Molecular mechanisms of epistasis within and between genes

Mechanisms of suppression: The wiring of genetic resilience

Recent analysis of genome sequences has identified individuals that are healthy despite carrying severe disease-associated mutations. A possible explanation is that these individuals carry a second genomic perturbation that can compensate for the detrimental effects of the disease allele, a phenomenon referred to as suppression. In model organisms, suppression interactions are generally divided into two classes: genomic suppressors which are secondary mutations in the genome that bypass a mutant phenotype, and dosage suppression interactions in which overexpression of a suppressor gene rescues a mutant phenotype. Here, we describe the general properties of genomic and dosage suppression, with an emphasis on the budding yeast. We propose that suppression interactions between genetic variants are likely relevant for determining the penetrance of human traits. Consequently, an understanding of suppression mechanisms may guide the discovery of protective variants in healthy individuals that carry disease alleles, which could direct the rational design of new therapeutics.

Functional regression method for whole genome eQTL epistasis analysis with sequencing data

Background

Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges.

Methods

We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions.

Results

By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples.

Conclusions

The proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3777-4) contains supplementary material, which is available to authorized users.

On the mechanistic nature of epistasis in a canonical cis-regulatory element

Understanding the relation between genotype and phenotype remains a major challenge. The difficulty of predicting individual mutation effects, and particularly the interactions between them, has prevented the development of a comprehensive theory that links genotypic changes to their phenotypic effects. We show that a general thermodynamic framework for gene regulation, based on a biophysical understanding of protein-DNA binding, accurately predicts the sign of epistasis in a canonical cis-regulatory element consisting of overlapping RNA polymerase and repressor binding sites. Sign and magnitude of individual mutation effects are sufficient to predict the sign of epistasis and its environmental dependence. Thus, the thermodynamic model offers the correct null prediction for epistasis between mutations across DNA-binding sites. Our results indicate that a predictive theory for the effects of cis-regulatory mutations is possible from first principles, as long as the essential molecular mechanisms and the constraints these impose on a biological system are accounted for.

DOI:http://dx.doi.org/10.7554/eLife.25192.001

Mutations are changes to DNA that provide the raw material upon which evolution can act. Therefore, to understand evolution, we need to know the effects of mutations, and how those mutations interact with each other (a phenomenon referred to as epistasis). So far, few mathematical models allow scientists to predict the effects of mutations, and even fewer are able to predict epistasis.

Biological systems are complex and consist of many proteins and other molecules. Genes are the sections of DNA that provide the instructions needed to produce these molecules, and some genes encode proteins that can bind to DNA to control whether other genes are switched on or off. Lagator, Paixão et al. have now used mathematical models and experiments to understand how the environment inside the cells of a bacterium known as E. coli, specifically the amount of particular proteins, affects epistasis.

These mathematical models are able to predict interactions between mutations in the most abundant class of DNA-binding sites in proteins. This approach found that the nature of the interaction between mutations can be explained through biophysical laws, combined with the basic knowledge of the logic of how genes regulate each other’s activities. Furthermore, the models allow Lagator, Paixão et al. to predict interactions between mutations in several different environments, such as the presence of a new food source or a toxin, defined by the amounts of relevant DNA-binding proteins in cells.

By providing new ways of understanding how genes are regulated in bacteria, and how gene regulation is affected by mutations, these findings contribute to our understanding of how organisms evolve. In addition, this work may help us to build artificial networks of genes that interact with each other to produce a desired response, such as more efficient production of fuel from ethanol or the break down of hazardous chemicals.

DOI:http://dx.doi.org/10.7554/eLife.25192.002

Gene editing and clonal isolation of human induced pluripotent stem cells using CRISPR/Cas9

Human induced pluripotent stem cells (hiPSCs) represent an ideal in vitro platform to study human genetics and biology. The recent advent of programmable nucleases makes also the human genome amenable to experimental genetics through either the correction of mutations in patient-derived iPSC lines or the de novo introduction of mutations into otherwise healthy iPSCs. The production of specific and sometimes complex genotypes in multiple cell lines requires efficient and streamlined gene editing technologies. In this article we provide protocols for gene editing in hiPSCs. We presently achieve high rates of gene editing at up to three loci using a modified iCRISPR system. This system includes a doxycycline inducible Cas9 and sgRNA/reporter plasmids for the enrichment of transfected cells by fluorescence-activated cell sorting (FACS). Here we cover the selection of target sites, vector construction, transfection, and isolation and genotyping of modified hiPSC clones.

Intrinsic incompatibilities evolving as a by-product of divergent ecological selection: Considering them in empirical studies on divergence with gene flow

The possibility of intrinsic barriers to gene flow is often neglected in empirical research on local adaptation and speciation with gene flow, for example when interpreting patterns observed in genome scans. However, we draw attention to the fact that, even with gene flow, divergent ecological selection may generate intrinsic barriers involving both ecologically selected and other interacting loci. Mechanistically, the link between the two types of barriers may be generated by genes that have multiple functions (i.e., pleiotropy), and/or by gene interaction networks. Because most genes function in complex networks, and their evolution is not independent of other genes, changes evolving in response to ecological selection can generate intrinsic barriers as a by-product. A crucial question is to what extent such by-product barriers contribute to divergence and speciation-that is whether they stably reduce gene flow. We discuss under which conditions by-product barriers may increase isolation. However, we also highlight that, depending on the conditions (e.g., the amount of gene flow and the strength of selection acting on the intrinsic vs. the ecological barrier component), the intrinsic incompatibility may actually destabilize barriers to gene flow. In practice, intrinsic barriers generated as a by-product of divergent ecological selection may generate peaks in genome scans that cannot easily be interpreted. We argue that empirical studies on divergence with gene flow should consider the possibility of both ecological and intrinsic barriers. Future progress will likely come from work combining population genomic studies, experiments quantifying fitness and molecular studies on protein function and interactions.

