Genomic profiling of drug sensitivities via induced haploinsufficiency

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.

Degradation of MAC13243 and studies of the interaction of resulting thiourea compounds with the lipoprotein targeting chaperone LolA

The discovery of novel small molecules that function as antibacterial agents or cellular probes of biology is hindered by our limited understanding of bacterial physiology and our ability to assign mechanism of action. We previously employed a chemical genomic strategy to identify a novel small molecule, MAC13243, as a likely inhibitor of the bacterial lipoprotein targeting chaperone, LolA. Here, we report on the degradation of MAC13243 into the active species, S-(4-chlorobenzyl)isothiourea. Analogs of this compound (e.g., A22) have previously been characterized as inhibitors of the bacterial actin-like protein, MreB. Herein, we demonstrate that the antibacterial activity of MAC13243 and the thiourea compounds are similar; these activities are suppressed or sensitized in response to increases or decreases of LolA copy number, respectively. We provide STD NMR data which confirms a physical interaction between LolA and the thiourea degradation product of MAC13243, with a Kd of ~150 μM. Taken together, we conclude that the thiourea series of compounds share a similar cellular mechanism that includes interaction with LolA in addition to the well-characterized target MreB.

Integrated compound profiling screens identify the mitochondrial electron transport chain as the molecular target of the natural products manassantin, sesquicillin, and arctigenin

Phenotypic compound screens can be used to identify novel targets in signaling pathways and disease processes, but the usefulness of these screens depends on the ability to quickly determine the target and mechanism of action of the molecules identified as hits. One fast route to discovering the mechanism of action of a compound is to profile its properties and to match this profile with those of compounds of known mechanism of action. In this work, the Novartis collection of over 12,000 pure natural products was screened for effects on early zebrafish development. The largest phenotypic class of hits, which caused developmental arrest without necrosis, contained known electron transport chain inhibitors and many compounds of unknown mechanism of action. High-throughput transcriptional profiling revealed that these compounds are mechanistically related to one another. Metabolic and biochemical assays confirmed that all of the molecules that induced developmental arrest without necrosis inhibited the electron transport chain. These experiments demonstrate that the electron transport chain is the target of the natural products manassantin, sesquicillin, and arctigenin. The overlap between the zebrafish and transcriptional profiling screens was not perfect, indicating that multiple profiling screens are necessary to fully characterize molecules of unknown function. Together, zebrafish screening and transcriptional profiling represent sensitive and scalable approaches for identifying bioactive compounds and elucidating their mechanism of action.

A novel calcineurin-independent activity of cyclosporin A in Saccharomyces cerevisiae

Fungi rely on regulatory networks to coordinate sensing of environmental stress with initiation of responses crucial for survival. Antifungal drugs are a specific type of environmental stress with broad clinical relevance. Small molecules with antifungal activity are ubiquitous in the environment, and are produced by a myriad of microbes in competitive natural communities. The echinocandins are fungal fermentation products and the most recently developed class of antifungals, with those in clinical use being semisynthetic derivatives that target the fungal cell wall by inhibiting 1,3-β-D-glucan synthase. Recent studies implicate the protein phosphatase calcineurin as a key regulator of cellular stress responses required for fungal survival of echinocandin-induced cell wall stress. Pharmacological inhibition of calcineurin can be achieved using the natural product and immunosuppressive drug cyclosporin A, which inhibits calcineurin by binding to the immunophilin Cpr1. This drug-protein complex inhibits the interaction between the regulatory and catalytic subunits of calcineurin, an interaction necessary for calcineurin function. Here, we report on potent activity of cyclosporin A when combined with the echinocandin micafungin against the model yeast Saccharomyces cerevisiae that is independent of its known mechanism of action of calcineurin inhibition. This calcineurin-independent synergy does not involve any of the 12 immunophilins known in yeast, individually or in combination, and is not mediated by any of the multidrug transporters encoded or controlled by YOR1, SNQ2, PDR5, PDR10, PDR11, YCF1, PDR15, ADP1, VMR1, NFT1, BPT1, YBT1, YNR070w, YOL075c, AUS1, PDR12, PDR1 and/or PDR3. Genome-wide haploinsufficiency profiling (HIP) and homozygous deletion profiling (HOP) strongly implicate the cell wall biosynthesis and integrity pathways as being central to the calcineurin-independent activity of cyclosporin A. Thus, systems level chemical genomic approaches implicate key cellular pathways in a novel mechanism of antifungal drug synergy.

