Impact of epistasis and pleiotropy on evolutionary adaptation

From molecular variations to behavioral adaptations: Unveiling adaptive epistasis in primate oxytocin system

OBJECTIVE

Our primary objective was to investigate the variability of oxytocin (OT) and the GAMEN binding motif within the LNPEP oxytocinase in primates.

MATERIALS AND METHODS

We sequenced the LNPEP segment encompassing the GAMEN motif in 34 Platyrrhini species, with 21 of them also sequenced for the OT gene. Our dataset was supplemented with primate sequences of LNPEP, OT, and the oxytocin receptor (OTR) sourced from public databases. Evolutionary analysis and coevolution predictions were made followed by the macroevolution analysis of relevant amino acids associated with phenotypic traits, such as mating systems, parental care, and litter size. To account for phylogenetic structure, we utilized two distinct statistical tests. Additionally, we calculated binding energies focusing on the interaction between Callithtrix jacchus VAMEN and Pro8OT.

RESULTS

We identified two novel motifs (AAMEN and VAMEN), challenging the current knowledge of motif conservation in placental mammals. Coevolution analysis demonstrated a correlation between GAMEN, AAMEN, and VAMEN and their corresponding OTs and OTRs. Callithrix jacchus exhibited a higher binding energy between VAMEN and Pro8OT than orthologous molecules found in humans (GAMEN and Leu8OT).

DISCUSSION

The coevolution of AAMEN and VAMEN with their corresponding OTs and OTRs suggests a functional relationship that could have contributed to specific reproductive and adaptive behaviors, including paternal care, social monogamy, and twin births, prominent traits in Cebidae species, such as marmosets and tamarins. Our findings underscore the coevolution of taxon-specific amino acids among the three studied molecules, shedding light on the oxytocinergic system as an adaptive epistatic repertoire in primates.

Quantifying higher-order epistasis: beware the chimera

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

Positive epistasis drives clavulanic acid resistance in double mutant libraries of BlaC β-lactamase

Phenotypic effects of mutations are highly dependent on the genetic backgrounds in which they occur, due to epistatic effects. To test how easily the loss of enzyme activity can be compensated for, we screen mutant libraries of BlaC, a β-lactamase from Mycobacterium tuberculosis, for fitness in the presence of carbenicillin and the inhibitor clavulanic acid. Using a semi-rational approach and deep sequencing, we prepare four double-site saturation libraries and determine the relative fitness effect for 1534/1540 (99.6%) of the unique library members at two temperatures. Each library comprises variants of a residue known to be relevant for clavulanic acid resistance as well as residue 105, which regulates access to the active site. Variants with greatly improved fitness were identified within each library, demonstrating that compensatory mutations for loss of activity can be readily found. In most cases, the fittest variants are a result of positive epistasis, indicating strong synergistic effects between the chosen residue pairs. Our study sheds light on a role of epistasis in the evolution of functional residues and underlines the highly adaptive potential of BlaC.

Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few mentions of protein space consider how protein phenotypes can be described in terms of their biophysical components, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these components. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [k_{cat}, K_{M}, K_{i}, and T_{m} (melting temperature)]. We then examine how combinations of three mutations (eight alleles in total) display pleiotropy, or unique effects on individual subspace traits. We examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that future applications to bioengineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Discovering pathways through ribozyme fitness landscapes using information theoretic quantification of epistasis

The identification of catalytic RNAs is typically achieved through primarily experimental means. However, only a small fraction of sequence space can be analyzed even with high-throughput techniques. Methods to extrapolate from a limited data set to predict additional ribozyme sequences, particularly in a human-interpretable fashion, could be useful both for designing new functional RNAs and for generating greater understanding about a ribozyme fitness landscape. Using information theory, we express the effects of epistasis (i.e., deviations from additivity) on a ribozyme. This representation was incorporated into a simple model of the epistatic fitness landscape, which identified potentially exploitable combinations of mutations. We used this model to theoretically predict mutants of high activity for a self-aminoacylating ribozyme, identifying potentially active triple and quadruple mutants beyond the experimental data set of single and double mutants. The predictions were validated experimentally, with nine out of nine sequences being accurately predicted to have high activity. This set of sequences included mutants that form a previously unknown evolutionary "bridge" between two ribozyme families that share a common motif. Individual steps in the method could be examined, understood, and guided by a human, combining interpretability and performance in a simple model to predict ribozyme sequences by extrapolation.

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'.

Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Multiple intermolecular interactions facilitate rapid evolution of essential genes

Essential genes are commonly assumed to function in basic cellular processes and to change slowly. However, it remains unclear whether all essential genes are similarly conserved or if their evolutionary rates can be accelerated by specific factors. To address these questions, we replaced 86 essential genes of Saccharomyces cerevisiae with orthologues from four other species that diverged from S. cerevisiae about 50, 100, 270 and 420 Myr ago. We identify a group of fast-evolving genes that often encode subunits of large protein complexes, including anaphase-promoting complex/cyclosome (APC/C). Incompatibility of fast-evolving genes is rescued by simultaneously replacing interacting components, suggesting it is caused by protein co-evolution. Detailed investigation of APC/C further revealed that co-evolution involves not only primary interacting proteins but also secondary ones, suggesting the evolutionary impact of epistasis. Multiple intermolecular interactions in protein complexes may provide a microenvironment facilitating rapid evolution of their subunits.

On the incongruence of genotype-phenotype and fitness landscapes

The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities that can transform the effects of mutations. For example, mutations may contribute additively to a phenotype, but their effects on fitness may combine non-additively because selection favors a low or intermediate value of that phenotype. This can cause incongruence between the topographical properties of a fitness landscape and its underlying genotype-phenotype landscape. Yet, genotype-phenotype landscapes are often used as a proxy for fitness landscapes to study the dynamics and predictability of evolution. Here, we use theoretical models and empirical data on transcription factor-DNA interactions to systematically study the incongruence of genotype-phenotype and fitness landscapes when selection favors a low or intermediate phenotypic value. Using the theoretical models, we prove a number of fundamental results. For example, selection for low or intermediate phenotypic values does not change simple sign epistasis into reciprocal sign epistasis, implying that genotype-phenotype landscapes with only simple sign epistasis motifs will always give rise to single-peaked fitness landscapes under such selection. More broadly, we show that such selection tends to create fitness landscapes that are more rugged than the underlying genotype-phenotype landscape, but this increased ruggedness typically does not frustrate adaptive evolution because the local adaptive peaks in the fitness landscape tend to be nearly as tall as the global peak. Many of these results carry forward to the empirical genotype-phenotype landscapes, which may help to explain why low- and intermediate-affinity transcription factor-DNA interactions are so prevalent in eukaryotic gene regulation.

