Genomic profiling of drug sensitivities via induced haploinsufficiency

Advances in exploring the therapeutic potential of marine natural products

Marine natural products represent novel and diverse chemotypes that serve as templates for the discovery and development of therapeutic agents with distinct mechanisms of action. These genetically encoded compounds produced by an evolutionary optimized biosynthetic machinery are usually quite complex and can be difficult to recreate in the laboratory. The isolation from the source organism results in limited amount of material; however, the development of advanced NMR technologies and dereplication strategies has enabled the structure elucidation on small scale. In order to rigorously explore the therapeutic potential of marine natural products and advance them further, the biological characterization has to keep pace with the chemical characterization. The limited marine natural product supply has been a serious challenge for thorough investigation of the biological targets. Several marine drugs have reached the markets or are in clinical trials, where those challenges have been overcome, including through the development of scalable syntheses. However, the identification of mechanisms of action of marine natural products early in the discovery process is potentially game changing, since effectively linking marine natural products to potential therapeutic applications in turn triggers motivation to tackle challenging syntheses and solve the supply problem. An increasing number of sensitive technologies and methods have been developed in recent years, some of which have been successfully applied to marine natural products, increasing the value of these compounds with respect to their biomedical utility. In this review, we discuss advances in overcoming the bottlenecks in marine natural product research, emphasizing on the development and advances of diverse target identification technologies applicable for marine natural product research.

Identifying chemogenetic interactions from CRISPR screens with drugZ

BACKGROUND

Chemogenetic profiling enables the identification of gene mutations that enhance or suppress the activity of chemical compounds. This knowledge provides insights into drug mechanism of action, genetic vulnerabilities, and resistance mechanisms, all of which may help stratify patient populations and improve drug efficacy. CRISPR-based screening enables sensitive detection of drug-gene interactions directly in human cells, but until recently has primarily been used to screen only for resistance mechanisms.

RESULTS

We present drugZ, an algorithm for identifying both synergistic and suppressor chemogenetic interactions from CRISPR screens. DrugZ identifies synthetic lethal interactions between PARP inhibitors and both known and novel members of the DNA damage repair pathway, confirms KEAP1 loss as a resistance factor for ERK inhibitors in oncogenic KRAS backgrounds, and defines the genetic context for temozolomide activity.

CONCLUSIONS

DrugZ is an open-source Python software for the analysis of genome-scale drug modifier screens. The software accurately identifies genetic perturbations that enhance or suppress drug activity. Interestingly, analysis of new and previously published data reveals tumor suppressor genes are drug-agnostic resistance genes in drug modifier screens. The software is available at github.com/hart-lab/drugz .

Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance

Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness–expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.

Large-scale chemical-genetics yields new M. tuberculosis inhibitor classes

New antibiotics are needed to combat rising levels of resistance, with new Mycobacterium tuberculosis (Mtb) drugs having the highest priority. However, conventional whole-cell and biochemical antibiotic screens have failed. Here we develop a strategy termed PROSPECT (primary screening of strains to prioritize expanded chemistry and targets), in which we screen compounds against pools of strains depleted of essential bacterial targets. We engineered strains that target 474 essential Mtb genes and screened pools of 100-150 strains against activity-enriched and unbiased compound libraries, probing more than 8.5 million chemical-genetic interactions. Primary screens identified over tenfold more hits than screening wild-type Mtb alone, with chemical-genetic interactions providing immediate, direct target insights. We identified over 40 compounds that target DNA gyrase, the cell wall, tryptophan, folate biosynthesis and RNA polymerase, as well as inhibitors that target EfpA. Chemical optimization yielded EfpA inhibitors with potent wild-type activity, thus demonstrating the ability of PROSPECT to yield inhibitors against targets that would have eluded conventional drug discovery.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

(Un)folding mechanisms of adaptation to ER stress: lessons from aneuploidy

During stress, accumulation of misfolded proteins in the endoplasmic reticulum (ER) triggers activation of the adaptive mechanisms that restore protein homeostasis. One mechanism that eukaryotic cells use to respond to ER stress is through activation of the unfolded protein response (UPR) signaling pathway, which initiates degradation of misfolded proteins and leads to inhibition of translation and increased expression of chaperones and oxidative folding components that enhance ER protein folding capacity. However, the mechanisms of adaptation to ER stress are not limited to the UPR. Using yeast Saccharomyces cerevisiae, we recently discovered that the protein folding burden in the ER can be alleviated in a UPR-independent manner through duplication of whole chromosomes containing ER stress-protective genes. Here we discuss our findings and their implication to our understanding of the mechanisms by which cells respond to protein misfolding in the ER.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Fungal Lanosterol 14α-demethylase: A target for next-generation antifungal design

The cytochrome P450 enzyme lanosterol 14α-demethylase (LDM) is the target of the azole antifungals used widely in medicine and agriculture as prophylaxis or treatments of infections or diseases caused by fungal pathogens. These drugs and agrochemicals contain an imidazole, triazole or tetrazole substituent, with one of the nitrogens in the azole ring coordinating as the sixth axial ligand to the LDM heme iron. Structural studies show that this membrane bound enzyme contains a relatively rigid ligand binding pocket comprised of a deeply buried heme-containing active site together with a substrate entry channel and putative product exit channel that reach to the membrane. Within the ligand binding pocket the azole antifungals have additional affinity determining interactions with hydrophobic side-chains, the polypeptide backbone and via water-mediated hydrogen bond networks. This review will describe the tools that can be used to identify and characterise the next generation of antifungals targeting LDM, with the goal of obtaining highly potent broad-spectrum fungicides that will be able to avoid target and drug efflux mediated antifungal resistance.

