Eco-evolutionary dynamics of adapting pathogens and host immunity

As pathogens spread in a population of hosts, immunity is built up, and the pool of susceptible individuals are depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host's immune systems adapt, and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high-frequency variants observed for influenza viruses.

Evolution of evolvability in rapidly adapting populations

Mutations can alter the short-term fitness of an organism, as well as the rates and benefits of future mutations. While numerous examples of these evolvability modifiers have been observed in rapidly adapting microbial populations, existing theory struggles to predict when they will be favoured by natural selection. Here we develop a mathematical framework for predicting the fates of genetic variants that modify the rates and benefits of future mutations in linked genomic regions. We derive analytical expressions showing how the fixation probabilities of these variants depend on the size of the population and the diversity of competing mutations. We find that competition between linked mutations can dramatically enhance selection for modifiers that increase the benefits of future mutations, even when they impose a strong direct cost on fitness. However, we also find that modest direct benefits can be sufficient to drive evolutionary dead ends to fixation. Our results suggest that subtle differences in evolvability could play an important role in shaping the long-term success of genetic variants in rapidly evolving microbial populations.

Genome-wide differentiation corresponds to climatic niches in two species of lichen-forming fungi

Lichens can withstand fluctuating environmental conditions such as hydration-desiccation cycles. Many species distribute across climate zones, suggesting population-level adaptations to conditions such as freezing and drought. Here, we aim to understand how climate affects population genomic patterns in lichenized fungi. We analysed population structure along elevational gradients in closely related Umbilicaria phaea (North American; two gradients) and Umbilicaria pustulata (European; three gradients). All gradients showed clear genomic breaks splitting populations into low-elevation (Mediterranean zone) and high-elevation (cold temperate zone). A total of 3301 SNPs in U. phaea and 138 SNPs in U. pustulata were driven to fixation between the two ends of the gradients. The difference between the species is likely due to differences in recombination rate: the sexually reproducing U. phaea has a higher recombination rate than the primarily asexually reproducing U. pustulata. Cline analysis revealed allele frequency transitions along all gradients at approximately 0°C, coinciding with the transition between the Mediterranean and cold temperate zones, suggesting freezing is a strong driver of population differentiation. Genomic scans further confirmed temperature-related selection targets. Both species showed similar differentiation patterns overall, but different selected alleles indicate convergent adaptation to freezing. Our results enrich our knowledge of fungal genomic functions related to temperature and climate, fungal population genomics, and species responses to environmental heterogeneity.

Continuously fluctuating selection reveals fine granularity of adaptation

Temporally fluctuating environmental conditions are a ubiquitous feature of natural habitats. Yet, how finely natural populations adaptively track fluctuating selection pressures via shifts in standing genetic variation is unknown1,2. Here we generated genome-wide allele frequency data every 1-2 generations from a genetically diverse population of Drosophila melanogaster in extensively replicated field mesocosms from late June to mid-December (a period of approximately 12 total generations). Adaptation throughout the fundamental ecological phases of population expansion, peak density and collapse was underpinned by extremely rapid, parallel changes in genomic variation across replicates. Yet, the dominant direction of selection fluctuated repeatedly, even within each of these ecological phases. Comparing patterns of change in allele frequency to an independent dataset procured from the same experimental system demonstrated that the targets of selection are predictable across years. In concert, our results reveal a fitness relevance of standing variation that is likely to be masked by inference approaches based on static population sampling or insufficiently resolved time-series data. We propose that such fine-scaled, temporally fluctuating selection may be an important force contributing to the maintenance of functional genetic variation in natural populations and an important stochastic force impacting genome-wide patterns of diversity at linked neutral sites, akin to genetic draft.

Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species

The accurate estimation of the distribution of fitness effects (DFE) of new mutations is critical for population genetic inference but remains a challenging task. While various methods have been developed for DFE inference using the site frequency spectrum of putatively neutral and selected sites, their applicability in species with diverse life history traits and complex demographic scenarios is not well understood. Selfing is common among eukaryotic species and can lead to decreased effective recombination rates, increasing the effects of selection at linked sites, including interference between selected alleles. We employ forward simulations to investigate the limitations of current DFE estimation approaches in the presence of selfing and other model violations, such as linkage, departures from semidominance, population structure, and uneven sampling. We find that distortions of the site frequency spectrum due to Hill-Robertson interference in highly selfing populations lead to mis-inference of the deleterious DFE of new mutations. Specifically, when inferring the distribution of selection coefficients, there is an overestimation of nearly neutral and strongly deleterious mutations and an underestimation of mildly deleterious mutations when interference between selected alleles is pervasive. In addition, the presence of cryptic population structure with low rates of migration and uneven sampling across subpopulations leads to the false inference of a deleterious DFE skewed towards effectively neutral/mildly deleterious mutations. Finally, the proportion of adaptive substitutions estimated at high rates of selfing is substantially overestimated. Our observations apply broadly to species and genomic regions with little/no recombination and where interference might be pervasive.

Signatures of selection in Mulinia lateralis underpinning its rapid adaptation to laboratory conditions

The dwarf surf clam, Mulinia lateralis, is considered as a model species for bivalves because of its rapid growth and short generation time. Recently, successful breeding of this species for multiple generations in our laboratory revealed its acquisition of adaptive advantages during artificial breeding. In this study, 310 individuals from five different generations were genotyped with 22,196 single nucleotide polymorphisms (SNPs) with the aim of uncovering the genetic basis of their adaptation to laboratory conditions. Results revealed that M. lateralis consistently maintained high genetic diversity across generations, characterized by high observed heterozygosity (H o: 0.2733-0.2934) and low levels of inbreeding (F is: -0.0244-0.0261). Population analysis indicated low levels of genetic differentiation among generations of M. lateralis during artificial breeding (F st <0.05). In total, 316 genomic regions exhibited divergent selection, with 168 regions under positive selection. Furthermore, 227 candidate genes were identified in the positive selection regions, which have functions including growth, stress resistance, and reproduction. Notably, certain selection signatures with significantly higher F st value were detected in genes associated with male reproduction, such as GAL3ST1, IFT88, and TSSK2, which were significantly upregulated during artificial breeding. This suggests a potential role of sperm-associated genes in the rapid evolutionary response of M. lateralis to selection in laboratory conditions. Overall, our findings highlight the phenotypic and genetic changes, as well as selection signatures, in M. lateralis during artificial breeding. This contributes to understanding their adaptation to laboratory conditions and underscores the potential for using this species to explore the adaptive evolution of bivalves.

