Clonal interference and the evolution of RNA viruses

Operator model for evolutionary dynamics

Drift, selection, and mutation are integral evolutionary factors. In this article, operator model is newly suggested to intuitively represent those evolutionary factors into mathematical operators, and to ultimately offer unconventional methodology for understanding evolutionary dynamics. To be specific, each of the drift, selection, and mutation was respectively interpreted as operator which in essence is a random matrix that acts upon the vector which contains population distribution information. The simulation results from the operator model coincided with the previous theoretical results for beneficial mutation accumulation rate in concurrent and successional regimes for asexually reproducing case. Furthermore, beneficial mutation accumulation in strong drift regime for asexually reproducing case was observed from the simulation while allowing the interactions of mutations with diverse selection coefficients. Lastly, methods to justify, reinforce, apply, and expand the operator model were discussed to scrutinize the implications of the model. With the operator model's unique characteristics, the model is expected to broaden perspective and to offer effective methodology for understanding the evolutionary process.

A dynamical limit to evolutionary adaptation

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis and find that under some circumstances, this can drive populations toward the boundary state, which can thus represent a long-term evolutionary attractor.

A dynamical limit to evolutionary adaptation

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis, and find that under some circumstances this can drive populations towards the boundary state, which can thus represent a long-term evolutionary attractor.

An improved algorithm for inferring mutational parameters from bar-seq evolution experiments

BACKGROUND

Genetic barcoding provides a high-throughput way to simultaneously track the frequencies of large numbers of competing and evolving microbial lineages. However making inferences about the nature of the evolution that is taking place remains a difficult task.

RESULTS

Here we describe an algorithm for the inference of fitness effects and establishment times of beneficial mutations from barcode sequencing data, which builds upon a Bayesian inference method by enforcing self-consistency between the population mean fitness and the individual effects of mutations within lineages. By testing our inference method on a simulation of 40,000 barcoded lineages evolving in serial batch culture, we find that this new method outperforms its predecessor, identifying more adaptive mutations and more accurately inferring their mutational parameters.

CONCLUSION

Our new algorithm is particularly suited to inference of mutational parameters when read depth is low. We have made Python code for our serial dilution evolution simulations, as well as both the old and new inference methods, available on GitHub ( https://github.com/FangfeiLi05/FitMut2 ), in the hope that it can find broader use by the microbial evolution community.

The emergence, spread and vanishing of a French SARS-CoV-2 variant exemplifies the fate of RNA virus epidemics and obeys the Mistigri rule

The nature and dynamics of mutations associated with the emergence, spread, and vanishing of SARS-CoV-2 variants causing successive waves are complex. We determined the kinetics of the most common French variant ("Marseille-4") for 10 months since its onset in July 2020. Here, we analyzed and classified into subvariants and lineages 7453 genomes obtained by next-generation sequencing. We identified two subvariants, Marseille-4A, which contains 22 different lineages of at least 50 genomes, and Marseille-4B. Their average lifetime was 4.1 ± 1.4 months, during which 4.1 ± 2.6 mutations accumulated. Growth rate was 0.079 ± 0.045, varying from 0.010 to 0.173. Most of the lineages exhibited a bell-shaped distribution. Several beneficial mutations at unpredicted sites initiated a new outbreak, while the accumulation of other mutations resulted in more viral heterogenicity, increased diversity and vanishing of the lineages. Marseille-4B emerged when the other Marseille-4 lineages vanished. Its ORF8 gene was knocked out by a stop codon, as reported in SARS-CoV-2 of mink and in the Alpha variant. This subvariant was associated with increased hospitalization and death rates, suggesting that ORF8 is a nonvirulence gene. We speculate that the observed heterogenicity of a lineage may predict the end of the outbreak.

The simple emergence of complex molecular function

At odds with a traditional view of molecular evolution that seeks a descent-with-modification relationship between functional sequences, new functions can emerge de novo with relative ease. At early times of molecular evolution, random polymers could have sufficed for the appearance of incipient chemical activity, while the cellular environment harbours a myriad of proto-functional molecules. The emergence of function is facilitated by several mechanisms intrinsic to molecular organization, such as redundant mapping of sequences into structures, phenotypic plasticity, modularity or cooperative associations between genomic sequences. It is the availability of niches in the molecular ecology that filters new potentially functional proposals. New phenotypes and subsequent levels of molecular complexity could be attained through combinatorial explorations of currently available molecular variants. Natural selection does the rest. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Microbial experimental evolution in a massively multiplexed and high-throughput era

Experimental evolution with microbial model systems has transformed our understanding of the basic rules underlying ecology and evolution. Experiments leveraging evolution as a central feature put evolutionary theories to the test, and modern sequencing and engineering tools then characterized the molecular basis of adaptation. As theory and experimentations refined our understanding of evolution, a need to increase throughput and experimental complexity has emerged. Here, we summarize recent technologies that have made high-throughput experiments practical and highlight studies that have capitalized on these tools, defining an exciting new era in microbial experimental evolution. Multiple research directions previously limited by experimental scale are now accessible for study and we believe applying evolutionary lessons from in vitro studies onto these applied settings has the potential for major innovations and discoveries across ecology and medicine.

Innate immune pathways act synergistically to constrain RNA virus evolution in Drosophila melanogaster

Host-pathogen interactions impose recurrent selective pressures that lead to constant adaptation and counter-adaptation in both competing species. Here, we sought to study this evolutionary arms-race and assessed the impact of the innate immune system on viral population diversity and evolution, using Drosophila melanogaster as model host and its natural pathogen Drosophila C virus (DCV). We isogenized eight fly genotypes generating animals defective for RNAi, Imd and Toll innate immune pathways as well as pathogen sensing and gut renewal pathways. Wild-type or mutant flies were then orally infected with DCV, and the virus was serially passaged ten times via reinfection in naïve flies. Viral population diversity was studied after each viral passage by high-throughput sequencing, and infection phenotypes were assessed at the beginning and at the end of the evolution experiment. We found that the absence of any of the various immune pathways studied increased viral genetic diversity while attenuating virulence. Strikingly, these effects were observed in a range of host factors described as having mainly antiviral or antibacterial functions. Together, our results indicate that the innate immune system as a whole, and not specific antiviral defense pathways in isolation, generally constrains viral diversity and evolution.

