BIONIC: biological network integration using convolutions.

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.

Exome and genome sequencing for rare genetic disease diagnosis: A scoping review and critical appraisal of clinical guidance documents produced by genetics professional organizations.

PURPOSE: Exome and genome sequencing have rapidly transitioned from research methods to widely used clinical tests for diagnosing rare genetic diseases. We sought to synthesize the topics covered and appraise the development processes of clinical guidance documents generated by genetics professional organizations. METHODS: We conducted a scoping review of guidance documents published since 2010, systematically identified in peer-reviewed and gray literature, using established methods and reporting guidelines. We coded verbatim recommendations by topic using content analysis and critically appraised documents using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool. RESULTS: We identified 30 guidance documents produced by 8 organizations (2012-2022), yielding 611 recommendations covering 21 topics. The most common topic related to findings beyond the primary testing indication. Mean AGREE II scores were low across all 6 quality domains; scores for items related to rigor of development were among the lowest. More recently published documents generally received higher scores. CONCLUSION: Guidance documents included a broad range of recommendations but were of low quality, particularly in their rigor of development. Developers should consider using tools such as AGREE II and basing recommendations on living knowledge syntheses to improve guidance development in this evolving space.

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.

Publisher Correction: Integrating yeast chemical genomics and mammalian cell pathway analysis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Decyl Gallate as a Possible Inhibitor of N-Glycosylation Process in Paracoccidioides lutzii.

The available antifungal therapeutic arsenal is limited. The search for alternative drugs with fewer side effects and new targets remains a major challenge. Decyl gallate (G14) is a derivative of gallic acid with a range of biological activities and broad-spectrum antifungal activity. Previously, our group demonstrated the promising anti-Paracoccidioides activity of G14. In this work, to evaluate the antifungal characteristics of G14 for Paracoccidioides lutzii, a chemical-genetic interaction analysis was conducted on a Saccharomyces cerevisiae model. N-glycosylation and/or the unfolded protein response pathway was identified as a high-confidence process for drug target prediction. The overactivation of unfolded protein response (UPR) signaling was confirmed using this model with IRE1/ATF6/PERK genes tagged with green fluorescent protein (GFP). In P. lutzii, this prediction was confirmed by the low activity of glycosylated enzymes [α-(1,3)-glucanase, N-acetyl-ß-d-glucosaminidase (NAGase), and α-(1,4)-amylase], by hyperexpression of genes involved with the UPR and glycosylated enzymes, and by the reduction in the amounts of glycosylated proteins and chitin. All of these components are involved in fungal cell wall integrity and are dependent on the N-glycosylation process. This loss of integrity was confirmed by the reduction in mitochondrial activity, impaired budding, enhancement of wall permeability, and a decrease in viability. These events led to a reduction of the ability of fungi to adhere on human lung epithelial cells (A549) in vitro Therefore, G14 may have an important role in balancing the inflammatory reaction caused by fungal infection, without interfering with the microbicidal activity of nitric oxide. This work provides new information on the activity of G14, a potential anti-Paracoccidioides compound.

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.

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.

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.

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.

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/ß-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.

Plant-derived antifungal agent poacic acid targets ß-1,3-glucan.

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 ß-1,3-glucan synthesis in vivo and in vitro, apparently by directly binding ß-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 ß-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.

Mechanism of imidazolium ionic liquids toxicity in Saccharomyces cerevisiae and rational engineering of a tolerant, xylose-fermenting strain.