Modeling of a negative feedback mechanism explains antagonistic pleiotropy in reproduction in domesticated Caenorhabditis elegans strains

Most biological traits and common diseases have a strong but complex genetic basis, controlled by large numbers of genetic variants with small contributions to a trait or disease risk. The effect-size of most genetic variants is not absolute and is instead dependent upon multiple factors such as the age and genetic background of an organism. In order to understand the mechanistic basis of these changes, we characterized heritable trait differences between two domesticated strains of C. elegans. We previously identified a major effect locus, caused in part by a mutation in a component of the NURF chromatin remodeling complex, that regulates reproductive output in an age-dependent manner. The effect-size of this locus changes from positive to negative over the course of an animal’s reproductive lifespan. Here, we use a previously published macroscale model of the egg-laying rate in C. elegans to show that time-dependent effect-size is explained by an unequal use of sperm combined with negative feedback between sperm and ovulation rate. We validate key predictions of this model with controlled mating experiments and quantification of oogenesis and sperm use. Incorporation of this model into QTL mapping allows us to identify and partition new QTLs into specific aspects of the egg-laying process. Finally, we show how epistasis between two genetic variants is predicted by this modeling as a consequence of the unequal use of sperm. This work demonstrates how modeling of multicellular communication systems can improve our ability to predict and understand the role of genetic variation on a complex phenotype. Negative autoregulatory feedback loops, common in transcriptional regulation, could play an important role in modifying genetic architecture in other traits.

Complex traits are influenced by the individual effects of genetic variants in addition to the interactions of the variants with the environment, age, and each other. While complex genetic architectures are ubiquitous in natural traits, little is known about the causal mechanisms that create their complex genetic architectures. Here we identify an example of age-dependent genetic architecture controlling the rate and timing of reproduction in the hermaphroditic nematode C. elegans. We use computational modeling to demonstrate how age-dependent genetic architecture can arise as a consequence of two factors: hormonal feedback on oocytes mediated by major sperm protein (MSP) released by sperm stored in the spermatheca and life history differences in sperm use caused by genetic variants. Our work also suggests how antagonistic pleiotropy can emerge from multicellular feedback systems.

Tissue-specific tumorigenesis: context matters

How can we treat cancer more effectively? Traditionally, tumours from the same anatomical site are treated as one tumour entity. This concept has been challenged by recent breakthroughs in cancer genomics and translational research that have enabled molecular tumour profiling. The identification and validation of cancer drivers that are shared between different tumour types, spurred the new paradigm to target driver pathways across anatomical sites by off-label drug use, or within so-called basket or umbrella trials which are designed to test whether molecular alterations in one tumour entity can be extrapolated to all others. However, recent clinical and preclinical studies suggest that there are tissue- and cell type-specific differences in tumorigenesis and the organization of oncogenic signalling pathways. In this Opinion article, we focus on the molecular, cellular, systemic and environmental determinants of organ-specific tumorigenesis and the mechanisms of context-specific oncogenic signalling outputs. Investigation, recognition and in-depth biological understanding of these differences will be vital for the design of next-generation clinical trials and the implementation of molecularly guided cancer therapies in the future.

Regulation of proteinaceous effector expression in phytopathogenic fungi

Effectors are molecules used by microbial pathogens to facilitate infection via effector-triggered susceptibility or tissue necrosis in their host. Much research has been focussed on the identification and elucidating the function of fungal effectors during plant pathogenesis. By comparison, knowledge of how phytopathogenic fungi regulate the expression of effector genes has been lagging. Several recent studies have illustrated the role of various transcription factors, chromosome-based control, effector epistasis, and mobilisation of endosomes within the fungal hyphae in regulating effector expression and virulence on the host plant. Improved knowledge of effector regulation is likely to assist in improving novel crop protection strategies.

Growth condition dependency is the major cause of non-responsiveness upon genetic perturbation

Investigating the role and interplay between individual proteins in biological processes is often performed by assessing the functional consequences of gene inactivation or removal. Depending on the sensitivity of the assay used for determining phenotype, between 66% (growth) and 53% (gene expression) of Saccharomyces cerevisiae gene deletion strains show no defect when analyzed under a single condition. Although it is well known that this non-responsive behavior is caused by different types of redundancy mechanisms or by growth condition/cell type dependency, it is not known what the relative contribution of these different causes is. Understanding the underlying causes of and their relative contribution to non-responsive behavior upon genetic perturbation is extremely important for designing efficient strategies aimed at elucidating gene function and unraveling complex cellular systems. Here, we provide a systematic classification of the underlying causes of and their relative contribution to non-responsive behavior upon gene deletion. The overall contribution of redundancy to non-responsive behavior is estimated at 29%, of which approximately 17% is due to homology-based redundancy and 12% is due to pathway-based redundancy. The major determinant of non-responsiveness is condition dependency (71%). For approximately 14% of protein complexes, just-in-time assembly can be put forward as a potential mechanistic explanation for how proteins can be regulated in a condition dependent manner. Taken together, the results underscore the large contribution of growth condition requirement to non-responsive behavior, which needs to be taken into account for strategies aimed at determining gene function. The classification provided here, can also be further harnessed in systematic analyses of complex cellular systems.

The thermodynamics of protein aggregation reactions may underpin the enhanced metabolic efficiency associated with heterosis, some balancing selection, and the evolution of ploidy levels

Identifying the physical basis of heterosis (or "hybrid vigor") has remained elusive despite over a hundred years of research on the subject. The three main theories of heterosis are dominance theory, overdominance theory, and epistasis theory. Kacser and Burns (1981) identified the molecular basis of dominance, which has greatly enhanced our understanding of its importance to heterosis. This paper aims to explain how overdominance, and some features of epistasis, can similarly emerge from the molecular dynamics of proteins. Possessing multiple alleles at a gene locus results in the synthesis of different allozymes at reduced concentrations. This in turn reduces the rate at which each allozyme forms soluble oligomers, which are toxic and must be degraded, because allozymes co-aggregate at low efficiencies. The model developed in this paper can explain how heterozygosity impacts the metabolic efficiency of an organism. It can also explain why the viabilities of some inbred lines seem to decline rapidly at high inbreeding coefficients (F > 0.5), which may provide a physical basis for truncation selection for heterozygosity. Finally, the model has implications for the ploidy level of organisms. It can explain why polyploids are frequently found in environments where severe physical stresses promote the formation of soluble oligomers. The model can also explain why complex organisms, which need to synthesize aggregation-prone proteins that contain intrinsically unstructured regions (IURs) and multiple domains because they facilitate complex protein interaction networks (PINs), tend to be diploid while haploidy tends to be restricted to relatively simple organisms.

A thousand empirical adaptive landscapes and their navigability

The adaptive landscape is an iconic metaphor that pervades evolutionary biology. It was mostly applied in theoretical models until recent years, when empirical data began to allow partial landscape reconstructions. Here, we exhaustively analyse 1,137 complete landscapes from 129 eukaryotic species, each describing the binding affinity of a transcription factor to all possible short DNA sequences. We find that the navigability of these landscapes through single mutations is intermediate to that of additive and shuffled null models, suggesting that binding affinity-and thereby gene expression-is readily fine-tuned via mutations in transcription factor binding sites. The landscapes have few peaks that vary in their accessibility and in the number of sequences they contain. Binding sites in the mouse genome are enriched in sequences found in the peaks of especially navigable landscapes and the genetic diversity of binding sites in yeast increases with the number of sequences in a peak. Our findings suggest that landscape navigability may have contributed to the enormous success of transcriptional regulation as a source of evolutionary adaptations and innovations.

