Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Mutations in an organism's DNA make the individual more likely to survive and reproduce in its environment, passing on its mutations to the next generation. Mutations can alter the proteins that a gene codes for in many ways. This leads to a situation where seemingly similar mutations ­ such as two mutations in the same gene ­ can have different effects. For example, two different mutations could affect the primary function of the encoded protein in the same way but have different side effects. One mutation might also cause the protein to interact with a new molecule or protein. Organisms possessing one or the other mutation will thus have similar odds of surviving and reproducing in some environments, but differences in environments where the new interaction is important. In microorganisms, mutations can lead to drug resistance. If drug-resistant mutations have different side effects, it can be challenging to treat microbial infections, as drug-resistant pathogens are often treated with sequential drug strategies. These strategies rely on mutations that cause resistance to the first drug all having susceptibility to the second drug. But if similar seeming mutations can have diverse side effects, predictions about how they will respond to a second drug are more complicated. To address this issue, Schmidlin, Apodaca et al. collected a diverse group of nearly a thousand mutant yeast strains that were resistant to a drug called fluconazole. Next, they asked to what extent the fitness ­ the ability to survive and reproduce ­ of these mutants responded similarly to environmental change. They used this information to cluster mutations into groups that likely have similar effects at the molecular level, finding at least six such groups with unique trade-offs across environments. For example, some groups resisted only low drug concentrations, and others were unique in that they resisted treatment with two single drugs but not their combination. These diverse types of fluconazole-resistant yeast lineages highlight the challenges of designing a simple sequential drug treatment that targets all drug-resistant mutants. However, the results also suggest some predictability in how drug-resistant infections can evolve and be treated.

Single-cell heterogeneity in ribosome content and the consequences for the growth laws.

Across species and environments, the ribosome content of cell populations correlates with population growth rate. The robustness and universality of this correlation have led to its classification as a "growth law." This law has fueled theories about how evolution selects for microbial organisms that maximize their growth rate based on nutrient availability, and it has informed models about how individual cells regulate their growth rates and ribosomal content. However, due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear whether individual cells tightly regulate their ribosome content to match their environment. Here, we employ recent groundbreaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we observe significant variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells exhibiting transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive that they have evolved to do things other than maximize their rate of growth. Overall, these results indicate that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability and cannot be predicted by a Gaussian process model that assumes measurements are sampled from a normal distribution centered on the population average. This work encourages the expansion of growth law and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity. To this end, we provide extensive data and analysis of ribosomal and transcriptomic variation across thousands of single cells from multiple conditions, replicates, and species.

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG).

While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are widely recognized in the fields of quantitative and evolutionary genetics, "environment-byenvironment interaction" (ExE) is a term used less often. In this study, we find that environmentby-environment interactions are a meaningful driver of phenotypes, and moreover, that they differ across different genotypes (suggestive of ExExG). To support this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. Our findings reveal that the effectiveness of a drug combination, relative to single drugs, often differs across drug resistant mutants. Remarkably, even mutants that differ by only a single nucleotide change can have dramatically different drug × drug (ExE) interactions. We also introduce a new framework that more accurately predicts the direction and magnitude of ExE interactions for some mutants. Understanding how ExE interactions change across genotypes (ExExG) is crucial not only for modeling the evolution of pathogenic microbes, but also for enhancing our knowledge of the underlying cell biology and the sources of phenotypic variance within populations. While the significance of ExExG interactions has been overlooked in evolutionary and population genetics, these fields and others stand to benefit from understanding how these interactions shape the complex behavior of living systems.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts.

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

Extreme Sensitivity of Fitness to Environmental Conditions: Lessons from #1BigBatch.

The phrase "survival of the fittest" has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcodes, all methods are limited in their precision to differentiate strains with small fitness differences. In this study, we rule out some major sources of imprecision, but still find that fitness measurements vary substantially from replicate to replicate. Our data suggest that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements. We conclude by discussing how fitness measurements should be interpreted given their extreme environment dependence. This work was inspired by the scientific community who followed us and gave us tips as we live tweeted a high-replicate fitness measurement experiment at #1BigBatch.

Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes.

All organisms are defined by the makeup of their DNA. Over billions of years, the structure and information contained in that DNA, often referred to as genetic architecture, have been honed by a multitude of evolutionary processes. Mutations that cause genetic elements to change in a way that results in beneficial phenotypic change are more likely to survive and propagate through the population in a process known as adaptation. Recent work reveals that the genetic targets of adaptation are varied and can change with genetic background. Further, seemingly similar adaptive mutations, even within the same gene, can have diverse and unpredictable effects on phenotype. These challenges represent major obstacles in predicting adaptation and evolution. In this review, we cover these concepts in detail and identify three emerging synergistic solutions: higher-throughput evolution experiments combined with updated genotype-phenotype mapping strategies and physiological models. Our review largely focuses on recent literature in yeast, and the field seems to be on the cusp of a new era with regard to studying the predictability of evolution.

