsynonymous substitutions in CpRP and NuCpRP tend to occur along the same branches in the Geraniaceae phylogeny. This coevolution pattern cannot be explained by physical interaction between amino acid residues. The forces driving accelerated coevolution varied with cellular compartment of the sequence. Increased ω in CpRP was mainly due to intensified positive selection whereas increased ω in NuCpRP was caused by relaxed purifying selection. In addition, the many indels identified in plastid rRNA genes in Geraniaceae may have contributed to changes in plastid subunits.

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

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

Constraints from protein structure and intra-molecular coevolution influence the fitness of HIV-1 recombinants

A major challenge for developing effective treatments for HIV-1 is the viruses' ability to generate new variants. Inter-strain recombination is a major contributor to this high evolutionary rate, since at least 20% of viruses are observed to be recombinant. However, the patterns of recombination vary across the viral genome. A number of factors influence recombination, including sequence identity and secondary RNA structure. In addition the recombinant genome must code for a functional virus, and expressed proteins must fold to stable and functional structures. Any intragenic recombination that disrupts internal residue contacts may therefore produce an unfolded protein. Here we find that contact maps based on protein structures predict recombination breakpoints observed in the HIV-1 pandemic. Moreover, many pairs of contacting residues that are unlikely to be disrupted by recombination are coevolving. We conclude that purifying selection arising from protein structure and intramolecular coevolutionary changes shapes the observed patterns of recombination in HIV-1.

Intrasubtype reassortments cause adaptive amino acid replacements in H3N2 influenza genes

Reassortments and point mutations are two major contributors to diversity of Influenza A virus; however, the link between these two processes is unclear. It has been suggested that reassortments provoke a temporary increase in the rate of amino acid changes as the viral proteins adapt to new genetic environment, but this phenomenon has not been studied systematically. Here, we use a phylogenetic approach to infer the reassortment events between the 8 segments of influenza A H3N2 virus since its emergence in humans in 1968. We then study the amino acid replacements that occurred in genes encoded in each segment subsequent to reassortments. In five out of eight genes (NA, M1, HA, PB1 and NS1), the reassortment events led to a transient increase in the rate of amino acid replacements on the descendant phylogenetic branches. In NA and HA, the replacements following reassortments were enriched with parallel and/or reversing replacements; in contrast, the replacements at sites responsible for differences between antigenic clusters (in HA) and at sites under positive selection (in NA) were underrepresented among them. Post-reassortment adaptive walks contribute to adaptive evolution in Influenza A: in NA, an average reassortment event causes at least 2.1 amino acid replacements in a reassorted gene, with, on average, 0.43 amino acid replacements per evolving post-reassortment lineage; and at least ~9% of all amino acid replacements are provoked by reassortments.

Evolutionary history of rat-borne Bartonella: the importance of commensal rats in the dissemination of bacterial infections globally

Emerging pathogens that originate from invasive species have caused numerous significant epidemics. Some bacteria of genus Bartonella are rodent-borne pathogens that can cause disease in humans and animals alike. We analyzed gltA sequences of 191 strains of rat-associated bartonellae from 29 rodent species from 17 countries to test the hypotheses that this bacterial complex evolved and diversified in Southeast Asia before being disseminated by commensal rats Rattus rattus (black rat) and Rattus norvegicus (Norway rat) to other parts of the globe. The analysis suggests that there have been numerous dispersal events within Asia and introductions from Asia to other regions, with six major clades containing Southeast Asian isolates that appear to have been dispersed globally. Phylogeographic analyses support the hypotheses that these bacteria originated in Southeast Asia and commensal rodents (R. rattus and R. norvegicus) play key roles in the evolution and dissemination of this Bartonella complex throughout the world.

Variable evolutionary routes to host establishment across repeated rabies virus host shifts among bats

Determining the genetic pathways that viruses traverse to establish in new host species is crucial to predict the outcome of cross-species transmission but poorly understood for most host-virus systems. Using sequences encoding 78% of the rabies virus genome, we explored the extent, repeatability and dynamic outcome of evolution associated with multiple host shifts among New World bats. Episodic bursts of positive selection were detected in several viral proteins, including regions associated with host cell interaction and viral replication. Host shifts involved unique sets of substitutions, and few sites exhibited repeated evolution across adaptation to many bat species, suggesting diverse genetic determinants over host range. Combining these results with genetic reconstructions of the demographic histories of individual viral lineages revealed that although rabies viruses shared consistent three-stage processes of emergence in each new bat species, host shifts involving greater numbers of positively selected substitutions had longer delays between cross-species transmission and enzootic viral establishment. Our results point to multiple evolutionary routes to host establishment in a zoonotic RNA virus that may influence the speed of viral emergence.