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.

Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates

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

Quantitative systems-based prediction of antimicrobial resistance evolution

Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.

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.

Proliferating active matter

The fascinating patterns of collective motion created by autonomously driven particles have fuelled active-matter research for over two decades. So far, theoretical active-matter research has often focused on systems with a fixed number of particles. This constraint imposes strict limitations on what behaviours can and cannot emerge. However, a hallmark of life is the breaking of local cell number conservation by replication and death. Birth and death processes must be taken into account, for example, to predict the growth and evolution of a microbial biofilm, the expansion of a tumour, or the development from a fertilized egg into an embryo and beyond. In this Perspective, we argue that unique features emerge in these systems because proliferation represents a distinct form of activity: not only do the proliferating entities consume and dissipate energy, they also inject biomass and degrees of freedom capable of further self-proliferation, leading to myriad dynamic scenarios. Despite this complexity, a growing number of studies document common collective phenomena in various proliferating soft-matter systems. This generality leads us to propose proliferation as another direction of active-matter physics, worthy of a dedicated search for new dynamical universality classes. Conceptual challenges abound, from identifying control parameters and understanding large fluctuations and nonlinear feedback mechanisms to exploring the dynamics and limits of information flow in self-replicating systems. We believe that, by extending the rich conceptual framework developed for conventional active matter to proliferating active matter, researchers can have a profound impact on quantitative biology and reveal fascinating emergent physics along the way.

Mutation bias and the predictability of evolution

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

Inspecting the interaction between human immunodeficiency virus and the immune system through genetic turnover

Chronic infections of the human immunodeficiency virus (HIV) create a very complex coevolutionary process, where the virus tries to escape the continuously adapting host immune system. Quantitative details of this process are largely unknown and could help in disease treatment and vaccine development. Here we study a longitudinal dataset of ten HIV-infected people, where both the B-cell receptors and the virus are deeply sequenced. We focus on simple measures of turnover, which quantify how much the composition of the viral strains and the immune repertoire change between time points. At the single-patient level, the viral-host turnover rates do not show any statistically significant correlation, however, they correlate if one increases the amount of statistics by aggregating the information across patients. We identify an anti-correlation: large changes in the viral pool composition come with small changes in the B-cell receptor repertoire. This result seems to contradict the naïve expectation that when the virus mutates quickly, the immune repertoire needs to change to keep up. However, a simple model of antagonistically evolving populations can explain this signal. If it is sampled at intervals comparable with the sweep time, one population has had time to sweep while the second cannot start a counter-sweep, leading to the observed anti-correlation. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Statistical genetics in and out of quasi-linkage equilibrium

This review is about statistical genetics, an interdisciplinary topic between statistical physics and population biology. The focus is on the phase ofquasi-linkage equilibrium(QLE). Our goals here are to clarify under which conditions the QLE phase can be expected to hold in population biology and how the stability of the QLE phase is lost. The QLE state, which has many similarities to a thermal equilibrium state in statistical mechanics, was discovered by M Kimura for a two-locus two-allele model, and was extended and generalized to the global genome scale byNeher&Shraiman (2011). What we will refer to as the Kimura-Neher-Shraiman theory describes a population evolving due to the mutations, recombination, natural selection and possibly genetic drift. A QLE phase exists at sufficiently high recombination rate (r) and/or mutation ratesµwith respect to selection strength. We show how in QLE it is possible to infer the epistatic parameters of the fitness function from the knowledge of the (dynamical) distribution of genotypes in a population. We further consider the breakdown of the QLE regime for high enough selection strength. We review recent results for the selection-mutation and selection-recombination dynamics. Finally, we identify and characterize a new phase which we call the non-random coexistence where variability persists in the population without either fixating or disappearing.

Coordinated evolution at amino acid sites of SARS-CoV-2 spike

SARS-CoV-2 has adapted in a stepwise manner, with multiple beneficial mutations accumulating in a rapid succession at origins of VOCs, and the reasons for this are unclear. Here, we searched for coordinated evolution of amino acid sites in the spike protein of SARS-CoV-2. Specifically, we searched for concordantly evolving site pairs (CSPs) for which changes at one site were rapidly followed by changes at the other site in the same lineage. We detected 46 sites which formed 45 CSP. Sites in CSP were closer to each other in the protein structure than random pairs, indicating that concordant evolution has a functional basis. Notably, site pairs carrying lineage defining mutations of the four VOCs that circulated before May 2021 are enriched in CSPs. For the Alpha VOC, the enrichment is detected even if Alpha sequences are removed from analysis, indicating that VOC origin could have been facilitated by positive epistasis. Additionally, we detected nine discordantly evolving pairs of sites where mutations at one site unexpectedly rarely occurred on the background of a specific allele at another site, for example on the background of wild-type D at site 614 (four pairs) or derived Y at site 501 (three pairs). Our findings hint that positive epistasis between accumulating mutations could have delayed the assembly of advantageous combinations of mutations comprising at least some of the VOCs.

The evolutionary and functional significance of germline immunoglobulin gene variation

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.

