The Role of Mutation Bias in Adaptive Evolution

Mutational biases favor complexity increases in protein interaction networks after gene duplication

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

Polygenic response of sex chromosomes to sexual antagonism

Sexual antagonism occurs when males and females differ in their phenotypic fitness optima but are constrained in their evolution to these optima because of their shared genome. The sex chromosomes, which have distinct evolutionary "interests" relative to the autosomes, are theorized to play an important role in sexually antagonistic conflict. However, the evolutionary responses of sex chromosomes and autosomes have usually been considered independently, that is, via contrasting the response of a gene located on either an X chromosome or an autosome. Here, we study the coevolutionary response of the X chromosome and autosomes to sexually antagonistic selection acting on a polygenic phenotype. We model a phenotype initially under stabilizing selection around a single optimum, followed by a sudden divergence of the male and female optima. We find that, in the absence of dosage compensation, the X chromosome promotes evolution toward the female optimum, inducing coevolutionary male-biased responses on the autosomes. Dosage compensation obscures the female-biased interests of the X, causing it to contribute equally to male and female phenotypic change. We further demonstrate that fluctuations in an adaptive landscape can generate prolonged intragenomic conflict and accentuate the differential responses of the X and autosomes to this conflict.

Four classic "de novo" genes all have plausible homologs and likely evolved from retro-duplicated or pseudogenic sequences

Despite being previously regarded as extremely unlikely, the idea that entirely novel protein-coding genes can emerge from non-coding sequences has gradually become accepted over the past two decades. Examples of "de novo origination", resulting in lineage-specific "orphan" genes, lacking coding orthologs, are now produced every year. However, many are likely cases of duplicates that are difficult to recognize. Here, I re-examine the claims and show that four very well-known examples of genes alleged to have emerged completely "from scratch"- FLJ33706 in humans, Goddard in fruit flies, BSC4 in baker's yeast and AFGP2 in codfish-may have plausible evolutionary ancestors in pre-existing genes. The first two are likely highly diverged retrogenes coding for regulatory proteins that have been misidentified as orphans. The antifreeze glycoprotein, moreover, may not have evolved from repetitive non-genic sequences but, as in several other related cases, from an apolipoprotein that could have become pseudogenized before later being reactivated. These findings detract from various claims made about de novo gene birth and show there has been a tendency not to invest the necessary effort in searching for homologs outside of a very limited syntenic or phylostratigraphic methodology. A robust approach is used for improving detection that draws upon similarities, not just in terms of statistical sequence analysis, but also relating to biochemistry and function, to obviate notable failures to identify homologs.

Mutation and evolution: Conceptual possibilities

Although random mutation is central to models of evolutionary change, a lack of clarity remains regarding the conceptual possibilities for thinking about the nature and role of mutation in evolution. We distinguish several claims at the intersection of mutation, evolution, and directionality and then characterize a previously unrecognized category: complex conditioned mutation. Empirical evidence in support of this category suggests that the historically famous fluctuation test should be revisited, and new experiments should be undertaken with emerging experimental techniques to facilitate detecting mutation rates within specific loci at an ultra-high, individual base pair resolution.

RNA m6 A Methylation Suppresses Insect Juvenile Hormone Degradation to Minimize Fitness Costs in Response to A Pathogenic Attack

Bioinsecticides and transgenic crops based on the bacterial pathogen Bacillus thuringiensis (Bt) can effectively control diverse agricultural insect pests, nevertheless, the evolution of resistance without obvious fitness costs has seriously eroded the sustainable use of these Bt products. Recently, it has been discovered that an increased titer of juvenile hormone (JH) favors an insect host (Plutella xylostella) to enhance fitness whilst resisting the Bt pathogen, however, the underlying regulatory mechanisms of the increased JH titer are obscure. Here, the involvement of N6 -methyladenosine (m6 A) RNA modification in modulating the availability of JH in this process is defined. Specifically, it is found that two m6 A methyltransferase subunit genes, PxMettl3 and PxMettl14, repress the expression of a key JH-degrading enzyme JH esterase (JHE) to induce an increased JH titer, mitigating the fitness costs associated with a robust defense against the Bt pathogen. This study identifies an as-yet uncharacterized m6 A-mediated epigenetic regulator of insect hormones for maintaining fitness during pathogen defense and unveils an emerging Bt resistance-related m6 A methylation atlas in insects, which further expands the functional landscape of m6 A modification and showcases the pivotal role of epigenetic regulation in host-pathogen interactions.

