Chemical-genetic Profiling Research Articles

Antifungal Activity of Disalt of Epipyrone A from Epicoccum nigrum Likely via Disrupted Fatty Acid Elongation and Sphingolipid Biosynthesis.

Multidrug-resistant fungal pathogens and antifungal drug toxicity have challenged our current ability to fight fungal infections. Therefore, there is a strong global demand for novel antifungal molecules with the distinct mode of action and specificity to service the medical and agricultural sectors. Polyenes are a class of antifungal drugs with the broadest spectrum of activity among the current antifungal drugs. Epipyrone A, a water-soluble antifungal molecule with a unique, linear polyene structure, was isolated from the fungus Epiccocum nigrum. Since small changes in a compound structure can significantly alter its cell target and mode of action, we present here a study on the antifungal mode of action of the disalt of epipyrone A (DEA) using chemical-genetic profiling, fluorescence microscopy, and metabolomics. Our results suggest the disruption of sphingolipid/fatty acid biosynthesis to be the primary mode of action of DEA, followed by the intracellular accumulation of toxic phenolic compounds, in particular p-toluic acid (4-methylbenzoic acid). Although membrane ergosterol is known to be the main cell target for polyene antifungal drugs, we found little evidence to support that is the case for DEA. Sphingolipids, on the other hand, are known for their important roles in fungal cell physiology, and their biosynthesis has been recognized as a potential fungal-specific cell target for the development of new antifungal drugs.

Rationally Designed Pooled CRISPRi-Seq Uncovers an Inhibitor of Bacterial Peptidyl-tRNA Hydrolase.

Bacterial mutant libraries in which antibiotic targets are downregulated are useful tools to functionally characterize novel antimicrobials. These libraries are used for chemical-genetic profiling as target-compound interactions can be inferred by differential fitness of mutants during pooled growth. Mutants that are functionally related to the antimicrobial mode of action are usually depleted from the pool upon exposure to the drug. Although powerful, this method can fail when the unequal proliferation of mutant strains before exposure causes mutants to fall below the detection level in the library pool. To address this issue, we constructed an arrayed essential gene mutant library (EGML) in the antibiotic-resistant bacterium Burkholderia cenocepacia using CRISPR interference (CRISPRi) and analyzed the growth parameters of individual mutant strains. We then modelled depletion levels during pooled growth and used the model to rationally design an optimized CRISPR interference-mediated pooled library of essential genes (CIMPLE). By adjusting the initial inoculum of the knockdown mutants, we achieved coverage of the bacterial essential genome with mutant sensitization. We exposed CIMPLE to a recently discovered antimicrobial of a novel class and discovered it inhibits the peptidyl-tRNA hydrolase, an essential bacterial enzyme. In summary, we demonstrate the utility of CIMPLE and CRISPRi-Seq to uncover the mechanism of action of novel antimicrobial compounds.

Application of combined CRISPR screening for genetic and chemical-genetic interaction profiling in Mycobacterium tuberculosis.

CRISPR screening, including CRISPR interference (CRISPRi) and CRISPR-knockout (CRISPR-KO) screening, has become a powerful technology in the genetic screening of eukaryotes. In contrast with eukaryotes, CRISPR-KO screening has not yet been applied to functional genomics studies in bacteria. Here, we constructed genome-scale CRISPR-KO and also CRISPRi libraries in Mycobacterium tuberculosis (Mtb). We first examined these libraries to identify genes essential for Mtb viability. Subsequent screening identified dozens of genes associated with resistance/susceptibility to the antitubercular drug bedaquiline (BDQ). Genetic and chemical validation of the screening results suggested that it provided a valuable resource to investigate mechanisms of action underlying the effects of BDQ and to identify chemical-genetic synergies that can be used to optimize tuberculosis therapy. In summary, our results demonstrate the potential for efficient genome-wide CRISPR-KO screening in bacteria and establish a combined CRISPR screening approach for high-throughput investigation of genetic and chemical-genetic interactions in Mtb.

Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles

MotivationChemical–genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances in artificial intelligence, chemical–genetic interaction profiles (CGIPs) can be leveraged to predict mechanism of action of compounds. This can be achieved by using machine learning, where the data from a CGIP is fed into the machine learning platform along with the chemical descriptors to develop a chemogenetically trained model. As small molecules can be considered non-structural data, graph convolutional neural networks, which can learn from the chemical structures directly, can be used to successfully predict molecular properties. Clustering analysis, on the other hand, is a critical approach to get insights into the underlying biological relationships between the gene products in the high-dimensional chemical-genetic data.Methods and resultsIn this study, we proposed a comprehensive framework based on the large-scale chemical-genetics dataset built in Mycobacterium tuberculosis for predicting CGIPs using graph-based deep learning models. Our approach is structured into three parts. First, by matching M. tuberculosis genes with homologous genes in Escherichia coli (E. coli) according to their gene products, we grouped the genes into clusters with distinct biological functions. Second, we employed a directed message passing neural network to predict growth inhibition against M. tuberculosis gene clusters using a collection of 50,000 chemicals with the profile. We compared the performance of different baseline models and implemented multi-label tasks in binary classification frameworks. Lastly, we applied the trained model to an externally curated drug set that had experimental results against M. tuberculosis genes to examine the effectiveness of our method. Overall, we demonstrate that our approach effectively created M. tuberculosis gene clusters, and the trained classifier is able to predict activity against essential M. tuberculosis targets with high accuracy.ConclusionThis work provides an analytical framework for modeling large-scale chemical-genetic datasets for predicting CGIPs and generating hypothesis about mechanism of action of novel drugs. In addition, this work highlights the importance of graph-based deep neural networks in drug discovery.

Clionamines stimulate autophagy, inhibit Mycobacterium tuberculosis survival in macrophages, and target Pik1

The pathogen Mycobacterium tuberculosis (Mtb) evades the innate immune system by interfering with autophagy and phagosomal maturation in macrophages, and, as a result, small molecule stimulation of autophagy represents a host-directed therapeutics (HDTs) approach for treatment of tuberculosis (TB). Here we show the marine natural product clionamines activate autophagy and inhibit Mtb survival in macrophages. A yeast chemical-genetics approach identified Pik1 as target protein of the clionamines. Biotinylated clionamine B pulled down Pik1 from yeast cell lysates and a clionamine analog inhibited phosphatidyl 4-phosphate (PI4P) production in yeast Golgi membranes. Chemical-genetic profiles of clionamines and cationic amphiphilic drugs (CADs) are closely related, linking the clionamine mode of action to co-localization with PI4P in a vesicular compartment. Small interfering RNA (siRNA) knockdown of PI4KB, a human homolog of Pik1, inhibited the survival of Mtb in macrophages, identifying PI4KB as an unexploited molecular target for efforts to develop HDT drugs for treatment of TB.

Indole-3-acetic acid is a physiological inhibitor of TORC1 in yeast.

Indole-3-acetic acid (IAA) is the most common, naturally occurring phytohormone that regulates cell division, differentiation, and senescence in plants. The capacity to synthesize IAA is also widespread among plant-associated bacterial and fungal species, which may use IAA as an effector molecule to define their relationships with plants or to coordinate their physiological behavior through cell-cell communication. Fungi, including many species that do not entertain a plant-associated life style, are also able to synthesize IAA, but the physiological role of IAA in these fungi has largely remained enigmatic. Interestingly, in this context, growth of the budding yeast Saccharomyces cerevisiae is sensitive to extracellular IAA. Here, we use a combination of various genetic approaches including chemical-genetic profiling, SAturated Transposon Analysis in Yeast (SATAY), and genetic epistasis analyses to identify the mode-of-action by which IAA inhibits growth in yeast. Surprisingly, these analyses pinpointed the target of rapamycin complex 1 (TORC1), a central regulator of eukaryotic cell growth, as the major growth-limiting target of IAA. Our biochemical analyses further demonstrate that IAA inhibits TORC1 both in vivo and in vitro. Intriguingly, we also show that yeast cells are able to synthesize IAA and specifically accumulate IAA upon entry into stationary phase. Our data therefore suggest that IAA contributes to proper entry of yeast cells into a quiescent state by acting as a metabolic inhibitor of TORC1.