Testing for fitness epistasis in a transplant experiment identifies a candidate adaptive locus in Timema stick insects

Identifying the genetic basis of adaptation is a central goal of evolutionary biology. However, identifying genes and mutations affecting fitness remains challenging because a large number of traits and variants can influence fitness. Selected phenotypes can also be difficult to know a priori, complicating top-down genetic approaches for trait mapping that involve crosses or genome-wide association studies. In such cases, experimental genetic approaches, where one maps fitness directly and attempts to infer the traits involved afterwards, can be valuable. Here, we re-analyse data from a transplant experiment involving Timema stick insects, where five physically clustered single-nucleotide polymorphisms associated with cryptic body coloration were shown to interact to affect survival. Our analysis covers a larger genomic region than past work and revealed a locus previously not identified as associated with survival. This locus resides near a gene, Punch (Pu), involved in pteridine pigments production, implying that it could be associated with an unmeasured coloration trait. However, by combining previous and newly obtained phenotypic data, we show that this trait is not eye or body coloration. We discuss the implications of our results for the discovery of traits, genes and mutations associated with fitness in other systems, as well as for supergene evolution. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'.

Genetic basis for the evolution of pelvic-fin brooding, a new mode of reproduction, in a Sulawesian fish

Modes of reproduction in animals are diverse, with different modes having evolved independently in multiple lineages across a variety of taxa. However, an understanding of the genomic change driving the transition between different modes of reproduction is limited. Several ricefishes (Adrianichthyidae) on the island of Sulawesi have a unique mode of reproduction called "pelvic-fin brooding," wherein females carry externally fertilized eggs until hatching using their pelvic fins. Phylogenomic analysis demonstrated pelvic-fin brooders to have evolved at least twice in two distant clades of the Adrianichthyidae. We investigated the genetic architecture of the evolution of this unique mode of reproduction. Morphological analyses and laboratory observations revealed that females of pelvic-fin brooders have longer pelvic fins and a deeper abdominal concavity, and that they can carry an egg clutch for longer than non-brooding adrianichthyids, suggesting that these traits play important roles in this reproductive mode. Quantitative trait locus mapping using a cross between a pelvic-fin brooder Oryzias eversi and a non-brooding O. dopingdopingensis reveals different traits involved in pelvic-fin brooding to be controlled by different loci on different chromosomes. Genomic analyses of admixture detected no signatures of introgression between two lineages with pelvic-fin brooders, indicating that introgression is unlikely to be responsible for repeated evolution of pelvic-fin brooding. These findings suggest that multiple independent mutations may have contributed to the convergent evolution of this novel mode of reproduction.

Rapid evolution generates synergism between multiple stressors: Linking theory and an evolution experiment

Global change encompasses many co-occurring anthropogenic stressors. Understanding the interactions between these multiple stressors, whether they be additive, antagonistic or synergistic, is critical for ecosystem managers when prioritizing which stressors to mitigate in the face of global change. While such interactions between stressors appear prevalent, it remains unclear if and how these interactions change over time, as the majority of multiple-stressor studies rarely span multiple generations of study organisms. Although meta-analyses have reported some intriguing temporal trends in stressor interactions, for example that synergism may take time to emerge, the mechanistic basis for such observations is unknown. In this study, by analysing data from an evolution experiment with the rotifer Brachionus calyciflorus (~35 generations and 31,320 observations), we show that adaptation to multiple stressors shifts stressor interactions towards synergism. We show that trade-offs, where populations cannot optimally perform multiple tasks (i.e. adapting to multiple stressors), generate this bias towards synergism. We also show that removal of stressors from evolved populations does not necessarily increase fitness and that there is variation in the evolutionary trajectories of populations that experienced the same stressor regimes. Our results highlight outstanding questions at the interface between evolution and global change biology, and illustrate the importance of considering rapid adaptation when managing or restoring ecosystems subjected to multiple stressors under global change.

Antagonistic Coevolution Limits the Range of Host Defense in C. elegans Populations

Host populations often evolve defenses against parasites due to the significant fitness costs imposed by infection. However, adaptation to a specific parasite may alter the effectiveness of the host’s defenses in general. Consequently, the specificity of host defense may be influenced by a host population’s evolutionary history with parasites. Further, the degree of reciprocal change within an interaction may profoundly alter the range of host defense, given that antagonistic coevolutionary interactions are predicted to favor defense against specific parasite genotypes. Here, we examined the effect of host evolutionary history on host defense range by assessing the mortality rates of Caenorhabditis elegans host populations exposed to an array of Serratia marcescens bacterial parasite strains. Importantly, each of the host populations were derived from the same genetic background but have different experimental evolution histories with parasites. Each of these histories (exposure to either heat-killed, fixed genotype, or coevolving parasites) carries a different level of evolutionary reciprocity. Overall, we observed an effect of host evolutionary history in that previously coevolved host populations were generally the most susceptible to novel parasite strains. This data demonstrates that host evolutionary history can have a significant impact on host defense, and that host-parasite coevolution can increase host susceptibility to novel parasites.

Back to the future: Implications of genetic complexity for the structure of hybrid breeding programs

Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate contribution of inbred lines reduce effective population size and constrain the accessible genetic space. Under these conditions, long-term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study, we therefore aim to propose a rationale for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this, we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space through program decentralization and disproportionate contribution of parental inbred lines, is required to expose additive genetic variation and thus facilitate heritable genetic gains under high levels of genetic complexity. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for “why things worked” in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.

Hidden suppressive interactions are common in higher-order drug combinations

The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive—that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss “hidden” cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order—single, 2-, 3-, and 4-drug—combinations. We found that a majority of all combinations—54%—contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments.

