A drug screen with approved compounds identifies amlexanox as a novel Wnt/ß-catenin activator inducing lung epithelial organoid formation.

BACKGROUND AND PURPOSE: Emphysema is an incurable disease characterized by loss of lung tissue leading to impaired gas exchange. Wnt/ß-catenin signalling is reduced in emphysema, and exogenous activation of the pathway in experimental models in vivo and in human ex vivo lung tissue improves lung function and structure. We sought to identify a pharmaceutical able to activate Wnt/ß-catenin signalling and assess its potential to activate lung epithelial cells and repair. EXPERIMENTAL APPROACH: We screened 1216 human-approved compounds for Wnt/ß-catenin signalling activation using luciferase reporter cells and selected candidates based on their computationally predicted protein targets. We further performed confirmatory luciferase reporter and metabolic activity assays. Finally, we studied the regenerative potential in murine adult epithelial cell-derived lung organoids and in vivo using a murine elastase-induced emphysema model. KEY RESULTS: The primary screen identified 16 compounds that significantly induced Wnt/ß-catenin-dependent luciferase activity. Selected compounds activated Wnt/ß-catenin signalling without inducing cell toxicity or proliferation. Two compounds were able to promote organoid formation, which was reversed by pharmacological Wnt/ß-catenin inhibition, confirming the Wnt/ß-catenin-dependent mechanism of action. Amlexanox was used for in vivo evaluation, and preventive treatment resulted in improved lung function and structure in emphysematous mouse lungs. Moreover, gene expression of Hgf, an important alveolar repair marker, was increased, whereas disease marker Eln was decreased, indicating that amlexanox induces pro-regenerative signalling in emphysema. CONCLUSION AND IMPLICATIONS: Using a drug screen based on Wnt/ß-catenin activity, organoid assays and a murine emphysema model, amlexanox was identified as a novel potential therapeutic agent for emphysema.

HitPickV2: a web server to predict targets of chemical compounds.

MOTIVATION: The identification of protein targets of novel compounds is essential to understand compounds' mechanisms of action leading to biological effects. Experimental methods to determine these protein targets are usually slow, costly and time consuming. Computational tools have recently emerged as cheaper and faster alternatives that allow the prediction of targets for a large number of compounds. RESULTS: Here, we present HitPickV2, a novel ligand-based approach for the prediction of human druggable protein targets of multiple compounds. For each query compound, HitPickV2 predicts up to 10 targets out of 2739 human druggable proteins. To that aim, HitPickV2 identifies the closest, structurally similar compounds in a restricted space within a vast chemical-protein interaction area, until 10 distinct protein targets are found. Then, HitPickV2 scores these 10 targets based on three parameters of the targets in such space: the Tanimoto coefficient (Tc) between the query and the most similar compound interacting with the target, a target rank that considers Tc and Laplacian-modified naïve Bayesian target models scores and a novel parameter introduced in HitPickV2, the number of compounds interacting with each target (occur). We present the performance results of HitPickV2 in cross-validation as well as in an external dataset. AVAILABILITY AND IMPLEMENTATION: HitPickV2 is available in www.hitpickv2.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systematic pharmacological screens uncover novel pathways involved in cerebral cavernous malformations.

Cerebral cavernous malformations (CCMs) are vascular lesions in the central nervous system causing strokes and seizures which currently can only be treated through neurosurgery. The disease arises through changes in the regulatory networks of endothelial cells that must be comprehensively understood to develop alternative, non-invasive pharmacological therapies. Here, we present the results of several unbiased small-molecule suppression screens in which we applied a total of 5,268 unique substances to CCM mutant worm, zebrafish, mouse, or human endothelial cells. We used a systems biology-based target prediction tool to integrate the results with the whole-transcriptome profile of zebrafish CCM2 mutants, revealing signaling pathways relevant to the disease and potential targets for small-molecule-based therapies. We found indirubin-3-monoxime to alleviate the lesion burden in murine preclinical models of CCM2 and CCM3 and suppress the loss-of-CCM phenotypes in human endothelial cells. Our multi-organism-based approach reveals new components of the CCM regulatory network and foreshadows novel small-molecule-based therapeutic applications for suppressing this devastating disease in patients.

A Novel Drug-Mouse Phenotypic Similarity Method Detects Molecular Determinants of Drug Effects.

The molecular mechanisms that translate drug treatment into beneficial and unwanted effects are largely unknown. We present here a novel approach to detect gene-drug and gene-side effect associations based on the phenotypic similarity of drugs and single gene perturbations in mice that account for the polypharmacological property of drugs. We scored the phenotypic similarity of human side effect profiles of 1,667 small molecules and biologicals to profiles of phenotypic traits of 5,384 mouse genes. The benchmarking with known relationships revealed a strong enrichment of physical and indirect drug-target connections, causative drug target-side effect links as well as gene-drug links involved in pharmacogenetic associations among phenotypically similar gene-drug pairs. The validation by in vitro assays and the experimental verification of an unknown connection between oxandrolone and prokineticin receptor 2 reinforces the ability of this method to provide new molecular insights underlying drug treatment. Thus, this approach may aid in the proposal of novel and personalized treatments.

Systematic Identification of Pharmacological Targets from Small-Molecule Phenotypic Screens.

Phenotypic drug discovery offers some advantages over target-based methods, mainly because it allows drug leads to be tested in systems that more closely model distinct disease states. However, a potential disadvantage is the difficulty of linking the observed phenotype to a specific cellular target. To address this problem, we developed DePick, a computational target de-convolution tool to determine targets specifically linked to small-molecule phenotypic screens. We applied DePick to eight publicly available screens and predicted 59 drug target-phenotype associations. In addition to literature-based evidence for our predictions, we provide experimental support for seven predicted associations. Interestingly, our analysis led to the discovery of a previously unrecognized connection between the Wnt signaling pathway and an aromatase, CYP19A1. These results demonstrate that the DePick approach can not only accelerate target de-convolution but also aid in discovery of new functionally relevant biological relationships.

Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes.

OBJECTIVE: Metformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin. RESEARCH DESIGN AND METHODS: We analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metformin-associated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways. RESULTS: We found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target. CONCLUSIONS: Our results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease.

Organ system heterogeneity DB: a database for the visualization of phenotypes at the organ system level.

Perturbations of mammalian organisms including diseases, drug treatments and gene perturbations in mice affect organ systems differently. Some perturbations impair relatively few organ systems while others lead to highly heterogeneous or systemic effects. Organ System Heterogeneity DB (http://mips.helmholtz-muenchen.de/Organ_System_Heterogeneity/) provides information on the phenotypic effects of 4865 human diseases, 1667 drugs and 5361 genetically modified mouse models on 26 different organ systems. Disease symptoms, drug side effects and mouse phenotypes are mapped to the System Organ Class (SOC) level of the Medical Dictionary of Regulatory Activities (MedDRA). Then, the organ system heterogeneity value, a measurement of the systemic impact of a perturbation, is calculated from the relative frequency of phenotypic features across all SOCs. For perturbations of interest, the database displays the distribution of phenotypic effects across organ systems along with the heterogeneity value and the distance between organ system distributions. In this way, it allows, in an easy and comprehensible fashion, the comparison of the phenotypic organ system distributions of diseases, drugs and their corresponding genetically modified mouse models of associated disease genes and drug targets. The Organ System Heterogeneity DB is thus a platform for the visualization and comparison of organ system level phenotypic effects of drugs, diseases and genes.

Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs.

BACKGROUND: The incomplete understanding of disease causes and drug mechanisms of action often leads to ineffective drug therapies or side effects. Therefore, new approaches are needed to improve treatment decisions and to elucidate molecular mechanisms underlying pathologies and unwanted drug effects. METHODS: We present here the first analysis of phenotypically related drug-disease pairs. The phenotypic similarity between 4,869 human diseases and 1,667 drugs was evaluated using an ontology-based semantic similarity approach to compare disease symptoms with drug side effects. We assessed and visualized the enrichment over random of clinical and molecular relationships among drug-disease pairs that share phenotypes using lift plots. To determine the associations between drug and disease classes enriched among phenotypically related pairs we employed a network-based approach combined with Fisher's exact test. RESULTS: We observed that molecularly and clinically related (for example, indication or contraindication) drugs and diseases are likely to share phenotypes. An analysis of the relations between drug mechanisms of action (MoAs) and disease classes among highly similar pairs revealed known and suspected MoA-disease relationships. Interestingly, we found that contraindications associated with high phenotypic similarity often involve diseases that have been reported as side effects of the drug, probably due to common mechanisms. Based on this, we propose a list of 752 precautions or potential contraindications for 486 drugs. CONCLUSIONS: Phenotypic similarity between drugs and diseases facilitates the proposal of contraindications and the mechanistic understanding of diseases and drug side effects.

