The chemical genomic portrait of yeast: uncovering a phenotype for all genes

Genetic basis of arsenite and cadmium tolerance in Saccharomyces cerevisiae

Background

Arsenic and cadmium are widely distributed in nature and pose serious threats to the environment and human health. Exposure to these nonessential toxic metals may result in a variety of human diseases including cancer. However, arsenic and cadmium toxicity targets and the cellular systems contributing to tolerance acquisition are not fully known.

Results

To gain insight into metal action and cellular tolerance mechanisms, we carried out genome-wide screening of the Saccharomyces cerevisiae haploid and homozygous diploid deletion mutant collections and scored for reduced growth in the presence of arsenite or cadmium. Processes found to be required for tolerance to both metals included sulphur and glutathione biosynthesis, environmental sensing, mRNA synthesis and transcription, and vacuolar/endosomal transport and sorting. We also identified metal-specific defence processes. Arsenite-specific defence functions were related to cell cycle regulation, lipid and fatty acid metabolism, mitochondrial biogenesis, and the cytoskeleton whereas cadmium-specific defence functions were mainly related to sugar/carbohydrate metabolism, and metal-ion homeostasis and transport. Molecular evidence indicated that the cytoskeleton is targeted by arsenite and that phosphorylation of the Snf1p kinase is required for cadmium tolerance.

Conclusion

This study has pin-pointed core functions that protect cells from arsenite and cadmium toxicity. It also emphasizes the existence of both common and specific defence systems. Since many of the yeast genes that confer tolerance to these agents have homologues in humans, similar biological processes may act in yeast and humans to prevent metal toxicity and carcinogenesis.

Computational systems biology of the cell cycle

One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.

Darwinian evolution in the light of genomics

Comparative genomics and systems biology offer unprecedented opportunities for testing central tenets of evolutionary biology formulated by Darwin in the Origin of Species in 1859 and expanded in the Modern Synthesis 100 years later. Evolutionary-genomic studies show that natural selection is only one of the forces that shape genome evolution and is not quantitatively dominant, whereas non-adaptive processes are much more prominent than previously suspected. Major contributions of horizontal gene transfer and diverse selfish genetic elements to genome evolution undermine the Tree of Life concept. An adequate depiction of evolution requires the more complex concept of a network or ‘forest’ of life. There is no consistent tendency of evolution towards increased genomic complexity, and when complexity increases, this appears to be a non-adaptive consequence of evolution under weak purifying selection rather than an adaptation. Several universals of genome evolution were discovered including the invariant distributions of evolutionary rates among orthologous genes from diverse genomes and of paralogous gene family sizes, and the negative correlation between gene expression level and sequence evolution rate. Simple, non-adaptive models of evolution explain some of these universals, suggesting that a new synthesis of evolutionary biology might become feasible in a not so remote future.

Asymmetric relationships between proteins shape genome evolution

An investigation of metabolic networks in E. coli and S. cerevisiae reveals that asymmetric protein interactions affect gene expression, the relative effect of gene-knockouts and genome evolution.

Background

The relationships between proteins are often asymmetric: one protein (A) depends for its function on another protein (B), but the second protein does not depend on the first. In metabolic networks there are multiple pathways that converge into one central pathway. The enzymes in the converging pathways depend on the enzymes in the central pathway, but the enzymes in the latter do not depend on any specific enzyme in the converging pathways. Asymmetric relations are analogous to the “if->then” logical relation where A implies B, but B does not imply A (A->B).

Results

We show that the majority of relationships between enzymes in metabolic flux models of metabolism in Escherichia coli and Saccharomyces cerevisiae are asymmetric. We show furthermore that these asymmetric relationships are reflected in the expression of the genes encoding those enzymes, the effect of gene knockouts and the evolution of genomes. From the asymmetric relative dependency, one would expect that the gene that is relatively independent (B) can occur without the other dependent gene (A), but not the reverse. Indeed, when only one gene of an A->B pair is expressed, is essential, is present in a genome after an evolutionary gain or loss, it tends to be the independent gene (B). This bias is strongest for genes encoding proteins whose asymmetric relationship is evolutionarily conserved.

