Mouse model phenotypes provide information about human drug targets

Motivation: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. Results: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. Availability and implementation: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com. Contact: leechuck@leechuck.de or roh25@aber.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  Michel Dumontier,et al.  Interoperability between Biomedical Ontologies through Relation Expansion, Upper-Level Ontologies and Automatic Reasoning , 2011, PloS one.

[2]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[3]  P. Bork,et al.  A side effect resource to capture phenotypic effects of drugs , 2010, Molecular systems biology.

[4]  P. Sanseau,et al.  Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.

[5]  Damian Smedley,et al.  MouseFinder: Candidate disease genes from mouse phenotype data , 2012, Human mutation.

[6]  Paul N. Schofield,et al.  Linking PharmGKB to Phenotype Studies and Animal Models of Disease for Drug Repurposing , 2011, Pacific Symposium on Biocomputing.

[7]  T. Gan,et al.  Diclofenac: an update on its mechanism of action and safety profile , 2010, Current medical research and opinion.

[8]  Douglas J. A. Adamson,et al.  Diclofenac antagonizes peroxisome proliferator-activated receptor-γ signaling , 2002 .

[9]  Hugo Y. K. Lam,et al.  Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes , 2012, Cell.

[10]  S. Sleigh,et al.  Repurposing Strategies for Therapeutics , 2010, Pharmaceutical Medicine.

[11]  W. Vach,et al.  A non-parametric approach for identifying differentially expressed genes in factorial microarray experiments , 2005, Genome Biology.

[12]  Ralf Paus,et al.  Scarring alopecia and the PPAR-gamma connection. , 2009, The Journal of investigative dermatology.

[13]  Steve D. M. Brown,et al.  The mouse ascending: perspectives for human-disease models , 2007, Nature Cell Biology.

[14]  Orval M. Klose,et al.  Bounds for the Variance of the Mann-Whitney Statistic , 1957 .

[15]  Cathy H. Wu,et al.  InterPro, progress and status in 2005 , 2004, Nucleic Acids Res..

[16]  Paul N. Schofield,et al.  PhenomeNET: a whole-phenome approach to disease gene discovery , 2011, Nucleic acids research.

[17]  Paul N. Schofield,et al.  An integrative, translational approach to understanding rare and orphan genetically based diseases , 2013, Interface Focus.

[18]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[19]  Boris Motik,et al.  OWL 2: The next step for OWL , 2008, J. Web Semant..

[20]  Cynthia L. Smith,et al.  Integrating phenotype ontologies across multiple species , 2010, Genome Biology.

[21]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[22]  Malorye Allison,et al.  NCATS launches drug repurposing program , 2012, Nature Biotechnology.

[23]  S. Lewis,et al.  Uberon, an integrative multi-species anatomy ontology , 2012, Genome Biology.

[24]  Dietrich Rebholz-Schuhmann,et al.  Improving Disease Gene Prioritization by Comparing the Semantic Similarity of Phenotypes in Mice with Those of Human Diseases , 2012, PloS one.

[25]  Cynthia L. Smith,et al.  The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information , 2004, Genome Biology.

[26]  P. Robinson,et al.  The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. , 2008, American journal of human genetics.

[27]  Kevin D Cooper,et al.  Hair follicle stem cell-specific PPARgamma deletion causes scarring alopecia. , 2009, The Journal of investigative dermatology.

[28]  P. Sexton,et al.  Molecular Pharmacology , 1965, Nature.

[29]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[30]  Phillip W. Lord,et al.  Semantic Similarity in Biomedical Ontologies , 2009, PLoS Comput. Biol..

[31]  Olivier Bodenreider,et al.  The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..

[32]  H Britt,et al.  A new drug classification for computer systems: the ATC extension code. , 1995, International journal of bio-medical computing.

[33]  Judith A. Blake,et al.  The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics , 2010, Nucleic Acids Res..

[34]  Catia Pesquita,et al.  Metrics for GO based protein semantic similarity: a systematic evaluation , 2008, BMC Bioinformatics.

[35]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[36]  R. Sharan,et al.  PREDICT: a method for inferring novel drug indications with application to personalized medicine , 2011, Molecular systems biology.

[37]  Ralf Paus,et al.  CommentaryScarring Alopecia and the PPAR-γ Connection , 2009 .

[38]  W. Wahli,et al.  Peroxisome proliferator-activated receptors (PPARs): from metabolic control to epidermal wound healing. , 2002, Swiss medical weekly.

[39]  L. Cardon,et al.  Use of genome-wide association studies for drug repositioning , 2012, Nature Biotechnology.

[40]  P. Bork,et al.  Systematic identification of proteins that elicit drug side effects , 2013, Molecular systems biology.

[41]  Mehryar Mohri,et al.  Confidence Intervals for the Area Under the ROC Curve , 2004, NIPS.

[42]  Russ B. Altman,et al.  Pharmacogenomics and bioinformatics: PharmGKB. , 2010, Pharmacogenomics.

[43]  R. Altman,et al.  Data-Driven Prediction of Drug Effects and Interactions , 2012, Science Translational Medicine.

[44]  Markus Krötzsch,et al.  Concurrent Classification of EL Ontologies , 2011, International Semantic Web Conference.

[45]  John M. Hancock,et al.  Using ontologies to describe mouse phenotypes , 2004, Genome Biology.

[46]  Damian Szklarczyk,et al.  STITCH 3: zooming in on protein–chemical interactions , 2011, Nucleic Acids Res..

[47]  Mark W. Moore,et al.  Towards an encyclopaedia of mammalian gene function: the International Mouse Phenotyping Consortium , 2012, Disease Models & Mechanisms.

[48]  R. Sartor,et al.  Impaired mucosal defense to acute colonic injury in mice lacking cyclooxygenase-1 or cyclooxygenase-2. , 2000, The Journal of clinical investigation.

[49]  Dietrich Rebholz-Schuhmann,et al.  Interoperability between phenotype and anatomy ontologies , 2010, Bioinform..

[50]  Paul Pavlidis,et al.  “Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks , 2012, PLoS Comput. Biol..