Toward genetics-based virus taxonomy: comparative analysis of a genetics-based classification and the taxonomy of picornaviruses

Virus taxonomy has received little attention from the research community despite its broad relevance. In an accompanying paper (C. Lauber and A. E. Gorbalenya, J. Virol. 86:3890-3904, 2012), we have introduced a quantitative approach to hierarchically classify viruses of a family using pairwise evolutionary distances (PEDs) as a measure of genetic divergence. When applied to the six most conserved proteins of the Picornaviridae, it clustered 1,234 genome sequences in groups at three hierarchical levels (to which we refer as the "GENETIC classification"). In this study, we compare the GENETIC classification with the expert-based picornavirus taxonomy and outline differences in the underlying frameworks regarding the relation of virus groups and genetic diversity that represent, respectively, the structure and content of a classification. To facilitate the analysis, we introduce two novel diagrams. The first connects the genetic diversity of taxa to both the PED distribution and the phylogeny of picornaviruses. The second depicts a classification and the accommodated genetic diversity in a standardized manner. Generally, we found striking agreement between the two classifications on species and genus taxa. A few disagreements concern the species Human rhinovirus A and Human rhinovirus C and the genus Aphthovirus, which were split in the GENETIC classification. Furthermore, we propose a new supergenus level and universal, level-specific PED thresholds, not reached yet by many taxa. Since the species threshold is approached mostly by taxa with large sampling sizes and those infecting multiple hosts, it may represent an upper limit on divergence, beyond which homologous recombination in the six most conserved genes between two picornaviruses might not give viable progeny.

Historical contingencies modulate the adaptability of Rice yellow mottle virus

The rymv1-2 and rymv1-3 alleles of the RYMV1 resistance to Rice yellow mottle virus (RYMV), coded by an eIF(iso)4G1 gene, occur in a few cultivars of the Asiatic (Oryza sativa) and African (O. glaberrima) rice species, respectively. The most salient feature of the resistance breaking (RB) process is the converse genetic barrier to rymv1-2 and rymv1-3 resistance breakdown. This specificity is modulated by the amino acid (glutamic acid vs. threonine) at codon 49 of the Viral Protein genome-linked (VPg), a position which is adjacent to the virulence codons 48 and 52. Isolates with a glutamic acid (E) do not overcome rymv1-3 whereas those with a threonine (T) rarely overcome rymv1-2. We found that isolates with T49 had a strong selective advantage over isolates with E49 in O. glaberrima susceptible cultivars. This explains the fixation of the mutation T49 during RYMV evolution and accounts for the diversifying selection estimated at codon 49. Better adapted to O. glaberrima, isolates with T49 are also more prone than isolates with E49 to fix rymv1-3 RB mutations at codon 52 in resistant O. glaberrima cultivars. However, subsequent genetic constraints impaired the ability of isolates with T49 to fix rymv1-2 RB mutations at codons 48 and 52 in resistant O. sativa cultivars. The origin and role of the amino acid at codon 49 of the VPg exemplifies the importance of historical contingencies in the ability of RYMV to overcome RYMV1 resistance.

Evolution of viral genomes: interplay between selection, recombination, and other forces

RNA viruses evolve very rapidly, often recombine, and are subject to strong host (immune response) and anthropogenic (antiretroviral drugs) selective forces. Given their compact and extensively sequenced genomes, comparative analysis of RNA viral data can provide important insights into the molecular mechanisms of adaptation, pathogenicity, immune evasion, and drug resistance. In this chapter, we present an example-based overview of recent advances in evolutionary models and statistical approaches that enable screening viral alignments for evidence of adaptive change in the presence of recombination, detecting bursts of directional adaptive evolution associated with the phenotypic changes, and detecting of coevolving sites in viral genes.

Why do RNA viruses recombine?

Recombination occurs in many RNA viruses and can be of major evolutionary significance. However, rates of recombination vary dramatically among RNA viruses, which can range from clonal to highly recombinogenic. Here, we review the factors that might explain this variation in recombination frequency and show that there is little evidence that recombination is favoured by natural selection to create advantageous genotypes or purge deleterious mutations, as predicted if recombination functions as a form of sexual reproduction. Rather, recombination rates seemingly reflect larger-scale patterns of viral genome organization, such that recombination may be a mechanistic by-product of the evolutionary pressures acting on other aspects of virus biology.

