Quantitative phenotyping via deep barcode sequencing

Design and analysis of Bar-seq experiments

High-throughput quantitative DNA sequencing enables the parallel phenotyping of pools of thousands of mutants. However, the appropriate analytical methods and experimental design that maximize the efficiency of these methods while maintaining statistical power are currently unknown. Here, we have used Bar-seq analysis of the Saccharomyces cerevisiae yeast deletion library to systematically test the effect of experimental design parameters and sequence read depth on experimental results. We present computational methods that efficiently and accurately estimate effect sizes and their statistical significance by adapting existing methods for RNA-seq analysis. Using simulated variation of experimental designs, we found that biological replicates are critical for statistical analysis of Bar-seq data, whereas technical replicates are of less value. By subsampling sequence reads, we found that when using four-fold biological replication, 6 million reads per condition achieved 96% power to detect a two-fold change (or more) at a 5% false discovery rate. Our guidelines for experimental design and computational analysis enables the study of the yeast deletion collection in up to 30 different conditions in a single sequencing lane. These findings are relevant to a variety of pooled genetic screening methods that use high-throughput quantitative DNA sequencing, including Tn-seq.

Manipulating the yeast genome: deletion, mutation, and tagging by PCR

Saccharomyces cerevisiae is an ideal model eukaryotic system for the systematic analysis of gene function due to the ease and precision with which its genome can be manipulated. The ability of budding yeast to undergo efficient homologous recombination with short stretches of sequence homology has led to an explosion of PCR-based methods to delete and mutate yeast genes and to create fusions to epitope tags and fluorescent proteins. Here, we describe commonly used methods to generate gene deletions, to integrate mutated versions of a gene into the yeast genome, and to construct N- and C-terminal gene fusions. Using a high-efficiency yeast transformation protocol, DNA fragments with as little as 40 bp of homology can accurately target integration into a particular region of the yeast genome.

DNA-damaging agents in cancer chemotherapy: serendipity and chemical biology

DNA-damaging agents have a long history of use in cancer chemotherapy. The full extent of their cellular mechanisms, which is essential to balance efficacy and toxicity, is often unclear. In addition, the use of many anticancer drugs is limited by dose-limiting toxicities as well as the development of drug resistance. Novel anticancer compounds are continually being developed in the hopes of addressing these limitations; however, it is essential to be able to evaluate these compounds for their mechanisms of action. This review covers the current DNA-damaging agents used in the clinic, discusses their limitations, and describes the use of chemical genomics to uncover new information about the DNA damage response network and to evaluate novel DNA-damaging compounds.

Yeast metabolic and signaling genes are required for heat-shock survival and have little overlap with the heat-induced genes

Genome-wide gene-expression studies have shown that hundreds of yeast genes are induced or repressed transiently by changes in temperature; many are annotated to stress response on this basis. To obtain a genome-scale assessment of which genes are functionally important for innate and/or acquired thermotolerance, we combined the use of a barcoded pool of ~4,800 nonessential, prototrophic Saccharomyces cerevisiae deletion strains with Illumina-based deep-sequencing technology. As reported in other recent studies that have used deletion mutants to study stress responses, we observed that gene deletions resulting in the highest thermosensitivity generally are not the same as those transcriptionally induced in response to heat stress. Functional analysis of identified genes revealed that metabolism, cellular signaling, and chromatin regulation play roles in regulating thermotolerance and in acquired thermotolerance. However, for most of the genes identified, the molecular mechanism behind this action remains unclear. In fact, a large fraction of identified genes are annotated as having unknown functions, further underscoring our incomplete understanding of the response to heat shock. We suggest that survival after heat shock depends on a small number of genes that function in assessing the metabolic health of the cell and/or regulate its growth in a changing environment.

Evasion of superinfection exclusion and elimination of primary viral RNA by an adapted strain of hepatitis C virus

Cells that are productively infected by hepatitis C virus (HCV) are refractory to a second infection by HCV via a block in viral replication known as superinfection exclusion. The block occurs at a postentry step and likely involves translation or replication of the secondary viral RNA, but the mechanism is largely unknown. To characterize HCV superinfection exclusion, we selected for an HCV variant that could overcome the block. We produced a high-titer HC-J6/JFH1 (Jc1) viral genome with a fluorescent reporter inserted between NS5A and NS5B and used it to infect Huh7.5 cells containing a Jc1 replicon. With multiple passages of these infected cells, we isolated an HCV variant that can superinfect cells at high levels. Notably, the superinfectious virus rapidly cleared the primary replicon from superinfected cells. Viral competition experiments, using a novel strategy of sequence-barcoding viral strains, as well as superinfection of replicon cells demonstrated that mutations in E1, p7, NS5A, and the poly(U/UC) tract of the 3' untranslated region were important for superinfection. Furthermore, these mutations dramatically increased the infectivity of the virus in naive cells. Interestingly, viruses with a shorter poly(U/UC) and an NS5A domain II mutation were most effective in overcoming the postentry block. Neither of these changes affected viral RNA translation, indicating that the major barrier to postentry exclusion occurs at viral RNA replication. The evolution of the ability to superinfect after less than a month in culture and the concomitant exclusion of the primary replicon suggest that superinfection exclusion dramatically affects viral fitness and dynamics in vivo.

Genomic phenotyping by barcode sequencing broadly distinguishes between alkylating agents, oxidizing agents, and non-genotoxic agents, and reveals a role for aromatic amino acids in cellular recovery after quinone exposure

Toxicity screening of compounds provides a means to identify compounds harmful for human health and the environment. Here, we further develop the technique of genomic phenotyping to improve throughput while maintaining specificity. We exposed cells to eight different compounds that rely on different modes of action: four genotoxic alkylating (methyl methanesulfonate (MMS), N-Methyl-N-nitrosourea (MNU), N,N′-bis(2-chloroethyl)-N-nitroso-urea (BCNU), N-ethylnitrosourea (ENU)), two oxidizing (2-methylnaphthalene-1,4-dione (menadione, MEN), benzene-1,4-diol (hydroquinone, HYQ)), and two non-genotoxic (methyl carbamate (MC) and dimethyl sulfoxide (DMSO)) compounds. A library of S. cerevisiae 4,852 deletion strains, each identifiable by a unique genetic ‘barcode’, were grown in competition; at different time points the ratio between the strains was assessed by quantitative high throughput ‘barcode’ sequencing. The method was validated by comparison to previous genomic phenotyping studies and 90% of the strains identified as MMS-sensitive here were also identified as MMS-sensitive in a much lower throughput solid agar screen. The data provide profiles of proteins and pathways needed for recovery after both genotoxic and non-genotoxic compounds. In addition, a novel role for aromatic amino acids in the recovery after treatment with oxidizing agents was suggested. The role of aromatic acids was further validated; the quinone subgroup of oxidizing agents were extremely toxic in cells where tryptophan biosynthesis was compromised.