Population genetics of polymorphism and divergence in rapidly evolving populations

In rapidly evolving populations, numerous beneficial and deleterious mutations can arise and segregate within a population at the same time. In this regime, evolutionary dynamics cannot be analyzed using traditional population genetic approaches that assume that sites evolve independently. Instead, the dynamics of many loci must be analyzed simultaneously. Recent work has made progress by first analyzing the fitness variation within a population, and then studying how individual lineages interact with this traveling fitness wave. However, these "traveling wave" models have previously been restricted to extreme cases where selection on individual mutations is either much faster or much slower than the typical coalescent timescale Tc. In this work, we show how the traveling wave framework can be extended to intermediate regimes in which the scaled fitness effects of mutations (Tcs) are neither large nor small compared to one. This enables us to describe the dynamics of populations subject to a wide range of fitness effects, and in particular, in cases where it is not immediately clear which mutations are most important in shaping the dynamics and statistics of genetic diversity. We use this approach to derive new expressions for the fixation probabilities and site frequency spectra of mutations as a function of their scaled fitness effects, along with related results for the coalescent timescale Tc and the rate of adaptation or Muller's ratchet. We find that competition between linked mutations can have a dramatic impact on the proportions of neutral and selected polymorphisms, which is not simply summarized by the scaled selection coefficient Tcs. We conclude by discussing the implications of these results for population genetic inferences.

Horizontal gene transfer and adaptive evolution in bacteria

Horizontal gene transfer (HGT) is arguably the most conspicuous feature of bacterial evolution. Evidence for HGT is found in most bacterial genomes. Although HGT can considerably alter bacterial genomes, not all transfer events may be biologically significant and may instead represent the outcome of an incessant evolutionary process that only occasionally has a beneficial purpose. When adaptive transfers occur, HGT and positive selection may result in specific, detectable signatures in genomes, such as gene-specific sweeps or increased transfer rates for genes that are ecologically relevant. In this Review, we first discuss the various mechanisms whereby HGT occurs, how the genetic signatures shape patterns of genomic variation and the distinct bioinformatic algorithms developed to detect these patterns. We then discuss the evolutionary theory behind HGT and positive selection in bacteria, and discuss the approaches developed over the past decade to detect transferred DNA that may be involved in adaptation to new environments.

Intra- and inter-host evolution of H9N2 influenza A virus in Japanese quail

Influenza A viruses (IAVs) are constantly evolving. Crucial steps in the infection cycle, such as sialic acid (SA) receptor binding on the host cell surface, can either promote or hamper the emergence of new variants. We previously assessed the relative fitness in Japanese quail of H9N2 variant viruses differing at a single amino acid position, residue 216 in the hemagglutinin (HA) viral surface protein. This site is known to modulate SA recognition. Our prior study generated a valuable set of longitudinal samples from quail transmission groups where the inoculum comprised different mixed populations of HA 216 variant viruses. Here, we leveraged these samples to examine the evolutionary dynamics of viral populations within and between inoculated and naïve contact quails. We found that positive selection dominated HA gene evolution, but fixation of the fittest variant depended on the competition mixture. Analysis of the whole genome revealed further evidence of positive selection acting both within and between hosts. Positive selection drove fixation of variants in non-HA segments within inoculated and contact quails. Importantly, transmission bottlenecks were modulated by the molecular signature at HA 216, revealing viral receptor usage as a determinant of transmitted diversity. Overall, we show that selection strongly shaped the evolutionary dynamics within and between quails. These findings support the notion that selective processes act effectively on IAV populations in poultry hosts, facilitating rapid viral evolution in this ecological niche.

Hidden role of mutations in the evolutionary process

Mutations not only alter allele frequencies in a genetic pool but may also determine the fate of an evolutionary process. Here we study which allele fixes in a one-step, one-way model including the wild type and two adaptive mutations. We study the effect of the four basic evolutionary mechanisms-genetic drift, natural selection, mutation, and gene flow-on mutant fixation and its kinetics. Determining which allele is more likely to fix is not simply a question of comparing fitnesses and mutation rates. For instance, if the allele of interest is less fit than the other, then not only must it have a greater mutation rate, but also its mutation rate must exceed a specific threshold for it to prevail. We find exact expressions for such conditions. Our conclusions are based on the mathematical description of two extreme but important regimes, as well as on simulations.

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.

A Novel SARS-CoV-2 Viral Sequence Bioinformatic Pipeline Has Found Genetic Evidence That the Viral 3' Untranslated Region (UTR) Is Evolving and Generating Increased Viral Diversity

An unprecedented amount of SARS-CoV-2 sequencing has been performed, however, novel bioinformatic tools to cope with and process these large datasets is needed. Here, we have devised a bioinformatic pipeline that inputs SARS-CoV-2 genome sequencing in FASTA/FASTQ format and outputs a single Variant Calling Format file that can be processed to obtain variant annotations and perform downstream population genetic testing. As proof of concept, we have analyzed over 229,000 SARS-CoV-2 viral sequences up until November 30, 2020. We have identified over 39,000 variants worldwide with increased polymorphisms, spanning the ORF3a gene as well as the 3' untranslated (UTR) regions, specifically in the conserved stem loop region of SARS-CoV-2 which is accumulating greater observed viral diversity relative to chance variation. Our analysis pipeline has also discovered the existence of SARS-CoV-2 hypermutation with low frequency (less than in 2% of genomes) likely arising through host immune responses and not due to sequencing errors. Among annotated non-sense variants with a population frequency over 1%, recurrent inactivation of the ORF8 gene was found. This was found to be present in the newly identified B.1.1.7 SARS-CoV-2 lineage that originated in the United Kingdom. Almost all VOC-containing genomes possess one stop codon in ORF8 gene (Q27∗), however, 13% of these genomes also contains another stop codon (K68∗), suggesting that ORF8 loss does not interfere with SARS-CoV-2 spread and may play a role in its increased virulence. We have developed this computational pipeline to assist researchers in the rapid analysis and characterization of SARS-CoV-2 variation.

VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples

We introduce VERSO, a two-step framework for the characterization of viral evolution from sequencing data of viral genomes, which is an improvement on phylogenomic approaches for consensus sequences. VERSO exploits an efficient algorithmic strategy to return robust phylogenies from clonal variant profiles, also in conditions of sampling limitations. It then leverages variant frequency patterns to characterize the intra-host genomic diversity of samples, revealing undetected infection chains and pinpointing variants likely involved in homoplasies. On simulations, VERSO outperforms state-of-the-art tools for phylogenetic inference. Notably, the application to 6,726 amplicon and RNA sequencing samples refines the estimation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution, while co-occurrence patterns of minor variants unveil undetected infection paths, which are validated with contact tracing data. Finally, the analysis of SARS-CoV-2 mutational landscape uncovers a temporal increase of overall genomic diversity and highlights variants transiting from minor to clonal state and homoplastic variants, some of which fall on the spike gene. Available at: https://github.com/BIMIB-DISCo/VERSO.

High-Resolution Evolutionary Analysis of Within-Host Hepatitis C Virus Infection

BACKGROUND

Despite recent breakthroughs in treatment of hepatitis C virus (HCV) infection, we have limited understanding of how virus diversity generated within individuals impacts the evolution and spread of HCV variants at the population scale. Addressing this gap is important for identifying the main sources of disease transmission and evaluating the risk of drug-resistance mutations emerging and disseminating in a population.

METHODS

We have undertaken a high-resolution analysis of HCV within-host evolution from 4 individuals coinfected with human immunodeficiency virus 1 (HIV-1). We used long-read, deep-sequenced data of full-length HCV envelope glycoprotein, longitudinally sampled from acute to chronic HCV infection to investigate the underlying viral population and evolutionary dynamics.

RESULTS

We found statistical support for population structure maintaining the within-host HCV genetic diversity in 3 out of 4 individuals. We also report the first population genetic estimate of the within-host recombination rate for HCV (0.28 × 10-7 recombination/site/year), which is considerably lower than that estimated for HIV-1 and the overall nucleotide substitution rate estimated during HCV infection.

CONCLUSIONS

Our findings indicate that population structure and strong genetic linkage shapes within-host HCV evolutionary dynamics. These results will guide the future investigation of potential HCV drug resistance adaptation during infection, and at the population scale.

Estimating viral bottleneck sizes for FMDV transmission within and between hosts and implications for the rate of viral evolution

RNA viruses exist as populations of closely related genomes, characterized by a high diversity of low-frequency variants. As viral genomes from one population disperse to establish new sites of replication, the fate of these low-frequency variants depends to a large extent on the size of the founding population. Focusing on foot-and-mouth disease virus (FMDV) we conjecture that variants are more likely to be transmitted through wide bottlenecks, but more likely to approach fixation in new populations following narrow bottlenecks; therefore, the longer-term rate of accumulation of 'nearly neutral' variants at high frequencies is likely to be inversely related to the bottleneck size. We examine this conjecture in vivo by estimating bottleneck sizes relating 'parent' and 'daughter' populations observed at different scales ranging from within host to between host (within the same herd, and in different herds) using a previously established method. Within hosts, we find bottleneck sizes to range from 5 to 20 viral genomes between populations transmitted from the pharynx to the serum, and from 4 to 54 between serum and lesion populations. Between hosts, we find bottleneck sizes to range from 2 to 39, suggesting inter-host bottlenecks are of a similar size to intra-host bottlenecks. We establish a statistically significant negative relationship between the probability of genomic consensus level change and bottleneck size, and present a simple sampling model that captures this empirical relationship. We also present a novel in vitro experiment to investigate the impact of bottleneck size on the frequency of mutations within FMDV populations, demonstrate that variant frequency in a population increases more rapidly during small population passages, and provide evidence for positive selection during the passage of large populations.

Uncovering the rules of microbial community invasions

Understanding the ecological and evolutionary processes determining the outcome of biological invasions has been the subject of decades of research with most work focusing on macro-organisms. In the context of microbes, invasions remain poorly understood despite being increasingly recognized as important. To shed light on the factors affecting the success of microbial community invasions, we perform simulations using an individual-based nearly neutral model that combines ecological and evolutionary processes. Our simulations qualitatively recreate many empirical patterns and lead to a description of five general rules of invasion: (1) larger communities evolve better invaders and better defenders; (2) where invader and resident fitness difference is large, invasion success is essentially deterministic; (3) propagule pressure contributes to invasion success, if and only if, invaders and residents are competitively similar; (4) increasing the diversity of invaders has a similar effect to increasing the number of invaders; and (5) more diverse communities more successfully resist invasion.

Clonal interference can cause wavelet-like oscillations of multilocus linkage disequilibrium

Within-host adaptation of pathogens such as human immunodeficiency virus (HIV) often occurs at more than two loci. Multiple beneficial mutations may arise simultaneously on different genetic backgrounds and interfere, affecting each other's fixation trajectories. Here, we explore how these evolutionary dynamics are mirrored in multilocus linkage disequilibrium (MLD), a measure of multi-way associations between alleles. In the parameter regime corresponding to HIV, we show that deterministic early infection models induce MLD to oscillate over time in a wavelet-like fashion. We find that the frequency of these oscillations is proportional to the rate of adaptation. This signature is robust to drift, but can be eroded by high variation in fitness effects of beneficial mutations. Our findings suggest that MLD oscillations could be used as a signature of interference among multiple equally advantageous mutations and may aid the interpretation of MLD in data.