Prevalence of epistasis in the evolution of influenza A surface proteins

The surface proteins of human influenza A viruses experience positive selection to escape both human immunity and, more recently, antiviral drug treatments. In bacteria and viruses, immune-escape and drug-resistant phenotypes often appear through a combination of several mutations that have epistatic effects on pathogen fitness. However, the extent and structure of epistasis in influenza viral proteins have not been systematically investigated. Here, we develop a novel statistical method to detect positive epistasis between pairs of sites in a protein, based on the observed temporal patterns of sequence evolution. The method rests on the simple idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic. We apply this method to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a non-epistatic null distribution, we detect substantial amounts of epistasis and determine the identities of putatively epistatic pairs of sites. In particular, using sequence data alone, our method identifies epistatic interactions between specific sites in neuraminidase that have recently been demonstrated, in vitro, to confer resistance to the drug oseltamivir; these epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today. This experimental validation demonstrates the predictive power of our method to identify epistatic sites of importance for viral adaptation and public health. We conclude that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites with , which would normally not be identified as positively selected, can facilitate viral adaptation through epistatic interactions with their partner sites. The knowledge of specific interactions among sites in influenza proteins may help us to predict the course of antigenic evolution and, consequently, to select more appropriate vaccines and drugs.

Epistasis describes non-additive interactions among genetic sites: the consequence of a mutation at one site may depend on the status of the genome at other sites. In an extreme case, a mutation may have no effect if it arises on one genetic background, but a strong effect on another background. Epistatic mutations in viruses and bacteria that live under severe conditions, such as antibiotic treatments or immune pressure, often allow pathogens to develop drug resistance or escape the immune system. In this paper we develop a new phylogenetic method for detecting epistasis, and we apply this method to the surface proteins of the influenza A virus, which are important targets of the immune system and drug treatments. The authors identify and characterize hundreds of epistatic mutations in these proteins. Among those identified, we find the specific epistatic mutations that were recently shown, experimentally, to confer resistance to the drug Tamiflu. The results of this study may help to predict the course of influenza's antigenic evolution and to select more appropriate vaccines and drugs.

Simple genomes, complex interactions: epistasis in RNA virus

Owed to their reduced size and low number of proteins encoded, RNA viruses and other subviral pathogens are often considered as being genetically too simple. However, this structural simplicity also creates the necessity for viral RNA sequences to encode for more than one protein and for proteins to carry out multiple functions, all together resulting in complex patterns of genetic interactions. In this work we will first review the experimental studies revealing that the architecture of viral genomes is dominated by antagonistic interactions among loci. Second, we will also review mathematical models and provide a description of computational tools for the study of RNA virus dynamics and evolution. As an application of these tools, we will finish this review article by analyzing a stochastic bit-string model of in silico virus replication. This model analyzes the interplay between epistasis and the mode of replication on determining the population load of deleterious mutations. The model suggests that, for a given mutation rate, the deleterious mutational load is always larger when epistasis is predominantly antagonistic than when synergism is the rule. However, the magnitude of this effect is larger if replication occurs geometrically than if it proceeds linearly.

Fitness epistasis and constraints on adaptation in a human immunodeficiency virus type 1 protein region

Fitness epistasis, the interaction among alleles at different loci in their effects on fitness, has potentially important consequences for adaptive evolution. We investigated fitness epistasis among amino acids of a functionally important region of the human immunodeficiency virus type 1 (HIV-1) exterior envelope glycoprotein (gp120). Seven mutations putatively involved in the adaptation of the second conserved to third variable protein region (C2-V3) to the use of an alternative host-cell chemokine coreceptor (CXCR4) for cell entry were engineered singly and in combinations on the wild-type genetic background and their effects on viral infectivity were measured. Epistasis was found to be common and complex, involving not only pairwise interactions, but also higher-order interactions. Interactions could also be surprisingly strong, changing fitness by more than 9 orders of magnitude, which is explained by some single mutations being practically lethal. A consequence of the observed epistasis is that many of the minimum-length mutational trajectories between the wild type and the mutant with highest fitness on cells expressing the alternative coreceptor are selectively inaccessible. These results may help explain the difficulty of evolving viruses that use the alternative coreceptor in culture and the delayed evolution of this phenotype in natural infection. Knowledge of common, complex, and strong fitness interactions among amino acids is necessary for a full understanding of protein evolution.