Constructive Neutral Evolution 20 Years Later.

Evolution has led to a great diversity that ranges from elegant simplicity to ornate complexity. Many complex features are often assumed to be more functional or adaptive than their simpler alternatives. However, in 1999, Arlin Stolzfus published a paper in the Journal of Molecular Evolution that outlined a framework in which complexity can arise through a series of non-adaptive steps. He called this framework Constructive Neutral Evolution (CNE). Despite its two-decade-old roots, many evolutionary biologists still appear to be unaware of this explanatory framework for the origins of complexity. In this perspective piece, we explain the theory of CNE and how it changes the order of events in narratives that describe the evolution of complexity. We also provide an extensive list of cellular features that may have become more complex through CNE. We end by discussing strategies to determine whether complexity arose through neutral or adaptive processes.

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation.

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.

One of the goals of evolutionary biology is to understand the relationship between genotype, phenotype, and fitness. An organism's genes ­ its genotype ­ determine its physical and behavioral traits ­ its phenotype. Phenotypes, in turn, affect the organisms' chances of survival and reproduction ­ its fitness. However, mapping the relationships among these three variables is far from easy. Recently researchers have become able to identify many genetic mutations that increase an organism's fitness, but it is more difficult to work out how these mutations affect an organism's phenotype, and why they are beneficial. The mutations that help organisms thrive in a particular environment are often limited to a handful of genes that affect similar biological processes. For example, microbes that grow in environments with limited sugar tend to accumulate mutations in genes involved in systems that determine whether to grow fast and carelessly or to be careful in case the sugar is never replenished. It is possible that these mutations all affect the same one or two phenotypes, such as the decision to grow or to hunker down. If this were the case, researchers should be able to easily predict how well these organisms adapt to new environments. However, it is possible that specific mutations affect several phenotypes, but these extra effects remain invisible until the environment changes and these phenotypes are revealed. To explore this possibility, Kinsler, Geiler-Samerotte, and Petrov obtained hundreds of individual yeast strains that each contained a different mutation that improved the yeast's fitness in a low sugar environment. They placed these strains into similar environments and measured their fitness. The patterns observed were used to build several models that predicted how many phenotypes each mutation must affect to explain the changes in fitness. Kinsler, Geiler-Samerotte and Petrov found that the model in which only five phenotypes were affected by the mutations was able to predict the fitness of the yeast in low-sugar environments. However, to predict the fitness of the same mutations in environments that were very different, the model had to include eight phenotypes. This suggests that although the mutations that helped yeast do well in the low sugar environment were similar in their benefits in this environment, they were not truly all the same. In fact, some mutations were quite different from the others in terms of their hidden phenotypic effects. The hidden effects of mutations can be positive or negative. One mutation might cause an organism to die in a new environment, whereas another might allow it to thrive. Understanding how this works has implications not only for evolutionary biology, but also for medical research. Pathogens that cause infection, and cells that cause cancer, often accumulate mutations in small numbers of crucial genes. Understanding how these mutations affect phenotypes that become important as the environment changes ­ for instance as the cells encounter new challenges as a tumor grows ­ and whether different mutations have different hidden effects, could improve treatments in the future.

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping.

Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

The Complete Genome Sequence of the Staphylococcus Bacteriophage Metroid.

Phages infecting bacteria of the genus Staphylococcus play an important role in their host's ecology and evolution. On one hand, horizontal gene transfer from phage can encourage the rapid adaptation of pathogenic Staphylococcus enabling them to escape host immunity or access novel environments. On the other hand, lytic phages are promising agents for the treatment of bacterial infections, especially those resistant to antibiotics. As part of an ongoing effort to gain novel insights into bacteriophage diversity, we characterized the complete genome of the Staphylococcus bacteriophage Metroid, a cluster C phage with a genome size of 151kb, encompassing 254 predicted protein-coding genes as well as 4 tRNAs. A comparative genomic analysis highlights strong similarities - including a conservation of the lysis cassette - with other Staphylococcus cluster C bacteriophages, several of which were previously characterized for therapeutic applications.

Emerging Frontiers in the Study of Molecular Evolution.

A collection of the editors of Journal of Molecular Evolution have gotten together to pose a set of key challenges and future directions for the field of molecular evolution. Topics include challenges and new directions in prebiotic chemistry and the RNA world, reconstruction of early cellular genomes and proteins, macromolecular and functional evolution, evolutionary cell biology, genome evolution, molecular evolutionary ecology, viral phylodynamics, theoretical population genomics, somatic cell molecular evolution, and directed evolution. While our effort is not meant to be exhaustive, it reflects research questions and problems in the field of molecular evolution that are exciting to our editors.

Why and how to study genetic changes with context-dependent effects.

The phenotypic impacts of a genetic change can depend on genetic background (e.g. epistasis), as well as other contexts including environment, developmental stage, cell type, disease state, and higher-order combinations thereof. Recent advances in high-throughput phenotyping are uncovering examples of context dependence faster than genotype-phenotype maps and other core concepts are changing to reflect the dynamic nature of biological systems. Here, we review several approaches to study context dependence and their findings. In our opinion, these findings encourage more studies that examine the spectrum of effects a genetic change may have, as opposed to studies that exclusively measure the impact of a genetic change in a particular context. Studies that elucidate the mechanisms that cause the effects of genetic change to vary with context are of special interest. Previous studies of the mechanisms underlying context dependence have improved predictions of phenotype from genotype and have provided insight about how biological systems function and evolve.

Decanalizing thinking on genetic canalization.

The concept of genetic canalization has had an abiding influence on views of complex-trait evolution. A genetically canalized system has evolved to become less sensitive to the effects of mutation. When a gene product that supports canalization is compromised, the phenotypic impacts of a mutation should be more pronounced. This expected increase in mutational effects not only has important consequences for evolution, but has also motivated strategies to treat disease. However, recent studies demonstrate that, when putative agents of genetic canalization are impaired, systems do not behave as expected. Here, we review the evidence that is used to infer whether particular gene products are agents of genetic canalization. Then we explain how such inferences often succumb to a converse error. We go on to show that several candidate agents of genetic canalization increase the phenotypic impacts of some mutations while decreasing the phenotypic impacts of others. These observations suggest that whether a gene product acts as a 'buffer' (lessening mutational effects) or a 'potentiator' (increasing mutational effects) is not a fixed property of the gene product but instead differs for the different mutations with which it interacts. To investigate features of genetic interactions that might predispose them toward buffering versus potentiation, we explore simulated gene-regulatory networks. Similarly to putative agents of genetic canalization, the gene products in simulated networks also modify the phenotypic effects of mutations in other genes without a strong overall tendency towards lessening or increasing these effects. In sum, these observations call into question whether complex traits have evolved to become less sensitive (i.e., are canalized) to genetic change, and the degree to which trends exist that predict how one genetic change might alter another's impact. We conclude by discussing approaches to address these and other open questions that are brought into focus by re-thinking genetic canalization.

Selection Transforms the Landscape of Genetic Variation Interacting with Hsp90.

The protein-folding chaperone Hsp90 has been proposed to buffer the phenotypic effects of mutations. The potential for Hsp90 and other putative buffers to increase robustness to mutation has had major impact on disease models, quantitative genetics, and evolutionary theory. But Hsp90 sometimes contradicts expectations for a buffer by potentiating rapid phenotypic changes that would otherwise not occur. Here, we quantify Hsp90's ability to buffer or potentiate (i.e., diminish or enhance) the effects of genetic variation on single-cell morphological features in budding yeast. We corroborate reports that Hsp90 tends to buffer the effects of standing genetic variation in natural populations. However, we demonstrate that Hsp90 tends to have the opposite effect on genetic variation that has experienced reduced selection pressure. Specifically, Hsp90 tends to enhance, rather than diminish, the effects of spontaneous mutations and recombinations. This result implies that Hsp90 does not make phenotypes more robust to the effects of genetic perturbation. Instead, natural selection preferentially allows buffered alleles to persist and thereby creates the false impression that Hsp90 confers greater robustness.

Development of a Comprehensive Genotype-to-Fitness Map of Adaptation-Driving Mutations in Yeast.

Adaptive evolution plays a large role in generating the phenotypic diversity observed in nature, yet current methods are impractical for characterizing the molecular basis and fitness effects of large numbers of individual adaptive mutations. Here, we used a DNA barcoding approach to generate the genotype-to-fitness map for adaptation-driving mutations from a Saccharomyces cerevisiae population experimentally evolved by serial transfer under limiting glucose. We isolated and measured the fitness of thousands of independent adaptive clones and sequenced the genomes of hundreds of clones. We found only two major classes of adaptive mutations: self-diploidization and mutations in the nutrient-responsive Ras/PKA and TOR/Sch9 pathways. Our large sample size and precision of measurement allowed us to determine that there are significant differences in fitness between mutations in different genes, between different paralogs, and even between different classes of mutations within the same gene.

Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates.

Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection.

Evaluation of common genetic variants in 82 candidate genes as risk factors for neural tube defects.

BACKGROUND: Neural tube defects (NTDs) are common birth defects (~1 in 1000 pregnancies in the US and Europe) that have complex origins, including environmental and genetic factors. A low level of maternal folate is one well-established risk factor, with maternal periconceptional folic acid supplementation reducing the occurrence of NTD pregnancies by 50-70%. Gene variants in the folate metabolic pathway (e.g., MTHFR rs1801133 (677 C > T) and MTHFD1 rs2236225 (R653Q)) have been found to increase NTD risk. We hypothesized that variants in additional folate/B12 pathway genes contribute to NTD risk. METHODS: A tagSNP approach was used to screen common variation in 82 candidate genes selected from the folate/B12 pathway and NTD mouse models. We initially genotyped polymorphisms in 320 Irish triads (NTD cases and their parents), including 301 cases and 341 Irish controls to perform case-control and family based association tests. Significantly associated polymorphisms were genotyped in a secondary set of 250 families that included 229 cases and 658 controls. The combined results for 1441 SNPs were used in a joint analysis to test for case and maternal effects. RESULTS: Nearly 70 SNPs in 30 genes were found to be associated with NTDs at the p < 0.01 level. The ten strongest association signals (p-value range: 0.0003-0.0023) were found in nine genes (MFTC, CDKN2A, ADA, PEMT, CUBN, GART, DNMT3A, MTHFD1 and T (Brachyury)) and included the known NTD risk factor MTHFD1 R653Q (rs2236225). The single strongest signal was observed in a new candidate, MFTC rs17803441 (OR = 1.61 [1.23-2.08], p = 0.0003 for the minor allele). Though nominally significant, these associations did not remain significant after correction for multiple hypothesis testing. CONCLUSIONS: To our knowledge, with respect to sample size and scope of evaluation of candidate polymorphisms, this is the largest NTD genetic association study reported to date. The scale of the study and the stringency of correction are likely to have contributed to real associations failing to survive correction. We have produced a ranked list of variants with the strongest association signals. Variants in the highest rank of associations are likely to include true associations and should be high priority candidates for further study of NTD risk.

The ecoresponsive genome of Daphnia pulex.

We describe the draft genome of the microcrustacean Daphnia pulex, which is only 200 megabases and contains at least 30,907 genes. The high gene count is a consequence of an elevated rate of gene duplication resulting in tandem gene clusters. More than a third of Daphnia's genes have no detectable homologs in any other available proteome, and the most amplified gene families are specific to the Daphnia lineage. The coexpansion of gene families interacting within metabolic pathways suggests that the maintenance of duplicated genes is not random, and the analysis of gene expression under different environmental conditions reveals that numerous paralogs acquire divergent expression patterns soon after duplication. Daphnia-specific genes, including many additional loci within sequenced regions that are otherwise devoid of annotations, are the most responsive genes to ecological challenges.

Misfolded proteins impose a dosage-dependent fitness cost and trigger a cytosolic unfolded protein response in yeast.

Evolving lineages face a constant intracellular threat: most new coding sequence mutations destabilize the folding of the encoded protein. Misfolded proteins form insoluble aggregates and are hypothesized to be intrinsically cytotoxic. Here, we experimentally isolate a fitness cost caused by toxicity of misfolded proteins. We exclude other costs of protein misfolding, such as loss of functional protein or attenuation of growth-limiting protein synthesis resources, by comparing growth rates of budding yeast expressing folded or misfolded variants of a gratuitous protein, YFP, at equal levels. We quantify a fitness cost that increases with misfolded protein abundance, up to as much as a 3.2% growth rate reduction when misfolded YFP represents less than 0.1% of total cellular protein. Comparable experiments on variants of the yeast gene orotidine-5'-phosphate decarboxylase (URA3) produce similar results. Quantitative proteomic measurements reveal that, within the cell, misfolded YFP induces coordinated synthesis of interacting cytosolic chaperone proteins in the absence of a wider stress response, providing evidence for an evolved modular response to misfolded proteins in the cytosol. These results underscore the distinct and evolutionarily relevant molecular threat of protein misfolding, independent of protein function. Assuming that most misfolded proteins impose similar costs, yeast cells express almost all proteins at steady-state levels sufficient to expose their encoding genes to selection against misfolding, lending credibility to the recent suggestion that such selection imposes a global constraint on molecular evolution.