Consequences of Epistasis on Growth in an Erhualian × White Duroc Pig Cross

Epistasis describes an interaction between the effects of loci. We included epistasis in quantitative trait locus (QTL) mapping of growth at a series of ages in a cross of a Chinese pig breed, Erhualian, with a commercial line, White Duroc. Erhualian pigs have much lower growth rates than White Duroc. We improved a method for genomewide testing of epistasis and present a clear analysis workflow. We also suggest a new approach for interpreting epistasis results where significant additive and dominance effects of a locus in specific backgrounds are determined. In total, seventeen QTL were found and eleven showed epistasis. Loci on chromosomes 2, 3, 4 and 7 were highlighted as affecting growth at more than one age or forming an interaction network. Epistasis resulted in both the QTL on chromosomes 3 and 7 having effects in opposite directions. We believe it is the first time for the chromosome 7 locus that an allele from a Chinese breed has been found to decrease growth. The consequences of epistasis were diverse. Results were impacted by using growth rather than body weight as the phenotype and by correcting for an effect of mother. Epistasis made a considerable contribution to growth in this population and modelling epistasis was important for accurately determining QTL effects.

Identification of Marker-Trait Associations for Lint Traits in Cotton

Harvesting high quality lint, a long-awaited breeding goal-accomplished partly, can be achieved by identifying DNA markers which could be used for diagnosing cotton plants containing the desired traits. In the present studies, a total of 185 cotton genotypes exhibiting diversity for lint traits were selected from a set of 546 genotypes evaluated for fiber traits in 2009. These genotypes were extensively studied for three consecutive years (2011-2013) at three different locations. Significant genetic variations were found for average boll weight, ginning out turn (GOT), micronaire value, staple length, fiber bundle strength, and uniformity index. IR-NIBGE-3701 showed maximum GOT (43.63%). Clustering of genotypes using Ward's method was found more informative than that of the clusters generated by principal component analysis. A total of 382 SSRs were surveyed on 10 Gossypium hirsutum genotypes exhibiting contrasting fiber traits. Out of these, 95 polymorphic SSR primer pairs were then surveyed on 185 genotypes. The gene diversity averaged 0.191 and the polymorphic information content (PIC) averaged 0.175. Unweighted pair group method with arithmetic mean (UPGMA), principal coordinate analysis (PCoA), and STRUCTURE software grouped these genotypes into four major clusters each. Genetic distance within the clusters ranged from 0.0587 to 0.1030. A total of 47 (25.41%) genotypes exhibited shared ancestry. In total 6.8% (r² ≥ 0.05) and 4.4% (r² ≥ 0.1) of the marker pairs showed significant linkage disequilibrium (LD). A number of marker-trait associations (in total 75) including 13 for average boll weight, 18 for GOT percentage, eight for micronaire value, 18 for staple length, three for fiber bundle strength, and 15 for uniformity index were calculated. Out of these, MGHES-51 was associated with all the traits. Most of the marker-trait associations were novel while few validated the associations reported in the previous studies. High frequency of favorable alleles in cultivated varieties is possibly due to fixation of desirable alleles by domestication. These favorable alleles can be used in marker assisted breeding or for gene cloning using next generation sequencing tools. The present studies would set a stage for harvesting high quality lint without compromising the yield potential-ascertaining natural fiber security.

Bayesian reversible-jump for epistasis analysis in genomic studies

Background

The large amount of data used in genomic analysis has allowed geneticists to achieve some understanding of the genetic architecture of complex traits. Although the information gathered by molecular markers has permitted gains in predictive accuracy and gene discovery, epistatic effects have been ignored based on exhaustive searches requesting estimates of its effects on the whole genome. In this work, we propose the reversible-jump technique to estimate epistasis in the genome without drastically altering the model dimension. To this end, we used a real maize dataset based on 256 F_2:3 progenies plus a simulation data set based on 300 F₂ individuals. In the simulation scenario, six QTL presenting main effects (additive and dominance) were combined with seven other epistatic effects totaling 13 QTL controlling the trait.

Results

Our model explored 18,624 candidate epistases, but even in this vast space, only one spurious interaction was found. The three epistases selected by our model, named here as 18x26, 56x68 and 59x93, were very close to simulated ones (19x25, 54x72, 59x91 and 59x94). In the real dataset, we estimate 33,024 epistatic effects, and several minor epistatic combinations were found to explain a significant proportion of the genetic variance. The broad participation of epistasis in the real dataset may indicate the presence of pervasive epistasis acting on maize grain yield.

Conclusions

The power of selecting true epistasis in thousands of possible combinations suggests the attractiveness of our model to handle genomic data

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3342-6) contains supplementary material, which is available to authorized users.

Inter-laboratory evolution of a model organism and its epistatic effects on mutagenesis screens

In theory, a few naturally occurring evolutionary changes in the genome of a model organism may have little or no observable impact on its wild type phenotype, and yet still substantially impact the phenotypes of mutant strains through epistasis. To see if this is happening in a model organism, we obtained nine different laboratories' wild type Myxococcus xanthus DK1622 "sublines" and sequenced each to determine if they had evolved after their physical separation. Under a common garden experiment, each subline satisfied the phenotypic prerequisites for wild type, but many differed to a significant degree in each of the four quantitative phenotypic traits we measured, with some sublines differing by several-fold. Genome resequencing identified 29 variants between the nine sublines, and eight had at least one unique variant within an Open Reading Frame (ORF). By disrupting the ORF MXAN7041 in two different sublines, we demonstrated substantial epistasis from these naturally occurring variants. The impact of such inter-laboratory wild type evolution is important to any genotype-to-phenotype study; an organism's phenotype may be sensitive to small changes in genetic background, so that results from phenotypic screens and other related experiments might not agree with prior published results or the results from other laboratories.

Powerful decomposition of complex traits in a diploid model

Explaining trait differences between individuals is a core and challenging aim of life sciences. Here, we introduce a powerful framework for complete decomposition of trait variation into its underlying genetic causes in diploid model organisms. We sequence and systematically pair the recombinant gametes of two intercrossed natural genomes into an array of diploid hybrids with fully assembled and phased genomes, termed Phased Outbred Lines (POLs). We demonstrate the capacity of this approach by partitioning fitness traits of 6,642 Saccharomyces cerevisiae POLs across many environments, achieving near complete trait heritability and precisely estimating additive (73%), dominance (10%), second (7%) and third (1.7%) order epistasis components. We map quantitative trait loci (QTLs) and find nonadditive QTLs to outnumber (3:1) additive loci, dominant contributions to heterosis to outnumber overdominant, and extensive pleiotropy. The POL framework offers the most complete decomposition of diploid traits to date and can be adapted to most model organisms.

Candidate gene study reveals DRD1 and DRD2 as putative interacting risk factors for youth depression

Alterations in the monoaminergic neurotransmission systems are suspected to be involved in the etiology of neuropsychiatric disorders, including depression. The role of these pathways in the risk of developing depressive symptoms during childhood or adolescence is still not completely clear. This study sought to identify putative genetic factors in genes of serotonergic and dopaminergic systems modulating the level of manifestation of depressive symptoms in children and adolescents. We analyzed 170 single nucleotide polymorphisms (SNPs) in 21 candidate dopaminergic and serotonergic genes in a non-clinical sample of 410 Costa Rican participants of ages between 7 and 18 years, assessing the severity of depressive symptoms through the Child Depression Inventory (CDI). Genotypic and haplotypic associations, as well as epistatic effects, were examined. A significant interaction effect was detected between rs1039089 in conjunction with rs877138 located upstream of the dopamine D1 receptor (DRD1) and the dopamine D2 receptor (DRD2) genes respectively, although no evidence was found for any single variant or haplotype related to a differential liability. This newly described genetic interaction among putative regulatory regions of dopamine receptors could affect the level of manifestation of depressive symptoms through an imbalance of D1-D2 heteromers and modulation of cognitive processes.

Changing preferences: deformation of single position amino acid fitness landscapes and evolution of proteins

The fitness landscape-the function that relates genotypes to fitness-and its role in directing evolution are a central object of evolutionary biology. However, its huge dimensionality precludes understanding of even the basic aspects of its shape. One way to approach it is to ask a simpler question: what are the properties of a function that assigns fitness to each possible variant at just one particular site-a single position fitness landscape-and how does it change in the course of evolution? Analyses of genomic data from multiple species and multiple individuals within a species have proved beyond reasonable doubt that fitness functions of positions throughout the genome do themselves change with time, thus shaping protein evolution. Here, I will briefly review the literature that addresses these dynamics, focusing on recent genome-scale analyses of fitness functions of amino acid sites, i.e. vectors of fitnesses of 20 individual amino acid variants at a given position of a protein. The set of amino acids that confer high fitness at a particular position changes with time, and the rate of this change is comparable with the rate at which a position evolves, implying that this process plays a major role in evolutionary dynamics. However, the causes of these changes remain largely unclear.

Multikernel linear mixed models for complex phenotype prediction

Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy.

Network of epistatic interactions within a yeast snoRNA

Epistatic interactions play a fundamental role in molecular evolution, but little is known about the spatial distribution of these interactions within genes. To systematically survey a model landscape of intragenic epistasis, we quantified the fitness of ~60,000 Saccharomyces cerevisiae strains expressing randomly mutated variants of the 333-nucleotide-long U3 small nucleolar RNA (snoRNA). The fitness effects of individual mutations were correlated with evolutionary conservation and structural stability. Many mutations had small individual effects but had large effects in the context of additional mutations, which indicated negative epistasis. Clusters of negative interactions were explained by local thermodynamic threshold effects, whereas positive interactions were enriched among large-effect sites and between base-paired nucleotides. We conclude that high-throughput mapping of intragenic epistasis can identify key structural and functional features of macromolecules.

Mitonuclear Epistasis for Development Time and Its Modification by Diet in Drosophila

Mitochondrial (mtDNA) and nuclear genes have to operate in a coordinated manner to maintain organismal function, and the regulation of this homeostasis presents a substantial source of potential epistatic (G × G) interactions. How these interactions shape the fitness landscape is poorly understood. Here we developed a novel mitonuclear epistasis model, using selected strains of the Drosophila Genetic Reference Panel (DGRP) and mitochondrial genomes from within Drosophila melanogaster and D. simulans to test the hypothesis that mtDNA × nDNA interactions influence fitness. In total we built 72 genotypes (12 nuclear backgrounds × 6 mtDNA haplotypes, with 3 from each species) to dissect the relationship between genotype and phenotype. Each genotype was assayed on four food environments. We found considerable variation in several phenotypes, including development time and egg-to-adult viability, and this variation was partitioned into genetic (G), environmental (E), and higher-order (G × G, G × E, and G × G × E) components. Food type had a significant impact on development time and also modified mitonuclear epistases, evidencing a broad spectrum of G × G × E across these genotypes. Nuclear background effects were substantial, followed by mtDNA effects and their G × G interaction. The species of mtDNA haplotype had negligible effects on phenotypic variation and there was no evidence that mtDNA variation has different effects on male and female fitness traits. Our results demonstrate that mitonuclear epistases are context dependent, suggesting the selective pressure acting on mitonuclear genotypes may vary with food environment in a genotype-specific manner.

Ascochyta blight disease of pea (Pisum sativum L.): defence-related candidate genes associated with QTL regions and identification of epistatic QTL

KEY MESSAGE

Advances have been made in our understanding of Ascochyta blight resistance genetics through mapping candidate genes associated with QTL regions and demonstrating the importance of epistatic interactions in determining resistance. Ascochyta blight disease of pea (Pisum sativum L.) is economically significant with worldwide distribution. The causal pathogens are Didymella pinodes, Phoma medicaginis var pinodella and, in South Australia, P. koolunga. This study aimed to identify candidate genes that map to quantitative trait loci (QTL) for Ascochyta blight field disease resistance and to explore the role of epistatic interactions. Candidate genes associated with QTL were identified beginning with 101 defence-related genes from the published literature. Synteny between pea and Medicago truncatula was used to narrow down the candidates for mapping. Fourteen pea candidate sequences were mapped in two QTL mapping populations, A26 × Rovar and A88 × Rovar. QTL peaks, or the intervals containing QTL peaks, for the Asc2.1, Asc4.2, Asc4.3 and Asc7.1 QTL were defined by four of these candidate genes, while another three candidate genes occurred within 1.0 LOD confidence intervals. Epistasis involving QTL × background marker and background marker × background marker interactions contributed to the disease response phenotypes observed in the two mapping populations. For each population, five pairwise interactions exceeded the 5% false discovery rate threshold. Two candidate genes were involved in significant pairwise interactions. Markers in three genomic regions were involved in two or more epistatic interactions. Therefore, this study has identified pea defence-related sequences that are candidates for resistance determination, and that may be useful for marker-assisted selection. The demonstration of epistasis informs breeders that the architecture of this complex quantitative resistance includes epistatic interactions with non-additive effects.

