Positive Epistasis Research Articles

Brn3a and Islet1 Act Epistatically to Regulate the Gene Expression Program of Sensory Differentiation

The combinatorial expression of transcription factors frequently marks cellular identity in the nervous system, yet how these factors interact to determine specific neuronal phenotypes is not well understood. Sensory neurons of the trigeminal ganglion (TG) and dorsal root ganglia (DRG) coexpress the homeodomain transcription factors Brn3a and Islet1, and past work has revealed partially overlapping programs of gene expression downstream of these factors. Here we examine sensory development in Brn3a/Islet1 double knock-out (DKO) mice. Sensory neurogenesis and the formation of the TG and DRG occur in DKO embryos, but the DRG are dorsally displaced, and the peripheral projections of the ganglia are markedly disturbed. Sensory neurons in DKO embryos show a profound loss of all early markers of sensory subtypes, including the Ntrk neurotrophin receptors, and the runt-family transcription factors Runx1 and Runx3. Examination of global gene expression in the E12.5 DRG of single and double mutant embryos shows that Brn3a and Islet1 are together required for nearly all aspects of sensory-specific gene expression, including several newly identified sensory markers. On a majority of targets, Brn3a and Islet1 exhibit negative epistasis, in which the effects of the individual knock-out alleles are less than additive in the DKO. Smaller subsets of targets exhibit positive epistasis, or are regulated exclusively by one factor. Brn3a/Islet1 double mutants also fail to developmentally repress neurogenic bHLH genes, and in vivo chromatin immunoprecipitation shows that Islet1 binds to a known Brn3a-regulated enhancer in the neurod4 gene, suggesting a mechanism of interaction between these genes.

Characterization of the Prolactin Receptor 3 (PRLR3) and Retinol-Binding Protein 4 (RBP4) Genes in the Birth Weight and Early Growth of Berkshire Pigs

To investigate the influences of candidate genes on the birth weight and the early stages of life, genotyping of the prolactin receptor 3 (PRLR3) and retinol-binding protein 4 (RBP4) genes was performed in 156 and 141 Berkshire pigs, respectively. The frequency of both PRLR3 alleles A and a was 0.50. The frequencies of the RBP4 alleles B and b were 0.42 and 0.58, respectively. Neither locus was in Hardy-Weinberg equilibrium. No significant associations of the PRLR3 alleles with birth or weaning weights and of the RBP4 alleles with birth weight were observed. The proportions of the phenotype variances due to the genotypes of PRLR3 in the feeder weights was 4.0% and those of RBP4 in the weaning and feeder weights were 11.9 and 3.3%, respectively (P < 0.05). The dominance effect of PRLR3 and RBP4 on feeder weights was 2.40 and 1.86 kg, respectively (P < 0.01). The additive and dominance effects of RBP4 on weaning weights were 0.332 and 0.682 kg, respectively (P < 0.01). Even if no significant epistasis of PRLR3 and RBP4 was detected, a considerable trend of consistent positive epistasis estimates of AA/BB and Aa/Bb was observed for all traits. The results of this study may have a considerable impact on early-stage growth by both loci, and a selection strategy should be designed separately for each marker in Berkshire pigs. (Key words : Prolactin receptor, Retinol-binding protein, Candidate genes)

Prevalence of Epistasis in the Evolution of Influenza A Surface Proteins

The surface proteins of human influenza A viruses experience positive selection to escape both human immunity and, more recently, antiviral drug treatments. In bacteria and viruses, immune-escape and drug-resistant phenotypes often appear through a combination of several mutations that have epistatic effects on pathogen fitness. However, the extent and structure of epistasis in influenza viral proteins have not been systematically investigated. Here, we develop a novel statistical method to detect positive epistasis between pairs of sites in a protein, based on the observed temporal patterns of sequence evolution. The method rests on the simple idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic. We apply this method to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a non-epistatic null distribution, we detect substantial amounts of epistasis and determine the identities of putatively epistatic pairs of sites. In particular, using sequence data alone, our method identifies epistatic interactions between specific sites in neuraminidase that have recently been demonstrated, in vitro, to confer resistance to the drug oseltamivir; these epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today. This experimental validation demonstrates the predictive power of our method to identify epistatic sites of importance for viral adaptation and public health. We conclude that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites with , which would normally not be identified as positively selected, can facilitate viral adaptation through epistatic interactions with their partner sites. The knowledge of specific interactions among sites in influenza proteins may help us to predict the course of antigenic evolution and, consequently, to select more appropriate vaccines and drugs.

