Soft-metal(loid)s induce protein aggregation in Escherichia coli.

Metal(loid) salts were used to treat infectious diseases in the past due to their exceptional biocidal properties at low concentrations. However, the mechanism of their toxicity has yet to be fully elucidated. The production of reactive oxygen species (ROS) has been linked to the toxicity of soft metal(loid)s such as Ag(I), Au(III), As(III), Cd(II), Hg(II), and Te(IV). Nevertheless, few reports have described the direct, or ROS-independent, effects of some of these soft-metal(loid)s on bacteria, including the dismantling of iron-sulfur clusters [4Fe-4S] and the accumulation of porphyrin IX. Here, we used genome-wide genetic, proteomic, and biochemical approaches under anaerobic conditions to evaluate the direct mechanisms of toxicity of these metal(loid)s in Escherichia coli. We found that certain soft-metal(loid)s promote protein aggregation in a ROS-independent manner. This aggregation occurs during translation in the presence of Ag(I), Au(III), Hg(II), or Te(IV) and post-translationally in cells exposed to Cd(II) or As(III). We determined that aggregated proteins were involved in several essential biological processes that could lead to cell death. For instance, several enzymes involved in amino acid biosynthesis were aggregated after soft-metal(loid) exposure, disrupting intracellular amino acid concentration. We also propose a possible mechanism to explain how soft-metal(loid)s act as proteotoxic agents.

High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds.

Morphological profiling is an omics-based approach for predicting intracellular targets of chemical compounds in which the dose-dependent morphological changes induced by the compound are systematically compared to the morphological changes in gene-deleted cells. In this study, we developed a reliable high-throughput (HT) platform for yeast morphological profiling using drug-hypersensitive strains to minimize compound use, HT microscopy to speed up data generation and analysis, and a generalized linear model to predict targets with high reliability. We first conducted a proof-of-concept study using six compounds with known targets: bortezomib, hydroxyurea, methyl methanesulfonate, benomyl, tunicamycin, and echinocandin B. Then we applied our platform to predict the mechanism of action of a novel diferulate-derived compound, poacidiene. Morphological profiling of poacidiene implied that it affects the DNA damage response, which genetic analysis confirmed. Furthermore, we found that poacidiene inhibits the growth of phytopathogenic fungi, implying applications as an effective antifungal agent. Thus, our platform is a new whole-cell target prediction tool for drug discovery.

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.

A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fingerprints and similarity coefficients have been limited by a lack of large-scale datasets that reflect biological similarities of compounds. To elucidate the relative performance of these alternatives, we systematically benchmarked 11 different molecular fingerprint encodings, each combined with 13 different similarity coefficients, using a large set of chemical-genetic interaction data from the yeast Saccharomyces cerevisiae as a systematic proxy for biological activity. We found that the performance of different molecular fingerprints and similarity coefficients varied substantially and that the all-shortest path fingerprints paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several compound collections. We further proposed a machine learning pipeline based on support vector machines that offered a fivefold improvement relative to the best unsupervised approach. Our results generally suggest that using high-dimensional chemical-genetic data as a basis for refining molecular fingerprints can be a powerful approach for improving prediction of biological functions from chemical structures.

Genome-wide screening of genes associated with momilactone B sensitivity in the fission yeast Schizosaccharomyces pombe.

Momilactone B is a natural product with dual biological activities, including antimicrobial and allelopathic properties, and plays a major role in plant chemical defense against competitive plants and pathogens. The pharmacological effects of momilactone B on mammalian cells have also been reported. However, little is known about the molecular and cellular mechanisms underlying its broad bioactivity. In this study, the genetic determinants of momilactone B sensitivity in yeast were explored to gain insight into its mode of action. We screened fission yeast mutants resistant to momilactone B from a pooled culture containing genome-wide gene-overexpressing strains in a drug-hypersensitive genetic background. Overexpression of pmd1, bfr1, pap1, arp9, or SPAC9E9.06c conferred resistance to momilactone B. In addition, a drug-hypersensitive, barcoded deletion library was newly constructed and the genes that imparted altered sensitivity to momilactone B upon deletion were identified. Gene Ontology and fission yeast phenotype ontology enrichment analyses predicted the biological pathways related to the mode of action of momilactone B. The validation of predictions revealed that momilactone B induced abnormal phenotypes such as multiseptated cells and disrupted organization of the microtubule structure. This is the first investigation of the mechanism underlying the antifungal activity of momilactone B against yeast. The results and datasets obtained in this study narrow the possible targets of momilactone B and facilitate further studies regarding its mode of action.

A marine microbiome antifungal targets urgent-threat drug-resistant fungi.