12621 Targeted Chemical-Genetic Screen Platform for Identifying Drug Modes-of-Action

ABSTRACT IMPACT: The key to advancing precision medicine is to deepen our understanding of drug modes-of-action (MOA). This project aims to develop a novel method for predicting MOA of potential drug compounds, providing an experimental and computational platform for more efficient drug discovery. OBJECTIVES/GOALS: To develop (1) a targeted CRISPR-Cas9 chemical-genetic screen approach, and (2) a computational method to predict drug mode-of-action from chemical-genetic interaction profiles. METHODS/STUDY POPULATION: Screening drugs against a gene deletion library can identify knockouts that modulate drug sensitivity. These chemical-genetic interaction (CGI) screens can be performed in human cell lines using a pooled lentiviral CRISPR-Cas9 approach to assess drug sensitivity/resistance of single-gene knockouts across the human genome. A targeted, rather than genome-wide, library can enable scaling these screens across many drugs.CGI profiles can be derived from phenotypic screen readouts. These profiles are analogous to genetic interaction (GI) profiles, which represent sensitivity/resistance of gene knockouts to a second gene knockout rather than a drug. To computationally predict a drug’s genetic target, we leverage the property that a drug’s CGI profile will be similar to its target’s GI profile. RESULTS/ANTICIPATED RESULTS: Five proof-of-principle screens will be conducted with compounds that have existing genome-wide profiles and well-characterized MOA. I will generate CGI profiles for these five compounds and identify genes that are drug-sensitizers or drug-suppressors. I will then evaluate whether targeted library screens can recapitulate the CGIs found in genome-wide screens. Finally, I will develop a computational tool to integrate these CGI profiles with GI profiles (derived from another project) to predict gene-level and bioprocess-level drug targets. These predictions (from both targeted and genome-wide profiles) will be benchmarked against a drug-target and drug-bioprocess standard. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work will develop a scalable, targeted chemical-genetic screen approach to discovering how putative therapeutics work. The targeted screen workflow provides a method for higher-throughput drug screening. The computational pipeline provides a powerful tool for exploring the MOA of uncharacterized drugs or repurposing FDA-approved drugs.

Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq

Chemical-genetic interaction profiling in model organisms has proven powerful in providing insights into compound mechanism of action and gene function. However, identifying chemical-genetic interactions in mammalian systems has been limited to low-throughput or computational methods. Here, we develop Quantitative and Multiplexed Analysis of Phenotype by Sequencing (QMAP-Seq), which leverages next-generation sequencing for pooled high-throughput chemical-genetic profiling. We apply QMAP-Seq to investigate how cellular stress response factors affect therapeutic response in cancer. Using minimal automation, we treat pools of 60 cell types—comprising 12 genetic perturbations in five cell lines—with 1440 compound-dose combinations, generating 86,400 chemical-genetic measurements. QMAP-Seq produces precise and accurate quantitative measures of acute drug response comparable to gold standard assays, but with increased throughput at lower cost. Moreover, QMAP-Seq reveals clinically actionable drug vulnerabilities and functional relationships involving these stress response factors, many of which are activated in cancer. Thus, QMAP-Seq provides a broadly accessible and scalable strategy for chemical-genetic profiling in mammalian cells.

Chemical-genetic profiling reveals limited cross-resistance between antimicrobial peptides with different modes of action

Antimicrobial peptides (AMPs) are key effectors of the innate immune system and promising therapeutic agents. Yet, knowledge on how to design AMPs with minimal cross-resistance to human host-defense peptides remains limited. Here, we systematically assess the resistance determinants of Escherichia coli against 15 different AMPs using chemical-genetics and compare to the cross-resistance spectra of laboratory-evolved AMP-resistant strains. Although generalizations about AMP resistance are common in the literature, we find that AMPs with different physicochemical properties and cellular targets vary considerably in their resistance determinants. As a consequence, cross-resistance is prevalent only between AMPs with similar modes of action. Finally, our screen reveals several genes that shape susceptibility to membrane- and intracellular-targeting AMPs in an antagonistic manner. We anticipate that chemical-genetic approaches could inform future efforts to minimize cross-resistance between therapeutic and human host AMPs.