Can Saccharomyces cerevisiae keep up as a model system in fungal azole susceptibility research?

The difficulty of manipulation and limited availability of genetic tools for use in many pathogenic fungi hamper fast and adequate investigation of cellular metabolism and consequent possibilities for antifungal therapies. S. cerevisiae is a model organism that is used to study many eukaryotic systems. In this review, we analyse the potency and relevance of this model system in investigating fungal susceptibility to azole drugs. Although many of the concepts apply to multiple pathogenic fungi, for the sake of simplicity, we will focus on the validity of using S. cerevisiae as a model organism for two Candida species, C. albicans and C. glabrata. Apart from the general benefits, we explore how S. cerevisiae can specifically be used to improve our knowledge on azole drug resistance and enables fast and efficient screening for novel drug targets in combinatorial therapy. We consider the shortcomings of the model system, yet conclude that it is still opportune to use S. cerevisiae as a model system for pathogenic fungi in this era.

A CRISPR Interference Platform for Efficient Genetic Repression in Candida albicans

Fungal pathogens are emerging as an important cause of human disease, and Candida albicans is among the most common causative agents of fungal infections. Studying this fungal pathogen is of the utmost importance and necessitates the development of molecular technologies to perform comprehensive genetic and functional genomic analysis. Here, we designed and developed a novel clustered regularly interspaced short palindromic repeat interference (CRISPRi) system for targeted genetic repression in C. albicans We engineered a nuclease-dead Cas9 (dCas9) construct that, paired with a guide RNA targeted to the promoter of an endogenous gene, is capable of targeting that gene for transcriptional repression. We further optimized a favorable promoter locus to achieve repression and demonstrated that fusion of dCas9 to an Mxi1 repressor domain was able to further enhance transcriptional repression. Finally, we demonstrated the application of this CRISPRi system through genetic repression of the essential molecular chaperone HSP90 This is the first demonstration of a functional CRISPRi repression system in C. albicans, and this valuable technology will enable many future applications in this critical fungal pathogen.IMPORTANCE Fungal pathogens are an increasingly important cause of human disease and mortality, and Candida albicans is among the most common causes of fungal disease. Studying this important fungal pathogen requires a comprehensive genetic toolkit to establish how different genetic factors play roles in the biology and virulence of this pathogen. Here, we developed a CRISPR-based genetic regulation platform to achieve targeted repression of C. albicans genes. This CRISPR interference (CRISPRi) technology exploits a nuclease-dead Cas9 protein (dCas9) fused to transcriptional repressors. The dCas9 fusion proteins pair with a guide RNA to target genetic promoter regions and to repress expression from these genes. We demonstrated the functionality of this system for repression in C. albicans and show that we can apply this technology to repress essential genes. Taking the results together, this work presents a new technology for efficient genetic repression in C. albicans, with important applications for genetic analysis in this fungal pathogen.

CRISPR/Cas9-Based Chemogenomic Profiling in Mammalian Cells

Chemogenomic profiling is a powerful and unbiased approach to elucidate pharmacological targets and the mechanism of bioactive compounds. It is based on identifying cellular hypersensitivity and resistance caused by individual gene modulations with genome-wide coverage. Due to the requirement of bar-coded, genome-wide deletion collections, high-resolution experiments of this nature have historically been limited to fungal systems. Pooled RNAi reagents have enabled similar attempts in mammalian cells but efforts have been hampered by significant off-target effects and experimental noise. The CRISPR/Cas9 system for the first time enables precise DNA editing at defined loci in a genome-wide fashion. Here we present the detailed protocol that leverages the CRISPR/Cas9 system for chemogenomic profiling and target identification of diverse chemical probes.

Inducible Cell Fusion Permits Use of Competitive Fitness Profiling in the Human Pathogenic Fungus Aspergillus fumigatus

Antifungal agents directed against novel therapeutic targets are required for treating invasive, chronic, and allergic Aspergillus infections. Competitive fitness profiling technologies have been used in a number of bacterial and yeast systems to identify druggable targets; however, the development of similar systems in filamentous fungi is complicated by the fact that they undergo cell fusion and heterokaryosis. Here, we demonstrate that cell fusion in Aspergillus fumigatus under standard culture conditions is not predominately constitutive, as with most ascomycetes, but can be induced by a range of extracellular stressors. Using this knowledge, we have developed a barcode-free genetic profiling system that permits high-throughput parallel determination of strain fitness in a collection of diploid A. fumigatus mutants. We show that heterozygous cyp51A and arf2 null mutants have reduced fitness in the presence of itraconazole and brefeldin A, respectively, and a heterozygous atp17 null mutant is resistant to brefeldin A.

MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action

Summary

Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks.

Availability and implementation

MOSAIC is available at http://mosaic.cs.umn.edu.

Contact

hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.

How Surrogate and Chemical Genetics in Model Organisms Can Suggest Therapies for Human Genetic Diseases

Genetic diseases are both inherited and acquired. Many genetic diseases fall under the paradigm of orphan diseases, a disease found in < 1 in 2000 persons. With rapid and cost-effective genome sequencing becoming the norm, many causal mutations for genetic diseases are being rapidly determined. In this regard, model organisms are playing an important role in validating if specific mutations identified in patients drive the observed phenotype. An emerging challenge for model organism researchers is the application of genetic and chemical genetic platforms to discover drug targets and drugs/drug-like molecules for potential treatment options for patients with genetic disease. This review provides an overview of how model organisms have contributed to our understanding of genetic disease, with a focus on the roles of yeast and zebrafish in gene discovery and the identification of compounds that could potentially treat human genetic diseases.

Yeast Chemogenomic Profiling Reveals Iron Chelation To Be the Principle Cell Inhibitory Mode of Action of Gossypol

Gossypol is an inhibitor of eukaryotic cells with an undetermined mode of action. Here we show that the chemogenomic profile of gossypol is strikingly similar to that of the iron chelators deferasirox and desferricoprogen. Iron import channels Fet1 and Fet3 are prominent in all three profiles. Furthermore, yeast inhibited by gossypol and deferasirox is rescued by the addition of Fe²⁺. We propose that Fe²⁺ chelation is in fact the principle mode of action of gossypol.

Machine learning and image-based profiling in drug discovery

The increase in imaging throughput, new analytical frameworks and high-performance computational resources open new avenues for data-rich phenotypic profiling of small molecules in drug discovery. Image-based profiling assays assessing single-cell phenotypes have been used to explore mechanisms of action, target efficacy and toxicity of small molecules. Technological advances to generate large data sets together with new machine learning approaches for the analysis of high-dimensional profiling data create opportunities to improve many steps in drug discovery. In this review, we will discuss how recent studies applied machine learning approaches in functional profiling workflows with a focus on chemical genetics. While their utility in image-based screening and profiling is predictably evident, examples of novel insights beyond the status quo based on the applications of machine learning approaches are just beginning to emerge. To enable discoveries, future studies also need to develop methodologies that lower the entry barriers to high-throughput profiling experiments by streamlining image-based profiling assays and providing applications for advanced learning technologies such as easy to deploy deep neural networks.

Artificial Mating-Type Conversion and Repetitive Mating for Polyploid Generation

The yeast Saccharomyces cerevisiae is one of the best-understood biological systems and can produce numerous useful compounds. Sexual hybridization (mating) can drive dramatic evolution of yeasts by the inheritance of half of the parental genomic information from each cell. Unfortunately, half of the parental genomic information is lost in individual cells in the next generation. Additionally, recombination of homologous chromosomes during meiosis gives rise to diversity in the next generation; hence, it is commonly employed to identify targets from diverse cell populations, based on the mating machinery. Here, we established a system for generating polyploids that inherit all genetic information from the parental strains via artificial mating-type conversion and repetitive mating. We prepared α-type haploid strains whose chromosomes were tagged with genes encoding fluorescent proteins or transcriptional factors. Only the mating-type locus was successfully converted from α-type to a-type sequence by the endonuclease Ho, and the resultant a-type cells mated with each α-type haploid to yield an a/α-type diploid strain with all genetic information from both parental strains. Importantly, we repeatedly converted the mating-type of polyploid cells to obtain a-type cells capable of mating with α-type cells. This approach can potentially facilitate yeast-strain development with unparalleled versatility, utilizing vast available resources.

Elucidating drug targets and mechanisms of action by genetic screens in mammalian cells

Phenotypic screening is a powerful approach to discover small molecules with desired effects on biological systems, which can then be developed into therapeutic drugs. The identification of the target and mechanism of action of compounds discovered in phenotypic screens remains a major challenge. This feature article describes the use of genetic tools to reveal drug targets and mechanisms in mammalian cells. Until recently, RNA interference was the method of choice for such studies. Here, we highlight very recent additions to the genetic toolkit in mammalian cells, including CRISPR, CRISPR interference, and CRISPR activation, and illustrate their usefulness for drug target identification.

Biosynthesis of fragin is controlled by a novel quorum sensing signal

Members of the diazeniumdiolate class of natural compounds show potential for drug development because of their antifungal, antibacterial, antiviral, and antitumor activities. Yet, their biosynthesis has remained elusive to date. Here, we identify a gene cluster directing the biosynthesis of the diazeniumdiolate compound fragin in Burkholderia cenocepacia H111. We provide evidence that fragin is a metallophore and that metal chelation is the molecular basis of its antifungal activity. A subset of the fragin biosynthetic genes is involved in the synthesis of a previously undescribed cell-to-cell signal molecule, valdiazen. RNA-Seq analyses reveal that valdiazen controls fragin biosynthesis and affects the expression of more than 100 genes. Homologs of the valdiazen biosynthesis genes are found in various bacteria, suggesting that valdiazen-like compounds may constitute a new class of signal molecules. We use structural information, in silico prediction of enzymatic functions and biochemical data to propose a biosynthesis route for fragin and valdiazen.