Sweepstakes reproductive success via pervasive and recurrent selective sweeps

Highly fecund natural populations characterized by high early mortality abound, yet our knowledge about their recruitment dynamics is somewhat rudimentary. This knowledge gap has implications for our understanding of genetic variation, population connectivity, local adaptation, and the resilience of highly fecund populations. The concept of sweepstakes reproductive success, which posits a considerable variance and skew in individual reproductive output, is key to understanding the distribution of individual reproductive success. However, it still needs to be determined whether highly fecund organisms reproduce through sweepstakes and, if they do, the relative roles of neutral and selective sweepstakes. Here, we use coalescent-based statistical analysis of population genomic data to show that selective sweepstakes likely explain recruitment dynamics in the highly fecund Atlantic cod. We show that the Kingman coalescent (modelling no sweepstakes) and the Xi-Beta coalescent (modelling random sweepstakes), including complex demography and background selection, do not provide an adequate fit for the data. The Durrett-Schweinsberg coalescent, in which selective sweepstakes result from recurrent and pervasive selective sweeps of new mutations, offers greater explanatory power. Our results show that models of sweepstakes reproduction and multiple-merger coalescents are relevant and necessary for understanding genetic diversity in highly fecund natural populations. These findings have fundamental implications for understanding the recruitment variation of fish stocks and general evolutionary genomics of high-fecundity organisms.

Contributions of adaptation and purifying selection to SARS-CoV-2 evolution

Continued evolution and adaptation of SARS-CoV-2 has led to more transmissible and immune-evasive variants with profound impacts on the course of the pandemic. Here I analyze the evolution of the virus over 2.5 years since its emergence and estimate the rates of evolution for synonymous and non-synonymous changes separately for evolution within clades-well-defined monophyletic groups with gradual evolution-and for the pandemic overall. The rate of synonymous mutation is found to be around 6 changes per year. Synonymous rates within variants vary little from variant to variant and are compatible with the overall rate of 7 changes per year (or [Formula: see text] per year and codon). In contrast, the rate at which variants accumulate amino acid changes (non-synonymous mutations) was initially around 12-16 changes per year, but in 2021 and 2022 it dropped to 6-9 changes per year. The overall rate of non-synonymous evolution, that is across variants, is estimated to be about 26 amino acid changes per year (or [Formula: see text] per year and codon). This strong acceleration of the overall rate compared to within clade evolution indicates that the evolutionary process that gave rise to the different variants is qualitatively different from that in typical transmission chains and likely dominated by adaptive evolution. I further quantify the spectrum of mutations and purifying selection in different SARS-CoV-2 proteins and show that the massive global sampling of SARS-CoV-2 is sufficient to estimate site-specific fitness costs across the entire genome. Many accessory proteins evolve under limited evolutionary constraints with little short-term purifying selection. About half of the mutations in other proteins are strongly deleterious.

Detecting bacterial adaptation within individual microbiomes

The human microbiome harbours a large capacity for within-person adaptive mutations. Commensal bacterial strains can stably colonize a person for decades, and billions of mutations are generated daily within each person's microbiome. Adaptive mutations emerging during health might be driven by selective forces that vary across individuals, vary within an individual, or are completely novel to the human population. Mutations emerging within individual microbiomes might impact the immune system, the metabolism of nutrients or drugs, and the stability of the community to perturbations. Despite this potential, relatively little attention has been paid to the possibility of adaptive evolution within complex human-associated microbiomes. This review discusses the promise of studying within-microbiome adaptation, the conceptual and technical limitations that may have contributed to an underappreciation of adaptive de novo mutations occurring within microbiomes to date, and methods for detecting recent adaptive evolution. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Selection in a growing colony biases results of mutation accumulation experiments

Mutations provide the raw material for natural selection to act. Therefore, understanding the variety and relative frequency of different type of mutations is critical to understanding the nature of genetic diversity in a population. Mutation accumulation (MA) experiments have been used in this context to estimate parameters defining mutation rates, distribution of fitness effects (DFE), and spectrum of mutations. MA experiments can be performed with different effective population sizes. In MA experiments with bacteria, a single founder is grown to a size of a colony (~ 108). It is assumed that natural selection plays a minimal role in dictating the dynamics of colony growth. In this work, we simulate colony growth via a mathematical model, and use our model to mimic an MA experiment. We demonstrate that selection ensures that, in an MA experiment, fraction of all mutations that are beneficial is over-represented by a factor of almost two, and that the distribution of fitness effects of beneficial and deleterious mutations are inaccurately captured in an MA experiment. Given this, the estimate of mutation rates from MA experiments is non-trivial. We then perform an MA experiment with 160 lines of E. coli, and show that due to the effect of selection in a growing colony, the size and sector of a colony from which the experiment is propagated impacts the results. Overall, we demonstrate that the results of MA experiments need to be revisited taking into account the action of selection in a growing colony.

What is N e, anyway?

Few doubt that effective population size (Ne) is one of the most important parameters in evolutionary biology, but how many can say they really understand the concept? Ne is the evolutionary analogue of the number of individuals (or adults) in the population, N. Whereas ecological consequences of population size depend on N, evolutionary consequences (rates of loss of genetic diversity and increase in inbreeding; relative effectiveness of selection) depend on Ne. Formal definitions typically relate effective size to a key population genetic parameter, such as loss of heterozygosity or variance in allele frequency. However, for practical application to real populations, it is more useful to define Ne in terms of three demographic parameters: number of potential parents (adult N), and mean and variance in offspring number. Defined this way, Ne determines the rate of random genetic drift across the entire genome in the offspring generation. Other evolutionary forces (mutation, migration, selection)—together with factors such as variation in recombination rate—can also affect genetic variation, and this leads to heterogeneity across the genome in observed rates of genetic change. For some, it has been convenient to interpret this heterogeneity in terms of heterogeneity in Ne, but unfortunately this has muddled the concepts of genetic drift and effective population size. A commonly-repeated misconception is that Ne is the number of parents that actually contribute genes to the next generation (NP). In reality, NP can be smaller or larger than Ne, and the NP/Ne ratio depends on the sex ratio, the mean and variance in offspring number, and whether inbreeding or variance Ne is of interest.