Phylogenetic analysis of population-based and deep sequencing data to identify coevolving sites in the nef gene of HIV-1

Rapidly evolving viruses such as HIV-1 display extensive sequence variation in response to host-specific selection, while simultaneously maintaining functions that are critical to replication and infectivity. This apparent conflict between diversifying and purifying selection may be resolved by an abundance of epistatic interactions such that the same functional requirements can be met by highly divergent sequences. We investigate this hypothesis by conducting an extensive characterization of sequence variation in the HIV-1 nef gene that encodes a highly variable multifunctional protein. Population-based sequences were obtained from 686 patients enrolled in the HOMER cohort in British Columbia, Canada, from which the distribution of nonsynonymous substitutions in the phylogeny was reconstructed by maximum likelihood. We used a phylogenetic comparative method on these data to identify putative epistatic interactions between residues. Two interactions (Y120/Q125 and N157/S169) were chosen to further investigate within-host evolution using HIV-1 RNA extractions from plasma samples from eight patients. Clonal sequencing confirmed strong linkage between polymorphisms at these sites in every case. We used massively parallel pyrosequencing (MPP) to reconstruct within-host evolution in these patients. Experimental error associated with MPP was quantified by performing replicates at two different stages of the protocol, which were pooled prior to analysis to reduce this source of variation. Phylogenetic reconstruction from these data revealed correlated substitutions at Y120/Q125 or N157/S169 repeated across multiple lineages in every host, indicating convergent within-host evolution shaped by epistatic interactions.

Rooting human parechovirus evolution in time

BACKGROUND

The Picornaviridae family contains a number of important pathogenic viruses, among which the recently reclassified human parechoviruses (HPeVs). These viruses are widespread and can be grouped in several types. Understanding the evolutionary history of HPeV could answer questions such as how long the circulating lineages last shared a common ancestor and how the evolution of this viral species is shaped by its population dynamics. Using both strict and relaxed clock Bayesian phylogenetics we investigated 1) the substitutions rates of the structural P1 and capsid VP1 regions and 2) evolutionary timescale of currently circulating HPeV lineages.

RESULTS

Our estimates reveal that human parechoviruses exhibit high substitution rates for both structural P1 and capsid VP1 regions, respectively 2.21 x 10(-3) (0.48 - 4.21 x 10(-3)) and 2.79 x 10(-3) (2.05 - 3.66 x 10(-3)) substitutions per site per year. These are within the range estimated for other picornaviruses. By employing a constant population size coalescent prior, the date of the most recent common ancestor was estimated to be at around 1600 (1427-1733). In addition, by looking at the frequency of synonymous and non-synonymous substitutions within the VP1 gene we show that purifying selection constitutes the dominating evolutionary force leading to strong amino acid conservation.

CONCLUSION

In conclusion, our estimates provide a timescale for the evolution of HPeVs and suggest that genetic diversity of current circulating HPeV types has arisen about 400 years ago.

Detecting natural selection in RNA virus populations using sequence summary statistics

At present, most analyses that aim to detect the action of natural selection upon viral gene sequences use phylogenetic estimates of the ratio of silent to replacement mutations. Such methods, however, are impractical to compute on large data sets comprising hundreds of complete viral genomes, which are becoming increasingly common due to advances in genome sequencing technology. Here we investigate the statistical performance of computationally efficient tests that are based on sequence summary statistics, and explore their applicability to RNA virus data sets in two ways. Firstly, we perform extensive simulations in order to measure the type I error of two well-known summary statistic methods - Tajima's D and the McDonald-Kreitman test - under a range of virus-like mutational and demographic scenarios. Secondly, we apply these methods to a compilation of approximately 100 RNA virus alignments that represent natural RNA virus populations. In addition, we develop and introduce a new implementation of the McDonald-Kreitman test and show that it greatly improves the test's statistical reliability on typical viral data sets. Our results suggest that variants of the McDonald-Kreitman test could prove useful in the analysis of very large sets of highly diverse viral genetic data.

