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Showing 4241 to 4244 of 5186 entries
Published on September 18, 2015
Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets.
Abstract: Genome-wide expression profiling has revolutionized biomedical research; vast amounts of expression data from numerous studies of many diseases are now available. Making the best use of this resource in order to better understand disease processes and treatment remains an open challenge. In particular, disease biomarkers detected in case-control studies suffer from low reliability and are only weakly reproducible. Here, we present a systematic integrative analysis methodology to overcome these shortcomings. We assembled and manually curated more than 14,000 expression profiles spanning 48 diseases and 18 expression platforms. We show that when studying a particular disease, judicious utilization of profiles from other diseases and information on disease hierarchy improves classification quality, avoids overoptimistic evaluation of that quality, and enhances disease-specific biomarker discovery. This approach yielded specific biomarkers for 24 of the analyzed diseases. We demonstrate how to combine these biomarkers with large-scale interaction, mutation and drug target data, forming a highly valuable disease summary that suggests novel directions in disease understanding and drug repurposing. Our analysis also estimates the number of samples required to reach a desired level of biomarker stability. This methodology can greatly improve the exploitation of the mountain of expression profiles for better disease analysis.
Published on September 15, 2015
GLASS: a comprehensive database for experimentally validated GPCR-ligand associations.
Abstract: MOTIVATION: G protein-coupled receptors (GPCRs) are probably the most attractive drug target membrane proteins, which constitute nearly half of drug targets in the contemporary drug discovery industry. While the majority of drug discovery studies employ existing GPCR and ligand interactions to identify new compounds, there remains a shortage of specific databases with precisely annotated GPCR-ligand associations. RESULTS: We have developed a new database, GLASS, which aims to provide a comprehensive, manually curated resource for experimentally validated GPCR-ligand associations. A new text-mining algorithm was proposed to collect GPCR-ligand interactions from the biomedical literature, which is then crosschecked with five primary pharmacological datasets, to enhance the coverage and accuracy of GPCR-ligand association data identifications. A special architecture has been designed to allow users for making homologous ligand search with flexible bioactivity parameters. The current database contains approximately 500 000 unique entries, of which the vast majority stems from ligand associations with rhodopsin- and secretin-like receptors. The GLASS database should find its most useful application in various in silico GPCR screening and functional annotation studies. AVAILABILITY AND IMPLEMENTATION: The website of GLASS database is freely available at http://zhanglab.ccmb.med.umich.edu/GLASS/. CONTACT: zhng@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Published on September 14, 2015
Stepwise substrate translocation mechanism revealed by free energy calculations of doxorubicin in the multidrug transporter AcrB.
Abstract: AcrB is the inner membrane transporter of the tripartite multidrug efflux pump AcrAB-TolC in E. coli, which poses a major obstacle to the treatment of bacterial infections. X-ray structures have identified two types of substrate-binding pockets in the porter domains of AcrB trimer: the proximal binding pocket (PBP) and the distal binding pocket (DBP), and suggest a functional rotating mechanism in which each protomer cycles consecutively through three distinct conformational states (access, binding and extrusion). However, the details of substrate binding and translocation between the binding pockets remain elusive. In this work, we performed atomic simulations to obtain the free energy profile of the translocation of an antibiotic drug doxorubicin (DOX) inside AcrB. Our simulation indicates that DOX binds at the PBP and DBP with comparable affinities in the binding state protomer, and overcomes a 3 kcal/mol energy barrier to transit between them. Obvious conformational changes including closing of the PC1/PC2 cleft and shrinking of the DBP were observed upon DOX binding in the PBP, resulting in an intermediate state between the access and binding states. Taken together, the simulation results reveal a detailed stepwise substrate binding and translocation process in the framework of functional rotating mechanism.
Published on September 9, 2015
An eigenvalue transformation technique for predicting drug-target interaction.
Abstract: The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. We further show that, in theory, eigenvalue transformation can be viewed as a feature transformation on the kernel matrix. Accordingly, although we only apply this technique to two algorithms in the current study, eigenvalue transformation also has the potential to be applied to other algorithms based on kernels.
Published on September 3, 2015
Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content.
Abstract: Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.
Published in August 2015
A Combined Omics Approach to Generate the Surface Atlas of Human Naive CD4+ T Cells during Early T-Cell Receptor Activation.
