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Network-based method to infer the contributions of proteins to the etiology of drug side effects

Frontiers of Electrical and Electronic Engineering in China 2015年第3期10卷 124-134页

作者： Rui Li (1) Ting Chen (1) (2) Shao Li (1) 1.MOE Key Laboratory of Bioinformatics and Bioinformatics Division TNLIST Department of Automation Tsinghua University Beijing 100084 China 2.Program in Computational Biology and Bioinformatics University of Southern California Los Angeles CA 90089 USA

Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development. Currently, however, computational methods are still unavailable to assess drug-protein interactions with the aim of globally inferring the contributions of various classes of proteins toward the etiology of side effects. In this work, we integrated data reflecting drug-side effect relationships, drug- target relationships, and protein-protein interactions to develop a novel network-based probabilistic model, SidePro, to evaluate the contributions of proteins toward the etiology of side effects. For a given side effect, the method applies an expectation--maximization algorithm and a diffusion kernel-based approach to estimate each protein＇s contribution. We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database. We also studied a specific side effect, nephrotoxicity, which is known to be associated with the irrational use of the Chinese herbal compound triptolide, a diterpenoid epoxide in the Thunder of God Vine, Tripterygium wilfordii （Lei-Gong-Teng）. Using triptolide as an example, we scored the target proteins of triptolide using our model and investigated the high-scoring proteins and their related biological processes. The results demonstrated that our model could differentiate between the potential side effect targets and therapeutic targets of triptolide. Overall, the proposed model could accurately pinpoint the molecular mechanisms of drug side effects, thus making contribution to safe and effective drug development.

关键词： network pharmacology drug targets side effects triptolide

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Network Analysis as a Grand Unifier in Biomedical Data Science

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Biomedical Data Science 1000年第1期1卷 153-180页

作者： Patrick McGillivray Declan Clarke William Meyerson Jing Zhang Donghoon Lee Mengting Gu Sushant Kumar Holly Zhou Mark Gerstein 1Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA email: mark@*** 2Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA 3Department of Computer Science Yale University New Haven Connecticut 06520 USA

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

关键词： network analysis molecular interaction systems biology cross-disciplinary research network medicine

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Perspectives on Human Population Structure at the Cusp of the Sequencing Era

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Genomics and Human Genetics 1000年第1期12卷 245-274页

作者： John Novembre Sohini Ramachandran *Both authors contributed equally. 1Department of Ecology and Evolutionary Biology and Interdepartmental Program on Bioinformatics University of California Los Angeles California 90403 email: jnovembre@ucla.edu 2Department of Ecology and Evolutionary Biology and Center for Computational Molecular Biology Brown University Providence Rhode Island 02912 email: sramachandran@brown.edu

Human groups show structured levels of genetic similarity as a consequence of factors such as geographical subdivision and genetic drift. Surveying this structure gives us a scientific perspective on human origins, sheds light on evolutionary processes that shape both human adaptation and disease, and is integral to effectively carrying out the mission of global medical genetics and personalized medicine. Surveys of population structure have been ongoing for decades, but in the past three years, single-nucleotide-polymorphism (SNP) array technology has provided unprecedented detail on human population structure at global and regional scales. These studies have confirmed well-known relationships between distantly related populations and uncovered previously unresolvable relationships among closely related human groups. SNPs represent the first dense genome-wide markers, and as such, their analysis has raised many challenges and insights relevant to the study of population genetics with whole-genome sequences. Here we draw on the lessons from these studies to anticipate the directions that will be most fruitful to pursue during the emerging whole-genome sequencing era.

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Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

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Biomedical Data Science 1000年第1期5卷 141-161页

作者： Bian Li Bowen Jin John A. Capra William S. Bush 1Department of Biological Sciences and Center for Structural Biology Vanderbilt University Nashville Tennessee USA 2Graduate Program in Systems Biology and Bioinformatics Department of Nutrition School of Medicine Case Western Reserve University Cleveland Ohio USA 3Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics University of California San Francisco California USA email: tony@*** 4Cleveland Institute for Computational Biology Department of Population and Quantitative Health Sciences Case Western Reserve University Cleveland Ohio USA email: wsb36@case.edu

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integratethese data sources will play increasingly important roles in disease gene discovery and variant interpretation.