Identifying the important HIV-1 recombination breakpoints

Recombinant HIV-1 genomes contribute significantly to the diversity of variants within the HIV/AIDS pandemic. It is assumed that some of these mosaic genomes may have novel properties that have led to their prevalence, particularly in the case of the circulating recombinant forms (CRFs). In regions of the HIV-1 genome where recombination has a tendency to convey a selective advantage to the virus, we predict that the distribution of breakpoints—the identifiable boundaries that delimit the mosaic structure—will deviate from the underlying null distribution. To test this hypothesis, we generate a probabilistic model of HIV-1 copy-choice recombination and compare the predicted breakpoint distribution to the distribution from the HIV/AIDS pandemic. Across much of the HIV-1 genome, we find that the observed frequencies of inter-subtype recombination are predicted accurately by our model. This observation strongly indicates that in these regions a probabilistic model, dependent on local sequence identity, is sufficient to explain breakpoint locations. In regions where there is a significant over- (either side of the env gene) or under- (short regions within gag, pol, and most of env) representation of breakpoints, we infer natural selection to be influencing the recombination pattern. The paucity of recombination breakpoints within most of the envelope gene indicates that recombinants generated in this region are less likely to be successful. The breakpoints at a higher frequency than predicted by our model are approximately at either side of env, indicating increased selection for these recombinants as a consequence of this region, or at least part of it, having a tendency to be recombined as an entire unit. Our findings thus provide the first clear indication of the existence of a specific portion of the genome that deviates from a probabilistic null model for recombination. This suggests that, despite the wide diversity of recombinant forms seen in the viral population, only a minority of recombination events appear to be of significance to the evolution of HIV-1.

Multiple variants of HIV can infect the same cell, and because each viral particle contains two copies of the viral genomic RNA, RNAs from different viruses can occasionally be incorporated together within a viral particle. When this virus subsequently infects another cell, genetic exchange (recombination) may occur between these two divergent copies of genomic RNA as a result of a switch between the RNA molecules while they are copied into DNA. This process is very important to understand as it contributes to the generation of new HIV variants. In this study, we have analysed a set of recombinant HIV genomes generated in the laboratory to construct a probabilistic model of the propensity for the switch to take place in specific regions of the genome, dependent on the local similarity of the parental viral sequences. This model allows us to predict the locations where recombination should occur more frequently. By comparing these predictions to the patterns of recombination observed in the HIV-1 pandemic, we identify the genomic regions in which recombination has been more important, in that it has provided an evolutionary advantage to the virus.

Comparative analysis of nearly full-length hepatitis C virus quasispecies from patients experiencing viral breakthrough during antiviral therapy: clustered mutations in three functional genes, E2, NS2, and NS5a

Viral breakthrough is a recognized response pattern to interferon-based antiviral therapy in patients with chronic hepatitis C virus (HCV) infection. The emergence of drug-resistant HCV quasispecies variants is assumed to be a major mechanism responsible for viral breakthrough. By using a long reverse transcription-PCR protocol recently developed in our lab, multiple nearly full-length HCV quasispecies variants were generated from 9.1-kb amplicons at both the baseline and breakthrough points in two patients experiencing viral breakthrough. Comparative analyses of consensus dominant quasispecies variants revealed that most mutations, occurring at the time of breakthrough, involved three functional viral genes, E2, NS2, and NS5a. Interestingly, similar mutation patterns were also observed in minor quasispecies variants at baseline. These three genes had the highest values of average amino acid complexity at the HCV 1a population level. No single amino acids were identified to be associated with viral breakthrough. Taken together, at the near-full-length HCV genome level, our data suggested that viral breakthrough might be attributed to the selection of minor quasispecies variants at the baseline with or without additional mutations during antiviral therapy. Furthermore, the pattern for mutation clustering indicated potential mutation linkage among E2, NS2, and NS5a due to structural or functional relatedness in HCV replication.

Reducing the false positive rate in the non-parametric analysis of molecular coevolution

Background

The strength of selective constraints operating on amino acid sites of proteins has a multifactorial nature. In fact, amino acid sites within proteins coevolve due to their functional and/or structural relationships. Different methods have been developed that attempt to account for the evolutionary dependencies between amino acid sites. Researchers have invested a significant effort to increase the sensitivity of such methods. However, the difficulty in disentangling functional co-dependencies from historical covariation has fuelled the scepticism over their power to detect biologically meaningful results. In addition, the biological parameters connecting linear sequence evolution to structure evolution remain elusive. For these reasons, most of the evolutionary studies aimed at identifying functional dependencies among protein domains have focused on the structural properties of proteins rather than on the information extracted from linear multiple sequence alignments (MSA). Non-parametric methods to detect coevolution have been reported to be especially susceptible to produce false positive results based on the properties of MSAs. However, no formal statistical analysis has been performed to definitively test the differential effects of these properties on the sensitivity of such methods.

Results

Here we test the effect that variations on the MSA properties have over the sensitivity of non-parametric methods to detect coevolution. We test the effect that the size of the MSA (number of sequences), mean pairwise amino acid distance per site and the strength of the coevolution signal have on the ability of non-parametric methods to detect coevolution. Our results indicate that all three factors have significant effects on the accuracy of non-parametric methods. Further, introducing statistical filters improves the sensitivity and increases the statistical power of the methods to detect functional coevolution. Statistical analysis of the physico-chemical properties of amino acid sites in the context of the protein structure reveals striking dependencies among amino acid sites. Results indicate a covariation trend in the hydrophobicities and molecular weight characteristics of amino acid sites when analysing a non-redundant set of 8000 protein structures. Using this biological information as filter in coevolutionary analyses minimises the false positive rate of these methods. Application of these filters to three different proteins with known functional domains supports the importance of using biological filters to detect coevolution.

Conclusion

Coevolutionary analyses using non-parametric methods have proved difficult and highly prone to provide spurious results depending on the properties of MSAs and on the strength of coevolution between amino acid sites. The application of statistical filters to the number of pairs detected as coevolving reduces significantly the number of artifactual results. Analysis of the physico-chemical properties of amino acid sites in the protein structure context reveals their structure-dependent covariation. The application of this known biological information to the analysis of covariation greatly enhances the functional coevolutionary signal and removes historical covariation. Simultaneous use of statistical and biological data is instrumental in the detection of functional amino acid sites dependencies and compensatory changes at the protein level.

Avoidance of protein fold disruption in natural virus recombinants

With the development of reliable recombination detection tools and an increasing number of available genome sequences, many studies have reported evidence of recombination in a wide range of virus genera. Recombination is apparently a major mechanism in virus evolution, allowing viruses to evolve more quickly by providing immediate direct access to many more areas of a sequence space than are accessible by mutation alone. Recombination has been widely described amongst the insect-transmitted plant viruses in the genus Begomovirus (family Geminiviridae), with potential recombination hot- and cold-spots also having been identified. Nevertheless, because very little is understood about either the biochemical predispositions of different genomic regions to recombine or what makes some recombinants more viable than others, the sources of the evolutionary and biochemical forces shaping distinctive recombination patterns observed in nature remain obscure. Here we present a detailed analysis of unique recombination events detectable in the DNA-A and DNA-A-like genome components of bipartite and monopartite begomoviruses. We demonstrate both that recombination breakpoint hot- and cold-spots are conserved between the two groups of viruses, and that patterns of sequence exchange amongst the genomes are obviously non-random. Using a computational technique designed to predict structural perturbations in chimaeric proteins, we demonstrate that observed recombination events tend to be less disruptive than sets of simulated ones. Purifying selection acting against natural recombinants expressing improperly folded chimaeric proteins is therefore a major determinant of natural recombination patterns in begomoviruses.

The exchange of genetic material between different virus species, called inter-species recombination, has the potential to generate, within a single genome replication cycle, an almost unimaginable number of genetically distinct virus strains, including many that might cause deadly new human, animal, or plant diseases. Many fear that inter-species recombination could provide viruses with quick access to evolutionary innovations such as broader host ranges, altered tissue tropisms, or increased severities. However, mounting evidence suggests that recombination is not an unconstrained process and that most inter-species recombinants that occur in nature are probably defective. It is suspected that networks of coevolved interactions between different parts of virus genomes and their encoded proteins must be kept intact for newly formed inter-species recombinants to have any chance of out-competing their parents. One category of coevolved interaction is that between contacting amino acids within the 3-D structures of folded proteins. Here we examine the distributions of recombination events across the genomes of a group of rampantly recombining plant viruses and find very good evidence that this class of interaction tends to be preserved amongst recombinant sequences sampled from nature. This indicates that selection against misfolded proteins strongly influences the survival of natural recombinants.

On the evolution of epistasis III: the haploid case with mutation

Whether interaction between genes is better represented by synergistic or antagonistic epistasis has been a focus of experimental research in bacterial population genetics. Our previous research on evolution of modifiers of epistasis in diploid systems has indicated that the strength of positive or negative epistasis should increase provided linkage disequilibrium is maintained. Here we study a modifier of epistasis in fitness between two loci in a haploid system. Epistasis is modified in the neighborhood of a mutation-selection balance. We show that when linkage in the three-locus system is tight, an increase in the frequency of a modifier allele that induces either more negative or more positive epistasis is possible. Epistasis here can be measured on either an additive or multiplicative scale.

Networks of genomic co-occurrence capture characteristics of human influenza A (H3N2) evolution

The recent availability of full genomic sequence data for a large number of human influenza A (H3N2) virus isolates over many years provides us an opportunity to analyze human influenza virus evolution by considering all gene segments simultaneously. However, such analysis requires development of new computational models that can capture the complex evolutionary features over the entire genome. By analyzing nucleotide co-occurrence over the entire genome of human H3N2 viruses, we have developed a network model to describe H3N2 virus evolutionary patterns and dynamics. The network model effectively captures the evolutionary antigenic features of H3N2 virus at the whole-genome level and accurately describes the complex evolutionary patterns between individual gene segments. Our analyses show that the co-occurring nucleotide modules apparently underpin the dynamics of human H3N2 evolution and that amino acid substitutions corresponding to nucleotide co-changes cluster preferentially in known antigenic regions of the viral HA. Therefore, our study demonstrates that nucleotide co-occurrence networks represent a powerful method for tracking influenza A virus evolution and that cooperative genomic interaction is a major force underlying influenza virus evolution.

An evolutionary-network model reveals stratified interactions in the V3 loop of the HIV-1 envelope

The third variable loop (V3) of the human immunodeficiency virus type 1 (HIV-1) envelope is a principal determinant of antibody neutralization and progression to AIDS. Although it is undoubtedly an important target for vaccine research, extensive genetic variation in V3 remains an obstacle to the development of an effective vaccine. Comparative methods that exploit the abundance of sequence data can detect interactions between residues of rapidly evolving proteins such as the HIV-1 envelope, revealing biological constraints on their variability. However, previous studies have relied implicitly on two biologically unrealistic assumptions: (1) that founder effects in the evolutionary history of the sequences can be ignored, and; (2) that statistical associations between residues occur exclusively in pairs. We show that comparative methods that neglect the evolutionary history of extant sequences are susceptible to a high rate of false positives (20%-40%). Therefore, we propose a new method to detect interactions that relaxes both of these assumptions. First, we reconstruct the evolutionary history of extant sequences by maximum likelihood, shifting focus from extant sequence variation to the underlying substitution events. Second, we analyze the joint distribution of substitution events among positions in the sequence as a Bayesian graphical model, in which each branch in the phylogeny is a unit of observation. We perform extensive validation of our models using both simulations and a control case of known interactions in HIV-1 protease, and apply this method to detect interactions within V3 from a sample of 1,154 HIV-1 envelope sequences. Our method greatly reduces the number of false positives due to founder effects, while capturing several higher-order interactions among V3 residues. By mapping these interactions to a structural model of the V3 loop, we find that the loop is stratified into distinct evolutionary clusters. We extend our model to detect interactions between the V3 and C4 domains of the HIV-1 envelope, and account for the uncertainty in mapping substitutions to the tree with a parametric bootstrap.

Understanding the evolutionary fate of finite populations: the dynamics of mutational effects

The most consistent result in more than two decades of experimental evolution is that the fitness of populations adapting to a constant environment does not increase indefinitely, but reaches a plateau. Using experimental evolution with bacteriophage, we show here that the converse is also true. In populations small enough such that drift overwhelms selection and causes fitness to decrease, fitness declines down to a plateau. We demonstrate theoretically that both of these phenomena must be due either to changes in the ratio of beneficial to deleterious mutations, the size of mutational effects, or both. We use mutation accumulation experiments and molecular data from experimental evolution to show that the most significant change in mutational effects is a drastic increase in the rate of beneficial mutation as fitness decreases. In contrast, the size of mutational effects changes little even as organismal fitness changes over several orders of magnitude. These findings have significant implications for the dynamics of adaptation.