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Most instances of suppressive interactions are missed by standard methods

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A majority (54%) of all antibiotic combinations tested contain hidden suppression

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Identifying hidden suppression can affect what combinations should be used

Adaptive Processes Change as Multiple Functions Evolve

Epistasis influences the gene-environment interactions that shape bacterial fitness through antibiotic exposure, which can ultimately affect the availability of certain resistance phenotypes to bacteria. The substitutions present within bla _TEM-50 confer both cephalosporin and β-lactamase inhibitor resistance. We wanted to compare the evolution of bla _TEM-50 with that of another variant, bla _TEM-85, which differs in that bla _TEM-85 contains only substitutions that contribute to cephalosporin resistance. Differences between the landscapes and epistatic interactions of these TEM variants are important for understanding their separate evolutionary responses to antibiotics. We hypothesized the substitutions within bla _TEM-50 would result in more epistatic interactions than for bla _TEM-85 As expected, we found more epistatic interactions between the substitutions present in bla _TEM-50 than in bla _TEM-85 Our results suggest that selection from many cephalosporins is required to achieve the full potential resistance to cephalosporins but that a single β-lactam and inhibitor combination will drive evolution of the full potential resistance phenotype. Surprisingly, we also found significantly positive increases in growth rates as antibiotic concentration increased for some of the strains expressing bla _TEM-85 precursor genotypes but not the bla _TEM-50 variants. This result further suggests that additive interactions more effectively optimize phenotypes than epistatic interactions, which means that exposure to numerous cephalosporins actually increases the ability of a TEM enzyme to confer resistance to any single cephalosporin.

Adaptation and co-adaptation of skin pigmentation and vitamin D genes in native Americans

We carried out an exhaustive review regarding human skin color variation and how much it may be related to vitamin D metabolism and other photosensitive molecules. We discuss evolutionary contexts that modulate this variability and hypotheses postulated to explain them; for example, a small amount of melanin in the skin facilitates vitamin D production, making it advantageous to have fair skin in an environment with little radiation incidence. In contrast, more melanin protects folate from degradation in an environment with a high incidence of radiation. Some Native American populations have a skin color at odds with what would be expected for the amount of radiation in the environment in which they live, a finding challenging the so-called "vitamin D-folate hypothesis." Since food is also a source of vitamin D, dietary habits should also be considered. Here we argue that a gene network approach provides tools to explain this phenomenon since it indicates potential alleles co-evolving in a compensatory way. We identified alleles of the vitamin D metabolism and pigmentation pathways segregated together, but in different proportions, in agriculturalists and hunter-gatherers. Finally, we highlight how an evolutionary approach can be useful to understand current topics of medical interest.

High-throughput analysis of adaptation using barcoded strains of Saccharomyces cerevisiae

BACKGROUND

Experimental evolution of microbes can be used to empirically address a wide range of questions about evolution and is increasingly employed to study complex phenomena ranging from genetic evolution to evolutionary rescue. Regardless of experimental aims, fitness assays are a central component of this type of research, and low-throughput often limits the scope and complexity of experimental evolution studies. We created an experimental evolution system in Saccharomyces cerevisiae that utilizes genetic barcoding to overcome this challenge.

RESULTS

We first confirm that barcode insertions do not alter fitness and that barcode sequencing can be used to efficiently detect fitness differences via pooled competition-based fitness assays. Next, we examine the effects of ploidy, chemical stress, and population bottleneck size on the evolutionary dynamics and fitness gains (adaptation) in a total of 76 experimentally evolving, asexual populations by conducting 1,216 fitness assays and analyzing 532 longitudinal-evolutionary samples collected from the evolving populations. In our analysis of these data we describe the strengths of this experimental evolution system and explore sources of error in our measurements of fitness and evolutionary dynamics.

CONCLUSIONS

Our experimental treatments generated distinct fitness effects and evolutionary dynamics, respectively quantified via multiplexed fitness assays and barcode lineage tracking. These findings demonstrate the utility of this new resource for designing and improving high-throughput studies of experimental evolution. The approach described here provides a framework for future studies employing experimental designs that require high-throughput multiplexed fitness measurements.

New Environment, New Invaders-Repeated Horizontal Transfer of LINEs to Sea Snakes

Although numerous studies have found horizontal transposon transfer (HTT) to be widespread across metazoans, few have focused on HTT in marine ecosystems. To investigate potential recent HTTs into marine species, we searched for novel repetitive elements in sea snakes, a group of elapids which transitioned to a marine habitat at most 18 Ma. Our analysis uncovered repeated HTTs into sea snakes following their marine transition. The seven subfamilies of horizontally transferred LINE retrotransposons we identified in the olive sea snake (Aipysurus laevis) are transcribed, and hence are likely still active and expanding across the genome. A search of 600 metazoan genomes found all seven were absent from other amniotes, including terrestrial elapids, with the most similar LINEs present in fish and marine invertebrates. The one exception was a similar LINE found in sea kraits, a lineage of amphibious elapids which independently transitioned to a marine environment 25 Ma. Our finding of repeated horizontal transfer events into marine snakes greatly expands past findings that the marine environment promotes the transfer of transposons. Transposons are drivers of evolution as sources of genomic sequence and hence genomic novelty. We identified 13 candidate genes for HTT-induced adaptive change based on internal or neighboring HTT LINE insertions. One of these, ADCY4, is of particular interest as a part of the KEGG adaptation pathway “Circadian Entrainment.” This provides evidence of the ecological interactions between species influencing evolution of metazoans not only through specific selection pressures, but also by contributing novel genomic material.

Moderate Amounts of Epistasis are Not Evolutionarily Stable in Small Populations

High mutation rates select for the evolution of mutational robustness where populations inhabit flat fitness peaks with little epistasis, protecting them from lethal mutagenesis. Recent evidence suggests that a different effect protects small populations from extinction via the accumulation of deleterious mutations. In drift robustness, populations tend to occupy peaks with steep flanks and positive epistasis between mutations. However, it is not known what happens when mutation rates are high and population sizes are small at the same time. Using a simple fitness model with variable epistasis, we show that the equilibrium fitness has a minimum as a function of the parameter that tunes epistasis, implying that this critical point is an unstable fixed point for evolutionary trajectories. In agent-based simulations of evolution at finite mutation rate, we demonstrate that when mutations can change epistasis, trajectories with a subcritical value of epistasis evolve to decrease epistasis, while those with supercritical initial points evolve towards higher epistasis. These two fixed points can be identified with mutational and drift robustness, respectively.