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.

Population-genomic analysis identifies a low rate of global adaptive fixation in the proteins of the cyclical parthenogen Daphnia magna

Daphnia are well-established ecological and evolutionary models, and the interaction between D. magna and its microparasites is widely considered a paragon of the host-parasite coevolutionary process. Like other well-studied arthropods such as Drosophila melanogaster and Anopheles gambiae, D. magna is a small, widespread, and abundant species that is therefore expected to display a large long-term population size and high rates of adaptive protein evolution. However, unlike these other species, D. magna is cyclically asexual and lives in a highly structured environment (ponds and lakes) with moderate levels of dispersal, both of which are predicted to impact upon long-term effective population size and adaptive protein evolution. To investigate patterns of adaptive protein fixation, we produced the complete coding genomes of 36 D. magna clones sampled from across the European range (Western Palaearctic), along with draft sequences for the close relatives D. similis and D. lumholtzi, used as outgroups. We analyzed genome-wide patterns of adaptive fixation, with a particular focus on genes that have an a priori expectation of high rates, such as those likely to mediate immune responses, RNA interference against viruses and transposable elements, and those with a strongly male-biased expression pattern. We find that, as expected, D. magna displays high levels of diversity and that this is highly structured among populations. However, compared with Drosophila, we find that D. magna proteins appear to have a high proportion of weakly deleterious variants and do not show evidence of pervasive adaptive fixation across its entire range. This is true of the genome as a whole, and also of putative ‘arms race’ genes that often show elevated levels of adaptive substitution in other species. In addition to the likely impact of extensive, and previously documented, local adaptation, we speculate that these findings may reflect reduced efficacy of selection associated with cyclical asexual reproduction.

Mutation bias shapes the spectrum of adaptive substitutions

How do mutational biases influence the process of adaptation? A common assumption is that selection alone determines the course of adaptation from abundant preexisting variation. Yet, theoretical work shows broad conditions under which the mutation rate to a given type of variant strongly influences its probability of contributing to adaptation. Here we introduce a statistical approach to analyzing how mutation shapes protein sequence adaptation. Using large datasets from three different species, we show that the mutation spectrum has a proportional influence on the types of changes fixed in adaptation. We also show via computer simulations that a variety of factors can influence how closely the spectrum of adaptive substitutions reflects the spectrum of variants introduced by mutation.

Evolutionary adaptation often occurs by the fixation of beneficial mutations. This mode of adaptation can be characterized quantitatively by a spectrum of adaptive substitutions, i.e., a distribution for types of changes fixed in adaptation. Recent work establishes that the changes involved in adaptation reflect common types of mutations, raising the question of how strongly the mutation spectrum shapes the spectrum of adaptive substitutions. We address this question with a codon-based model for the spectrum of adaptive amino acid substitutions, applied to three large datasets covering thousands of amino acid changes identified in natural and experimental adaptation in Saccharomyces cerevisiae, Escherichia coli, and Mycobacterium tuberculosis. Using species-specific mutation spectra based on prior knowledge, we find that the mutation spectrum has a proportional influence on the spectrum of adaptive substitutions in all three species. Indeed, we find that by inferring the mutation rates that best explain the spectrum of adaptive substitutions, we can accurately recover the species-specific mutation spectra. However, we also find that the predictive power of the model differs substantially between the three species. To better understand these differences, we use population simulations to explore the factors that influence how closely the spectrum of adaptive substitutions mirrors the mutation spectrum. The results show that the influence of the mutation spectrum decreases with increasing mutational supply (Nμ) and that predictive power is strongly affected by the number and diversity of beneficial mutations.

Codon Usage Bias in Phytoplankton

Non-random usage of synonymous codons, known as “codon bias”, has been described in many organisms, from bacteria to Drosophila, but little is known about it in phytoplankton. This phenomenon is thought to be driven by selection for translational efficiency. As the efficacy of selection is proportional to the effective population size, species with large population sizes, such as phytoplankton, are expected to have strong codon bias. To test this, we measured codon bias in 215 strains from Haptophyta, Chlorophyta, Ochrophyta (except diatoms that were studied previously), Dinophyta, Cryptophyta, Ciliophora, unicellular Rhodophyta and Chlorarachniophyta. Codon bias is modest in most groups, despite the astronomically large population sizes of marine phytoplankton. The strength of the codon bias, measured with the effective number of codons, is the strongest in Haptophyta and the weakest in Chlorarachniophyta. The optimal codons are GC-ending in most cases, but several shifts to AT-ending codons were observed (mainly in Ochrophyta and Ciliophora). As it takes a long time to reach a new equilibrium after such shifts, species having AT-ending codons show a lower frequency of optimal codons compared to other species. Genetic diversity, calculated for species with more than three strains sequenced, is modest, indicating that the effective population sizes are many orders of magnitude lower than the astronomically large census population sizes, which helps to explain the modest codon bias in marine phytoplankton. This study represents the first comparative analysis of codon bias across multiple major phytoplankton groups.

Evolutionary Dynamics of Asexual Hypermutators Adapting to a Novel Environment

How microbes adapt to a novel environment is a central question in evolutionary biology. Although adaptive evolution must be fueled by beneficial mutations, whether higher mutation rates facilitate the rate of adaptive evolution remains unclear. To address this question, we cultured Escherichia coli hypermutating populations, in which a defective methyl-directed mismatch repair pathway causes a 140-fold increase in single-nucleotide mutation rates. In parallel with wild-type E. coli, populations were cultured in tubes containing Luria-Bertani broth, a complex medium known to promote the evolution of subpopulation structure. After 900 days of evolution, in three transfer schemes with different population-size bottlenecks, hypermutators always exhibited similar levels of improved fitness as controls. Fluctuation tests revealed that the mutation rates of hypermutator lines converged evolutionarily on those of wild-type populations, which may have contributed to the absence of fitness differences. Further genome-sequence analysis revealed that, although hypermutator populations have higher rates of genomic evolution, this largely reflects strong genetic linkage. Despite these linkage effects, the evolved population exhibits parallelism in fixed mutations, including those potentially related to biofilm formation, transcription regulation, and mutation-rate evolution. Together, these results are generally inconsistent with a hypothesized positive relationship between the mutation rate and the adaptive speed of evolution, and provide insight into how clonal adaptation occurs in novel environments.

Interplay between extreme drift and selection intensities favors the fixation of beneficial mutations in selfing maize populations

Population and quantitative genetic models provide useful approximations to predict long-term selection responses sustaining phenotypic shifts, and underlying multilocus adaptive dynamics. Valid across a broad range of parameters, their use for understanding the adaptive dynamics of small selfing populations undergoing strong selection intensity (thereafter High Drift-High selection regime, HDHS) remains to be explored. Saclay Divergent Selection Experiments (DSEs) on maize flowering time provide an interesting example of populations evolving under HDHS, with significant selection responses over 20 generations in two directions. We combined experimental data from Saclay DSEs, forward individual-based simulations, and theoretical predictions to dissect the evolutionary mechanisms at play in the observed selection responses. We asked two main questions: How do mutations arise, spread, and reach fixation in populations evolving under HDHS? How does the interplay between drift and selection influence observed phenotypic shifts? We showed that the long-lasting response to selection in small populations is due to the rapid fixation of mutations occurring during the generations of selection. Among fixed mutations, we also found a clear signal of enrichment for beneficial mutations revealing a limited cost of selection. Both environmental stochasticity and variation in selection coefficients likely contributed to exacerbate mutational effects, thereby facilitating selection grasp and fixation of small-effect mutations. Together our results highlight that despite a small number of polymorphic loci expected under HDHS, adaptive variation is continuously fueled by a vast mutational target. We discuss our results in the context of breeding and long-term survival of small selfing populations.

Ciprofloxacin induced antibiotic resistance in Salmonella Typhimurium mutants and genome analysis

Antibiotic resistance of Salmonella species is well reported. Ciprofloxacin is the frontline antibiotic for salmonellosis. The repeated exposure to ciprofloxacin leads to resistant strains. After 20 cycles of antibiotic exposure, resistant bacterial clones were evaluated. The colony size of the mutants was small and had an extended lag phase compared to parent strain. The whole genome sequencing showed 40,513 mutations across the genome. Small percentage (5.2%) of mutations was non-synonymous. Four-fold more transitions were observed than transversions. Ratio of < 1 transition vs transversion showed a positive selection for antibiotic resistant trait. Mutation distribution across the genome was uniform. The native plasmid was an exception and 2 mutations were observed on 90 kb plasmid. The important genes like dnaE, gyrA, iroC, metH and rpoB involved in antibiotic resistance had point mutations. The genome analysis revealed most of the metabolic pathways were affected.

Decomposing the sources of SARS-CoV-2 fitness variation in the United States

The fitness of a pathogen is a composite phenotype determined by many different factors influencing growth rates both within and between hosts. Determining what factors shape fitness at the host population-level is especially challenging because both intrinsic factors like pathogen genetics and extrinsic factors such as host behavior influence between-host transmission potential. This challenge has been highlighted by controversy surrounding the population-level fitness effects of mutations in the SARS-CoV-2 genome and their relative importance when compared against non-genetic factors shaping transmission dynamics. Building upon phylodynamic birth-death models, we develop a new framework to learn how hundreds of genetic and non-genetic factors have shaped the fitness of SARS-CoV-2. We estimate the fitness effects of all amino acid variants and several structural variants that have circulated in the United States between February 2020 and March 2021 from viral phylogenies. We also estimate how much fitness variation among pathogen lineages is attributable to genetic versus non-genetic factors such as spatial heterogeneity in transmission rates. Before September 2020, most fitness variation between lineages can be explained by background spatial heterogeneity in transmission rates across geographic regions. Starting in late 2020, genetic variation in fitness increased dramatically with the emergence of several new lineages including B.1.1.7, B.1.427, B.1.429 and B.1.526. Our analysis also indicates that genetic variants in less well-explored genomic regions outside of Spike may be contributing significantly to overall fitness variation in the viral population.

Evolution of resistance under alternative models of selective interference

The use of multiple pesticides or drugs can lead to a simultaneous selection pressure for resistance alleles at different loci. Models of resistance evolution focus on how this can delay the spread of resistance through a population, but often neglect how this can also reduce the probability that a resistance allele spreads. This neglected factor has been studied in a parallel literature as selective interference. Models of interference use alternative constructions of fitness, where selection coefficients from different loci either add or multiply. Although these are equivalent under weak selection, the two constructions make alternative predictions under the strong selection that characterizes resistance evolution. Here, simulations are used to examine the effects of interference on the probability of fixation and time to fixation of a new and strongly beneficial mutation in the presence of another strongly beneficial allele with variable starting frequency. The results from simulations show a complicated pattern of effects. The key result is that, under multiplicativity, the presence of the strongly beneficial allele leads to a small reduction in the probability of fixation for the new beneficial mutation up to ~10%, and a negligible increase in the average time to fixation up to ~2%, whereas under additivity, the effect is more substantial at up to ~50% for the probability of fixation and ~100% for the average time to fixation. Consequently, the effect of interference is only an important feature of resistance evolution under additivity. Current evidence from studies of experimental evolution provides widespread support for the basic features of additivity, which suggests that interference may afford resistance a different pattern of evolution than other adaptations: rather than the gradual and simultaneous selection of many alleles with small effects, the rapid evolution of resistance may involve the sequential selection of alleles with large effects.

Environmental Heterogeneity Leads to Spatial Differences in Genetic Diversity and Demographic Structure of Acer caudatifolium

Under climate fluctuation, species dispersal may be disturbed by terrain and local climate, resulting in uneven spatial-genetic structure. In addition, organisms at different latitudes may be differentially susceptible to climate change. Here, we tracked the seed dispersal of Acer caudatifolium using chloroplast DNA to explore the relationships of terrain and local climate heterogeneity with range shifts and demography in Taiwan. Our results showed that the extant populations have shifted upward and northward to the mountains since the Last Glacial Maximum. The distributional upshift of A. caudatifolium is in contrast to the downward expansion of its closest relative in Taiwan, A. morrisonense. The northern populations of A. caudatifolium have acquired multiple-source chlorotypes and harbor high genetic diversity. However, effective gene flow between the north and south is interrupted by topography, geographic distance, north-south differences in October rainfall, and other climate heterogeneities, blocking southward genetic rescue. In addition, winter monsoon-driven rainfall may cause regional differences in the phenological schedule, resulting in adaptive effects on the timing of range shift and the genetic draft of chlorotype distribution. Terrain, distance, and local climate also differentiate the northernmost populations from the others, supporting the previous taxonomic treatment of Acer kawakamii var. taitonmontanum as an independent variety.

Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment

Gut microbial communities can respond to antibiotic perturbations by rapidly altering their taxonomic and functional composition. However, little is known about the strain-level processes that drive this collective response. Here, we characterize the gut microbiome of a single individual at high temporal and genetic resolution through a period of health, disease, antibiotic treatment, and recovery. We used deep, linked-read metagenomic sequencing to track the longitudinal trajectories of thousands of single nucleotide variants within 36 species, which allowed us to contrast these genetic dynamics with the ecological fluctuations at the species level. We found that antibiotics can drive rapid shifts in the genetic composition of individual species, often involving incomplete genome-wide sweeps of pre-existing variants. These genetic changes were frequently observed in species without obvious changes in species abundance, emphasizing the importance of monitoring diversity below the species level. We also found that many sweeping variants quickly reverted to their baseline levels once antibiotic treatment had concluded, demonstrating that the ecological resilience of the microbiota can sometimes extend all the way down to the genetic level. Our results provide new insights into the population genetic forces that shape individual microbiomes on therapeutically relevant timescales, with potential implications for personalized health and disease.

Divergent Evolution of Mutation Rates and Biases in the Long-Term Evolution Experiment with Escherichia coli

All organisms encode enzymes that replicate, maintain, pack, recombine, and repair their genetic material. For this reason, mutation rates and biases also evolve by mutation, variation, and natural selection. By examining metagenomic time series of the Lenski long-term evolution experiment (LTEE) with Escherichia coli (Good BH, McDonald MJ, Barrick JE, Lenski RE, Desai MM. 2017. The dynamics of molecular evolution over 60,000 generations. Nature 551(7678):45–50.), we find that local mutation rate variation has evolved during the LTEE. Each LTEE population has evolved idiosyncratic differences in their rates of point mutations, indels, and mobile element insertions, due to the fixation of various hypermutator and antimutator alleles. One LTEE population, called Ara+3, shows a strong, symmetric wave pattern in its density of point mutations, radiating from the origin of replication. This pattern is largely missing from the other LTEE populations, most of which evolved missense, indel, or structural mutations in topA, fis, and dusB—loci that all affect DNA topology. The distribution of mutations in those genes over time suggests epistasis and historical contingency in the evolution of DNA topology, which may have in turn affected local mutation rates. Overall, the replicate populations of the LTEE have largely diverged in their mutation rates and biases, even though they have adapted to identical abiotic conditions.

Understanding the evolution of multiple drug resistance in structured populations

The evolution of multidrug resistance (MDR) is a pressing public health concern. Yet many aspects, such as the role played by population structure, remain poorly understood. Here, we argue that studying MDR evolution by focusing upon the dynamical equations for linkage disequilibrium (LD) can greatly simplify the calculations, generate more insight, and provide a unified framework for understanding the role of population structure. We demonstrate how a general epidemiological model of MDR evolution can be recast in terms of the LD equations. These equations reveal how the different forces generating and propagating LD operate in a dynamical setting at both the population and metapopulation levels. We then apply these insights to show how the LD perspective: (i) explains equilibrium patterns of MDR, (ii) provides a simple interpretative framework for transient evolutionary dynamics, and (iii) can be used to assess the consequences of different drug prescription strategies for MDR evolution.

Efficient inference, potential, and limitations of site-specific substitution models

Natural selection imposes a complex filter on which variants persist in a population resulting in evolutionary patterns that vary greatly along the genome. Some sites evolve close to neutrally, while others are highly conserved, allow only specific states, or only change in concert with other sites. On one hand, such constraints on sequence evolution can be to infer biological function, one the other hand they need to be accounted for in phylogenetic reconstruction. Phylogenetic models often account for this complexity by partitioning sites into a small number of discrete classes with different rates and/or state preferences. Appropriate model complexity is typically determined by model selection procedures. Here, we present an efficient algorithm to estimate more complex models that allow for different preferences at every site and explore the accuracy at which such models can be estimated from simulated data. Our iterative approximate maximum likelihood scheme uses information in the data efficiently and accurately estimates site-specific preferences from large data sets with moderately diverged sequences and known topology. However, the joint estimation of site-specific rates, and site-specific preferences, and phylogenetic branch length can suffer from identifiability problems, while ignoring variation in preferences across sites results in branch length underestimates. Site-specific preferences estimated from large HIV pol alignments show qualitative concordance with intra-host estimates of fitness costs. Analysis of these substitution models suggests near saturation of divergence after a few hundred years. Such saturation can explain the inability to infer deep divergence times of HIV and SIVs using molecular clock approaches and time-dependent rate estimates.