关键词： protein 3D structure population genetics variant interpretation disease gene discovery data integration

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Evolution and Medicine: An Inquiry-Based High School Curriculum Supplement

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Evolution: Education and Outreach 2011年第4期4卷 603-612页

作者： Beardsley, Paul M. Stuhlsatz, Molly A.M. Kruse, Rebecca A. Eckstrand, Irene A. Gordon, Shefa D. Odenwald, Ward F. BSCS (Biological Sciences Curriculum Study) 5415 Mark Dabling Blvd Colorado Springs 80918 CO United States National Institute of General Medical Sciences Division of Genetics and Developmental Biology Center for Bioinformatics and Computational Biology 45 Center Drive Bethesda 20892-6200 MD United States National Eye Institute Office of Program Planning and Analysis 9000 Rockville Pike Bethesda 20892-6200 MD United States National Institute of Neurological Disorders and Stroke Neural Cell-Fate Determinants Section Bethesda 20892-6200 MD United States Center for Excellence in Mathematics and Science Teaching and Department of Biological Sciences CEMaST 4-2-515 California State Polytechnic University 3801 W. Temple Ave. Pomona 91768 CA United States

Evolution and Medicine is a curriculum supplement designed by the National Institutes of Health (NIH) and the Biological Sciences Curriculum Study (BSCS) for high school students. The supplement is freely available from NIH's Office of Science Education (OSE) as a part of the NIH curriculum supplement series. Development of the supplement was a collaborative effort that included input from a panel of experts in medicine, evolution, education, and educational technology. In total, the curriculum supplement includes five inquiry-based lessons that are integrated into the BSCS 5E instructional model (based on constructivist learning theory). The goal was to develop a 2-week curriculum to help students understand major concepts of evolution using the dynamic, modern, and relevant context of medicine. A diverse group of students and teachers across the US participated in a formative evaluation of a field test version of the curriculum. High school students made significant learning gains from pretest to posttest, with a relatively large effect size for student understanding of common ancestry and a relatively small effect size for student understanding of natural selection. There was no statistically significant difference in achievement gains between white students and all other racial/ethnic categories. Overall, the evaluation suggests that a curriculum that emphasizes the role of evolution in medicine, uses a constructivist instructional model, and is grounded in inquiry is relatively well-received by teachers and students and shows promise for increasing student learning in evolution. © 2011 Springer Science+Business Media, LLC.

关键词： Common ancestry Evolution Evolution and medicine High school Inquiry Instructional model Natural selection

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Causes and Consequences of Apparent Timescaling Across All Estimated Evolutionary Rates

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Ecology, Evolution, and Systematics 1000年第1期52卷 587-609页

作者： Luke J. Harmon Matthew W. Pennell L. Francisco Henao-Diaz Jonathan Rolland Breanna N. Sipley Josef C. Uyeda 1Institute for Bioinformatics and Evolutionary Studies (IBEST) and Department of Biological Sciences University of Idaho Moscow Idaho 83844 USA email: lukeh@uidaho.edu 2Department of Zoology and Biodiversity Research Centre University of British Columbia Vancouver British Columbia V6T 1Z4 Canada 3Laboratoire Evolution et Diversité Biologique CNRS UMR5174 Université Toulouse III–Paul Sabatier 31062 Toulouse France 4Program for Bioinformatics and Computational Biology University of Idaho Moscow Idaho 83844 USA 5Department of Biological Sciences Virginia Tech University Blacksburg Virginia 24061 USA

Evolutionary rates play a central role in connecting micro- and macroevolution. All evolutionary rate estimates, including rates of molecular evolution, trait evolution, and lineage diversification, share a similar scaling pattern with time: The highest rates are those measured over the shortest time interval. This creates a disconnect between micro- and macroevolution, although the pattern is the opposite of what some might expect: Patterns of change over short timescales predict that evolution has tremendous potential to create variation and that potential is barely tapped by macroevolution. In this review, we discuss this shared scaling pattern across evolutionary rates. We break down possible explanations for scaling into two categories, estimation error and model misspecification, and discuss how both apply to each type of rate. We also discuss the consequences of this ubiquitous pattern, which can lead to unexpected results when comparing ratesover different timescales. Finally, after addressing purely statistical concerns, we explore a few possibilities for a shared unifying explanation across the three types of rates that results from a failure to fully understand and account for how biological processes scale over time.

