Evolutionary Landscapes of Host-Virus Arms Races.

Vertebrate immune systems suppress viral infection using both innate restriction factors and adaptive immunity. Viruses mutate to escape these defenses, driving hosts to counterevolve to regain fitness. This cycle recurs repeatedly, resulting in an evolutionary arms race whose outcome depends on the pace and likelihood of adaptation by host and viral genes. Although viruses evolve faster than their vertebrate hosts, their proteins are subject to numerous functional constraints that impact the probability of adaptation. These constraints are globally defined by evolutionary landscapes, which describe the fitness and adaptive potential of all possible mutations. We review deep mutational scanning experiments mapping the evolutionary landscapes of both host and viral proteins engaged in arms races. For restriction factors and some broadly neutralizing antibodies, landscapes favor the host, which may help to level the evolutionary playing field against rapidly evolving viruses. We discuss the biophysical underpinnings of these landscapes and their therapeutic implications.

Mutation Rate Heterogeneity Increases Odds of Survival in Unpredictable Environments.

Mutation rates affect both a population's present fitness and its capacity to adapt to future environmental changes. When the available genetic variability limits adaptation to environmental change, natural selection favors high mutations rates. However, constitutively high mutation rates compromise the fitness of a population in stable environments. This problem may be resolved if an increase in mutation rates is limited to times of stress, restricted to some genomic regions, and occurs only in a subpopulation of cells. Such within-population heterogeneity of mutation rates can result from genetic, environmental, and stochastic effects. The presence of subpopulations of transient mutator cells does not jeopardize the overall fitness of a population under stable environmental conditions. However, they can increase the odds of survival in changing environments because they represent reservoirs of increased genetic variability. This article presents evidence that such heterogeneity of mutation rates is more the norm than the exception.

Suppressors of fixation can increase average fitness beyond amplifiers of selection.

Evolutionary dynamics on graphs has remarkable features: For example, it has been shown that amplifiers of selection exist that-compared to an unstructured population-increase the fixation probability of advantageous mutations, while they decrease the fixation probability of disadvantageous mutations. So far, the theoretical literature has focused on the case of a single mutant entering a graph-structured population, asking how the graph affects the probability that a mutant takes over a population and the time until this typically happens. For continuously evolving systems, the more relevant case is that mutants constantly arise in an evolving population. Typically, such mutations occur with a small probability during reproduction events. We thus focus on the low mutation rate limit. The probability distribution for the fitness in this process converges to a steady state at long times. Intuitively, amplifiers of selection are expected to increase the population's mean fitness in the steady state. Similarly, suppressors of selection are expected to decrease the population's mean fitness in the steady state. However, we show that another set of graphs, called suppressors of fixation, can attain the highest population mean fitness. The key reason behind this is their ability to efficiently reject deleterious mutants. This illustrates the importance of the deleterious mutant regime for the long-term evolutionary dynamics, something that seems to have been overlooked in the literature so far.

Mitochondrial genomes revisited: why do different lineages retain different genes?

The mitochondria contain their own genome derived from an alphaproteobacterial endosymbiont. From thousands of protein-coding genes originally encoded by their ancestor, only between 1 and about 70 are encoded on extant mitochondrial genomes (mitogenomes). Thanks to a dramatically increasing number of sequenced and annotated mitogenomes a coherent picture of why some genes were lost, or relocated to the nucleus, is emerging. In this review, we describe the characteristics of mitochondria-to-nucleus gene transfer and the resulting varied content of mitogenomes across eukaryotes. We introduce a 'burst-upon-drift' model to best explain nuclear-mitochondrial population genetics with flares of transfer due to genetic drift.

Population Genetic Considerations Regarding Evidence for Biased Mutation Rates in Arabidopsis thaliana.

It has recently been proposed that lower mutation rates in gene bodies compared with upstream and downstream sequences in Arabidopsis thaliana are the result of an "adaptive" modification of the rate of beneficial and deleterious mutations in these functional regions. This claim was based both on analyses of mutation accumulation lines and on population genomics data. Here, we show that several questionable assumptions were used in the population genomics analyses. In particular, we demonstrate that the difference between gene bodies and less selectively constrained sequences in the magnitude of Tajima's D can in principle be explained by the presence of sites subject to purifying selection and does not require lower mutation rates in regions experiencing selective constraints.

