Magnitude and sign epistasis among deleterious mutations in a positive-sense plant RNA virus

How epistatic interactions between mutations determine the genetic architecture of fitness is of central importance in evolution. The study of epistasis is particularly interesting for RNA viruses because of their genomic compactness, lack of genetic redundancy, and apparent low complexity. Moreover, interactions between mutations in viral genomes determine traits such as resistance to antiviral drugs, virulence and host range. In this study we generated 53 Tobacco etch potyvirus genotypes carrying pairs of single-nucleotide substitutions and measured their separated and combined deleterious fitness effects. We found that up to 38% of pairs had significant epistasis for fitness, including both positive and negative deviations from the null hypothesis of multiplicative effects. Interestingly, the sign of epistasis was correlated with viral protein-protein interactions in a model network, being predominantly positive between linked pairs of proteins and negative between unlinked ones. Furthermore, 55% of significant interactions were cases of reciprocal sign epistasis (RSE), indicating that adaptive landscapes for RNA viruses maybe highly rugged. Finally, we found that the magnitude of epistasis correlated negatively with the average effect of mutations. Overall, our results are in good agreement to those previously reported for other viruses and further consolidate the view that positive epistasis is the norm for small and compact genomes that lack genetic robustness.     

Slow Protein Dynamics Elicits New Enzymatic Functions by Means of Epistatic Interactions

Protein evolution depends on the adaptation of these molecules to different functional challenges. This occurs by tuning their biochemical, biophysical, and structural traits through the accumulation of mutations. While the role of protein dynamics in biochemistry is well recognized, there are limited examples providing experimental evidence of the optimization of protein dynamics during evolution. Here we report an NMR study of four variants of the CTX-M β-lactamases, in which the interplay of two mutations outside the active site enhances the activity against a cephalosporin substrate, ceftazidime. The crystal structures of these enzymes do not account for this activity enhancement. By using NMR, here we show that the combination of these two mutations increases the backbone dynamics in a slow timescale and the exposure to the solvent of an otherwise buried β-sheet. The two mutations located in this β-sheet trigger conformational changes in loops located at the opposite side of the active site. We postulate that the most active variant explores alternative conformations that enable binding of the more challenging substrate ceftazidime. The impact of the mutations in the dynamics is context-dependent, in line with the epistatic effect observed in the catalytic activity of the different variants. These results reveal the existence of a dynamic network in CTX-M β-lactamases that has been exploited in evolution to provide a net gain-of-function, highlighting the role of alternative conformations in protein evolution.     

Evolution of model proteins on a foldability landscape

We model the evolution of simple lattice proteins as a random walk in a fitness landscape, where the fitness represents the ability of the protein to fold. At higher selective pressure, the evolutionary trajectories are confined to neutral networks where the native structure is conserved and the dynamics are non self-averaging and nonexponential. The optimizability of the corresponding native structure has a strong effect on the size of these neutral networks and thus on the nature of the evolutionary process. Proteins 29:461–466, 1997. © 1997 Wiley-Liss, Inc.     

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.     

Molecular Evolution in Static and Dynamical Landscapes

In an attempt to understand protein evolution, we address the issues ofhow much variety in the sequences is needed to prompt the evolution ofan enzyme from random polypeptides and how does cellular interactionaffect the dynamics of molecular evolution to allow genetic diversity inpopulation. The experimental evolution of phage-displayed randompolypeptides of about 140 amino acid residues panned with transition stateanalogue for an esterase reaction showed that even with a population sizeas small as ten, not only could significant varieties be found but also therandom polypeptides in each of the generation had great promise towardsdeveloping into functional proteins. Hence, it is evident that the enzymeevolution is prompted even within a small local area of the static landscapeof the sequence space. Considering that interaction among living cells is aninevitable event in natural evolution, its role was investigated through threeconsecutive rounds of random mutagenesis on the glutamine synthetasegene and chemostat culture of the transformed Escherichia colicellscontaining the mutated genes. The molecular phylogeny and populationdynamics show the coexistence of some mutants having different level ofglutamine synthetase at each generation. In addition, it was confirmed thatcellular interaction via the medium influences the stability of the coexistenceand bring forth fitness change to the coexisting members of the population,thereby, leading to a dynamical landscape. Based on experimental resultsreflecting the extent of interaction among members in population, here, Iproposed that protein evolution could change its mode from theoptimization on static landscape to diversification on dynamicallandscape.     

