An in silico analysis of microRNAs: mining the miRNAome.

Systematic analysis of literature- and experimentally-derived datasets using text mining with ontological enrichment and network modeling revealed global trends in the microRNA (miRNA) interactome. A total of 756 unique miRNAs were resolved from PubMed abstracts and 1165 direct relationships between 270 miRNAs and 581 genes were identified as phrase groups using semantic search techniques. These miRNA:gene interactions were built into a bipartite network (the miRNAome) which displays scale-free degree distribution. Functional classification of miRNA-target genes using PANTHER revealed 189 distinct molecular functions, with significant enrichment of nucleic acid binding, transcription and protein phosphorylation. Pathway analysis revealed a network of 176 miRNAs linked to 368 OMIM disorders via their target genes, which are enriched (p = 0.0047) for disease-associated SNP variations. Reference to a database of drug targets revealed that 24.8% of all published miRNA-targets are targets for drug development programs, while a sub-set (18.2%) are targets for FDA-approved drugs. Consistent with topological analysis of the miRNA-disease network, the most prevalent class of FDA-approved drugs is anti-neoplastic agents against published miRNA-target genes. Linking miRNAs to biological process and diseases reveals distinct co-regulation of phenotypes that could aid in understanding the role miRNA-based gene regulation plays in biological phenomena.

[1]  E. Levanon,et al.  Human housekeeping genes are compact. , 2003, Trends in genetics : TIG.

[2]  Q. Cui,et al.  An Analysis of Human MicroRNA and Disease Associations , 2008, PloS one.

[3]  D. Bartel.  MicroRNAs: Target Recognition and Regulatory Functions , 2009, Cell.

[4]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2004, Nucleic Acids Res..

[5]  Anton J. Enright,et al.  Human MicroRNA Targets , 2004, PLoS biology.

[6]  J. Castle,et al.  Definition, conservation and epigenetics of housekeeping and tissue-enriched genes , 2009, BMC Genomics.

[7]  M. Campbell,et al.  PANTHER: a library of protein families and subfamilies indexed by function. , 2003, Genome research.

[8]  Brad T. Sherman,et al.  DAVID: Database for Annotation, Visualization, and Integrated Discovery , 2003, Genome Biology.

[9]  Yadong Wang,et al.  miR2Disease: a manually curated database for microRNA deregulation in human disease , 2008, Nucleic Acids Res..

[10]  Wei Liu,et al.  Mapping similarities in mTOR pathway perturbations in mouse lupus nephritis models and human lupus nephritis , 2008, Arthritis research & therapy.

[11]  Alexander A. Morgan,et al.  FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease , 2008, Genome Biology.

[12]  N. Rajewsky,et al.  Natural selection on human microRNA binding sites inferred from SNP data , 2006, Nature Genetics.

[13]  H. Horvitz,et al.  Most Caenorhabditis elegans microRNAs Are Individually Not Essential for Development or Viability , 2007, PLoS genetics.

[14]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[15]  Gail Mandel,et al.  Homeostatic regulation of MeCP2 expression by a CREB-induced microRNA , 2007, Nature Neuroscience.

[16]  Lin He,et al.  MicroRNAs: small RNAs with a big role in gene regulation , 2004, Nature reviews genetics.

[17]  Hongfang Liu,et al.  Gene name ambiguity of eukaryotic nomenclatures , 2005, Bioinform..

[18]  Chris Sander,et al.  Prediction of human microRNA targets. , 2006, Methods in molecular biology.

[19]  Ben Lehner,et al.  Tissue specificity and the human protein interaction network , 2009, Molecular systems biology.

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

[21]  W. Ritchie,et al.  Predicting microRNA targets and functions: traps for the unwary , 2009, Nature Methods.

[22]  Sam Griffiths-Jones,et al.  The microRNA Registry , 2004, Nucleic Acids Res..

[23]  Carlo M. Croce,et al.  MicroRNAs 17-5p–20a–106a control monocytopoiesis through AML1 targeting and M-CSF receptor upregulation , 2007, Nature Cell Biology.

[24]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[25]  C. Croce,et al.  MicroRNAs in diseases and drug response. , 2008, Current opinion in pharmacology.

[26]  Jian-Fu Chen,et al.  The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation , 2006, Nature Genetics.

[27]  Yoshihiro Yamanishi,et al.  KEGG for linking genomes to life and the environment , 2007, Nucleic Acids Res..

[28]  K. Gunsalus,et al.  Combinatorial microRNA target predictions , 2005, Nature Genetics.

[29]  Yuriy Gusev,et al.  Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA. , 2008, Methods.

[30]  D. Banerjee,et al.  MiRSNPs or MiR-polymorphisms, new players in microRNA mediated regulation of the cell: Introducing microRNA pharmacogenomics , 2008, Cell cycle.

[31]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[32]  Stijn van Dongen,et al.  miRBase: microRNA sequences, targets and gene nomenclature , 2005, Nucleic Acids Res..

[33]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[34]  D. Vitkup,et al.  Network properties of genes harboring inherited disease mutations , 2008, Proceedings of the National Academy of Sciences.

[35]  Fidel Ramírez,et al.  Computing topological parameters of biological networks , 2008, Bioinform..

[36]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[37]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[38]  David B. Searls,et al.  Can literature analysis identify innovation drivers in drug discovery? , 2009, Nature Reviews Drug Discovery.

[39]  N. Colburn,et al.  MicroRNA-21 promotes cell transformation by targeting the programmed cell death 4 gene , 2008, Oncogene.

[40]  Stijn van Dongen,et al.  miRBase: tools for microRNA genomics , 2007, Nucleic Acids Res..

[41]  Sergei Egorov,et al.  MedScan, a natural language processing engine for MEDLINE abstracts , 2003, Bioinform..

[42]  P. Bork,et al.  Literature mining for the biologist: from information retrieval to biological discovery , 2006, Nature Reviews Genetics.

[43]  George A Calin,et al.  Identification of differentially expressed microRNAs by microarray: A possible role for microRNA genes in pituitary adenomas , 2007, Journal of cellular physiology.

[44]  Michael Krauthammer,et al.  GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles , 2001, ISMB.

[45]  Nikolaus Rajewsky,et al.  Computational identification of microRNA targets. , 2004 .

[46]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[47]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[48]  Roger Hale.  Text mining: getting more value from literature resources. , 2005, Drug discovery today.

[49]  Ian H. Witten,et al.  Managing Gigabytes: Compressing and Indexing Documents and Images , 1999 .

[50]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[51]  Anushya Muruganujan,et al.  Applications for protein sequence–function evolution data: mRNA/protein expression analysis and coding SNP scoring tools , 2006, Nucleic Acids Res..

[52]  Ralf Zimmer,et al.  miRSel: Automated extraction of associations between microRNAs and genes from the biomedical literature , 2010, BMC Bioinformatics.