A highly precise and portable genome engineering method allows comparison of mutational effects across bacterial species

Currently available tools for multiplex bacterial genome engineering are optimized for a few laboratory model strains, demand extensive prior modification of the host strain, and lead to the accumulation of numerous off-target modifications. Building on prior development of multiplex automated genome engineering (MAGE), our work addresses these problems in a single framework. Using a dominant-negative mutant protein of the methyl-directed mismatch repair (MMR) system, we achieved a transient suppression of DNA repair in Escherichia coli, which is necessary for efficient oligonucleotide integration. By integrating all necessary components into a broad-host vector, we developed a new workflow we term pORTMAGE. It allows efficient modification of multiple loci, without any observable off-target mutagenesis and prior modification of the host genome. Because of the conserved nature of the bacterial MMR system, pORTMAGE simultaneously allows genome editing and mutant library generation in other biotechnologically and clinically relevant bacterial species. Finally, we applied pORTMAGE to study a set of antibiotic resistance-conferring mutations in Salmonella enterica and E. coli. Despite over 100 million y of divergence between the two species, mutational effects remained generally conserved. In sum, a single transformation of a pORTMAGE plasmid allows bacterial species of interest to become an efficient host for genome engineering. These advances pave the way toward biotechnological and therapeutic applications. Finally, pORTMAGE allows systematic comparison of mutational effects and epistasis across a wide range of bacterial species.

Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross

The extent and strength of epistasis is commonly unresolved in genetic studies, and observed epistasis is often difficult to interpret in terms of biological consequences or overall genetic architecture. We investigated the prevalence and consequences of epistasis by analyzing four body composition phenotypes—body weight, body fat percentage, femoral density, and femoral circumference—in a large F2 intercross of B6-lit/lit and C3.B6-lit/lit mice. We used Combined Analysis of Pleiotropy and Epistasis (CAPE) to examine interactions for the four phenotypes simultaneously, which revealed an extensive directed network of genetic loci interacting with each other, circulating IGF1, and sex to influence these phenotypes. The majority of epistatic interactions had small effects relative to additive effects of individual loci, and tended to stabilize phenotypes towards the mean of the population rather than extremes. Interactive effects of two alleles inherited from one parental strain commonly resulted in phenotypes closer to the population mean than the additive effects from the two loci, and often much closer to the mean than either single-locus model. Alternatively, combinations of alleles inherited from different parent strains contribute to more extreme phenotypes not observed in either parental strain. This class of phenotype-stabilizing interactions has effects that are close to additive and are thus difficult to detect except in very large intercrosses. Nevertheless, we found these interactions to be useful in generating hypotheses for functional relationships between genetic loci. Our findings suggest that while epistasis is often weak and unlikely to account for a large proportion of heritable variance, even small-effect genetic interactions can facilitate hypotheses of underlying biology in well-powered studies.

The role of statistical epistasis in the genetic architecture of complex traits has been of great interest to the genetics community since Fisher introduced the concept in 1918. However, assessing epistasis in human and model organism populations has been impeded by limited statistical power. To mitigate this limitation, we analyzed bone and body composition traits in an unusually large mouse intercross population of over 2000 mice, paired with a recently-developed computational approach that leverages information to detect interactions across multiple phenotypes. We discovered a large network of highly significant genetic interactions between variants that influence complex body composition traits. Although epistasis was abundant, the interaction network was dominated by epistasis that stabilizes phenotypes by reducing phenotypic deviation from the parent strains. Nevertheless, the observed network provides an overview of genetic architecture and specific hypotheses of how QTL combine to affect phenotypes. These findings suggest that epistatic effects are generally of lesser magnitude than main QTL effects, and therefore are unlikely to account for major components of variance, but also reinforce genetic interaction analysis as a potent tool for dissecting the biology of complex traits.

Cancer Drug Resistance: A Brief Overview from a Genetic Viewpoint

Cancer drug resistance leading to therapeutic failure in the treatment of many cancers encompasses various mechanisms and may be intrinsic relying on the patient's genetic makeup or be acquired by tumors that are initially sensitive to cancer drugs. All in all, it may be responsible for treatment failure in over 90 % of patients with metastatic cancer. Cancer drug resistance, in particular acquired resistance, may stem from the micro-clonality/micro-genetic heterogeneity of the tumors whereby, among others, the following mechanisms may entail resistance: altered expression of drug influx/efflux transporters in the tumor cells mediating lower drug uptake and/or greater efflux of the drug; altered role of DNA repair and impairment of apoptosis; role of epigenomics/epistasis by methylation, acetylation, and altered levels of microRNAs leading to alterations in upstream or downstream effectors; mutation of drug targets in targeted therapy and alterations in the cell cycle and checkpoints; and tumor microenvironment that are briefly reviewed.

A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions

BACKGROUND

Genetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering pathway organization and understanding the relationship between genotype, phenotype and disease.

RESULTS

To investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, "buffering by induced dependency" and "alleviation by derepression".

CONCLUSIONS

These mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships.

Epistatic Interactions in the Arabinose Cis-Regulatory Element

Changes in gene expression are an important mode of evolution; however, the proximate mechanism of these changes is poorly understood. In particular, little is known about the effects of mutations within cis binding sites for transcription factors, or the nature of epistatic interactions between these mutations. Here, we tested the effects of single and double mutants in two cis binding sites involved in the transcriptional regulation of the Escherichia coli araBAD operon, a component of arabinose metabolism, using a synthetic system. This system decouples transcriptional control from any posttranslational effects on fitness, allowing a precise estimate of the effect of single and double mutations, and hence epistasis, on gene expression. We found that epistatic interactions between mutations in the araBAD cis-regulatory element are common, and that the predominant form of epistasis is negative. The magnitude of the interactions depended on whether the mutations are located in the same or in different operator sites. Importantly, these epistatic interactions were dependent on the presence of arabinose, a native inducer of the araBAD operon in vivo, with some interactions changing in sign (e.g., from negative to positive) in its presence. This study thus reveals that mutations in even relatively simple cis-regulatory elements interact in complex ways such that selection on the level of gene expression in one environment might perturb regulation in the other environment in an unpredictable and uncorrelated manner.

