Sex- and APOE-specific genetic risk factors for late-onset Alzheimer's disease: Evidence from gene-gene interaction of longevity-related loci

Advanced age is the largest risk factor for late-onset Alzheimer's disease (LOAD), a disease in which susceptibility correlates to almost all hallmarks of aging. Shared genetic signatures between LOAD and longevity were frequently hypothesized, likely characterized by distinctive epistatic and pleiotropic interactions. Here, we applied a multidimensional reduction approach to detect gene-gene interactions affecting LOAD in a large dataset of genomic variants harbored by genes in the insulin/IGF1 signaling, DNA repair, and oxidative stress pathways, previously investigated in human longevity. The dataset was generated from a collection of publicly available Genome Wide Association Studies, comprising a total of 2,469 gene variants genotyped in 20,766 subjects of Northwestern European ancestry (11,038 LOAD cases and 9,728 controls). The stratified analysis according to APOE*4 status and sex corroborated evidence that pathways leading to longevity also contribute to LOAD. Among the significantly interacting genes, PTPN1, TXNRD1, and IGF1R were already found enriched in gene-gene interactions affecting survival to old age. Furthermore, interacting variants associated with LOAD in a sex- and APOE-specific way. Indeed, while in APOE*4 female carriers we found several inter-pathway interactions, no significant epistasis was found in APOE*4 negative females; conversely, in males, significant intra- and inter-pathways epistasis emerged according to APOE*4 status. These findings suggest that interactions of risk factors may drive different trajectories of cognitive aging. Beyond helping to disentangle the genetic architecture of LOAD, such knowledge may improve precision in predicting the risk of dementia and enable effective sex- and APOE-stratified preventive and therapeutic interventions for LOAD.

Co-amplification of CBX3 with EGFR or RAC1 in human cancers corroborated by a conserved genetic interaction among the genes

Chromobox Protein 3 (CBX3) overexpression is a common event occurring in cancer, promotes cancer cell proliferation and represents a poor prognosis marker in a plethora of human cancers. Here we describe that a wide spectrum of human cancers harbors a co-amplification of CBX3 gene with either EGFR or RAC1, which yields a statistically significant increase of both mRNA and protein levels of CBX3, EGFR and RAC1. We also reveal that the simultaneous overexpression of CBX3, RAC1 and EGFR gene products correlates with a worse prognosis compared to the condition when CBX3, RAC1 and EGFR are singularly upregulated. Furthermore, we also show that a co-occurrence of low-grade amplification, in addition to high-grade amplification, between CBX3 and EGFR or RAC1 is associated with a reduced patient lifespan. Finally, we find that CBX3 and RAC1/EGFR genetically interact in the model organism Drosophila melanogaster, suggesting that the simultaneous overexpression as well as well the co-occurrence of high- or low-grade copy number alterations in these genes is not accidental and could reflect evolutionarily conserved functional relationships.

The Genetic Landscape of a Metabolic Interaction

Enzyme abundance, catalytic activity, and ultimately sequence are all shaped by the need of growing cells to maintain metabolic flux while minimizing accumulation of deleterious intermediates. While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focused on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We used deep mutational scanning to quantify the growth rate effect of 2,696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.

Plant biochemical genetics in the multiomics era

Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.

Fast kernel-based association testing of non-linear genetic effects for biobank-scale data

Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a kernel-based approach that can test for non-linear effects of a set of variants on a quantitative trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of unrelated individuals from a homogeneous population. We apply FastKAST to 53 quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with non-linear effects at genome-wide significance.

Gene Interactions in Human Disease Studies-Evidence Is Mounting

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Ecotype formation and prophage domestication during gut bacterial evolution

How much bacterial evolution occurs in our intestines and which factors control it are currently burning questions. The formation of new ecotypes, some of which capable of coexisting for long periods of time, is highly likely in our guts. Horizontal gene transfer driven by temperate phages that can perform lysogeny is also widespread in mammalian intestines. Yet, the roles of mutation and especially lysogeny as key drivers of gut bacterial adaptation remain poorly understood. The mammalian gut contains hundreds of bacterial species, each with many strains and ecotypes, whose abundance varies along the lifetime of a host. A continuous high input of mutations and horizontal gene transfer events mediated by temperate phages drives that diversity. Future experiments to study the interaction between mutations that cause adaptation in microbiomes and lysogenic events with different costs and benefits will be key to understand the dynamic microbiomes of mammals. Also see the video abstract here: https://youtu.be/Zjqsiyb5Pk0.

Ancient transcriptional regulators can easily evolve new pair-wise cooperativity

Cells regulate gene expression by the specific binding of transcription regulators to cis-regulatory sequences. Pair-wise cooperativity between regulators-whereby two different regulators physically interact and bind DNA in a cooperative manner-is common and permits complex modes of gene regulation. Over evolutionary timescales, the formation of new combinations of regulators represents a major source of phenotypic novelty, facilitating new network structures. How functional, pair-wise cooperative interactions arise between regulators is poorly understood, despite the abundance of examples in extant species. Here, we explore a protein-protein interaction between two ancient transcriptional regulators-the homeodomain protein Matα2 and the MADS box protein Mcm1-that was gained approximately 200 million y ago in a clade of ascomycete yeasts that includes Saccharomyces cerevisiae. By combining deep mutational scanning with a functional selection for cooperative gene expression, we tested millions of possible alternative evolutionary solutions to this interaction interface. The artificially evolved, functional solutions are highly degenerate, with diverse amino acid chemistries permitted at all positions but with widespread epistasis limiting success. Nonetheless, approximately ~45% of the random sequences sampled function as well or better in controlling gene expression than the naturally evolved sequence. From these variants (which are unconstrained by historical contingency), we discern structural rules and epistatic constraints governing the emergence of cooperativity between these two transcriptional regulators. This work provides a mechanistic basis for long-standing observations of transcription network plasticity and highlights the importance of epistasis in the evolution of new protein-protein interactions.

CovET: A covariation-evolutionary trace method that identifies protein structure-function modules

Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.

Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort

Alzheimer's disease (AD) is the main cause of dementia worldwide, and the genetic mechanism of which is not yet fully understood. Much evidence has accumulated over the past decade to suggest that after the first large-scale genome-wide association studies (GWAS) were conducted, the problem of "missing heritability" in AD is still a great challenge. Epistasis has been considered as one of the main causes of "missing heritability" in AD, which has been largely ignored in human genetics. The focus of current genome-wide epistasis studies is usually on single nucleotide polymorphisms (SNPs) that have significant individual effects, and the amount of heritability explained by which was very low. Moreover, AD is characterized by progressive cognitive decline and neuronal damage, and some studies have suggested that hyperphosphorylated tau (P-tau) mediates neuronal death by inducing necroptosis and inflammation in AD. Therefore, this study focused on identifying epistasis between two-marker interactions at marginal main effects across the whole genome using cerebrospinal fluid (CSF) P-tau as quantitative trait (QT). We sought to detect interactions between SNPs in a multi-GPU based linear regression method by using age, gender, and clinical diagnostic status (cds) as covariates. We then used the STRING online tool to perform the PPI network and identify two-marker epistasis at the level of gene-gene interaction. A total of 758 SNP pairs were found to be statistically significant. Particularly, between the marginal main effect SNP pairs, highly significant SNP-SNP interactions were identified, which explained a relatively high variance at the P-tau level. In addition, 331 AD-related genes were identified, 10 gene-gene interaction pairs were replicated in the PPI network. The identified gene-gene interactions and genes showed associations with AD in terms of neuroinflammation and neurodegeneration, neuronal cells activation and brain development, thereby leading to cognitive decline in AD, which is indirectly associated with the P-tau pathological feature of AD and in turn supports the results of this study. Thus, the results of our study might be beneficial for explaining part of the "missing heritability" of AD.

Identification of Driver Epistatic Gene Pairs Combining Germline and Somatic Mutations in Cancer

Cancer arises from the complex interplay of various factors. Traditionally, the identification of driver genes focuses primarily on the analysis of somatic mutations. We describe a new method for the detection of driver gene pairs based on an epistasis analysis that considers both germline and somatic variations. Specifically, the identification of significantly mutated gene pairs entails the calculation of a contingency table, wherein one of the co-mutated genes can exhibit a germline variant. By adopting this approach, it is possible to select gene pairs in which the individual genes do not exhibit significant associations with cancer. Finally, a survival analysis is used to select clinically relevant gene pairs. To test the efficacy of the new algorithm, we analyzed the colon adenocarcinoma (COAD) and lung adenocarcinoma (LUAD) samples available at The Cancer Genome Atlas (TCGA). In the analysis of the COAD and LUAD samples, we identify epistatic gene pairs significantly mutated in tumor tissue with respect to normal tissue. We believe that further analysis of the gene pairs detected by our method will unveil new biological insights, enhancing a better description of the cancer mechanism.

Transient mutation bias increases the predictability of evolution on an empirical genotype-phenotype landscape

Predicting how a population will likely navigate a genotype-phenotype landscape requires consideration of selection in combination with mutation bias, which can skew the likelihood of following a particular trajectory. Strong and persistent directional selection can drive populations to ascend toward a peak. However, with a greater number of peaks and more routes to reach them, adaptation inevitably becomes less predictable. Transient mutation bias, which operates only on one mutational step, can influence landscape navigability by biasing the mutational trajectory early in the adaptive walk. This sets an evolving population upon a particular path, constraining the number of accessible routes and making certain peaks and routes more likely to be realized than others. In this work, we employ a model system to investigate whether such transient mutation bias can reliably and predictably place populations on a mutational trajectory to the strongest selective phenotype or usher populations to realize inferior phenotypic outcomes. For this we use motile mutants evolved from ancestrally non-motile variants of the microbe Pseudomonas fluorescens SBW25, of which one trajectory exhibits significant mutation bias. Using this system, we elucidate an empirical genotype-phenotype landscape, where the hill-climbing process represents increasing strength of the motility phenotype, to reveal that transient mutation bias can facilitate rapid and predictable ascension to the strongest observed phenotype in place of equivalent and inferior trajectories. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Impact of population size on early adaptation in rugged fitness landscapes

Owing to stochastic fluctuations arising from finite population size, known as genetic drift, the ability of a population to explore a rugged fitness landscape depends on its size. In the weak mutation regime, while the mean steady-state fitness increases with population size, we find that the height of the first fitness peak encountered when starting from a random genotype displays various behaviours versus population size, even among small and simple rugged landscapes. We show that the accessibility of the different fitness peaks is key to determining whether this height overall increases or decreases with population size. Furthermore, there is often a finite population size that maximizes the height of the first fitness peak encountered when starting from a random genotype. This holds across various classes of model rugged landscapes with sparse peaks, and in some experimental and experimentally inspired ones. Thus, early adaptation in rugged fitness landscapes can be more efficient and predictable for relatively small population sizes than in the large-size limit.

This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.

A tractable physical model for the yeast polarity predicts epistasis and fitness

Accurate phenotype prediction based on genetic information has numerous societal applications, such as crop design or cellular factories. Epistasis, when biological components interact, complicates modelling phenotypes from genotypes. Here we show an approach to mitigate this complication for polarity establishment in budding yeast, where mechanistic information is abundant. We coarse-grain molecular interactions into a so-called mesotype, which we combine with gene expression noise into a physical cell cycle model. First, we show with computer simulations that the mesotype allows validation of the most current biochemical polarity models by quantitatively matching doubling times. Second, the mesotype elucidates epistasis emergence as exemplified by evaluating the predicted mutational effect of key polarity protein Bem1p when combined with known interactors or under different growth conditions. This example also illustrates how unlikely evolutionary trajectories can become more accessible. The tractability of our biophysically justifiable approach inspires a road-map towards bottom-up modelling complementary to statistical inferences.

This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.

Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects

Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from Chlamydomonas reinhardtii, we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.

Genome-wide association and epistasis studies reveal the genetic basis of saline-alkali tolerance at the germination stage in rice

Introduction

Saline-alkali stress is one of the main abiotic factors limiting rice production worldwide. With the widespread use of rice direct seeding technology, it has become increasingly important to improve rice saline-alkali tolerance at the germination stage.

Methods

To understand the genetic basis of saline-alkali tolerance and facilitate breeding efforts for developing saline-alkali tolerant rice varieties, the genetic basis of rice saline-alkali tolerance was dissected by phenotyping seven germination-related traits of 736 diverse rice accessions under the saline-alkali stress and control conditions using genome-wide association and epistasis analysis (GWAES).

Results

Totally, 165 main-effect quantitative trait nucleotides (QTNs) and 124 additional epistatic QTNs were identified as significantly associated with saline-alkali tolerance, which explained a significant portion of the total phenotypic variation of the saline-alkali tolerance traits in the 736 rice accessions. Most of these QTNs were located in genomic regions either harboring saline-alkali tolerance QTNs or known genes for saline-alkali tolerance reported previously. Epistasis as an important genetic basis of rice saline-alkali tolerance was validated by genomic best linear unbiased prediction in which inclusion of both main-effect and epistatic QTNs showed a consistently better prediction accuracy than either main-effect or epistatic QTNs alone. Candidate genes for two pairs of important epistatic QTNs were suggested based on combined evidence from the high-resolution mapping plus their reported molecular functions. The first pair included a glycosyltransferase gene LOC_Os02g51900 (UGT85E1) and an E3 ligase gene LOC_Os04g01490 (OsSIRP4), while the second pair comprised an ethylene-responsive transcriptional factor, AP59 (LOC_Os02g43790), and a Bcl-2-associated athanogene gene, OsBAG1 (LOC_Os09g35630) for salt tolerance. Detailed haplotype analyses at both gene promoter and CDS regions of these candidate genes for important QTNs identified favorable haplotype combinations with large effects on saline-alkali tolerance, which can be used to improve rice saline-alkali tolerance by selective introgression.

Discussion

Our findings provided saline-alkali tolerant germplasm resources and valuable genetic information to be used in future functional genomic and breeding efforts of rice saline-alkali tolerance at the germination stage.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Health Disparities by Sexual Orientation: Implications of Genetic and Environmental Explanations

Xu and colleagues add to the growing research literature on the role of potential familial (i.e., genetic and shared environment) causes in sexual orientation and in the relation between sexual orientation and poor mental health, in which lesbian, gay, bisexual, and other sexual minority individuals have poorer mental health than heterosexual individuals. I address several implications of the findings by Xu et al. and others. First, the nuance or complexity of genetics is considered, given the implications of genetics for health via a single gene, epistasis, or epigenetics. Second, I highlight the magnitude of the genetic relation between sexual orientation and psychopathology, suggesting some, but not all, sexual minority individuals may differ from heterosexual individuals on psychiatric vulnerability. In turn, this suggests that research should identify for whom or under what circumstances the relation holds. Third, I underscore the need for a within-subjects design, in which attention turns to differences among sexual minority individuals. Fourth, the need to reevaluate Minority Stress Theory, the predominant theory accounting for health disparities by sexual orientation, is addressed because of the implications of genetics for health. Fifth, I acknowledge the concern attached to genetic research for potentially compounding the stigmatization already experienced by sexual minority individuals. Nevertheless, this research is occurring and will likely increase in volume. It will inform understanding of sexual minority individuals.

Epistasis reduces fitness costs of influenza A virus escape from stem-binding antibodies

The hemagglutinin (HA) stem region is a major target of universal influenza vaccine efforts owing to the presence of highly conserved epitopes across multiple influenza A virus (IAV) strains and subtypes. To explore the potential impact of vaccine-induced immunity targeting the HA stem, we examined the fitness effects of viral escape from stem-binding broadly neutralizing antibodies (stem-bnAbs). Recombinant viruses containing each individual antibody escape substitution showed diminished replication compared to wild-type virus, indicating that stem-bnAb escape incurred fitness costs. A second-site mutation in the HA head domain (N129D; H1 numbering) reduced the fitness effects observed in primary cell cultures and likely enabled the selection of escape mutations. Functionally, this putative permissive mutation increased HA avidity for its receptor. These results suggest a mechanism of epistasis in IAV, wherein modulating the efficiency of attachment eases evolutionary constraints imposed by the requirement for membrane fusion. Taken together, the data indicate that viral escape from stem-bnAbs is costly but highlights the potential for epistatic interactions to enable evolution within the functionally constrained HA stem domain.

S:D614G and S:H655Y are gateway mutations that act epistatically to promote SARS-CoV-2 variant fitness

SARS-CoV-2 variants bearing complex combinations of mutations that confer increased transmissibility, COVID-19 severity, and immune escape, were first detected after S:D614G had gone to fixation, and likely originated during persistent infection of immunocompromised hosts. To test the hypothesis that S:D614G facilitated emergence of such variants, S:D614G was reverted to the ancestral sequence in the context of sequential Spike sequences from an immunocompromised individual, and within each of the major SARS-CoV-2 variants of concern. In all cases, infectivity of the S:D614G revertants was severely compromised. The infectivity of atypical SARS-CoV-2 lineages that propagated in the absence of S:D614G was found to be dependent upon either S:Q613H or S:H655Y. Notably, Gamma and Omicron variants possess both S:D614G and S:H655Y, each of which contributed to infectivity of these variants. Among sarbecoviruses, S:Q613H, S:D614G, and S:H655Y are only detected in SARS-CoV-2, which is also distinguished by a polybasic S1/S2 cleavage site. Genetic and biochemical experiments here showed that S:Q613H, S:D614G, and S:H655Y each stabilize Spike on virions, and that they are dispensable in the absence of S1/S2 cleavage, consistent with selection of these mutations by the S1/S2 cleavage site. CryoEM revealed that either S:D614G or S:H655Y shift the Spike receptor binding domain (RBD) towards the open conformation required for ACE2-binding and therefore on pathway for infection. Consistent with this, an smFRET reporter for RBD conformation showed that both S:D614G and S:H655Y spontaneously adopt the conformation that ACE2 induces in the parental Spike. Data from these orthogonal experiments demonstrate that S:D614G and S:H655Y are convergent adaptations to the polybasic S1/S2 cleavage site which stabilize S1 on the virion in the open RBD conformation and act epistatically to promote the fitness of variants bearing complex combinations of clinically significant mutations.

Widespread epistasis among beneficial genetic variants revealed by high-throughput genome editing

The phenotypic effect of any genetic variant can be altered by variation at other genomic loci. Known as epistasis, these genetic interactions shape the genotype-phenotype map of every species, yet their origins remain poorly understood. To investigate this, we employed high-throughput genome editing to measure the fitness effects of 1,826 naturally polymorphic variants in four strains of Saccharomyces cerevisiae. About 31% of variants affect fitness, of which 24% have strain-specific fitness effects indicative of epistasis. We found that beneficial variants are more likely to exhibit genetic interactions and that these interactions can be mediated by specific traits such as flocculation ability. This work suggests that adaptive evolution will often involve trade-offs where a variant is only beneficial in some genetic backgrounds, potentially explaining why many beneficial variants remain polymorphic. In sum, we provide a framework to understand the factors influencing epistasis with single-nucleotide resolution, revealing widespread epistasis among beneficial variants.

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

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

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

Causes and consequences of linkage disequilibrium among transposable elements within eukaryotic genomes

Sex and recombination can affect the dynamics of transposable elements (TEs) in various ways: while sex is expected to help TEs to spread within populations, the deleterious effect of ectopic recombination among transposons represents a possible source of purifying selection limiting their number. Furthermore, recombination may also increase the efficiency of selection against TEs by reducing selective interference among loci. In order to better understand the effects of recombination and reproductive systems on TE dynamics, this article provides analytical expressions for the linkage disequilibrium (LD) among TEs in a classical model in which TE number is stabilized by synergistic purifying selection. The results show that positive LD is predicted in infinite populations despite negative epistasis, due to the effect of the transposition process. Positive LD may substantially inflate the variance in the number of elements per genome in the case of partially selfing or partially clonal populations. Finite population size tends to generate negative LD (Hill-Robertson effect), the relative importance of this effect increasing with the degree of linkage among loci. The model is then extended in order to explore how TEs may affect selection for recombination. While positive LD generated by transposition generally disfavors recombination, the Hill-Robertson effect may represent a non-negligible source of indirect selection for recombination when TEs are abundant. However, the direct fitness cost imposed by ectopic recombination among elements generally drives the population towards low-recombination regimes, at which TEs cannot be maintained at a stable equilibrium.