New antifungal drugs are urgently needed to address the emergence and transcontinental spread of fungal infectious diseases, such as pandrug-resistant Candida auris. Leveraging the microbiomes of marine animals and cutting-edge metabolomics and genomic tools, we identified encouraging lead antifungal molecules with in vivo efficacy. The most promising lead, turbinmicin, displays potent in vitro and mouse-model efficacy toward multiple-drug-resistant fungal pathogens, exhibits a wide safety index, and functions through a fungal-specific mode of action, targeting Sec14 of the vesicular trafficking pathway. The efficacy, safety, and mode of action distinct from other antifungal drugs make turbinmicin a highly promising antifungal drug lead to help address devastating global fungal pathogens such as C. auris.

From Yeast to Humans: Leveraging New Approaches in Yeast to Accelerate Discovery of Therapeutic Targets for Synucleinopathies.

Neurodegenerative diseases (ND) represent a growing, global health crisis, one that lacks any disease-modifying therapeutic strategy. This critical need for new therapies must be met with an exhaustive approach to exploit all tools available. A yeast (Saccharomyces cerevisiae) model of α-synuclein toxicity-the protein causally linked to Parkinson's disease and other synucleinopathies-offers a powerful approach that takes advantage of the unique offerings of this system: tractable genetics, robust high-throughput screening strategies, unparalleled data repositories, powerful computational tools, and extensive evolutionary conservation of fundamental biological pathways. These attributes have enabled genetic and small molecule screens that have revealed toxic phenotypes and drug targets that translate directly to patient-derived iPSC neurons. Extending these insights, recent advances in genetic network analyses have generated the first "humanized" α-synuclein network, which has identified druggable proteins and led to validation of the toxic phenotypes in patient-derived cells. Unbiased phenotypic small molecule screens can identify compounds targeting critical proteins within α-synuclein networks. While identification of direct drug targets for phenotypic screen hits represents a bottleneck, high-throughput chemical genetic methods provide a means to uncover cellular targets and pathways for large numbers of compounds in parallel. Taken together, the yeast α-synuclein model and associated tools can reveal insights into underlying cellular pathologies, lead molecules and their cognate targets, and strategies to translate mechanisms of toxicity and cytoprotection into complex neuronal systems.

Resistance against Sclerotinia sclerotiorum in soybean involves a reprogramming of the phenylpropanoid pathway and up-regulation of antifungal activity targeting ergosterol biosynthesis.

Sclerotinia sclerotiorum, a predominately necrotrophic fungal pathogen with a broad host range, causes a significant yield-limiting disease of soybean called Sclerotinia stem rot. Resistance mechanisms against this pathogen in soybean are poorly understood, thus hindering the commercial deployment of resistant varieties. We used a multiomic approach utilizing RNA-sequencing, gas chromatography-mass spectrometry-based metabolomics and chemical genomics in yeast to decipher the molecular mechanisms governing resistance to S. sclerotiorum in soybean. Transcripts and metabolites of two soybean recombinant inbred lines, one resistant and one susceptible to S. sclerotiorum were analysed in a time course experiment. The combined results show that resistance to S. sclerotiorum in soybean is associated in part with an early accumulation of JA-Ile ((+)-7-iso-jasmonoyl-L-isoleucine), a bioactive jasmonate, increased ability to scavenge reactive oxygen species, and importantly, a reprogramming of the phenylpropanoid pathway leading to increased antifungal activities. Indeed, we noted that phenylpropanoid pathway intermediates, such as 4-hydroxybenzoate, cinnamic acid, ferulic acid and caffeic acid, were highly accumulated in the resistant line. In vitro assays show that these metabolites and total stem extracts from the resistant line clearly affect S. sclerotiorum growth and development. Using chemical genomics in yeast, we further show that this antifungal activity targets ergosterol biosynthesis in the fungus, by disrupting enzymes involved in lipid and sterol biosynthesis. Overall, our results are consistent with a model where resistance to S. sclerotiorum in soybean coincides with an early recognition of the pathogen, leading to the modulation of the redox capacity of the host and the production of antifungal metabolites.

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.

Inhibiting Stearoyl-CoA Desaturase Ameliorates α-Synuclein Cytotoxicity.

The lack of disease-modifying treatments for neurodegenerative disease stems in part from our rudimentary understanding of disease mechanisms and the paucity of targets for therapeutic intervention. Here we used an integrated discovery paradigm to identify a new therapeutic target for diseases caused by α-synuclein (α-syn), a small lipid-binding protein that misfolds and aggregates in Parkinson's disease and other disorders. Using unbiased phenotypic screening, we identified a series of compounds that were cytoprotective against α-syn-mediated toxicity by inhibiting the highly conserved enzyme stearoyl-CoA desaturase (SCD). Critically, reducing the levels of unsaturated membrane lipids by inhibiting SCD reduced α-syn toxicity in human induced pluripotent stem cell (iPSC) neuronal models. Taken together, these findings suggest that inhibition of fatty acid desaturation has potential as a therapeutic approach for the treatment of Parkinson's disease and other synucleinopathies.

