Abstract: The irrefutable change in the expression of brain-enriched microRNAs (miRNAs) following ischemic stroke has promoted the development of radical miRNA-based therapeutics encompassing neuroprotection and neuronal restoration. Our previous report on the systems-level prediction of miR-9 in post-stroke-induced neurogenesis served as a premise to experimentally uncover the functional role of miR-9 in post-ischemic neuronal survival and regeneration. The oxygen-glucose deprivation (OGD) in SH-SY5Y cells significantly reduced miR-9 expression, while miR-9 mimic transfection enhanced post-ischemic neuronal cell viability. The next major objective involved the execution of a drug repositioning strategy to augment miR-9 expression via structure-based screening of Food and Drug Administration (FDA)-approved drugs that bind to Histone Deacetylase 4 (HDAC4), a known miR-9 target. Glucosamine emerged as the top hit and its binding potential to HDAC4 was verified by Molecular Dynamics (MD) Simulation, Drug Affinity Responsive Target Stability (DARTS) assay, and MALDI-TOF MS. It was intriguing that the glucosamine treatment 1-h post-OGD was associated with the increased miR-9 level as well as enhanced neuronal viability. miR-9 mimic or post-OGD glucosamine treatment significantly increased the cellular proliferation (BrdU assay), while the neurite outgrowth assay displayed elongated neurites. The enhanced BCL2 and VEGF parallel with the reduced NFkappaB1, TNF-alpha, IL-1beta, and iNOS mRNA levels in miR-9 mimic or glucosamine-treated cells further substantiated their post-ischemic neuroprotective and regenerative efficacy. Hence, this study unleashes a potential therapeutic approach that integrates neuronal survival and regeneration via small-molecule-based regulation of miR-9 favoring long-term recovery against ischemic stroke.

Abstract: Online health discussion forums as information exchange repository are used by different patient groups for sharing experience and seeking advice. Their accessibility is tremendously expanded in the last decade with the rapid growth of mobile internet. Among many popular topics, "drug-drug interactions" (DDIs) forum embeds a large number of DDIs hazards patient experienced however not published. In this paper, we intend to uncover the potential DDIs from the online forums and formulate the task as a sub-graph detection problem, such that co-mentioned drugs and symptoms are modeled as vertices, along with the occurrences are modeled as weighted edges. Therefore, a connected sub-graph consisting of both symptoms and drug vertices reveals DDIs occurrence. We then propose a novel bi-submodular function to characterize the likelihood of DDI occurrence within a connected sub-graph and apply an approximated algorithm to resolve the bi-submodular optimization (BSMO). The complexity of the algorithm is nearly linear. Our extensive experiments demonstrate the effectiveness and efficiency of the proposed approach.

Abstract: In recent years, an increasing number of patients have had diabetes and cancer simultaneously; thus, it is crucial for physicians to select hypoglycemic drugs with the lowest risk of inducing cancer. Gliclazide is a widely used sulfonylurea hypoglycemic drug, but its cancer risk remains controversial. Here, we explored the primary targets of gliclazide and its associated genes by querying an available database to construct a biological network. By using DrugBank and STRING, we found two primary targets of gliclazide and 50 gliclazide-associated genes, which were then enrolled for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using WebGestalt. From this analysis, we obtained the top 15 KEGG pathways. Accurate analysis of these KEGG pathways revealed that two pathways, one linked to bladder cancer and the other linked to the phosphoinositide 3-kinase-AKT signaling pathway, are functionally associated with gliclazide, and from these we identified four overlapping genes. Finally, genomic analysis using cBioPortal showed that genomic alterations of these four overlapping genes predict poor prognosis for patients with bladder cancer. In conclusion, gliclazide should be used with caution as a hypoglycemic drug for diabetic patients with cancer, especially bladder cancer. In addition, this study provides a functional network analysis to flexibly explore drug interaction systems and estimate their safety.

Abstract: There is mounting evidence that seemingly diverse psychiatric disorders share genetic etiology, but the biological substrates mediating this overlap are not well characterized. Here we leverage the unique Integrative Psychiatric Research Consortium (iPSYCH) study, a nationally representative cohort ascertained through clinical psychiatric diagnoses indicated in Danish national health registers. We confirm previous reports of individual and cross-disorder single-nucleotide polymorphism heritability for major psychiatric disorders and perform a cross-disorder genome-wide association study. We identify four novel genome-wide significant loci encompassing variants predicted to regulate genes expressed in radial glia and interneurons in the developing neocortex during mid-gestation. This epoch is supported by partitioning cross-disorder single-nucleotide polymorphism heritability, which is enriched at regulatory chromatin active during fetal neurodevelopment. These findings suggest that dysregulation of genes that direct neurodevelopment by common genetic variants may result in general liability for many later psychiatric outcomes.