Simulating the Effect of Environmental Change on Evolving Populations.

This study uses evolutionary simulations to explore the strategies that emerge to enable populations to cope with random environmental changes in situations where lifetime learning approaches are not available to accommodate them. In particular, it investigates how the average magnitude of change per unit time and the persistence of the changes (and hence the resulting autocorrelation of the environmental time series) affect the change tolerances, population diversities, and extinction timescales that emerge. Although it is the change persistence (often discussed in terms of environmental noise color) that has received most attention in the recent literature, other factors, particularly the average change magnitude, interact with this and can be more important drivers of the adaptive strategies that emerge. Moreover, when running simulations, the choice of change representation and normalization can also affect the outcomes. Detailed simulations are presented that are designed to explore all these issues. They also reveal significant dependences on the associated mutation rates and the extent to which they can evolve, and they clarify how evolution often leads populations into strategies with higher risks of extinction. Overall, this study shows how modeling the effect of environmental change requires more care than may have previously been realized.

Distribution of mutation rates challenges evolutionary predictability.

Natural selection is commonly assumed to act on extensive standing genetic variation. Yet, accumulating evidence highlights the role of mutational processes creating this genetic variation: to become evolutionarily successful, adaptive mutants must not only reach fixation, but also emerge in the first place, i.e. have a high enough mutation rate. Here, we use numerical simulations to investigate how mutational biases impact our ability to observe rare mutational pathways in the laboratory and to predict outcomes in experimental evolution. We show that unevenness in the rates at which mutational pathways produce adaptive mutants means that most experimental studies lack power to directly observe the full range of adaptive mutations. Modelling mutation rates as a distribution, we show that a substantially larger target size ensures that a pathway mutates more commonly. Therefore, we predict that commonly mutated pathways are conserved between closely related species, but not rarely mutated pathways. This approach formalizes our proposal that most mutations have a lower mutation rate than the average mutation rate measured experimentally. We suggest that the extent of genetic variation is overestimated when based on the average mutation rate.

High level of somatic mutations detected in a diploid banana wild relative Musa basjoo.

Plants are thought to lack an early segregating germline and often retain both asexual and sexual reproduction, both of which may allow somatic mutations to enter the gametes or clonal progeny, and thereby impact plant evolution. It is yet unclear how often these somatic mutations occur during plant development and what proportion is transmitted to their sexual or cloned offspring. Asexual "seedless" propagation has contributed greatly to the breeding in many fruit crops, such as citrus, grapes and bananas. Whether plants in these lineages experience substantial somatic mutation accumulation is unknown. To estimate the somatic mutation accumulation and inheritance among a clonal population of plant, here we assess somatic mutation accumulation in Musa basjoo, a diploid banana wild relative, using 30 whole-genome resequenced samples collected from five structures, including leaves, sheaths, panicle, roots and underground rhizome connecting three clonal individuals. We observed 18.5 high proportion de novo somatic mutations on average between each two adjacent clonal suckers, equivalent to ~ 2.48 × 10-8 per site per asexual generation, higher than the per site per sexual generation rates (< 1 × 10-8) reported in Arabidopsis and peach. Interestingly, most of these inter-ramet somatic mutations were shared simultaneously in different tissues of the same individual with a high level of variant allele fractions, suggesting that these somatic mutations arise early in ramet development and that each individual may develop only from a few apical stem cells. These results thus suggest substantial mutation accumulation in a wild relative of banana. Our work reveals the significance of somatic mutation in Musa basjoo genetics variations and contribute to the trait improvement breeding of bananas and other asexual clonal crops.

Antibiotic-Induced Genetic Variation: How It Arises and How It Can Be Prevented.

By targeting essential cellular processes, antibiotics provoke metabolic perturbations and induce stress responses and genetic variation in bacteria. Here we review current knowledge of the mechanisms by which these molecules generate genetic instability. They include production of reactive oxygen species, as well as induction of the stress response regulons, which lead to enhancement of mutation and recombination rates and modulation of horizontal gene transfer. All these phenomena influence the evolution and spread of antibiotic resistance. The use of strategies to stop or decrease the generation of resistant variants is also discussed.

Ancestral reproductive bias in branching processes.

Consider a branching process whose reproduction law is homogeneous. Sampling a single cell uniformly from the population at a time [Formula: see text] and looking along the sampled cell's ancestral lineage, we find that the reproduction law is heterogeneous-the expected reproductive output of ancestral cells on the lineage from time 0 to time T continuously increases with time. This 'inspection paradox' is due to sampling bias, that cells with a larger number of offspring are more likely to have one of their descendants sampled by virtue of their prolificity. The bias's strength changes with the random population size and/or the sampling time T. Our main result explicitly characterises the evolution of reproduction rates and sizes along the sampled ancestral lineage as a mixture of Poisson processes, which simplifies in special cases. The ancestral bias helps to explain recently observed variation in mutation rates along lineages of the developing human embryo.

Fragaria mitogenomes evolve rapidly in structure but slowly in sequence and incur frequent multinucleotide mutations mediated by microinversions.

Plant mitochondrial DNA has been described as evolving rapidly in structure but slowly in sequence. However, many of the noncoding portions of plant mitogenomes are not homologous among species, raising questions about the rate and spectrum of mutations in noncoding regions. Recent studies have suggested that the lack of homology in noncoding regions could be due to increased sequence divergence. We compared 30 kb of coding and 200 kb of noncoding DNA from 13 sequenced Fragaria mitogenomes, followed by analysis of the rate of sequence divergence, microinversion events and structural variations. Substitution rates in synonymous sites and nongenic sites are nearly identical, suggesting that the genome-wide point mutation rate is generally consistent. A surprisingly high number of large multinucleotide substitutions were detected in Fragaria mitogenomes, which may have resulted from microinversion events and could affect phylogenetic signal and local rate estimates. Fragaria mitogenomes preferentially accumulate deletions relative to insertions and substantial genomic arrangements, whereas mutation rates could positively associate with these sequence and structural changes among species. Together, these observations suggest that plant mitogenomes exhibit low point mutations genome-wide but exceptionally high structural variations, and our results favour a gain-and-loss model for the rapid loss of homology among plant mitogenomes.

Evolutionary Trajectories to Antibiotic Resistance.

The ability to predict the evolutionary trajectories of antibiotic resistance would be of great value in tailoring dosing regimens of antibiotics so as to maximize the duration of their usefulness. Useful prediction of resistance evolution requires information about (a) the mutation supply rate, (b) the level of resistance conferred by the resistance mechanism, (c) the fitness of the antibiotic-resistant mutant bacteria as a function of drug concentration, and (d) the strength of selective pressures. In addition, processes including epistatic interactions and compensatory evolution, coselection of drug resistances, and population bottlenecks and clonal interference can strongly influence resistance evolution and thereby complicate attempts at prediction. Currently, the very limited quantitative data on most of these parameters severely limit attempts to accurately predict trajectories of resistance evolution.

Mutational analysis of 16 STR markers in the Slovak population.

BACKGROUND: Short tandem repeats (STRs) are genetic markers frequently used for human identification and paternity testing. They are highly mutable, which may occasionally lead to inconsistencies between the genotypes of parents and their children. As the mutation rates of individual STR markers can vary among populations, population-specific data are of high importance. AIM: To investigate the mutation rates of 16 STR markers in the Slovak population. SUBJECTS AND METHODS: In this study, we analysed the germline mutation rates of 16 STR markers (TH01, D3S1358, vWA, D21S11, D16S539, D1S1656, D19S433, SE33, D10S1248, D22S1045, D12S391, D8S1179, D2S1338, D2S441, D18S51 and FGA) in the Slovak population. At these loci, we analysed 42 096 allelic transfers and identified 61 mutation events. RESULTS: The loci with the highest overall mutation rates were SE33 and FGA, while no mutations were identified in TH01, D19S433 and D22S1045. The average paternal mutation rate was higher than the maternal mutation rate. All but one mutation consisted of gains or losses of a single repeat unit and the overall mutation rate was estimated to be 1.45 x10-3 per meiosis. CONCLUSION: This study provides data which can be used to further strengthen the correct paternity index calculations and reliability of paternity testing in Slovakia.

A Bayesian Framework for Inferring the Influence of Sequence Context on Point Mutations.

The probability of point mutations is expected to be highly influenced by the flanking nucleotides that surround them, known as the sequence context. This phenomenon may be mainly attributed to the enzyme that modifies or mutates the genetic material, because most enzymes tend to have specific sequence contexts that dictate their activity. Here, we develop a statistical model that allows for the detection and evaluation of the effects of different sequence contexts on mutation rates from deep population sequencing data. This task is computationally challenging, as the complexity of the model increases exponentially as the context size increases. We established our novel Bayesian method based on sparse model selection methods, with the leading assumption that the number of actual sequence contexts that directly influence mutation rates is minuscule compared with the number of possible sequence contexts. We show that our method is highly accurate on simulated data using pentanucleotide contexts, even when accounting for noisy data. We next analyze empirical population sequencing data from polioviruses and HIV-1 and detect a significant enrichment in sequence contexts associated with deamination by the cellular deaminases ADAR 1/2 and APOBEC3G, respectively. In the current era, where next-generation sequencing data are highly abundant, our approach can be used on any population sequencing data to reveal context-dependent base alterations and may assist in the discovery of novel mutable sites or editing sites.

Selective and non-selective bottlenecks as drivers of the evolution of hypermutable bacterial loci.

Bottlenecks reduce the size of the gene pool within populations of all life forms with implications for their subsequent survival. Here, we examine the effects of bottlenecks on bacterial commensal-pathogens during transmission between, and dissemination within, hosts. By reducing genetic diversity, bottlenecks may alter individual or population-wide adaptive potential. A diverse range of hypermutable mechanisms have evolved in infectious agents that allow for rapid generation of genetic diversity in specific genomic loci as opposed to the variability arising from increased genome-wide mutation rates. These localised hypermutable mechanisms include multi-gene phase variation (PV) of outer membrane components, multi-allele PV of restriction systems and recombination-driven antigenic variation. We review selected experimental and theoretical (mathematical) models pertaining to the hypothesis that localised hypermutation (LH) compensates for fitness losses caused by bottlenecks and discuss whether bottlenecks have driven the evolution of hypermutable loci.

DNA sequence reconstruction based on innovated hybridization technique of probabilistic cellular automata and particle swarm optimization.

DNA sequence reconstruction is a challenging research problem in the computational biology field. The evolution of the DNA is too complex to be characterized by a few parameters. Therefore, there is a need for a modeling approach for analyzing DNA patterns. In this paper, we proposed a novel framework for DNA pattern analysis. The proposed framework consists of two main stages. The first stage is for analyzing the DNA sequences evolution, whereas the other stage is for the reconstruction process. We utilized cellular automata (CA) rules for analyzing and predicting the DNA sequence. Then, a modified procedure for the reconstruction process is introduced, which is based on the Probabilistic Cellular Automata (PCA) integrated with Particle Swarm Optimization (PSO) algorithm. This integration makes the proposed framework more efficient and achieves optimum transition rules. Our innovated model leans on the hypothesis that mutations are probabilistic events. As a result, their evolution can be simulated as a PCA model. The main objective of this paper is to analyze various DNA sequences to predict the changes that occur in DNA during evolution (mutations). We used a similarity score as a fitness measure to detect symmetry relations, which is appropriate for numerous extremely long sequences. Results are given for the CpG-methylation-deamination processes, which are regions of DNA where a guanine nucleotide follows a cytosine nucleotide in the linear sequence of bases. The DNA evolution is handled as the evolved colored paradigms. Therefore, incorporating probabilistic components help to produce a tool capable of foretelling the likelihood of specific mutations. Besides, it shows their capabilities in dealing with complex relations.

Identification and characterization of novel rapidly mutating Y-chromosomal short tandem repeat markers.