Abstract: Naive CD4(+) T cells are the common precursors of multiple effector and memory T-cell subsets and possess a high plasticity in terms of differentiation potential. This stem-cell-like character is important for cell therapies aiming at regeneration of specific immunity. Cell surface proteins are crucial for recognition and response to signals mediated by other cells or environmental changes. Knowledge of cell surface proteins of human naive CD4(+) T cells and their changes during the early phase of T-cell activation is urgently needed for a guided differentiation of naive T cells and may support the selection of pluripotent cells for cell therapy. Periodate oxidation and aniline-catalyzed oxime ligation technology was applied with subsequent quantitative liquid chromatography-tandem MS to generate a data set describing the surface proteome of primary human naive CD4(+) T cells and to monitor dynamic changes during the early phase of activation. This led to the identification of 173 N-glycosylated surface proteins. To independently confirm the proteomic data set and to analyze the cell surface by an alternative technique a systematic phenotypic expression analysis of surface antigens via flow cytometry was performed. This screening expanded the previous data set, resulting in 229 surface proteins, which were expressed on naive unstimulated and activated CD4(+) T cells. Furthermore, we generated a surface expression atlas based on transcriptome data, experimental annotation, and predicted subcellular localization, and correlated the proteomics result with this transcriptional data set. This extensive surface atlas provides an overall naive CD4(+) T cell surface resource and will enable future studies aiming at a deeper understanding of mechanisms of T-cell biology allowing the identification of novel immune targets usable for the development of therapeutic treatments.
Published in August 2015
Many InChIs and quite some feat.
Abstract:
Published in August 2015
Pilot Trial of Selecting Molecularly Guided Therapy for Patients with Non-V600 BRAF-Mutant Metastatic Melanoma: Experience of the SU2C/MRA Melanoma Dream Team.
Abstract: Targeted therapies and immunotherapies have led to significant improvements in the treatment of advanced cancers, including metastatic melanoma. However, new strategies are desperately needed to overcome therapeutic resistance to these agents, as well as to identify effective treatment approaches for cancer patients that fall outside major targetable mutational subtypes (e.g., non-V600 BRAF melanoma). One such strategy is to extend the paradigm of individually tailored, molecularly targeted therapy into a broader spectrum of melanoma patients, particularly those bearing tumors without commonly recognized therapeutic targets, as well as having failed or were ineligible for immunotherapy. In this nontreatment pilot study, next-generation sequencing (NGS) technologies were utilized, including whole genome and whole transcriptome sequencing, to identify molecular aberrations in patients with non-V600 BRAF metastatic melanoma. This information was then rationally matched to an appropriate clinical treatment from a defined pharmacopeia. Five patients with advanced non-V600 BRAF metastatic melanoma were enrolled. We demonstrated successful performance of the following during a clinically relevant time period: patient tumor biopsy, quality DNA/RNA extraction, DNA/RNA-based sequencing for gene expression analysis, analysis utilizing a series of data integration methodologies, report generation, and tumor board review with formulated treatment plan. Streamlining measures were conducted based on the experiences of enrolling, collecting specimens, and analyzing the molecular signatures of patients. We demonstrated the feasibility of using NGS to identify molecular aberrations and generate an individualized treatment plan in this patient population. A randomized treatment study utilizing lessons learned from the conduct of this pilot study is currently underway.
Published in August 2015
Visualization of multi-property landscapes for compound selection and optimization.
Abstract: Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.
Published in August 2015
Comedications alter drug-induced liver injury reporting frequency: Data mining in the WHO VigiBase.
Abstract: Polypharmacy is common, and may modify mechanisms of drug-induced liver injury. We examined the effect of these drug-drug interactions on liver safety reports of four drugs highly associated with hepatotoxicity. In the WHO VigiBase, liver event reports were examined for acetaminophen, isoniazid, valproic acid, and amoxicillin/clavulanic acid. Then, we evaluated the liver event reporting frequency of these 4 drugs in the presence of co-reported medications. Each of the 4 primary drugs was reported as having more than 2000 liver events, and co-reported with more than 600 different medications. Overall, the effect of 2275 co-reported drugs (316 drug classes) on the reporting frequency was analyzed. Decreased liver event reporting frequency was associated with 245 drugs/122 drug classes, including anti-TNFalpha, opioids, and folic acid. Increased liver event reporting frequency was associated with 170 drugs/82 drug classes; in particular, halogenated hydrocarbons, carboxamides, and bile acid sequestrants. After adjusting for age, gender, and other co-reported drug classes, multiple co-reported drug classes were significantly associated with decreased/increased liver event reporting frequency in a drug-specific/unspecific manner. In conclusion, co-reported medications were associated with changes in the liver event reporting frequency of drugs commonly associated with hepatotoxicity, suggesting that comedications may modify drug hepatic safety.
Showing 4241 to 4244 of 5186 entries