Directed Evolution of a Mycobacteriophage

Bacteriophages represent an alternative strategy to combat pathogenic bacteria. Currently, Mycobacterium tuberculosis infections constitute a major public health problem due to extensive antibiotic resistance in some strains. Using a non-pathogenic species of the same genus as an experimental model, Mycobacterium smegmatis, here we have set up a basic methodology for mycobacteriophage growth and we have explored directed evolution as a tool for increasing phage infectivity and lytic activity. We demonstrate mycobacteriophage adaptation to its host under different conditions. Directed evolution could be used for the development of future phage therapy applications against mycobacteria.

Evolthon: A community endeavor to evolve lab evolution

In experimental evolution, scientists evolve organisms in the lab, typically by challenging them to new environmental conditions. How best to evolve a desired trait? Should the challenge be applied abruptly, gradually, periodically, sporadically? Should one apply chemical mutagenesis, and do strains with high innate mutation rate evolve faster? What are ideal population sizes of evolving populations? There are endless strategies, beyond those that can be exposed by individual labs. We therefore arranged a community challenge, Evolthon, in which students and scientists from different labs were asked to evolve Escherichia coli or Saccharomyces cerevisiae for an abiotic stress—low temperature. About 30 participants from around the world explored diverse environmental and genetic regimes of evolution. After a period of evolution in each lab, all strains of each species were competed with one another. In yeast, the most successful strategies were those that used mating, underscoring the importance of sex in evolution. In bacteria, the fittest strain used a strategy based on exploration of different mutation rates. Different strategies displayed variable levels of performance and stability across additional challenges and conditions. This study therefore uncovers principles of effective experimental evolutionary regimens and might prove useful also for biotechnological developments of new strains and for understanding natural strategies in evolutionary arms races between species. Evolthon constitutes a model for community-based scientific exploration that encourages creativity and cooperation.

This Community Page article describes Evolthon; a first-of-its-kind community-based effort, involving about 30 participant labs around the world, aiming to explore the best strategy for evolving microorganisms to cope with an environmental challenge.

Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis

Some of the most common infectious diseases are caused by bacteria that naturally colonise humans asymptomatically. Combating these opportunistic pathogens requires an understanding of the traits that differentiate infecting strains from harmless relatives. Staphylococcus epidermidis is carried asymptomatically on the skin and mucous membranes of virtually all humans but is a major cause of nosocomial infection associated with invasive procedures. Here we address the underlying evolutionary mechanisms of opportunistic pathogenicity by combining pangenome-wide association studies and laboratory microbiology to compare S. epidermidis from bloodstream and wound infections and asymptomatic carriage. We identify 61 genes containing infection-associated genetic elements (k-mers) that correlate with in vitro variation in known pathogenicity traits (biofilm formation, cell toxicity, interleukin-8 production, methicillin resistance). Horizontal gene transfer spreads these elements, allowing divergent clones to cause infection. Finally, Random Forest model prediction of disease status (carriage vs. infection) identifies pathogenicity elements in 415 S. epidermidis isolates with 80% accuracy, demonstrating the potential for identifying risk genotypes pre-operatively.

Population genomics of bacterial host adaptation

Some bacteria can transfer to new host species, and this poses a risk to human health. Indeed, an estimated 60% of all human pathogens have originated from other animal species. Similarly, human-to-animal transitions are recognized as a major threat to sustainable livestock production, and emerging pathogens impose an increasing burden on crop yield and global food security. Recent advances in high-throughput sequencing technologies have enabled comparative genomic analyses of bacterial populations from multiple hosts. Such studies are providing new insights into the evolutionary processes that underpin the establishment of bacteria in new host niches. A better understanding of the genetic and mechanistic basis for bacterial host adaptation may reveal novel targets for controlling infection or inform the design of approaches to limit the emergence of new pathogens.

Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution

In experimental evolution, laboratory-controlled conditions select for the adaptation of species, which can be monitored in real time. Despite the current popularity of such experiments, nature's most pervasive biological force was long believed to be observable only on time scales that transcend a researcher's life-span, and studying evolution by natural selection was therefore carried out solely by comparative means. Eventually, microorganisms' propensity for fast evolutionary changes proved us wrong, displaying strong evolutionary adaptations over a limited time, nowadays massively exploited in laboratory evolution experiments. Here, we formulate a guide to experimental evolution with microorganisms, explaining experimental design and discussing evolutionary dynamics and outcomes and how it is used to assess ecoevolutionary theories, improve industrially important traits, and untangle complex phenotypes. Specifically, we give a comprehensive overview of the setups used in experimental evolution. Additionally, we address population dynamics and genetic or phenotypic diversity during evolution experiments and expand upon contributing factors, such as epistasis and the consequences of (a)sexual reproduction. Dynamics and outcomes of evolution are most profoundly affected by the spatiotemporal nature of the selective environment, where changing environments might lead to generalists and structured environments could foster diversity, aided by, for example, clonal interference and negative frequency-dependent selection. We conclude with future perspectives, with an emphasis on possibilities offered by fast-paced technological progress. This work is meant to serve as an introduction to those new to the field of experimental evolution, as a guide to the budding experimentalist, and as a reference work to the seasoned expert.

Experimental Studies of Evolutionary Dynamics in Microbes

Evolutionary dynamics in laboratory microbial evolution experiments can be surprisingly complex. In the past two decades, observations of these dynamics have challenged simple models of adaptation and have shown that clonal interference, hitchhiking, ecological diversification, and contingency are widespread. In recent years, advances in high-throughput strain maintenance and phenotypic assays, the dramatically reduced cost of genome sequencing, and emerging methods for lineage barcoding have made it possible to observe evolutionary dynamics at unprecedented resolution. These new methods can now begin to provide detailed measurements of key aspects of fitness landscapes and of evolutionary outcomes across a range of systems. These measurements can highlight challenges to existing theoretical models and guide new theoretical work towards the complications that are most widely important.

