Discovery of the neuroprotective effects of alvespimycin by computational prioritization of potential anti‐parkinson agents

Based on public gene expression data, we propose a computational approach to optimize gene expression signatures for the use with Connectivity Map (CMap) to reposition drugs or discover lead compounds for Parkinson's disease. This approach integrates genetic information from the Gene Expression Omnibus (GEO) database, the Parkinson's disease gene expression database (ParkDB), the Online Mendelian Inheritance in Man (OMIM) database and the Comparative Toxicogenomics Database (CTD), with the aim of identifying a set of interesting genes for use in computational drug screening via CMap. The results showed that CMap, using the top 20 differentially expressed genes identified by our approach as a gene expression signature, outperformed the same method using all differentially expressed genes (n = 535) as a signature. Utilizing this approach, the candidate compound alvespimycin (17‐DMAG) was selected for experimental evaluation in a model of rotenone‐induced toxicity in human SH‐SY5Y neuroblastoma cells and isolated rat brain mitochondria. The results showed that 17‐DMAG significantly attenuated rotenone‐induced toxicity, as reflected by the increase of cell viability, the reduction of intracellular reactive oxygen species generation and a reduction in mitochondrial respiratory dysfunction. In conclusion, this computational method provides an effective systematic approach for drug repositioning or lead compound discovery for Parkinson's disease, and the discovery of the neuroprotective effects of 17‐DMAG supports the practicability of this method.

[1]  P J McLean,et al.  Molecular chaperones as rational drug targets for Parkinson's disease therapeutics. , 2010, CNS & neurological disorders drug targets.

[2]  Todd B. Sherer,et al.  Mechanism of Toxicity in Rotenone Models of Parkinson's Disease , 2003, The Journal of Neuroscience.

[3]  Weixiong Zhang,et al.  A general co-expression network-based approach to gene expression analysis: comparison and applications , 2010, BMC Systems Biology.

[4]  P. McLean,et al.  Molecular Chaperones and Co-Chaperones in Parkinson Disease , 2012, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[5]  L. Moran,et al.  Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson’s disease , 2006, Neurogenetics.

[6]  S. Endo,et al.  L347P PINK1 mutant that fails to bind to Hsp90/Cdc37 chaperones is rapidly degraded in a proteasome-dependent manner , 2008, Neuroscience Research.

[7]  Mauro Fasano,et al.  Cellular models to investigate biochemical pathways in Parkinson’s disease , 2012, The FEBS journal.

[8]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[9]  William L Jorgensen,et al.  Efficient drug lead discovery and optimization. , 2009, Accounts of chemical research.

[10]  Monica L Guzman,et al.  Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data. , 2008, Blood.

[11]  Jiansong Fang,et al.  In silico Target Fishing for the Potential Targets and Molecular Mechanisms of Baicalein as an Antiparkinsonian Agent: Discovery of the Protective Effects on NMDA Receptor‐Mediated Neurotoxicity , 2013, Chemical biology & drug design.

[12]  David Gomez-Cabrero,et al.  ParkDB: a Parkinson’s disease gene expression database , 2011, Database J. Biol. Databases Curation.

[13]  Jing-nan Sun,et al.  Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. , 2006, Journal of environmental sciences.

[14]  David S. Wishart,et al.  Nucleic Acids Research Polysearch: a Web-based Text Mining System for Extracting Relationships between Human Diseases, Genes, Mutations, Drugs Polysearch: a Web-based Text Mining System for Extracting Relationships between Human Diseases, Genes, Mutations, Drugs and Metabolites , 2008 .

[15]  D. Maraganore,et al.  A Genomic Pathway Approach to a Complex Disease: Axon Guidance and Parkinson Disease , 2007, PLoS genetics.

[16]  Yang Song,et al.  Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery , 2011, Nucleic Acids Res..

[17]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2013 , 2012, Nucleic Acids Res..

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

[19]  P. Robinson,et al.  Walking the interactome for prioritization of candidate disease genes. , 2008, American journal of human genetics.

[20]  P. Auluck,et al.  Pharmacological prevention of Parkinson disease in Drosophila , 2002, Nature Medicine.

[21]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[22]  Luonan Chen,et al.  Network-based drug repositioning. , 2013, Molecular bioSystems.

[23]  D. Frank Hsu,et al.  Consensus Scoring Criteria for Improving Enrichment in Virtual Screening , 2005, J. Chem. Inf. Model..

[24]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[25]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[26]  K. Bhalla,et al.  Phase I study of the heat shock protein 90 inhibitor alvespimycin (KOS-1022, 17-DMAG) administered intravenously twice weekly to patients with acute myeloid leukemia , 2010, Leukemia.

[27]  P. Workman,et al.  A Phase I Study of the Heat Shock Protein 90 Inhibitor Alvespimycin (17-DMAG) Given Intravenously to Patients with Advanced Solid Tumors , 2011, Clinical Cancer Research.

[28]  R. Piro,et al.  Computational approaches to disease‐gene prediction: rationale, classification and successes , 2012, The FEBS journal.

[29]  J. Trojanowski,et al.  Convergence of heat shock protein 90 with ubiquitin in filamentous alpha-synuclein inclusions of alpha-synucleinopathies. , 2006, The American journal of pathology.

[30]  Christian Haass,et al.  Mitochondrial dysfunction in Parkinson's disease: molecular mechanisms and pathophysiological consequences , 2012, The EMBO journal.

[31]  John Q. Trojanowski,et al.  Convergence of Heat Shock Protein 90 with Ubiquitin in Filamentous α-Synuclein Inclusions of α-Synucleinopathies , 2006 .

[32]  Yumei Wang,et al.  Geldanamycin Induces Heat Shock Protein 70 and Protects against MPTP-induced Dopaminergic Neurotoxicity in Mice* , 2005, Journal of Biological Chemistry.

[33]  H. Federoff,et al.  Altered Gene Expression Profiles Reveal Similarities and Differences Between Parkinson Disease and Model Systems , 2005, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[34]  Tao Han,et al.  Cross-platform comparability of microarray technology: Intra-platform consistency and appropriate data analysis procedures are essential , 2005, BMC Bioinformatics.

[35]  T. Mosmann.  Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. , 1983, Journal of immunological methods.

[36]  G. Du,et al.  Protective effects of baicalein against rotenone-induced neurotoxicity in PC12 cells and isolated rat brain mitochondria. , 2012, European journal of pharmacology.

[37]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[38]  Carol A. Bocchini,et al.  A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®) , 2011, Human mutation.

[39]  Fumiaki Tanaka,et al.  Heat shock proteins in neurodegenerative diseases: Pathogenic roles and therapeutic implications , 2009, International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group.

[40]  H. Cai,et al.  The Chaperone Activity of Heat Shock Protein 90 Is Critical for Maintaining the Stability of Leucine-Rich Repeat Kinase 2 , 2008, The Journal of Neuroscience.

[41]  Todd B. Sherer,et al.  An In Vitro Model of Parkinson's Disease: Linking Mitochondrial Impairment to Altered α-Synuclein Metabolism and Oxidative Damage , 2002, The Journal of Neuroscience.

[42]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[43]  Alexander A. Morgan,et al.  Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data , 2011, Science Translational Medicine.

[44]  Justin Lamb,et al.  The Connectivity Map: a new tool for biomedical research , 2007, Nature Reviews Cancer.

[45]  G. Sobue,et al.  17-DMAG ameliorates polyglutamine-mediated motor neuron degeneration through well-preserved proteasome function in an SBMA model mouse. , 2009, Human molecular genetics.

[46]  Deepak K Rajpal,et al.  Applications of Connectivity Map in drug discovery and development. , 2012, Drug discovery today.

[47]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[48]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2011 , 2010, Nucleic Acids Res..

[49]  G. Giaccone,et al.  Update on Hsp90 inhibitors in clinical trial. , 2009, Current topics in medicinal chemistry.

[50]  Duc-Hau Le,et al.  GPEC: A Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection , 2012, Comput. Biol. Chem..

[51]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[52]  P. McLean,et al.  Molecular chaperones in Parkinson's disease--present and future. , 2011, Journal of Parkinson's disease.

[53]  R. Shields,et al.  mRNA Expression Signatures of Human Skeletal Muscle Atrophy Identify a Natural Compound that Increases Muscle Mass , 2011, Cell metabolism.

[54]  Todd B. Sherer,et al.  Chronic systemic pesticide exposure reproduces features of Parkinson's disease , 2000, Nature Neuroscience.

[55]  Mengsu Yang,et al.  Proteomic and transcriptomic study on the action of a cytotoxic saponin (Polyphyllin D): Induction of endoplasmic reticulum stress and mitochondria‐mediated apoptotic pathways , 2008, Proteomics.