Abstract: Computational analysis of human-virus protein-protein interaction (PPI) data is an effective way toward systems understanding the molecular mechanism of viral infection. Previous work has mainly focused on characterizing the global properties of viral targets within the entire human PPI network. In comparison, how viruses manipulate host local networks (e.g., human protein complexes) has been rarely addressed from a computational perspective. By mainly integrating information about human-virus PPIs, human protein complexes, and gene expression profiles, we performed a large-scale analysis of virally targeted complexes (VTCs) related to five common human-pathogenic viruses, including influenza A virus subtype H1N1, human immunodeficiency virus type 1, Epstein-Barr virus, human papillomavirus, and hepatitis C virus. We found that viral targets are enriched within human protein complexes. We observed in the context of VTCs that viral targets tended to have a high within-complex degree and to be scaffold and housekeeping proteins. Complexes that are essential for viral propagation were simultaneously targeted by multiple viruses. We characterized the periodic expression patterns of VTCs and provided the corresponding candidates that may be involved in the manipulation of the host cell cycle. As a potential application of the current analysis, we proposed a VTC-based antiviral drug target discovery strategy. Finally, we developed an online VTC-related platform known as VTcomplex (http://zzdlab.com/vtcomplex/index.php or http://systbio.cau.edu.cn/vtcomplex/index.php). We hope that the current analysis can provide new insights into the global landscape of human-virus PPIs at the VTC level and that the developed VTcomplex will become a vital resource for the community. IMPORTANCE Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections.

Abstract: BACKGROUND: In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples. RESULTS: In the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD. CONCLUSIONS: The proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

Abstract: Potent Ebolavirus (EBOV) inhibitors will help to curtail outbreaks such as that which occurred in 2014-16 in West Africa. EBOV has on its surface a single glycoprotein (GP) critical for viral entry and membrane fusion. Recent high-resolution complexes of EBOV GP with a variety of approved drugs revealed that binding to a common cavity prevented fusion of the virus and endosomal membranes, inhibiting virus infection. We performed docking experiments, screening a database of natural compounds to identify those likely to bind at this site. Using both inhibition assays of HIV-1-derived pseudovirus cell entry and structural analyses of the complexes of the compounds with GP, we show here that two of these compounds attach in the common binding cavity, out of eight tested. In both cases, two molecules bind in the cavity. The two compounds are chemically similar, but the tighter binder has an additional chlorine atom that forms good halogen bonds to the protein and achieves an IC50 of 50 nM, making it the most potent GP-binding EBOV inhibitor yet identified, validating our screening approach for the discovery of novel antiviral compounds.

Abstract: BACKGROUND: The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics. METHODS: To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome. RESULTS: We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses. CONCLUSIONS: Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

Abstract: Blockade of the human ether-a-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drugs. In this study, we develop a deep learning approach, termed deephERG, for prediction of hERG blockers of small molecules in drug discovery and postmarketing surveillance. In total, we assemble 7,889 compounds with well-defined experimental data on the hERG and with diverse chemical structures. We find that deephERG models built by a multitask deep neural network (DNN) algorithm outperform those built by single-task DNN, naive Bayes (NB), support vector machine (SVM), random forest (RF), and graph convolutional neural network (GCNN). Specifically, the area under the receiver operating characteristic curve (AUC) value for the best model of deephERG is 0.967 on the validation set. Furthermore, based on 1,824 U.S. Food and Drug Administration (FDA) approved drugs, 29.6% drugs are computationally identified to have potential hERG inhibitory activities by deephERG, highlighting the importance of hERG risk assessment in early drug discovery. Finally, we showcase several novel predicted hERG blockers on approved antineoplastic agents, which are validated by clinical case reports, experimental evidence, and the literature. In summary, this study presents a powerful deep learning-based tool for risk assessment of hERG-mediated cardiotoxicities in drug discovery and postmarketing surveillance.

Abstract: Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations.