The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae

Genome-scale analysis of interactions between genetic perturbations and natural variation

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 8046 CRISPRi perturbations targeting 1721 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Loss of mitochondrial DNA is associated with reduced DNA content variability in Saccharomyces cerevisiae

DNA content measurement by fluorescence-assisted cell sorting (FACS) provides information on cell cycle progression and DNA content variability. Saccharomyces cerevisiae mutants with DNA content variability that was reduced relative to wild-type strains had defects in mitochondrial DNA (mtDNA) maintenance and mitochondrial gene expression and were correlated with strains found to lack mtDNA ([ rho 0 ] cells) by genome sequencing and fluorescence microscopy. In contrast, mutants with increased variability had defects in cell cycle progression, which may indicate a loss of coordination between mtDNA and nuclear DNA replication. Thus, FACS measurement of DNA content variability can provide insight into cell-to-cell heterogeneity in mtDNA copy number.

Strain background of Candida albicans interacts with SIR2 to alter phenotypic switching

The genetic background between strains of a single species and within a single strain lineage can significantly impact the expression of biological traits. This genetic variation may also reshape epigenetic mechanisms of cell identity and environmental responses that are controlled by interconnected transcriptional networks and chromatin-modifying enzymes. Histone deacetylases, including sirtuins, are critical regulators of chromatin state and have been directly implicated in governing the phenotypic transition between the 'sterile' white state and the mating-competent opaque state in Candida albicans, a common fungal commensal and pathogen of humans. Here, we found that a previously ambiguous role for the sirtuin SIR2 in C. albicans phenotypic switching is likely linked to the genetic background of mutant strains produced in the RM lineage of SC5314. SIR2 mutants in a specific lineage of BWP17 displayed increased frequencies of switching to the opaque state compared to the wild-type. Loss of SIR2 in other SC5314-derived backgrounds, including newly constructed BWP17 sir2Δ/Δ mutants, failed to recapitulate the increased white-opaque switching frequencies observed in the original BWP17 sir2Δ/Δ mutant background. Whole-genome sequencing revealed the presence of multiple imbalanced chromosomes and large loss of heterozygosity tracts that likely interact with SIR2 to increase phenotypic switching in this BWP17 sir2Δ/Δ mutant lineage. These genomic changes are not found in other SC5314-derived sir2Δ/Δ mutants that do not display increased opaque cell formation. Thus, complex karyotypes can emerge during strain construction that modify mutant phenotypes and highlight the importance of validating strain background when interpreting phenotypes.

Genome-scale analysis of interactions between genetic perturbations and natural variation

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 7,700 CRISPRi perturbations targeting 1,712 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

Cross-feeding promotes heterogeneity within yeast cell populations

Cellular heterogeneity in cell populations of isogenic origin is driven by intrinsic factors such as stochastic gene expression, as well as external factors like nutrient availability and interactions with neighbouring cells. Heterogeneity promotes population fitness and thus has important implications in antimicrobial and anticancer treatments, where stress tolerance plays a significant role. Here, we study plasmid retention dynamics within a population of plasmid-complemented ura3∆0 yeast cells, and show that the exchange of complementary metabolites between plasmid-carrying prototrophs and plasmid-free auxotrophs allows the latter to survive and proliferate in selective environments. This process also affects plasmid copy number in plasmid-carrying prototrophs, further promoting cellular functional heterogeneity. Finally, we show that targeted genetic engineering can be used to suppress cross-feeding and reduce the frequency of plasmid-free auxotrophs, or to exploit it for intentional population diversification and division of labour in co-culture systems.