Modeling Within-Host Evolution of HIV: Mutation,Competition and Strain Replacement

Virus evolution during infection of a single individual is a well-known feature of disease progression in chronic viral diseases. However, the simplest models of virus competition for host resources show the existence of a single dominant strain that grows most rapidly during the initial period of infection and competitively excludes all other virus strains. Here, we examine the dynamics of strain replacement in a simple model that includes a convex trade-off between rapid virus reproduction and long-term host cell survival. Strains are structured according to their within-cell replication rate. Over the course of infection, we find a progression in the dominant strain from fast- to moderately-replicating virus strains featuring distinct jumps in the replication rate of the dominant strain over time. We completely analyze the model and provide estimates for the replication rate of the initial dominant strain and its successors. Our model lays the groundwork for more detailed models of HIV selection and mutation. We outline future directions and application of related models to other biological situations.     

Epistatic interactions of spontaneous mutations in haploid strains of the yeast Saccharomyces cerevisiae

Several important biological phenomena, including genetic recombination and sexual reproduction, could have evolved to counteract genome contamination by deleterious mutations. This postulate would be especially relevant if it were shown that deleterious mutations interact in such a way that their individual negative effects are reinforced by each other. The hypothesis of synergism can be tested experimentally by crossing organisms bearing deleterious mutations and comparing the fitness of the parents and their progeny. The present study used laboratory strains of the budding yeast burdened with mutations resulting from absence of a major DNA mismatch repair function. Only in one, or possibly two, crosses out of eight did fitness of the progeny deviate from that of their parents in a direction indicating synergism. Furthermore, the distributions of progeny fitness were not skewed as would be expected if strong interactions were present. The choice of experimental material ensured that genetic recombination was extensive, all four meiotic products were available for fitness assays, and that the mutations were probably numerous. Despite this generally favourable experimental setting, synergism did not appear to be a dominating force shaping fitness of yeast containing randomly generated mutations.     

On the Classification and Evolution of Protein Modules

Our efforts to classify the functional units of many proteins, the modules, are reviewed. The data from the sequencing projects for various model organisms are extremely helpful in deducing the evolution of proteins and modules. For example, a dramatic increase of modular proteins can be observed from yeast to C. elegans in accordance with new protein functions that had to be introduced in multicellular organisms. Our sequence characterization of modules relies on sensitive similarity search algorithms and the collection of multiple sequence alignments for each module. To trace the evolution of modules and to further automate the classification, we have developed a sequence and a module alerting system that checks newly arriving sequence data for the presence of already classified modules. Using these systems, we were able to identify an unexpected similarity between extracellular C1Q modules with bacterial proteins.     

The foldability landscape of model proteins

Molecular evolution may be considered as a walk in a multidimensional fitness landscape, where the fitness at each point is associated with features such as the function, stability, and survivability of these molecules. We present a simple model for the evolution of protein sequences on a landscape with a precisely defined fitness function. We use simple lattice models to represent protein structures, with the ability of a protein sequence to fold into the structure with lowest energy, quantified as the foldability, representing the fitness of the sequence. The foldability of the sequence is characterized based on the spin glass model of protein folding. We consider evolution as a walk in this foldability landscape and study the nature of the landscape and the resulting dynamics. Selective pressure is explicitly included in this model in the form of a minimum foldability requirement. We find that different native structures are not evenly distributed in interaction space, with similar structures and structures with similar optimal foldabilities clustered together. Evolving proteins marginally fulfill the selective criteria of foldability. As the selective pressure is increased, evolutionary trajectories become increasingly confined to “neutral networks,” where the sequence and the interactions can be significantly changed while a constant structure is maintained. © 1997 John Wiley & Sons, Inc. Biopoly 42: 427–438, 1997     

Epistatic interactions between ancestral genotype and beneficial mutations shape evolvability in Pseudomonas aeruginosa

The idea that interactions between mutations influence adaptation by driving populations to low and high fitness peaks on adaptive landscapes is deeply ingrained in evolutionary theory. Here, we investigate the impact of epistasis on evolvability by challenging populations of two Pseudomonas aeruginosa clones bearing different initial mutations (in rpoB conferring rifampicin resistance, and the type IV pili gene network) to adaptation to a medium containing l ‐serine as the sole carbon source. Despite being initially indistinguishable in fitness, populations founded by the two ancestral genotypes reached different fitness following 300 generations of evolution. Genome sequencing revealed that the difference could not be explained by acquiring mutations in different targets of selection; the majority of clones from both ancestors converged on one of the following two strategies: (1) acquiring mutations in either PA2449 (gcsR, an l ‐serine‐metabolism RpoN enhancer binding protein) or (2) protease genes. Additionally, populations from both ancestors converged on loss‐of‐function mutations in the type IV pili gene network, either due to ancestral or acquired mutations. No compensatory or reversion mutations were observed in RNA polymerase (RNAP) genes, in spite of the large fitness costs typically associated with mutations in rpoB. Although current theory points to sign epistasis as the dominant constraint on evolvability, these results suggest that the role of magnitude epistasis in constraining evolvability may be underappreciated. The contribution of magnitude epistasis is likely to be greatest under the biologically relevant mutation supply rates that make back mutations probabilistically unlikely.     