Evolving new protein-protein interaction specificity through promiscuous intermediates

Interacting proteins typically coevolve, and the identification of coevolving amino acids can pinpoint residues required for interaction specificity. This approach often assumes that an interface-disrupting mutation in one protein drives selection of a compensatory mutation in its partner during evolution. However, this model requires a non-functional intermediate state prior to the compensatory change. Alternatively, a mutation in one protein could first broaden its specificity, allowing changes in its partner, followed by a specificity-restricting mutation. Using bacterial toxin-antitoxin systems, we demonstrate the plausibility of this second, promiscuity-based model. By screening large libraries of interface mutants, we show that toxins and antitoxins with high specificity are frequently connected in sequence space to more promiscuous variants that can serve as intermediates during a reprogramming of interaction specificity. We propose that the abundance of promiscuous variants promotes the expansion and diversification of toxin-antitoxin systems and other paralogous protein families during evolution.

Humoral Immunity to Cytomegalovirus Glycoprotein B in Patients With Breast Cancer and Matched Controls: Contribution of Immunoglobulin γ, κ, and Fcγ Receptor Genes

Increasing evidence implicates human cytomegalovirus (HCMV) in the etiopathogenesis of breast cancer. Antibodies to this virus in patients with breast cancer have been reported, but no large-scale studies have been conducted to determine whether the antibody levels differ between patients and matched controls. Using specimens from a large (1712 subjects) multiethnic case-control study, we aimed to determine whether the levels of antibodies to the HCMV glycoprotein B (gB) differed between patients and controls and whether they were associated with particular immunoglobulin γ marker (GM), κ marker (KM), and Fcγ receptor (FcγR) genotypes. A combined analysis showed that anti-gB immunoglobulin G antibody levels were higher in healthy controls than in patients (P < .0001). Stratified analyses showed population-specific differences in the magnitude of anti-gB antibody responsiveness and in the contribution of particular GM, KM, and FcγR genotypes to these responses. These findings may have implications for HCMV-based immunotherapy against breast cancer and other HCMV-associated diseases.

Cancer type-dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types

Cancers, like many diseases, are normally caused by combinations of genetic alterations rather than by changes affecting single genes. It is well established that the genetic alterations that drive cancer often interact epistatically, having greater or weaker consequences in combination than expected from their individual effects. In a stringent statistical analysis of data from > 3,000 tumors, we find that the co-occurrence and mutual exclusivity relationships between cancer driver alterations change quite extensively in different types of cancer. This cannot be accounted for by variation in tumor heterogeneity or unrecognized cancer subtypes. Rather, it suggests that how genomic alterations interact cooperatively or partially redundantly to driver cancer changes in different types of cancers. This re-wiring of epistasis across cell types is likely to be a basic feature of genetic architecture, with important implications for understanding the evolution of multicellularity and human genetic diseases. In addition, if this plasticity of epistasis across cell types is also true for synthetic lethal interactions, a synthetic lethal strategy to kill cancer cells may frequently work in one type of cancer but prove ineffective in another.

Association genetics in Populus reveals the interactions between Pt-miR397a and its target genes

Recent studies have revealed associations between single nucleotide polymorphisms (SNPs) in microRNA (miRNA) genes and diseases. However, association studies to decipher the interactions between miRNAs and their target genes remain to be conducted. Here, we investigated the association of growth and wood traits with SNPs in Pt-miR397a and its targets, in 261 individuals from a natural population of Populus tomentosa. Of the 57 SNPs identified in Pt-miR397a, three strongly affect its secondary stability, and SNPs in target sites in Pt-LAC20 and Pt-HSP40 changed the binding affinity of Pt-miR397a. Single-SNP association analysis revealed that SNPs in Pt-miR397a significantly associated with α-cellulose content and stem volume, and SNPs in target genes also associated with growth and wood-property traits. Multi-SNP association analysis with additive and dominant models found that SNPs in six potential target genes associated with at least one trait in common with Pt-miR397a, revealing a possible genetic interaction between Pt-miR397a and its targets. Furthermore, epistasis analysis revealed epistatic interactions between SNPs in Pt-miR397a and its target genes. Thus, our study indicated that SNPs in Pt-miR397a and six target genes affect wood formation and that association studies can reveal the interactions between miRNAs and their target genes.

Network wiring of pleiotropic kinases yields insight into protective role of diabetes on aneurysm

Recent studies suggest a protective role of diabetes in the development of aneurysm, but the biological mechanisms behind this are still unknown. This type of association is not present in the case of diabetes and atherosclerosis despite similar risk factors for aneurysm and atherosclerosis. We postulate the existence of genes that disrupt the pathways needed for the onset of aneurysm in the presence of diabetes. Motivated by the significance of genetic interactions in understanding disease-disease associations, we tackle this problem by integrating protein-protein interaction and genetic interaction data, i.e., we examine the biological pathways related to the three diseases that contain genes involved in the following genetic interactions: one gene in a genetic interaction is part of a diabetes pathway, the other gene is part of an aneurysm, or an atherosclerosis pathway. We create a protein-protein interaction sub-network that contains disease pathways described above. We then use a "brokerage" measure - a topological measure that identifies proteins in this sub-network whose removal severely affects the interconnectedness of their neighbourhood, enabling such proteins to disrupt the pathway they are in. We identify a set of proteins with high brokerage values and find this set to be enriched in biological functions, including cell-matrix adhesion, which facilitates mechanisms that have already been suggested as possible causes of diabetes-aneurysm association. We further narrow down our set to 16 proteins that are involved in an aneurysm or an atherosclerosis pathway and are encoded by genes participating in genetic interactions with a gene in a diabetes pathway. This set is enriched in kinases and phosphorylation processes, with two pleiotropic kinases that are involved in both aneurysm and atherosclerosis pathways. Kinases can turn on or off proteins, explaining how functional changes of such proteins could result in the disruption of pathways. So if in an aneurysm-related pathway a gene is turned off, the onset of the disease could be prevented. However, mutations of pleiotropic genes could have effects only on one of the traits, which explains why pleiotropic kinases that are involved in both aneurysm and atherosclerosis pathways could disrupt aneurysm pathways explaining the reduced risk of aneurysm in diabetes patients, but not affect the atherosclerosis pathways.