BACKGROUND: Imidazolium ionic liquids (IILs) underpin promising technologies that generate fermentable sugars from lignocellulose for future biorefineries. However, residual IILs are toxic to fermentative microbes such as Saccharomyces cerevisiae, making IIL-tolerance a key property for strain engineering. To enable rational engineering, we used chemical genomic profiling to understand the effects of IILs on S. cerevisiae. RESULTS: We found that IILs likely target mitochondria as their chemical genomic profiles closely resembled that of the mitochondrial membrane disrupting agent valinomycin. Further, several deletions of genes encoding mitochondrial proteins exhibited increased sensitivity to IIL. High-throughput chemical proteomics confirmed effects of IILs on mitochondrial protein levels. IILs induced abnormal mitochondrial morphology, as well as altered polarization of mitochondrial membrane potential similar to valinomycin. Deletion of the putative serine/threonine kinase PTK2 thought to activate the plasma-membrane proton efflux pump Pma1p conferred a significant IIL-fitness advantage. Conversely, overexpression of PMA1 conferred sensitivity to IILs, suggesting that hydrogen ion efflux may be coupled to influx of the toxic imidazolium cation. PTK2 deletion conferred resistance to multiple IILs, including [EMIM]Cl, [BMIM]Cl, and [EMIM]Ac. An engineered, xylose-converting ptk2∆ S. cerevisiae (Y133-IIL) strain consumed glucose and xylose faster and produced more ethanol in the presence of 1 % [BMIM]Cl than the wild-type PTK2 strain. We propose a model of IIL toxicity and resistance. CONCLUSIONS: This work demonstrates the utility of chemical genomics-guided biodesign for development of superior microbial biocatalysts for the ever-changing landscape of fermentation inhibitors.

STAT6 mutations enriched at diffuse large B-cell lymphoma relapse reshape the tumor microenvironment.

Diffuse large B-cell lymphoma (DLBCL) relapses in approximately 40% of patients following frontline therapy. We reported that STAT6D419 mutations are enriched in relapsed/refractory DLBCL (rrDLBCL) samples, suggesting that JAK/STAT signaling plays a role in therapeutic resistance. We hypothesized that STAT6D419 mutations can improve DLBCL cell survival by reprogramming the microenvironment to sustain STAT6 activation. Thus, we investigated the role of STAT6D419 mutations on DLBCL cell growth and its microenvironment. We found that phospho-STAT6D419N was retained in the nucleus longer than phospho-STAT6WT following IL-4 stimulation, and STAT6D419N recognized a more restricted DNA-consensus sequence than STAT6WT. Upon IL-4 induction, STAT6D419N expression led to a higher magnitude of gene expression changes, but in a more selective list of gene targets compared with STATWT. The most significantly expressed genes induced by STAT6D419N were those implicated in survival, proliferation, migration, and chemotaxis, in particular CCL17. This chemokine, also known as TARC, attracts helper T-cells to the tumor microenvironment, especially in Hodgkin's lymphoma. To this end, in DLBCL, phospho-STAT6+ rrDLBCL cells had a greater proportion of infiltrating CD4+ T-cells than phospho-STAT6- tumors. Our findings suggest that STAT6D419 mutations in DLBCL lead to cell autonomous changes, enhanced signaling, and altered composition of the tumor microenvironment.

Regulation of vacuolar H+-ATPase activity by the Cdc42 effector Ste20 in Saccharomyces cerevisiae.

In the budding yeast Saccharomyces cerevisiae, the Cdc42 effector Ste20 plays a crucial role in the regulation of filamentous growth, a response to nutrient limitation. Using the split-ubiquitin technique, we found that Ste20 forms a complex with Vma13, an important regulatory subunit of vacuolar H(+)-ATPase (V-ATPase). This protein-protein interaction was confirmed by a pulldown assay and coimmunoprecipitation. We also demonstrate that Ste20 associates with vacuolar membranes and that Ste20 stimulates V-ATPase activity in isolated vacuolar membranes. This activation requires Ste20 kinase activity and does not depend on increased assembly of the V1 and V0 sectors of the V-ATPase, which is a major regulatory mechanism. Furthermore, loss of V-ATPase activity leads to a strong increase in invasive growth, possibly because these cells fail to store and mobilize nutrients efficiently in the vacuole in the absence of the vacuolar proton gradient. In contrast to the wild type, which grows in rather small, isolated colonies on solid medium during filamentation, hyperinvasive vma mutants form much bigger aggregates in which a large number of cells are tightly clustered together. Genetic data suggest that Ste20 and the protein kinase A catalytic subunit Tpk2 are both activated in the vma13Δ strain. We propose that during filamentous growth, Ste20 stimulates V-ATPase activity. This would sustain nutrient mobilization from vacuolar stores, which is beneficial for filamentous growth.