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.

Water-soluble phenolic compounds produced from extractive ammonia pretreatment exerted binary inhibitory effects on yeast fermentation using synthetic hydrolysate.

Biochemical conversion of lignocellulosic biomass to liquid fuels requires pretreatment and enzymatic hydrolysis of the biomass to produce fermentable sugars. Degradation products produced during thermochemical pretreatment, however, inhibit the microbes with regard to both ethanol yield and cell growth. In this work, we used synthetic hydrolysates (SynH) to study the inhibition of yeast fermentation by water-soluble components (WSC) isolated from lignin streams obtained after extractive ammonia pretreatment (EA). We found that SynH with 20g/L WSC mimics real hydrolysate in cell growth, sugar consumption and ethanol production. However, a long lag phase was observed in the first 48 h of fermentation of SynH, which is not observed during fermentation with the crude extraction mixture. Ethyl acetate extraction was conducted to separate phenolic compounds from other water-soluble components. These phenolic compounds play a key inhibitory role during ethanol fermentation. The most abundant compounds were identified by Liquid Chromatography followed by Mass Spectrometry (LC-MS) and Gas Chromatography followed by Mass Spectrometry (GC-MS), including coumaroyl amide, feruloyl amide and coumaroyl glycerol. Chemical genomics profiling was employed to fingerprint the gene deletion response of yeast to different groups of inhibitors in WSC and AFEX-Pretreated Corn Stover Hydrolysate (ACSH). The sensitive/resistant genes cluster patterns for different fermentation media revealed their similarities and differences with regard to degradation compounds.

Chemical genomic guided engineering of gamma-valerolactone tolerant yeast.

BACKGROUND: Gamma valerolactone (GVL) treatment of lignocellulosic bomass is a promising technology for degradation of biomass for biofuel production; however, GVL is toxic to fermentative microbes. Using a combination of chemical genomics with the yeast (Saccharomyces cerevisiae) deletion collection to identify sensitive and resistant mutants, and chemical proteomics to monitor protein abundance in the presence of GVL, we sought to understand the mechanism toxicity and resistance to GVL with the goal of engineering a GVL-tolerant, xylose-fermenting yeast. RESULTS: Chemical genomic profiling of GVL predicted that this chemical affects membranes and membrane-bound processes. We show that GVL causes rapid, dose-dependent cell permeability, and is synergistic with ethanol. Chemical genomic profiling of GVL revealed that deletion of the functionally related enzymes Pad1p and Fdc1p, which act together to decarboxylate cinnamic acid and its derivatives to vinyl forms, increases yeast tolerance to GVL. Further, overexpression of Pad1p sensitizes cells to GVL toxicity. To improve GVL tolerance, we deleted PAD1 and FDC1 in a xylose-fermenting yeast strain. The modified strain exhibited increased anaerobic growth, sugar utilization, and ethanol production in synthetic hydrolysate with 1.5% GVL, and under other conditions. Chemical proteomic profiling of the engineered strain revealed that enzymes involved in ergosterol biosynthesis were more abundant in the presence of GVL compared to the background strain. The engineered GVL strain contained greater amounts of ergosterol than the background strain. CONCLUSIONS: We found that GVL exerts toxicity to yeast by compromising cellular membranes, and that this toxicity is synergistic with ethanol. Deletion of PAD1 and FDC1 conferred GVL resistance to a xylose-fermenting yeast strain by increasing ergosterol accumulation in aerobically grown cells. The GVL-tolerant strain fermented sugars in the presence of GVL levels that were inhibitory to the unmodified strain. This strain represents a xylose fermenting yeast specifically tailored to GVL produced hydrolysates.

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.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Coculture of Marine Invertebrate-Associated Bacteria and Interdisciplinary Technologies Enable Biosynthesis and Discovery of a New Antibiotic, Keyicin.