Genotype network intersections promote evolutionary innovation

Evolutionary innovations are qualitatively novel traits that emerge through evolution and increase biodiversity. The genetic mechanisms of innovation remain poorly understood. A systems view of innovation requires the analysis of genotype networks—the vast networks of genetic variants that produce the same phenotype. Innovations can occur at the intersection of two different genotype networks. However, the experimental characterization of genotype networks has been hindered by the vast number of genetic variants that need to be functionally analyzed. Here, we use high-throughput sequencing to study the fitness landscape at the intersection of the genotype networks of two catalytic RNA molecules (ribozymes). We determined the ability of numerous neighboring RNA sequences to catalyze two different chemical reactions, and we use these data as a proxy for a genotype to fitness map where two functions come in close proximity. We find extensive functional overlap, and numerous genotypes can catalyze both functions. We demonstrate through evolutionary simulations that these numerous points of intersection facilitate the discovery of a new function. However, the rate of adaptation of the new function depends upon the local ruggedness around the starting location in the genotype network. As a consequence, one direction of adaptation is more rapid than the other. We find that periods of neutral evolution increase rates of adaptation to the new function by allowing populations to spread out in their genotype network. Our study reveals the properties of a fitness landscape where genotype networks intersect and the consequences for evolutionary innovations. Our results suggest that historic innovations in natural systems may have been facilitated by overlapping genotype networks.

The determination of the empirical fitness landscape at the genotypic intersection between two different catalytic RNA (ribozyme) functions reveals details about how novel traits can emerge through evolutionary innovation.

Impact of antiretroviral resistance and virological failure on HIV-1 informational entropy

Objectives

The present study investigated the relationship between genomic variability and resistance of HIV-1 sequences in protease (PR) and reverse transcriptase (RT) regions of the pol gene. In addition, we analysed the resistance among 651 individuals presenting antiretroviral virological failure, from 2009 to 2011, in the state of São Paulo, Brazil.

Methods

The genomic variability was quantified by using informational entropy methods and the relationship between resistance and replicative fitness, as inferred by the residual viral load and CD4+ T cell count.

Results

The number of antiretroviral schemes is related to the number of resistance mutations in the HIV-1 PR (α = 0.2511, P = 0.0003, R2 = 0.8672) and the RT (α = 0.7892, P = 0.0001, R2 = 0.9141). Increased informational entropy rate is related to lower levels of HIV-1 viral loads (α = -0.0121, P = 0.0471, R2 = 0.7923), lower levels of CD4+ T cell counts (α = -0.0120, P = 0.0335, R2 = 0.8221) and a higher number of antiretroviral resistance-related mutations.

Conclusions

Less organized HIV genomes as inferred by higher levels of informational entropy relate to less competent host immune systems, lower levels of HIV replication and HIV genetic evolution as a consequence of antiretroviral resistance.

Transgenerational response to early spring warming in Daphnia

Temperature and photoperiod regulate key fitness traits in plants and animals. However, with temperature increase due to global warming, temperature cue thresholds are experienced at shorter photoperiods, disrupting the optimal seasonal timing of physiological, developmental and reproductive events in many species. Understanding the mechanisms of adaptation to the asynchrony between temperature and photoperiod is key to inform our understanding of how species will respond to global warming. Here, we studied the transgenerational mechanisms of responses of the cyclical parthenogen Daphnia magna to different photoperiod lengths co-occurring with warm temperature thereby assessing the impact of earlier spring warming on its fitness. Daphnia uses temperature and photoperiod cues to time dormancy, and to switch between sexual and asexual reproduction. Daphnia life cycle offers the opportunity to measure the relative contribution of plastic and genetic responses to environmental change across generations and over evolutionary time. We use transgenerational common garden experiments on three populations 'resurrected' from a biological archive experiencing temperature increase over five decades. Our results suggest that response to early spring warming evolved underpinned by a complex interaction between plastic and genetic mechanisms while a positive maternal contribution at matching environments between parental and offspring generation was also observed.

No Cost of Complexity in Bacteriophages Adapting to a Complex Environment

A long-standing prediction in evolutionary biology is that organisms experience a so-called "cost of complexity" manifested as a decreasing rate of adaptation in populations as organisms or selective environments become increasingly complex. This theory assumes the ubiquity of antagonistic pleiotropy, or trade-offs in fitness, for mutations affecting multiple traits or phenotypes. A particular manifestation of antagonism thought to be at play in adaptive dynamics involves the relationship between viral growth rate and capsid stability, an interaction that may impede the adaptation of viral pathogens to novel hosts and environments. Here, we present a comparison of the genetics of adaptation for populations of bacteriophages undergoing complete adaptive walks under both simple and complex selective conditions, with complexity being determined by the number of traits under directional selection. We found no evidence for a long-term cost of complexity in viruses experiencing complex selection, with on average at least as great a rate of adaptation under more complex conditions, and rampant evidence for synergistic, rather than antagonistic, pleiotropy. The lack of evident trade-offs between multiple phenotypes implies that emerging pathogens may be able to improve their growth in many different hosts or environments simultaneously, and to do so at a faster rate than previously anticipated.

Long-term evolution on complex fitness landscapes when mutation is weak

Understanding evolution on complex fitness landscapes is difficult both because of the large dimensionality of sequence space and the stochasticity inherent to population-genetic processes. Here, I present an integrated suite of mathematical tools for understanding evolution on time-invariant fitness landscapes when mutations occur sufficiently rarely that the population is typically monomorphic and evolution can be modeled as a sequence of well-separated fixation events. The basic intuition behind this suite of tools is that surrounding any particular genotype lies a region of the fitness landscape that is easy to evolve to, while other pieces of the fitness landscape are difficult to evolve to (due to distance, being across a fitness valley, etc.). I propose a rigorous definition for this "dynamical neighborhood" of a genotype which captures several aspects of the distribution of waiting times to evolve from one genotype to another. The neighborhood structure of the landscape as a whole can be summarized as a matrix, and I show how this matrix can be used to approximate the expected waiting time for certain evolutionary events to occur and to provide an intuitive interpretation to existing formal results on the index of dispersion of the molecular clock.

Mutation network-based understanding of pleiotropic and epistatic mutational behavior of Enterococcus faecalis FMN-dependent azoreductase

We previously identified a highly active homodimeric FMN-dependent NADH-preferred azoreductase (AzoA) from Enterococcus faecalis, which cleaves the azo bonds (R-N˭N-R) of diverse azo dyes, and determined its crystal structure. The preliminary network-based mutational analysis suggested that the two residues, Arg-21 and Asn-121, have an apparent mutational potential for fine-tuning of AzoA, based on their beneficial pleiotropic feedbacks. However, epistasis between the two promising mutational spots in AzoA has not been obtained in terms of substrate binding and azoreductase activity. In this study, we further quantified, visualized, and described the pleiotropic and/or epistatic behavior of six single or double mutations at the positions, Arg-21 and Asn-121, as a further research endeavor for beneficial fine-tuning of AzoA. Based on this network-based mutational analysis, we showed that pleiotropy and epistasis are common, sensitive, and complex mutational behaviors, depending mainly on the structural and functional responsibility and the physicochemical properties of the residue(s) in AzoA.

Negative Epistasis in Experimental RNA Fitness Landscapes

Mutations and their effects on fitness are a fundamental component of evolution. The effects of some mutations change in the presence of other mutations, and this is referred to as epistasis. Epistasis can occur between mutations in different genes or within the same gene. A systematic study of epistasis requires the analysis of numerous mutations and their combinations, which has recently become feasible with advancements in DNA synthesis and sequencing. Here we review the mutational effects and epistatic interactions within RNA molecules revealed by several recent high-throughput mutational studies involving two ribozymes studied in vitro, as well as a tRNA and a snoRNA studied in yeast. The data allow an analysis of the distribution of fitness effects of individual mutations as well as combinations of two or more mutations. Two different approaches to measuring epistasis in the data both reveal a predominance of negative epistasis, such that higher combinations of two or more mutations are typically lower in fitness than expected from the effect of each individual mutation. These data are in contrast to past studies of epistasis that used computationally predicted secondary structures of RNA that revealed a predominance of positive epistasis. The RNA data reviewed here are more similar to that found from mutational experiments on individual protein enzymes, suggesting that a common thermodynamic framework may explain negative epistasis between mutations within macromolecules.

Gene Conversion Facilitates Adaptive Evolution on Rugged Fitness Landscapes

Gene conversion is a ubiquitous phenomenon that leads to the exchange of genetic information between homologous DNA regions and maintains coevolving multi-gene families in most prokaryotic and eukaryotic organisms. In this paper, we study its implications for the evolution of a single functional gene with a silenced duplicate, using two different models of evolution on rugged fitness landscapes. Our analytical and numerical results show that, by helping to circumvent valleys of low fitness, gene conversion with a passive duplicate gene can cause a significant speedup of adaptation, which depends nontrivially on the frequency of gene conversion and the structure of the landscape. We find that stochastic effects due to finite population sizes further increase the likelihood of exploiting this evolutionary pathway. A universal feature appearing in both deterministic and stochastic analysis of our models is the existence of an optimal gene conversion rate, which maximizes the speed of adaptation. Our results reveal the potential for duplicate genes to act as a "scratch paper" that frees evolution from being limited to strictly beneficial mutations in strongly selective environments.

Time-Resolved Tracking of Mutations Reveals Diverse Allele Dynamics during Escherichia coli Antimicrobial Adaptive Evolution to Single Drugs and Drug Pairs

Understanding the evolutionary processes that lead to antibiotic resistance can help to achieve better treatment strategies. Yet, little is known about the dynamics of the resistance alleles during adaptation. Here, we use population sequencing to monitor genetic changes in putative resistance loci at several time-points during adaptive evolution experiments involving five different antibiotic conditions. We monitor the mutational spectra in lineages evolved to be resistant to single antibiotics [amikacin (AMK), chloramphenicol (CHL), and ciprofloxacin (CIP)], as well as antibiotic combinations (AMK + CHL and CHL + CIP). We find that lineages evolved to antibiotic combinations exhibit different resistance allele dynamics compared with those of single-drug evolved lineages, especially for a drug pair with reciprocal collateral sensitivity. During adaptation, we observed interfering, superimposing and fixation allele dynamics. To further understand the selective forces driving specific allele dynamics, a subset of mutations were introduced into the ancestral wild type enabling differentiation between clonal interference and negative epistasis.

REVIEW: Epistasis and dominance in the emergence of catalytic function as exemplified by the evolution of plant terpene synthases

Epistasis, the interaction between mutations and the genetic background, is a pervasive force in evolution that is difficult to predict yet derives from a simple principle - biological systems are interconnected. Therefore, one effect may be intimately linked to another, hence interdependent. Untangling epistatic interactions between and within genes is a vibrant area of research. Deriving a mechanistic understanding of epistasis is a major challenge. Particularly, elucidating how epistasis can attenuate the effects of otherwise dominant mutations that control phenotypes. Using the emergence of terpene cyclization in specialized metabolism as an excellent example, this review describes the process of discovery and interpretation of dominance and epistasis in relation to current efforts. Specifically, we outline experimental approaches to isolating epistatic networks of mutations in protein structure, formally quantifying epistatic interactions, then building biochemical models with chemical mechanisms in efforts to achieve an understanding of the physical basis for epistasis. From these models we describe informed conjectures about past evolutionary events that underlie the emergence, divergence and specialization of terpene synthases to illustrate key principles of the constraining forces of epistasis in enzyme function.

Evolutionary game theory using agent-based methods

Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection-strong mutation limit), but that mathematics is crucial to validate the computational simulations.

Adaptation in protein fitness landscapes is facilitated by indirect paths

The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20^L) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 20⁴ = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve.

DOI:http://dx.doi.org/10.7554/eLife.16965.001

Proteins can evolve over time by changing their component parts, which are called amino acids. These changes usually happen one at a time and natural selection tends to preserve those changes that make the protein more efficient at its specific tasks, while discarding those that impair the protein’s activity. However the effect of each change depends on the protein as a whole, and so two changes that separately make the protein worse can make it much better if they occur together. This phenomenon is called epistasis and in some cases it can trap proteins in a sub-optimal form and prevent them from improving further.

Proteins are made from twenty different kinds of amino acid, and there are millions of different combinations of amino acids that could, in theory, make a protein of a given length. Studying protein evolution involves making variants of the same protein, each with just a few changes, and comparing how efficient, or “fit”, they are. Previous studies only measured the fitness of a few variants and showed that epistasis could block protein evolution by requiring the protein to lose some fitness before it could improve further. However, new techniques have now made it easier to study protein evolution by testing many more protein variants.

Wu, Dai et al. focused on four amino acids in part of a protein called GB1 and tested the efficiency of every possible combination of these four amino acids, a total of 160,000 (20⁴) variants. Contrary to expectations, the results suggested that the protein could evolve quickly to maximise fitness despite there being epistasis between the four amino acids. Overcoming epistasis typically involved making a change to one amino acid that paved the way for further changes while avoiding the need to lose fitness. The original change could then be reversed once the epistasis was overcome. The complexity of this solution means it can only be seen by studying a large number of protein variants that represent many alternative sequences of protein changes.

Wu, Dai et al. conclude that proteins are able to achieve a higher level of fitness through evolution by exploring a large number of changes. There are many possible changes for each protein and it is this variety that, despite epistasis, allows proteins to become naturally optimised for the tasks that they perform. While the full complexity of protein evolution cannot be explored at the moment, as technology advances it will become possible to study more protein variants. Such advances would therefore hopefully allow researchers to discover even more about the natural mechanisms of protein evolution.

DOI:http://dx.doi.org/10.7554/eLife.16965.002

Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein

Epistatic interactions between residues determine a protein’s adaptability and shape its evolutionary trajectory. When a protein experiences a changed environment, it is under strong selection to find a peak in the new fitness landscape. It has been shown that strong selection increases epistatic interactions as well as the ruggedness of the fitness landscape, but little is known about how the epistatic interactions change under selection in the long-term evolution of a protein. Here we analyze the evolution of epistasis in the protease of the human immunodeficiency virus type 1 (HIV-1) using protease sequences collected for almost a decade from both treated and untreated patients, to understand how epistasis changes and how those changes impact the long-term evolvability of a protein. We use an information-theoretic proxy for epistasis that quantifies the co-variation between sites, and show that positive information is a necessary (but not sufficient) condition that detects epistasis in most cases. We analyze the “fossils” of the evolutionary trajectories of the protein contained in the sequence data, and show that epistasis continues to enrich under strong selection, but not for proteins whose environment is unchanged. The increase in epistasis compensates for the information loss due to sequence variability brought about by treatment, and facilitates adaptation in the increasingly rugged fitness landscape of treatment. While epistasis is thought to enhance evolvability via valley-crossing early-on in adaptation, it can hinder adaptation later when the landscape has turned rugged. However, we find no evidence that the HIV-1 protease has reached its potential for evolution after 9 years of adapting to a drug environment that itself is constantly changing. We suggest that the mechanism of encoding new information into pairwise interactions is central to protein evolution not just in HIV-1 protease, but for any protein adapting to a changing environment.

Evolution is often viewed as a process that occurs “mutation by mutation”, suggesting that the effect of each mutation is independent of that of others. However, in reality the effect of a mutation often depends on the context of other mutations, a dependence known as “epistasis”. Even though epistasis can constrain protein evolution, it is actually very common. Such interactions are particularly pervasive in proteins that evolve resistance to a drug via mutations that create defects, and that must be repaired with compensatory mutations. We study how epistasis between protein residues evolves over time in a new and changing environment, and compare these findings to protein evolution in a constant environment. We analyze the sequences of the human immunodeficiency virus type 1 (HIV-1) protease enzyme collected over a period of 9 years from patients treated with anti-viral drugs (as well as from patients that went untreated), and find that epistasis between residues continues to increase as more potent anti-viral drugs enter the market, while epistasis is unchanging in the proteins exposed to a constant environment. Yet, the proteins adapting to the changing landscape do not appear to be constrained by the epistatic interactions and continue to manage to evade new drugs.

Biophysical principles predict fitness landscapes of drug resistance

Fitness landscapes of drug resistance constitute powerful tools to elucidate mutational pathways of antibiotic escape. Here, we developed a predictive biophysics-based fitness landscape of trimethoprim (TMP) resistance for Escherichia coli dihydrofolate reductase (DHFR). We investigated the activity, binding, folding stability, and intracellular abundance for a complete set of combinatorial DHFR mutants made out of three key resistance mutations and extended this analysis to DHFR originated from Chlamydia muridarum and Listeria grayi We found that the acquisition of TMP resistance via decreased drug affinity is limited by a trade-off in catalytic efficiency. Protein stability is concurrently affected by the resistant mutants, which precludes a precise description of fitness from a single molecular trait. Application of the kinetic flux theory provided an accurate model to predict resistance phenotypes (IC50) quantitatively from a unique combination of the in vitro protein molecular properties. Further, we found that a controlled modulation of the GroEL/ES chaperonins and Lon protease levels affects the intracellular steady-state concentration of DHFR in a mutation-specific manner, whereas IC50 is changed proportionally, as indeed predicted by the model. This unveils a molecular rationale for the pleiotropic role of the protein quality control machinery on the evolution of antibiotic resistance, which, as we illustrate here, may drastically confound the evolutionary outcome. These results provide a comprehensive quantitative genotype-phenotype map for the essential enzyme that serves as an important target of antibiotic and anticancer therapies.

Environmental changes bridge evolutionary valleys

In the basic fitness landscape metaphor for molecular evolution, evolutionary pathways are presumed to follow uphill steps of increasing fitness. How evolution can cross fitness valleys is an open question. One possibility is that environmental changes alter the fitness landscape such that low-fitness sequences reside on a hill in alternate environments. We experimentally test this hypothesis on the antibiotic resistance gene TEM-15 β-lactamase by comparing four evolutionary strategies shaped by environmental changes. The strategy that included initial steps of selecting for low antibiotic resistance (negative selection) produced superior alleles compared with the other three strategies. We comprehensively examined possible evolutionary pathways leading to one such high-fitness allele and found that an initially deleterious mutation is key to the allele's evolutionary history. This mutation is an initial gateway to an otherwise relatively inaccessible area of sequence space and participates in higher-order, positive epistasis with a number of neutral to slightly beneficial mutations. The ability of negative selection and environmental changes to provide access to novel fitness peaks has important implications for natural evolutionary mechanisms and applied directed evolution.

Synergistic Pleiotropy Overrides the Costs of Complexity in Viral Adaptation

Adaptive evolution progresses as a series of steps toward a multidimensional phenotypic optimum, and organismal or environmental complexity determines the number of phenotypic dimensions, or traits, under selection. Populations evolving in complex environments may experience costs of complexity such that improvement in one or more traits is impeded by selection on others. We compared the fitness effects of the first fixed mutations for populations of single-stranded DNA bacteriophage evolving under simple selection for growth rate to those of populations evolving under more complex selection for growth rate as well as capsid stability. We detected a cost of complexity manifested as a smaller growth rate improvement for mutations fixed under complex conditions. We found that, despite imposing a cost for growth rate improvement, strong complex selection resulted in the greatest overall fitness improvement, even for single mutations. Under weaker secondary selective pressures, tradeoffs between growth rate and stability were pervasive, but strong selection on the secondary trait resulted largely in mutations beneficial to both traits. Strength of selection therefore determined the nature of pleiotropy governing observed trait evolution, and strong positive selection forced populations to find mutations that improved multiple traits, thereby overriding costs incurred as a result of a more complex selective environment. The costs of complexity, however, remained substantial when considering the effects on a single trait in the context of selection on multiple traits.