Genome-wide study of the adaptation of Saccharomyces cerevisiae to the early stages of wine fermentation

This work was designed to identify yeast cellular functions specifically affected by the stress factors predominating during the early stages of wine fermentation, and genes required for optimal growth under these conditions. The main experimental method was quantitative fitness analysis by means of competition experiments in continuous culture of whole genome barcoded yeast knockout collections. This methodology allowed the identification of haploinsufficient genes, and homozygous deletions resulting in growth impairment in synthetic must. However, genes identified as haploproficient, or homozygous deletions resulting in fitness advantage, were of little predictive power concerning optimal growth in this medium. The relevance of these functions for enological performance of yeast was assessed in batch cultures with single strains. Previous studies addressing yeast adaptation to winemaking conditions by quantitative fitness analysis were not specifically focused on the proliferative stages. In some instances our results highlight the importance of genes not previously linked to winemaking. In other cases they are complementary to those reported in previous studies concerning, for example, the relevance of some genes involved in vacuolar, peroxisomal, or ribosomal functions. Our results indicate that adaptation to the quickly changing growth conditions during grape must fermentation require the function of different gene sets in different moments of the process. Transport processes and glucose signaling seem to be negatively affected by the stress factors encountered by yeast in synthetic must. Vacuolar activity is important for continued growth during the transition to stationary phase. Finally, reduced biogenesis of peroxisomes also seems to be advantageous. However, in contrast to what was described for later stages, reduced protein synthesis is not advantageous for the early (proliferative) stages of the fermentation process. Finally, we found adenine and lysine to be in short supply for yeast growth in some natural grape musts.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Miniature short hairpin RNA screens to characterize antiproliferative drugs

The application of new proteomics and genomics technologies support a view in which few drugs act solely by inhibiting a single cellular target. Indeed, drug activity is modulated by complex, often incompletely understood cellular mechanisms. Therefore, efforts to decipher mode of action through genetic perturbation such as RNAi typically yields "hits" that fall into several categories. Of particular interest to the present study, we aimed to characterize secondary activities of drugs on cells. Inhibiting a known target can result in clinically relevant synthetic phenotypes. In one scenario, drug perturbation could, for example, improperly activate a protein that normally inhibits a particular kinase. In other cases, additional, lower affinity targets can be inhibited as in the example of inhibition of c-Kit observed in Bcr-Abl-positive cells treated with Gleevec. Drug transport and metabolism also play an important role in the way any chemicals act within the cells. Finally, RNAi per se can also affect cell fitness by more general off-target effects, e.g., via the modulation of apoptosis or DNA damage repair. Regardless of the root cause of these unwanted effects, understanding the scope of a drug's activity and polypharmacology is essential for better understanding its mechanism(s) of action, and such information can guide development of improved therapies. We describe a rapid, cost-effective approach to characterize primary and secondary effects of small-molecules by using small-scale libraries of virally integrated short hairpin RNAs. We demonstrate this principle using a "minipool" composed of shRNAs that target the genes encoding the reported protein targets of approved drugs. Among the 28 known reported drug-target pairs, we successfully identify 40% of the targets described in the literature and uncover several unanticipated drug-target interactions based on drug-induced synthetic lethality. We provide a detailed protocol for performing such screens and for analyzing the data. This cost-effective approach to mammalian knockdown screens, combined with the increasing maturation of RNAi technology will expand the accessibility of similar approaches in academic settings.

Microfluidic genome-wide profiling of intrinsic electrical properties in Saccharomyces cerevisiae

Methods to analyze the intrinsic physical properties of cells - for example, size, density, rigidity, or electrical properties - are an active area of interest in the microfluidics community. Although the physical properties of cells are determined at a fundamental level by gene expression, the relationship between the two remains exceptionally complex and poorly characterized, limiting the adoption of intrinsic separation technologies. To improve our current understanding of how a cell's genotype maps to a measurable physical characteristic and quantitatively investigate the potential of using these characteristics as biomarkers, we have developed a novel screen that combines microfluidic cell sorting with high-throughput sequencing and the haploid yeast deletion library to identify genes whose functions modulate one such characteristic - intrinsic electrical properties. Using this screen, we are able to establish a high-content electrical profile of the haploid yeast gene deletion strains. We find that individual genetic deletions can appreciably alter the electrical properties of cells, affecting ~10% of the 4432 gene deletion strains screened. Additionally, we find that gene deletions affecting electrical properties in specific ways (i.e. increasing or decreasing effective conductivity at higher or lower electric field frequencies) are strongly associated with an enriched subset of fundamental biological processes that can be traced to specific pathways and complexes. The screening approach demonstrated here and the attendant results are immediately applicable to the intrinsic separations community.

Dissecting a complex chemical stress: chemogenomic profiling of plant hydrolysates

Complex chemical stress arises during the production of biofuels. Large-scale mutant fitness profiling was used to identify bacterial and yeast tolerance genes and to model fitness in a complex hydrolysate mixture. The resulting model can be used to engineer more tolerant strains.

Genome-wide fitness profiling was used to identify plant hydrolysate tolerance genes in Zymomonas mobilis and Saccharomyces cerevisiae.

We modeled fitness in hydrolysate as a mixture of fitness in its components.

Outliers in our model led to the identification of a previously unknown component of hydrolysate.

Overexpression of a Z. mobilis tolerance gene of unknown function improved ethanol productivity in plant hydrolysate.