In any population, two factors determine whether the average fitness of individuals will increase (adaptation) or decrease: the size of the population and the distribution of mutational effects (i.e., the relative rates and effect sizes of beneficial and deleterious mutations). Although it is relatively simple to get quantitative information on population size, it is much harder to gain insight into the distribution of mutational effects. Very little information exists on the relative rates of beneficial versus deleterious effects, on the shapes of mutational distributions, or on whether the distributions change over time. Thus, it remains difficult to even speculate whether a given population will adapt over time. Here, we use laboratory evolution of a bacterial virus to quantify the distribution of mutational effects. Our results reveal that the average impact of a mutation is approximately constant with respect to fitness, that most mutations have small effects, and that the rate of beneficial mutation depends on the fitness of the organism. Our study demonstrates the simple, but perhaps underappreciated fact that mutational effects are dynamic. It also proposes and tests an explicit model of adaptation in which organismal fitness specifies both the rate and distribution of deleterious and beneficial mutations, and it presents specific and testable predictions of the circumstances under which populations will adapt.

Experimental evolution of bacteriophage reveals that mutational effects are dynamic and dependent on genetic background, thus providing fundamental and testable insights into the nature of adaptation.

Distinguishing functional amino acid covariation from background linkage disequilibrium in HIV protease and reverse transcriptase

Correlated amino acid mutation analysis has been widely used to infer functional interactions between different sites in a protein. However, this analysis can be confounded by important phylogenetic effects broadly classifiable as background linkage disequilibrium (BLD). We have systematically separated the covariation induced by selective interactions between amino acids from background LD, using synonymous (S) vs. amino acid (A) mutations. Covariation between two amino acid mutations, (A,A), can be affected by selective interactions between amino acids, whereas covariation within (A,S) pairs or (S,S) pairs cannot. Our analysis of the pol gene — including the protease and the reverse transcriptase genes — in HIV reveals that (A,A) covariation levels are enormously higher than for either (A,S) or (S,S), and thus cannot be attributed to phylogenetic effects. The magnitude of these effects suggests that a large portion of (A,A) covariation in the HIV pol gene results from selective interactions. Inspection of the most prominent (A,A) interactions in the HIV pol gene showed that they are known sites of independently identified drug resistance mutations, and physically cluster around the drug binding site. Moreover, the specific set of (A,A) interaction pairs was reproducible in different drug treatment studies, and vanished in untreated HIV samples. The (S,S) covariation curves measured a low but detectable level of background LD in HIV.

A distinct group of hepacivirus/pestivirus-like internal ribosomal entry sites in members of diverse picornavirus genera: evidence for modular exchange of functional noncoding RNA elements by recombination

The 5' untranslated regions (UTRs) of the RNA genomes of Flaviviridae of the Hepacivirus and Pestivirus genera contain internal ribosomal entry sites (IRESs) that are unrelated to the two principal classes of IRESs of Picornaviridae. The mechanism of translation initiation on hepacivirus/pestivirus (HP) IRESs, which involves factor-independent binding to ribosomal 40S subunits, also differs fundamentally from initiation on these picornavirus IRESs. Ribosomal binding to HP IRESs requires conserved sequences that form a pseudoknot and the adjacent IIId and IIIe domains; analogous elements do not occur in the two principal groups of picornavirus IRESs. Here, comparative sequence analysis was used to identify a subset of picornaviruses from multiple genera that contain 5' UTR sequences with significant similarities to HP IRESs. They are avian encephalomyelitis virus, duck hepatitis virus 1, duck picornavirus, porcine teschovirus, porcine enterovirus 8, Seneca Valley virus, and simian picornavirus. Their 5' UTRs are predicted to form several structures, in some of which the peripheral elements differ from the corresponding HP IRES elements but in which the core pseudoknot, domain IIId, and domain IIIe elements are all closely related. These findings suggest that HP-like IRESs have been exchanged between unrelated virus families by recombination and support the hypothesis that RNA viruses consist of modular coding and noncoding elements that can exchange and evolve independently.

Phylogenetic evidence for deleterious mutation load in RNA viruses and its contribution to viral evolution

Populations of RNA viruses are often characterized by abundant genetic variation. However, the relative fitness of these mutations is largely unknown, although this information is central to our understanding of viral emergence, immune evasion, and drug resistance. Here we develop a phylogenetic method, based on the distribution of nonsynonymous and synonymous changes, to assess the relative fitness of polymorphisms in the structural genes of 143 RNA viruses. This reveals that a substantial proportion of the amino acid variation observed in natural populations of RNA viruses comprises transient deleterious mutations that are later purged by purifying selection, potentially limiting virus adaptability. We also demonstrate, for the first time, the existence of a relationship between amino acid variability and the phylogenetic distribution of polymorphisms. From this relationship, we propose an empirical threshold for the maximum viable deleterious mutation load in RNA viruses.