Lysosomal disruption preferentially targets acute myeloid leukemia cells and progenitors

Despite efforts to understand and treat acute myeloid leukemia (AML), there remains a need for more comprehensive therapies to prevent AML-associated relapses. To identify new therapeutic strategies for AML, we screened a library of on- and off-patent drugs and identified the antimalarial agent mefloquine as a compound that selectively kills AML cells and AML stem cells in a panel of leukemia cell lines and in mice. Using a yeast genome-wide functional screen for mefloquine sensitizers, we identified genes associated with the yeast vacuole, the homolog of the mammalian lysosome. Consistent with this, we determined that mefloquine disrupts lysosomes, directly permeabilizes the lysosome membrane, and releases cathepsins into the cytosol. Knockdown of the lysosomal membrane proteins LAMP1 and LAMP2 resulted in decreased cell viability, as did treatment of AML cells with known lysosome disrupters. Highlighting a potential therapeutic rationale for this strategy, leukemic cells had significantly larger lysosomes compared with normal cells, and leukemia-initiating cells overexpressed lysosomal biogenesis genes. These results demonstrate that lysosomal disruption preferentially targets AML cells and AML progenitor cells, providing a rationale for testing lysosomal disruption as a novel therapeutic strategy for AML.

Discovering thiamine transporters as targets of chloroquine using a novel functional genomics strategy

Chloroquine (CQ) and other quinoline-containing antimalarials are important drugs with many therapeutic benefits as well as adverse effects. However, the molecular targets underlying most such effects are largely unknown. By taking a novel functional genomics strategy, which employs a unique combination of genome-wide drug-gene synthetic lethality (DGSL), gene-gene synthetic lethality (GGSL), and dosage suppression (DS) screens in the model organism Saccharomyces cerevisiae and is thus termed SL/DS for simplicity, we found that CQ inhibits the thiamine transporters Thi7, Nrt1, and Thi72 in yeast. We first discovered a thi3Δ mutant as hypersensitive to CQ using a genome-wide DGSL analysis. Using genome-wide GGSL and DS screens, we then found that a thi7Δ mutation confers severe growth defect in the thi3Δ mutant and that THI7 overexpression suppresses CQ-hypersensitivity of this mutant. We subsequently showed that CQ inhibits the functions of Thi7 and its homologues Nrt1 and Thi72. In particular, the transporter activity of wild-type Thi7 but not a CQ-resistant mutant (Thi7^T287N) was completely inhibited by the drug. Similar effects were also observed with other quinoline-containing antimalarials. In addition, CQ completely inhibited a human thiamine transporter (SLC19A3) expressed in yeast and significantly inhibited thiamine uptake in cultured human cell lines. Therefore, inhibition of thiamine uptake is a conserved mechanism of action of CQ. This study also demonstrated SL/DS as a uniquely effective methodology for discovering drug targets.

By using a novel SL/DS methodology in the model organism yeast, we discovered that the antimalarial drug CQ inhibits thiamine transporters and consequently causes thiamine (vitamin B1) deficiency and growth defects. This mechanism of action (MOA) is conserved in human cells and possibly also in other organisms. Given that both thiamine deficiency and treatment with CQ cause retinal, neurological, and cardiovascular disorders in humans, our results suggest that thiamine deficiency might be a root cause of some of CQ's adverse effects, which might be preventable with concomitant dietary thiamine supplementation. Such a MOA by CQ could also be responsible for its therapeutic effects against malarial parasites, which need exogenous thiamine for survival. Such a possibility needs to be investigated before dietary thiamine supplementation can be used to prevent CQ's adverse effects.