Nonadaptive host-use specificity in tropical armored scale insects

Most herbivorous insects are diet specialists in spite of the apparent advantages of being a generalist. This conundrum might be explained by fitness trade-offs on alternative host plants, yet the evidence of such trade-offs has been elusive. Another hypothesis is that specialization is nonadaptive, evolving through neutral population-genetic processes and within the bounds of historical constraints. Here, we report on a striking lack of evidence for the adaptiveness of specificity in tropical canopy communities of armored scale insects. We find evidence of pervasive diet specialization, and find that host use is phylogenetically conservative, but also find that more-specialized species occur on fewer of their potential hosts than do less-specialized species, and are no more abundant where they do occur. Of course local communities might not reflect regional diversity patterns. But based on our samples, comprising hundreds of species of hosts and armored scale insects at two widely separated sites, more-specialized species do not appear to outperform more generalist species.

What We Don't Know About Diet-Breadth Evolution in Herbivorous Insects

Half a million species of herbivorous insects have been described. Most of them are diet specialists, using only a few plant species as hosts. Biologists suspect that their specificity is key to their diversity. But why do herbivorous insects tend to be diet specialists? In this review, we catalog a broad range of explanations. We review the evidence for each and suggest lines of research to obtain the evidence we lack. We then draw attention to a second major question, namely how changes in diet breadth affect the rest of a species’ biology. In particular, we know little about how changes in diet breadth feed back on genetic architecture, the population genetic environment, and other aspects of a species’ ecology. Knowing more about how generalists and specialists differ should go a long way toward sorting out potential explanations of specificity, and yield a deeper understanding of herbivorous insect diversity.

Imposed mutational meltdown as an antiviral strategy

Following widespread infections of the most recent coronavirus known to infect humans, SARS‐CoV‐2, attention has turned to potential therapeutic options. With no drug or vaccine yet approved, one focal point of research is to evaluate the potential value of repurposing existing antiviral treatments, with the logical strategy being to identify at least a short‐term intervention to prevent within‐patient progression, while long‐term vaccine strategies unfold. Here, we offer an evolutionary/population‐genetic perspective on one approach that may overwhelm the capacity for pathogen defense (i.e., adaptation) – induced mutational meltdown – providing an overview of key concepts, review of previous theoretical and experimental work of relevance, and guidance for future research. Applied with appropriate care, including target specificity, induced mutational meltdown may provide a general, rapidly implemented approach for the within‐patient eradication of a wide range of pathogens or other undesirable microorganisms.

Selective Interference and the Evolution of Sex

Selection acts upon genes linked together on chromosomes. This physical connection reduces the efficiency by which selection can act because, in the absence of sex, alleles must rise and fall together in frequency with the genome in which they are found. This selective interference underlies such phenomena as clonal interference and Muller’s Ratchet and is broadly termed Hill-Robertson interference. In this review, I examine the potential for selective interference to account for the evolution and maintenance of sex, discussing the positive and negative evidence from both theoretical and empirical studies, and highlight the gaps that remain.

Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution

Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence-only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth.

Horizontal transmission and recombination maintain forever young bacterial symbiont genomes

Bacterial symbionts bring a wealth of functions to the associations they participate in, but by doing so, they endanger the genes and genomes underlying these abilities. When bacterial symbionts become obligately associated with their hosts, their genomes are thought to decay towards an organelle-like fate due to decreased homologous recombination and inefficient selection. However, numerous associations exist that counter these expectations, especially in marine environments, possibly due to ongoing horizontal gene flow. Despite extensive theoretical treatment, no empirical study thus far has connected these underlying population genetic processes with long-term evolutionary outcomes. By sampling marine chemosynthetic bacterial-bivalve endosymbioses that range from primarily vertical to strictly horizontal transmission, we tested this canonical theory. We found that transmission mode strongly predicts homologous recombination rates, and that exceedingly low recombination rates are associated with moderate genome degradation in the marine symbionts with nearly strict vertical transmission. Nonetheless, even the most degraded marine endosymbiont genomes are occasionally horizontally transmitted and are much larger than their terrestrial insect symbiont counterparts. Therefore, horizontal transmission and recombination enable efficient natural selection to maintain intermediate symbiont genome sizes and substantial functional genetic variation.

Differential loss of effector genes in three recently expanded pandemic clonal lineages of the rice blast fungus

BACKGROUND

Understanding the mechanisms and timescales of plant pathogen outbreaks requires a detailed genome-scale analysis of their population history. The fungus Magnaporthe (Syn. Pyricularia) oryzae-the causal agent of blast disease of cereals- is among the most destructive plant pathogens to world agriculture and a major threat to the production of rice, wheat, and other cereals. Although M. oryzae is a multihost pathogen that infects more than 50 species of cereals and grasses, all rice-infecting isolates belong to a single genetically defined lineage. Here, we combined the two largest genomic datasets to reconstruct the genetic history of the rice-infecting lineage of M. oryzae based on 131 isolates from 21 countries.

RESULTS

The global population of the rice blast fungus consists mainly of three well-defined genetic groups and a diverse set of individuals. Multiple population genetic tests revealed that the rice-infecting lineage of the blast fungus probably originated from a recombining diverse group in Southeast Asia followed by three independent clonal expansions that took place over the last ~ 200 years. Patterns of allele sharing identified a subpopulation from the recombining diverse group that introgressed with one of the clonal lineages before its global expansion. Remarkably, the four genetic lineages of the rice blast fungus vary in the number and patterns of presence and absence of candidate effector genes. These genes encode secreted proteins that modulate plant defense and allow pathogen colonization. In particular, clonal lineages carry a reduced repertoire of effector genes compared with the diverse group, and specific combinations of presence and absence of effector genes define each of the pandemic clonal lineages.

CONCLUSIONS

Our analyses reconstruct the genetic history of the rice-infecting lineage of M. oryzae revealing three clonal lineages associated with rice blast pandemics. Each of these lineages displays a specific pattern of presence and absence of effector genes that may have shaped their adaptation to the rice host and their evolutionary history.

