Abstract: The hedgehog (Hh) pathway plays a central role in the development and repair of our bodies. Therefore, dysregulation of the Hh pathway is responsible for many developmental diseases and cancers. Basal cell carcinoma and medulloblastoma have well-established links to the Hh pathway, as well as many other cancers with Hh-dysregulated subtypes. A smoothened (SMO) receptor plays a central role in regulating the Hh signaling in the cells. However, the complexities of the receptor structural mechanism of action and other pathway members make it difficult to find Hh pathway inhibitors efficient in a wide range. Recent crystal structure of SMO with cholesterol indicates that it may be a natural ligand for SMO activation. Structural similarity of fluorinated corticosterone derivatives to cholesterol motivated us to study the effect of dexamethasone, fludrocortisone, and corticosterone on the Hh pathway activity. We identified an inhibitory effect of these three drugs on the Hh pathway using a functional assay in NIH3T3 glioma response element cells. Studies using BODIPY-cyclopamine and 20(S)-hydroxy cholesterol [20(S)-OHC] as competitors for the transmembrane (TM) and extracellular cysteine-rich domain (CRD) binding sites showed a non-competitive effect and suggested an alternative or allosteric binding site for the three drugs. Furthermore, the three steroids showed an additive effect on Hh pathway inhibition when tested in combination with cyclopamine. Our study reports the antagonistic effect of dexamethasone, fludrocortisone, and corticosterone on the Hh pathway using functional assay and confirmed that they do not bind to the CRD or adjacent TM binding cavities of SMO. The study also suggests that dexamethasone could be additionally beneficial as the adjuvant therapy for cancer patients with an established link to the dysregulated Hh pathway.

Abstract: Bladder cancer (BC) is one of the most common urogenital malignancies. However, present studies of its multiple gene interaction and cellular pathways remain unable to accurately verify the genesis and the development of BC. The aim of the present study was to investigate the genetic signatures of BC and identify its potential molecular mechanisms. The gene expression profiles of GSE31189 were downloaded from the Gene Expression Omnibus database. The GSE31189 dataset contained 92 samples, including 52 BC and 40 non-cancerous urothelial cells. To further examine the biological functions of the identified differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and a protein-protein interaction (PPI) network was mapped using Cytoscape software. In total, 976 DEGs were identified in BC, including 457 upregulated genes and 519 downregulated genes. GO and KEGG pathway enrichment analyses indicated that upregulated genes were significantly enriched in the cell cycle and the negative regulation of the apoptotic process, while the downregulated genes were mainly involved in cell proliferation, cell adhesion molecules and oxidative phosphorylation pathways (P<0.05). From the PPI network, the 12 nodes with the highest degrees were screened as hub genes; these genes were involved in certain pathways, including the chemokine-mediated signaling pathway, fever generation, inflammatory response and the immune response nucleotide oligomerization domain-like receptor signaling pathway. The present study used bioinformatics analysis of gene profile datasets and identified potential therapeutic targets for BC.

Abstract: STUDY OBJECTIVE: Serotonergic adverse drug events (ADEs) are caused by enhanced intrasynaptic concentrations of 5-hydroxytryptamine (5-HT). No systematic process currently exists for evaluating cumulative 5-HT and off-target toxicity of serotonergic drugs. The primary study aim was to create a Serotonergic Expanded Bioactivity Matrix (SEBM) by using a molecular bioinformatics, polypharmacologic approach for assessment of the participation of individual 5-HT drugs in serotonin syndrome (SS) reports. DATA SOURCES: Publicly available databases including the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), ChEMBL, DrugBank, PubChem, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were queried for computational and pharmacologic data. DESIGN: An in-house bioinformatics TargetSearch program ( http://dxulab.org/software) was used to characterize 71 serotonergic drugs interacting at 13 serotonin receptor subtypes and serotonin reuptake transporter protein (SERT). In addition, off-target interactions at norepinephrine transporter (NET), monoamine oxidase (MAO), and muscarinic receptors were included to define seven polypharmacological drug cohorts. Serotonin syndrome reports for each serotonergic drug were extracted from FAERS by using the Sternbach and Hunter criteria. MEASUREMENTS AND MAIN RESULTS: A proportional reporting adverse drug reaction (ADR) ratio (PRR) was calculated from each drug's total ADEs and SS case reports and aggregated by drug bioactivity cohorts. Triple-receptor interactions had a disproportionately higher number of SS cases using both the Hunter criteria (mean PRR 1.72, 95% CI 1.05-2.39) and Sternbach (mean PRR 1.54, 95% CI 1.29-1.79). 5-Hydroxytryptamine agonists were associated with a significantly lower proportion of SS cases using the Hunter and Sternbach criteria, respectively (mean PRR 0.49, 95% CI 0.17-0.81 and mean PRR 0.49, 95% CI 0.15-0.83). Drugs with disproportionately higher participation in SS vary considerably between the two diagnostic criteria. CONCLUSION: The SEBM model suggests a possible polypharmacological role in SS. Although further research is needed, off-target receptor activity may help explain differences in severity of toxicity and clinical presentation.