Vacuolar H+-ATPase works in parallel with the HOG pathway to adapt Saccharomyces cerevisiae cells to osmotic stress.

Hyperosmotic stress activates an array of cellular detoxification mechanisms, including the high-osmolarity glycerol (HOG) pathway. We report here that vacuolar H(+)-ATPase (V-ATPase) activity helps provide osmotic tolerance in Saccharomyces cerevisiae. V-ATPase subunit genes exhibit complex haploinsufficiency interactions with HOG pathway components. vma mutants lacking V-ATPase function are sensitive to high concentrations of salt and exhibit Hog1p activation even at low salt concentrations, as demonstrated by phosphorylation of Hog1p, a shift in Hog1-green fluorescent protein localization, transcriptional activation of a subset of HOG pathway effectors, and transcriptional inhibition of parallel mitogen-activated protein kinase pathway targets. vma2Δ hog1Δ and vma3Δ pbs2Δ double mutants have a synthetic growth phenotype, poor salt tolerance, and an aberrant, hyper-elongated morphology on solid media, accompanied by activation of a filamentous response element-LacZ construct, indicating cross talk into the filamentous growth pathway. Vacuoles isolated from wild-type cells briefly exposed to salt show higher levels of V-ATPase activity, and Na(+)/H(+) exchange in isolated vacuolar vesicles suggests a biochemical basis for the genetic interactions observed. V-ATPase activity is upregulated during salt stress by increasing assembly of the catalytic V(1) sector with the membrane-bound V(o) sector. Together, these data suggest that the V-ATPase acts in parallel with the HOG pathway in order to mediate salt detoxification.

Identification of triazenyl indoles as inhibitors of fungal fatty acid biosynthesis with broad-spectrum activity.

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.

Jerveratrum-Type Steroidal Alkaloids Inhibit ß-1,6-Glucan Biosynthesis in Fungal Cell Walls.

The limited number of available effective agents necessitates the development of new antifungals. We report that jervine, a jerveratrum-type steroidal alkaloid isolated from Veratrum californicum, has antifungal activity. Phenotypic comparisons of cell wall mutants, K1 killer toxin susceptibility testing, and quantification of cell wall components revealed that ß-1,6-glucan biosynthesis was significantly inhibited by jervine. Temperature-sensitive mutants defective in essential genes involved in ß-1,6-glucan biosynthesis, including BIG1, KEG1, KRE5, KRE9, and ROT1, were hypersensitive to jervine. In contrast, point mutations in KRE6 or its paralog SKN1 produced jervine resistance, suggesting that jervine targets Kre6 and Skn1. Jervine exhibited broad-spectrum antifungal activity and was effective against human-pathogenic fungi, including Candida parapsilosis and Candida krusei. It was also effective against phytopathogenic fungi, including Botrytis cinerea and Puccinia recondita. Jervine exerted a synergistic effect with fluconazole. Therefore, jervine, a jerveratrum-type steroidal alkaloid used in pharmaceutical products, represents a new class of antifungals active against mycoses and plant-pathogenic fungi. IMPORTANCE Non-Candida albicans Candida species (NCAC) are on the rise as a cause of mycosis. Many antifungal drugs are less effective against NCAC, limiting the available therapeutic agents. Here, we report that jervine, a jerveratrum-type steroidal alkaloid, is effective against NCAC and phytopathogenic fungi. Jervine acts on Kre6 and Skn1, which are involved in ß-1,6-glucan biosynthesis. The skeleton of jerveratrum-type steroidal alkaloids has been well studied, and more recently, their anticancer properties have been investigated. Therefore, jerveratrum-type alkaloids could potentially be applied as treatments for fungal infections and cancer.

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.

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.