Epistatic QTLs for yield heterosis in tomato

Controlled population development and genome-wide association studies have proven powerful in uncovering genes and alleles underlying complex traits. An underexplored dimension of such studies is the phenotypic contribution of nonadditive interactions between quantitative trait loci (QTLs). Capturing of such epistasis in a genome-wide manner requires very large populations to represent replicated combinations of loci whose interactions determine phenotypic outcomes. Here, we dissect epistasis using a densely genotyped population of 1,400 backcross inbred lines (BILs) between a modern processing tomato inbred (Solanum lycopersicum) and the Lost Accession (LA5240) of a distant, green-fruited, drought-tolerant wild species, Solanum pennellii. The homozygous BILs, each harboring an average of 11 introgressions and their hybrids with the recurrent parents, were phenotyped for tomato yield components. Population-wide mean yield of the BILs was less than 50% of that of their hybrids (BILHs). All the homozygous introgressions across the genome reduced yield relative to recurrent parent, while several QTLs of the BILHs independently improved productivity. Analysis of two QTL scans showed 61 cases of less-than-additive interactions and 19 cases of more-than-additive interactions. Strikingly, a single epistatic interaction involving S. pennellii QTLs on chromosomes 1 and 7, that independently did not affect yield, increased fruit yield by 20 to 50% in the double introgression hybrid grown in irrigated and dry fields over a period of 4 y. Our work demonstrates the power of large, interspecific controlled population development to uncover hidden QTL phenotypes and how rare epistatic interactions can improve crop productivity via heterosis.

Network Analysis Reveals the Molecular Bases of Statin Pleiotropy That Vary with Genetic Background

Many approved drugs are pleiotropic: for example, statins, whose main cholesterol-lowering activity is complemented by anticancer and prodiabetogenic mechanisms involving poorly characterized genetic interaction networks. We investigated these using the Saccharomyces cerevisiae genetic model, where most genetic interactions known are limited to the statin-sensitive S288C genetic background. We therefore broadened our approach by investigating gene interactions to include two statin-resistant genetic backgrounds: UWOPS87-2421 and Y55. Networks were functionally focused by selection of HMG1 and BTS1 mevalonate pathway genes for detection of genetic interactions. Networks, multilayered by genetic background, were analyzed for key genes using network centrality (degree, betweenness, and closeness), pathway enrichment, functional community modules, and Gene Ontology. Specifically, we found modification genes related to dysregulated endocytosis and autophagic cell death. To translate results to human cells, human orthologues were searched for other drug targets, thus identifying candidates for synergistic anticancer bioactivity. IMPORTANCE Atorvastatin is a highly successful drug prescribed to lower cholesterol and prevent cardiovascular disease in millions of people. Though much of its effect comes from inhibiting a key enzyme in the cholesterol biosynthetic pathway, genes in this pathway interact with genes in other pathways, resulting in 15% of patients suffering painful muscular side effects and 50% having inadequate responses. Such multigenic complexity may be unraveled using gene networks assembled from overlapping pairs of genes that complement each other. We used the unique power of yeast genetics to construct genome-wide networks specific to atorvastatin bioactivity in three genetic backgrounds to represent the genetic variation and varying response to atorvastatin in human individuals. We then used algorithms to identify key genes and their associated FDA-approved drugs in the networks, which resulted in the distinction of drugs that may synergistically enhance the known anticancer activity of atorvastatin.

The genetic architecture of behavioral canalization

Behaviors are components of fitness and contribute to adaptive evolution. Behaviors represent the interactions of an organism with its environment, yet innate behaviors display robustness in the face of environmental change, which we refer to as 'behavioral canalization'. We hypothesize that positive selection of hub genes of genetic networks stabilizes the genetic architecture for innate behaviors by reducing variation in the expression of interconnected network genes. Robustness of these stabilized networks would be protected from deleterious mutations by purifying selection or suppressing epistasis. We propose that, together with newly emerging favorable mutations, epistatically suppressed mutations can generate a reservoir of cryptic genetic variation that could give rise to decanalization when genetic backgrounds or environmental conditions change to allow behavioral adaptation.

From individual behaviors to collective outcomes: fruiting body formation in Dictyostelium as a group-level phenotype

Collective phenotypes, which arise from the interactions among individuals, can be important for the evolution of higher levels of biological organization. However, how a group's composition determines its collective phenotype remains poorly understood. When starved, cells of the social amoeba Dictyostelium discoideum cooperate to build a multicellular fruiting body, and the morphology of the fruiting body is likely advantageous to the surviving spores. We assessed how the number of strains, as well as their genetic and geographic relationships to one another, impact the group's morphology and productivity. We find that some strains consistently enhance or detract from the productivity of their groups, regardless of the identity of the other group members. We also detect extensive pairwise and higher-order genotype interactions, which collectively have a large influence on the group phenotype. Whereas previous work in Dictyostelium has focused almost exclusively on whether spore production is equitable when strains cooperate to form multicellular fruiting bodies, our results suggest a previously unrecognized impact of chimeric co-development on the group phenotype. Our results demonstrate how interactions among members of a group influence collective phenotypes and how group phenotypes might in turn impact selection on the individual.

Nonparallel genome changes within subpopulations over time contributed to genetic diversity within the US Holstein population

Maintaining genetic variation in a population is important for long-term genetic gain. The existence of subpopulations within a breed helps maintain genetic variation and diversity. The 20,990 genotyped animals, representing the breeding animals in the year 2014, were identified as the sires of animals born after 2010 with at least 25 progenies, and females measured for type traits within the last 2 yr of data. K-means clustering with 5 clusters (C1, C2, C3, C4, and C5) was applied to the genomic relationship matrix based on 58,990 SNP markers to stratify the selected candidates into subpopulations. The general higher inbreeding resulting from within-cluster mating than across-cluster mating suggests the successful stratification into genetically different groups. The largest cluster (C4) contained animals that were less related to each animal within and across clusters. The average fixation index was 0.03, indicating that the populations were differentiated, and allele differences across the subpopulations were not due to drift alone. Starting with the selected candidates within each cluster, a family unit was identified by tracing back through the pedigree, identifying the genotyped ancestors, and assigning them to a pseudogeneration. Each of the 5 families (F1, F2, F3, F4, and F5) was traced back for 10 generations, allowing for changes in frequency of individual SNPs over time to be observed, which we call allele frequencies change. Alternative procedures were used to identify SNPs changing in a parallel or nonparallel way across families. For example, markers that have changed the most in the whole population, markers that have changed differently across families, and genes previously identified as those that have changed in allele frequency. The genomic trajectory taken by each family involves selective sweeps, polygenic changes, hitchhiking, and epistasis. The replicate frequency spectrum was used to measure the similarity of change across families and showed that populations have changed differently. The proportion of markers that reversed direction in allele frequency change varied from 0.00 to 0.02 if the rate of change was greater than 0.02 per generation, or from 0.14 to 0.24 if the rate of change was greater than 0.005 per generation within each family. Cluster-specific SNP effects for stature were estimated using only females and applied to obtain indirect genomic predictions for males. Reranking occurs depending on SNP effects used. Additive genetic correlations between clusters show possible differences in populations. Further research is required to determine how this knowledge can be applied to maintain diversity and optimize selection decisions in the future.

AGO1 and HSP90 buffer different genetic variants in Arabidopsis thaliana

Argonaute 1 (AGO1), the principal protein component of microRNA-mediated regulation, plays a key role in plant growth and development. AGO1 physically interacts with the chaperone HSP90, which buffers cryptic genetic variation in plants and animals. We sought to determine whether genetic perturbation of AGO1 in Arabidopsis thaliana would also reveal cryptic genetic variation, and if so, whether AGO1-dependent loci overlap with those dependent on HSP90. To address these questions, we introgressed a hypomorphic mutant allele of AGO1 into a set of mapping lines derived from the commonly used Arabidopsis strains Col-0 and Ler. Although we identified several cases in which AGO1 buffered genetic variation, none of the AGO1-dependent loci overlapped with those buffered by HSP90 for the traits assayed. We focused on 1 buffered locus where AGO1 perturbation uncoupled the traits days to flowering and rosette leaf number, which are otherwise closely correlated. Using a bulk segregant approach, we identified a nonfunctional Ler hua2 mutant allele as the causal AGO1-buffered polymorphism. Introduction of a nonfunctional hua2 allele into a Col-0 ago1 mutant background recapitulated the Ler-dependent ago1 phenotype, implying that coupling of these traits involves different molecular players in these closely related strains. Taken together, our findings demonstrate that even though AGO1 and HSP90 buffer genetic variation in the same traits, these robustness regulators interact epistatically with different genetic loci, suggesting that higher-order epistasis is uncommon. Plain Language Summary Argonaute 1 (AGO1), a key player in plant development, interacts with the chaperone HSP90, which buffers environmental and genetic variation. We found that AGO1 buffers environmental and genetic variation in the same traits; however, AGO1-dependent and HSP90-dependent loci do not overlap. Detailed analysis of a buffered locus found that a nonfunctional HUA2 allele decouples days to flowering and rosette leaf number in an AGO1-dependent manner, suggesting that the AGO1-dependent buffering acts at the network level.

Beyond redundant kill: A fundamental explanation of how insecticide mixtures work for resistance management

The use of insecticide mixtures for resistance management has been a controversial topic for many decades. Here, we provide a reassessment of the fundamental theory of insecticide mixtures. First, we examine how mixtures differ from other strategies. We suggest that the fundamental strategy concept of a mixture is defined by the simultaneous use of insecticides and their overlapping exposure. Second, we provide a simple, illustrative model to show how mixtures affect resistance evolution. Following the existing literature, we identify a role for 'redundant kill' acting against resistant individuals, which we link to the overlapping exposure of insecticides. We also identify the occurrence of 'additional kill' acting against susceptible individuals, which is the immediate consequence of the simultaneous use of insecticides. Third, we take a basic approach to the comparison of mixtures and other strategies using a simple model. We find that a common comparison of the time to resistance alone leaves the effects of additional kill unaccounted for. Moreover, we demonstrate that different approaches to comparison can lead to different results because of biases that are introduced in the comparison setup. Fourth, still using the same model, we showcase a more sophisticated approach to comparison using optimised strategies. We find that optimised mixtures always perform better than other strategies due to the combination of redundant and additional kill. We suggest that the comparison of optimised strategies is unbiased because each strategy is performing the best that it can. On this basis, in theory (but not necessarily practice), we believe that mixtures are better than other strategies and, through the steps of our argument, we can tie this success back to the fundamental properties (of simultaneous use and overlapping exposure) that distinguish mixtures from other strategy concepts. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Systematic analysis reveals novel insight into the molecular determinants of function, diversity and evolution of sweet taste receptors T1R2/T1R3 in primates

Sweet taste is a primary sensation for the preference and adaption of primates to diet, which is crucial for their survival and fitness. It is clear now that the sweet perception is mediated by a G protein-coupled receptor (GPCR)-sweet taste receptor T1R2/T1R3, and many behavioral or physiological experiments have described the diverse sweet taste sensitivities in primates. However, the structure-function relationship of T1R2s/T1R3s in primates, especially the molecular basis for their species-dependent sweet taste, has not been well understood until now. In this study, we performed a comprehensive sequence, structural and functional analysis of sweet taste receptors in primates to elucidate the molecular determinants mediating their species-dependent sweet taste recognition. Our results reveal distinct taxonomic distribution and significant characteristics (interaction, coevolution and epistasis) of specific key function-related residues, which could partly account for the previously reported behavioral results of taste perception in primates. Moreover, the prosimians Lemuriformes species, which were reported to have no sensitivity to aspartame, could be proposed to be aspartame tasters based on the present analysis. Collectively, our study provides new insights and promotes a better understanding for the diversity, function and evolution of sweet taste receptors in primates.

RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Genetic architecture of resistance to spot blotch in wheat was examined using a Genome-Wide Association Study (GWAS) involving an association panel comprising 303 diverse genotypes. The association panel was evaluated at two different locations in India including Banaras Hindu University (BHU), Varanasi (Uttar Pradesh), and Borlaug Institute for South Asia (BISA), Pusa, Samastipur (Bihar) for two consecutive years (2017-2018 and 2018-2019), thus making four environments (E1, BHU 2017-18; E2, BHU 2018-19; E3, PUSA, 2017-18; E4, PUSA, 2018-19). The panel was genotyped for 12,196 SNPs based on DArT-seq (outsourced to DArT Ltd by CIMMYT); these SNPs included 5,400 SNPs, which could not be assigned to individual chromosomes and were therefore, described as unassigned by the vendor. Phenotypic data was recorded on the following three disease-related traits: (i) Area Under Disease Progress Curve (AUDPC), (ii) Incubation Period (IP), and (iii) Lesion Number (LN). GWAS was conducted using each of five different models, which included two single-locus models (CMLM and SUPER) and three multi-locus models (MLMM, FarmCPU, and BLINK). This exercise gave 306 MTAs, but only 89 MTAs (33 for AUDPC, 30 for IP and 26 for LN) including a solitary MTA detected using all the five models and 88 identified using four of the five models (barring SUPER) were considered to be important. These were used for further analysis, which included identification of candidate genes (CGs) and their annotation. A majority of these MTAs were novel. Only 70 of the 89 MTAs were assigned to individual chromosomes; the remaining 19 MTAs belonged to unassigned SNPs, for which chromosomes were not known. Seven MTAs were selected on the basis of minimum P value, number of models, number of environments and location on chromosomes with respect to QTLs reported earlier. These 7 MTAs, which included five main effect MTAs and two for epistatic interactions, were considered to be important for marker-assisted selection (MAS). The present study thus improved our understanding of the genetics of resistance against spot blotch in wheat and provided seven MTAs, which may be used for MAS after due validation.

Quantifying the local adaptive landscape of a nascent bacterial community

The fitness effects of all possible mutations available to an organism largely shape the dynamics of evolutionary adaptation. Yet, whether and how this adaptive landscape changes over evolutionary times, especially upon ecological diversification and changes in community composition, remains poorly understood. We sought to fill this gap by analyzing a stable community of two closely related ecotypes ("L" and "S") shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). We engineered genome-wide barcoded transposon libraries to measure the invasion fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different, ecologically relevant conditions. We find consistent statistical patterns of fitness effect variation across both genetic background and community composition, despite the idiosyncratic behavior of individual knockouts. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to shape the adaptive landscape in a nascent ecological community.

Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody

Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the V_H-V_L interface is essential for binding a divergent H1. Collectively, this is the first work to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.

Evolution and molecular basis of a novel allosteric property of crocodilian hemoglobin

The extraordinary breath-hold diving capacity of crocodilians has been ascribed to a unique mode of allosterically regulating hemoglobin (Hb)-oxygenation in circulating red blood cells. We investigated the origin and mechanistic basis of this novel biochemical phenomenon by performing directed mutagenesis experiments on resurrected ancestral Hbs. Comparisons of Hb function between the common ancestor of archosaurs (the group that includes crocodilians and birds) and the last common ancestor of modern crocodilians revealed that regulation of Hb-O2 affinity via allosteric binding of bicarbonate ions represents a croc-specific innovation that evolved in combination with the loss of allosteric regulation by ATP binding. Mutagenesis experiments revealed that evolution of the novel allosteric function in crocodilians and the concomitant loss of ancestral function were not mechanistically coupled and were caused by different sets of substitutions. The gain of bicarbonate sensitivity in crocodilian Hb involved the direct effect of few amino acid substitutions at key sites in combination with indirect effects of numerous other substitutions at structurally disparate sites. Such indirect interaction effects suggest that evolution of the novel protein function was conditional on neutral mutations that produced no adaptive benefit when they first arose but that contributed to a permissive background for subsequent function-altering mutations at other sites. Due to the context dependence of causative substitutions, the unique allosteric properties of crocodilian Hb cannot be easily transplanted into divergent homologs of other species.

Shifting Paradigm from Gene Expressions to Pathways Reveals Physiological Mechanisms in Blood Pressure Control in Causation

Genetics for blood pressure (BP) in human and animals has been partitioned into two separate specialties. However, this divide is mechanistically-misleading. BP physiology is mechanistically participated by products of quantitative trait loci (QTLs). The key to unlocking its mechanistic mystery lies in the past with mammalian ancestors before humans existed. By pivoting from effects to causes, physiological mechanisms determining BP by six QTLs have been implicated. Our work relies on congenic knock-in genetics in vivo using rat models, and has reproduced the physiological outcome based on a QTL being molecularly equal to one gene. A gene dose for a QTL is irrelevant to physiological BP controls in causation. Together, QTLs join one another as a group in modularized Mendelian fashion to achieve polygenicity. Mechanistically, QTLs in the same module appear to function in a common pathway. Each is involved in a different step in the pathway toward polygenic hypertension. This work has implicated previously-concealed components of these pathways. This emerging concept is a departure from the human-centric precept that the level of QTL expressions, not physiology, would ultimately determine BP. The modularity/pathway paradigm breaks a unique conceptual ground for unravelling the physiological mechanisms of polygenic and quantitative traits like BP.

Incompatibility and Interchangeability in Molecular Evolution

There is remarkable variation in the rate at which genetic incompatibilities in molecular interactions accumulate. In some cases, minor changes-even single-nucleotide substitutions-create major incompatibilities when hybridization forces new variants to function in a novel genetic background from an isolated population. In other cases, genes or even entire functional pathways can be horizontally transferred between anciently divergent evolutionary lineages that span the tree of life with little evidence of incompatibilities. In this review, we explore whether there are general principles that can explain why certain genes are prone to incompatibilities while others maintain interchangeability. We summarize evidence pointing to four genetic features that may contribute to greater resistance to functional replacement: (1) function in multisubunit enzyme complexes and protein-protein interactions, (2) sensitivity to changes in gene dosage, (3) rapid rate of sequence evolution, and (4) overall importance to cell viability, which creates sensitivity to small perturbations in molecular function. We discuss the relative levels of support for these different hypotheses and lay out future directions that may help explain the striking contrasts in patterns of incompatibility and interchangeability throughout the history of molecular evolution.

Coordinated evolution at amino acid sites of SARS-CoV-2 spike

SARS-CoV-2 has adapted in a stepwise manner, with multiple beneficial mutations accumulating in a rapid succession at origins of VOCs, and the reasons for this are unclear. Here, we searched for coordinated evolution of amino acid sites in the spike protein of SARS-CoV-2. Specifically, we searched for concordantly evolving site pairs (CSPs) for which changes at one site were rapidly followed by changes at the other site in the same lineage. We detected 46 sites which formed 45 CSP. Sites in CSP were closer to each other in the protein structure than random pairs, indicating that concordant evolution has a functional basis. Notably, site pairs carrying lineage defining mutations of the four VOCs that circulated before May 2021 are enriched in CSPs. For the Alpha VOC, the enrichment is detected even if Alpha sequences are removed from analysis, indicating that VOC origin could have been facilitated by positive epistasis. Additionally, we detected nine discordantly evolving pairs of sites where mutations at one site unexpectedly rarely occurred on the background of a specific allele at another site, for example on the background of wild-type D at site 614 (four pairs) or derived Y at site 501 (three pairs). Our findings hint that positive epistasis between accumulating mutations could have delayed the assembly of advantageous combinations of mutations comprising at least some of the VOCs.