Improved functional overview of protein complexes using inferred epistatic relationships

BackgroundEpistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.ResultsWe introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.ConclusionOverall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.

Genome-Wide Scoring of Positive and Negative Epistasis through Decomposition of Quantitative Genetic Interaction Fitness Matrices

Recent technological developments in genetic screening approaches have offered the means to start exploring quantitative genotype-phenotype relationships on a large-scale. What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means. To address these questions, we introduce here a computational approach for improving the quantitative discrimination power encoded in the genetic interaction screening data. Our matrix approximation model decomposes the original double-mutant fitness matrix into separate components, representing variability across the array and query mutants, which can be utilized for estimating and correcting the single-mutant fitness effects, respectively. When applied to three large-scale quantitative interaction datasets in yeast, we could improve the accuracy of scoring various interaction classes beyond that obtained with the original fitness data, especially in synthetic genetic array (SGA) and in genetic interaction mapping (GIM) datasets. In addition to the known pairs of interactions used in the evaluation of the computational approach, a number of novel interaction pairs were also predicted, along with underlying biological mechanisms, which remained undetected by the original datasets. It was shown that the optimal choice of the scoring function depends heavily on the screening approach and on the interaction class under analysis. Moreover, a simple preprocessing of the fitness matrix could further enhance the discrimination power of the epistatic miniarray profiling (E-MAP) dataset. These systematic evaluation results provide in-depth information on the optimal analysis of the future, large-scale screening experiments. In general, the modeling framework, enabling accurate identification and classification of genetic interactions, provides a solid basis for completing and mining the genetic interaction networks in yeast and other organisms.

Prevalent positive epistasis in Escherichia coli and Saccharomyces cerevisiae metabolic networks

Epistasis refers to the interaction between genes. Although high-throughput epistasis data from model organisms are being generated and used to construct genetic networks1-3, to what extent genetic epistasis reflects biologically meaningful interactions remains unclear4-6. We address this question by in silico mapping of positive and negative epistatic interactions amongst biochemical reactions within the metabolic networks of E. coli and S. cerevisiae using flux balance analysis. We found that negative epistasis occurs mainly between nonessential reactions with overlapping functions, whereas positive epistasis usually involves essential reactions, is highly abundant, and surprisingly, often occurs between reactions without overlapping functions. We offered mechanistic explanations of these findings and experimentally validated them for 61 S. cerevisiae gene pairs.

Time to fixation in the presence of recombination

We study the evolutionary dynamics of a haploid population of infinite size recombining with a probability r in a two locus model. Starting from a low fitness locus, the population is evolved under mutation, selection and recombination until a finite fraction of the population reaches the fittest locus. An analytical method is developed to calculate the fixation time T to the fittest locus for various choices of epistasis. We find that: (1) for negative epistasis, T decreases slowly for small r but decays fast at larger r ; (2) for positive epistasis, T increases linearly for small r and mildly for large r ; (3) for compensatory mutation, T diverges as a power law with logarithmic corrections as the recombination fraction approaches a critical value. Our calculations are seen to be in good agreement with the exact numerical results.

A Developmental Systems Perspective on Epistasis: Computational Exploration of Mutational Interactions in Model Developmental Regulatory Networks