The virtue of innovation: innovation through the lenses of biological evolution

We rehearse the processes of innovation and discovery in general terms, using as our main metaphor the biological concept of an evolutionary fitness landscape. Incremental and disruptive innovations are seen, respectively, as successful searches carried out locally or more widely. They may also be understood as reflecting evolution by mutation (incremental) versus recombination (disruptive). We also bring a platonic view, focusing on virtue and memory. We use 'virtue' as a measure of efforts, including the knowledge required to come up with disruptive and incremental innovations, and 'memory' as a measure of their lifespan, i.e. how long they are remembered. Fostering innovation, in the evolutionary metaphor, means providing the wherewithal to promote novelty, good objective functions that one is trying to optimize, and means to improve one's knowledge of, and ability to navigate, the landscape one is searching. Recombination necessarily implies multi- or inter-disciplinarity. These principles are generic to all kinds of creativity, novel ideas formation and the development of new products and technologies.

A tortoise-hare pattern seen in adapting structured and unstructured populations suggests a rugged fitness landscape in bacteria

In the context of Wright's adaptive landscape, genetic epistasis can yield a multipeaked or "rugged" topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to fix rapidly on one, which may not be the highest peak. In a spatially structured population, on the other hand, beneficial mutations take longer to spread. This slowdown allows distant parts of the population to explore the landscape semiindependently. Such a population can simultaneously discover multiple peaks, and the genotype at the highest discovered peak is expected to dominate eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the fable of the tortoise and the hare, the structured population (tortoise) starts relatively slow but eventually surpasses the unstructured population (hare) in average fitness. In contrast, on single-peak landscapes that lack epistasis, all uphill paths converge. Given such "smooth" topography, breadth of search is devalued and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the tortoise-hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a tortoise-hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wright's shifting balance process.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Comparative functional pan-genome analyses to build connections between genomic dynamics and phenotypic evolution in polycyclic aromatic hydrocarbon metabolism in the genus Mycobacterium

Background

The bacterial genus Mycobacterium is of great interest in the medical and biotechnological fields. Despite a flood of genome sequencing and functional genomics data, significant gaps in knowledge between genome and phenome seriously hinder efforts toward the treatment of mycobacterial diseases and practical biotechnological applications. In this study, we propose the use of systematic, comparative functional pan-genomic analysis to build connections between genomic dynamics and phenotypic evolution in polycyclic aromatic hydrocarbon (PAH) metabolism in the genus Mycobacterium.

Results

Phylogenetic, phenotypic, and genomic information for 27 completely genome-sequenced mycobacteria was systematically integrated to reconstruct a mycobacterial phenotype network (MPN) with a pan-genomic concept at a network level. In the MPN, mycobacterial phenotypes show typical scale-free relationships. PAH degradation is an isolated phenotype with the lowest connection degree, consistent with phylogenetic and environmental isolation of PAH degraders. A series of functional pan-genomic analyses provide conserved and unique types of genomic evidence for strong epistatic and pleiotropic impacts on evolutionary trajectories of the PAH-degrading phenotype. Under strong natural selection, the detailed gene gain/loss patterns from horizontal gene transfer (HGT)/deletion events hypothesize a plausible evolutionary path, an epistasis-based birth and pleiotropy-dependent death, for PAH metabolism in the genus Mycobacterium. This study generated a practical mycobacterial compendium of phenotypic and genomic changes, focusing on the PAH-degrading phenotype, with a pan-genomic perspective of the evolutionary events and the environmental challenges.

Conclusions

Our findings suggest that when selection acts on PAH metabolism, only a small fraction of possible trajectories is likely to be observed, owing mainly to a combination of the ambiguous phenotypic effects of PAHs and the corresponding pleiotropy- and epistasis-dependent evolutionary adaptation. Evolutionary constraints on the selection of trajectories, like those seen in PAH-degrading phenotypes, are likely to apply to the evolution of other phenotypes in the genus Mycobacterium.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-015-0302-8) contains supplementary material, which is available to authorized users.

Emergence of terpene cyclization in Artemisia annua

The emergence of terpene cyclization was critical to the evolutionary expansion of chemical diversity yet remains unexplored. Here we report the first discovery of an epistatic network of residues that controls the onset of terpene cyclization in Artemisia annua. We begin with amorpha-4,11-diene synthase (ADS) and (E)-β-farnesene synthase (BFS), a pair of terpene synthases that produce cyclic or linear terpenes, respectively. A library of ~27,000 enzymes is generated by breeding combinations of natural amino-acid substitutions from the cyclic into the linear producer. We discover one dominant mutation is sufficient to activate cyclization, and together with two additional residues comprise a network of strongly epistatic interactions that activate, suppress or reactivate cyclization. Remarkably, this epistatic network of equivalent residues also controls cyclization in a BFS homologue from Citrus junos. Fitness landscape analysis of mutational trajectories provides quantitative insights into a major epoch in specialized metabolism.

Terpene cyclases are ring-forming enzymes found in many biosynthetic pathways, but the evolutionary origins of the cyclization mechanism is not well understood. Here, the authors use structure-guided breeding to identify an epistatic network that controls the onset of cyclization activity in Artemisia annua.

Epistasis constrains mutational pathways of hemoglobin adaptation in high-altitude pikas

A fundamental question in evolutionary genetics concerns the roles of mutational pleiotropy and epistasis in shaping trajectories of protein evolution. This question can be addressed most directly by using site-directed mutagenesis to explore the mutational landscape of protein function in experimentally defined regions of sequence space. Here, we evaluate how pleiotropic trade-offs and epistatic interactions influence the accessibility of alternative mutational pathways during the adaptive evolution of hemoglobin (Hb) function in high-altitude pikas (Mammalia: Lagomorpha). By combining ancestral protein resurrection with a combinatorial protein-engineering approach, we examined the functional effects of sequential mutational steps in all possible pathways that produced an increased Hb–O₂ affinity. These experiments revealed that the effects of mutations on Hb–O₂ affinity are highly dependent on the temporal order in which they occur: Each of three β-chain substitutions produced a significant increase in Hb–O₂ affinity on the ancestral genetic background, but two of these substitutions produced opposite effects when they occurred as later steps in the pathway. The experiments revealed pervasive epistasis for Hb–O₂ affinity, but affinity-altering mutations produced no significant pleiotropic trade-offs. These results provide insights into the properties of adaptive substitutions in naturally evolved proteins and suggest that the accessibility of alternative mutational pathways may be more strongly constrained by sign epistasis for positively selected biochemical phenotypes than by antagonistic pleiotropy.