The efficient production of biofuels from cellulosic feedstocks will require the efficient fermentation of the sugars in hydrolyzed plant material. Unfortunately, plant hydrolysates also contain many compounds that inhibit microbial growth and fermentation. We used DNA-barcoded mutant libraries to identify genes that are important for hydrolysate tolerance in both Zymomonas mobilis (44 genes) and Saccharomyces cerevisiae (99 genes). Overexpression of a Z. mobilis tolerance gene of unknown function (ZMO1875) improved its specific ethanol productivity 2.4-fold in the presence of miscanthus hydrolysate. However, a mixture of 37 hydrolysate-derived inhibitors was not sufficient to explain the fitness profile of plant hydrolysate. To deconstruct the fitness profile of hydrolysate, we profiled the 37 inhibitors against a library of Z. mobilis mutants and we modeled fitness in hydrolysate as a mixture of fitness in its components. By examining outliers in this model, we identified methylglyoxal as a previously unknown component of hydrolysate. Our work provides a general strategy to dissect how microbes respond to a complex chemical stress and should enable further engineering of hydrolysate tolerance.

High-throughput sequencing of cytosine methylation in plant DNA

: Cytosine methylation is a significant and widespread regulatory factor in plant systems. Methods for the high-throughput sequencing of methylation have allowed a greatly improved characterisation of the methylome. Here we discuss currently available methods for generation and analysis of high-throughput sequencing of methylation data. We also discuss the results previously acquired through sequencing plant methylomes, and highlight remaining challenges in this field.

Systems-level antimicrobial drug and drug synergy discovery

Here, we review the 'target-centric' genomic strategy to antimicrobial discovery and share our perspective on identification, validation and prioritization of potential antimicrobial drug targets in the context of emerging chemical biology, genomics and phenotypic screening strategies. We propose that coupling the dual processes of antimicrobial small-molecule screening and target identification in a whole-cell context is essential to empirically annotate 'druggable' targets and advance early stage antimicrobial discovery. We also advocate a systems-level approach to annotating synthetic-lethal genetic interactions comprehensively within yeast and bacteria models. The resulting genetic interaction networks provide a landscape to rationally predict and exploit drug synergy between cognate inhibitors. We posit that synergistic combination agents provide an important and largely unexploited strategy to 'repurpose' existing chemical space and simultaneously address issues of potency, spectrum, toxicity and drug resistance in early stages of antimicrobial drug discovery.

The impact of next-generation sequencing technology on preimplantation genetic diagnosis and screening

Largely because of efforts required to complete the Human Genome Project, DNA sequencing has undergone a steady transformation with still-ongoing developments of high-throughput sequencing machines for which the cost per reaction is falling drastically. Similarly, the fast-changing landscape of reproductive technologies has been improved by genetic approaches. Preimplantation genetic diagnosis and screening were established more than two decades ago for selecting genetically normal embryos to avoid inherited diseases and to give the highest potential to achieve stable pregnancies. Most recent additions to the IVF practices (blastocyst/trophectoderm biopsy, embryo vitrification) and adoption of new genetics tools such as array comparative genome hybridization have allowed setting up more precise and efficient programs for clinical embryo diagnosis. Nevertheless, there is always room for improvements. Remarkably, a recent explosion in the release of advanced sequencing benchtop platforms, together with a certain maturity of bioinformatics tools, has set the target goal of sequencing individual cells for embryo diagnosis to be a realistically feasible scenario for the near future. Next-generation sequencing technology should provide the opportunity to simultaneously analyze single-gene disorders and perform an extensive comprehensive chromosome screening/diagnosis by concurrently sequencing, counting, and accurately assembling millions of DNA reads.

Target identification for small bioactive molecules: finding the needle in the haystack

Identification and confirmation of bioactive small-molecule targets is a crucial, often decisive step both in academic and pharmaceutical research. Through the development and availability of several new experimental techniques, target identification is, in principle, feasible, and the number of successful examples steadily grows. However, a generic methodology that can successfully be applied in the majority of the cases has not yet been established. Herein we summarize current methods for target identification of small molecules, primarily for a chemistry audience but also the biological community, for example, the chemist or biologist attempting to identify the target of a given bioactive compound. We describe the most frequently employed experimental approaches for target identification and provide several representative examples illustrating the state-of-the-art. Among the techniques currently available, protein affinity isolation using suitable small-molecule probes (pulldown) and subsequent mass spectrometric analysis of the isolated proteins appears to be most powerful and most frequently applied. To provide guidance for rapid entry into the field and based on our own experience we propose a typical workflow for target identification, which centers on the application of chemical proteomics as the key step to generate hypotheses for potential target proteins.

A functional variomics tool for discovering drug-resistance genes and drug targets

Comprehensive discovery of genetic mechanisms of drug resistance and identification of in vivo drug targets represent significant challenges. Here we present a functional variomics technology in the model organism Saccharomyces cerevisiae. This tool analyzes numerous genetic variants and effectively tackles both problems simultaneously. Using this tool, we discovered almost all genes that, due to mutations or modest overexpression, confer resistance to rapamycin, cycloheximide, and amphotericin B. Most significant among the resistance genes were drug targets, including multiple targets of a given drug. With amphotericin B, we discovered the highly conserved membrane protein Pmp3 as a potent resistance factor and a possible target. Widespread application of this tool should allow rapid identification of conserved resistance mechanisms and targets of many more compounds. New genes and alleles that confer resistance to other stresses can also be discovered. Similar tools in other systems, such as human cell lines, will also be useful.

Saccharomyces cerevisiae genetics predicts candidate therapeutic genetic interactions at the mammalian replication fork

The concept of synthetic lethality has gained popularity as a rational guide for predicting chemotherapeutic targets based on negative genetic interactions between tumor-specific somatic mutations and a second-site target gene. One hallmark of most cancers that can be exploited by chemotherapies is chromosome instability (CIN). Because chromosome replication, maintenance, and segregation represent conserved and cell-essential processes, they can be modeled effectively in simpler eukaryotes such as Saccharomyces cerevisiae. Here we analyze and extend genetic networks of CIN cancer gene orthologs in yeast, focusing on essential genes. This identifies hub genes and processes that are candidate targets for synthetic lethal killing of cancer cells with defined somatic mutations. One hub process in these networks is DNA replication. A nonessential, fork-associated scaffold, CTF4, is among the most highly connected genes. As Ctf4 lacks enzymatic activity, potentially limiting its development as a therapeutic target, we exploited its function as a physical interaction hub to rationally predict synthetic lethal interactions between essential Ctf4-binding proteins and CIN cancer gene orthologs. We then validated a subset of predicted genetic interactions in a human colorectal cancer cell line, showing that siRNA-mediated knockdown of MRE11A sensitizes cells to depletion of various replication fork-associated proteins. Overall, this work describes methods to identify, predict, and validate in cancer cells candidate therapeutic targets for tumors with known somatic mutations in CIN genes using data from yeast. We affirm not only replication stress but also the targeting of DNA replication fork proteins themselves as potential targets for anticancer therapeutic development.