The way in which the information contained in genotypes is translated into complex phenotypic traits (i.e. embryonic expression patterns) depends on its decoding by a multilayered hierarchy of biomolecular systems (regulatory networks). Each layer of this hierarchy displays its own regulatory schemes (i.e. operational rules such as +/− feedback) and associated control parameters, resulting in characteristic variational constraints. This process can be conceptualized as a mapping issue, and in the context of highly-dimensional genotype-phenotype mappings (GPMs) epistatic events have been shown to be ubiquitous, manifested in non-linear correspondences between changes in the genotype and their phenotypic effects. In this study I concentrate on epistatic phenomena pervading levels of biological organization above the genetic material, more specifically the realm of molecular networks. At this level, systems approaches to studying GPMs are specially suitable to shed light on the mechanistic basis of epistatic phenomena. To this aim, I constructed and analyzed ensembles of highly-modular (fully interconnected) networks with distinctive topologies, each displaying dynamic behaviors that were categorized as either arbitrary or functional according to early patterning processes in the Drosophila embryo. Spatio-temporal expression trajectories in virtual syncytial embryos were simulated via reaction-diffusion models. My in silico mutational experiments show that: 1) the average fitness decay tendency to successively accumulated mutations in ensembles of functional networks indicates the prevalence of positive epistasis, whereas in ensembles of arbitrary networks negative epistasis is the dominant tendency; and 2) the evaluation of epistatic coefficients of diverse interaction orders indicates that, both positive and negative epistasis are more prevalent in functional networks than in arbitrary ones. Overall, I conclude that the phenotypic and fitness effects of multiple perturbations are strongly conditioned by both the regulatory architecture (i.e. pattern of coupled feedback structures) and the dynamic nature of the spatio-temporal expression trajectories displayed by the simulated networks.

Positive Epistasis Drives the Acquisition of Multidrug Resistance

The evolution of multiple antibiotic resistance is an increasing global problem. Resistance mutations are known to impair fitness, and the evolution of resistance to multiple drugs depends both on their costs individually and on how they interact—epistasis. Information on the level of epistasis between antibiotic resistance mutations is of key importance to understanding epistasis amongst deleterious alleles, a key theoretical question, and to improving public health measures. Here we show that in an antibiotic-free environment the cost of multiple resistance is smaller than expected, a signature of pervasive positive epistasis among alleles that confer resistance to antibiotics. Competition assays reveal that the cost of resistance to a given antibiotic is dependent on the presence of resistance alleles for other antibiotics. Surprisingly we find that a significant fraction of resistant mutations can be beneficial in certain resistant genetic backgrounds, that some double resistances entail no measurable cost, and that some allelic combinations are hotspots for rapid compensation. These results provide additional insight as to why multi-resistant bacteria are so prevalent and reveal an extra layer of complexity on epistatic patterns previously unrecognized, since it is hidden in genome-wide studies of genetic interactions using gene knockouts.

Epistasis among Deleterious Mutations in the HIV-1 Protease

A central goal in molecular evolution is to understand how genetic interactions between protein mutations shape protein function and fitness. While intergenic epistasis has been extensively explored in eukaryotes, bacteria, and viruses, intragenic epistatic interactions have been insufficiently studied. Here, we employ a model system in which lambda phage fitness correlates with the enzymatic activity of human immunodeficiency virus type 1 (HIV-1) protease to systematically determine the epistatic interactions between intragenic pairs of deleterious protein substitutions. We generated 114 genotypes of the HIV-1 protease, each carrying pairs of nucleotide substitution mutations whose separated and combined deleterious effects on fitness were then determined. A high proportion (39%) of pairs displayed lethality. Several pairs exhibited significant interactions for fitness, including positive and negative epistasis. Significant negative epistatic interactions predominated (15%) over positive interactions (2%). However, the average ± SD epistatic effect, ē = 0.0025 ± 0.1334, was not significantly different from zero ( p = 0.8368). Notably, epistatic interactions, regardless of epistatic direction, tend to be more frequent in the context of less deleterious mutations. In the present study, the high frequencies of lethality and negative epistasis indicate that the HIV-1 protease is highly sensitive to the effects of deleterious mutations. Therefore, proteins may not be as robust to mutational change as is usually expected.

Sex Is Always Well Worth Its Two-Fold Cost

Sex is considered as an evolutionary paradox, since its positive contribution to Darwinian fitness remains unverified for some species. Defenses against unpredictable threats (parasites, fluctuating environment and deleterious mutations) are indeed significantly improved by wider genetic variability and by positive epistasis gained by sexual reproduction. The corresponding evolutionary advantages, however, do not overcome universally the barrier of the two-fold cost for sharing half of one's offspring genome with another member of the population. Here we show that sexual reproduction emerges and is maintained even when its Darwinian fitness is twice as low as the fitness of asexuals. We also show that more than two sexes (inheritance of genetic material from three or even more parents) are always evolutionary unstable. Our approach generalizes the evolutionary game theory to analyze species whose members are able to sense the sexual state of their conspecifics and to adapt their own sex consequently, either by switching or by taxis towards the highest concentration of the complementary sex. The widespread emergence and maintenance of sex follows therefore from its co-evolution with the even more widespread environmental sensing abilities.