The impact of epistasis in the heterosis and combining ability analyses

The current theoretical knowledge concerning the influence of epistasis on heterosis is based on a simplified multiplicative model. The objective of this study was to assess how epistasis affects the heterosis and combining ability analyses, assuming additive model, hundreds of genes, linkage disequilibrium (LD), dominance, and seven types of digenic epistasis. We developed the quantitative genetics theory for supporting the simulation of the individual genotypic values in nine populations, the selfed populations, the 36 interpopulation crosses, 180 doubled haploids (DHs), and their 16,110 crosses, assuming 400 genes on 10 chromosomes of 200 cM. Epistasis only affects population heterosis if there is LD. Only additive × additive and dominance × dominance epistasis can affect the components of the heterosis and combining ability analyses of populations. Epistasis can have a negative impact on the heterosis and combining ability analysis of populations, leading to wrong inferences regarding the identification of superior and most divergent populations. However, this depends on the type of epistasis, percentage of epistatic genes, and magnitude of their effects. Except for duplicate genes with cumulative effects and non-epistatic genic interaction, there was a decrease in the average heterosis by increasing the percentage of epistatic genes and the magnitude of their effects. The same results are generally true for the combining ability analysis of DHs. The combining ability analyses of subsets of 20 DHs showed no significant average impact of epistasis on the identification of the most divergent ones, regardless of the number of epistatic genes and magnitude of their effects. However, a negative effect on the assessment of the superior DHs can occur assuming 100% of epistatic genes, but depending on the epistasis type and the epistatic effect magnitude.

A biophysical model of viral escape from polyclonal antibodies

A challenge in studying viral immune escape is determining how mutations combine to escape polyclonal antibodies, which can potentially target multiple distinct viral epitopes. Here we introduce a biophysical model of this process that partitions the total polyclonal antibody activity by epitope and then quantifies how each viral mutation affects the antibody activity against each epitope. We develop software that can use deep mutational scanning data to infer these properties for polyclonal antibody mixtures. We validate this software using a computationally simulated deep mutational scanning experiment and demonstrate that it enables the prediction of escape by arbitrary combinations of mutations. The software described in this paper is available at https://jbloomlab.github.io/polyclonal.

Experimental and In Silico Analysis of TEM β-Lactamase Adaptive Evolution

Multiple mutations often have non-additive (epistatic) phenotypic effects. Epistasis is of fundamental biological relevance but is not well understood mechanistically. Adaptive evolution, i.e., the evolution of new biochemical activities, is rich in epistatic interactions. To better understand the principles underlying epistasis during genetic adaptation, we studied the evolution of TEM-1 β-lactamase variants exhibiting cefotaxime resistance. We report the collection of a library of 487 observed evolutionary trajectories for TEM-1 and determine the epistasis status based on cefotaxime resistance phenotype for 206 combinations of 2-3 TEM-1 mutations involving 17 positions under adaptive selective pressure. Gain-of-function (GOF) mutations are gatekeepers for adaptation. To see if GOF phenotypes can be inferred based solely on sequence data, we calculated the enrichment of GOF mutations in the different categories of epistatic pairs. Our results suggest that this is possible because GOF mutations are particularly enriched in sign and reciprocal sign epistasis, which leave a major imprint on the sequence space accessible to evolution. We also used FoldX to explore the relationship between thermodynamic stability and epistasis. We found that mutations in observed evolutionary trajectories tend to destabilize the folded structure of the protein, albeit their cumulative effects are consistently below the protein's free energy of folding. The destabilizing effect is stronger for epistatic pairs, suggesting that modest or local alterations in folding stability can modulate catalysis. Finally, we report a significant relationship between epistasis and the degree to which two protein positions are structurally and dynamically coupled, even in the absence of ligand.

Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis

Adaptive evolution of clonally dividing cells and microbes is the ultimate cause of cancer and infectious diseases. The possibility of constraining the adaptation of cell populations, by inhibiting proteins enhancing the evolvability, has therefore attracted interest. However, our current understanding of how genes influence adaptation kinetics is limited, partly because accurately measuring adaptation for many cell populations is challenging. We used a high-throughput adaptive laboratory evolution platform to track the adaptation of >18,000 cell populations corresponding to single-gene deletion strains in the haploid yeast deletion collection. We report that the preadaptation fitness of gene knockouts near-perfectly (R2= 0.91) predicts their adaptation to arsenic, leaving at the most a marginal role for dedicated evolvability gene functions. We tracked the adaptation of another >23,000 gene knockout populations to a diverse range of selection pressures and generalized the almost perfect (R2=0.72-0.98) capacity of preadaptation fitness to predict adaptation. We also reconstructed mutations in FPS1, ASK10, and ARR3, which together account for almost all arsenic adaptation in wild-type cells, in gene deletions covering a broad fitness range and show that the predictability of arsenic adaptation can be understood as a by global epistasis, where excluding arsenic is more beneficial to arsenic unfit cells. The paucity of genes with a meaningful evolvability effect on adaptation diminishes the prospects of developing adjuvant drugs aiming to slow antimicrobial and chemotherapy resistance.

Impact of epistasis effects on the accuracy of predicting phenotypic values of residual feed intake in U. S Holstein cows

The impact of genomic epistasis effects on the accuracy of predicting the phenotypic values of residual feed intake (RFI) in U.S. Holstein cows was evaluated using 6215 Holstein cows and 78,964 SNPs. Two SNP models and seven epistasis models were initially evaluated. Heritability estimates and the accuracy of predicting the RFI phenotypic values from 10-fold cross-validation studies identified the model with SNP additive effects and additive × additive (A×A) epistasis effects (A + A×A model) to be the best prediction model. Under the A + A×A model, additive heritability was 0.141, and A×A heritability was 0.263 that consisted of 0.260 inter-chromosome A×A heritability and 0.003 intra-chromosome A×A heritability, showing that inter-chromosome A×A effects were responsible for the accuracy increases due to A×A. Under the SNP additive model (A-only model), the additive heritability was 0.171. In the 10 validation populations, the average accuracy for predicting the RFI phenotypic values was 0.246 (with range 0.197-0.333) under A + A×A model and was 0.231 (with range of 0.188-0.319) under the A-only model. The average increase in the accuracy of predicting the RFI phenotypic values by the A + A×A model over the A-only model was 6.49% (with range of 3.02-14.29%). Results in this study showed A×A epistasis effects had a positive impact on the accuracy of predicting the RFI phenotypic values when combined with additive effects in the prediction model.

A possible genomic footprint of polygenic adaptation on population divergence in seed beetles?

Efforts to unravel the genomic basis of incipient speciation are hampered by a mismatch between our toolkit and our understanding of the ecology and genetics of adaptation. While the former is focused on detecting selective sweeps involving few independently acting or linked speciation genes, the latter states that divergence typically occurs in polygenic traits under stabilizing selection. Here, we ask whether a role of stabilizing selection on polygenic traits in population divergence may be unveiled by using a phenotypically informed integrative approach, based on genome‐wide variation segregating in divergent populations. We compare three divergent populations of seed beetles (Callosobruchus maculatus) where previous work has demonstrated a prominent role for stabilizing selection on, and population divergence in, key life history traits that reflect rate‐dependent metabolic processes. We derive and assess predictions regarding the expected pattern of covariation between genetic variation segregating within populations and genetic differentiation between populations. Population differentiation was considerable (mean F_ST = 0.23–0.26) and was primarily built by genes showing high selective constraints and an imbalance in inferred selection in different populations (positive Tajima's D_NS in one and negative in one), and this set of genes was enriched with genes with a metabolic function. Repeatability of relative population differentiation was low at the level of individual genes but higher at the level of broad functional classes, again spotlighting metabolic genes. Absolute differentiation (d_XY) showed a very different general pattern at this scale of divergence, more consistent with an important role for genetic drift. Although our exploration is consistent with stabilizing selection on polygenic metabolic phenotypes as an important engine of genome‐wide relative population divergence and incipient speciation in our study system, we note that it is exceedingly difficult to firmly exclude other scenarios.

Multifactorial methods integrating haplotype and epistasis effects for genomic estimation and prediction of quantitative traits

The rapid growth in genomic selection data provides unprecedented opportunities to discover and utilize complex genetic effects for improving phenotypes, but the methodology is lacking. Epistasis effects are interaction effects, and haplotype effects may contain local high-order epistasis effects. Multifactorial methods with SNP, haplotype, and epistasis effects up to the third-order are developed to investigate the contributions of global low-order and local high-order epistasis effects to the phenotypic variance and the accuracy of genomic prediction of quantitative traits. These methods include genomic best linear unbiased prediction (GBLUP) with associated reliability for individuals with and without phenotypic observations, including a computationally efficient GBLUP method for large validation populations, and genomic restricted maximum estimation (GREML) of the variance and associated heritability using a combination of EM-REML and AI-REML iterative algorithms. These methods were developed for two models, Model-I with 10 effect types and Model-II with 13 effect types, including intra- and inter-chromosome pairwise epistasis effects that replace the pairwise epistasis effects of Model-I. GREML heritability estimate and GBLUP effect estimate for each effect of an effect type are derived, except for third-order epistasis effects. The multifactorial models evaluate each effect type based on the phenotypic values adjusted for the remaining effect types and can use more effect types than separate models of SNP, haplotype, and epistasis effects, providing a methodology capability to evaluate the contributions of complex genetic effects to the phenotypic variance and prediction accuracy and to discover and utilize complex genetic effects for improving the phenotypes of quantitative traits.

The evolutionary potential of influenza A virus hemagglutinin is highly constrained by epistatic interactions with neuraminidase

Antigenic evolution of the influenza A virus (IAV) hemagglutinin (HA) gene limits efforts to effectively control the spread of the virus in the population. Efforts to understand the mechanisms governing HA antigenic evolution typically examine the HA gene in isolation. This can ignore the importance of balancing HA receptor binding activities with the receptor-destroying activities of the viral neuraminidase (NA) to maintain viral fitness. We hypothesize that the need to maintain functional balance with NA significantly constrains the evolutionary potential of the HA. We use deep mutational scanning and show that variation in NA activity significantly reshapes the HA fitness landscape by modulating the overall mutational robustness of HA. Consistent with this, we observe that different NA backgrounds support the emergence of distinct repertoires of HA escape variants under neutralizing antibody pressure. Our results reveal a critical role for intersegment epistasis in influencing the evolutionary potential of the HA gene.

A bacterial pan-genome makes gene essentiality strain-dependent and evolvable

Many bacterial species are represented by a pan-genome, whose genetic repertoire far outstrips that of any single bacterial genome. Here we investigate how a bacterial pan-genome might influence gene essentiality and whether essential genes that are initially critical for the survival of an organism can evolve to become non-essential. By using Transposon insertion sequencing (Tn-seq), whole-genome sequencing and RNA-seq on a set of 36 clinical Streptococcus pneumoniae strains representative of >68% of the species' pan-genome, we identify a species-wide 'essentialome' that can be subdivided into universal, core strain-specific and accessory essential genes. By employing 'forced-evolution experiments', we show that specific genetic changes allow bacteria to bypass essentiality. Moreover, by untangling several genetic mechanisms, we show that gene essentiality can be highly influenced by and/or be dependent on: (1) the composition of the accessory genome, (2) the accumulation of toxic intermediates, (3) functional redundancy, (4) efficient recycling of critical metabolites and (5) pathway rewiring. While this functional characterization underscores the evolvability potential of many essential genes, we also show that genes with differential essentiality remain important antimicrobial drug target candidates, as their inactivation almost always has a severe fitness cost in vivo.