Integrating yeast chemical genomics and mammalian cell pathway analysis.

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces cerevisiae. Here, we use yeast as a surrogate system for studying compounds that are active against metazoan targets. Large-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target predictions. To discover compounds that have the potential to function like therapeutic agents with known targets, we also analyzed a reference library of approved drugs. Previously uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells, and new modulators with specific modes of action were validated. This analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

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.

A simple and inexpensive quantitative technique for determining chemical sensitivity in Saccharomyces cerevisiae

Chemical sensitivity, growth inhibition in response to a chemical, is a powerful phenotype that can reveal insight into diverse cellular processes. Chemical sensitivity assays are used in nearly every model system, however the yeast Saccharomyces cerevisiae provides a particularly powerful platform for discovery and mechanistic insight from chemical sensitivity assays. Here we describe a simple and inexpensive approach to determine chemical sensitivity quantitatively in yeast in the form of half maximal inhibitory concentration (IC50) using common laboratory equipment. We demonstrate the utility of this method using chemicals commonly used to monitor changes in membrane traffic. When compared to traditional agar-based plating methods, this method is more sensitive and can detect defects not apparent using other protocols. Additionally, this method reduces the experimental protocol from five days to 18 hours for the toxic amino acid canavanine. Furthermore, this method provides reliable results using lower amounts of chemicals. Finally, this method is easily adapted to additional chemicals as demonstrated with an engineered system that activates the spindle assembly checkpoint in response to rapamycin with differing efficiencies. This approach provides researchers with a cost-effective method to perform chemical genetic profiling without specialized equipment.

Combined CRISPRi/a-Based Chemical Genetic Screens Reveal that Rigosertib Is a Microtubule-Destabilizing Agent

SummaryChemical libraries paired with phenotypic screens can now readily identify compounds with therapeutic potential. A central limitation to exploiting these compounds, however, has been in identifying their relevant cellular targets. Here, we present a two-tiered CRISPR-mediated chemical-genetic strategy for target identification: combined genome-wide knockdown and overexpression screening as well as focused, comparative chemical-genetic profiling. Application of these strategies to rigosertib, a drug in phase 3 clinical trials for high-risk myelodysplastic syndrome whose molecular target had remained controversial, pointed singularly to microtubules as rigosertib’s target. We showed that rigosertib indeed directly binds to and destabilizes microtubules using cell biological, in vitro, and structural approaches. Finally, expression of tubulin with a structure-guided mutation in the rigosertib-binding pocket conferred resistance to rigosertib, establishing that rigosertib kills cancer cells by destabilizing microtubules. These results demonstrate the power of our chemical-genetic screening strategies for pinpointing the physiologically relevant targets of chemical agents.

Functional annotation of chemical libraries across diverse biological processes.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Systematic analysis of Ca2+ homeostasis in Saccharomyces cerevisiae based on chemical-genetic interaction profiles.

We investigated the global landscape of Ca2+ homeostasis in budding yeast based on high-dimensional chemical-genetic interaction profiles. The morphological responses of 62 Ca2+-sensitive (cls) mutants were quantitatively analyzed with the image processing program CalMorph after exposure to a high concentration of Ca2+ After a generalized linear model was applied, an analysis of covariance model was used to detect significant Ca2+-cls interactions. We found that high-dimensional, morphological Ca2+-cls interactions were mixed with positive (86%) and negative (14%) chemical-genetic interactions, whereas one-dimensional fitness Ca2+-cls interactions were all negative in principle. Clustering analysis with the interaction profiles revealed nine distinct gene groups, six of which were functionally associated. In addition, characterization of Ca2+-cls interactions revealed that morphology-based negative interactions are unique signatures of sensitized cellular processes and pathways. Principal component analysis was used to discriminate between suppression and enhancement of the Ca2+-sensitive phenotypes triggered by inactivation of calcineurin, a Ca2+-dependent phosphatase. Finally, similarity of the interaction profiles was used to reveal a connected network among the Ca2+ homeostasis units acting in different cellular compartments. Our analyses of high-dimensional chemical-genetic interaction profiles provide novel insights into the intracellular network of yeast Ca2+ homeostasis.