Interaction between mutation type and gene pleiotropy drives parallel evolution in the laboratory

What causes evolution to be repeatable is a fundamental question in evolutionary biology. Pleiotropy, i.e. the effect of an allele on multiple traits, is thought to enhance repeatability by constraining the number of available beneficial mutations. Additionally, pleiotropy may promote repeatability by allowing large fitness benefits of single mutations via adaptive combinations of phenotypic effects. Yet, this latter evolutionary potential may be reaped solely by specific types of mutations able to realize optimal combinations of phenotypic effects while avoiding the costs of pleiotropy. Here, we address the interaction of gene pleiotropy and mutation type on evolutionary repeatability in a meta-analysis of experimental evolution studies with Escherichia coli. We hypothesize that single nucleotide polymorphisms (SNPs) are principally able to yield large fitness benefits by targeting highly pleiotropic genes, whereas indels and structural variants (SVs) provide smaller benefits and are restricted to genes with lower pleiotropy. By using gene connectivity as proxy for pleiotropy, we show that non-disruptive SNPs in highly pleiotropic genes yield the largest fitness benefits, since they contribute more to parallel evolution, especially in large populations, than inactivating SNPs, indels and SVs. Our findings underscore the importance of considering genetic architecture together with mutation type for understanding evolutionary repeatability. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

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.

A fitness trade-off explains the early fate of yeast aneuploids with chromosome gains

The early development of aneuploidy from an accidental chromosome missegregation shows contrasting effects. On the one hand, it is associated with significant cellular stress and decreased fitness. On the other hand, it often carries a beneficial effect and provides a quick (but typically transient) solution to external stress. These apparently controversial trends emerge in several experimental contexts, particularly in the presence of duplicated chromosomes. However, we lack a mathematical evolutionary modeling framework that comprehensively captures these trends from the mutational dynamics and the trade-offs involved in the early stages of aneuploidy. Here, focusing on chromosome gains, we address this point by introducing a fitness model where a fitness cost of chromosome duplications is contrasted by a fitness advantage from the dosage of specific genes. The model successfully captures the experimentally measured probability of emergence of extra chromosomes in a laboratory evolution setup. Additionally, using phenotypic data collected in rich media, we explored the fitness landscape, finding evidence supporting the existence of a per-gene cost of extra chromosomes. Finally, we show that the substitution dynamics of our model, evaluated in the empirical fitness landscape, explains the relative abundance of duplicated chromosomes observed in yeast population genomics data. These findings lay a firm framework for the understanding of the establishment of newly duplicated chromosomes, providing testable quantitative predictions for future observations.

Adaptive Evolution of Rhizobial Symbiosis beyond Horizontal Gene Transfer: From Genome Innovation to Regulation Reconstruction

There are ubiquitous variations in symbiotic performance of different rhizobial strains associated with the same legume host in agricultural practices. This is due to polymorphisms of symbiosis genes and/or largely unexplored variations in integration efficiency of symbiotic function. Here, we reviewed cumulative evidence on integration mechanisms of symbiosis genes. Experimental evolution, in concert with reverse genetic studies based on pangenomics, suggests that gain of the same circuit of key symbiosis genes through horizontal gene transfer is necessary but sometimes insufficient for bacteria to establish an effective symbiosis with legumes. An intact genomic background of the recipient may not support the proper expression or functioning of newly acquired key symbiosis genes. Further adaptive evolution, through genome innovation and reconstruction of regulation networks, may confer the recipient of nascent nodulation and nitrogen fixation ability. Other accessory genes, either co-transferred with key symbiosis genes or stochastically transferred, may provide the recipient with additional adaptability in ever-fluctuating host and soil niches. Successful integrations of these accessory genes with the rewired core network, regarding both symbiotic and edaphic fitness, can optimize symbiotic efficiency in various natural and agricultural ecosystems. This progress also sheds light on the development of elite rhizobial inoculants using synthetic biology procedures.