Species-wide survey of the expressivity and complexity spectrum of traits in yeast

Assessing the complexity and expressivity of traits at the species level is an essential first step to better dissect the genotype-phenotype relationship. As trait complexity behaves dynamically, the classic dichotomy between monogenic and complex traits is too simplistic. However, no systematic assessment of this complexity spectrum has been carried out on a population scale to date. In this context, we generated a large diallel hybrid panel composed of 190 unique hybrids coming from 20 natural isolates representative of the S. cerevisiae genetic diversity. For each of these hybrids, a large progeny of 160 individuals was obtained, leading to a total of 30,400 offspring individuals. Their mitotic growth was evaluated on 38 conditions inducing various cellular stresses. We developed a classification algorithm to analyze the phenotypic distributions of offspring and assess the trait complexity. We clearly found that traits are mainly complex at the population level. On average, we found that 91.2% of cross/trait combinations exhibit high complexity, while monogenic and oligogenic cases accounted for only 4.1% and 4.7%, respectively. However, the complexity spectrum is very dynamic, trait specific and tightly related to genetic backgrounds. Overall, our study provided greater insight into trait complexity as well as the underlying genetic basis of its spectrum in a natural population.

Paralog dispensability shapes homozygous deletion patterns in tumor genomes

Genomic instability is a hallmark of cancer, resulting in tumor genomes having large numbers of genetic aberrations, including homozygous deletions of protein coding genes. That tumor cells remain viable in the presence of such gene loss suggests high robustness to genetic perturbation. In model organisms and cancer cell lines, paralogs have been shown to contribute substantially to genetic robustness-they are generally more dispensable for growth than singletons. Here, by analyzing copy number profiles of > 10,000 tumors, we test the hypothesis that the increased dispensability of paralogs shapes tumor genome evolution. We find that genes with paralogs are more likely to be homozygously deleted and that this cannot be explained by other factors known to influence copy number variation. Furthermore, features that influence paralog dispensability in cancer cell lines correlate with paralog deletion frequency in tumors. Finally, paralogs that are broadly essential in cancer cell lines are less frequently deleted in tumors than non-essential paralogs. Overall, our results suggest that homozygous deletions of paralogs are more frequently observed in tumor genomes because paralogs are more dispensable.

Role of genetic architecture in phenotypic plasticity

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

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.

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 ENA1 Na+-ATPase Gene Is Regulated by the SPS Sensing Pathway and the Stp1/Stp2 Transcription Factors

The Saccharomyces cerevisiae ENA1 gene, encoding a Na+-ATPase, responds transcriptionally to the alkalinization of the medium by means of a network of signals that involves the Rim101, the Snf1 and PKA kinases, and the calcineurin/Crz1 pathways. We show here that the ENA1 promoter also contains a consensus sequence, located at nt −553/−544, for the Stp1/2 transcription factors, the downstream components of the amino acid sensing SPS pathway. Mutation of this sequence or deletion of either STP1 or STP2 decreases the activity of a reporter containing this region in response to alkalinization as well as to changes in the amino acid composition in the medium. Expression driven from the entire ENA1 promoter was affected with similar potency by the deletion of PTR3, SSY5, or simultaneous deletion of STP1 and STP2 when cells were exposed to alkaline pH or moderate salt stress. However, it was not altered by the deletion of SSY1, encoding the amino acid sensor. In fact, functional mapping of the ENA1 promoter reveals a region spanning from nt −742 to −577 that enhances transcription, specifically in the absence of Ssy1. We also found that the basal and alkaline pH-induced expression from the HXT2, TRX2, and, particularly, SIT1 promoters was notably decreased in an stp1 stp2 deletion mutant, whereas the PHO84 and PHO89 gene reporters were unaffected. Our findings add a further layer of complexity to the regulation of ENA1 and suggest that the SPS pathway might participate in the regulation of a subset of alkali-inducible genes.

High throughput mutagenesis and screening for yeast engineering

The eukaryotic yeast Saccharomyces cerevisiae is a model host utilized for whole cell biocatalytic conversions, protein evolution, and scientific inquiries into the pathogenesis of human disease. Over the past decade, the scale and pace of such studies has drastically increased alongside the advent of novel tools for both genome-wide studies and targeted genetic mutagenesis. In this review, we will detail past and present (e.g., CRISPR/Cas) genome-scale screening platforms, typically employed in the context of growth-based selections for improved whole cell phenotype or for mechanistic interrogations. We will further highlight recent advances that enable the rapid and often continuous evolution of biomolecules with improved function. Additionally, we will detail the corresponding advances in high throughput selection and screening strategies that are essential for assessing or isolating cellular and protein improvements. Finally, we will describe how future developments can continue to advance yeast high throughput engineering.

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.

Functional Divergence in a Multi-gene Family Is a Key Evolutionary Innovation for Anaerobic Growth in Saccharomyces cerevisiae

The amplification and diversification of genes into large multi-gene families often mark key evolutionary innovations, but this process often creates genetic redundancy that hinders functional investigations. When the model budding yeast Saccharomyces cerevisiae transitions to anaerobic growth conditions, the cell massively induces the expression of seven serine/threonine-rich anaerobically-induced cell wall mannoproteins (anCWMPs): TIP1, TIR1, TIR2, TIR3, TIR4, DAN1, and DAN4. Here, we show that these genes likely derive evolutionarily from a single ancestral anCWMP locus, which was duplicated and translocated to new genomic contexts several times both prior to and following the budding yeast whole genome duplication (WGD) event. Based on synteny and their phylogeny, we separate the anCWMPs into four gene subfamilies. To resolve prior inconclusive genetic investigations of these genes, we constructed a set of combinatorial deletion mutants to determine their contributions toward anaerobic growth in S. cerevisiae. We found that two genes, TIR1 and TIR3, were together necessary and sufficient for the anCWMP contribution to anaerobic growth. Overexpressing either gene alone was insufficient for anaerobic growth, implying that they encode non-overlapping functional roles in the cell during anaerobic growth. We infer from the phylogeny of the anCWMP genes that these two important genes derive from an ancient duplication that predates the WGD event, whereas the TIR1 subfamily experienced gene family amplification after the WGD event. Taken together, the genetic and molecular evidence suggests that one key anCWMP gene duplication event, several auxiliary gene duplication events, and functional divergence underpin the evolution of anaerobic growth in budding yeasts.

Increasing Ethanol Tolerance and Ethanol Production in an Industrial Fuel Ethanol Saccharomyces cerevisiae Strain

The stress imposed by ethanol to Saccharomyces cerevisiae cells are one of the most challenging limiting factors in industrial fuel ethanol production. Consequently, the toxicity and tolerance to high ethanol concentrations has been the subject of extensive research, allowing the identification of several genes important for increasing the tolerance to this stress factor. However, most studies were performed with well-characterized laboratory strains, and how the results obtained with these strains work in industrial strains remains unknown. In the present work, we have tested three different strategies known to increase ethanol tolerance by laboratory strains in an industrial fuel–ethanol producing strain: the overexpression of the TRP1 or MSN2 genes, or the overexpression of a truncated version of the MSN2 gene. Our results show that the industrial CAT-1 strain tolerates up to 14% ethanol, and indeed the three strategies increased its tolerance to ethanol. When these strains were subjected to fermentations with high sugar content and cell recycle, simulating the industrial conditions used in Brazilian distilleries, only the strain with overexpression of the truncated MSN2 gene showed improved fermentation performance, allowing the production of 16% ethanol from 33% of total reducing sugars present in sugarcane molasses. Our results highlight the importance of testing genetic modifications in industrial yeast strains under industrial conditions in order to improve the production of industrial fuel ethanol by S. cerevisiae.

Loss-of-function mutation survey revealed that genes with background-dependent fitness are rare and functionally related in yeast

In different individuals, the same mutation can lead to different phenotypes due to genetic background effects. This is commonly observed in various systems, including many human diseases. While isolated examples of such background effects have been observed, a systematic view across a large number of individuals is still lacking. Here, we surveyed genetic background effects associated with gene loss-of-function mutations across a population of natural isolates of the yeast Saccharomyces cerevisiae. We found that ∼15% of genes can display a background-dependent fitness change. Genes related to mitochondrial functions are significantly overrepresented, and showed reversed patterns of fitness gain or loss with genes involved in transcription and chromatin remodeling as well as in nuclear–cytoplasmic transport, suggesting a potential functional rewiring.