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.

Selective and specific inhibition of the plasmodium falciparum lysyl-tRNA synthetase by the fungal secondary metabolite cladosporin

With renewed calls for malaria eradication, next-generation antimalarials need be active against drug-resistant parasites and efficacious against both liver- and blood-stage infections. We screened a natural product library to identify inhibitors of Plasmodium falciparum blood- and liver-stage proliferation. Cladosporin, a fungal secondary metabolite whose target and mechanism of action are not known for any species, was identified as having potent, nanomolar, antiparasitic activity against both blood and liver stages. Using postgenomic methods, including a yeast deletion strains collection, we show that cladosporin specifically inhibits protein synthesis by directly targeting P. falciparum cytosolic lysyl-tRNA synthetase. Further, cladosporin is >100-fold more potent against parasite lysyl-tRNA synthetase relative to the human enzyme, which is conferred by the identity of two amino acids within the enzyme active site. Our data indicate that lysyl-tRNA synthetase is an attractive, druggable, antimalarial target that can be selectively inhibited.

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.

Chemical genetics: budding yeast as a platform for drug discovery and mapping of genetic pathways

The budding yeast Saccharomyces cerevisiae is a widely used model organism, and yeast genetic methods are powerful tools for discovery of novel functions of genes. Recent advancements in chemical-genetics and chemical-genomics have opened new avenues for development of clinically relevant drug treatments. Systematic mapping of genetic networks by high-throughput chemical-genetic screens have given extensive insight in connections between genetic pathways. Here, I review some of the recent developments in chemical-genetic techniques in budding yeast.

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.

CMRF: analyzing differential gene regulation in two group perturbation experiments

BACKGROUND

Microarray experiments often measure expressions of genes taken from sample tissues in the presence of external perturbations such as medication, radiation, or disease. The external perturbation can change the expressions of some genes directly or indirectly through gene interaction network. In this paper, we focus on an important class of such microarray experiments that inherently have two groups of tissue samples. When such different groups exist, the changes in expressions for some of the genes after the perturbation can be different between the two groups. It is not only important to identify the genes that respond differently across the two groups, but also to mine the reason behind this differential response. In this paper, we aim to identify the cause of this differential behavior of genes, whether because of the perturbation or due to interactions with other genes.

RESULTS

We propose a new probabilistic Bayesian method CMRF based on Markov Random Field to identify such genes. CMRF leverages the information about gene interactions as the prior of the model. We compare the accuracy of CMRF with SSEM and Student's t test and our old method SMRF on semi-synthetic dataset generated from microarray data. CMRF obtains high accuracy and outperforms all the other three methods. We also conduct a statistical significance test using a parametric noise based experiment to evaluate the accuracy of our method. In this experiment, CMRF generates significant regions of confidence for various parameter settings.

CONCLUSIONS

In this paper, we solved the problem of finding primarily differentially regulated genes in the presence of external perturbations when the data is sampled from two groups. The probabilistic Bayesian method CMRF based on Markov Random Field incorporates dependency structure of the gene networks as the prior to the model. Experimental results on synthetic and real datasets demonstrated the superiority of CMRF compared to other simple techniques.

Compound prioritization methods increase rates of chemical probe discovery in model organisms

Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided.

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).

Using transcriptome sequencing to identify mechanisms of drug action and resistance

Determining mechanisms of drug action in human cells remains a major challenge. Here we describe an approach in which multiple-drug-resistant clones are isolated and transcriptome sequencing is used to find mutations in each clone. Further analysis of mutations common to more than one clone can identify a drug's physiological target and indirect resistance mechanisms, as indicated by our proof-of-concept studies of the cytotoxic anticancer drugs BI 2536 and bortezomib.

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.

Identification and validation of protein targets of bioactive small molecules

Identification and validation of protein targets of bioactive small molecules is an important problem in chemical biology and drug discovery. Currently, no single method is satisfactory for this task. Here, we provide an overview of common methods for target identification and validation that historically were most successful. We have classified for the first time the existing methods into two distinct and complementary types, the 'top-down' and 'bottom-up' approaches. In a typical top-down approach, the cellular phenotype is used as a starting point and the molecular target is approached through systematic narrowing down of possibilities by taking advantage of the detailed existing knowledge of cellular pathways and processes. In contrast, the bottom-up approach entails the direct detection and identification of the molecular targets using affinity-based or genetic methods. A special emphasis is placed on target validation, including correlation analysis and genetic methods, as this area is often ignored despite its importance.

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.

Genetic and proteomic approaches to identify cancer drug targets

While target-based small-molecule discovery has taken centre-stage in the pharmaceutical industry, there are many cancer-promoting proteins not easily addressed with a traditional target-based screening approach. In order to address this problem, as well as to identify modulators of biological states in the absence of knowing the protein target of the state switch, alternative phenotypic screening approaches, such as gene expression-based and high-content imaging, have been developed. With this renewed interest in phenotypic screening, however, comes the challenge of identifying the binding protein target(s) of small-molecule hits. Emerging technologies have the potential to improve the process of target identification. In this review, we discuss the application of genomic (gene expression-based), genetic (short hairpin RNA and open reading frame screening), and proteomic approaches to protein target identification.

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.

Power cut: inhibiting mitochondrial translation to target leukemia

In this issue of Cancer Cell, Skrtic et al. demonstrate that inhibition of mitochondrial ribosomes with tigecycline, a known antimicrobial, selectively kills leukemia cells. This finding highlights the metabolic susceptibility of leukemia cells to mitochondrial translational inhibition and identifies a compound with significant efficacy in an in vivo preclinical model.

Inhibition of mitochondrial translation as a therapeutic strategy for human acute myeloid leukemia

To identify FDA-approved agents targeting leukemic cells, we performed a chemical screen on two human leukemic cell lines and identified the antimicrobial tigecycline. A genome-wide screen in yeast identified mitochondrial translation inhibition as the mechanism of tigecycline-mediated lethality. Tigecycline selectively killed leukemia stem and progenitor cells compared to their normal counterparts and also showed antileukemic activity in mouse models of human leukemia. ShRNA-mediated knockdown of EF-Tu mitochondrial translation factor in leukemic cells reproduced the antileukemia activity of tigecycline. These effects were derivative of mitochondrial biogenesis that, together with an increased basal oxygen consumption, proved to be enhanced in AML versus normal hematopoietic cells and were also important for their difference in tigecycline sensitivity.

Target identification and validation of novel antimalarials

It has been recognized that new antimalarials with a novel mode of action are critical to combat the continued emergence and dissemination of drug-resistant parasites that threaten the efficacy of current malaria treatments. Thus, recent high-throughput screening campaigns have been initiated using asexual intraerythrocytic stage cell-based assays of Plasmodium falciparum. These have led to the unprecedented identification of over 10,000 new antimalarial compounds. Inherently, novel compounds identified by cell-based assays will have poorly defined modes of action. While some of these compounds may have recognizable targets, the majority of cell-based hits are comprised of unique chemical scaffolds usually lacking cross-resistance with known drugs. It is likely that these novel antimalarial scaffolds will reveal new targets. A challenge for the community will be to assign these small molecules to their targets. In this article, we review methodologies to assist in the determination of a compound's mode of action.

Inferring regulatory mechanisms from patterns of evolutionary divergence

The number of sequenced species is increasing at a staggering rate, calling for new approaches for incorporating evolutionary information in the study of biological mechanisms. Evolutionary conservation is widely used for assigning a function to new proteins and for predicting functional coding or non-coding sequences. Here, we argue for a complementary approach that focuses on the divergence of regulatory programs. Regulatory mechanisms can be learned from patterns of evolutionary divergence in regulatory properties such as gene expression, transcription factor binding or nucleosome positioning. We review examples of this concept using yeast as a model system, and highlight a hybrid-based approach that is highly instrumental in this analysis.