Multivariate selection and the making and breaking of mutational pleiotropy

The role of mutations have been subject to many controversies since the formation of the Modern Synthesis of evolution in the early 1940ties. Geneticists in the early half of the twentieth century tended to view mutations as a limiting factor in evolutionary change. In contrast, natural selection was largely viewed as a “sieve” whose main role was to sort out the unfit but which could not create anything novel alone. This view gradually changed with the development of mathematical population genetics theory, increased appreciation of standing genetic variation and the discovery of more complex forms of selection, including balancing selection. Short-term evolutionary responses to selection are mainly influenced by standing genetic variation, and are predictable to some degree using information about the genetic variance–covariance matrix (G) and the strength and form of selection (e. g. the vector of selection gradients, β). However, predicting long-term evolution is more challenging, and requires information about the nature and supply of novel mutations, summarized by the mutational variance–covariance matrix (M). Recently, there has been increased attention to the role of mutations in general and M in particular. Some evolutionary biologists argue that evolution is largely mutation-driven and claim that mutation bias frequently results in mutation-biased adaptation. Strong similarities between G and M have also raised questions about the non-randomness of mutations. Moreover, novel mutations are typically not isotropic in their phenotypic effects and mutational pleiotropy is common. Here I discuss the evolutionary origin and consequences of mutational pleiotropy and how multivariate selection directly shapes G and indirectly M through changed epistatic relationships. I illustrate these ideas by reviewing recent literature and models about correlational selection, evolution of G and M, sexual selection and the fitness consequences of sexual antagonism.

Accelerated Evolution by Diversity-Generating Retroelements

Diversity-generating retroelements (DGRs) create vast amounts of targeted, functional diversity by facilitating the rapid evolution of ligand-binding protein domains. Thousands of DGRs have been identified in bacteria, archaea, and their respective viruses. They are broadly distributed throughout the microbial world, with enrichment observed in certain taxa and environments. The diversification machinery works through a novel mechanism termed mutagenic retrohoming, whereby nucleotide sequence information is copied from an invariant DNA template repeat (TR) into an RNA intermediate, selectively mutagenized at TR adenines during cDNA synthesis by a DGR-encoded reverse transcriptase, and transferred to a variable repeat (VR) region within a variable-protein gene (54). This unidirectional flow of information leaves TR-DNA sequences unmodified, allowing for repeated rounds of mutagenic retrohoming to optimize variable-protein function. DGR target genes are often modular and can encode one or more of a wide variety of discrete functional domains appended to a diversifiable ligand-binding motif. Bacterial variable proteins often localize to cell surfaces, although a subset appear to be cytoplasmic, while phage-encoded DGRs commonly diversify tail fiber-associated receptor-binding proteins. Here, we provide a comprehensive review of the mechanism and consequences of accelerated protein evolution by these unique and beneficial genetic elements. Expected final online publication date for the Annual Review of Microbiology, Volume 76 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Population size mediates the contribution of high-rate and large-benefit mutations to parallel evolution

Mutations with large fitness benefits and mutations occurring at high rates may both cause parallel evolution, but their contribution is predicted to depend on population size. Moreover, high-rate and large-benefit mutations may have different long-term adaptive consequences. We show that small and 100-fold larger bacterial populations evolve resistance to a β-lactam antibiotic by using similar numbers, but different types of mutations. Small populations frequently substitute similar high-rate structural variants and loss-of-function point mutations, including the deletion of a low-activity β-lactamase, and evolve modest resistance levels. Large populations more often use low-rate, large-benefit point mutations affecting the same targets, including mutations activating the β-lactamase and other gain-of-function mutations, leading to much higher resistance levels. Our results demonstrate the separation by clonal interference of mutation classes with divergent adaptive consequences, causing a shift from high-rate to large-benefit mutations with increases in population size.

Genetic Basis of High-Pressure Tolerance of a Vibrio parahaemolyticus Mutant and Its Pathogenicity

Foodborne pathogens with high-pressure processing (HPP) tolerance and their pathogenicity have gained considerable attention in the field of food safety. However, tolerance to pressure treatment varies among microorganisms and growth phases, and the mechanism by which Vibrio parahaemolyticus can become tolerant of HPP is currently not known. In this study, 183 strains of V. parahaemolyticus were isolated from seafood products, and one strain, C4, carried a thermostable direct hemolysin (tdh) gene. A strain, N11, which was acquired from the C4 strain through adaptive laboratory evolution under HPP stress, could tolerate up to 200 MPa for 10 min. Compared with the C4 strain, the catalase and Na⁺/K⁺-ATPase activities in N11 strain were increased by about 2–3 times, and the cells maintained an intact cell membrane structure under HPP treatment. As shown by murine infection trials, the C4 and N11 strains impacted the physiological activities of mice and damaged liver and spleen cells. Comparative genomic analysis showed that 19 nucleotides were mutated in the N11 strain, which led to sustained high expression of mlaC and mlaD genes in this strain. Knockout of these genes confirmed that they were involved in the high-pressure stress response, and also related to pathogenicity of V. parahaemolyticus. Thereby, our findings revealed a HPP tolerance mechanism of V. parahaemolyticus, and the high-pressure-tolerant strain still retained pathogenicity in mice with skin and fur pleating and lethargy, indicating the pressure-tolerant foodborne pathogens present health risks.

A pan-cancer analysis on the carcinogenic effect of human adenomatous polyposis coli

Adenomatous polyposis coli (APC) is the most commonly mutated gene in colon cancer and can cause familial adenomatous polyposis (FAP). Hypermethylation of the APC promoter can also promote the development of breast cancer, indicating that APC is not limited to association with colorectal neoplasms. However, no pan-cancer analysis has been conducted. We studied the location and structure of APC and the expression and potential role of APC in a variety of tumors by using The Cancer Genome Atlas and Gene Expression Omnibus databases and online bioinformatics analysis tools. The APC is located at 5q22.2, and its protein structure is conserved among H. sapiens, M. musculus with C. elaphus hippelaphus. The APC identity similarity between homo sapiens and mus musculus reaches 90.1%. Moreover, APC is highly specifically expressed in brain tissues and bipolar cells but has low expression in most cancers. APC is mainly expressed on the cell membrane and is not detected in plasma by mass spectrometry. APC is low expressed in most tumor tissues, and there is a significant correlation between the expressed level of APC and the main pathological stages as well as the survival and prognosis of tumor patients. In most tumors, APC gene has mutation and methylation and an enhanced phosphorylation level of some phosphorylation sites, such as T1438 and S2260. The expressed level of APC is also involved in the level of CD8+ T-cell infiltration, Tregs infiltration, and cancer-associated fibroblast infiltration. We conducted a gene correlation study, but the findings seemed to contradict the previous analysis results of the low expression of the APC gene in most cancers. Our research provides a comparative wholesale understanding of the carcinogenic effects of APC in various cancers, which will help anti-cancer research.

The zinc-finger bearing xenogeneic silencer MucR in α-proteobacteria balances adaptation and regulatory integrity

Foreign AT-rich genes drive bacterial adaptation to new niches while challenging the existing regulation network. Here we report that MucR, a conserved regulator in α-proteobacteria, balances adaptation and regulatory integrity in Sinorhizobium fredii, a facultative microsymbiont of legumes. Chromatin immunoprecipitation sequencing coupled with transcriptomic data reveal that average transcription levels of both target and non-target genes, under free-living and symbiotic conditions, increase with their conservation levels. Targets involved in environmental adaptation and symbiosis belong to genus or species core and can be repressed or activated by MucR in a condition-dependent manner, implying regulatory integrations. However, most targets are enriched in strain-specific genes of lower expression levels and higher AT%. Within each conservation levels, targets have higher AT% and average transcription levels than non-target genes and can be further up-regulated in the mucR mutant. This is consistent with higher AT% of spacers between -35 and -10 elements of promoters for target genes, which enhances transcription. The MucR recruitment level linearly increases with AT% and the number of a flexible pattern (with periodic repeats of Ts) of target sequences. Collectively, MucR directly represses AT-rich foreign genes with predisposed high transcription potential while progressive erosions of its target sites facilitate regulatory integrations of foreign genes.

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.

Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes

Morphospaces-representations of phenotypic characteristics-are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavoring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that noncoding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon a random sampling of sequences. We show that: 1) only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored; 2) remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far; and 3) perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon a uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather a strong phenotype bias in the RNA genotype-phenotype map, a type of developmental bias or "findability constraint," which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to "find."