关键词： speciation extinction genetics phenotypes microevolution macroevolution

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Erratum: Yellow Dwarf Viruses of Cereals: Taxonomy and Molecular Mechanisms

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Phytopathology 1000年第1期

作者： W. Allen Miller Zachary Lozier 2Bioinformatics and Computational Biology Program Iowa State University Ames Iowa USA 1Department of Plant Pathology and Microbiology Iowa State University Ames Iowa USA email: wamiller@iastate.edu

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'Isotopo' a database application for facile analysis and management of mass isotopomer data

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Database : the journal of biological databases and curation 2014年第2014期2014卷 bau077页

作者： Ahmed, Zeeshan Zeeshan, Saman Huber, Claudia Hensel, Michael Schomburg, Dietmar Münch, Richard Eylert, Eva Eisenreich, Wolfgang Dandekar, Thomas Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Universität München Division of Microbiology University of Osnabrück Department of Bioinformatics and Biochemistry Technical University Braunschweig Institute for Microbiology Biozentrum Technical University Braunschweig Germany and Computational biology and structures program European Molecular Biology Laboratory Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Universität München Division of Microbiology University of Osnabrück Department of Bioinformatics and Biochemistry Technical University Braunschweig Institute for Microbiology Biozentrum Technical University Braunschweig Germany and Computational biology and structures program European Molecular Biology Laboratory 97074 Wuerzburg 97074 Wuerzburg Carl-Neuberg-Str. 1 Lichtenbergstraße 4 Barbarastraße 11 Gebäude 36 49076 Osnabrück Langer Kamp 19B 2. Obergeschoss Spielmannstraße 7 Meyerhofstr. 1 97074 Wuerzburg 97074 Wuerzburg Carl-Neuberg-Str. 1 Lichtenbergstraße 4 Barbarastraße 11 Gebäude 36 49076 Osnabrück Langer Kamp 19B 2. Obergeschoss Spielmannstraße 7 Braunschweig 38106 Hanover Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Univer

UNLABELLED: The composition of stable-isotope labelled isotopologues/isotopomers in metabolic products can be measured by mass spectrometry and supports the analysis of pathways and fluxes. As a prerequisite, the original mass spectra have to be processed, managed and stored to rapidly calculate, analyse and compare isotopomer enrichments to study, for instance, bacterial metabolism in infection. For such applications, we provide here the database application 'Isotopo'. This software package includes (i) a database to store and process isotopomer data, (ii) a parser to upload and translate different data formats for such data and (iii) an improved application to process and convert signal intensities from mass spectra of (13)C-labelled metabolites such as tertbutyldimethylsilyl-derivatives of amino acids. Relative mass intensities and isotopomer distributions are calculated applying a partial least square method with iterative refinement for high precision data. The data output includes formats such as graphs for overall enrichments in amino acids. The package is user-friendly for easy and robust data management of multiple experiments.;AVAILABILITY: The 'Isotopo' software is available at the following web link (section Download): http://***/bioinformatics/isotopo/. The package contains three additional files: software executable setup (installer), one data set file (discussed in this article) and one excel file (which can be used to convert data from excel to '.iso' format). The 'Isotopo' software is compatible only with the Microsoft Windows operating system.;DATABASE URL: http://***/bioinformatics/isotopo/. © The Author(s) 2014. Published by Oxford University Press.

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The Functional Genomic Circuitry of Human Glioblastoma Stem Cells

SSRN

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SSRN 2018年

作者： MacLeod, Graham Bozek, Danielle A. Rajakulendran, Nishani Monteiro, Vernon Ahmadi, Moloud Steinhart, Zachary Kushida, Michelle M. Yu, Helen Coutinho, Fiona J. Restall, Ian Hao, Xiaoguang Hart, Traver Luchman, H. Artee Weiss, Samuel Dirks, Peter B. Angers, Stephane Leslie Dan Faculty of Pharmacy University of Toronto ONM5S 3M2 Canada Hotchkiss Brain Institute Department of Cell Biology and Anatomy Cumming School of Medicine University of Calgary CalgaryABT2N 4N1 Canada Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain Tumour Research Centre The Hospital for Sick Children TorontoONM5G 0A4 Canada Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Molecular Genetics Department of Laboratory Medicine and Pathobiology Division of Neurosurgery Department of Surgery University of Toronto ON Canada Division of Neurosurgery The Hospital for Sick Children TorontoONM5G 0A4 Canada Department of Biochemistry Faculty of Medicine University of Toronto ONM5G 0A4 Canada