Sampangine inhibits heme biosynthesis in both yeast and human

The azaoxoaporphine alkaloid sampangine exhibits strong antiproliferation activity in various organisms. Previous studies suggested that it somehow affects heme metabolism and stimulates production of reactive oxygen species (ROS). In this study, we show that inhibition of heme biosynthesis is the primary mechanism of action by sampangine and that increases in the levels of reactive oxygen species are secondary to heme deficiency. We directly demonstrate that sampangine inhibits heme synthesis in the yeast Saccharomyces cerevisiae. It also causes accumulation of uroporphyrinogen and its decarboxylated derivatives, intermediate products of the heme biosynthesis pathway. Our results also suggest that sampangine likely works through an unusual mechanism-by hyperactivating uroporhyrinogen III synthase-to inhibit heme biosynthesis. We also show that the inhibitory effect of sampangine on heme synthesis is conserved in human cells. This study also reveals a surprising essential role for the interaction between the mitochondrial ATP synthase and the electron transport chain.

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.

Chemical genetics

Chemical genetics can be defined as the study of biological systems using small molecule tools. Cell permeable and selective small molecules modulate gene product function rapidly, reversibly and can be administered conditionally in either a cellular or organismal context. The small molecule approach provides exacting temporal and quantitative control and is therefore an extremely powerful tool for dissecting biological processes. This tutorial review has been written to introduce the subject to a broad audience and highlights recent developments within the field in four key areas of biology: modulating protein-protein interactions, malaria research, hepatitis C virus research, and disrupting RNA interference pathways.

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.

Regulation of white and opaque cell-type formation in Candida albicans by Rtt109 and Hst3

How different cell types with the same genotype are formed and heritability maintained is a fundamental question in biology. We utilized white-opaque switching in Candida albicans as a system to study mechanisms of cell-type formation and maintenance. Each cell type has tractable characters, which are maintained over many cell divisions. Cell-type specification is under the control of interlocking transcriptional feedback loops, with Wor1 being the master regulator of the opaque cell type. Here we show that deletion of RTT109, encoding the acetyltransferase for histone H3K56, impairs stochastic and environmentally stimulated white-opaque switching. Ectopic expression of WOR1 mostly bypasses the requirement for RTT109, but opaque cells lacking RTT109 cannot be maintained. We have also discovered that nicotinamide induces opaque cell formation, and this activity of nicotinamide requires RTT109. Reducing the copy number of HST3, which encodes the H3K56 deacetylase, also leads to increased opaque formation. We further show that the Hst3 level is downregulated in the presence of genotoxins and ectopic expression of HST3 blocks genotoxin induced switching. This finding links genotoxin induced switching to Hst3 regulation. Together, these findings suggest RTT109 and HST3 genes play an important role in the regulation of white-opaque switching in C. albicans.

The end game of chemical genetics: target identification

The use of classical genetic and molecular biology methods along with the sequencing of many genomes has proven crucial for elucidating complex biological processes. Despite being invaluable tools, their limitations have led to a search for more versatile alternatives and, thus, to the use of small molecules. Chemical genetics is a rapidly emerging field that uses small-molecule techniques to probe biological systems and is composed of three parts: natural product or small-molecule libraries, phenotypic screening and target identification. Currently, the biggest hurdle in the overall process of chemical genetics is target identification. Efforts to overcome this obstacle have led to advances in the areas of affinity chromatography, yeast haploinsufficiency, complementary DNA (cDNA) overexpression, DNA microarray, small-molecule microarray and RNA interference (RNAi) technologies. While these technologies continue to undergo further optimization, they have been integral in the identification and/or confirmation of many cellular targets and have seen an increase in applications to the drug-development process.