The SAM domain of ANKS6 has different interacting partners and mutations can induce different cystic phenotypes

The ankyrin repeat and sterile α motif (SAM) domain-containing six gene (Anks6) is a candidate for polycystic kidney disease (PKD). Originally identified in the PKD/Mhm(cy/+) rat model of PKD, the disease is caused by a mutation (R823W) in the SAM domain of the encoded protein. Recent studies support the etiological role of the ANKS6 SAM domain in human cystic diseases, but its function in kidney remains unknown. To investigate the role of ANKS6 in cyst formation, we screened an archive of N-ethyl-N-nitrosourea-treated mice and derived a strain carrying a missense mutation (I747N) within the SAM domain of ANKS6. This mutation is only six amino acids away from the PKD-causing mutation (R823W) in cy/+ rats. Evidence of renal cysts in these mice confirmed the crucial role of the SAM domain of ANKS6 in kidney function. Comparative phenotype analysis in cy/+ rats and our Anks6(I747N) mice further showed that the two models display noticeably different PKD phenotypes and that there is a defective interaction between ANKS6 with ANKS3 in the rat and between ANKS6 and BICC1 (bicaudal C homolog 1) in the mouse. Thus, our data demonstrate the importance of ANKS6 for kidney structure integrity and the essential mediating role of its SAM domain in the formation of protein complexes.

Genes as cues: phenotypic integration of genetic and epigenetic information from a Darwinian perspective

The development of multicellular organisms involves a delicate interplay between genetic and environmental influences. It is often useful to think of developmental systems as integrating available sources of information about current conditions to produce organisms. Genes and inherited physiology provide cues, as does the state of the environment during development. The integration systems themselves are under genetic control and subject to Darwinian selection, so we expect them to evolve to produce organisms that fit well with current ecological (including social) conditions. We argue for the scientific value of this explicitly informational perspective by providing detailed examples of how it can elucidate taxonomically diverse phenomena. We also present a general framework for linking genetic and phenotypic variation from an informational perspective. This application of Darwinian logic at the organismal level can elucidate genetic influences on phenotypic variation in novel and counterintuitive ways.

Network-based analysis of the sphingolipid metabolism in hypertension

Common diseases like essential hypertension or diabetes mellitus are complex as they are polygenic in nature, such that each genetic variation only has a small influence on the disease. Genes operates in integrated networks providing the blue-print for all biological processes and conditional of the complex genotype determines the state and dynamics of any trait, which may be modified to various extent by non-genetic factors. Thus, diseases are heterogenous ensembles of conditions with a common endpoint. Numerous studies have been performed to define genes of importance for a trait or disease, but only a few genes with small effect have been identified. The major reasons for this modest progress is the unresolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition. Here, a two-step procedure is presented in which physiological heterogeneity is disentangled and genetic effects are analyzed by variance decomposition of genetic interactions and by an information theoretical approach including 162 single nucleotide polymorphisms (SNP) in 84 genes in the sphingolipid metabolism and related networks in blood pressure regulation. As expected, almost no genetic main effects were detected. In contrast, two-gene interactions established the entire sphingolipid metabolic and related genetic network to be highly involved in the regulation of blood pressure. The pattern of interaction clearly revealed that epistasis does not necessarily reflects the topology of the metabolic pathways i.e., the flow of metabolites. Rather, the enzymes and proteins are integrated in complex cellular substructures where communication flows between the components of the networks, which may be composite in structure. The heritabilities for diastolic and systolic blood pressure were estimated to be 0.63 and 0.01, which may in fact be the maximum heritabilities of these traits. This procedure provide a platform for studying and capturing the genetic networks of any polygenic trait, condition, or disease.

Fighting microbial drug resistance: a primer on the role of evolutionary biology in public health

Although microbes have been evolving resistance to antimicrobials for millennia, the spread of resistance in pathogen populations calls for the development of new drugs and treatment strategies. We propose that successful, long-term resistance management requires a better understanding of how resistance evolves in the first place. This is an opportunity for evolutionary biologists to engage in public health, a collaboration that has substantial precedent. Resistance evolution has been an important tool for developing and testing evolutionary theory, especially theory related to the genetic basis of new traits and constraints on adaptation. The present era is no exception. The articles in this issue highlight the breadth of current research on resistance evolution and also its challenges. In this introduction, we review the conceptual advances that have been achieved from studying resistance evolution and describe a path forward.

Protein evolution. Pervasive degeneracy and epistasis in a protein-protein interface

Mapping protein sequence space is a difficult problem that necessitates the analysis of 20(N) combinations for sequences of length N. We systematically mapped the sequence space of four key residues in the Escherichia coli protein kinase PhoQ that drive recognition of its substrate PhoP. We generated a library containing all 160,000 variants of PhoQ at these positions and used a two-step selection coupled to next-generation sequencing to identify 1659 functional variants. Our results reveal extensive degeneracy in the PhoQ-PhoP interface and epistasis, with the effect of individual substitutions often highly dependent on context. Together, epistasis and the genetic code create a pattern of connectivity of functional variants in sequence space that likely constrains PhoQ evolution. Consequently, the diversity of PhoQ orthologs is substantially lower than that of functional PhoQ variants.

An overview of the neurobiology of suicidal behaviors as one meta-system

Suicidal behaviors (SB) may be regarded as the outmost consequence of mental illnesses, or as a distinct entity per se. Regardless, the consequences of SB are very large to both society and affected individuals. The path leading to SB is clearly a complex one involving interactions between the subject's biology and environmental influences throughout life. With the aim to generate a representative and diversified overview of the different neurobiological components hypothesized or shown implicated across the entire SB field up to date by any approach, we selected and compiled a list of 212 gene symbols from the literature. An increasing number of novel gene (products) have been introduced as candidates, with half being implicated in SB in only the last 4 years. These candidates represent different neuro systems and functions and might therefore be regarded as competing or redundant explanations. We then adopted a unifying approach by treating them all as parts of the same meta-system, using bioinformatic tools. We present a network of all components connected by physical protein-protein interactions (the SB interactome). We proceeded by exploring the differences between the highly connected core (~30% of the candidate genes) and its peripheral parts, observing more functional homogeneity at the core, with multiple signal transduction pathways and actin-interacting proteins connecting a subset of receptors in nerve cell compartments as well as development/morphology phenotypes and the stress-sensitive synaptic plasticity processes of long term potentiation/depression. We suggest that SB neurobiology might also be viewed as one meta-system and perhaps be explained as intrinsic unbalances acting within the core or as imbalances arising between core and specific peripheral components.