Abstract: BACKGROUND/AIM: Genomic DNA copy number alterations (CNAs) are frequent in tumors and have been catalogued by The Cancer Genome Atlas project. Emergence of chemoresistance frequently renders drug therapies ineffective. MATERIALS AND METHODS: We analyzed how CNAs recurrently found in the genomes of TCGA patients of thirty-one tumor types affect protein targets of antineoplastic (AN) agents. RESULTS: CNA deletions more frequently affected the targets of AN agents than CNA amplifications. Interestingly, in seven tumors we observed signs of compensatory CNAs. For example, in glioblastoma multiforme, two target genes (FLT1, FLT3) of the experimental drug sorafenib were recurrently deleted, whereas another target (KDR) of sorafenib was recurrently amplified. In renal clear cell carcinoma, the target FLT1 of pazopanib, sunitinib, sorafenib, and axitinib was recurrently deleted, whereas FLT4 bound by the same drugs, was recurrently amplified. CONCLUSION: Deletions of AN target proteins can be compensated by amplification of alternative targets.

Abstract: Despite decades of clinical use and research, the mechanism of action (MOA) of antidepressant medications remains poorly understood. Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) are the most commonly prescribed antidepressants-atypical antidepressants such as bupropion have also proven effective, while exhibiting a divergent clinical phenotype. The difference in phenotypic profiles presumably lies in the differences among the MOAs of SSRIs/SNRIs and bupropion. We integrated the ensemble of bupropion's affinities for all its receptors with the expression levels of those targets in nervous system tissues. This "combined target tissue" profile of bupropion was compared to those of duloxetine, fluoxetine, and venlafaxine to isolate the unique target tissue effects of bupropion. Our results suggest that the three monoamines-serotonin, norepinephrine, and dopamine-all contribute to the common antidepressant effects of SSRIs, SNRIs, and bupropion. At the same time, bupropion is unique in its action on 5-HT3AR in the dorsal root ganglion and nicotinic acetylcholine receptors in the pineal gland. These unique tissue-specific activities may explain unique therapeutic effects of bupropion, such as pain management and smoking cessation, and, given melatonin's association with nicotinic acetylcholine receptors and depression, highlight the underappreciated role of the melatonergic system in bupropion's MOA.

Abstract: Constructing gene interaction networks (GINs) from high-throughput gene expression data is an important and challenging problem in systems biology. Existing algorithms produce networks that either have undirected and unweighted edges, or else are constrained to contain no cycles, both of which are biologically unrealistic. In the present paper we propose a new algorithm, based on a concept from probability theory known as the varphi-mixing coefficient, that produces networks whose edges are weighted and directed, and are permitted to contain cycles. Specifically, we inferred networks for two subtypes of lung cancer small cell (SCLC) and non-small cell (NSCLC) as well as normal lung tissue. Then we compared with the outcomes of siRNA screening of 19,000+ genes on 11 NSCLC cell lines, and found that the higher the degree of a gene in the inferred network, the more essential it is to the survival of a cell. We also analyzed data from a ChIP-Seq experiment to determine putative downstream targets of ASCL1. The SCLC network was enriched for ChIP-seq neighbors of this oncogenic transcription factor, but not in the NSCLC network. We also reverse-engineered whole-genome interaction networks for two distinct subtypes of breast cancer, namely Luminal-A and Basal (also known as triple negative).

Abstract: The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

Abstract: BACKGROUND: The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity. RESULTS: In this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions. CONCLUSIONS: Our model's capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process.

Abstract: Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.

Abstract: Understanding the genetic basis of complex diseases is challenging. Prior work shows that disease-related proteins do not typically function in isolation. Rather, they often interact with each other to form a network module that underlies dysfunctional mechanistic pathways. Identifying such disease modules will provide insights into a systems-level understanding of molecular mechanisms of diseases. Owing to the incompleteness of our knowledge of disease proteins and limited information on the biological mediators of pathobiological processes, the key proteins (seed proteins) for many diseases appear scattered over the human protein-protein interactome and form a few small branches, rather than coherent network modules. In this paper, we develop a network-based algorithm, called the Seed Connector algorithm (SCA), to pinpoint disease modules by adding as few additional linking proteins (seed connectors) to the seed protein pool as possible. Such seed connectors are hidden disease module elements that are critical for interpreting the functional context of disease proteins. The SCA aims to connect seed disease proteins so that disease mechanisms and pathways can be decoded based on predicted coherent network modules. We validate the algorithm using a large corpus of 70 complex diseases and binding targets of over 200 drugs, and demonstrate the biological relevance of the seed connectors. Lastly, as a specific proof of concept, we apply the SCA to a set of seed proteins for coronary artery disease derived from a meta-analysis of large-scale genome-wide association studies and obtain a coronary artery disease module enriched with important disease-related signaling pathways and drug targets not previously recognized.