In natural populations, the same mutation can lead to different phenotypic outcomes due to the genetic variation that exists among individuals. Such genetic background effects are commonly observed, including in the context of many human diseases. However, systematic characterization of these effects at the species level is still lacking to date. Here, we sought to comprehensively survey background-dependent traits associated with gene loss-of-function (LoF) mutations in 39 natural isolates of Saccharomyces cerevisiae using a transposon saturation strategy. By analyzing the modeled fitness variability of a total of 4,469 genes, we found that 15% of them, when impacted by a LoF mutation, exhibited a significant gain- or loss-of-fitness phenotype in certain natural isolates compared with the reference strain S288C. Out of these 632 genes with predicted background-dependent fitness effects, around 2/3 impact multiple backgrounds with a gradient of predicted fitness change while 1/3 are specific to a single genetic background. Genes related to mitochondrial function are significantly overrepresented in the set of genes showing a continuous variation and display a potential functional rewiring with other genes involved in transcription and chromatin remodeling as well as in nuclear–cytoplasmic transport. Such rewiring effects are likely modulated by both the genetic background and the environment. While background-specific cases are rare and span diverse cellular processes, they can be functionally related at the individual level. All genes with background-dependent fitness effects tend to have an intermediate connectivity in the global genetic interaction network and have shown relaxed selection pressure at the population level, highlighting their potential evolutionary characteristics.

Evolutionary history and strength of selection determine the rate of antibiotic resistance adaptation

Bacterial adaptation to stressful environments often produces evolutionary constraints whereby increases in resistance are associated with reduced fitness in a different environment. The exploitation of this resistance-cost trade-off has been proposed as the basis of rational antimicrobial treatment strategies designed to limit the evolution of drug resistance in bacterial pathogens. Recent theoretical, laboratory, and clinical studies have shown that fluctuating selection can maintain drug efficacy and even restore drug susceptibility, but can also increase the rate of adaptation and promote cross-resistance to other antibiotics. In this paper, we combine mathematical modeling, experimental evolution, and whole-genome sequencing to follow evolutionary trajectories towards β-lactam resistance under fluctuating selective conditions. Our experimental model system consists of eight populations of Escherichia coli K12 evolving in parallel to a serial dilution protocol designed to dynamically control the strength of selection for resistance. We implemented adaptive ramps with mild and strong selection, resulting in evolved populations with similar levels of resistance, but with different evolutionary dynamics and diverging genotypic profiles. We found that mutations that emerged under strong selection are unstable in the absence of selection, in contrast to resistance mutations previously selected in the mild selection regime that were stably maintained in drug-free environments and positively selected for when antibiotics were reintroduced. Altogether, our population dynamics model and the phenotypic and genomic analysis of the evolved populations show that the rate of resistance adaptation is contingent upon the strength of selection, but also on evolutionary constraints imposed by prior drug exposures.

Exploring conditional gene essentiality through systems genetics approaches in yeast

An essential gene encodes for a cellular function that is required for viability. Although viability is a straightforward phenotype to analyze in yeast, defining a gene as essential is not always trivial. Gene essentiality has generally been studied in specific laboratory strains and under standard growth conditions, however, essentiality can vary across species, strains, and environments. Recent systematic studies of gene essentiality revealed that two sets of essential genes exist: core essential genes that are always required for viability and conditional essential genes that vary in essentiality in different genetic and environmental contexts. Here, we review recent advances made in the systematic analysis of gene essentiality in yeast and discuss the properties that distinguish core from context-dependent essential genes.

Genetic Background Matters: Population-Based Studies in Model Organisms for Translational Research

We are all similar but a bit different. These differences are partially due to variations in our genomes and are related to the heterogeneity of symptoms and responses to treatments that patients exhibit. Most animal studies are performed in one single strain with one manipulation. However, due to the lack of variability, therapies are not always reproducible when treatments are translated to humans. Panels of already sequenced organisms are valuable tools for mimicking human phenotypic heterogeneities and gene mapping. This review summarizes the current knowledge of mouse, fly, and yeast panels with insightful applications for translational research.