Challenges in dengue research: A computational perspective

The dengue virus is now the most widespread arbovirus affecting human populations, causing significant economic and social impact in South America and South-East Asia. Increasing urbanization and globalization, coupled with insufficient resources for control, misguided policies or lack of political will, and expansion of its mosquito vectors are some of the reasons why interventions have so far failed to curb this major public health problem. Computational approaches have elucidated on dengue's population dynamics with the aim to provide not only a better understanding of the evolution and epidemiology of the virus but also robust intervention strategies. It is clear, however, that these have been insufficient to address key aspects of dengue's biology, many of which will play a crucial role for the success of future control programmes, including vaccination. Within a multiscale perspective on this biological system, with the aim of linking evolutionary, ecological and epidemiological thinking, as well as to expand on classic modelling assumptions, we here propose, discuss and exemplify a few major computational avenues-real-time computational analysis of genetic data, phylodynamic modelling frameworks, within-host model frameworks and GPU-accelerated computing. We argue that these emerging approaches should offer valuable research opportunities over the coming years, as previously applied and demonstrated in the context of other pathogens.

Influenza A virus hemagglutinin glycosylation compensates for antibody escape fitness costs

Rapid antigenic evolution enables the persistence of seasonal influenza A and B viruses in human populations despite widespread herd immunity. Understanding viral mechanisms that enable antigenic evolution is critical for designing durable vaccines and therapeutics. Here, we utilize the primerID method of error-correcting viral population sequencing to reveal an unexpected role for hemagglutinin (HA) glycosylation in compensating for fitness defects resulting from escape from anti-HA neutralizing antibodies. Antibody-free propagation following antigenic escape rapidly selected viruses with mutations that modulated receptor binding avidity through the addition of N-linked glycans to the HA globular domain. These findings expand our understanding of the viral mechanisms that maintain fitness during antigenic evolution to include glycan addition, and highlight the immense power of high-definition virus population sequencing to reveal novel viral adaptive mechanisms.

Sexual recombination and increased mutation rate expedite evolution of Escherichia coli in varied fitness landscapes

Sexual recombination and mutation rate are theorized to play different roles in adaptive evolution depending on the fitness landscape; however, direct experimental support is limited. Here we examine how these factors affect the rate of adaptation utilizing a “genderless” strain of Escherichia coli capable of continuous in situ sexual recombination. The results show that the populations with increased mutation rate, and capable of sexual recombination, outperform all the other populations. We further characterize two sexual and two asexual populations with increased mutation rate and observe maintenance of beneficial mutations in the sexual populations through mutational sweeps. Furthermore, we experimentally identify the molecular signature of a mating event within the sexual population that combines two beneficial mutations to generate a fitter progeny; this evidence suggests that the recombination event partially alleviates clonal interference. We present additional data suggesting that stochasticity plays an important role in the combinations of mutations observed.

Sexual recombination and mutation rate may play different roles in adaptive evolution depending on the fitness landscape. Here, Peabody et al. examine how the two factors affect the rate of adaptation of an E. coli strain capable of sexual recombination, under different conditions during experimental evolution.

Rapid adaptation in large populations with very rare sex: Scalings and spontaneous oscillations

Genetic exchange in microbes and other facultative sexuals can be rare enough that evolution is almost entirely asexual and populations almost clonal. But the benefits of genetic exchange depend crucially on the diversity of genotypes in a population. How very rare recombination together with the accumulation of new mutations shapes the diversity of large populations and gives rise to faster adaptation is still poorly understood. This paper analyzes a particularly simple model: organisms with two asexual chromosomes that can reassort during rare matings that occur at a rate r. The speed of adaptation for large population sizes, N, is found to depend on the ratio ∼log(Nr)∕log(N). For larger populations, the r needed to yield the same speed decreases as a power of N. Remarkably, the population undergoes spontaneous oscillations alternating between phases when the fittest individuals are created by mutation and when they are created by reassortment, which - in contrast to conventional regimes - decreases the diversity. Between the two phases, the mean fitness jumps rapidly. The oscillatory dynamics and the strong fluctuations this induces have implications for the diversity and coalescent statistics. The results are potentially applicable to large microbial populations, especially viruses that have a small number of chromosomes. Some of the key features may be more broadly applicable for large populations with other types of rare genetic exchange.

Pathogen population bottlenecks and adaptive landscapes: overcoming the barriers to disease emergence

Emerging diseases are a major challenge to public health. Revealing the evolutionary processes that allow novel pathogens to adapt to new hosts, also the potential barriers to host adaptation, is central to understanding the drivers of disease emergence. In particular, it is unclear how the genetics and ecology of pathogens interact to shape the likelihood of successful cross-species transmission. To better understand the determinants of host adaptation and emergence, we modelled key aspects of pathogen evolutionary dynamics at both intra- and inter-host scales, using parameter values similar to those observed in influenza virus. We considered the possibility of acquiring the necessary host adaptive mutations both before ('off-the-shelf' emergence) and after ('tailor-made' emergence) a virus is transmitted from a donor to a new recipient species. Under both scenarios, population bottlenecks at inter-host transmission act as a major barrier to host adaptation, greatly limiting the number of adaptive mutations that are able to cross the species barrier. In addition, virus emergence is hindered if the fitness valley between the donor and recipient hosts is either too steep or too shallow. Overall, our results reveal where in evolutionary parameter space a virus could adapt to and become transmissible in a new species.

Within-host Evolution of Segments Ratio for the Tripartite Genome of Alfalfa Mosaic Virus

The existence of multipartite viruses is an intriguing mystery in evolutionary virology. Several hypotheses suggest benefits that should outweigh the costs of a reduced transmission efficiency and of segregation of coadapted genes associated with encapsidating each segment into a different particle. Advantages range from increasing genome size despite high mutation rates, faster replication, more efficient selection resulting from reassortment during mixed infections, better regulation of gene expression, or enhanced virion stability and cell-to-cell movement. However, support for these hypotheses is scarce. Here we report experiments testing whether an evolutionary stable equilibrium exists for the three genomic RNAs of Alfalfa mosaic virus (AMV). Starting infections with different segment combinations, we found that the relative abundance of each segment evolves towards a constant ratio. Population genetic analyses show that the segment ratio at this equilibrium is determined by frequency-dependent selection. Replication of RNAs 1 and 2 was coupled and collaborative, whereas the replication of RNA 3 interfered with the replication of the other two. We found that the equilibrium solution is slightly different for the total amounts of RNA produced and encapsidated, suggesting that competition exists between all RNAs during encapsidation. Finally, we found that the observed equilibrium appears to be host-species dependent.