Antitumor/Antifungal Celecoxib Derivative AR-12 is a Non-Nucleoside Inhibitor of the ANL-Family Adenylating Enzyme Acetyl CoA Synthetase.

AR-12/OSU-03012 is an antitumor celecoxib-derivative that has progressed to Phase I clinical trial as an anticancer agent and has activity against a number of infectious agents including fungi, bacteria and viruses. However, the mechanism of these activities has remained unclear. Based on a chemical-genetic profiling approach in yeast, we have found that AR-12 is an ATP-competitive, time-dependent inhibitor of yeast acetyl coenzyme A synthetase. AR-12-treated fungal cells show phenotypes consistent with the genetic reduction of acetyl CoA synthetase activity, including induction of autophagy, decreased histone acetylation, and loss of cellular integrity. In addition, AR-12 is a weak inhibitor of human acetyl CoA synthetase ACCS2. Acetyl CoA synthetase activity is essential in many fungi and parasites. In contrast, acetyl CoA is primarily synthesized by an alternate enzyme, ATP-citrate lyase, in mammalian cells. Taken together, our results indicate that AR-12 is a non-nucleoside acetyl CoA synthetase inhibitor and that acetyl CoA synthetase may be a feasible antifungal drug target.

The Monoterpene Carvacrol Generates Endoplasmic Reticulum Stress in the Pathogenic Fungus Candida albicans

The monoterpene carvacrol, the major component of oregano and thyme oils, is known to exert potent antifungal activity against the pathogenic yeast Candida albicans. This monoterpene has been the subject of a considerable number of investigations that uncovered extensive pharmacological properties, including antifungal and antibacterial effects. However, its mechanism of action remains elusive. Here, we used integrative chemogenomic approaches, including genome-scale chemical-genetic and transcriptional profiling, to uncover the mechanism of action of carvacrol associated with its antifungal property. Our results clearly demonstrated that fungal cells require the unfolded protein response (UPR) signaling pathway to resist carvacrol. The mutants most sensitive to carvacrol in our genome-wide competitive fitness assay in the yeast Saccharomyces cerevisiae expressed mutations of the transcription factor Hac1 and the endonuclease Ire1, which is required for Hac1 activation by removing a nonconventional intron from the 3' region of HAC1 mRNA. Confocal fluorescence live-cell imaging revealed that carvacrol affects the morphology and the integrity of the endoplasmic reticulum (ER). Transcriptional profiling of pathogenic yeast C. albicans cells treated with carvacrol demonstrated a bona fide UPR transcriptional signature. Ire1 activity detected by the splicing of HAC1 mRNA in C. albicans was activated by carvacrol. Furthermore, carvacrol was found to potentiate antifungal activity of the echinocandin antifungal caspofungin and UPR inducers dithiothreitol and tunicamycin against C. albicans. This comprehensive chemogenomic investigation demonstrated that carvacrol exerts its antifungal activity by altering ER integrity, leading to ER stress and the activation of the UPR to restore protein-folding homeostasis.

Using Yeast to Develop Anti‐Tumor Therapeutic Agents That Cause Liponecrotic Death of Cancer Cells by Remodeling Lipid Metabolism