Identification of genes affecting vacuole membrane fragmentation in Saccharomyces cerevisiae

The equilibrium of membrane fusion and fission influences the volume and copy number of organelles. Fusion of yeast vacuoles has been well characterized but their fission and the mechanisms determining vacuole size and abundance remain poorly understood. We therefore attempted to systematically characterize factors necessary for vacuole fission. Here, we present results of an in vivo screening for deficiencies in vacuolar fragmentation activity of an ordered collection deletion mutants, representing 4881 non-essential genes of the yeast Saccharomyces cerevisiae. The screen identified 133 mutants with strong defects in vacuole fragmentation. These comprise numerous known fragmentation factors, such as the Fab1p complex, Tor1p, Sit4p and the V-ATPase, thus validating the approach. The screen identified many novel factors promoting vacuole fragmentation. Among those are 22 open reading frames of unknown function and three conspicuous clusters of proteins with known function. The clusters concern the ESCRT machinery, adaptins, and lipases, which influence the production of diacylglycerol and phosphatidic acid. A common feature of these factors of known function is their capacity to change membrane curvature, suggesting that they might promote vacuole fragmentation via this property.

Forward chemical genetics in yeast for discovery of chemical probes targeting metabolism

The many virtues that made the yeast Saccharomyces cerevisiae a dominant model organism for genetics and molecular biology, are now establishing its role in chemical genetics. Its experimental tractability (i.e., rapid doubling time, simple culture conditions) and the availability of powerful tools for drug-target identification, make yeast an ideal organism for high-throughput phenotypic screening. It may be especially applicable for the discovery of chemical probes targeting highly conserved cellular processes, such as metabolism and bioenergetics, because these probes would likely inhibit the same processes in higher eukaryotes (including man). Importantly, changes in normal cellular metabolism are associated with a variety of diseased states (including neurological disorders and cancer), and exploiting these changes for therapeutic purposes has accordingly gained considerable attention. Here, we review progress and challenges associated with forward chemical genetic screening in yeast. We also discuss evidence supporting these screens as a useful strategy for discovery of new chemical probes and new druggable targets related to cellular metabolism.

The genomic landscape and evolutionary resolution of antagonistic pleiotropy in yeast

Antagonistic pleiotropy (AP), or genetic tradeoff, is an important concept that is frequently invoked in theories of aging, cancer, genetic disease, and other common phenomena. However, the prevalence of AP, which genes are subject to AP, and to what extent and how AP may be resolved remain unclear. By measuring the fitness difference between the wild-type and null alleles of ~5,000 nonessential genes in yeast, we found that in any given environment, yeast expresses hundreds of genes that harm rather than benefit the organism, demonstrating widespread AP. Nonetheless, under sufficient selection, AP is often resolvable through regulatory evolution, primarily by trans-acting changes, although in one case we also detected a cis-acting change and localized its causal mutation. However, AP is resolved more slowly in smaller populations, predicting more unresolved AP in multicellular organisms than in yeast. These findings provide an empirical foundation for AP-dependent theories and have broad biomedical and evolutionary implications.

Functional analysis with a barcoder yeast gene overexpression system

Systematic analysis of gene overexpression phenotypes provides an insight into gene function, enzyme targets, and biological pathways. Here, we describe a novel functional genomics platform that enables a highly parallel and systematic assessment of overexpression phenotypes in pooled cultures. First, we constructed a genome-level collection of ~5100 yeast barcoder strains, each of which carries a unique barcode, enabling pooled fitness assays with a barcode microarray or sequencing readout. Second, we constructed a yeast open reading frame (ORF) galactose-induced overexpression array by generating a genome-wide set of yeast transformants, each of which carries an individual plasmid-born and sequence-verified ORF derived from the Saccharomyces cerevisiae full-length EXpression-ready (FLEX) collection. We combined these collections genetically using synthetic genetic array methodology, generating ~5100 strains, each of which is barcoded and overexpresses a specific ORF, a set we termed “barFLEX.” Additional synthetic genetic array allows the barFLEX collection to be moved into different genetic backgrounds. As a proof-of-principle, we describe the properties of the barFLEX overexpression collection and its application in synthetic dosage lethality studies under different environmental conditions.

An algorithm for chemical genomic profiling that minimizes batch effects: bucket evaluations

Background

Chemical genomics is an interdisciplinary field that combines small molecule perturbation with traditional genomics to understand gene function and to study the mode(s) of drug action. A benefit of chemical genomic screens is their breadth; each screen can capture the sensitivity of comprehensive collections of mutants or, in the case of mammalian cells, gene knock-downs, simultaneously. As with other large-scale experimental platforms, to compare and contrast such profiles, e.g. for clustering known compounds with uncharacterized compounds, a robust means to compare a large cohort of profiles is required. Existing methods for correlating different chemical profiles include diverse statistical discriminant analysis-based methods and specific gene filtering or normalization methods. Though powerful, none are ideal because they typically require one to define the disrupting effects, commonly known as batch effects, to detect true signal from experimental variation. These effects are not always known, and they can mask true biological differences. We present a method, Bucket Evaluations (BE) that surmounts many of these problems and is extensible to other datasets such as those obtained via gene expression profiling and which is platform independent.

Results

We designed an algorithm to analyse chemogenomic profiles to identify potential targets of known drugs and new chemical compounds. We used levelled rank comparisons to identify drugs/compounds with similar profiles that minimizes batch effects and avoids the requirement of pre-defining the disrupting effects. This algorithm was also tested on gene expression microarray data and high throughput sequencing chemogenomic screens and found the method is applicable to a variety of dataset types.