Plasmid and clonal interference during post horizontal gene transfer evolution

Plasmids are nucleic acid molecules that can drive their own replication in a living cell. They can be transmitted horizontally and can thrive in the host cell to high-copy numbers. Plasmid replication and gene expression consume cellular resources and cells carrying plasmids incur fitness costs. But many plasmids carry genes that can be beneficial under certain conditions, allowing the cell to endure in the presence of antibiotics, toxins, competitors or parasites. Horizontal transfer of plasmid-encoded genes can thus instantaneously confer differential adaptation to local or transient selection conditions. This conflict between cellular fitness and plasmid spread sets the scene for multilevel selection processes. We have engineered a system to study the short-term evolutionary impact of different synonymous versions of a plasmid-encoded antibiotic resistance gene. Applying experimental evolution under different selection conditions and deep sequencing allowed us to show rapid local adaptation to the presence of antibiotic and to the specific version of the resistance gene transferred. We describe the presence of clonal interference at two different levels: at the within-cell level, because a single cell can carry several plasmids, and at the between-cell level, because a bacterial population may contain several clones carrying different plasmids and displaying different fitness in the presence/absence of antibiotic. Understanding the within-cell and between-cell dynamics of plasmids after horizontal gene transfer is essential to unravel the dense network of mobile elements underlying the worldwide threat to public health of antibiotic resistance.

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.

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.

Competition along trajectories governs adaptation rates towards antimicrobial resistance

The increasing availability of genotype-phenotype maps for different combinations of mutations has empowered evolutionary biologists with the tools to interrogate the predictability of adaptive evolution, especially in the context of the evolution of antimicrobial resistance. Large microbial populations are known to generate competing beneficial mutations, but determining how these mutations contribute to the adaptive trajectories that are most likely to be followed remains a challenge. Despite a recognition that there may also be competition between successive alleles on the same trajectory, prior studies have not fully considered how this impacts adaptation rates along, or likelihood of following, individual trajectories. Here, we develop a metric that quantifies the competition between successive alleles along adaptive trajectories and show how this competition largely governs the rate of evolution in simulations on empirical fitness landscapes for proteins involved in drug resistance in two species of malaria (Plasmodium falciparum and P. vivax). Our findings reveal that a trajectory with a larger-than-average initial fitness increase may have smaller fitness increases in later steps, which slows adaptation. In some circumstances, these trajectories may be outcompeted by alleles on faster alternative trajectories that are being explored simultaneously. The ability to predict adaptation rates along accessible trajectories has implications for efforts to manage antimicrobial resistance in real-world settings and for the broader intellectual pursuit of predictive evolution in complex adaptive fitness landscapes for a variety of application domains.

Exceptional Heterogeneity in Viral Evolutionary Dynamics Characterises Chronic Hepatitis C Virus Infection

The treatment of HCV infection has seen significant progress, particularly since the approval of new direct-acting antiviral drugs. However these clinical achievements have been made despite an incomplete understanding of HCV replication and within-host evolution, especially compared with HIV-1. Here, we undertake a comprehensive analysis of HCV within-host evolution during chronic infection by investigating over 4000 viral sequences sampled longitudinally from 15 HCV-infected patients. We compare our HCV results to those from a well-studied HIV-1 cohort, revealing key differences in the evolutionary behaviour of these two chronic-infecting pathogens. Notably, we find an exceptional level of heterogeneity in the molecular evolution of HCV, both within and among infected individuals. Furthermore, these patterns are associated with the long-term maintenance of viral lineages within patients, which fluctuate in relative frequency in peripheral blood. Together, our findings demonstrate that HCV replication behavior is complex and likely comprises multiple viral subpopulations with distinct evolutionary dynamics. The presence of a structured viral population can explain apparent paradoxes in chronic HCV infection, such as rapid fluctuations in viral diversity and the reappearance of viral strains years after their initial detection.

Our knowledge of HCV within-host evolution is substantially limited, which is surprising given that highly successful therapies against the virus have been developed. Key aspects of HCV infection, such as rapid fluctuations in viral diversity and the reappearance of viral strains years after their initial detection, remain unexplained. To better understand this problem, we analyse viral sequences from HCV-infected patients sampled over several years. Our findings suggest that the replication dynamics during chronic HCV infection are distinct from those of HIV-1, and dominated by the co-circulation of multiple viral strains. Although a major difference between the two chronic-infecting viruses is the level of recombination, our results indicate that HCV within-host evolution is most likely to be shaped by a structured viral population. Crucially, our study shows that HCV sampled from blood does not fully represent the within-host viral population at that time. This may have important implications for HCV treatment, especially in patients that have seemingly cleared the virus, as well as for molecular epidemiology studies investigating HCV transmission.

Constrained evolvability of interferon suppression in an RNA virus

Innate immunity responses controlled by interferon (IFN) are believed to constitute a major selective pressure shaping viral evolution. Viruses encode a variety of IFN suppressors, but these are often multifunctional proteins that also play essential roles in other steps of the viral infection cycle, possibly limiting their evolvability. Here, we experimentally evolved a vesicular stomatitis virus (VSV) mutant carrying a defect in the matrix protein (M∆51) that abolishes IFN suppression and that has been previously used in the context of oncolytic virotherapy. Serial transfers of this virus in normal, IFN-secreting cells led to a modest recovery of IFN blocking capacity and to weak increases in viral fitness. Full-genome ultra-deep sequencing and phenotypic analysis of population variants revealed that the anti-IFN function of the matrix protein was not restored, and that the Mdelta51 defect was instead compensated by changes in the viral phosphoprotein. We also show that adaptation to IFN-secreting cells can be driven by the selection of fast-growing viruses with no IFN suppression capacity, and that these population variants can be trans-complemented by other, IFN-suppressing variants. Our results thus suggest that virus-virus interactions and alternative strategies of innate immunity evasion can determine the evolution of IFN suppression in a virus.