Advances in genomics and metabolomics have made clear in recent years that microbial biosynthetic capacities on Earth far exceed previous expectations. This is attributable, in part, to the realization that most microbial natural product (NP) producers harbor biosynthetic machineries not readily amenable to classical laboratory fermentation conditions. Such "cryptic" or dormant biosynthetic gene clusters (BGCs) encode for a vast assortment of potentially new antibiotics and, as such, have become extremely attractive targets for activation under controlled laboratory conditions. We report here that coculturing of a Rhodococcus sp. and a Micromonospora sp. affords keyicin, a new and otherwise unattainable bis-nitroglycosylated anthracycline whose mechanism of action (MOA) appears to deviate from those of other anthracyclines. The structure of keyicin was elucidated using high resolution MS and NMR technologies, as well as detailed molecular modeling studies. Sequencing of the keyicin BGC (within the Micromonospora genome) enabled both structural and genomic comparisons to other anthracycline-producing systems informing efforts to characterize keyicin. The new NP was found to be selectively active against Gram-positive bacteria including both Rhodococcus sp. and Mycobacterium sp. E. coli-based chemical genomics studies revealed that keyicin's MOA, in contrast to many other anthracyclines, does not invoke nucleic acid damage.

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 in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, 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 the 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 seven 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.

Chemical Genomics, Structure Elucidation, and in Vivo Studies of the Marine-Derived Anticlostridial Ecteinamycin.

A polyether antibiotic, ecteinamycin (1), was isolated from a marine Actinomadura sp., cultivated from the ascidian Ecteinascidia turbinata. 13C enrichment, high resolution NMR spectroscopy, and molecular modeling enabled elucidation of the structure of 1, which was validated on the basis of comparisons with its recently reported crystal structure. Importantly, ecteinamycin demonstrated potent activity against the toxigenic strain of Clostridium difficile NAP1/B1/027 (MIC = 59 ng/µL), as well as other toxigenic and nontoxigenic C. difficile isolates both in vitro and in vivo. Additionally, chemical genomics studies using Escherichia coli barcoded deletion mutants led to the identification of sensitive mutants such as trkA and kdpD involved in potassium cation transport and homeostasis supporting a mechanistic proposal that ecteinamycin acts as an ionophore antibiotic. This is the first antibacterial agent whose mechanism of action has been studied using E. coli chemical genomics. On the basis of these data, we propose ecteinamycin as an ionophore antibiotic that causes C. difficile detoxification and cell death via potassium transport dysregulation.

Accumulation of heme biosynthetic intermediates contributes to the antibacterial action of the metalloid tellurite.

The metalloid tellurite is highly toxic to microorganisms. Several mechanisms of action have been proposed, including thiol depletion and generation of hydrogen peroxide and superoxide, but none of them can fully explain its toxicity. Here we use a combination of directed evolution and chemical and biochemical approaches to demonstrate that tellurite inhibits heme biosynthesis, leading to the accumulation of intermediates of this pathway and hydroxyl radical. Unexpectedly, the development of tellurite resistance is accompanied by increased susceptibility to hydrogen peroxide. Furthermore, we show that the heme precursor 5-aminolevulinic acid, which is used as an antimicrobial agent in photodynamic therapy, potentiates tellurite toxicity. Our results define a mechanism of tellurite toxicity and warrant further research on the potential use of the combination of tellurite and 5-aminolevulinic acid in antimicrobial therapy.

Mutations That Alter the Bacterial Cell Envelope Increase Lipid Production.

Lipids from microbes offer a promising source of renewable alternatives to petroleum-derived compounds. In particular, oleaginous microbes are of interest because they accumulate a large fraction of their biomass as lipids. In this study, we analyzed genetic changes that alter lipid accumulation in Rhodobacter sphaeroides By screening an R. sphaeroides Tn5 mutant library for insertions that increased fatty acid content, we identified 10 high-lipid (HL) mutants for further characterization. These HL mutants exhibited increased sensitivity to drugs that target the bacterial cell envelope and changes in shape, and some had the ability to secrete lipids, with two HL mutants accumulating ~60% of their total lipids extracellularly. When one of the highest-lipid-secreting strains was grown in a fed-batch bioreactor, its lipid content was comparable to that of oleaginous microbes, with the majority of the lipids secreted into the medium. Based on the properties of these HL mutants, we conclude that alterations of the cell envelope are a previously unreported approach to increase microbial lipid production. We also propose that this approach may be combined with knowledge about biosynthetic pathways, in this or other microbes, to increase production of lipids and other chemicals.IMPORTANCE This paper reports on experiments to understand how to increase microbial lipid production. Microbial lipids are often cited as one renewable replacement for petroleum-based fuels and chemicals, but strategies to increase the yield of these compounds are needed to achieve this goal. While lipid biosynthesis is often well understood, increasing yields of these compounds to industrially relevant levels is a challenge, especially since genetic, synthetic biology, or engineering approaches are not feasible in many microbes. We show that altering the bacterial cell envelope can be used to increase microbial lipid production. We also find that the utility of some of these alterations can be enhanced by growing cells in bioreactor configurations that can be used industrially. We propose that our findings can inform current and future efforts to increase production of microbial lipids, other fuels, or chemicals that are currently derived from petroleum.