Fragin is a diazeniumdiolate metabolite with antifungal activity, produced by some bacteria. Here, Jenul et al. show that metal chelation is the molecular basis of fragin’s antifungal activity, and that a gene cluster directing fragin biosynthesis is also involved in the synthesis of a signal molecule.

CRISPR Approaches to Small Molecule Target Identification

A long-standing challenge in drug development is the identification of the mechanisms of action of small molecules with therapeutic potential. A number of methods have been developed to address this challenge, each with inherent strengths and limitations. We here provide a brief review of these methods with a focus on chemical-genetic methods that are based on systematically profiling the effects of genetic perturbations on drug sensitivity. In particular, application of these methods to mammalian systems has been facilitated by the recent advent of CRISPR-based approaches, which enable one to readily repress, induce, or delete a given gene and determine the resulting effects on drug sensitivity.

Target Identification and Mechanism of Action of Picolinamide and Benzamide Chemotypes with Antifungal Properties

Invasive fungal infections are accompanied by high mortality rates that range up to 90%. At present, only three different compound classes are available for use in the clinic, and these often suffer from low bioavailability, toxicity, and drug resistance. These issues emphasize an urgent need for novel antifungal agents. Herein, we report the identification of chemically versatile benzamide and picolinamide scaffolds with antifungal properties. Chemogenomic profiling and biochemical assays with purified protein identified Sec14p, the major phosphatidylinositol/phosphatidylcholine transfer protein in Saccharomyces cerevisiae, as the sole essential target for these compounds. A functional variomics screen identified resistance-conferring residues that localized to the lipid-binding pocket of Sec14p. Determination of the X-ray co-crystal structure of a Sec14p-compound complex confirmed binding in this cavity and rationalized both the resistance-conferring residues and the observed structure-activity relationships. Taken together, these findings open new avenues for rational compound optimization and development of novel antifungal agents.

Inhibiting mitochondrial phosphate transport as an unexploited antifungal strategy

The development of effective antifungal therapeutics remains a formidable challenge due to the close evolutionary relationship between humans and fungi. Mitochondrial function may present an exploitable vulnerability due to its differential utilization in fungi and its pivotal roles in fungal morphogenesis, virulence, and drug resistance already demonstrated by others. We now report mechanistic characterization of ML316, a thiohydantoin which kills drug-resistant Candida species at nanomolar concentrations through fungal-selective inhibition of the mitochondrial phosphate carrier Mir1. We established ML316 as the first Mir1 inhibitor using genetic, biochemical, and metabolomic approaches. Inhibition of Mir1 by ML316 in respiring yeast diminished mitochondrial oxygen consumption resulting in an unusual metabolic catastrophe marked by citrate accumulation, and death. In a mouse model of azole-resistant oropharyngeal candidiasis, ML316 reduced fungal burden and enhanced azole activity. Targeting Mir1 could provide a new, much needed therapeutic strategy to address the rapidly rising burden of drug-resistant fungal infection.

H+ and Pi Byproducts of Glycosylation Affect Ca2+ Homeostasis and Are Retrieved from the Golgi Complex by Homologs of TMEM165 and XPR1

Glycosylation reactions in the Golgi complex and the endoplasmic reticulum utilize nucleotide sugars as donors and produce inorganic phosphate (Pi) and acid (H⁺) as byproducts. Here we show that homologs of mammalian XPR1 and TMEM165 (termed Erd1 and Gdt1) recycle luminal Pi and exchange luminal H⁺ for cytoplasmic Ca²⁺, respectively, thereby promoting growth of yeast cells in low Pi and low Ca²⁺ environments. As expected for reversible H⁺/Ca²⁺ exchangers, Gdt1 also promoted growth in high Ca²⁺ environments when the Golgi-localized V-ATPase was operational but had the opposite effect when the V-ATPase was eliminated. Gdt1 activities were negatively regulated by calcineurin signaling and by Erd1, which recycled the Pi byproduct of glycosylation reactions and prevented the loss of this nutrient to the environment via exocytosis. Thus, Erd1 transports Pi in the opposite direction from XPR1 and other EXS family proteins and facilitates byproduct removal from the Golgi complex together with Gdt1.

Small molecule inhibition of apicomplexan FtsH1 disrupts plastid biogenesis in human pathogens

The malaria parasite Plasmodium falciparum and related apicomplexan pathogens contain an essential plastid organelle, the apicoplast, which is a key anti-parasitic target. Derived from secondary endosymbiosis, the apicoplast depends on novel, but largely cryptic, mechanisms for protein/lipid import and organelle inheritance during parasite replication. These critical biogenesis pathways present untapped opportunities to discover new parasite-specific drug targets. We used an innovative screen to identify actinonin as having a novel mechanism-of-action inhibiting apicoplast biogenesis. Resistant mutation, chemical-genetic interaction, and biochemical inhibition demonstrate that the unexpected target of actinonin in P. falciparum and Toxoplasma gondii is FtsH1, a homolog of a bacterial membrane AAA+ metalloprotease. PfFtsH1 is the first novel factor required for apicoplast biogenesis identified in a phenotypic screen. Our findings demonstrate that FtsH1 is a novel and, importantly, druggable antimalarial target. Development of FtsH1 inhibitors will have significant advantages with improved drug kinetics and multistage efficacy against multiple human parasites.

