Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes-such as transcriptional profiles-at scale. Here, we develop Perturb-seq, combining single-cell RNA sequencing (RNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbations to perform many such assays in a pool. We demonstrate Perturb-seq by analyzing 200,000 cells in immune cells and cell lines, focusing on transcription factors regulating the response of dendritic cells to lipopolysaccharide (LPS). Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions. We posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation. By decomposing many high content measurements into the effects of perturbations, their interactions, and diverse cell metadata, Perturb-seq dramatically increases the scope of pooled genomic assays.

Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato

Structural variants (SVs) underlie important crop improvement and domestication traits. However, resolving the extent, diversity, and quantitative impact of SVs has been challenging. We used long-read nanopore sequencing to capture 238,490 SVs in 100 diverse tomato lines. This panSV genome, along with 14 new reference assemblies, revealed large-scale intermixing of diverse genotypes, as well as thousands of SVs intersecting genes and cis-regulatory regions. Hundreds of SV-gene pairs exhibit subtle and significant expression changes, which could broadly influence quantitative trait variation. By combining quantitative genetics with genome editing, we show how multiple SVs that changed gene dosage and expression levels modified fruit flavor, size, and production. In the last example, higher order epistasis among four SVs affecting three related transcription factors allowed introduction of an important harvesting trait in modern tomato. Our findings highlight the underexplored role of SVs in genotype-to-phenotype relationships and their widespread importance and utility in crop improvement.

Epistasis in protein evolution

The structure, function, and evolution of proteins depend on physical and genetic interactions among amino acids. Recent studies have used new strategies to explore the prevalence, biochemical mechanisms, and evolutionary implications of these interactions-called epistasis-within proteins. Here we describe an emerging picture of pervasive epistasis in which the physical and biological effects of mutations change over the course of evolution in a lineage-specific fashion. Epistasis can restrict the trajectories available to an evolving protein or open new paths to sequences and functions that would otherwise have been inaccessible. We describe two broad classes of epistatic interactions, which arise from different physical mechanisms and have different effects on evolutionary processes. Specific epistasis-in which one mutation influences the phenotypic effect of few other mutations-is caused by direct and indirect physical interactions between mutations, which nonadditively change the protein's physical properties, such as conformation, stability, or affinity for ligands. In contrast, nonspecific epistasis describes mutations that modify the effect of many others; these typically behave additively with respect to the physical properties of a protein but exhibit epistasis because of a nonlinear relationship between the physical properties and their biological effects, such as function or fitness. Both types of interaction are rampant, but specific epistasis has stronger effects on the rate and outcomes of evolution, because it imposes stricter constraints and modulates evolutionary potential more dramatically; it therefore makes evolution more contingent on low-probability historical events and leaves stronger marks on the sequences, structures, and functions of protein families.

Stability-mediated epistasis constrains the evolution of an influenza protein

John Maynard Smith compared protein evolution to the game where one word is converted into another a single letter at a time, with the constraint that all intermediates are words: WORD→WORE→GORE→GONE→GENE. In this analogy, epistasis constrains evolution, with some mutations tolerated only after the occurrence of others. To test whether epistasis similarly constrains actual protein evolution, we created all intermediates along a 39-mutation evolutionary trajectory of influenza nucleoprotein, and also introduced each mutation individually into the parent. Several mutations were deleterious to the parent despite becoming fixed during evolution without negative impact. These mutations were destabilizing, and were preceded or accompanied by stabilizing mutations that alleviated their adverse effects. The constrained mutations occurred at sites enriched in T-cell epitopes, suggesting they promote viral immune escape. Our results paint a coherent portrait of epistasis during nucleoprotein evolution, with stabilizing mutations permitting otherwise inaccessible destabilizing mutations which are sometimes of adaptive value.

DOI:http://dx.doi.org/10.7554/eLife.00631.001

During evolution, the effect of one mutation on a protein can depend on whether another mutation is also present. This phenomenon is similar to the game in which one word is converted to another word, one letter at a time, subject to the rule that all the intermediate steps are also valid words: for example, the word WORD can be converted to the word GENE as follows: WORD→WORE→GORE→GONE→GENE. In this example, the D must be changed to an E before the W is changed to a G, because GORD is not a valid word.

Similarly, during the evolution of a virus, a mutation that helps the virus evade the human immune system might only be tolerated if the virus has acquired another mutation beforehand. This type of mutational interaction would constrain the evolution of the virus, since its capacity to take advantage of the second mutation depends on the first mutation having already occurred.

Gong et al. examined whether such interactions have indeed constrained evolution of the influenza virus. Between 1968 and 2007, the nucleoprotein—which acts as a scaffold for the replication of genetic material—in the human H3N2 influenza virus underwent a series of 39 mutations. To test whether all of these mutations could have been tolerated by the 1968 virus, Gong et al. introduced each one individually into the 1968 nucleoprotein. They found that several mutations greatly reduced the fitness of the 1968 virus when introduced on their own, which strongly suggests that these ‘constrained mutations’ became part of the virus’s genetic makeup as a result of interactions with ‘enabling’ mutations.

The constrained mutations decreased the stability of the nucleoprotein at high temperatures, while the enabling mutations counteracted this effect. It may, therefore, be possible to identify enabling mutations based on their effects on thermal stability. Intriguingly, the constrained mutations helped the virus overcome one form of human immunity to influenza, suggesting that interactions between mutations might limit the rate at which viruses evolve to evade the immune system.

Overall, these results show that interactions among mutations constrain the evolution of the influenza nucleoprotein in a fashion that can be largely understood in terms of protein stability. If the same is true for other proteins and viruses, this work could lead to a deeper understanding of the constraints that govern evolution at the molecular level.

DOI:http://dx.doi.org/10.7554/eLife.00631.002

The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers

BACKGROUND

Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits.

METHODS

The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background.

RESULTS

The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis.

CONCLUSIONS

Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures.

IMPACT

Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126-35. ©2016 AACR.

Antimicrobial resistance in Mycobacterium tuberculosis: mechanistic and evolutionary perspectives

Antibiotic-resistant Mycobacterium tuberculosis strains are threatening progress in containing the global tuberculosis epidemic. Mycobacterium tuberculosis is intrinsically resistant to many antibiotics, limiting the number of compounds available for treatment. This intrinsic resistance is due to a number of mechanisms including a thick, waxy, hydrophobic cell envelope and the presence of drug degrading and modifying enzymes. Resistance to the drugs which are active against M. tuberculosis is, in the absence of horizontally transferred resistance determinants, conferred by chromosomal mutations. These chromosomal mutations may confer drug resistance via modification or overexpression of the drug target, as well as by prevention of prodrug activation. Drug resistance mutations may have pleiotropic effects leading to a reduction in the bacterium's fitness, quantifiable e.g. by a reduction in the in vitro growth rate. Secondary so-called compensatory mutations, not involved in conferring resistance, can ameliorate the fitness cost by interacting epistatically with the resistance mutation. Although the genetic diversity of M. tuberculosis is low compared to other pathogenic bacteria, the strain genetic background has been demonstrated to influence multiple aspects in the evolution of drug resistance. The rate of resistance evolution and the fitness costs of drug resistance mutations may vary as a function of the genetic background.

Genetic interactions between polymorphisms that affect gene expression in yeast

Interactions between polymorphisms at different quantitative trait loci (QTLs) are thought to contribute to the genetics of many traits, and can markedly affect the power of genetic studies to detect QTLs. Interacting loci have been identified in many organisms. However, the prevalence of interactions, and the nucleotide changes underlying them, are largely unknown. Here we search for naturally occurring genetic interactions in a large set of quantitative phenotypes--the levels of all transcripts in a cross between two strains of Saccharomyces cerevisiae. For each transcript, we searched for secondary loci interacting with primary QTLs detected by their individual effects. Such locus pairs were estimated to be involved in the inheritance of 57% of transcripts; statistically significant pairs were identified for 225 transcripts. Among these, 67% of secondary loci had individual effects too small to be significant in a genome-wide scan. Engineered polymorphisms in isogenic strains confirmed an interaction between the mating-type locus MAT and the pheromone response gene GPA1. Our results indicate that genetic interactions are widespread in the genetics of transcript levels, and that many QTLs will be missed by single-locus tests but can be detected by two-stage tests that allow for interactions.

Bypassing Negative Epistasis on Yield in Tomato Imposed by a Domestication Gene

Selection for inflorescence architecture with improved flower production and yield is common to many domesticated crops. However, tomato inflorescences resemble wild ancestors, and breeders avoided excessive branching because of low fertility. We found branched variants carry mutations in two related transcription factors that were selected independently. One founder mutation enlarged the leaf-like organs on fruits and was selected as fruit size increased during domestication. The other mutation eliminated the flower abscission zone, providing "jointless" fruit stems that reduced fruit dropping and facilitated mechanical harvesting. Stacking both beneficial traits caused undesirable branching and sterility due to epistasis, which breeders overcame with suppressors. However, this suppression restricted the opportunity for productivity gains from weak branching. Exploiting natural and engineered alleles for multiple family members, we achieved a continuum of inflorescence complexity that allowed breeding of higher-yielding hybrids. Characterizing and neutralizing similar cases of negative epistasis could improve productivity in many agricultural organisms. VIDEO ABSTRACT.

Mapping the Genetic Landscape of Human Cells

Seminal yeast studies have established the value of comprehensively mapping genetic interactions (GIs) for inferring gene function. Efforts in human cells using focused gene sets underscore the utility of this approach, but the feasibility of generating large-scale, diverse human GI maps remains unresolved. We developed a CRISPR interference platform for large-scale quantitative mapping of human GIs. We systematically perturbed 222,784 gene pairs in two cancer cell lines. The resultant maps cluster functionally related genes, assigning function to poorly characterized genes, including TMEM261, a new electron transport chain component. Individual GIs pinpoint unexpected relationships between pathways, exemplified by a specific cholesterol biosynthesis intermediate whose accumulation induces deoxynucleotide depletion, causing replicative DNA damage and a synthetic-lethal interaction with the ATR/9-1-1 DNA repair pathway. Our map provides a broad resource, establishes GI maps as a high-resolution tool for dissecting gene function, and serves as a blueprint for mapping the genetic landscape of human cells.

A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function

The ability of a protein to carry out a given function results from fundamental physicochemical properties that include the protein's structure, mechanism of action, and thermodynamic stability. Traditional approaches to study these properties have typically required the direct measurement of the property of interest, oftentimes a laborious undertaking. Although protein properties can be probed by mutagenesis, this approach has been limited by its low throughput. Recent technological developments have enabled the rapid quantification of a protein's function, such as binding to a ligand, for numerous variants of that protein. Here, we measure the ability of 47,000 variants of a WW domain to bind to a peptide ligand and use these functional measurements to identify stabilizing mutations without directly assaying stability. Our approach is rooted in the well-established concept that protein function is closely related to stability. Protein function is generally reduced by destabilizing mutations, but this decrease can be rescued by stabilizing mutations. Based on this observation, we introduce partner potentiation, a metric that uses this rescue ability to identify stabilizing mutations, and identify 15 candidate stabilizing mutations in the WW domain. We tested six candidates by thermal denaturation and found two highly stabilizing mutations, one more stabilizing than any previously known mutation. Thus, physicochemical properties such as stability are latent within these large-scale protein functional data and can be revealed by systematic analysis. This approach should allow other protein properties to be discovered.

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1

The quantitative characterization of mutational landscapes is a task of outstanding importance in evolutionary and medical biology: It is, for example, of central importance for our understanding of the phenotypic effect of mutations related to disease and antibiotic drug resistance. Here we develop a novel inference scheme for mutational landscapes, which is based on the statistical analysis of large alignments of homologs of the protein of interest. Our method is able to capture epistatic couplings between residues, and therefore to assess the dependence of mutational effects on the sequence context where they appear. Compared with recent large-scale mutagenesis data of the beta-lactamase TEM-1, a protein providing resistance against beta-lactam antibiotics, our method leads to an increase of about 40% in explicative power as compared with approaches neglecting epistasis. We find that the informative sequence context extends to residues at native distances of about 20 Å from the mutated site, reaching thus far beyond residues in direct physical contact.

Ongoing global and regional adaptive evolution of SARS-CoV-2

Understanding the trends in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution is paramount to control the COVID-19 pandemic. We analyzed more than 300,000 high-quality genome sequences of SARS-CoV-2 variants available as of January 2021. The results show that the ongoing evolution of SARS-CoV-2 during the pandemic is characterized primarily by purifying selection, but a small set of sites appear to evolve under positive selection. The receptor-binding domain of the spike protein and the region of the nucleocapsid protein associated with nuclear localization signals (NLS) are enriched with positively selected amino acid replacements. These replacements form a strongly connected network of apparent epistatic interactions and are signatures of major partitions in the SARS-CoV-2 phylogeny. Virus diversity within each geographic region has been steadily growing for the entirety of the pandemic, but analysis of the phylogenetic distances between pairs of regions reveals four distinct periods based on global partitioning of the tree and the emergence of key mutations. The initial period of rapid diversification into region-specific phylogenies that ended in February 2020 was followed by a major extinction event and global homogenization concomitant with the spread of D614G in the spike protein, ending in March 2020. The NLS-associated variants across multiple partitions rose to global prominence in March to July, during a period of stasis in terms of interregional diversity. Finally, beginning in July 2020, multiple mutations, some of which have since been demonstrated to enable antibody evasion, began to emerge associated with ongoing regional diversification, which might be indicative of speciation.

Capturing the mutational landscape of the beta-lactamase TEM-1

Adaptation proceeds through the selection of mutations. The distribution of mutant fitness effect and the forces shaping this distribution are therefore keys to predict the evolutionary fate of organisms and their constituents such as enzymes. Here, by producing and sequencing a comprehensive collection of 10,000 mutants, we explore the mutational landscape of one enzyme involved in the spread of antibiotic resistance, the beta-lactamase TEM-1. We measured mutation impact on the enzyme activity through the estimation of amoxicillin minimum inhibitory concentration on a subset of 990 mutants carrying a unique missense mutation, representing 64% of possible amino acid changes in that protein reachable by point mutation. We established that mutation type, solvent accessibility of residues, and the predicted effect of mutations on protein stability primarily determined alone or in combination changes in minimum inhibitory concentration of mutants. Moreover, we were able to capture the drastic modification of the mutational landscape induced by a single stabilizing point mutation (M182T) by a simple model of protein stability. This work thereby provides an integrated framework to study mutation effects and a tool to understand/define better the epistatic interactions.

The Red Queen and Fluctuating Epistasis: A Population Genetic Analysis of Antagonistic Coevolution

Host-parasite coevolution has been shown to provide an advantage to recombination, but the selective mechanism underlying this advantage is unclear. One possibility is that recombination increases the frequency of advantageous genotypes that are disproportionately rare because of fluctuating epistasis. However, for this mechanism to work, epistasis for fitness must fluctuate over a very narrow timescale: two to five generations. Alternatively, recombination may speed up the response to directional selection by breaking up linkage disequilibria that decrease additive genetic variance. Here we analyze the results of a numerical simulation of host-parasite coevolution to assess the importance of these two mechanisms. We find that linkage disequilibria may tend to increase, rather than decrease, additive genetic variance. In addition, the sign of epistasis changes every two to five generations under several of the parameter values investigated, and epistasis and linkage disequilibrium are frequently of opposite signs. These results are consistent with the idea that selection for recombination is mediated by fluctuating epistasis. Finally, we explore the conditions under which an allele causing free recombination can spread in a nonrecombining host population and find general agreement between the predictions of a population genetic model of fluctuating epistasis and our simulation model.

Crosslinking snapshots of bacterial chemoreceptor squads

The team signaling model for bacterial chemoreceptors proposes that receptor dimers of different detection specificities form mixed trimers of dimers. These receptor "squads" then recruit the cytoplasmic signaling proteins CheA and CheW to form ternary signaling teams, which typically cluster at the poles of the cell. We devised cysteine-directed in vivo crosslinking approaches to ask whether mixed receptor squads could form in the absence of CheA and CheW and, if so, whether the underlying structural interactions conformed to trimer-of-dimers geometry. One approach used cysteine reporters at positions in the serine (Tsr) and aspartate (Tar) receptors that should form disulfide-linked Tsr approximately Tar products when juxtaposed at the interface of a mixed trimer. Another approach used a cysteine reporter with trigonal geometry near the trimer contact region and a trifunctional maleimide reagent with a spacer length appropriate for capturing the three axial subunits in a trimer of dimers. Both approaches detected mixed receptor-crosslinking products in cells lacking CheA and CheW. Under these conditions, receptor methylation and ligand-binding state had no discernable effect on crosslinking efficiencies. Crosslinking with the trigonal reporter was rapid and did not increase with longer treatment times or higher reagent concentrations, suggesting that this method produces a short-exposure snapshot of the receptor population. The extent of crosslinking indicated that most of the cell's receptor molecules were organized in higher-order groups. Crosslinking in receptor trimer contact mutants correlated with their signaling behaviors, suggesting that trimers of dimers are both structural and functional precursors of chemoreceptor signaling teams in bacteria.

Human Adaptation of Ebola Virus during the West African Outbreak

The 2013–2016 outbreak of Ebola virus (EBOV) in West Africa was the largest recorded. It began following the cross-species transmission of EBOV from an animal reservoir, most likely bats, into humans, with phylogenetic analysis revealing the co-circulation of several viral lineages. We hypothesized that this prolonged human circulation led to genomic changes that increased viral transmissibility in humans. We generated a synthetic glycoprotein (GP) construct based on the earliest reported isolate and introduced amino acid substitutions that defined viral lineages. Mutant GPs were used to generate a panel of pseudoviruses, which were used to infect different human and bat cell lines. These data revealed that specific amino acid substitutions in the EBOV GP have increased tropism for human cells, while reducing tropism for bat cells. Such increased infectivity may have enhanced the ability of EBOV to transmit among humans and contributed to the wide geographic distribution of some viral lineages.

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EBOV adapted to humans during the West African outbreak

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Amino acid substitutions in the EBOV glycoprotein increase human cell tropism

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The same glycoprotein amino acid substitutions decrease tropism for bat cells

Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein

The RNA recognition motif (RRM) is the most common RNA-binding domain in eukaryotes. Differences in RRM sequences dictate, in part, both RNA and protein-binding specificities and affinities. We used a deep mutational scanning approach to study the sequence-function relationship of the RRM2 domain of the Saccharomyces cerevisiae poly(A)-binding protein (Pab1). By scoring the activity of more than 100,000 unique Pab1 variants, including 1246 with single amino acid substitutions, we delineated the mutational constraints on each residue. Clustering of residues with similar mutational patterns reveals three major classes, composed principally of RNA-binding residues, of hydrophobic core residues, and of the remaining residues. The first class also includes a highly conserved residue not involved in RNA binding, G150, which can be mutated to destabilize Pab1. A comparison of the mutational sensitivity of yeast Pab1 residues to their evolutionary conservation reveals that most residues tolerate more substitutions than are present in the natural sequences, although other residues that tolerate fewer substitutions may point to specialized functions in yeast. An analysis of ∼40,000 double mutants indicates a preference for a short distance between two mutations that display an epistatic interaction. As examples of interactions, the mutations N139T, N139S, and I157L suppress other mutations that interfere with RNA binding and protein stability. Overall, this study demonstrates that living cells can be subjected to a single assay to analyze hundreds of thousands of protein variants in parallel.

Modeling Epistasis in Genomic Selection

Modeling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the reproducing kernel Hilbert space regression (RKHS) are two attractive approaches that reduce the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches, explicitly modeling epistatic effects. Moreover, we have shown why the RKHS model based on a Gaussian kernel captures epistatic effects among markers. Using experimental data sets in wheat and maize, we compared different genomic selection approaches and concluded that prediction accuracy can be improved by modeling epistasis for selfing species but may not for outcrossing species.

Plasticity of genetic interactions in metabolic networks of yeast

Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness.

The Causes and Consequences of Genetic Interactions (Epistasis)

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.

Adaptation in protein fitness landscapes is facilitated by indirect paths

The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20^L) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 20⁴ = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve.

DOI:http://dx.doi.org/10.7554/eLife.16965.001

Proteins can evolve over time by changing their component parts, which are called amino acids. These changes usually happen one at a time and natural selection tends to preserve those changes that make the protein more efficient at its specific tasks, while discarding those that impair the protein’s activity. However the effect of each change depends on the protein as a whole, and so two changes that separately make the protein worse can make it much better if they occur together. This phenomenon is called epistasis and in some cases it can trap proteins in a sub-optimal form and prevent them from improving further.

Proteins are made from twenty different kinds of amino acid, and there are millions of different combinations of amino acids that could, in theory, make a protein of a given length. Studying protein evolution involves making variants of the same protein, each with just a few changes, and comparing how efficient, or “fit”, they are. Previous studies only measured the fitness of a few variants and showed that epistasis could block protein evolution by requiring the protein to lose some fitness before it could improve further. However, new techniques have now made it easier to study protein evolution by testing many more protein variants.

Wu, Dai et al. focused on four amino acids in part of a protein called GB1 and tested the efficiency of every possible combination of these four amino acids, a total of 160,000 (20⁴) variants. Contrary to expectations, the results suggested that the protein could evolve quickly to maximise fitness despite there being epistasis between the four amino acids. Overcoming epistasis typically involved making a change to one amino acid that paved the way for further changes while avoiding the need to lose fitness. The original change could then be reversed once the epistasis was overcome. The complexity of this solution means it can only be seen by studying a large number of protein variants that represent many alternative sequences of protein changes.

Wu, Dai et al. conclude that proteins are able to achieve a higher level of fitness through evolution by exploring a large number of changes. There are many possible changes for each protein and it is this variety that, despite epistasis, allows proteins to become naturally optimised for the tasks that they perform. While the full complexity of protein evolution cannot be explored at the moment, as technology advances it will become possible to study more protein variants. Such advances would therefore hopefully allow researchers to discover even more about the natural mechanisms of protein evolution.

DOI:http://dx.doi.org/10.7554/eLife.16965.002

Evidence for polygenic adaptation to pathogens in the human genome

Most approaches aiming at finding genes involved in adaptive events have focused on the detection of outlier loci, which resulted in the discovery of individually "significant" genes with strong effects. However, a collection of small effect mutations could have a large effect on a given biological pathway that includes many genes, and such a polygenic mode of adaptation has not been systematically investigated in humans. We propose here to evidence polygenic selection by detecting signals of adaptation at the pathway or gene set level instead of analyzing single independent genes. Using a gene-set enrichment test to identify genome-wide signals of adaptation among human populations, we find that most pathways globally enriched for signals of positive selection are either directly or indirectly involved in immune response. We also find evidence for long-distance genotypic linkage disequilibrium, suggesting functional epistatic interactions between members of the same pathway. Our results show that past interactions with pathogens have elicited widespread and coordinated genomic responses, and suggest that adaptation to pathogens can be considered as a primary example of polygenic selection.

Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants

A key goal in the study of influenza virus evolution is to forecast which viral strains will persist and which ones will die out. Here we experimentally measure the effects of all amino acid mutations to the hemagglutinin protein from a human H3N2 influenza strain on viral growth in cell culture. We show that these measurements have utility for distinguishing among viral strains that do and do not succeed in nature. Overall, our work suggests that new high-throughput experimental approaches may be useful for understanding virus evolution in nature.

Human influenza virus rapidly accumulates mutations in its major surface protein hemagglutinin (HA). The evolutionary success of influenza virus lineages depends on how these mutations affect HA’s functionality and antigenicity. Here we experimentally measure the effects on viral growth in cell culture of all single amino acid mutations to the HA from a recent human H3N2 influenza virus strain. We show that mutations that are measured to be more favorable for viral growth are enriched in evolutionarily successful H3N2 viral lineages relative to mutations that are measured to be less favorable for viral growth. Therefore, despite the well-known caveats about cell-culture measurements of viral fitness, such measurements can still be informative for understanding evolution in nature. We also compare our measurements for H3 HA to similar data previously generated for a distantly related H1 HA and find substantial differences in which amino acids are preferred at many sites. For instance, the H3 HA has less disparity in mutational tolerance between the head and stalk domains than the H1 HA. Overall, our work suggests that experimental measurements of mutational effects can be leveraged to help understand the evolutionary fates of viral lineages in nature—but only when the measurements are made on a viral strain similar to the ones being studied in nature.

Plasmid persistence: costs, benefits, and the plasmid paradox

Plasmids are extrachromosomal DNA elements that can be found throughout bacteria, as well as in other domains of life. Nonetheless, the evolutionary processes underlying the persistence of plasmids are incompletely understood. Bacterial plasmids may encode genes for traits that are sometimes beneficial to their hosts, such as antimicrobial resistance, virulence, heavy metal tolerance, and the catabolism of unique nutrient sources. In the absence of selection for these traits, however, plasmids generally impose a fitness cost on their hosts. As such, plasmid persistence presents a conundrum: models predict that costly plasmids will be lost over time or that beneficial plasmid genes will be integrated into the host genome. However, laboratory and comparative studies have shown that plasmids can persist for long periods, even in the absence of positive selection. Several hypotheses have been proposed to explain plasmid persistence, including host-plasmid co-adaptation, plasmid hitchhiking, cross-ecotype transfer, and high plasmid transfer rates, but there is no clear evidence that any one model adequately resolves the plasmid paradox.

Azithromycin Resistance through Interspecific Acquisition of an Epistasis-Dependent Efflux Pump Component and Transcriptional Regulator in Neisseria gonorrhoeae

Mosaic interspecifically acquired alleles of the multiple transferable resistance (mtr) efflux pump operon correlate with increased resistance to azithromycin in Neisseria gonorrhoeae in epidemiological studies. However, whether and how these alleles cause resistance is unclear. Here, we use population genomics, transformations, and transcriptional analyses to dissect the relationship between variant mtr alleles and azithromycin resistance. We find that the locus encompassing the mtrR transcriptional repressor and the mtrCDE pump is a hot spot of interspecific recombination introducing alleles from Neisseria meningitidis and Neisseria lactamica into N. gonorrhoeae, with multiple rare haplotypes in linkage disequilibrium at mtrD and the mtr promoter region. Transformations demonstrate that resistance to azithromycin, as well as to other antimicrobial compounds such as polymyxin B and crystal violet, is mediated through epistasis between these two loci and that the full-length mosaic mtrD allele is required. Gene expression profiling reveals the mechanism of resistance in mosaics couples novel mtrD alleles with promoter mutations that increase expression of the pump. Overall, our results demonstrate that epistatic interactions at mtr gained from multiple neisserial species has contributed to increased gonococcal resistance to diverse antimicrobial agents.IMPORTANCENeisseria gonorrhoeae is the sexually transmitted bacterial pathogen responsible for more than 100 million cases of gonorrhea worldwide each year. The incidence of resistance to the macrolide azithromycin has increased in the past decade; however, a large proportion of the genetic basis of resistance remains unexplained. This study is the first to conclusively demonstrate the acquisition of macrolide resistance through mtr alleles from other Neisseria species, demonstrating that commensal Neisseria bacteria are a reservoir for antibiotic resistance to macrolides, extending the role of interspecies mosaicism in resistance beyond what has been previously described for cephalosporins. Ultimately, our results emphasize that future fine-mapping of genome-wide interspecies mosaicism may be valuable in understanding the pathways to antimicrobial resistance. Our results also have implications for diagnostics and public health surveillance and control, as they can be used to inform the development of sequence-based tools to monitor and control the spread of antibiotic-resistant gonorrhea.