The Evolution of Epistasis and Its Links With Genetic Robustness, Complexity and Drift in a Phenotypic Model of Adaptation

The epistatic interactions among mutations have a large effect on the evolution of populations. In this article we provide a formalism under which epistatic interactions among pairs of mutations have a distribution whose mean can be modulated. We find that the mean epistasis is correlated to the effect of mutations or genetic robustness, which suggests that such formalism is in good agreement with most in silico models of evolution where the same pattern is observed. We further show that the evolution of epistasis is highly dependant on the intensity of drift and of how complex the organisms are, and that either positive or negative epistasis could be selected for, depending on the balance between the efficiency of selection and the intensity of drift.

THE EVOLUTION OF STRONG REPRODUCTIVE ISOLATION

Felsenstein distinguished two ways by which selection can directly strengthen isolation. First, a modifier that strengthens prezygotic isolation can be favored everywhere. This fits with the traditional view of reinforcement as an adaptation to reduce deleterious hybridization by strengthening assortative mating. Second, selection can favor association between different incompatibilities, despite recombination. We generalize this "two allele" model to follow associations among any number of incompatibilities, which may include both assortment and hybrid inviability. Our key argument is that this process, of coupling between incompatibilities, may be quite different from the usual view of reinforcement: strong isolation can evolve through the coupling of any kind of incompatibility, whether prezygotic or postzygotic. Single locus incompatibilities become coupled because associations between them increase the variance in compatibility, which in turn increases mean fitness if there is positive epistasis. Multiple incompatibilities, each maintained by epistasis, can become coupled in the same way. In contrast, a single-locus incompatibility can become coupled with loci that reduce the viability of haploid hybrids because this reduces harmful recombination. We obtain simple approximations for the limits of tight linkage, and strong assortment, and show how assortment alleles can invade through associations with other components of reproductive isolation.

Interactions Between Stressful Environment and Gene Deletions Alleviate the Expected Average Loss of Fitness in Yeast

The conjecture that the deleterious effects of mutations are amplified by stress or interaction with one another remains unsatisfactorily tested. It is now possible to reapproach this problem systematically by using genomic collections of mutants and applying stress-inducing conditions with a well-recognized impact on metabolism. We measured the maximum growth rate of single- and double-gene deletion strains of yeast in several stress-inducing treatments, including poor nutrients, elevated temperature, high salinity, and the addition of caffeine. The negative impact of deletions on the maximum growth rate was relatively smaller in stressful than in favorable conditions. In both benign and harsh environments, double-deletion strains grew on average slightly faster than expected from a multiplicative model of interaction between single growth effects, indicating positive epistasis for the rate of growth. This translates to even higher positive epistasis for fitness defined as the number of progeny. We conclude that the negative impact of metabolic disturbances, regardless of whether they are of environmental or genetic origin, is absolutely and relatively highest when growth is fastest. The effect of further damages tends to be weaker. This results in an average alleviating effect of interactions between stressful environment and gene deletions and among gene deletions.

On the evolution of epistasis III: The haploid case with mutation

Whether interaction between genes is better represented by synergistic or antagonistic epistasis has been a focus of experimental research in bacterial population genetics. Our previous research on evolution of modifiers of epistasis in diploid systems has indicated that the strength of positive or negative epistasis should increase provided linkage disequilibrium is maintained. Here we study a modifier of epistasis in fitness between two loci in a haploid system. Epistasis is modified in the neighborhood of a mutation–selection balance. We show that when linkage in the three-locus system is tight, an increase in the frequency of a modifier allele that induces either more negative or more positive epistasis is possible. Epistasis here can be measured on either an additive or multiplicative scale.

Epistatic interactions alter dynamics of multilocus gene-for-gene coevolution.