Chemical genetics in drug discovery

Chemical-genetic approaches are based on measuring the cellular outcome of combining genetic and chemical perturbations in large-numbers in tandem. In these approaches the contribution of every gene to the fitness of an organism is measured upon exposure to different chemicals. Current technological advances enable the application of chemical genetics to almost any organism and at an unprecedented throughput. Here we review the underlying concepts behind chemical genetics, present its different vignettes and illustrate how such approaches can propel drug discovery.

Network-assisted target identification for haploinsufficiency and homozygous profiling screens

Chemical genomic screens have recently emerged as a systematic approach to drug discovery on a genome-wide scale. Drug target identification and elucidation of the mechanism of action (MoA) of hits from these noisy high-throughput screens remain difficult. Here, we present GIT (Genetic Interaction Network-Assisted Target Identification), a network analysis method for drug target identification in haploinsufficiency profiling (HIP) and homozygous profiling (HOP) screens. With the drug-induced phenotypic fitness defect of the deletion of a gene, GIT also incorporates the fitness defects of the gene's neighbors in the genetic interaction network. On three genome-scale yeast chemical genomic screens, GIT substantially outperforms previous scoring methods on target identification on HIP and HOP assays, respectively. Finally, we showed that by combining HIP and HOP assays, GIT further boosts target identification and reveals potential drug's mechanism of action.

Identification of a novel NAMPT inhibitor by CRISPR/Cas9 chemogenomic profiling in mammalian cells

Chemogenomic profiling is a powerful and unbiased approach to elucidate pharmacological targets and the mechanism of bioactive compounds. Until recently, genome-wide, high-resolution experiments of this nature have been limited to fungal systems due to lack of mammalian genome-wide deletion collections. With the example of a novel nicotinamide phosphoribosyltransferase (NAMPT) inhibitor, we demonstrate that the CRISPR/Cas9 system enables the generation of transient homo- and heterozygous deletion libraries and allows for the identification of efficacy targets and pathways mediating hypersensitivity and resistance relevant to the compound mechanism of action.

Competitive Growth Enhances Conditional Growth Mutant Sensitivity to Antibiotics and Exposes a Two-Component System as an Emerging Antibacterial Target in Burkholderia cenocepacia

Chemogenetic approaches to profile an antibiotic mode of action are based on detecting differential sensitivities of engineered bacterial strains in which the antibacterial target (usually encoded by an essential gene) or an associated process is regulated. We previously developed an essential-gene knockdown mutant library in the multidrug-resistant Burkholderia cenocepacia by transposon delivery of a rhamnose-inducible promoter. In this work, we used Illumina sequencing of multiplex-PCR-amplified transposon junctions to track individual mutants during pooled growth in the presence of antibiotics. We found that competition from nontarget mutants magnified the hypersensitivity of a clone underexpressing gyrB to novobiocin by 8-fold compared with hypersensitivity measured during clonal growth. Additional profiling of various antibiotics against a pilot library representing most categories of essential genes revealed a two-component system with unknown function, which, upon depletion of the response regulator, sensitized B. cenocepacia to novobiocin, ciprofloxacin, tetracycline, chloramphenicol, kanamycin, meropenem, and carbonyl cyanide 3-chlorophenylhydrazone, but not to colistin, hydrogen peroxide, and dimethyl sulfoxide. We named the gene cluster esaSR for enhanced sensitivity to antibiotics sensor and response regulator. Mutational analysis and efflux activity assays revealed that while esaS is not essential and is involved in antibiotic-induced efflux, esaR is an essential gene and regulates efflux independently of antibiotic-mediated induction. Furthermore, microscopic analysis of cells stained with propidium iodide provided evidence that depletion of EsaR has a profound effect on the integrity of cell membranes. In summary, we unraveled a previously uncharacterized two-component system that can be targeted to reduce antibiotic resistance in B. cenocepacia.

Identification and Evaluation of Novel Drug Targets against the Human Fungal Pathogen Aspergillus fumigatus with Elaboration on the Possible Role of RNA-Binding Protein

BACKGROUND

Aspergillus fumigatus is an airborne opportunistic fungal pathogen that can cause fatal infections in immunocompromised patients. Although the current anti-fungal therapies are relatively efficient, some issues such as drug toxicity, drug interactions, and the emergence of drug-resistant fungi have promoted the intense research toward finding the novel drug targets.

METHODS

In search of new antifungal drug targets, we have used a bioinformatics approach to identify novel drug targets. We compared the whole proteome of this organism with yeast Saccharomyces cerevisiae to come up with 153 specific proteins. Further screening of these proteins revealed 50 potential molecular targets in A. fumigatus. Amongst them, RNA-binding protein (RBP) was selected for further examination. The aspergillus fumigatus RBP (AfuRBP), as a peptidylprolyl isomerase, was evaluated by homology modeling and bioinformatics tools. RBP-deficient mutant strains of A. fumigatus were generated and characterized. Furthermore, the susceptibility of these strains to known peptidylprolyl isomerase inhibitors was assessed.