Useful insights from evolutionary biology for developing perennial grain crops

Annual grain crops dominate agricultural landscapes and provide the majority of calories consumed by humanity. Perennial grain crops could potentially ameliorate the land degradation and off-site impacts associated with annual grain cropping. However, herbaceous perennial plants with constitutively high allocation to harvestable seeds are rare to absent in nature. Recent trade-off theory models suggest that rugged fitness landscapes may explain the absence of this form better than sink competition models. Artificial selection for both grain production and multiyear lifespan can lead to more rapid progress in the face of fitness and genetic trade-offs than natural selection but is likely to result in plant types that differ substantially from all current domestic crops. Perennial grain domestication is also likely to require the development of selection strategies that differ from published crop breeding methods, despite their success in improving long-domesticated crops; for this purpose, we have reviewed literature in the areas of population and evolutionary genetics, domestication, and molecular biology. Rapid domestication will likely require genes with large effect that are expected to exhibit strong pleiotropy and epistasis. Cryptic genetic variation will need to be deliberately exposed both to purge mildly deleterious alleles and to generate novel agronomic phenotypes. We predict that perennial grain domestication programs will benefit from population subdivision followed by selection for simple traits in each subpopulation, the evaluation of very large populations, high selection intensity, rapid cycling through generations, and heterosis. The latter may be particularly beneficial in the development of varieties with stable yield and tolerance to crowding.

Payoffs, not tradeoffs, in the adaptation of a virus to ostensibly conflicting selective pressures

The genetic architecture of many phenotypic traits is such that genes often contribute to multiple traits, and mutations in these genes can therefore affect multiple phenotypes. These pleiotropic interactions often manifest as tradeoffs between traits where improvement in one property entails a cost in another. The life cycles of many pathogens include periods of growth within a host punctuated with transmission events, such as passage through a digestive tract or a passive stage of exposure in the environment. Populations exposed to such fluctuating selective pressures are expected to acquire mutations showing tradeoffs between reproduction within and survival outside of a host. We selected for individual mutations under fluctuating selective pressures for a ssDNA microvirid bacteriophage by alternating selection for increased growth rate with selection on biophysical properties of the phage capsid in high-temperature or low-pH conditions. Surprisingly, none of the seven unique mutations identified showed a pleiotropic cost; they all improved both growth rate and pH or temperature stability, suggesting that single mutations even in a simple genetic system can simultaneously improve two distinct traits. Selection on growth rate alone revealed tradeoffs, but some mutations still benefited both traits. Tradeoffs were therefore prevalent when selection acted on a single trait, but payoffs resulted when multiple traits were selected for simultaneously. We employed a molecular-dynamics simulation method to determine the mechanisms underlying beneficial effects for three heat-shock mutations. All three mutations significantly enhanced the affinities of protein-protein interfacial bindings, thereby improving capsid stability. The ancestral residues at the mutation sites did not contribute to protein-protein interfacial binding, indicating that these sites acquired a new function. Computational models, such as those used here, may be used in future work not only as predictive tools for mutational effects on protein stability but, ultimately, for evolution.

Role of pleiotropy during adaptation of TEM-1 β-lactamase to two novel antibiotics

Pleiotropy is a key feature of the genotype–phenotype map, and its form and extent have many evolutionary implications, including for the dynamics of adaptation and the evolution of specialization. Similarly, pleiotropic effects of antibiotic resistance mutations may affect the evolution of antibiotic resistance in the simultaneous or fluctuating presence of different antibiotics. Here, we study the role of pleiotropy during the in vitro adaptation of the enzyme TEM-1 β-lactamase to two novel antibiotics, cefotaxime (CTX) and ceftazidime (CAZ). We subject replicate lines for four rounds of evolution to selection with CTX and CAZ alone, and in their combined and fluctuating presence. Evolved alleles show positive correlated responses when selecting with single antibiotics. Nevertheless, pleiotropic constraints are apparent from the effects of single mutations and from selected alleles showing smaller correlated than direct responses and smaller responses after simultaneous and fluctuating selection with both than with single antibiotics. We speculate that these constraints result from structural changes in the oxyanion pocket surrounding the active site, where accommodation of CTX and the larger CAZ is balanced against their positioning with respect to the active site. Our findings suggest limited benefits from the combined or fluctuating application of these related cephalosporins for containing antibiotic resistance.

Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model

Much of the current theory of adaptation is based on Gillespie's mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyze a simple fitness landscape model with tunable ruggedness based on the rough Mount Fuji (RMF) model originally introduced by Aita et al. in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulas for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly fit first-step background in a recent evolution experiment with a microvirid bacteriophage.

Trade-offs drive resource specialization and the gradual establishment of ecotypes

BACKGROUND

Speciation is driven by many different factors. Among those are trade-offs between different ways an organism utilizes resources, and these trade-offs can constrain the manner in which selection can optimize traits. Limited migration among allopatric populations and species interactions can also drive speciation, but here we ask if trade-offs alone are sufficient to drive speciation in the absence of other factors.

RESULTS

We present a model to study the effects of trade-offs on specialization and adaptive radiation in asexual organisms based solely on competition for limiting resources, where trade-offs are stronger the greater an organism's ability to utilize resources. In this model resources are perfectly substitutable, and fitness is derived from the consumption of these resources. The model contains no spatial parameters, and is therefore strictly sympatric. We quantify the degree of specialization by the number of ecotypes evolved and the niche breadth of the population, and observe that these are sensitive to resource influx and trade-offs. Resource influx has a strong effect on the degree of specialization, with a clear transition between minimal diversification at high influx and multiple species evolving at low resource influx. At low resource influx the degree of specialization further depends on the strength of the trade-offs, with more ecotypes evolving the stronger trade-offs are. The specialized organisms persist through negative frequency-dependent selection. In addition, by analyzing one of the evolutionary radiations in greater detail we demonstrate that a single mutation alone is not enough to establish a new ecotype, even though phylogenetic reconstruction identifies that mutation as the branching point. Instead, it takes a series of additional mutations to ensure the stable coexistence of the new ecotype in the background of the existing ones.

CONCLUSIONS

Trade-offs are sufficient to drive the evolution of specialization in sympatric asexual populations. Without trade-offs to restrain traits, generalists evolve and diversity decreases. The observation that several mutations are required to complete speciation, even when a single mutation creates the new species, highlights the gradual nature of speciation and the importance of phyletic evolution.