Fitness costs associated with resistance or virulence genes are thought to play a key role in determining the dynamics of gene-for-gene (GFG) host-parasite coevolution. However, the nature of interactions between fitness effects of multiple resistance or virulence genes (epistasis) has received less attention. To examine effects of the functional form of epistasis on the dynamics of GFG host-parasite coevolution we modified a classic multilocus GFG model framework. We show that the type of epistasis between virulence genes largely determines coevolutionary dynamics, and that coevolutionary fluctuations are more likely with acceleratingly costly (negative) than with linear or deceleratingly costly (positive) epistasis. Our results demonstrate that the specific forms of interaction between multiple resistance or virulence genes are a crucial determinant of host-parasite coevolutionary dynamics.

EVOLUTION OF GENETIC ARCHITECTURE UNDER DIRECTIONAL SELECTION

We investigate the multilinear epistatic model under mutation-limited directional selection. We confirm previous results that only directional epistasis, in which genes on average reinforce or diminish each other's effects, contribute to the initial evolution of mutational effects. Thus, either canalization or decanalization can occur under directional selection, depending on whether positive or negative epistasis is prevalent. We then focus on the evolution of the epistatic coefficients themselves. In the absence of higher-order epistasis, positive pairwise epistasis will tend to weaken relative to additive effects, while negative pairwise epistasis will tend to become strengthened. Positive third-order epistasis will counteract these effects, while negative third-order epistasis will reinforce them. More generally, gene interactions of all orders have an inherent tendency for negative changes under directional selection, which can only be modified by higher-order directional epistasis. We identify three types of nonadditive quasi-equilibrium architectures that, although not strictly stable, can be maintained for an extended time: (1) nondirectional epistatic architectures; (2) canalized architectures with strong epistasis; and (3) near-additive architectures in which additive effects keep increasing relative to epistasis.

A Phylogenetic Method for Detecting Positive Epistasis in Gene Sequences and Its Application to RNA Virus Evolution

RNA virus genomes are compact, often containing multiple overlapping reading frames and functional secondary structure. Consequently, it is thought that evolutionary interactions between nucleotide sites are commonplace in the genomes of these infectious agents. However, the role of epistasis in natural populations of RNA viruses remains unclear. To investigate the pervasiveness of epistasis in RNA viruses, we used a parsimony-based computational method to identify pairs of co-occurring mutations along phylogenies of 177 RNA virus genes. This analysis revealed widespread evidence for positive epistatic interactions at both synonymous and nonsynonymous nucleotide sites and in both clonal and recombining viruses, with the majority of these interactions spanning very short sequence regions. These findings have important implications for understanding the key aspects of RNA virus evolution, including the dynamics of adaptation. Additionally, many comparative analyses that utilize the phylogenetic relationships among gene sequences assume that mutations represent independent, uncorrelated events. Our results show that this assumption may often be invalid.

Effect of Varying Epistasis on the Evolution of Recombination

Whether recombination decelerates or accelerates a population's response to selection depends, at least in part, on how fitness-determining loci interact. Realistically, all genomes likely contain fitness interactions both with positive and with negative epistasis. Therefore, it is crucial to determine the conditions under which the potential beneficial effects of recombination with negative epistasis prevail over the detrimental effects of recombination with positive epistasis. Here, we examine the simultaneous effects of diverse epistatic interactions with different strengths and signs in a simplified model system with independent pairs of interacting loci and selection acting only on the haploid phase. We find that the average form of epistasis does not predict the average amount of linkage disequilibrium generated or the impact on a recombination modifier when compared to results using the entire distribution of epistatic effects and associated single-mutant effects. Moreover, we show that epistatic interactions of a given strength can produce very different effects, having the greatest impact when selection is weak. In summary, we observe that the evolution of recombination at mutation-selection balance might be driven by a small number of interactions with weak selection rather than by the average epistasis of all interactions. We illustrate this effect with an analysis of published data of Saccharomyces cerevisiae. Thus to draw conclusions on the evolution of recombination from experimental data, it is necessary to consider the distribution of epistatic interactions together with the associated selection coefficients.

Response to Comment on "Evidence for Positive Epistasis in HIV-1"

Wang et al . analyzed artificially biased data to show that our results can be explained by a bias against sequences with low fitness. We explicitly acknowledged this potential caveat in our original study. Showing that an artificially introduced bias can produce a spurious signal of positive epistasis does not demonstrate that such bias exists in our original data.