Successful glioblastoma (GBM) therapies have remained elusive due to limitations in understanding mechanisms of growth and survival of the tumorigenic population. Using CRISPR-Cas9 approaches in patientderived GBM stem cells to interrogate function of the coding genome, we identify diverse actionable pathways responsible for growth that reveal the gene-essential circuitry of GBM stemness. In particular, we describe the Sox developmental transcription factor family;H3K79 methylation by DOT1L;and ufmylation stress responsiveness programs as essential for GBM stemness. Additionally, we find mechanisms of temozolomide resistance and sensitivity that could lead to combination strategies with this standard of care treatment. By reaching beyond static genome analysis of bulk tumors, with a genome wide functional approach, we dive deep into a broad range of biological processes to provide new understanding of GBM growth and treatment resistance. © 2018, The Authors. All rights reserved.

关键词： Stem cells

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Genomic profiling in advanced stage non-small-cell lung cancer patients with platinum-based chemotherapy identifies germline variants with prognostic value in SMYD2

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Cancer Treatment and Research Communications 2018年 15卷 21-31页

作者： Galván-Femenía, Iván Guindo, Marta Duran, Xavier Calabuig-Fariñas, Sílvia Mercader, Josep Maria Ramirez, Jose Luis Rosell, Rafael Torrents, David Carreras, Anna Kohno, Takashi Jantus-Lewintre, Eloisa Camps, Carlos Perucho, Manuel Sumoy, Lauro Yokota, Jun de Cid, Rafael Genomes For life-GCAT Lab. Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain Barcelona Supercomputing Center (BSC-CNS) Joint BSC-CRG-IRB Research Program in Computational Biology Carrer de Jordi Girona 29-31 Barcelona 08034 Spain Department of Medical Oncology Hospital General Universitario de Valencia Avenida Tres Cruces 2 València 46014 Spain Molecular Oncology Laboratory Fundación Hospital General Universitario de Valencia Avda. Tres Cruces s/n València 46014 Spain Department of Pathology Universitat de València Av. de Blasco Ibáñez 13 València 46010 Spain Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain ICREA Catalan Institution for Research and Advanced Studies Spain Division of Genome Biology National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo 104-0045 Japan Molecular Oncology Laboratory Fundación Hospital General Universitario de València Avda. Tres Cruces s/n València 46014 Spain Department of Biotechnology Universitat Politècnica de València Camí de Vera s/n València 46022 Spain Department of Medicine Universitat de València Av. de Blasco Ibáñez 13 València 46010 Spain Genomics and Bioinformatics. Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain Programs in Metabolism and Medical & Population Genetics Broad Institute of Harvard and MIT Cambridge Massachusetts United States Diabetes Unit and Center for Human Genetic Research Massachusetts General Hospital Boston Massachuse

Objective: The aim of the study was to investigate the relationship between germline variations as a prognosis biomarker in patients with advanced Non-Small-Cell-Lung-Cancer (NSCLC) subjected to first-line platinum-based treatment. Materials and Methods: We carried out a two-stage genome-wide-association study in non-small-cell lung cancer patients with platinum-based chemotherapy in an exploratory sample of 181 NSCLC patients from Caucasian origin, followed by a validation on 356 NSCLC patients from the same ancestry (Valencia, Spain). Results: We identified germline variants in SMYD2 as a prognostic factor for survival in patients with advanced NSCLC receiving chemotherapy. SMYD2 alleles are associated to a decreased overall survival and with a reduced Time to Progression. In addition, enrichment pathway analysis identified 361 variants in 40 genes to be involved in poorer outcome in advanced-stage NSCLC patients. Conclusion: Germline SMYD2 alleles are associated with bad clinical outcome of first-line platinum-based treatment in advanced NSCLC patients. This result supports the role of SMYD2 in the carcinogenic process, and might be used as prognostic signature directing patient stratification and the choice of therapy. A two-Stage Genome wide association study in Caucasian population reveals germline genetic variation in SMYD2 associated to progression disease in first-line platinum-based treatment in advanced NSCLC patients. SMYD2 profiling might have prognostic / predictive value directing choice of therapy and enlighten current knowledge on pathways involved in human carcinogenesis as well in resistance to chemotherapy. © 2018 The Authors

关键词： Advanced stage Genome-Wide-Association Studies Lung cancer NSCLC Prognostic factors

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