RESULTS

AfuRBP-deficient mutants demonstrated a normal growth phenotype. MIC assay results using inhibitors of peptidylprolyl isomerase confirmed a higher sensitivity of these mutants compared to the wild type.

CONCLUSION

Our bioinformatics approach revealed a number of fungal-specific proteins that may be considered as new targets for drug discovery purposes. Peptidylprolyl isomerase, as a possible drug target, was evaluated against two potential inhibitors and the promising results were investigated mechanistically. Future studies would confirm the impact of such target on the antifungal discovery investigations.

High-Resolution Genetics Identifies the Lipid Transfer Protein Sec14p as Target for Antifungal Ergolines

Invasive infections by fungal pathogens cause more deaths than malaria worldwide. We found the ergoline compound NGx04 in an antifungal screen, with selectivity over mammalian cells. High-resolution chemogenomics identified the lipid transfer protein Sec14p as the target of NGx04 and compound-resistant mutations in Sec14p define compound-target interactions in the substrate binding pocket of the protein. Beyond its essential lipid transfer function in a variety of pathogenic fungi, Sec14p is also involved in secretion of virulence determinants essential for the pathogenicity of fungi such as Cryptococcus neoformans, making Sec14p an attractive antifungal target. Consistent with this dual function, we demonstrate that NGx04 inhibits the growth of two clinical isolates of C. neoformans and that NGx04-related compounds have equal and even higher potency against C. neoformans. Furthermore NGx04 analogues showed fungicidal activity against a fluconazole resistant C. neoformans strain. In summary, we present genetic evidence that NGx04 inhibits fungal Sec14p and initial data supporting NGx04 as a novel antifungal starting point.

Emerging resistance to antibiotics led to an inglorious revival of infectious diseases. Furthermore, in the past 30 years, only one novel anti-fungal target has been discovered which was used to develop therapies against. Therefore pathogen-selective targets and knowledge about possible resistance determinants are of utmost importance to successfully develop new medicines. Here we describe the identification of anti-fungal ergolines, targeting the lipid transfer protein Sec14p, and inhibiting the growth of two clinical isolates of the pathogenic fungus Cryptococcus neoformans. Both, compound and target represent attractive points for further investigations: Sec14p as it differs significantly from the human homolog and as it has been implicated in fungal viability and pathogenicity, and, ergolines as they are used in the clinic against a variety of diseases demonstrating both efficacy and safety.

Antifungal Drugs: The Current Armamentarium and Development of New Agents

Invasive fungal infections are becoming an increasingly important cause of human mortality and morbidity, particularly for immunocompromised populations. The fungal pathogens Candida albicans , Cryptococcus neoformans , and Aspergillus fumigatus collectively contribute to over 1 million human deaths annually. Hence, the importance of safe and effective antifungal therapeutics for the practice of modern medicine has never been greater. Given that fungi are eukaryotes like their human host, the number of unique molecular targets that can be exploited for drug development remains limited. Only three classes of molecules are currently approved for the treatment of invasive mycoses. The efficacy of these agents is compromised by host toxicity, fungistatic activity, or the emergence of drug resistance in pathogen populations. Here we describe our current arsenal of antifungals and highlight current strategies that are being employed to improve the therapeutic safety and efficacy of these drugs. We discuss state-of-the-art approaches to discover novel chemical matter with antifungal activity and highlight some of the most promising new targets for antifungal drug development. We feature the benefits of combination therapy as a strategy to expand our current repertoire of antifungals and discuss the antifungal combinations that have shown the greatest potential for clinical development. Despite the paucity of new classes of antifungals that have come to market in recent years, it is clear that by leveraging innovative approaches to drug discovery and cultivating collaborations between academia and industry, there is great potential to bolster the antifungal armamentarium.

Functional Genomics Using the Saccharomyces cerevisiae Yeast Deletion Collections

Constructed by a consortium of 16 laboratories, the Saccharomyces genome-wide deletion collections have, for the past decade, provided a powerful, rapid, and inexpensive approach for functional profiling of the yeast genome. Loss-of-function deletion mutants were systematically created using a polymerase chain reaction (PCR)-based gene deletion strategy to generate a start-to-stop codon replacement of each open reading frame by homologous recombination. Each strain carries two molecular barcodes that serve as unique strain identifiers, enabling their growth to be analyzed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays or through the use of next-generation sequencing technologies. Functional profiling of the deletion collections, using either strain-by-strain or parallel assays, provides an unbiased approach to systematically survey the yeast genome. The Saccharomyces yeast deletion collections have proved immensely powerful in contributing to the understanding of gene function, including functional relationships between genes and genetic pathways in response to diverse genetic and environmental perturbations.