Conclusions

The asymmetric relations between proteins that arise from the system properties of metabolic networks affect gene expression, the relative effect of gene knockouts and genome evolution in a predictable manner.

Chemical biology approaches to probe the proteome

Understanding disease-associated cellular defects at a molecular level is critical for the development of pharmacological intervention strategies. Recent breakthroughs in microarray and sequencing technologies have provided powerful tools to rapidly reveal the cellular defects caused by alterations in the genome or transcriptome. However, the picture of how the cellular proteome is affected in a disease state and how changes in DNA and RNA affect protein function is often incomplete. This is perhaps not surprising because the functions of proteins are not just determined by primary sequence and abundance, but are under the control of many regulatory mechanisms. Here, we highlight several recent advances in proteomics technologies that are being developed to generate comprehensive human proteome maps and discuss them in the context of strategies that have been developed in simple model organisms. Chemical biology will play a critical role in drafting a map of the proteome with functional information. Chemical genetic approaches that use high-throughput small molecule screening have resulted in the public availability of small molecule datasets through web interfaces such as PubChem. With such approaches, the opportunities to investigate disease and to explore the proteome with chemistry are rapidly increasing. In addition, new tools are being developed to probe protein function. Here we highlight recent developments in chemical biology and the exciting opportunities that are arising with them.

Dyclonine and alverine citrate enhance the cytotoxic effects of proteasome inhibitor MG132 on breast cancer cells

Proteasome is an important target in cancer therapy. To enhance the efficacy of proteasome inhibitors is a challenging task due to the paucity of understanding the functional interactions between proteasome and other cellular pathways in mammalian cells. Taking advantage of the knowledge gained from Saccharomyces cerevisiae, we show that dyclonine and alverine citrate, the major components of two over-the-counter medicines, can substantially enhance the cytotoxic effects of proteasome inhibitor MG132 on breast cancer cells. This study also highlights an important yeast genetic approach to identification of potential therapeutics that can be used for combination therapy with proteasome inhibitors.

A complex-based reconstruction of the Saccharomyces cerevisiae interactome

Most cellular processes are performed by proteomic units that interact with each other. These units are often stoichiometrically stable complexes comprised of several proteins. To obtain a faithful view of the protein interactome we must view it in terms of these basic units (complexes and proteins) and the interactions between them. This study makes two contributions toward this goal. First, it provides a new algorithm for reconstruction of stable complexes from a variety of heterogeneous biological assays; our approach combines state-of-the-art machine learning methods with a novel hierarchical clustering algorithm that allows clusters to overlap. We demonstrate that our approach constructs over 40% more known complexes than other recent methods and that the complexes it produces are more biologically coherent even compared with the reference set. We provide experimental support for some of our novel predictions, identifying both a new complex involved in nutrient starvation and a new component of the eisosome complex. Second, we provide a high accuracy algorithm for the novel problem of predicting transient interactions involving complexes. We show that our complex level network, which we call ComplexNet, provides novel insights regarding the protein-protein interaction network. In particular, we reinterpret the finding that “hubs” in the network are enriched for being essential, showing instead that essential proteins tend to be clustered together in essential complexes and that these essential complexes tend to be large.

Design of 240,000 orthogonal 25mer DNA barcode probes

DNA barcodes linked to genetic features greatly facilitate screening these features in pooled formats using microarray hybridization, and new tools are needed to design large sets of barcodes to allow construction of large barcoded mammalian libraries such as shRNA libraries. Here we report a framework for designing large sets of orthogonal barcode probes. We demonstrate the utility of this framework by designing 240,000 barcode probes and testing their performance by hybridization. From the test hybridizations, we also discovered new probe design rules that significantly reduce cross-hybridization after their introduction into the framework of the algorithm. These rules should improve the performance of DNA microarray probe designs for many applications.

Combining chemical genomics screens in yeast to reveal spectrum of effects of chemical inhibition of sphingolipid biosynthesis

BACKGROUND

Single genome-wide screens for the effect of altered gene dosage on drug sensitivity in the model organism Saccharomyces cerevisiae provide only a partial picture of the mechanism of action of a drug.

RESULTS

Using the example of the tumor cell invasion inhibitor dihydromotuporamine C, we show that a more complete picture of drug action can be obtained by combining different chemical genomics approaches--analysis of the sensitivity of rho0 cells lacking mitochondrial DNA, drug-induced haploinsufficiency, suppression of drug sensitivity by gene overexpression and chemical-genetic synthetic lethality screening using strains deleted of nonessential genes. Killing of yeast by this chemical requires a functional mitochondrial electron-transport chain and cytochrome c heme lyase function. However, we find that it does not require genes associated with programmed cell death in yeast. The chemical also inhibits endocytosis and intracellular vesicle trafficking and interferes with vacuolar acidification in yeast and in human cancer cells. These effects can all be ascribed to inhibition of sphingolipid biosynthesis by dihydromotuporamine C.

CONCLUSION

Despite their similar conceptual basis, namely altering drug sensitivity by modifying gene dosage, each of the screening approaches provided a distinct set of information that, when integrated, revealed a more complete picture of the mechanism of action of a drug on cells.

Why is the correlation between gene importance and gene evolutionary rate so weak?

One of the few commonly believed principles of molecular evolution is that functionally more important genes (or DNA sequences) evolve more slowly than less important ones. This principle is widely used by molecular biologists in daily practice. However, recent genomic analysis of a diverse array of organisms found only weak, negative correlations between the evolutionary rate of a gene and its functional importance, typically measured under a single benign lab condition. A frequently suggested cause of the above finding is that gene importance determined in the lab differs from that in an organism's natural environment. Here, we test this hypothesis in yeast using gene importance values experimentally determined in 418 lab conditions or computationally predicted for 10,000 nutritional conditions. In no single condition or combination of conditions did we find a much stronger negative correlation, which is explainable by our subsequent finding that always-essential (enzyme) genes do not evolve significantly more slowly than sometimes-essential or always-nonessential ones. Furthermore, we verified that functional density, approximated by the fraction of amino acid sites within protein domains, is uncorrelated with gene importance. Thus, neither the lab-nature mismatch nor a potentially biased among-gene distribution of functional density explains the observed weakness of the correlation between gene importance and evolutionary rate. We conclude that the weakness is factual, rather than artifactual. In addition to being weakened by population genetic reasons, the correlation is likely to have been further weakened by the presence of multiple nontrivial rate determinants that are independent from gene importance. These findings notwithstanding, we show that the principle of slower evolution of more important genes does have some predictive power when genes with vastly different evolutionary rates are compared, explaining why the principle can be practically useful despite the weakness of the correlation.

The fact that functionally more important genes or DNA sequences evolve more slowly than less important ones is commonly believed and frequently used by molecular biologists. However, previous genome-wide studies of a diverse array of organisms found only weak, negative correlations between the importance of a gene and its evolutionary rate. We show, here, that the weakness of the correlation is not because gene importance measured in lab conditions deviates from that in an organism's natural environments. Neither is it due to a potentially biased among-gene distribution of functional density. We suggest that the weakness of the correlation is factual, rather than artifactual. These findings notwithstanding, we show that the principle of slower evolution of more important genes does have some predictive power when genes with vastly different evolutionary rates are compared, explaining why the principle can be practically useful for tasks such as identifying functional non-coding sequences despite the weakness of the correlation.

Chemogenomic approaches to elucidation of gene function and genetic pathways

The approximately 6,000 strains in the yeast deletion collection can be studied in a single culture by using a microarray to detect the 20 bp DNA "barcodes" or "tags" contained in each strain. Barcode intensities measured by microarray are compared across time-points or across conditions to analyze the relative fitness of each strain. The development of this pooled fitness assay has greatly facilitated the functional annotation of the yeast genome by making genome-wide gene-deletion studies faster and easier, and has led to the development of high throughput methods for studying drug action in yeast. Pooled screens can be used for identifying gene functions, measuring the functional relatedness of gene pairs to group genes into pathways, identifying drug targets, and determining a drug's mechanism of action. This process involves five main steps: preparing aliquots of pooled cells, pooled growth, isolation of genomic DNA and PCR amplification of the barcodes, array hybridization, and data analysis. In addition to yeast fitness applications, the general method of studying pooled samples with barcode arrays can also be adapted for use with other types of samples, such as mutant collections in other organisms, siRNA vectors, and molecular inversion probes.

Synthetic genetic interactions allele dependence, uses, and conservation

Genetic interactions occur between a pair of genes when the phenotype of the double mutant leads to an unexpected phenotype, one that is not predicted from the phenotypes of the single mutants alone. Here, we focus on genetic enhancements, otherwise known as synthetic genetic interactions, where the double mutant phenotype is more severe than expected. Such interactions are rife in natural populations and underlie complex traits, variable penetrance, variable expressivity, and genetic predisposition. Such interactions can also contribute valuable information for functional genomics analysis. Pairwise synthetic genetic interactions are now being systematically uncovered for some simple model genomes. These data are affording us an unparalleled opportunity to examine, understand and exploit genetic enhancements. Here we focus on some key lessons, insights, and confusions arising from these large-scale datasets. We consider if genome-wide datasets support traditional assumptions about the functional relationships between gene products that underlie genetic enhancements. We argue that the genetic enhancement network of an organism is not uniform in nature and is highly dependent on the nature of the interacting alleles. We consider how such genetic networks can be exploited to inform gene product function. Finally, we consider the extent to which genetic enhancement networks are conserved between species.

Complex fate of paralogs

Background

Thanks to recent high coverage mass-spectrometry studies and reconstructed protein complexes, we are now in an unprecedented position to study the evolution of biological systems. Gene duplications, known to be a major source of innovation in evolution, can now be readily examined in the context of protein complexes.

Results

We observe that paralogs operating in the same complex fulfill different roles: mRNA dosage increase for more than a hundred cytosolic ribosomal proteins, mutually exclusive participation of at least 54 paralogs resulting in alternative forms of complexes, and 24 proteins contributing to bona fide structural growth. Inspection of paralogous proteins participating in two independent complexes shows that an ancient, pre-duplication protein functioned in both multi-protein assemblies and a gene duplication event allowed the respective copies to specialize and split their roles.

Conclusion

Variants with conditionally assembled, paralogous subunits likely have played a role in yeast's adaptation to anaerobic conditions. In a number of cases the gene duplication has given rise to one duplicate that is no longer part of a protein complex and shows an accelerated rate of evolution. Such genes could provide the raw material for the evolution of new functions.

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

Combination chemical genetics

Predicting the behavior of living organisms is an enormous challenge given their vast complexity. Efforts to model biological systems require large datasets generated by physical binding experiments and perturbation studies. Genetic perturbations have proven important and are greatly facilitated by the advent of comprehensive mutant libraries in model organisms. Small-molecule chemical perturbagens provide a complementary approach, especially for systems that lack mutant libraries, and can easily probe the function of essential genes. Though single chemical or genetic perturbations provide crucial information associating individual components (for example, genes, proteins or small molecules) with pathways or phenotypes, functional relationships between pathways and modules of components are most effectively obtained from combined perturbation experiments. Here we review the current state of and discuss some future directions for 'combination chemical genetics', the systematic application of multiple chemical or mixed chemical and genetic perturbations, both to gain insight into biological systems and to facilitate medical discoveries.

Spontaneous reaction silencing in metabolic optimization

Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances. Yet, the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood. Here we predict computationally and analytically that any organism evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical nonoptimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all. We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways, shedding new light on microbial evolution, robustness, and versatility for the execution of specific biochemical tasks. In particular, the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function.

Cellular growth and other integrated metabolic functions are manifestations of the coordinated interconversion of a large number of chemical compounds. But what is the relation between such whole-cell behaviors and the activity pattern of the individual biochemical reactions? In this study, we have used flux balance-based methods and reconstructed networks of Helicobacter pylori, Staphylococcus aureus, Escherichia coli, and Saccharomyces cerevisiae to show that a cell seeking to optimize a metabolic objective, such as growth, has a tendency to spontaneously inactivate a significant number of its metabolic reactions, while all such reactions are recruited for use in typical suboptimal states. The mechanisms governing this behavior not only provide insights into why numerous genes can often be disabled without affecting optimal growth but also lay a foundation for the recently proposed synthetic rescue of metabolic function in which the performance of suboptimally operating cells can be enhanced by disabling specific metabolic reactions. Our findings also offer explanation for another experimentally observed behavior, in which some inactive reactions are temporarily activated following a genetic or environmental perturbation. The latter is of utmost importance given that nonoptimal and transient metabolic behaviors are arguably common in natural environments.

Drug:H+ antiporters in chemical stress response in yeast

The emergence of widespread multidrug resistance (MDR) is a serious challenge for therapeutics, food-preservation and crop protection. Frequently, MDR is a result of the action of drug-efflux pumps, which are able to catalyze the extrusion of unrelated chemical compounds. This review summarizes the current knowledge on the Saccharomyces cerevisiae drug:H+ antiporters of the major facilitator superfamily (MFS), a group of MDR transporters that is still characterized poorly in eukaryotes. Particular focus is given here to the physiological role and expression regulation of these transporters, while we provide a unified view of new data emerging from functional genomics approaches. Although traditionally described as drug pumps, evidence reviewed here corroborates the hypothesis that several MFS-MDR transporters might have a natural substrate and that drug transport might occur only fortuitously or opportunistically. Their role in MDR might even result from the transport of endogenous metabolites that affect the partition of cytotoxic compounds indirectly. Finally, the extrapolation of the gathered knowledge on the MDR phenomenon in yeast to pathogenic fungi and higher eukaryotes is discussed.

A critical view of metabolic network adaptations

There has been considerable recent interest in deciphering the adaptive properties underlying the structure and function of metabolic networks. Various features of metabolic networks such as the global topology, distribution of fluxes, and mutational robustness, have been proposed to have adaptive significance and hence reflect design principles. However, whether evolutionary processes alternative to direct selection on the trait under investigation also play a role is often ignored and the selection pressures maintaining a given metabolic trait often remain speculative. Some systems-level traits might simply arise as by-products of selection on other traits or even through random genetic drift. Here, we ask which systems-level aspects of metabolism are likely to have adaptive utility and which could be better explained as by-products of other evolutionary forces. We conclude that the global topological characteristics of metabolic networks and their mutational robustness are unlikely to be directly shaped by natural selection. Conversely, models of optimal design revealed that various aspects of individual pathways and the behavior of the whole network show signs of adaptations, even though the exact selective forces often remain elusive. Comparative and experimental approaches, which so far have been relatively rarely employed, could help to distinguish between alternative adaptive scenarios.

Chemical-genetic profile analysis in yeast suggests that a previously uncharacterized open reading frame, YBR261C, affects protein synthesis

Background

Functional genomics has received considerable attention in the post-genomic era, as it aims to identify function(s) for different genes. One way to study gene function is to investigate the alterations in the responses of deletion mutants to different stimuli. Here we investigate the genetic profile of yeast non-essential gene deletion array (yGDA, ~4700 strains) for increased sensitivity to paromomycin, which targets the process of protein synthesis.

Results

As expected, our analysis indicated that the majority of deletion strains (134) with increased sensitivity to paromomycin, are involved in protein biosynthesis. The remaining strains can be divided into smaller functional categories: metabolism (45), cellular component biogenesis and organization (28), DNA maintenance (21), transport (20), others (38) and unknown (39). These may represent minor cellular target sites (side-effects) for paromomycin. They may also represent novel links to protein synthesis. One of these strains carries a deletion for a previously uncharacterized ORF, YBR261C, that we term TAE1 for Translation Associated Element 1. Our focused follow-up experiments indicated that deletion of TAE1 alters the ribosomal profile of the mutant cells. Also, gene deletion strain for TAE1 has defects in both translation efficiency and fidelity. Miniaturized synthetic genetic array analysis further indicates that TAE1 genetically interacts with 16 ribosomal protein genes. Phenotypic suppression analysis using TAE1 overexpression also links TAE1 to protein synthesis.

Conclusion

We show that a previously uncharacterized ORF, YBR261C, affects the process of protein synthesis and reaffirm that large-scale genetic profile analysis can be a useful tool to study novel gene function(s).

Essential role for fibrillin-2 in zebrafish notochord and vascular morphogenesis

Recent studies demonstrate that lysyl oxidase cuproenzymes are critical for zebrafish notochord formation, but the molecular mechanisms of copper-dependent notochord morphogenesis are incompletely understood. We, therefore, conducted a forward genetic screen for zebrafish mutants that exhibit notochord sensitivity to lysyl oxidase inhibition, yielding a mutant with defects in notochord and vascular morphogenesis, puff daddygw1 (pfdgw1). Meiotic mapping and cloning reveal that the pfdgw1 phenotype results from disruption of the gene encoding the extracellular matrix protein fibrillin-2, and the spatiotemporal expression of fibrillin-2 is consistent with the pfdgw1 phenotype. Furthermore, each aspect of the pfdgw1 phenotype is recapitulated by morpholino knockdown of fibrillin-2. Taken together, the data reveal a genetic interaction between fibrillin-2 and the lysyl oxidases in notochord formation and demonstrate the importance of fibrillin-2 in specific early developmental processes in zebrafish.

Zebrafish mutants calamity and catastrophe define critical pathways of gene-nutrient interactions in developmental copper metabolism

Nutrient availability is an important environmental variable during development that has significant effects on the metabolism, health, and viability of an organism. To understand these interactions for the nutrient copper, we used a chemical genetic screen for zebrafish mutants sensitive to developmental copper deficiency. In this screen, we isolated two mutants that define subtleties of copper metabolism. The first contains a viable hypomorphic allele of atp7a and results in a loss of pigmentation when exposed to mild nutritional copper deficiency. This mutant displays incompletely penetrant skeletal defects affected by developmental copper availability. The second carries an inactivating mutation in the vacuolar ATPase that causes punctate melanocytes and embryonic lethality. This mutant, catastrophe, is sensitive to copper deprivation revealing overlap between ion metabolic pathways. Together, the two mutants illustrate the utility of chemical genetic screens in zebrafish to elucidate the interaction of nutrient availability and genetic polymorphisms in cellular metabolism.

Copper is an essential nutrient required for multiple biologic functions. Proper uptake, transport, and excretion of copper are critical for use of this metal while reducing its inherent toxicity. While several key proteins involved in this process have been identified, there are still gaps in our understanding of copper metabolism—particularly during early development. We have used zebrafish, a genetically useful animal model system, to study genetic interactions with copper deficiency during development. We treated mutant embryonic zebrafish with a chelator that reduces the level of available copper and screened for mutants that displayed a copper deficient phenotype only in the presence of the chelator. We identified and characterized two mutants that advance our understanding of copper metabolism during the early periods of development, as well as show an interaction between copper metabolism and another fundamental pathway—that of proton transport. Our results expand our knowledge of copper metabolism and illustrate the power of this type of genetic screen in zebrafish to elucidate mechanisms of nutrient metabolism.

Mgr3p and Mgr1p are adaptors for the mitochondrial i-AAA protease complex

By screening yeast knockouts for their dependence upon the mitochondrial genome, we identified Mgr3p, a protein that associates with the i-AAA protease complex in the mitochondrial inner membrane. Mgr3p and Mgr1p, another i-AAA-interacting protein, form a subcomplex that bind to the i-AAA subunit Yme1p. We find that loss of Mgr3p, like the lack of Mgr1p, reduces proteolysis by Yme1p. Mgr3p and Mgr1p can bind substrate even in the absence of Yme1p, and both proteins are needed for maximal binding of an unfolded substrate by the i-AAA complex. We speculate that Mgr3p and Mgr1p function in an adaptor complex that targets substrates to the i-AAA protease for degradation.

Has nature already identified all useful antibacterial targets?

Novel antimicrobial targets are urgently needed to overcome rising antibiotic resistance of important human pathogens. However, evidence from previous antimicrobial screenings, in silico analysis, and experimental target evaluation suggests that the number of novel bacterial broad-spectrum targets might be severely limited. This is because of the poor conservation of genes among diverse bacterial pathogens, partial functional redundancy and nutrient-rich host environments. Remaining opportunities under these circumstances include the development of narrow-spectrum antibiotics against specific pathogens and the exploration of target combinations.

Dispensability of mammalian DNA

In the lab, the cis-regulatory network seems to exhibit great functional redundancy. Many experiments testing enhancer activity of neighboring cis-regulatory elements show largely overlapping expression domains. Of recent interest, mice in which cis-regulatory ultraconserved elements were knocked out showed no obvious phenotype, further suggesting functional redundancy. Here, we present a global evolutionary analysis of mammalian conserved nonexonic elements (CNEs), and find strong evidence to the contrary. Given a set of CNEs conserved between several mammals, we characterize functional dispensability as the propensity for the ancestral element to be lost in mammalian species internal to the spanned species tree. We show that ultraconserved-like elements are over 300-fold less likely than neutral DNA to have been lost during rodent evolution. In fact, many thousands of noncoding loci under purifying selection display near uniform indispensability during mammalian evolution, largely irrespective of nucleotide conservation level. These findings suggest that many genomic noncoding elements possess functions that contribute noticeably to organism fitness in naturally evolving populations.

Yeast chemical genomics and drug discovery: an update

The Saccharomyces cerevisiae sequencing project (the first eukaryotic genome decoded) was completed in 1995 and, subsequently, the first version of the yeast knockout collection was made available in 2002. Since then, many diverse studies have applied these resources to understand drug mechanism of action and to identify novel drug targets and target pathways. In this update of an earlier review, we present a snapshot of the current state of chemical genomic approaches in yeast, propose a set of integrated chemical genomic assays to move the field forward and consider its near-term future.

Chemogenetic fingerprinting by analysis of cellular growth dynamics

Background

A fundamental goal in chemical biology is the elucidation of on- and off-target effects of drugs and biocides. To this aim chemogenetic screens that quantify drug induced changes in cellular fitness, typically taken as changes in composite growth, is commonly applied.

Results

Using the model organism Saccharomyces cerevisiae we here report that resolving cellular growth dynamics into its individual components, growth lag, growth rate and growth efficiency, increases the predictive power of chemogenetic screens. Both in terms of drug-drug and gene-drug interactions did the individual growth variables capture distinct and only partially overlapping aspects of cell physiology. In fact, the impact on cellular growth dynamics represented functionally distinct chemical fingerprints.

Discussion

Our findings suggest that the resolution and quantification of all facets of growth increases the informational and interpretational output of chemogenetic screening. Hence, by facilitating a physiologically more complete analysis of gene-drug and drug-drug interactions the here reported results may simplify the assignment of mode-of-action to orphan bioactive compounds.

Off-target effects of psychoactive drugs revealed by genome-wide assays in yeast

To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac) interfered with establishment of cell polarity, cyproheptadine (Periactin) targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil) interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril) had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol) and pimozide (Orap). Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes.

Dissecting colony development of Neurospora crassa using mRNA profiling and comparative genomics approaches

Colony development, which includes hyphal extension, branching, anastomosis, and asexual sporulation, is a fundamental aspect of the life cycle of filamentous fungi; genetic mechanisms underlying these phenomena are poorly understood. We conducted transcriptional profiling during colony development of the model filamentous fungus Neurospora crassa, using 70-mer oligonucleotide microarrays. Relative mRNA expression levels were determined for six sections of defined age excised from a 27-h-old N. crassa colony. Functional category analysis showed that the expression of genes involved in cell membrane biosynthesis, polar growth, and cellular signaling was enriched at the periphery of the colony. The relative expression of genes involved in protein synthesis and energy production was enriched in the middle section of the colony, while sections of the colony undergoing asexual development (conidiogenesis) were enriched in expression of genes involved in protein/peptide degradation and unclassified proteins. A cross-examination of the N. crassa data set with a published data set of Aspergillus niger revealed shared patterns in the spatiotemporal regulation of gene orthologs during colony development. At present, less than 50% of genes in N. crassa have functional annotation, which imposes the chief limitation on data analysis. Using an evolutionary approach, we observed that the expression of phylogenetically conserved groups of genes was enriched in the middle section of an N. crassa colony whereas expression of genes unique to euascomycete species and of N. crassa orphan genes was enriched at the colony periphery and in the older, conidiating sections of a fungal colony.

Determination of antibiotic hypersensitivity among 4,000 single-gene-knockout mutants of Escherichia coli

We have tested the entire Keio collection of close to 4,000 single-gene knockouts in Escherichia coli for increased susceptibility to one of seven different antibiotics (ciprofloxacin, rifampin, vancomycin, ampicillin, sulfamethoxazole, gentamicin, or metronidazole). We used high-throughput screening of several subinhibitory concentrations of each antibiotic and reduced more than 65,000 data points to a set of 140 strains that display significantly increased sensitivities to at least one of the antibiotics, determining the MIC in each case. These data provide targets for the design of "codrugs" that can potentiate existing antibiotics. We have made a number of double mutants with greatly increased sensitivity to ciprofloxacin, and these overcome the resistance generated by certain gyrA mutations. Many of the gene knockouts in E. coli are hypersensitive to more than one antibiotic. Together, all of these data allow us to outline the cell's "intrinsic resistome," which provides innate resistance to antibiotics.

A comprehensive strategy enabling high-resolution functional analysis of the yeast genome

Functional genomic studies in Saccharomyces cerevisiae have contributed enormously to our understanding of cellular processes. Their full potential, however, has been hampered by the limited availability of reagents to systematically study essential genes and the inability to quantify the small effects of most gene deletions on growth. Here we describe the construction of a library of hypomorphic alleles of essential genes and a high-throughput growth competition assay to measure fitness with unprecedented sensitivity. These tools dramatically increase the breadth and precision with which quantitative genetic analysis can be performed in yeast. We illustrate the value of these approaches by using genetic interactions to reveal new relationships between chromatin-modifying factors and to create a functional map of the proteasome. Finally, by measuring the fitness of strains in the yeast deletion library, we addressed an enigma regarding the apparent prevalence of gene dispensability and found that most genes do contribute to growth.

An integrated platform of genomic assays reveals small-molecule bioactivities

Bioactive compounds are widely used to modulate protein function and can serve as important leads for drug development. Identifying the in vivo targets of these compounds remains a challenge. Using yeast, we integrated three genome-wide gene-dosage assays to measure the effect of small molecules in vivo. A single TAG microarray was used to resolve the fitness of strains derived from pools of (i) homozygous deletion mutants, (ii) heterozygous deletion mutants and (iii) genomic library transformants. We demonstrated, with eight diverse reference compounds, that integration of these three chemogenomic profiles improves the sensitivity and specificity of small-molecule target identification. We further dissected the mechanism of action of two protein phosphatase inhibitors and in the process developed a framework for the rational design of multidrug combinations to sensitize cells with specific genotypes more effectively. Finally, we applied this platform to 188 novel synthetic chemical compounds and identified both potential targets and structure-activity relationships.