Gene loss and compensatory evolution promotes the emergence of morphological novelties in budding yeast

Deleterious mutations are generally considered to be irrelevant for morphological evolution. However, they could be compensated by conditionally beneficial mutations, thereby providing access to new adaptive paths. Here we use high-dimensional phenotyping of laboratory-evolved budding yeast lineages to demonstrate that new cellular morphologies emerge exceptionally rapidly as a by-product of gene loss and subsequent compensatory evolution. Unexpectedly, the capacities for invasive growth, multicellular aggregation and biofilm formation also spontaneously evolve in response to gene loss. These multicellular phenotypes can be achieved by diverse mutational routes and without reactivating the canonical regulatory pathways. These ecologically and clinically relevant traits originate as pleiotropic side effects of compensatory evolution and have no obvious utility in the laboratory environment. The extent of morphological diversity in the evolved lineages is comparable to that of natural yeast isolates with diverse genetic backgrounds and lifestyles. Finally, we show that both the initial gene loss and subsequent compensatory mutations contribute to new morphologies, with their synergistic effects underlying specific morphological changes. We conclude that compensatory evolution is a previously unrecognized source of morphological diversity and phenotypic novelties.

Rapid intraspecies evolution of fitness effects of yeast genes

Organisms within species have numerous genetic and phenotypic variations. Growing evidences show intraspecies variation of mutant phenotypes may be more complicated than expected. Current studies on intraspecies variations of mutant phenotypes are limited to just a few strains. This study investigated the intraspecies variation of fitness effects of 5,630 gene mutants in ten Saccharomyces cerevisiae strains using CRISPR–Cas9 screening. We found that the variability of fitness effects induced by gene disruptions is very large across different strains. Over 75% of genes affected cell fitness in a strain-specific manner to varying degrees. The strain specificity of the fitness effect of a gene is related to its evolutionary and functional properties. Subsequent analysis revealed that younger genes, especially those newly acquired in S. cerevisiae species, are more likely to be strongly strain-specific. Intriguingly, there seems to exist a ceiling of fitness effect size for strong strain-specific genes, and among them, the newly acquired genes are still evolving and have yet to reach this ceiling. Additionally, for a large proportion of protein complexes, the strain specificity profile is inconsistent among genes encoding the same complex. Taken together, these results offer a genome-wide map of intraspecies variation for fitness effect as a mutant phenotype and provide an updated insight on intraspecies phenotypic evolution.

Domestication reprogrammed the budding yeast life cycle

Domestication of plants and animals is the foundation for feeding the world human population but can profoundly alter the biology of the domesticated species. Here we investigated the effect of domestication on one of our prime model organisms, the yeast Saccharomyces cerevisiae, at a species-wide level. We tracked the capacity for sexual and asexual reproduction and the chronological life span across a global collection of 1,011 genome-sequenced yeast isolates and found a remarkable dichotomy between domesticated and wild strains. Domestication had systematically enhanced fermentative and reduced respiratory asexual growth, altered the tolerance to many stresses and abolished or impaired the sexual life cycle. The chronological life span remained largely unaffected by domestication and was instead dictated by clade-specific evolution. We traced the genetic origins of the yeast domestication syndrome using genome-wide association analysis and genetic engineering and disclosed causative effects of aneuploidy, gene presence/absence variations, copy number variations and single-nucleotide polymorphisms. Overall, we propose domestication to be the most dramatic event in budding yeast evolution, raising questions about how much domestication has distorted our understanding of the natural biology of this key model species.

Genetic basis of a spontaneous mutation’s expressivity

Genetic background often influences the phenotypic consequences of mutations, resulting in variable expressivity. How standing genetic variants collectively cause this phenomenon is not fully understood. Here, we comprehensively identify loci in a budding yeast cross that impact the growth of individuals carrying a spontaneous missense mutation in the nuclear-encoded mitochondrial ribosomal gene MRP20. Initial results suggested that a single large effect locus influences the mutation’s expressivity, with one allele causing inviability in mutants. However, further experiments revealed this simplicity was an illusion. In fact, many additional loci shape the mutation’s expressivity, collectively leading to a wide spectrum of mutational responses. These results exemplify how complex combinations of alleles can produce a diversity of qualitative and quantitative responses to the same mutation.