Systematic Mapping of Chemical-Genetic Interactions in Saccharomyces cerevisiae

Chemical-genetic interactions (CGIs) describe a phenomenon where the effects of a chemical compound (i.e., a small molecule) on cell growth are dependent on a particular gene. CGIs can reveal important functional information about genes and can also be powerful indicators of a compound's mechanism of action. Mapping CGIs can lead to the discovery of new chemical probes, which, in contrast to genetic perturbations, operate at the level of the gene product (or pathway) and can be fast-acting, tunable, and reversible. The simple culture conditions required for yeast and its rapid growth, as well as the availability of a complete set of barcoded gene deletion strains, facilitate systematic mapping of CGIs in this organism. This process involves two basic steps: first, screening chemical libraries to identify bioactive compounds affecting growth and, second, measuring the effects of these compounds on genome-wide collections of mutant strains. Here, we introduce protocols for both steps that have great potential for the discovery and development of new small-molecule tools and medicines.

3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping

Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in target prediction was observed for HIV reverse transcriptase (HIVRT) compounds, which consist of diverse scaffold hopping compounds targeting the nucleotidyltransferase binding site. CSNAP3D was further applied to a set of antimitotic compounds identified in a cell-based chemical screen and identified novel small molecules that share a pharmacophore with Taxol and display a Taxol-like mechanism of action, which were validated experimentally using in vitro microtubule polymerization assays and cell-based assays.

Next-generation phenotypic screening

Phenotypic drug discovery (PDD) strategies are defined by screening and selection of hit or lead compounds based on quantifiable phenotypic endpoints without prior knowledge of the drug target. We outline the challenges associated with traditional phenotypic screening strategies and propose solutions and new opportunities to be gained by adopting modern PDD technologies. We highlight both historical and recent examples of approved drugs and new drug candidates discovered by modern phenotypic screening. Finally, we offer a prospective view of a new era of PDD underpinned by a wealth of technology advances in the areas of in vitro model development, high-content imaging and image informatics, mechanism-of-action profiling and target deconvolution.

Identifying antimalarial compounds targeting dihydrofolate reductase-thymidylate synthase (DHFR-TS) by chemogenomic profiling

The mode of action of many antimalarial drugs is unknown. Chemogenomic profiling is a powerful method to address this issue. This experimental approach entails disruption of gene function and phenotypic screening for changes in sensitivity to bioactive compounds. Here, we describe the application of reverse genetics for chemogenomic profiling in Plasmodium. Plasmodium falciparum parasites harbouring a transgenic insertion of the glmS ribozyme downstream of the dihydrofolate reductase-thymidylate synthase (DHFR-TS) gene were used for chemogenomic profiling of antimalarial compounds to identify those which target DHFR-TS. DHFR-TS expression can be attenuated by exposing parasites to glucosamine. Parasites with attenuated DHFR-TS expression were significantly more sensitive to antifolate drugs known to target DHFR-TS. In contrast, no change in sensitivity to other antimalarial drugs with different modes of action was observed. Chemogenomic profiling was performed using the Medicines for Malaria Venture (Switzerland) Malaria Box compound library, and two compounds were identified as novel DHFR-TS inhibitors. We also tested the glmS ribozyme in Plasmodium berghei, a rodent malaria parasite. The expression of reporter genes with downstream glmS ribozyme could be attenuated in transgenic parasites comparable with that obtained in P. falciparum. The chemogenomic profiling method was applied in a P. berghei line expressing a pyrimethamine-resistant Toxoplasma gondii DHFR-TS reporter gene under glmS ribozyme control. Parasites with attenuated expression of this gene were significantly sensitised to antifolates targeting DHFR-TS, but not other drugs with different modes of action. In conclusion, these data show that the glmS ribozyme reverse genetic tool can be applied for identifying primary targets of antimalarial compounds in human and rodent malaria parasites.

Utilizing yeast chemogenomic profiles for the prediction of pharmacogenomic associations in humans

Understanding the genetic basis underlying individual responses to drug treatment is a fundamental task with implications to drug development and administration. Pharmacogenomics is the study of the genes that affect drug response. The study of pharmacogenomic associations between a drug and a gene that influences the interindividual drug response, which is only beginning, holds much promise and potential. Although relatively few pharmacogenomic associations between drugs and specific genes were mapped in humans, large systematic screens have been carried out in the yeast Saccharomyces cerevisiae, motivating the constructing of a projection method. We devised a novel approach for the prediction of pharmacogenomic associations in humans using genome-scale chemogenomic data from yeast. We validated our method using both cross-validation and comparison to known drug-gene associations extracted from multiple data sources, attaining high AUC scores. We show that our method outperforms a previous technique, as well as a similar method based on known human associations. Last, we analyze the predictions and demonstrate their biological relevance to understanding drug response.

A Protein Complex Map of Trypanosoma brucei

The functions of the majority of trypanosomatid-specific proteins are unknown, hindering our understanding of the biology and pathogenesis of Trypanosomatida. While protein-protein interactions are highly informative about protein function, a global map of protein interactions and complexes is still lacking for these important human parasites. Here, benefiting from in-depth biochemical fractionation, we systematically interrogated the co-complex interactions of more than 3354 protein groups in procyclic life stage of Trypanosoma brucei, the protozoan parasite responsible for human African trypanosomiasis. Using a rigorous methodology, our analysis led to identification of 128 high-confidence complexes encompassing 716 protein groups, including 635 protein groups that lacked experimental annotation. These complexes correlate well with known pathways as well as for proteins co-expressed across the T. brucei life cycle, and provide potential functions for a large number of previously uncharacterized proteins. We validated the functions of several novel proteins associated with the RNA-editing machinery, identifying a candidate potentially involved in the mitochondrial post-transcriptional regulation of T. brucei. Our data provide an unprecedented view of the protein complex map of T. brucei, and serve as a reliable resource for further characterization of trypanosomatid proteins. The presented results in this study are available at: www.TrypsNetDB.org.