Unveiling new biological relationships using shared hits of chemical screening assay pairs.

MOTIVATION: Although the integration and analysis of the activity of small molecules across multiple chemical screens is a common approach to determine the specificity and toxicity of hits, the suitability of these approaches to reveal novel biological information is less explored. Here, we test the hypothesis that assays sharing selective hits are biologically related. RESULTS: We annotated the biological activities (i.e. biological processes or molecular activities) measured in assays and constructed chemical hit profiles with sets of compounds differing on their selectivity level for 1640 assays of ChemBank repository. We compared the similarity of chemical hit profiles of pairs of assays with their biological relationships and observed that assay pairs sharing non-promiscuous chemical hits tend to be biologically related. A detailed analysis of a network containing assay pairs with the highest hit similarity confirmed biological meaningful relationships. Furthermore, the biological roles of predicted molecular targets of the shared hits reinforced the biological associations between assay pairs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations.

MOTIVATION: Diseases and adverse drug reactions are frequently caused by disruptions in gene functionality. Gaining insight into the global system properties governing the relationships between genotype and phenotype is thus crucial to understand and interfere with perturbations in complex organisms such as diseases states. RESULTS: We present a systematic analysis of phenotypic information of 5047 perturbations of single genes in mice, 4766 human diseases and 1666 drugs that examines the relationships between different gene properties and the phenotypic impact at the organ system level in mammalian organisms. We observe that while single gene perturbations and alterations of nonessential, tissue-specific genes or those with low betweenness centrality in protein-protein interaction networks often show organ-specific effects, multiple gene alterations resulting e.g. from complex disorders and drug treatments have a more widespread impact. Interestingly, certain cellular localizations are distinctly associated to systemic effects in monogenic disease genes and mouse gene perturbations, such as the lumen of intracellular organelles and transcription factor complexes, respectively. In summary, we show that the broadness of the phenotypic effect is clearly related to certain gene properties and is an indicator of the severity of perturbations. This work contributes to the understanding of gene properties influencing the systemic effects of diseases and drugs.

Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding.

In pharmacology, it is crucial to understand the complex biological responses that drugs elicit in the human organism and how well they can be inferred from model organisms. We therefore identified a large set of drug-induced transcriptional modules from genome-wide microarray data of drug-treated human cell lines and rat liver, and first characterized their conservation. Over 70% of these modules were common for multiple cell lines and 15% were conserved between the human in vitro and the rat in vivo system. We then illustrate the utility of conserved and cell-type-specific drug-induced modules by predicting and experimentally validating (i) gene functions, e.g., 10 novel regulators of cellular cholesterol homeostasis and (ii) new mechanisms of action for existing drugs, thereby providing a starting point for drug repositioning, e.g., novel cell cycle inhibitors and new modulators of α-adrenergic receptor, peroxisome proliferator-activated receptor and estrogen receptor. Taken together, the identified modules reveal the conservation of transcriptional responses towards drugs across cell types and organisms, and improve our understanding of both the molecular basis of drug action and human biology.

HitPick: a web server for hit identification and target prediction of chemical screenings.

MOTIVATION: High-throughput phenotypic assays reveal information about the molecules that modulate biological processes, such as a disease phenotype and a signaling pathway. In these assays, the identification of hits along with their molecular targets is critical to understand the chemical activities modulating the biological system. Here, we present HitPick, a web server for identification of hits in high-throughput chemical screenings and prediction of their molecular targets. HitPick applies the B-score method for hit identification and a newly developed approach combining 1-nearest-neighbor (1NN) similarity searching and Laplacian-modified naïve Bayesian target models to predict targets of identified hits. The performance of the HitPick web server is presented and discussed. AVAILABILITY: The server can be accessed at http://mips.helmholtz-muenchen.de/proj/hitpick. CONTACT: monica.campillos@helmholtz-muenchen.de.

Systematic identification of proteins that elicit drug side effects.

Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug-target relations to identify overrepresented protein-side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.

Novel biomarkers for pre-diabetes identified by metabolomics.

Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4×10(-4) to 2.1×10(-13). Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite-protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

Role of CXCL13 in asthma: novel therapeutic target.

BACKGROUND: B cells play an important role in allergic asthma. However, the mechanisms by which these cells are activated in the airways remain poorly understood. METHODS: We used a mouse model of ovalbumin (OVA)-induced allergic inflammation to study CXCL13 and to investigate the concentration of this chemokine in the BAL fluid derived from asthmatic and normal control subjects. RESULTS: We found that OVA-challenged mice upregulate the CXCL13/CXCR5 axis, which is associated with several changes in their airways, including recruitment of B and CD4(+) cells, development of bronchial-associated lymphoid tissue, and airway inflammation. Treating sensitized mice with an anti-CXCL13 antibody reduced cell recruitment, bronchial-associated lymphoid tissue formation, and airways inflammation. Interestingly, measurements of CXCL13 using enzyme-linked immunosorbent assay showed that levels of this cytokine were significantly elevated in BAL fluid from subjects with asthma compared with control subjects (median, 162 [range, 120-296] vs 31 [range, 120-156] pg/mL; P = .005). CONCLUSIONS: All together, these findings suggest that CXCL13 is involved in the allergic airway inflammatory process, and targeting this chemokine may constitute a novel approach in asthma.

Drug-induced regulation of target expression.

Drug perturbations of human cells lead to complex responses upon target binding. One of the known mechanisms is a (positive or negative) feedback loop that adjusts the expression level of the respective target protein. To quantify this mechanism systems-wide in an unbiased way, drug-induced differential expression of drug target mRNA was examined in three cell lines using the Connectivity Map. To overcome various biases in this valuable resource, we have developed a computational normalization and scoring procedure that is applicable to gene expression recording upon heterogeneous drug treatments. In 1290 drug-target relations, corresponding to 466 drugs acting on 167 drug targets studied, 8% of the targets are subject to regulation at the mRNA level. We confirmed systematically that in particular G-protein coupled receptors, when serving as known targets, are regulated upon drug treatment. We further newly identified drug-induced differential regulation of Lanosterol 14-alpha demethylase, Endoplasmin, DNA topoisomerase 2-alpha and Calmodulin 1. The feedback regulation in these and other targets is likely to be relevant for the success or failure of the molecular intervention.

SIREs: searching for iron-responsive elements.

The iron regulatory protein/iron-responsive element regulatory system plays a crucial role in the post-transcriptional regulation of gene expression and its disruption results in human disease. IREs are cis-acting regulatory motifs present in mRNAs that encode proteins involved in iron metabolism. They function as binding sites for two related trans-acting factors, namely the IRP-1 and -2. Among cis-acting RNA regulatory elements, the IRE is one of the best characterized. It is defined by a combination of RNA sequence and structure. However, currently available programs to predict IREs do not show a satisfactory level of sensitivity and fail to detect some of the functional IREs. Here, we report an improved software for the prediction of IREs implemented as a user-friendly web server tool. The SIREs web server uses a simple data input interface and provides structure analysis, predicted RNA folds, folding energy data and an overall quality flag based on properties of well characterized IREs. Results are reported in a tabular format and as a schematic visual representation that highlights important features of the IRE. The SIREs (Search for iron-responsive elements) web server is freely available on the web at http://ccbg.imppc.org/sires/index.html.

A side effect resource to capture phenotypic effects of drugs.

The molecular understanding of phenotypes caused by drugs in humans is essential for elucidating mechanisms of action and for developing personalized medicines. Side effects of drugs (also known as adverse drug reactions) are an important source of human phenotypic information, but so far research on this topic has been hampered by insufficient accessibility of data. Consequently, we have developed a public, computer-readable side effect resource (SIDER) that connects 888 drugs to 1450 side effect terms. It contains information on frequency in patients for one-third of the drug-side effect pairs. For 199 drugs, the side effect frequency of placebo administration could also be extracted. We illustrate the potential of SIDER with a number of analyses. The resource is freely available for academic research at http://sideeffects.embl.de.

STITCH 2: an interaction network database for small molecules and proteins.

Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/.

Drug target identification using side-effect similarity.

Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.