Conclusions

BE, along with various correlation methods on a collection of datasets proved to be highly accurate for locating similarity between experiments. BE is a non-parametric correlation approach, which is suitable for locating correlations in somewhat perturbed datasets such as chemical genomic profiles. We created software and a user interface for using BE, which is publically available.

Multiplex assay for condition-dependent changes in protein-protein interactions

Changes in protein-protein interactions that occur in response to environmental cues are difficult to uncover and have been poorly characterized to date. Here we describe a yeast-based assay that allows many binary protein interactions to be assessed in parallel and under various conditions. This method combines molecular bar-coding and tag array technology with the murine dihydrofolate reductase-based protein-fragment complementation assay. A total of 238 protein-fragment complementation assay strains, each representing a unique binary protein complex, were tagged with molecular barcodes, pooled, and then interrogated against a panel of 80 diverse small molecules. Our method successfully identified specific disruption of the Hom3:Fpr1 interaction by the immunosuppressant FK506, illustrating the assay's capacity to identify chemical inhibitors of protein-protein interactions. Among the additional findings was specific cellular depletion of the Dst1:Rbp9 complex by the anthracycline drug doxorubicin, but not by the related drug idarubicin. The assay also revealed chemical-induced accumulation of several binary multidrug transporter complexes that largely paralleled increases in transcript levels. Further assessment of two such interactions (Tpo1:Pdr5 and Snq2:Pdr5) in the presence of 1,246 unique chemical compounds revealed a positive correlation between drug lipophilicity and the drug response in yeast.

Genome-scale genetic manipulation methods for exploring bacterial molecular biology

Bacteria are diverse and abundant, playing key roles in human health and disease, the environment, and biotechnology. Despite progress in genome sequencing and bioengineering, much remains unknown about the functional organization of prokaryotes. For instance, roughly a third of the protein-coding genes of the best-studied model bacterium, Escherichia coli, currently lack experimental annotations. Systems-level experimental approaches for investigating the functional associations of bacterial genes and genetic structures are essential for defining the fundamental molecular biology of microbes, preventing the spread of antibacterial resistance in the clinic, and driving the development of future biotechnological applications. This review highlights recently introduced large-scale genetic manipulation and screening procedures for the systematic exploration of bacterial gene functions, molecular relationships, and the global organization of bacteria at the gene, pathway, and genome levels.

Evolutionary and Biochemical Aspects of Chemical Stress Resistance in Saccharomyces cerevisiae

Large-scale chemical genetics screens (chemogenomics) in yeast have been widely used to find drug targets, understand the mechanism-of-action of compounds, and unravel the biochemistry of drug resistance. Chemogenomics is based on the comparison of growth of gene deletants in the presence and absence of a chemical substance. Such studies showed that more than 90% of the yeast genes are required for growth in the presence of at least one chemical. Analysis of these data, using computational approaches, has revealed non-trivial features of the natural chemical tolerance systems. As a result two non-overlapping sets of genes are seen to respectively impart robustness and evolvability in the context of natural chemical resistance. The former is composed of multidrug-resistance genes, whereas the latter comprises genes sharing chemical genetic profiles with many others. Recent publications showing the potential applications chemogenomics in studying the pharmacological basis of various drugs are discussed, as well as the expansion of chemogenomics to other organisms. Finally, integration of chemogenomics with sensitive sequence analysis and ubiquitination/phosphorylation data led to the discovery of a new conserved domain and important post-translational modification pathways involved in stress resistance.

Yeast as a model eukaryote in toxinology: a functional genomics approach to studying the molecular basis of action of pharmacologically active molecules

Yeast Saccharomyces cerevisiae has proven to be a relevant and convenient model organism for the study of diverse biological phenomena, due to its straightforward genetics, cost-effectiveness and rapid growth, combined with the typical characteristics of a eukaryotic cell. More than 40% of yeast proteins share at least part of their primary amino acid sequence with the corresponding human protein, making yeast a valuable model in biomedical research. In the last decade, high-throughput and genome-wide experimental approaches developed in yeast have paved the way to functional genomics that aims at a global understanding of the relationship between genotype and phenotype. In this review we first present the yeast strain and plasmid collections for genome-wide experimental approaches to study complex interactions between genes, proteins and endo- or exogenous small molecules. We describe methods for protein-protein, protein-DNA, genetic and chemo-genetic interactions, as well as localization studies, focussing on their application in research on small pharmacologically active molecules. Next we review the use of yeast as a model organism in neurobiology, emphasizing work done towards elucidating the pathogenesis of neurodegenerative diseases and the mechanism of action of neurotoxic phospholipases A(2).

The high-throughput yeast deletion fitness data and the theories of dominance

The development of high-throughput fitness measurement methods provides unprecedented power to test evolutionary theories. However, with this comes new challenges regarding data quality and data analysis. We illustrate this by reanalysing the fitness distribution in several environments of yeast mutants (homo- and heterozygous) from the yeast deletion project. Originally created to study functional properties of genes, evolutionary biologists took advantage of this database to study evolutionary questions, such as dominance for fitness of mutations. We uncover several problems in this data set strongly affecting these questions that have remained unnoticed despite the numerous studies based on it. High-throughput methodologies are necessarily challenging, both experimentally and for data analysis: our point is not to criticize these approaches, but to pinpoint these challenges and to propose several improvements that may help avoid several shortcomings. Further, in the light of this finding, we question the conclusions regarding theories of dominance that have been made using this data set. We show that the data on deletion of small effects are not sufficiently reliable to be informative on this question. On the other hand, deletions of large effect exhibit no correlation between homo- and heterozygous fitness effects, a pattern that sheds new light on the h-s correlation issue, with several consequences for the debate over the different theories of dominance.

Pair-barcode high-throughput sequencing for large-scale multiplexed sample analysis

BACKGROUND

The multiplexing becomes the major limitation of the next-generation sequencing (NGS) in application to low complexity samples. Physical space segregation allows limited multiplexing, while the existing barcode approach only permits simultaneously analysis of up to several dozen samples.

RESULTS

Here we introduce pair-barcode sequencing (PBS), an economic and flexible barcoding technique that permits parallel analysis of large-scale multiplexed samples. In two pilot runs using SOLiD sequencer (Applied Biosystems Inc.), 32 independent pair-barcoded miRNA libraries were simultaneously discovered by the combination of 4 unique forward barcodes and 8 unique reverse barcodes. Over 174,000,000 reads were generated and about 64% of them are assigned to both of the barcodes. After mapping all reads to pre-miRNAs in miRBase, different miRNA expression patterns are captured from the two clinical groups. The strong correlation using different barcode pairs and the high consistency of miRNA expression in two independent runs demonstrates that PBS approach is valid.

CONCLUSIONS

By employing PBS approach in NGS, large-scale multiplexed pooled samples could be practically analyzed in parallel so that high-throughput sequencing economically meets the requirements of samples which are low sequencing throughput demand.

Mitochondrial electron transport is the cellular target of the oncology drug elesclomol

Elesclomol is a first-in-class investigational drug currently undergoing clinical evaluation as a novel cancer therapeutic. The potent antitumor activity of the compound results from the elevation of reactive oxygen species (ROS) and oxidative stress to levels incompatible with cellular survival. However, the molecular target(s) and mechanism by which elesclomol generates ROS and subsequent cell death were previously undefined. The cellular cytotoxicity of elesclomol in the yeast S. cerevisiae appears to occur by a mechanism similar, if not identical, to that in cancer cells. Accordingly, here we used a powerful and validated technology only available in yeast that provides critical insights into the mechanism of action, targets and processes that are disrupted by drug treatment. Using this approach we show that elesclomol does not work through a specific cellular protein target. Instead, it targets a biologically coherent set of processes occurring in the mitochondrion. Specifically, the results indicate that elesclomol, driven by its redox chemistry, interacts with the electron transport chain (ETC) to generate high levels of ROS within the organelle and consequently cell death. Additional experiments in melanoma cells involving drug treatments or cells lacking ETC function confirm that the drug works similarly in human cancer cells. This deeper understanding of elesclomol's mode of action has important implications for the therapeutic application of the drug, including providing a rationale for biomarker-based stratification of patients likely to respond in the clinical setting.

Barcode sequencing for understanding drug-gene interactions

With the advent of next-generation sequencing (NGS) technology, methods previously developed for microarrays have been adapted for use by NGS. Here we describe in detail a protocol for Barcode analysis by sequencing (Bar-seq) to assess pooled competitive growth of individually barcoded yeast deletion mutants. This protocol has been optimized on two sequencing platforms: Illumina's Genome Analyzer IIx/HiSeq2000 and Life Technologies SOLiD3/5500. In addition, we provide guidelines for assessment of human knockdown cells using short-hairpin RNAs (shRNA) and an Illumina sequencing readout.

Chemical-genomic profiling: systematic analysis of the cellular targets of bioactive molecules

Chemical-genomic (CG) profiling of bioactive compounds is a powerful approach for drug target identification and mode of action studies. Within the last decade, research focused largely on the development and application of CG approaches in the model yeast Saccharomyces cerevisiae. The success of these methods has sparked interest in transitioning CG profiling to other biological systems to extend clinical and evolutionary relevance. Additionally, CG profiling has proven to enhance drug-synergy screens for developing combinatorial therapies. Herein, we briefly review CG profiling, focusing on emerging cross-species technologies and novel drug-synergy applications, as well as outlining needs within the field.

Bugs, drugs and chemical genomics

The serendipitous discovery of penicillin inspired intensive research into how small molecules affect basic cellular processes and their potential to treat disease. Biochemical and genetic approaches have been fundamental for clarifying small-molecule modes of action. Genomic technologies have permitted the use of chemical-genetic strategies that comprehensively study compound-target relationships in the context of a living cell, providing a systems biology view of both the cellular targets and the interdependent networks that respond to chemical stress. These studies highlight the fact that in vitro determinations of mechanism rarely translate into a complete understanding of drug behavior in the cell. Here, we review key discoveries that gave rise to the field of chemical genetics, with particular attention to chemical-genetic strategies developed for bakers' yeast, their extension to clinically relevant microbial pathogens, and the potential of these approaches to affect antimicrobial drug discovery.

Competition experiments coupled with high-throughput analyses for functional genomics studies in yeast

Competition experiments are an effective way to provide a measurement of the fitness of yeast strains. The availability of the Saccharomyces cerevisiae yeast knock-out (YKO) deletion collection allows scientists to retrieve fitness data for the ~6,000 S. cerevisiae genes at the same time in a given environment. The molecular barcodes, characterizing each yeast mutant, serve as strain identifiers, which can be detected in a single microarray analysis. Competition experiments in continuous culture using chemically defined media allow a more specific discrimination of the strains based on their fitness profile. With this high-throughput approach, a series of genes that, when one allele is missing, result in either defective (haplo-insufficient) or favored (haplo-proficient) growth phenotype have been discovered, for each nutrient-limiting condition tested. While haplo-insufficient genes seemed to overlap largely across all the media used, the haplo-proficient ones seem to be more environment specific. For example, genes involved in the protein secretion pathway were highly haplo-insufficient in all the contexts, whereas most of the genes encoding for proteasome components showed a haplo-proficient phenotype specific to nitrogen-limiting conditions. In this chapter, the method used for implementation of competition experiments for high-throughput studies in yeast is presented.

Evidence-based annotation of gene function in Shewanella oneidensis MR-1 using genome-wide fitness profiling across 121 conditions

Most genes in bacteria are experimentally uncharacterized and cannot be annotated with a specific function. Given the great diversity of bacteria and the ease of genome sequencing, high-throughput approaches to identify gene function experimentally are needed. Here, we use pools of tagged transposon mutants in the metal-reducing bacterium Shewanella oneidensis MR-1 to probe the mutant fitness of 3,355 genes in 121 diverse conditions including different growth substrates, alternative electron acceptors, stresses, and motility. We find that 2,350 genes have a pattern of fitness that is significantly different from random and 1,230 of these genes (37% of our total assayed genes) have enough signal to show strong biological correlations. We find that genes in all functional categories have phenotypes, including hundreds of hypotheticals, and that potentially redundant genes (over 50% amino acid identity to another gene in the genome) are also likely to have distinct phenotypes. Using fitness patterns, we were able to propose specific molecular functions for 40 genes or operons that lacked specific annotations or had incomplete annotations. In one example, we demonstrate that the previously hypothetical gene SO_3749 encodes a functional acetylornithine deacetylase, thus filling a missing step in S. oneidensis metabolism. Additionally, we demonstrate that the orphan histidine kinase SO_2742 and orphan response regulator SO_2648 form a signal transduction pathway that activates expression of acetyl-CoA synthase and is required for S. oneidensis to grow on acetate as a carbon source. Lastly, we demonstrate that gene expression and mutant fitness are poorly correlated and that mutant fitness generates more confident predictions of gene function than does gene expression. The approach described here can be applied generally to create large-scale gene-phenotype maps for evidence-based annotation of gene function in prokaryotes.

Many computationally predicted gene annotations in bacteria are incomplete or wrong. Consequently, experimental methods to systematically determine gene function in bacteria are required. Here, we describe a genetic approach to meet this challenge. We constructed a large transposon mutant library in the metal-reducing bacterium Shewanella oneidensis MR-1 and profiled the fitness of this collection in more than 100 diverse experimental conditions. In addition to identifying a phenotype for more than 2,000 genes, we demonstrate that mutant fitness profiles can be used to assign “evidence-based” gene annotations for enzymes, signaling proteins, transporters, and transcription factors, a subset of which we verify experimentally.

Multiple means to the same end: the genetic basis of acquired stress resistance in yeast

In nature, stressful environments often occur in combination or close succession, and thus the ability to prepare for impending stress likely provides a significant fitness advantage. Organisms exposed to a mild dose of stress can become tolerant to what would otherwise be a lethal dose of subsequent stress; however, the mechanism of this acquired stress tolerance is poorly understood. To explore this, we exposed the yeast gene-deletion libraries, which interrogate all essential and non-essential genes, to successive stress treatments and identified genes necessary for acquiring subsequent stress resistance. Cells were exposed to one of three different mild stress pretreatments (salt, DTT, or heat shock) and then challenged with a severe dose of hydrogen peroxide (H₂O₂). Surprisingly, there was little overlap in the genes required for acquisition of H₂O₂ tolerance after different mild-stress pretreatments, revealing distinct mechanisms of surviving H₂O₂ in each case. Integrative network analysis of these results with respect to protein–protein interactions, synthetic–genetic interactions, and functional annotations identified many processes not previously linked to H₂O₂ tolerance. We tested and present several models that explain the lack of overlap in genes required for H₂O₂ tolerance after each of the three pretreatments. Together, this work shows that acquired tolerance to the same severe stress occurs by different mechanisms depending on prior cellular experiences, underscoring the context-dependent nature of stress tolerance.

Cells experience stressful conditions in the real world that can threaten physiology. Therefore, organisms have evolved intricate defense systems to protect themselves against environmental stress. Many organisms can increase their stress tolerance at the first sign of a problem through a phenomenon called acquired stress resistance: when pre-exposed to a mild dose of one stress, cells can become super-tolerant to subsequent stresses that would kill unprepared cells. This response is observed in many organisms, from bacteria to plants to humans, and has application in human health and disease treatment; however, its mechanism remains poorly understood. We used yeast as a model to identify genes important for acquired resistance to severe oxidative stress after pretreatment with three different mild stresses (osmotic, heat, or reductive shock). Surprisingly, there was little overlap in the genes required to survive the same severe stress after each pretreatment. This reveals that the mechanism of acquiring tolerance to the same severe stress occurs through different routes depending on the mild stressor. We leveraged available datasets of physical and genetic interaction networks to address the mechanism and regulation of stress tolerance. We find that acquired stress resistance is a unique phenotype that can uncover new insights into stress biology.

Genome-wide assessment of the carriers involved in the cellular uptake of drugs: a model system in yeast

Background

The uptake of drugs into cells has traditionally been considered to be predominantly via passive diffusion through the bilayer portion of the cell membrane. The recent recognition that drug uptake is mostly carrier-mediated raises the question of which drugs use which carriers.

Results

To answer this, we have constructed a chemical genomics platform built upon the yeast gene deletion collection, using competition experiments in batch fermenters and robotic automation of cytotoxicity screens, including protection by 'natural' substrates. Using these, we tested 26 different drugs and identified the carriers required for 18 of the drugs to gain entry into yeast cells.

Conclusions

As well as providing a useful platform technology, these results further substantiate the notion that the cellular uptake of pharmaceutical drugs normally occurs via carrier-mediated transport and indicates that establishing the identity and tissue distribution of such carriers should be a major consideration in the design of safe and effective drugs.

Genomic phenotyping of the essential and non-essential yeast genome detects novel pathways for alkylation resistance

BACKGROUND

A myriad of new chemicals has been introduced into our environment and exposure to these agents can damage cells and induce cytotoxicity through different mechanisms, including damaging DNA directly. Analysis of global transcriptional and phenotypic responses in the yeast S. cerevisiae provides means to identify pathways of damage recovery upon toxic exposure.

RESULTS

Here we present a phenotypic screen of S. cerevisiae in liquid culture in a microtiter format. Detailed growth measurements were analyzed to reveal effects on ~5,500 different haploid strains that have either non-essential genes deleted or essential genes modified to generate unstable transcripts. The pattern of yeast mutants that are growth-inhibited (compared to WT cells) reveals the mechanisms ordinarily used to recover after damage. In addition to identifying previously-described DNA repair and cell cycle checkpoint deficient strains, we also identified new functional groups that profoundly affect MMS sensitivity, including RNA processing and telomere maintenance.

CONCLUSIONS

We present here a data-driven method to reveal modes of toxicity of different agents that impair cellular growth. The results from this study complement previous genomic phenotyping studies as we have expanded the data to include essential genes and to provide detailed mutant growth analysis for each individual strain. This eukaryotic testing system could potentially be used to screen compounds for toxicity, to identify mechanisms of toxicity, and to reduce the need for animal testing.

A barcode screen for epigenetic regulators reveals a role for the NuB4/HAT-B histone acetyltransferase complex in histone turnover

Dynamic modification of histone proteins plays a key role in regulating gene expression. However, histones themselves can also be dynamic, which potentially affects the stability of histone modifications. To determine the molecular mechanisms of histone turnover, we developed a parallel screening method for epigenetic regulators by analyzing chromatin states on DNA barcodes. Histone turnover was quantified by employing a genetic pulse-chase technique called RITE, which was combined with chromatin immunoprecipitation and high-throughput sequencing. In this screen, the NuB4/HAT-B complex, containing the conserved type B histone acetyltransferase Hat1, was found to promote histone turnover. Unexpectedly, the three members of this complex could be functionally separated from each other as well as from the known interacting factor and histone chaperone Asf1. Thus, systematic and direct interrogation of chromatin structure on DNA barcodes can lead to the discovery of genes and pathways involved in chromatin modification and dynamics.

Systems biology of infectious diseases: a focus on fungal infections

The study of infectious disease concerns the interaction between the host species and a pathogen organism. The analysis of such complex systems is improving with the evolution of high-throughput technologies and advanced computational resources. This article reviews integrative, systems-oriented approaches to understanding mechanisms underlying infection, immune response and inflammation to find biomarkers of disease and design new drugs. We focus on the systems biology process, especially the data gathering and analysis techniques rather than the experimental technologies or latest computational resources.

Competitive genomic screens of barcoded yeast libraries

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily. The tempo of these advances is accelerating, promising greater depth and breadth. In light of these extraordinary advances, the need for fast, parallel methods to define gene function becomes ever more important. Collections of genome-wide deletion mutants in yeasts and E. coli have served as workhorses for functional characterization of gene function, but this approach is not scalable, current gene-deletion approaches require each of the thousands of genes that comprise a genome to be deleted and verified. Only after this work is complete can we pursue high-throughput phenotyping. Over the past decade, our laboratory has refined a portfolio of competitive, miniaturized, high-throughput genome-wide assays that can be performed in parallel. This parallelization is possible because of the inclusion of DNA 'tags', or 'barcodes,' into each mutant, with the barcode serving as a proxy for the mutation and one can measure the barcode abundance to assess mutant fitness. In this study, we seek to fill the gap between DNA sequence and barcoded mutant collections. To accomplish this we introduce a combined transposon disruption-barcoding approach that opens up parallel barcode assays to newly sequenced, but poorly characterized microbes. To illustrate this approach we present a new Candida albicans barcoded disruption collection and describe how both microarray-based and next generation sequencing-based platforms can be used to collect 10,000-1,000,000 gene-gene and drug-gene interactions in a single experiment.

FASTA barcodes: a simple method for the identification of yeast ORF deletions

A consortium of yeast geneticists have created -6000 individual ORF deletions, representing > 96% of the currently verified or predicted ORFs in S. cerevisiae. Importantly, molecular barcodes (each a unique 20 bp sequence termed either Uptag or Downtag) were used as identifiers for every ORF deletion. Microarray analyses of pooled yeast deletions has been used to identify thousands of genes involved in general fitness, haploinsufficiency, drug resistance and DNA damage repair. However, application of this powerful technology requires considerable expense, expertise and specialized equipment. While standard PCR techniques and specifically designed PCR primers can be used to confirm that a given ORF is in fact deleted, this procedure cannot be used to identify unknown deletions. In theory, every ORF deletion could be determined by barcode sequencing. However, neither a consolidated barcode database nor a reliable search engine is currently available for this purpose. To address this need, we have adapted a FASTA sequence program that utilizes the unique barcode database to allow users to identify individual ORF deletions, based upon simple sequencing reactions of PCR amplifications of either Uptag or Downtag barcodes. In silico and practical testing of this application reveals that it is an inexpensive, reliable and reproducible method for rapidly identifying unknown deletions. This approach allows laboratories to conduct small- or large-scale genetic screens with pooled yeast deletion strains and identify or verify any ORF deletion without the need for microarray technology.

Advances in bacterial transcriptome and transposon insertion-site profiling using second-generation sequencing

The arrival of second-generation sequencing has revolutionized the study of bacteria within a short period. The sequence information generated from these platforms has helped in our understanding of bacterial development, adaptation and diversity and how bacteria cause disease. Furthermore, these technologies have quickly been adapted for high-throughput studies that were previously performed using DNA cloning or microarray-based applications. This has facilitated a more comprehensive study of bacterial transcriptomes through RNA sequencing (RNA-Seq) and the systematic determination of gene function by 'transposon monitoring'. In this review, we provide an outline of these powerful tools and the in silico analyses used in their application, and also highlight the biological questions being addressed in these approaches.

Mining high-throughput experimental data to link gene and function

Nearly 2200 genomes that encode around 6 million proteins have now been sequenced. Around 40% of these proteins are of unknown function, even when function is loosely and minimally defined as 'belonging to a superfamily'. In addition to in silico methods, the swelling stream of high-throughput experimental data can give valuable clues for linking these unknowns with precise biological roles. The goal is to develop integrative data-mining platforms that allow the scientific community at large to access and utilize this rich source of experimental knowledge. To this end, we review recent advances in generating whole-genome experimental datasets, where this data can be accessed, and how it can be used to drive prediction of gene function.

Reduction of non-insert sequence reads by dimer eliminator LNA oligonucleotide for small RNA deep sequencing

Here we describe a method for constructing small RNA libraries for high-throughput sequencing in which we have made a significant improvement to commonly available standard protocols. We added a locked nucleic acid (LNA) oligonucleotide--named dimer eliminator--that is complementary to the adapter-dimer ligation products during the reverse transcription reaction. It reduces adapter-dimers, which often contaminate standard libraries and increase the number of non-insert sequence reads. This simple technology can be used for simultaneous multiplex sequencing of various barcoded samples as well as nonbarcoded small RNA library sequencing. In this study we also evaluated the reproducibility and quantitative design of the eight barcoded tags by comparing the Pearson's correlation values in the expression analysis between each barcoded sample. This method improves the sequencing yield and efficiency, while simplifying library construction, and makes it easier to perform large-scale small RNA analysis under multiple conditions with next-generation sequencers.