Investigation of gene effects and epistatic interactions between Akt1 and neuregulin 1 in the regulation of behavioral phenotypes and social functions in genetic mouse models of schizophrenia

Accumulating evidence from human genetic studies has suggested several functional candidate genes that might contribute to susceptibility to schizophrenia, including AKT1 and neuregulin 1 (NRG1). Recent findings also revealed that NRG1 stimulates the PI3-kinase/AKT signaling pathway, which might be involved in the functional outcomes of some schizophrenic patients. The aim of this study was to evaluate the effect of Akt1-deficiency and Nrg1-deficiency alone or in combination in the regulation of behavioral phenotypes, cognition, and social functions using genetically modified mice as a model. Male Akt1^+/−, Nrg1^+/−, and double mutant mice were bred and compared with their wild-type (WT) littermate controls. In Experiment 1, general physical examination revealed that all mutant mice displayed a normal profile of body weight during development and a normal brain activity with microPET scan. In Experiment 2, no significant genotypic differences were found in our basic behavioral phenotyping, including locomotion, anxiety-like behavior, and sensorimotor gating function. However, both Nrg1^+/− and double mutant mice exhibited impaired episodic-like memory. Double mutant mice also had impaired sociability. In Experiment 3, a synergistic epistasis between Akt1 and Nrg1 was further confirmed in double mutant mice in that they had impaired social interaction compared to the other 3 groups, especially encountering with a novel male or an ovariectomized female. Double mutant and Nrg1^+/− mice also emitted fewer female urine-induced ultrasonic vocalization calls. Collectively, our results indicate that double deficiency of Akt1 and Nrg1 can result in the impairment of social cognitive functions, which might be pertinent to the pathogenesis of schizophrenia-related social cognition.

Dynamic epistasis under varying environmental perturbations

Epistasis describes the phenomenon that mutations at different loci do not have independent effects with regard to certain phenotypes. Understanding the global epistatic landscape is vital for many genetic and evolutionary theories. Current knowledge for epistatic dynamics under multiple conditions is limited by the technological difficulties in experimentally screening epistatic relations among genes. We explored this issue by applying flux balance analysis to simulate epistatic landscapes under various environmental perturbations. Specifically, we looked at gene-gene epistatic interactions, where the mutations were assumed to occur in different genes. We predicted that epistasis tends to become more positive from glucose-abundant to nutrient-limiting conditions, indicating that selection might be less effective in removing deleterious mutations in the latter. We also observed a stable core of epistatic interactions in all tested conditions, as well as many epistatic interactions unique to each condition. Interestingly, genes in the stable epistatic interaction network are directly linked to most other genes whereas genes with condition-specific epistasis form a scale-free network. Furthermore, genes with stable epistasis tend to have similar evolutionary rates, whereas this co-evolving relationship does not hold for genes with condition-specific epistasis. Our findings provide a novel genome-wide picture about epistatic dynamics under environmental perturbations.

A model of substitution trajectories in sequence space and long-term protein evolution

The nature of factors governing the tempo and mode of protein evolution is a fundamental issue in evolutionary biology. Specifically, whether or not interactions between different sites, or epistasis, are important in directing the course of evolution became one of the central questions. Several recent reports have scrutinized patterns of long-term protein evolution claiming them to be compatible only with an epistatic fitness landscape. However, these claims have not yet been substantiated with a formal model of protein evolution. Here, we formulate a simple covarion-like model of protein evolution focusing on the rate at which the fitness impact of amino acids at a site changes with time. We then apply the model to the data on convergent and divergent protein evolution to test whether or not the incorporation of epistatic interactions is necessary to explain the data. We find that convergent evolution cannot be explained without the incorporation of epistasis and the rate at which an amino acid state switches from being acceptable at a site to being deleterious is faster than the rate of amino acid substitution. Specifically, for proteins that have persisted in modern prokaryotic organisms since the last universal common ancestor for one amino acid substitution approximately ten amino acid states switch from being accessible to being deleterious, or vice versa. Thus, molecular evolution can only be perceived in the context of rapid turnover of which amino acids are available for evolution.

A systematic survey of an intragenic epistatic landscape

Mutations are the source of evolutionary variation. The interactions of multiple mutations can have important effects on fitness and evolutionary trajectories. We have recently described the distribution of fitness effects of all single mutations for a nine-amino-acid region of yeast Hsp90 (Hsp82) implicated in substrate binding. Here, we report and discuss the distribution of intragenic epistatic effects within this region in seven Hsp90 point mutant backgrounds of neutral to slightly deleterious effect, resulting in an analysis of more than 1,000 double mutants. We find negative epistasis between substitutions to be common, and positive epistasis to be rare--resulting in a pattern that indicates a drastic change in the distribution of fitness effects one step away from the wild type. This can be well explained by a concave relationship between phenotype and genotype (i.e., a concave shape of the local fitness landscape), suggesting mutational robustness intrinsic to the local sequence space. Structural analyses indicate that, in this region, epistatic effects are most pronounced when a solvent-inaccessible position is involved in the interaction. In contrast, all 18 observations of positive epistasis involved at least one mutation at a solvent-exposed position. By combining the analysis of evolutionary and biophysical properties of an epistatic landscape, these results contribute to a more detailed understanding of the complexity of protein evolution.

Concerted evolution of life stage performances signals recent selection on yeast nitrogen use

Exposing natural selection driving phenotypic and genotypic adaptive differentiation is an extraordinary challenge. Given that an organism's life stages are exposed to the same environmental variations, we reasoned that fitness components, such as the lag, rate, and efficiency of growth, directly reflecting performance in these life stages, should often be selected in concert. We therefore conjectured that correlations between fitness components over natural isolates, in a particular environmental context, would constitute a robust signal of recent selection. Critically, this test for selection requires fitness components to be determined by different genetic loci. To explore our conjecture, we exhaustively evaluated the lag, rate, and efficiency of asexual population growth of natural isolates of the model yeast Saccharomyces cerevisiae in a large variety of nitrogen-limited environments. Overall, fitness components were well correlated under nitrogen restriction. Yeast isolates were further crossed in all pairwise combinations and coinheritance of each fitness component and genetic markers were traced. Trait variations tended to map to quantitative trait loci (QTL) that were private to a single fitness component. We further traced QTLs down to single-nucleotide resolution and uncovered loss-of-function mutations in RIM15, PUT4, DAL1, and DAL4 as the genetic basis for nitrogen source use variations. Effects of SNPs were unique for a single fitness component, strongly arguing against pleiotropy between lag, rate, and efficiency of reproduction under nitrogen restriction. The strong correlations between life stage performances that cannot be explained by pleiotropy compellingly support adaptive differentiation of yeast nitrogen source use and suggest a generic approach for detecting selection.