Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and ...automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.
A rise in resistance to current antifungals necessitates strategies to identify alternative sources of effective fungicides. We report the discovery of poacic acid, a potent antifungal compound found ...in lignocellulosic hydrolysates of grasses. Chemical genomics using Saccharomyces cerevisiae showed that loss of cell wall synthesis and maintenance genes conferred increased sensitivity to poacic acid. Morphological analysis revealed that cells treated with poacic acid behaved similarly to cells treated with other cell wall-targeting drugs and mutants with deletions in genes involved in processes related to cell wall biogenesis. Poacic acid causes rapid cell lysis and is synergistic with caspofungin and fluconazole. The cellular target was identified; poacic acid localized to the cell wall and inhibited β-1,3-glucan synthesis in vivo and in vitro, apparently by directly binding β-1,3-glucan. Through its activity on the glucan layer, poacic acid inhibits growth of the fungi Sclerotinia sclerotiorum and Alternaria solani as well as the oomycete Phytophthora sojae . A single application of poacic acid to leaves infected with the broad-range fungal pathogen S. sclerotiorum substantially reduced lesion development. The discovery of poacic acid as a natural antifungal agent targeting β-1,3-glucan highlights the potential side use of products generated in the processing of renewable biomass toward biofuels as a source of valuable bioactive compounds and further clarifies the nature and mechanism of fermentation inhibitors found in lignocellulosic hydrolysates.
Significance The search for new antifungal compounds is struggling to keep pace with emerging fungicide resistance. Through chemoprospecting of an untapped reservoir of inhibitory compounds, lignocellulosic hydrolysates, we have identified a previously undescribed antifungal agent, poacic acid. Using both chemical genomics and morphological analysis together for the first time, to our knowledge, we identified the cellular target of poacic acid as β-1,3-glucan. Through its action on the glucan layer of fungal cell walls, poacic acid is a natural antifungal agent against economically significant fungi and oomycete plant pathogens. This work highlights the chemical diversity within lignocellulosic hydrolysates as a source of potentially valuable chemicals.
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-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.
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.
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•Clionamines activate autophagy and inhibit Mtb survival in macrophages•Yeast chemical genetics identified Pik1 as a clionamine target protein•SiRNA knockdown of PI4KB inhibited the survival of Mtb in macrophages•PI4KB has been identified as an unexploited target for drugs to treat tuberculosis
Persaud et al. have shown that the sponge natural product clionamines activate autophagy, inhibit Mtb in macrophages, and target the PI4 kinase Pik1 in yeast. siRNA knockdown of PI4KB, a human homolog of Pik1, inhibited Mtb survival in macrophages, identifying PI4KB as an unexploited target for HDT drugs to treat tuberculosis.
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.
Abstract
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.
Supplementary information
Supplementary data are available at Bioinformatics online.
Rising drug resistance among pathogenic fungi, paired with a limited antifungal arsenal, poses an increasing threat to human health. To identify antifungal compounds, we screened the RIKEN natural ...product depository against representative isolates of four major human fungal pathogens. This screen identified NPD6433, a triazenyl indole with broad-spectrum activity against all screening strains, as well as the filamentous mold Aspergillus fumigatus. Mechanistic studies indicated that NPD6433 targets the enoyl reductase domain of fatty acid synthase 1 (Fas1), covalently inhibiting its flavin mononucleotide-dependent NADPH-oxidation activity and arresting essential fatty acid biosynthesis. Robust Fas1 inhibition kills Candida albicans, while sublethal inhibition impairs diverse virulence traits. At well-tolerated exposures, NPD6433 extended the lifespan of nematodes infected with azole-resistant C. albicans. Overall, identification of NPD6433 provides a tool with which to explore lipid homeostasis as a therapeutic target in pathogenic fungi and reveals a mechanism by which Fas1 function can be inhibited.
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•Chemical library screen identifies a triazenyl indole as broad-spectrum antifungal•Compound covalently targets enoyl reductase domain of fungal fatty acid synthase•At sublethal levels, target inhibition impairs diverse fungal virulence traits•Compound provides a new tool to explore lipid homeostasis as an antifungal target
New agents are needed to counter drug-resistant fungal infections. Iyer et al. screened the RIKEN natural product depository against isolates of four major human fungal pathogens. Chemogenomic and biochemical data established the most promising hit as a covalent inhibitor of Fas1. Findings support inhibiting this target as a promising antifungal strategy.
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.
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