Discovering the targets of drugs via computational systems biology

Computational systems biology is empowering the study of drug action. Studies on biological effects of chemical compounds have increased in scale and accessibility, allowing integration with other large-scale experimental data types. Here, we review computational approaches for elucidating the mechanisms of both intended and undesirable effects of drugs, with the collective potential to change the nature of drug discovery and pharmacological therapy.

The Budding Yeast "Saccharomyces cerevisiae" as a Drug Discovery Tool to Identify Plant-Derived Natural Products with Anti-Proliferative Properties

The budding yeast Saccharomyces cerevisiae is a valuable system to study cell-cycle regulation, which is defective in cancer cells. Due to the highly conserved nature of the cell-cycle machinery between yeast and humans, yeast studies are directly relevant to anticancer-drug discovery. The budding yeast is also an excellent model system for identifying and studying antifungal compounds because of the functional conservation of fungal genes. Moreover, yeast studies have also contributed greatly to our understanding of the biological targets and modes of action of bioactive compounds. Understanding the mechanism of action of clinically relevant compounds is essential for the design of improved second-generation molecules. Here we describe our methodology for screening a library of plant-derived natural products in yeast in order to identify and characterize new compounds with anti-proliferative properties.

Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole

The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.

The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.

Chemical screens with a library enriched for known drugs identified a diverse set of 148 compounds that potentiated the action of the antifungal drug fluconazole against the fungal pathogens Cryptococcus neoformans, Cryptococcus gattii and Candida albicans, and the model yeast Saccharomyces cerevisiae, often in a species-specific manner.

Chemogenomic profiles of six confirmed hits in S. cerevisiae revealed different modes of action and enabled the prediction of additional synergistic combinations; three-way synergistic interactions exhibited even stronger synergies at low doses of fluconazole.

The synergistic combination of fluconazole and the antidepressant sertraline was active against fluconazole-resistant clinical fungal isolates and in an in vivo model of Cryptococcal infection.

Rising fungal infection rates, especially among immune-suppressed individuals, represent a serious clinical challenge (Gullo, 2009). Cancer, organ transplant and HIV patients, for example, often succumb to opportunistic fungal pathogens. The limited repertoire of approved antifungal agents and emerging drug resistance in the clinic further complicate the effective treatment of systemic fungal infections. At the molecular level, the paucity of fungal-specific essential targets arises from the conserved nature of cellular functions from yeast to humans, as well as from the fact that many essential yeast genes can confer viability at a fraction of wild-type dosage (Yan et al, 2009). Although only ∼1100 of the ∼6000 genes in yeast are essential, almost all genes become essential in specific genetic backgrounds in which another non-essential gene has been deleted or otherwise attenuated, an effect termed synthetic lethality (Tong et al, 2001). Genome-scale surveys suggest that over 200 000 binary synthetic lethal gene combinations dominate the yeast genetic landscape (Costanzo et al, 2010). The genetic buffering phenomenon is also manifest as a plethora of differential chemical–genetic interactions in the presence of sublethal doses of bioactive compounds (Hillenmeyer et al, 2008). These observations frame the difficulty of interdicting network functions in eukaryotic pathogens with single agent therapeutics. At the same time, however, this genetic network organization suggests that judicious combinations of small molecule inhibitors of both essential and non-essential targets may elicit additive or synergistic effects on cell growth (Sharom et al, 2004; Lehar et al, 2008). Unbiased screens for drugs that synergistically enhance a specific bioactive effect, but which are not themselves individually active—termed a syncretic combination—are one means to substantially elaborate chemical space (Keith et al, 2005). Indeed, compounds that enhance the activity of known agents in model yeast and cancer cell line systems have been identified both by focused small molecule library screens and by computational methods (Borisy et al, 2003; Lehar et al, 2007; Nelander et al, 2008; Jansen et al, 2009; Zinner et al, 2009).

To extend the stratagem of chemical synthetic lethality to clinically relevant fungal pathogens, we screened a bioactive library of known drugs for synergistic enhancers of the widely used fungistatic drug fluconazole against the clinically relevant pathogens C. albicans, C. neoformans and C. gattii, as well as the genetically tractable budding yeast S. cerevisiae. Fluconazole is an azole drug that inhibits lanosterol 14α-demethylase, the gene product of ERG11, an essential cytochrome P450 enzyme in the ergosterol biosynthetic pathway (Groll et al, 1998). We identified 148 drugs that potentiate the antifungal action of fluconazole against the four species. These syncretic compounds had not been previously recognized in the clinic as antifungal agents, and many acted in a species-specific manner, often in a potent fungicidal manner.

To understand the mechanisms of synergism, we interrogated six syncretic drugs—trifluoperazine, tamoxifen, clomiphene, sertraline, suloctidil and L-cycloserine—in genome-wide chemogenomic profiles of the S. cerevisiae deletion strain collection (Giaever et al, 1999). These profiles revealed that membrane, vesicle trafficking and lipid biosynthesis pathways are targeted by five of the synergizers, whereas the sphingolipid biosynthesis pathway is targeted by L-cycloserine. Cell biological assays confirmed the predicted membrane disruption effects of the former group of compounds, which may perturb ergosterol metabolism, impair fluconazole export by drug efflux pumps and/or affect active import of fluconazole (Kuo et al, 2010; Mansfield et al, 2010). Based on the integration of chemical–genetic and genetic interaction space, a signature set of deletion strains that are sensitive to the membrane active synergizers correctly predicted additional drug synergies with fluconazole. Similarly, the L-cycloserine chemogenomic profile correctly predicted a synergistic interaction between fluconazole and myriocin, another inhibitor of sphingolipid biosynthesis. The structure of genetic networks suggests that it should be possible to devise higher order drug combinations with even greater selectivity and potency (Sharom et al, 2004). In an initial test of this concept, we found that the combination of a non-synergistic pair drawn from the membrane active and sphingolipid target classes exhibited potent three-way synergism with a low dose of fluconazole. Finally, the combination of sertraline and fluconazole was active in a G. mellonella model of Cryptococcal infection, and was also efficacious against fluconazole-resistant clinical isolates of C. albicans and C. glabrata.

Collectively, these results demonstrate that the combinatorial redeployment of known drugs defines a powerful antifungal strategy and establish a number of potential lead combinations for future clinical assessment.

Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off-patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species- or genus-specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole-resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.

Confronting the challenges of natural product-based antifungal discovery

Starting with the discovery of penicillin, the pharmaceutical industry has relied extensively on natural products (NPs) as an unparalleled source of bioactive small molecules suitable for antibiotic development. However, the discovery of structurally novel and chemically tractable NPs with suitable pharmacological properties as antibiotic leads has waned in recent decades. Today, the repetitive "rediscovery" of previously known NP classes with limited antibiotic lead potential dominates most industrial efforts. This limited productivity, exacerbated by the significant financial and resource requirements of such activities, has led to a broad de-emphasis of NP research by most pharmaceutical companies, including most recently Merck. Here we review our strategies--both technological and philosophical--in addressing current antifungal discovery bottlenecks in target identification and validation and how such efforts may improve NP-based antimicrobial discoveries when aligned with NP screening and dereplication.

Identification, in vitro activity and mode of action of phosphoinositide-dependent-1 kinase inhibitors as antifungal molecules

Although protein kinases have recently emerged as important drug targets, the anti-infective potential of protein kinase inhibitors has not been developed extensively. We identified the mammalian PDK1 inhibitor KP-372-1 as a potent antifungal molecule with activity against yeast and fungal biofilms using a screening strategy for protein kinase inhibitors that block the cell wall stress response in yeast. Genetic and biochemical studies indicate that KP-372-1 inhibits fungal PDK1 orthologs (Pkh kinases) as part of its mode of action and support a role for Pkh kinases in eisosome assembly. Two other structurally distinct molecules that inhibit PDK1, OSU-03012 and UCN-01, also have antifungal activity. Taken together, these data indicate that fungal PDK1 orthologs are promising targets for new antifungal drug development.

A comprehensive platform for highly multiplexed mammalian functional genetic screens

Background

Genome-wide screening in human and mouse cells using RNA interference and open reading frame over-expression libraries is rapidly becoming a viable experimental approach for many research labs. There are a variety of gene expression modulation libraries commercially available, however, detailed and validated protocols as well as the reagents necessary for deconvolving genome-scale gene screens using these libraries are lacking. As a solution, we designed a comprehensive platform for highly multiplexed functional genetic screens in human, mouse and yeast cells using popular, commercially available gene modulation libraries. The Gene Modulation Array Platform (GMAP) is a single microarray-based detection solution for deconvolution of loss and gain-of-function pooled screens.

Results

Experiments with specially constructed lentiviral-based plasmid pools containing ~78,000 shRNAs demonstrated that the GMAP is capable of deconvolving genome-wide shRNA "dropout" screens. Further experiments with a larger, ~90,000 shRNA pool demonstrate that equivalent results are obtained from plasmid pools and from genomic DNA derived from lentivirus infected cells. Parallel testing of large shRNA pools using GMAP and next-generation sequencing methods revealed that the two methods provide valid and complementary approaches to deconvolution of genome-wide shRNA screens. Additional experiments demonstrated that GMAP is equivalent to similar microarray-based products when used for deconvolution of open reading frame over-expression screens.

Conclusion

Herein, we demonstrate four major applications for the GMAP resource, including deconvolution of pooled RNAi screens in cells with at least 90,000 distinct shRNAs. We also provide detailed methodologies for pooled shRNA screen readout using GMAP and compare next-generation sequencing to GMAP (i.e. microarray) based deconvolution methods.

Probing bacterial pathogenesis with genetics, genomics, and chemical biology: past, present, and future approaches

Classical genetic approaches for studying bacterial pathogenesis have provided a solid foundation for our current understanding of microbial physiology and the interactions between pathogen and host. During the past decade however, advances in several arenas have expanded the ways in which the biology of pathogens can be studied. This review discussed the impact of these advances on bacterial genetics, including the application of genomics and chemical biology to the study of pathogenesis.

The synthetic genetic interaction network reveals small molecules that target specific pathways in Sacchromyces cerevisiae

High-throughput elucidation of synthetic genetic interactions (SGIs) has contributed to a systems-level understanding of genetic robustness and fault-tolerance encoded in the genome. Pathway targets of various compounds have been predicted by comparing chemical-genetic synthetic interactions to a network of SGIs. We demonstrate that the SGI network can also be used in a powerful reverse pathway-to-drug approach for identifying compounds that target specific pathways of interest. Using the SGI network, the method identifies an indicator gene that may serve as a good candidate for screening a library of compounds. The indicator gene is selected so that compounds found to produce sensitivity in mutants deleted for the indicator gene are likely to abrogate the target pathway. We tested the utility of the SGI network for pathway-to-drug discovery using the DNA damage checkpoint as the target pathway. An analysis of the compendium of synthetic lethal interactions in yeast showed that superoxide dismutase 1 (SOD1) has significant SGI connectivity with a large subset of DNA damage checkpoint and repair (DDCR) genes in Saccharomyces cerevisiae, and minimal SGIs with non-DDCR genes. We screened a sod1Δ strain against three National Cancer Institute (NCI) compound libraries using a soft agar high-throughput halo assay. Fifteen compounds out of ∼3100 screened showed selective toxicity toward sod1Δ relative to the isogenic wild type (wt) strain. One of these, 1A08, caused a transient increase in growth in the presence of sublethal doses of DNA damaging agents, suggesting that 1A08 inhibits DDCR signaling in yeast. Genome-wide screening of 1A08 against the library of viable homozygous deletion mutants further supported DDCR as the relevant targeted pathway of 1A08. When assayed in human HCT-116 colorectal cancer cells, 1A08 caused DNA-damage resistant DNA synthesis and blocked the DNA-damage checkpoint selectively in S-phase.