Exploiting the Adaptation Dynamics to Predict the Distribution of Beneficial Fitness Effects

Adaptation of asexual populations is driven by beneficial mutations and therefore the dynamics of this process, besides other factors, depends on the distribution of beneficial fitness effects. It is known that on uncorrelated fitness landscapes, this distribution can only be of three types: truncated, exponential and power law. We performed extensive stochastic simulations to study the adaptation dynamics on rugged fitness landscapes, and identified two quantities that can be used to distinguish the underlying distribution of beneficial fitness effects. The first quantity studied here is the fitness difference between successive mutations that spread in the population, which is found to decrease in the case of truncated distributions, remains nearly a constant for exponentially decaying distributions and increases when the fitness distribution decays as a power law. The second quantity of interest, namely, the rate of change of fitness with time also shows quantitatively different behaviour for different beneficial fitness distributions. The patterns displayed by the two aforementioned quantities are found to hold good for both low and high mutation rates. We discuss how these patterns can be exploited to determine the distribution of beneficial fitness effects in microbial experiments.

Sex speeds adaptation by altering the dynamics of molecular evolution

Sex and recombination are pervasive throughout nature despite their substantial costs¹. Understanding the evolutionary forces that maintain these phenomena is a central challenge in biology^2,3. One longstanding hypothesis argues that sex is beneficial because recombination speeds adaptation⁴. Theory has proposed a number of distinct population genetic mechanisms that could underlie this advantage. For example, sex can promote the fixation of beneficial mutations either by alleviating interference competition (the Fisher-Muller effect)^5,6 or by separating them from deleterious load (the ruby in the rubbish effect)^7,8. Previous experiments confirm that sex can increase the rate of adaptation^9–17, but these studies did not observe the evolutionary dynamics that drive this effect at the genomic level. Here, we present the first comparison between the sequence-level dynamics of adaptation in experimental sexual and asexual populations, which allows us to identify the specific mechanisms by which sex speeds adaptation. We find that sex alters the molecular signatures of evolution by changing the spectrum of mutations that fix, and confirm theoretical predictions that it does so by alleviating clonal interference. We also show that substantially deleterious mutations hitchhike to fixation in adapting asexual populations. In contrast, recombination prevents such mutations from fixing. Our results demonstrate that sex both speeds adaptation and alters its molecular signature by allowing natural selection to more efficiently sort beneficial from deleterious mutations.

Experimental evolution in biofilm populations

Biofilms are a major form of microbial life in which cells form dense surface associated communities that can persist for many generations. The long-life of biofilm communities means that they can be strongly shaped by evolutionary processes. Here, we review the experimental study of evolution in biofilm communities. We first provide an overview of the different experimental models used to study biofilm evolution and their associated advantages and disadvantages. We then illustrate the vast amount of diversification observed during biofilm evolution, and we discuss (i) potential ecological and evolutionary processes behind the observed diversification, (ii) recent insights into the genetics of adaptive diversification, (iii) the striking degree of parallelism between evolution experiments and real-life biofilms and (iv) potential consequences of diversification. In the second part, we discuss the insights provided by evolution experiments in how biofilm growth and structure can promote cooperative phenotypes. Overall, our analysis points to an important role of biofilm diversification and cooperation in bacterial survival and productivity. Deeper understanding of both processes is of key importance to design improved antimicrobial strategies and diagnostic techniques.

This review paper provides an overview of (i) the different experimental models used to study biofilm evolution, (ii) the vast amount of diversification observed during biofilm evolution (including potential causes and consequences) and (iii) recent insights in how growth in biofilms can lead to the evolution of cooperative phenotypes.

Investigating the Consequences of Interference between Multiple CD8+ T Cell Escape Mutations in Early HIV Infection

During early human immunodeficiency virus (HIV) infection multiple CD8⁺ T cell responses are elicited almost simultaneously. These responses exert strong selective pressures on different parts of HIV’s genome, and select for mutations that escape recognition and are thus beneficial to the virus. Some studies reveal that the later these escape mutations emerge, the more slowly they go to fixation. This pattern of escape rate decrease(ERD) can arise by distinct mechanisms. In particular, in large populations with high beneficial mutation rates interference among different escape strains –an effect that can emerge in evolution with asexual reproduction and results in delayed fixation times of beneficial mutations compared to sexual reproduction– could significantly impact the escape rates of mutations. In this paper, we investigated how interference between these concurrent escape mutations affects their escape rates in systems with multiple epitopes, and whether it could be a source of the ERD pattern. To address these issues, we developed a multilocus Wright-Fisher model of HIV dynamics with selection, mutation and recombination, serving as a null-model for interference. We also derived an interference-free null model assuming initial neutral evolution before immune response elicitation. We found that interference between several equally selectively advantageous mutations can generate the observed ERD pattern. We also found that the number of loci, as well as recombination rates substantially affect ERD. These effects can be explained by the underexponential decline of escape rates over time. Lastly, we found that the observed ERD pattern in HIV infected individuals is consistent with both independent, interference-free mutations as well as interference effects. Our results confirm that interference effects should be considered when analyzing HIV escape mutations. The challenge in estimating escape rates and mutation-associated selective coefficients posed by interference effects cannot simply be overcome by improved sampling frequencies or sizes. This problem is a consequence of the fundamental shortcomings of current estimation techniques under interference regimes. Hence, accounting for the stochastic nature of competition between mutations demands novel estimation methodologies based on the analysis of HIV strains, rather than mutation frequencies.