Energy Profile of the Space of Model Protein Sequences

A numerical study of the energy landscape of the space of model proteinsequences is carried out. As a consequence of the heterogeneity of thecontact energies among amino acids, the energy landscape displays a veryrough profile, a behaviour typical of frustrated systems. This givesraise to a hierarchical clustering of low-energy sequences and can have evolutionary consequences.     

蛋白质定向进化的研究进展

在工业生物催化过程和生物细胞工厂构建方面,蛋白质定向进化被广泛地应用于酶的分子改造.蛋白质定向进化不仅可以针对某一目的蛋白进行改造,还可以改善代谢途径、优化代谢网络、获得期望表型细胞.为了获得更高效的突变效率,快捷、高通量的筛选方法,提高蛋白质定向进化的效果,研究者不断开发蛋白质体内、体外进化方法,取得了新的进展和应用.本文介绍了最近发展的蛋白质定向进化技术的原理、方法及特点,总结了突变文库的筛选方法和蛋白质定向进化的最新应用,最后讨论了蛋白质定向进化存在的挑战和未来发展方向.     

Epistatic effects of promoter and repressor functions of the Tn10 tetracycline-resistance operon on the fitness of Escherichia coli

We have been studying the effects of expression of plasmid-borne, Tn10-encoded, tetracycline resistance on the fitness of Escherichia coli K12. We previously demonstrated large reductions in fitness resulting from induced or constitutive expression of the resistance protein; however, any residual expression by the repressed operon was so slight that possession of an inducible resistance function imposed essentially no burden in the absence of antibiotic. Here, we demonstrate two distinct disadvantages for inducible genotypes relative to isogenic constitutive constructs. During the transition from antibiotic-free to antibiotic-containing media, the inducible genotype experiences a longer lag phase prior to growth. In the sustained presence of antibiotic, full induction of the resistance function in the inducible genotype is prevented by the continued action of its repressor. However, these disadvantages may be reduced by increasing the strength of the promoter for the resistance gene in the inducible genotype. Simultaneous consideration of the mode of gene regulation (i.e. constitutive or inducible) and the strength of the resistance-gene promoter (i.e. maximum level of expression) indicates an adaptive landscape with very strong epistasis and, perhaps, multiple fitness peaks.     

Comparative analysis of sequence covariation methods to mine evolutionary hubs: Examples from selected GPCR families

Covariation between positions in a multiple sequence alignment may reflect structural, functional, and/or phylogenetic constraints and can be analyzed by a wide variety of methods. We explored several of these methods for their ability to identify covarying positions related to the divergence of a protein family at different hierarchical levels. Specifically, we compared seven methods on a model system composed of three nested sets of G‐protein‐coupled receptors (GPCRs) in which a divergence event occurred. The covariation methods analyzed were based on: χ² test, mutual information, substitution matrices, and perturbation methods. We first analyzed the dependence of the covariation scores on residue conservation (measured by sequence entropy), and then we analyzed the networking structure of the top pairs. Two methods out of seven—OMES (Observed minus Expected Squared) and ELSC (Explicit Likelihood of Subset Covariation)—favored pairs with intermediate entropy and a networking structure with a central residue involved in several high‐scoring pairs. This networking structure was observed for the three sequence sets. In each case, the central residue corresponded to a residue known to be crucial for the evolution of the GPCR family and the subfamily specificity. These central residues can be viewed as evolutionary hubs, in relation with an epistasis‐based mechanism of functional divergence within a protein family. Proteins 2014; 82:2141–2156. © 2014 Wiley Periodicals, Inc.     

Evolution of pleiotropic costs in experimental populations

The fitness of populations adapting to new environments is expected to decline in different environments, but empirical studies often do not lend support for such adaptation costs. We test the idea that the initial fitness of the selected populations in the environment where the cost is estimated is key for interpreting tests of ecological trade‐offs. We isolated single clones of the yeast Saccharomyces cerevisiae every ~250 generations from replicate experimental lineages that had been selected during 5000 generations in a glucose‐limited environment. We then selected these clones in a galactose‐limited environment for ~120 generations. Finally, we estimated single‐clone fitness in both environments, before and after selection on galactose. The pleiotropic effects on glucose of selection on galactose evolved from positive to negative as fitness in glucose increased, providing strong support for the importance of initial fitness for determining the sign and magnitude of pleiotropic effects. This demonstrates that the sign of pleiotropic effects for fitness following adaptation to a new environment can change during long‐term adaptation to an original environment. We also found no relationship between the size of the fitness changes in galactose and glucose, such that pleiotropic effects in glucose became relatively smaller as the sizes of direct effects on galactose increased.     