Both Binding Strength and Evolutionary Accessibility Affect the Population Frequency of Transcription Factor Binding Sequences in Arabidopsis thaliana

Mutations in DNA sequences that bind transcription factors and thus modulate gene expression are a source of adaptive variation in gene expression. To understand how transcription factor binding sequences evolve in natural populations of the thale cress Arabidopsis thaliana, we integrated genomic polymorphism data for loci bound by transcription factors with in vitro data on binding affinity for these transcription factors. Specifically, we studied 19 different transcription factors, and the allele frequencies of 8,333 genomic loci bound in vivo by these transcription factors in 1,135 A. thaliana accessions. We find that transcription factor binding sequences show very low genetic diversity, suggesting that they are subject to purifying selection. High frequency alleles of such binding sequences tend to bind transcription factors strongly. Conversely, alleles that are absent from the population tend to bind them weakly. In addition, alleles with high frequencies also tend to be the endpoints of many accessible evolutionary paths leading to these alleles. We show that both high affinity and high evolutionary accessibility contribute to high allele frequency for at least some transcription factors. Although binding sequences with stronger affinity are more frequent, we did not find them to be associated with higher gene expression levels. Epistatic interactions among individual mutations that alter binding affinity are pervasive and can help explain variation in accessibility among binding sequences. In summary, combining in vitro binding affinity data with in vivo binding sequence data can help understand the forces that affect the evolution of transcription factor binding sequences in natural populations.

Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes

Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.

The contribution of mutation and selection to multivariate quantitative genetic variance in an outbred population of Drosophila serrata

Genetic variance is not equal for all multivariate combinations of traits. This inequality, in which some combinations of traits have abundant genetic variation while others have very little, biases the rate and direction of multivariate phenotypic evolution. However, we still understand little about what causes genetic variance to differ among trait combinations. Here, we investigate the relative roles of mutation and selection in determining the genetic variance of multivariate phenotypes. We accumulated mutations in an outbred population of Drosophila serrata and analyzed wing shape and size traits for over 35,000 flies to simultaneously estimate the additive genetic and additive mutational (co)variances. This experimental design allowed us to gain insight into the phenotypic effects of mutation as they arise and come under selection in naturally outbred populations. Multivariate phenotypes associated with more (less) genetic variance were also associated with more (less) mutational variance, suggesting that differences in mutational input contribute to differences in genetic variance. However, mutational correlations between traits were stronger than genetic correlations, and most mutational variance was associated with only one multivariate trait combination, while genetic variance was relatively more equal across multivariate traits. Therefore, selection is implicated in breaking down trait covariance and resulting in a different pattern of genetic variance among multivariate combinations of traits than that predicted by mutation and drift. Overall, while low mutational input might slow evolution of some multivariate phenotypes, stabilizing selection appears to reduce the strength of evolutionary bias introduced by pleiotropic mutation.

Self-selection of evolutionary strategies: adaptive versus non-adaptive forces

The evolution of complex genetic networks is shaped over the course of many generations through multiple mechanisms. These mechanisms can be broken into two predominant categories: adaptive forces, such as natural selection, and non-adaptive forces, such as recombination, genetic drift, and random mutation. Adaptive forces are influenced by the environment, where individuals better suited for their ecological niche are more likely to reproduce. This adaptive force results in a selective pressure which creates a bias in the reproduction of individuals with beneficial traits. Non-adaptive forces, in contrast, are not influenced by the environment: Random mutations occur in offspring regardless of whether they improve the fitness of the offspring. Both adaptive and non-adaptive forces play critical roles in the development of a species over time, and both forces are intrinsically linked to one another. We hypothesize that even under a simple sexual reproduction model, selective pressure will result in changes in the mutation rate and genome size. We tested this hypothesis by evolving Boolean networks using a modified genetic algorithm. Our results demonstrate that changes in environmental signals can result in selective pressure which affects mutation rate.

Correlational selection in the age of genomics

Ecologists and evolutionary biologists are well aware that natural and sexual selection do not operate on traits in isolation, but instead act on combinations of traits. This long-recognized and pervasive phenomenon is known as multivariate selection, or-in the particular case where it favours correlations between interacting traits-correlational selection. Despite broad acknowledgement of correlational selection, the relevant theory has often been overlooked in genomic research. Here, we discuss theory and empirical findings from ecological, quantitative genetic and genomic research, linking key insights from different fields. Correlational selection can operate on both discrete trait combinations and quantitative characters, with profound implications for genomic architecture, linkage, pleiotropy, evolvability, modularity, phenotypic integration and phenotypic plasticity. We synthesize current knowledge and discuss promising research approaches that will enable us to understand how correlational selection shapes genomic architecture, thereby linking quantitative genetic approaches with emerging genomic methods. We suggest that research on correlational selection has great potential to integrate multiple fields in evolutionary biology, including developmental and functional biology, ecology, quantitative genetics, phenotypic polymorphisms, hybrid zones and speciation processes.