Short tandem repeat polymorphisms on the male-specific part of the human Y-chromosome (Y-STRs) are valuable tools in many areas of human genetics. Although their paternal inheritance and moderate mutation rate (~10-3 mutations per marker per meiosis) allow detecting paternal relationships, they typically fail to separate male relatives. Previously, we identified 13 Y-STR markers with untypically high mutation rates (>10-2 ), termed rapidly mutating (RM) Y-STRs, and showed that they improved male relative differentiation over standard Y-STRs. By applying a newly developed in silico search approach to the Y-chromosome reference sequence, we identified 27 novel RM Y-STR candidates. Genotyping them in 1,616 DNA-confirmed father-son pairs for mutation rate estimation empirically highlighted 12 novel RM Y-STRs. Their capacity to differentiate males related by 1, 2, and 3 meioses was 27%, 47%, and 61%, respectively, while for all 25 currently known RM Y-STRs, it was 44%, 69%, and 83%. Of the 647 Y-STR mutations observed in total, almost all were single repeat changes, repeat gains, and losses were well balanced; allele length and fathers' age were positively correlated with mutation rate. We expect these new RM Y-STRs, together with the previously known ones, to significantly improving male relative differentiation in future human genetic applications.

Oak symbolism in the light of genomics.

Throughout the Northern Hemisphere, human societies, political systems, and religions have appropriated oaks in symbolic representations. In this review, we explore the possible associations between recent genetic and genomic findings and the symbolic representations of oaks. We first consider the ways in which evolutionary history during the Holocene has tightened links between humans and oaks in Europe, and how this may have led to symbolic representations. We then show how recent findings concerning the structure and evolution of the oak genome have provided additional knowledge about symbolic representations, such as longevity, cohesiveness, and robustness.

Low mutation rates promote the evolution of advantageous traits by preventing interference from deleterious mutations.

We studied how mutation rates promote the evolution of advantageous traits in an asexual population. First, to examine the effects of mutation rates on the evolution of an advantageous trait (high competitive ability), we carried out simulation analyses with competition between individuals for survival. Second, to examine the mechanism underlying the promotion of advantageous trait evolution, we calculated the probabilities that new favorable effects of mutations on the phenotype were acquired and that existing favorable effects were maintained. In the simulation analyses, advantageous traits evolved in the population with a low mutation rate; however, when the mutation rate was extremely low, advantageous traits evolved slowly because few beneficial mutations occurred. Then, the numerical calculations showed that the probability of acquiring new favorable effects of mutations on the phenotype and the probability of maintaining existing favorable effects are high if the mutation rate is low. The former occurs because, if the mutation rate is high, multiple mutations may occur in a genome, and even if beneficial mutations occur, their favorable effects may be masked by simultaneously occurring deleterious mutations. However, if the mutation rate is low, it is likely that only one beneficial mutation occurs, and its favorable effect on the phenotype is direct. In conclusion, low mutation rates are advantageous because they promote favorable phenotypic effects of mutations without interference from deleterious mutations; these low rates not only prevent the occurrence of deleterious mutations but also help maintain existing beneficial mutations and promote the evolution of advantageous traits.

A simple stochastic model describing genomic evolution over time of GC content in microbial symbionts.

An organism's genomic base composition is usually summarized by its AT or GC content due to Chargaff's parity laws. Variation in prokaryotic GC content can be substantial between taxa but is generally small within microbial genomes. This variation has been found to correlate with both phylogeny and environmental factors. Since novel single-nucleotide polymorphisms (SNPs) within genomes are at least partially linked to the environment through natural selection, SNP GC content can be considered a compound measure of an organism's environmental influences, lifestyle, phylogeny as well as other more or less random processes. While there are several models describing genomic GC content few, if any, consider AT/GC mutation rates subjected to random perturbations. We present a mathematical model that describes how GC content in microbial genomes evolves over time as a function of the AT â†’ GC and GC â†’ AT mutation rates with Gaussian white noise disturbances. The model, which is suited specifically to non-recombining vertically transmitted prokaryotic symbionts, suggests that small differences in the AT/GC mutation rates can lead to profound differences in outcome due to the ensuing stochastic process. In other words, the model indicates that time to extinction could be a consequence of the mutation rate trajectory on which the symbiont embarked early on in its evolutionary history.