Toxicology knowledge graph for structural birth defects.

BACKGROUND: Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes. METHODS: To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules. RESULTS: Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes. CONCLUSIONS: ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.

While birth defects are common, for most birth defects there are no known causes. During pregnancy, developing babies are exposed to drugs, cosmetics, food, and environmental pollutants that may cause birth defects. However, exactly how these environmental factors are involved in producing birth defects is difficult to discern. Also, birth defects can be a consequence of the genes inherited from the parents. We combined general data about human genes and drugs with specific data previously implicating genes and drugs in inducing birth defects to create a knowledge graph representation that connects genes, drugs, and birth defects. This knowledge graph can be used to explore new links that may explain why birth defects occur, particularly those that result from a combination of inherited and environmental influences.

Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease.

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.

AlphaFold illuminates half of the dark human proteins.

We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination.

BACKGROUND: LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. RESULTS: Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG's resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. CONCLUSIONS: The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Getting Started with the IDG KMC Datasets and Tools.

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.

Optical induction of autophagy via Transcription factor EB (TFEB) reduces pathological tau in neurons.

Pathological accumulation of microtubule associated protein tau in neurons is a major neuropathological hallmark of Alzheimer's disease (AD) and related tauopathies. Several attempts have been made to promote clearance of pathological tau (p-Tau) from neurons. Transcription factor EB (TFEB) has shown to clear p-Tau from neurons via autophagy. However, sustained TFEB activation and autophagy can create burden on cellular bioenergetics and can be deleterious. Here, we modified previously described two-plasmid systems of Light Activated Protein (LAP) from bacterial transcription factor-EL222 and Light Responsive Element (LRE) to encode TFEB. Upon blue-light (465 nm) illumination, the conformation changes in LAP induced LRE-driven expression of TFEB, its nuclear entry, TFEB-mediated expression of autophagy-lysosomal genes and clearance of p-Tau from neuronal cells and AD patient-derived human iPSC-neurons. Turning the blue-light off reversed the expression of TFEB-target genes and attenuated p-Tau clearance. Together, these results suggest that optically regulated TFEB expression unlocks the potential of opto-therapeutics to treat AD and other dementias.

The contribution of Candida albicans vacuolar ATPase subunit V1B, encoded by VMA2, to stress response, autophagy, and virulence is independent of environmental pH.

Candida albicans vacuoles are central to many critical biological processes, including filamentation and in vivo virulence. The V-ATPase proton pump is a multisubunit complex responsible for organellar acidification and is essential for vacuolar biogenesis and function. To study the function of the V1B subunit of C. albicans V-ATPase, we constructed a tetracycline-regulatable VMA2 mutant, tetR-VMA2. Inhibition of VMA2 expression resulted in the inability to grow at alkaline pH and altered resistance to calcium, cold temperature, antifungal drugs, and growth on nonfermentable carbon sources. Furthermore, V-ATPase was unable to fully assemble at the vacuolar membrane and was impaired in proton transport and ATPase-specific activity. VMA2 repression led to vacuolar alkalinization in addition to abnormal vacuolar morphology and biogenesis. Key virulence-related traits, including filamentation and secretion of degradative enzymes, were markedly inhibited. These results are consistent with previous studies of C. albicans V-ATPase; however, differential contributions of the V-ATPase Vo and V1 subunits to filamentation and secretion are observed. We also make the novel observation that inhibition of C. albicans V-ATPase results in increased susceptibility to osmotic stress. Notably, V-ATPase inhibition under conditions of nitrogen starvation results in defects in autophagy. Lastly, we show the first evidence that V-ATPase contributes to virulence in an acidic in vivo system by demonstrating that the tetR-VMA2 mutant is avirulent in a Caenorhabditis elegans infection model. This study illustrates the fundamental requirement of V-ATPase for numerous key virulence-related traits in C. albicans and demonstrates that the contribution of V-ATPase to virulence is independent of host pH.

Candida albicans VMA3 is necessary for V-ATPase assembly and function and contributes to secretion and filamentation.

The vacuolar membrane ATPase (V-ATPase) is a protein complex that utilizes ATP hydrolysis to drive protons from the cytosol into the vacuolar lumen, acidifying the vacuole and modulating several key cellular response systems in Saccharomyces cerevisiae. To study the contribution of V-ATPase to the biology and virulence attributes of the opportunistic fungal pathogen Candida albicans, we created a conditional mutant in which VMA3 was placed under the control of a tetracycline-regulated promoter (tetR-VMA3 strain). Repression of VMA3 in the tetR-VMA3 strain prevents V-ATPase assembly at the vacuolar membrane and reduces concanamycin A-sensitive ATPase-specific activity and proton transport by more than 90%. Loss of C. albicans V-ATPase activity alkalinizes the vacuolar lumen and has pleiotropic effects, including pH-dependent growth, calcium sensitivity, and cold sensitivity. The tetR-VMA3 strain also displays abnormal vacuolar morphology, indicative of defective vacuolar membrane fission. The tetR-VMA3 strain has impaired aspartyl protease and lipase secretion, as well as attenuated virulence in an in vitro macrophage killing model. Repression of VMA3 suppresses filamentation, and V-ATPase-dependent filamentation defects are not rescued by overexpression of RIM8, MDS3, EFG1, CST20, or UME6, which encode positive regulators of filamentation. Specific chemical inhibition of Vma3p function also results in defective filamentation. These findings suggest either that V-ATPase functions downstream of these transcriptional regulators or that V-ATPase function during filamentation involves independent mechanisms and alternative signaling pathways. Taken together, these data indicate that V-ATPase activity is a fundamental requirement for several key virulence-associated traits in C. albicans.

Deletion of vacuolar proton-translocating ATPase V(o)a isoforms clarifies the role of vacuolar pH as a determinant of virulence-associated traits in Candida albicans.

Vacuolar proton-translocating ATPase (V-ATPase) is a central regulator of cellular pH homeostasis, and inactivation of all V-ATPase function has been shown to prevent infectivity in Candida albicans. V-ATPase subunit a of the Vo domain (Voa) is present as two fungal isoforms: Stv1p (Golgi) and Vph1p (vacuole). To delineate the individual contribution of Stv1p and Vph1p to C. albicans physiology, we created stv1Δ/Δ and vph1Δ/Δ mutants and compared them to the corresponding reintegrant strains (stv1Δ/ΔR and vph1Δ/ΔR). V-ATPase activity, vacuolar physiology, and in vitro virulence-related phenotypes were unaffected in the stv1Δ/Δ mutant. The vph1Δ/Δ mutant exhibited defective V1Vo assembly and a 90% reduction in concanamycin A-sensitive ATPase activity and proton transport in purified vacuolar membranes, suggesting that the Vph1p isoform is essential for vacuolar V-ATPase activity in C. albicans. The vph1Δ/Δ cells also had abnormal endocytosis and vacuolar morphology and an alkalinized vacuolar lumen (pHvph1Δ/Δ = 6.8 versus pHvph1Δ/ΔR = 5.8) in both yeast cells and hyphae. Secreted protease and lipase activities were significantly reduced, and M199-induced filamentation was impaired in the vph1Δ/Δ mutant. However, the vph1Δ/Δ cells remained competent for filamentation induced by Spider media and YPD, 10% FCS, and biofilm formation and macrophage killing were unaffected in vitro. These studies suggest that different virulence mechanisms differentially rely on acidified vacuoles and that the loss of both vacuolar (Vph1p) and non-vacuolar (Stv1p) V-ATPase activity is necessary to affect in vitro virulence-related phenotypes. As a determinant of C. albicans pathogenesis, vacuolar pH alone may prove less critical than originally assumed.