Purine Auxotrophic Starvation Evokes Phenotype Similar to Stationary Phase Cells in Budding Yeast

Purine auxotrophy is an abundant trait among eukaryotic parasites and a typical marker for many budding yeast strains. Supplementation with an additional purine source (such as adenine) is necessary to cultivate these strains. If not supplied in adequate amounts, purine starvation sets in. We explored purine starvation effects in a model organism, a budding yeast Saccharomyces cerevisiae ade8 knockout, at the level of cellular morphology, central carbon metabolism, and global transcriptome. We observed that purine-starved cells stopped their cycle in G1/G0 state and accumulated trehalose, and the intracellular concentration of AXP decreased, but adenylate charge remained stable. Cells became tolerant to severe environmental stresses. Intracellular RNA concentration decreased, and massive downregulation of ribosomal biosynthesis genes occurred. We proved that the expression of new proteins during purine starvation is critical for cells to attain stress tolerance phenotype Msn2/4p targets are upregulated in purine-starved cells when compared to cells cultivated in purine-rich media. The overall transcriptomic response to purine starvation resembles that of stationary phase cells. Our results demonstrate that the induction of a strong stress resistance phenotype in budding yeast can be caused not only by natural starvation, but also starvation for metabolic intermediates, such as purines.

Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound.

The roles of history, chance, and natural selection in the evolution of antibiotic resistance

History, chance, and selection are the fundamental factors that drive and constrain evolution. We designed evolution experiments to disentangle and quantify effects of these forces on the evolution of antibiotic resistance. Previously, we showed that selection of the pathogen Acinetobacter baumannii in both structured and unstructured environments containing the antibiotic ciprofloxacin produced distinct genotypes and phenotypes, with lower resistance in biofilms as well as collateral sensitivity to β-lactam drugs (Santos-Lopez et al., 2019). Here we study how this prior history influences subsequent evolution in new β-lactam antibiotics. Selection was imposed by increasing concentrations of ceftazidime and imipenem and chance differences arose as random mutations among replicate populations. The effects of history were reduced by increasingly strong selection in new drugs, but not erased, at times revealing important contingencies. A history of selection in structured environments constrained resistance to new drugs and led to frequent loss of resistance to the initial drug by genetic reversions and not compensatory mutations. This research demonstrates that despite strong selective pressures of antibiotics leading to genetic parallelism, history can etch potential vulnerabilities to orthogonal drugs.

Natural variants suppress mutations in hundreds of essential genes

The consequence of a mutation can be influenced by the context in which it operates. For example, loss of gene function may be tolerated in one genetic background, and lethal in another. The extent to which mutant phenotypes are malleable, the architecture of modifiers and the identities of causal genes remain largely unknown. Here, we measure the fitness effects of ~ 1,100 temperature‐sensitive alleles of yeast essential genes in the context of variation from ten different natural genetic backgrounds and map the modifiers for 19 combinations. Altogether, fitness defects for 149 of the 580 tested genes (26%) could be suppressed by genetic variation in at least one yeast strain. Suppression was generally driven by gain‐of‐function of a single, strong modifier gene, and involved both genes encoding complex or pathway partners suppressing specific temperature‐sensitive alleles, as well as general modifiers altering the effect of many alleles. The emerging frequency of suppression and range of possible mechanisms suggest that a substantial fraction of monogenic diseases could be managed by modulating other gene products.

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .

The impact of genetic diversity on gene essentiality within the Escherichia coli species

Bacteria from the same species can differ widely in their gene content. In Escherichia coli, the set of genes shared by all strains, known as the core genome, represents about half the number of genes present in any strain. Although recent advances in bacterial genomics have unravelled genes required for fitness in various experimental conditions, most studies have focused on single model strains. As a result, the impact of the species' genetic diversity on core processes of the bacterial cell remains largely under-investigated. Here, we have developed a CRISPR interference platform for high-throughput gene repression that is compatible with most E. coli isolates and closely related species. We have applied it to assess the importance of ~3,400 nearly ubiquitous genes in three growth conditions in 18 representative E. coli strains spanning most common phylogroups and lifestyles of the species. Our screens revealed extensive variations in gene essentiality between strains and conditions. Investigation of the genetic determinants for these variations highlighted the importance of epistatic interactions with mobile genetic elements. In particular, we have shown how prophage-encoded defence systems against phage infection can trigger the essentiality of persistent genes that are usually non-essential. This study provides broad insights into the evolvability of gene essentiality and argues for the importance of studying various isolates from the same species under diverse conditions.

LI Detector: a framework for sensitive colony-based screens regardless of the distribution of fitness effects

Microbial growth characteristics have long been used to investigate fundamental questions of biology. Colony-based high-throughput screens enable parallel fitness estimation of thousands of individual strains using colony growth as a proxy for fitness. However, fitness estimation is complicated by spatial biases affecting colony growth, including uneven nutrient distribution, agar surface irregularities, and batch effects. Analytical methods that have been developed to correct for these spatial biases rely on the following assumptions: (1) that fitness effects are normally distributed, and (2) that most genetic perturbations lead to minor changes in fitness. Although reasonable for many applications, these assumptions are not always warranted and can limit the ability to detect small fitness effects. Beneficial fitness effects, in particular, are notoriously difficult to detect under these assumptions. Here, we developed the linear interpolation-based detector (LI Detector) framework to enable sensitive colony-based screening without making prior assumptions about the underlying distribution of fitness effects. The LI Detector uses a grid of reference colonies to assign a relative fitness value to every colony on the plate. We show that the LI Detector is effective in correcting for spatial biases and equally sensitive toward increase and decrease in fitness. LI Detector offers a tunable system that allows the user to identify small fitness effects with unprecedented sensitivity and specificity. LI Detector can be utilized to develop and refine gene-gene and gene-environment interaction networks of colony-forming organisms, including yeast, by increasing the range of fitness effects that can be reliably detected.

The complex role of genetic background in shaping the effects of spontaneous and induced mutations

Spontaneous and induced mutations frequently show different phenotypic effects across genetically distinct individuals. It is generally appreciated that these background effects mainly result from genetic interactions between the mutations and segregating loci. However, the architectures and molecular bases of these genetic interactions are not well understood. Recent work in a number of model organisms has tried to advance knowledge of background effects both by using large‐scale screens to find mutations that exhibit this phenomenon and by identifying the specific loci that are involved. Here, we review this body of research, emphasizing in particular the insights it provides into both the prevalence of background effects across different mutations and the mechanisms that cause these background effects.

A large fraction of mutations show different effects in distinct individuals.

These background effects are mainly caused by epistasis with segregating loci.

Mapping studies show a diversity of genetic architectures can be involved.

Genetically complex changes in gene expression are often, but not always, causative.

Transcriptional Rewiring, Adaptation, and the Role of Gene Duplication in the Metabolism of Ethanol of Saccharomyces cerevisiae

Ethanol is the main by-product of yeast sugar fermentation that affects microbial growth parameters, being considered a dual molecule, a nutrient and a stressor. Previous works demonstrated that the budding yeast arose after an ancient hybridization process resulted in a tier of duplicated genes within its genome, many of them with implications in this ethanol "produce-accumulate-consume" strategy. The evolutionary link between ethanol production, consumption, and tolerance versus ploidy and stability of the hybrids is an ongoing debatable issue. The implication of ancestral duplicates in this metabolic rewiring, and how these duplicates differ transcriptionally, remains unsolved. Here, we study the transcriptomic adaptive signatures to ethanol as a nonfermentative carbon source to sustain clonal yeast growth by experimental evolution, emphasizing the role of duplicated genes in the adaptive process. As expected, ethanol was able to sustain growth but at a lower rate than glucose. Our results demonstrate that in asexual populations a complete transcriptomic rewiring was produced, strikingly by downregulation of duplicated genes, mainly whole-genome duplicates, whereas small-scale duplicates exhibited significant transcriptional divergence between copies. Overall, this study contributes to the understanding of evolution after gene duplication, linking transcriptional divergence with duplicates' fate in a multigene trait as ethanol tolerance.IMPORTANCE Gene duplication events have been related with increasing biological complexity through the tree of life, but also with illnesses, including cancer. Early evolutionary theories indicated that duplicated genes could explore alternative functions due to relaxation of selective constraints in one of the copies, as the other remains as ancestral-function backup. In unicellular eukaryotes like yeasts, it has been demonstrated that the fate and persistence of duplicates depend on duplication mechanism (whole-genome or small-scale events), shaping their actual genomes. Although it has been shown that small-scale duplicates tend to innovate and whole-genome duplicates specialize in ancestral functions, the implication of duplicates' transcriptional plasticity and transcriptional divergence on environmental and metabolic responses remains largely obscure. Here, by experimental adaptive evolution, we show that Saccharomyces cerevisiae is able to respond to metabolic stress (ethanol as nonfermentative carbon source) due to the persistence of duplicated genes. These duplicates respond by transcriptional rewiring, depending on their transcriptional background. Our results shed light on the mechanisms that determine the role of duplicates, and on their evolvability.

Identification of Pathogen Genomic Differences That Impact Human Immune Response and Disease during Cryptococcus neoformans Infection

Patient outcomes during infection are due to a complex interplay between the quality of medical care, host immunity factors, and the infecting pathogen's characteristics. To probe the influence of pathogen genotype on human survival, immune response, and other parameters of disease, we examined Cryptococcus neoformans isolates collected during the Cryptococcal Optimal Antiretroviral Therapy (ART) Timing (COAT) Trial in Uganda. We measured human participants' survival, meningitis disease parameters, immunologic phenotypes, and pathogen in vitro growth characteristics. We compared those clinical data to whole-genome sequences from 38 C. neoformans isolates of the most frequently observed sequence type (ST), ST93, in our Ugandan participant population and to sequences from an additional 18 strains of 9 other sequence types representing the known genetic diversity within the Ugandan Cryptococcus clinical isolates. We focused our analyses on 652 polymorphisms that were variable among the ST93 genomes, were not in centromeres or extreme telomeres, and were predicted to have a fitness effect. Logistic regression and principal component analysis identified 40 candidate Cryptococcus genes and 3 hypothetical RNAs associated with human survival, immunologic response, or clinical parameters. We infected mice with 17 available KN99α gene deletion strains for these candidate genes and found that 35% (6/17) directly influenced murine survival. Four of the six gene deletions that impacted murine survival were novel. Such bedside-to-bench translational research identifies important candidate genes for future studies on virulence-associated traits in human Cryptococcus infections.IMPORTANCE Even with the best available care, mortality rates in cryptococcal meningitis range from 20% to 60%. Disease is often due to infection by the fungus Cryptococcus neoformans and involves a complex interaction between the human host and the fungal pathogen. Although previous studies have suggested genetic differences in the pathogen impact human disease, it has proven quite difficult to identify the specific C. neoformans genes that impact the outcome of the human infection. Here, we take advantage of a Ugandan patient cohort infected with closely related C. neoformans strains to examine the role of pathogen genetic variants on several human disease characteristics. Using a pathogen whole-genome sequencing approach, we showed that 40 C. neoformans genes are associated with human disease. Surprisingly, many of these genes are specific to Cryptococcus and have unknown functions. We also show deletion of some of these genes alters disease in a mouse model of infection, confirming their role in disease. These findings are particularly important because they are the first to identify C. neoformans genes associated with human cryptococcal meningitis and lay the foundation for future studies that may lead to new treatment strategies aimed at reducing patient mortality.