Within-host evolution of human immunodeficiency virus (HIV) is shaped by strong immune responses mounted against the virus. Multiple CD8⁺ T cell populations, each recognizing a specific part of an HIV protein, simultaneously suppress HIV growth. Escape mutations that arise in HIV genome regions coding for these virion protein parts, impair CD8⁺ T cell recognition and are consequently strongly selected. The emergence and rise of these escape mutations exhibits an intriguing temporal pattern: the earlier an escape mutation arises, the faster it goes to fixation. This pattern is termed escape rate decrease (ERD). In this paper, we tested computationally whether interference, i.e. the coexistence of multiple genetically distinct HIV strains engaged in competitive interaction within the host, could be a possible source of ERD. As an alternative, we also mathematically derived the temporal pattern of escapes under interference-free conditions, and compared this with data. We found that interference between multiple beneficial mutations could generate ERD. However, ERD does not imply the presence of interference. Thus, more detailed data is required to unambiguously determine whether interference effects influence ERD generation. Nevertheless, interference should be considered when studying the within-host evolution of HIV. Ignoring its effects on population dynamics can severely underestimate the protective capacity of CD8⁺ T cells.

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

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

Population genomics of intrapatient HIV-1 evolution

Many microbial populations rapidly adapt to changing environments with multiple variants competing for survival. To quantify such complex evolutionary dynamics in vivo, time resolved and genome wide data including rare variants are essential. We performed whole-genome deep sequencing of HIV-1 populations in 9 untreated patients, with 6-12 longitudinal samples per patient spanning 5-8 years of infection. The data can be accessed and explored via an interactive web application. We show that patterns of minor diversity are reproducible between patients and mirror global HIV-1 diversity, suggesting a universal landscape of fitness costs that control diversity. Reversions towards the ancestral HIV-1 sequence are observed throughout infection and account for almost one third of all sequence changes. Reversion rates depend strongly on conservation. Frequent recombination limits linkage disequilibrium to about 100 bp in most of the genome, but strong hitch-hiking due to short range linkage limits diversity.

On the relative roles of background selection and genetic hitchhiking in shaping human cytomegalovirus genetic diversity

A central focus of population genetics has been examining the contribution of selective and neutral processes in shaping patterns of intraspecies diversity. In terms of selection specifically, surveys of higher organisms have shown considerable variation in the relative contributions of background selection and genetic hitchhiking in shaping the distribution of polymorphisms, although these analyses have rarely been extended to bacteria and viruses. Here, we study the evolution of a ubiquitous, viral pathogen, human cytomegalovirus (HCMV), by analysing the relationship among intraspecies diversity, interspecies divergence and rates of recombination. We show that there is a strong correlation between diversity and divergence, consistent with expectations of neutral evolution. However, after correcting for divergence, there remains a significant correlation between intraspecies diversity and recombination rates, with additional analyses suggesting that this correlation is largely due to the effects of background selection. In addition, a small number of loci, centred on long noncoding RNAs, also show evidence of selective sweeps. These data suggest that HCMV evolution is dominated by neutral mechanisms as well as background selection, expanding our understanding of linked selection to a novel class of organisms.

Selective Sweeps and Parallel Pathoadaptation Drive Pseudomonas aeruginosa Evolution in the Cystic Fibrosis Lung

Pulmonary infections caused by Pseudomonas aeruginosa are a recalcitrant problem in cystic fibrosis (CF) patients. While the clinical implications and long-term evolutionary patterns of these infections are well studied, we know little about the short-term population dynamics that enable this pathogen to persist despite aggressive antimicrobial therapy. Here, we describe a short-term population genomic analysis of 233 P. aeruginosa isolates collected from 12 sputum specimens obtained over a 1-year period from a single patient. Whole-genome sequencing and antimicrobial susceptibility profiling identified the expansion of two clonal lineages. The first lineage originated from the coalescence of the entire sample less than 3 years before the end of the study and gave rise to a high-diversity ancestral population. The second expansion occurred 2 years later and gave rise to a derived population with a strong signal of positive selection. These events show characteristics consistent with recurrent selective sweeps. While we cannot identify the specific mutations responsible for the origins of the clonal lineages, we find that the majority of mutations occur in loci previously associated with virulence and resistance. Additionally, approximately one-third of all mutations occur in loci that are mutated multiple times, highlighting the importance of parallel pathoadaptation. One such locus is the gene encoding penicillin-binding protein 3, which received three independent mutations. Our functional analysis of these alleles shows that they provide differential fitness benefits dependent on the antibiotic under selection. These data reveal that bacterial populations can undergo extensive and dramatic changes that are not revealed by lower-resolution analyses.

IMPORTANCE

Pseudomonas aeruginosa is a bacterial opportunistic pathogen responsible for significant morbidity and mortality in cystic fibrosis (CF) patients. Once it has colonized the lung in CF, it is highly resilient and rarely eradicated. This study presents a deep sampling examination of the fine-scale evolutionary dynamics of P. aeruginosa in the lungs of a chronically infected CF patient. We show that diversity of P. aeruginosa is driven by recurrent clonal emergence and expansion within this patient and identify potential adaptive variants associated with these events. This high-resolution sequencing strategy thus reveals important intraspecies dynamics that explain a clinically important phenomenon not evident at a lower-resolution analysis of community structure.

What do mathematical models tell us about killing rates during HIV-1 infection?

Over the past few decades the extent to which cytotoxic T lymphocytes (CTLs) control human immunodeficiency virus (HIV) replication has been studied extensively, yet their role and mode of action remain controversial. In some studies, CTLs were found to kill a large fraction of the productively infected cells relative to the viral cytopathicity, whereas in others CTLs were suggested to kill only a small fraction of infected cells. In this review, we compile published estimates of CTL-mediated death rates, and examine whether these studies permit determining the rate at which CTLs kill HIV-1 infected cells. We highlight potential misinterpretations of the CTL-killing rates from the escape rates of mutants, and from perturbations of the steady state viral load during chronic infection. Our major conclusion is that CTL-mediated killing rates remain unknown. But contrary to current consensus, we argue that killing rates higher than one per day are perfectly consistent with the experimental data, which would imply that the majority of the productively infected cells could still die from CTL-mediated killing rather than from viral cytopathicity.

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

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