Due to high evolutionary divergence of trypanosomatid pathogens from other eukaryotes, accurate prediction of functional roles for most of their proteins is not feasible based on homology-based approaches. Although protein co-complex maps provide a compelling tool for the functional annotation of proteins, as subunits of a complex are expected to be involved in similar biological processes, the current knowledge about these maps is still rudimentary. Here, we systematically examined the protein co-complex membership of more than one third of T. brucei proteome using two orthogonal fractionation approaches. A high-confidence network of co-complex relationships predicts the network context of 866 proteins, including many hypothetical and experimentally unannotated proteins. To our knowledge, this study presents the largest proteomics-based interaction map of trypanosomatid parasites to date, providing a useful resource for formulating new biological hypothesises and further experimental leads.

Select microtubule inhibitors increase lysosome acidity and promote lysosomal disruption in acute myeloid leukemia (AML) cells

To identify new biological vulnerabilities in acute myeloid leukemia, we screened a library of natural products for compounds cytotoxic to TEX leukemia cells. This screen identified the novel small molecule Deoxysappanone B 7,4' dimethyl ether (Deox B 7,4), which possessed nanomolar anti-leukemic activity. To determine the anti-leukemic mechanism of action of Deox B 7,4, we conducted a genome-wide screen in Saccharomyces cerevisiae and identified enrichment of genes related to mitotic cell cycle as well as vacuolar acidification, therefore pointing to microtubules and vacuolar (V)-ATPase as potential drug targets. Further investigations into the mechanisms of action of Deox B 7,4 and a related analogue revealed that these compounds were reversible microtubule inhibitors that bound near the colchicine site. In addition, Deox B 7,4 and its analogue increased lysosomal V-ATPase activity and lysosome acidity. The effects on microtubules and lysosomes were functionally important for the anti-leukemic effects of these drugs. The lysosomal effects were characteristic of select microtubule inhibitors as only the Deox compounds and nocodazole, but not colchicine, vinca alkaloids or paclitaxel, altered lysosome acidity and induced lysosomal disruption. Thus, our data highlight a new mechanism of action of select microtubule inhibitors on lysosomal function.

Re-engineering of PIP3-antagonist triazole PITENIN's chemical scaffold: development of novel antifungal leads

A novel 4-(1-phenyl-1-hydroxyethyl)-1-(o-hydroxyphenyl)-1H-1,2,3-triazole was designed by integrating the structural features of triazole PITENIN anticancer agents and the azole class of antifungal drugs.

Microbial and biochemical basis of a Fusarium wilt-suppressive soil

Crops lack genetic resistance to most necrotrophic pathogens. To compensate for this disadvantage, plants recruit antagonistic members of the soil microbiome to defend their roots against pathogens and other pests. The best examples of this microbially based defense of roots are observed in disease-suppressive soils in which suppressiveness is induced by continuously growing crops that are susceptible to a pathogen, but the molecular basis of most is poorly understood. Here we report the microbial characterization of a Korean soil with specific suppressiveness to Fusarium wilt of strawberry. In this soil, an attack on strawberry roots by Fusarium oxysporum results in a response by microbial defenders, of which members of the Actinobacteria appear to have a key role. We also identify Streptomyces genes responsible for the ribosomal synthesis of a novel heat-stable antifungal thiopeptide antibiotic inhibitory to F. oxysporum and the antibiotic's mode of action against fungal cell wall biosynthesis. Both classical- and community-oriented approaches were required to dissect this suppressive soil from the field to the molecular level, and the results highlight the role of natural antibiotics as weapons in the microbial warfare in the rhizosphere that is integral to plant health, vigor and development.

Advances in identification and validation of protein targets of natural products without chemical modification

This review focuses on and reports case studies of the latest advances in target protein identification methods for label-free natural products. The integration of newly developed technologies will provide new insights and highlight the value of natural products for use as biological probes and new drug candidates.

Functional genomics to uncover drug mechanism of action

The upswing in US Food and Drug Administration and European Medicines Agency drug approvals in 2014 may have marked an end to the dry spell that has troubled the pharmaceutical industry over the past decade. Regardless, the attrition rate of drugs in late clinical phases remains high, and a lack of target validation has been highlighted as an explanation. This has led to a resurgence in appreciation of phenotypic drug screens, as these may be more likely to yield compounds with relevant modes of action. However, cell-based screening approaches do not directly reveal cellular targets, and hence target deconvolution and a detailed understanding of drug action are needed for efficient lead optimization and biomarker development. Here, recently developed functional genomics technologies that address this need are reviewed. The approaches pioneered in model organisms, particularly in yeast, and more recently adapted to mammalian systems are discussed. Finally, areas of particular interest and directions for future tool development are highlighted.