Estimation of Epistatic Variance Components and Heritability in Founder Populations and Crosses

Genetic association studies have explained only a small proportion of the estimated heritability of complex traits, leaving the remaining heritability “missing.” Genetic interactions have been proposed as an explanation for this, because they lead to overestimates of the heritability and are hard to detect. Whether this explanation is true depends on the proportion of variance attributable to genetic interactions, which is difficult to measure in outbred populations. Founder populations exhibit a greater range of kinship than outbred populations, which helps in fitting the epistatic variance. We extend classic theory to founder populations, giving the covariance between individuals due to epistasis of any order. We recover the classic theory as a limit, and we derive a recently proposed estimator of the narrow sense heritability as a corollary. We extend the variance decomposition to include dominance. We show in simulations that it would be possible to estimate the variance from pairwise interactions with samples of a few thousand from strongly bottlenecked human founder populations, and we provide an analytical approximation of the standard error. Applying these methods to 46 traits measured in a yeast (Saccharomyces cerevisiae) cross, we estimate that pairwise interactions explain 10% of the phenotypic variance on average and that third- and higher-order interactions explain 14% of the phenotypic variance on average. We search for third-order interactions, discovering an interaction that is shared between two traits. Our methods will be relevant to future studies of epistatic variance in founder populations and crosses.     

Divergent Evolution of a Protein–Protein Interaction Revealed through Ancestral Sequence Reconstruction and Resurrection

The postsynaptic density extends across the postsynaptic dendritic spine with discs large (DLG) as the most abundant scaffolding protein. DLG dynamically alters the structure of the postsynaptic density, thus controlling the function and distribution of specific receptors at the synapse. DLG contains three PDZ domains and one important interaction governing postsynaptic architecture is that between the PDZ3 domain from DLG and a protein called cysteine-rich interactor of PDZ3 (CRIPT). However, little is known regarding functional evolution of the PDZ3:CRIPT interaction. Here, we subjected PDZ3 and CRIPT to ancestral sequence reconstruction, resurrection, and biophysical experiments. We show that the PDZ3:CRIPT interaction is an ancient interaction, which was likely present in the last common ancestor of Eukaryotes, and that high affinity is maintained in most extant animal phyla. However, affinity is low in nematodes and insects, raising questions about the physiological function of the interaction in species from these animal groups. Our findings demonstrate how an apparently established protein–protein interaction involved in cellular scaffolding in bilaterians can suddenly be subject to dynamic evolution including possible loss of function.     

Cambrian explosion and Permian quiescence: Implications of rugged fitness landscapes

Summary The transition from the late Precambrian to Cambrian coincided with a massive increase in diversity of multicellular organisms and the rapid establishment of a large number of phyla. In contrast, the Permian extinction 200 million years ago was followed by as rapid an increase at the family level, but no new phyla or classes emerged. This asymmetry has suggested alternative theories based on greater ecological opportunity in the Cambrian, or special developmental canalization locking in development by the Permian. I suggest instead that the asymmetry reflects generic features of adaptive evolution on rugged fitness landscapes.     

Connectivity of Neutral Networks,Overdispersion, and Structural Conservation in Protein Evolution

Abstract Protein structures are much more conserved than sequences during evolution. Based on this observation, we investigate the consequences of structural conservation on protein evolution. We study seven of the most studied protein folds, determining that an extended neutral network in sequence space is associated with each of them. Within our model, neutral evolution leads to a non-Poissonian substitution process, due to the broad distribution of connectivities in neutral networks. The observation that the substitution process has non-Poissonian statistics has been used to argue against the original Kimura neutral theory, while our model shows that this is a generic property of neutral evolution with structural conservation. Our model also predicts that the substitution rate can strongly fluctuate from one branch to another of the evolutionary tree. The average sequence similarity within a neutral network is close to the threshold of randomness, as observed for families of sequences sharing the same fold. Nevertheless, some positions are more difficult to mutate than others. We compare such structurally conserved positions to positions conserved in protein evolution, suggesting that our model can be a valuable tool to distinguish structural from functional conservation in databases of protein families. These results indicate that a synergy between database analysis and structurally based computational studies can increase our understanding of protein evolution.