We discovered a novel form of programmed cell death (PCD) called ʺliponecrosisʺ. It can be triggered by an exposure of the yeast Saccharomyces cerevisiae to exogenous palmitoleic acid (POA), a monounsaturated fatty acid. We revealed a molecular mechanism underlying liponecrotic PCD. It is initiated if bulk quantities of exogenously added POA are incorporated into phospholipids (PL) that amass in the endoplasmic reticulum membrane, mitochondrial membranes and the plasma membrane. This triggers a cascade of events committing yeast to liponecrotic PCD, which is executed via an autophagic degradation of various cellular organelles and macromolecules. Two pathways of lipid metabolism can protect yeast from liponecrosis by reducing the flow of POA into PL synthesis pathways; these pro-survival pathways include POA oxidation within peroxisomes and POA incorporation into neutral lipids deposited in lipid droplets. Of note, the relative rates of all these pathways of lipid metabolism in rapidly proliferating yeast cells are known to be similar to those in cancer cells. Moreover, various small molecules have been identified by chemical-genetic profiling in yeast as target-specific inhibitors or activators of different reactions involved in these pathways. We discuss how some of these small molecules, added alone or in different combinations, can be used for such remodeling of various lipid metabolism pathways in proliferating yeast cells that makes these cells susceptible to liponecrotic PCD. We infer that such screens in yeast can be used to develop anti-tumor therapeutic agents eliciting liponecrotic death of cancer cells via specific remodeling of certain lipid metabolism pathways. Supported by NSERC of Canada.

Pleiotropic drug-resistance attenuated genomic library improves elucidation of drug mechanisms

Identifying Saccharomyces cerevisiae genome-wide gene deletion mutants that confer hypersensitivity to a xenobiotic aids the elucidation of its mechanism of action (MoA). However, the biological activities of many xenobiotics are masked by the pleiotropic drug resistance (PDR) network which effluxes xenobiotics that are PDR substrates. The PDR network in S. cerevisiae is almost entirely under the control of two functionally homologous transcription factors Pdr1p and Pdr3p. Herein we report the construction of a PDR-attenuated haploid non-essential DMA (PA-DMA), lacking PDR1 and PDR3, which permits the MoA elucidation of xenobiotics that are PDR substrates at low concentrations. The functionality of four key cellular processes commonly activated in response to xenobiotic stress: oxidative stress response, general stress response, unfolded stress response and calcium signalling pathways were assessed in the absence of PDR1 and PDR3 genes and were found to unaltered, therefore, these key chemogenomic signatures are not lost when using the PA-DMA. Efficacy of the PA-DMA was demonstrated using cycloheximide and latrunculin A at low nanomolar concentrations to attain chemical genetic profiles that were more specific to their known main mechanisms. We also found a two-fold increase in the number of compounds that are bioactive in the pdr1Δpdr3Δ compared to the wild type strain in screening the commercially available LOPAC(1280) library. The PA-DMA should be particularly applicable to mechanism determination of xenobiotics that have limited availability, such as natural products.

The Natural Diyne-Furan Fatty Acid EV-086 Is an Inhibitor of Fungal Delta-9 Fatty Acid Desaturation with Efficacy in a Model of Skin Dermatophytosis

Human fungal infections represent a therapeutic challenge. Although effective strategies for treatment are available, resistance is spreading, and many therapies have unacceptable side effects. A clear need for novel antifungal targets and molecules is thus emerging. Here, we present the identification and characterization of the plant-derived diyne-furan fatty acid EV-086 as a novel antifungal compound. EV-086 has potent and broad-spectrum activity in vitro against Candida, Aspergillus, and Trichophyton spp., whereas activities against bacteria and human cell lines are very low. Chemical-genetic profiling of Saccharomyces cerevisiae deletion mutants identified lipid metabolic processes and organelle organization and biogenesis as targets of EV-086. Pathway modeling suggested that EV-086 inhibits delta-9 fatty acid desaturation, an essential process in S. cerevisiae, depending on the delta-9 fatty acid desaturase OLE1. Delta-9 unsaturated fatty acids-but not saturated fatty acids-antagonized the EV-086-mediated growth inhibition, and transcription of the OLE1 gene was strongly upregulated in the presence of EV-086. EV-086 increased the ratio of saturated to unsaturated free fatty acids and phosphatidylethanolamine fatty acyl chains, respectively. Furthermore, EV-086 was rapidly taken up into the lipid fraction of the cell and incorporated into phospholipids. Together, these findings demonstrate that EV-086 is an inhibitor of delta-9 fatty acid desaturation and that the mechanism of inhibition might involve an EV-086-phospholipid. Finally, EV-086 showed efficacy in a guinea pig skin dermatophytosis model of topical Trichophyton infection, which demonstrates that delta-9 fatty acid desaturation is a valid antifungal target, at least for dermatophytoses.