Elevated temperature increases genome-wide selection on de novo mutations

Adaptation in new environments depends on the amount of genetic variation available for evolution, and the efficacy by which natural selection discriminates among this variation. However, whether some ecological factors reveal more genetic variation, or impose stronger selection pressures than others, is typically not known. Here, we apply the enzyme kinetic theory to show that rising global temperatures are predicted to intensify natural selection throughout the genome by increasing the effects of DNA sequence variation on protein stability. We test this prediction by (i) estimating temperature-dependent fitness effects of induced mutations in seed beetles adapted to ancestral or elevated temperature, and (ii) calculate 100 paired selection estimates on mutations in benign versus stressful environments from unicellular and multicellular organisms. Environmental stress per se did not increase mean selection on de novo mutation, suggesting that the cost of adaptation does not generally increase in new ecological settings to which the organism is maladapted. However, elevated temperature increased the mean strength of selection on genome-wide polymorphism, signified by increases in both mutation load and mutational variance in fitness. These results have important implications for genetic diversity gradients and the rate and repeatability of evolution under climate change.

Natural selection mediated by seasonal time constraints increases the alignment between evolvability and developmental plasticity

Phenotypic plasticity can either hinder or promote adaptation to novel environments. Recent studies that have quantified alignments between plasticity, genetic variation, and divergence propose that such alignments may reflect constraints that bias future evolutionary trajectories. Here, we emphasize that such alignments may themselves be a result of natural selection and do not necessarily indicate constraints on adaptation. We estimated developmental plasticity and broad sense genetic covariance matrices (G) among damselfly populations situated along a latitudinal gradient in Europe. Damselflies were reared at photoperiod treatments that simulated the seasonal time constraints experienced at northern (strong constraints) and southern (relaxed constraints) latitudes. This allowed us to partition the effects of (1) latitude, (2) photoperiod, and (3) environmental novelty on G and its putative alignment with adaptive plasticity and divergence. Environmental novelty and latitude did not affect G, but photoperiod did. Photoperiod increased evolvability in the direction of observed adaptive divergence and developmental plasticity when G was assessed under strong seasonal time constraints at northern (relative to southern) photoperiod. Because selection and adaptation under time constraints is well understood in Lestes damselflies, our results suggest that natural selection can shape the alignment between divergence, plasticity, and evolvability.

Mutation bias can shape adaptation in large asexual populations experiencing clonal interference

The extended evolutionary synthesis invokes a role for development in shaping adaptive evolution, which in population genetics terms corresponds to mutation-biased adaptation. Critics have claimed that clonal interference makes mutation-biased adaptation rare. We consider the behaviour of two simultaneously adapting traits, one with larger mutation rate U, the other with larger selection coefficient s, using asexual travelling wave models. We find that adaptation is dominated by whichever trait has the faster rate of adaptation v in isolation, with the other trait subject to evolutionary stalling. Reviewing empirical claims for mutation-biased adaptation, we find that not all occur in the 'origin-fixation' regime of population genetics where v is only twice as sensitive to s as to U. In some cases, differences in U are at least ten to twelve times larger than differences in s, as needed to cause mutation-biased adaptation even in the 'multiple mutations' regime. Surprisingly, when U > s in the 'diffusive-mutation' regime, the required sensitivity ratio is also only two, despite pervasive clonal interference. Given two traits with identical v, the benefit of having higher s is surprisingly small, occurring largely when one trait is at the boundary between the origin-fixation and multiple mutations regimes.