Sex Chromosome Degeneration by Regulatory Evolution

In many species, the Y (or W) sex chromosome is degenerate. Current theory proposes that this degeneration follows the arrest of recombination and results from the accumulation of deleterious mutations due to selective interference-the inefficacy of natural selection on non-recombining genomic regions. This theory requires very few assumptions, but it does not robustly predict fast erosion of the Y (or W) in large populations or the stepwise degeneration of several small non-recombining strata. We propose a new mechanism for Y/W erosion that works over faster timescales, in large populations, and for small non-recombining regions (down to a single sex-linked gene). The mechanism is based on the instability and divergence of cis-regulatory sequences in non-recombining genome regions, which become selectively haploidized to mask deleterious mutations on coding sequences. This haploidization is asymmetric, because cis-regulators on the X cannot be silenced (otherwise there would be no expression in females). This process causes rapid Y/W degeneration and simultaneous evolution of dosage compensation, provided that autosomal trans-regulatory sequences with sex-limited effects are available to compensate for cis-regulatory divergence. Although this "degeneration by regulatory evolution" does not require selective interference, both processes may act in concert to further accelerate Y degeneration.

A Theoretical Framework for Evolutionary Cell Biology

One of the last uncharted territories in evolutionary biology concerns the link with cell biology. Because all phenotypes ultimately derive from events at the cellular level, this connection is essential to building a mechanism-based theory of evolution. Given the impressive developments in cell biological methodologies at the structural and functional levels, the potential for rapid progress is great. The primary challenge for theory development is the establishment of a quantitative framework that transcends species boundaries. Two approaches to the problem are presented here: establishing the long-term steady-state distribution of mean phenotypes under specific regimes of mutation, selection, and drift and evaluating the energetic costs of cellular structures and functions. Although not meant to be the final word, these theoretical platforms harbor potential for generating insight into a diversity of unsolved problems, ranging from genome structure to cellular architecture to aspects of motility in organisms across the Tree of Life.

Saturation Mutagenesis Genome Engineering of Infective ΦX174 Bacteriophage via Unamplified Oligo Pools and Golden Gate Assembly

Here we present a novel protocol for the construction of saturation single-site-and massive multisite-mutant libraries of a bacteriophage. We segmented the ΦX174 genome into 14 nontoxic and nonreplicative fragments compatible with Golden Gate assembly. We next used nicking mutagenesis with oligonucleotides prepared from unamplified oligo pools with individual segments as templates to prepare near-comprehensive single-site mutagenesis libraries of genes encoding the F capsid protein (421 amino acids scanned) and G spike protein (172 amino acids scanned). Libraries possessed greater than 99% of all 11 860 programmed mutations. Golden Gate cloning was then used to assemble the complete ΦX174 mutant genome and generate libraries of infective viruses. This protocol will enable reverse genetics experiments for studying viral evolution and, with some modifications, can be applied for engineering therapeutically relevant bacteriophages with larger genomes.

Fungal Endophyte Colonization Patterns Alter Over Time in the Novel Association Between Lolium perenne and Epichloë Endophyte AR37

Infection of the pasture grass Lolium perenne with the seed-transmitted fungal endophyte Epichloë festucae enhances its resilience to biotic and abiotic stress. Agricultural benefits of endophyte infection can be increased by generating novel symbiotic associations through inoculating L. perenne with selected Epichloë strains. Natural symbioses have coevolved over long periods. Thus, artificial symbioses will probably not have static properties, but symbionts will coadapt over time improving the fitness of the association. Here we report for the first time on temporal changes in a novel association of Epichloë strain AR37 and the L. perenne cultivar Grasslands Samson. Over nine generations, a seed maintenance program had increased the endophyte seed transmission rates to > 95% (from an initial 76%). We observed an approximately fivefold decline in endophyte biomass concentration in vegetative tissues over time (between generations 2 and 9). This indicates strong selection pressure toward reducing endophyte-related fitness costs by reducing endophyte biomass, without compromising the frequency of endophyte transmission to seed. We observed no obvious changes in tillering and only minor transcriptomic changes in infected plants over time. Functional analysis of 40 plant genes, showing continuously decreasing expression over time, suggests that adaptation of host metabolism and defense mechanisms are important for increasing the fitness of this association, and possibly fitness of such symbioses in general. Our results indicate that fitness of novel associations is likely to improve over time and that monitoring changes in novel associations can assist in identifying key features of endophyte-mediated enhancement of host fitness.

Patterns of Genetic Variability in Genomic Regions with Low Rates of Recombination

The amount of DNA sequence variability in a genomic region is often positively correlated with its rate of crossing over (CO) [1-3]. This pattern is caused by selection acting on linked sites, which reduces genetic variability and biases the frequency distribution of segregating variants toward more rare variants than are expected without selection (skew). These effects may involve the spread of beneficial mutations (selective sweeps [SSWs]), the elimination of deleterious mutations (background selection [BGS]), or both, and are expected to be stronger with lower CO rates [1-3]. However, in a recent study of human populations, the skew was reduced in the lowest CO regions compared with regions with somewhat higher CO rates [4]. A low skew in very low CO regions, compared with theoretical predictions, is seen in the population genomic studies of Drosophila simulans described here and in other Drosophila species. Here, we propose an explanation for lower than expected skew in low CO regions, and validate it using computer simulations; explanations for higher skew with higher CO rates, as in D. simulans, will be explored elsewhere. Partially recessive, linked deleterious mutations can increase neutral variability when the product of the effective population size (N_e) and the selection coefficient against homozygous carriers of mutations (s) is ≤1, i.e., there is associative overdominance (AOD) rather than BGS [5]. AOD can operate in low CO regions, producing a lower skew than in its absence. This opens up a new perspective on how selection affects patterns of variability at linked sites.