Loss of Heterozygosity and Base Mutation Rates Vary Among Saccharomyces cerevisiae Hybrid Strains

A growing body of evidence suggests that mutation rates exhibit intra-species specific variation. We estimated genome-wide loss of heterozygosity (LOH), gross chromosomal changes, and single nucleotide mutation rates to determine intra-species specific differences in hybrid and homozygous strains of Saccharomyces cerevisiae. The mutation accumulation lines of the S. cerevisiae hybrid backgrounds - S288c/YJM789 (S/Y) and S288c/RM11-1a (S/R) were analyzed along with the homozygous diploids RM11, S288c, and YJM145. LOH was extensive in both S/Y and S/R hybrid backgrounds. The S/Y background also showed longer LOH tracts, gross chromosomal changes, and aneuploidy. Short copy number aberrations were observed in the S/R background. LOH data from the S/Y and S/R hybrids were used to construct a LOH map for S288c to identify hotspots. Further, we observe up to a sixfold difference in single nucleotide mutation rates among the S. cerevisiae S/Y and S/R genetic backgrounds. Our results demonstrate LOH is common during mitotic divisions in S. cerevisiae hybrids and also highlight genome-wide differences in LOH patterns and rates of single nucleotide mutations between commonly used S. cerevisiae hybrid genetic backgrounds.

Characterization and genomic analysis of a diesel-degrading bacterium, Acinetobacter calcoaceticus CA16, isolated from Canadian soil

Background

With the high demand for diesel across the world, environmental decontamination from its improper usage, storage and accidental spills becomes necessary. One highly environmentally friendly and cost-effective decontamination method is to utilize diesel-degrading microbes as a means for bioremediation. Here, we present a newly isolated and identified strain of Acinetobacter calcoaceticus (‘CA16’) as a candidate for the bioremediation of diesel-contaminated areas.

Results

Acinetobacter calcoaceticus CA16 was able to survive and grow in minimal medium with diesel as the only source of carbon. We determined through metabolomics that A. calcoaceticus CA16 appears to be efficient at diesel degradation. Specifically, CA16 is able to degrade 82 to 92% of aliphatic alkane hydrocarbons (C_nH_n + 2; where n = 12–18) in 28 days. Several diesel-degrading genes (such as alkM and xcpR) that are present in other microbes were also found to be activated in CA16.

Conclusions

The results presented here suggest that Acinetobacter strain CA16 has good potential in the bioremediation of diesel-polluted environments.

The emergence of new trophic levels in eco-evolutionary models with naturally-bounded traits

Ecosystems and food webs are structured into trophic levels of who eats whom. Species that occupy higher trophic levels have less available energy and higher energetic costs than species at lower trophic levels. So why do higher trophic levels exist? What processes generate new trophic levels? We consider a heuristic eco-evolutionary model based on simple Lotka-Volterra equations, where the evolution of traits is described by a generalisation of Lande's equation. The transition from competition to predation in this simplest of models is a successful, safe strategy for a population, and suggests a propensity to develop new trophic levels may be an inherent property of ecosystems. Numerical simulations with a more complex eco-evolutionary model of interacting plant and herbivore populations display the emergence of a new trophic level as an alternative to continued competition. These simulations reveal that new trophic levels may arise naturally from ecosystems because a robust strategy for a population in the presence of a strong competitor that could dominate or potentially extinguish them, is to predate upon the competitor. The same properties that make the competitor strong make it an ideal prey, suggesting the rubric that it is better to eat a strong competitor than to continue competing.

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.

Codon Usage in the Iflaviridae Family Is Not Diverse Though the Family Members Are Isolated from Diverse Host Taxa

All iflavirus members belong to the unique genus, Iflavirus, of the family, Iflaviridae. The host taxa and sequence identities of these viruses are diverse. A codon usage bias, maintained by a balance between selection, mutation, and genetic drift, exists in a wide variety of organisms. We characterized the codon usage patterns of 44 iflavirus genomes that were isolated from the classes, Insecta, Arachnida, Mammalia, and Malacostraca. Iflaviruses lack a strong codon usage bias when they are evaluated using an effective number of codons. The odds ratios of the majority of dinucleotides are within the normal range. However, the dinucleotides at the 1st-2nd codon positions are more biased than those at the 2nd-3rd codon positions. Plots of effective numbers of codons, relative neutrality analysis, and PR2 bias analysis all indicate that selection pressure dominates mutations in shaping codon usage patterns in the family, Iflaviridae. When these viruses were grouped into their host taxa, we found that the indices, including the nucleotide composition, effective number of codons, relative synonymous codon usage, and the influencing factors behind the codon usage patterns, all show that there are non-significant differences between the six host-taxa-groups. Our results disagree with our assumption that diverse viruses should possess diverse codon usage patterns, suggesting that the nucleotide composition and codon usage in the family, Iflaviridae, are not host taxa-specific signatures.