The transcriptome's drugable frequenters.

Microarray studies are widely employed in the exploratory phase of the drug discovery process. Expectations raised by the genomics revolution led to the belief that they would rapidly lead to the identification of novel drug targets. However, a few basic questions were often overlooked. Are members of drugable gene families properly represented in the transcriptome? Or are they poorly expressed and below the detection limit of the microarray technology? This review explores the representation of drug targets and components of downstream cellular signaling pathways in the transcriptome. It appears that members of drugable gene families are underrepresented in the transcriptomes of non-pathological human tissues. But, they are represented at or above the expected frequency in the differential transcriptome (i.e. the set of genes that changes expression upon a change in cellular environment). Analysis of differential gene expression on a genome-wide scale will therefore give a comprehensive overview of cellular pathways and possible drug targets.

[1]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[2]  M. Reinders,et al.  Genetic network modeling. , 2002, Pharmacogenomics.

[3]  David E. Misek,et al.  Discordant Protein and mRNA Expression in Lung Adenocarcinomas * , 2002, Molecular & Cellular Proteomics.

[4]  Lukasz Salwinski,et al.  In silico simulation of biological network dynamics , 2004, Nature Biotechnology.

[5]  Gregory Stephanopoulos,et al.  Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data. , 2004, Genome research.

[6]  D. Neri,et al.  Modulation of gene expression by extracellular pH variations in human fibroblasts: A transcriptomic and proteomic study , 2003, Proteomics.

[7]  D. Slonim From patterns to pathways: gene expression data analysis comes of age , 2002, Nature Genetics.

[8]  T. Hughes,et al.  Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. , 2000, Science.

[9]  G. Superti-Furga,et al.  Rediscovering the sweet spot in drug discovery. , 2003, Drug discovery today.

[10]  Francis D. Gibbons,et al.  Judging the quality of gene expression-based clustering methods using gene annotation. , 2002, Genome research.

[11]  N. Socci,et al.  Cytokine-induced Patterns of Gene Expression in Skeletal Muscle Tissue* , 2003, Journal of Biological Chemistry.

[12]  Yudong D. He,et al.  Functional Discovery via a Compendium of Expression Profiles , 2000, Cell.

[13]  Christine L Mummery,et al.  Comprehensive Microarray Analysis of Bone Morphogenetic Protein 2‐Induced Osteoblast Differentiation Resulting in the Identification of Novel Markers for Bone Development , 2002, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[14]  M. Thattai,et al.  Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[15]  David Botstein,et al.  The Stanford Microarray Database , 2001, Nucleic Acids Res..

[16]  P. Brown,et al.  Drug target validation and identification of secondary drug target effects using DNA microarrays , 1998, Nature Medicine.

[17]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[18]  Manuel Hidalgo,et al.  Transcriptional profiles in peripheral blood mononuclear cells prognostic of clinical outcomes in patients with advanced renal cell carcinoma. , 2005, Clinical cancer research : an official journal of the American Association for Cancer Research.

[19]  P. Workman,et al.  Gene expression microarray technologies in the development of new therapeutic agents. , 2004, European journal of cancer.

[20]  Todd,et al.  Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.

[21]  K. Park,et al.  Analysis of proteome and transcriptome of tumor necrosis factor α stimulated vascular smooth muscle cells with or without alpha lipoic acid , 2004, Proteomics.

[22]  B. Aronow,et al.  Transcriptome signature of irreversible senescence in human papillomavirus-positive cervical cancer cells , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Gavin Thurston,et al.  Haploinsufficiency of delta-like 4 ligand results in embryonic lethality due to major defects in arterial and vascular development. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[24]  S. De Flora,et al.  Proteomic analysis as related to transcriptome data in the lung of chromium(VI)-treated rats. , 2004, International journal of oncology.

[25]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[26]  Ertugrul M. Ozbudak,et al.  Regulation of noise in the expression of a single gene , 2002, Nature Genetics.

[27]  M. Gerstein,et al.  Comparing protein abundance and mRNA expression levels on a genomic scale , 2003, Genome Biology.

[28]  Sergio Contrino,et al.  ArrayExpress—a public repository for microarray gene expression data at the EBI , 2004, Nucleic Acids Res..

[29]  A. Arkin,et al.  Stochastic mechanisms in gene expression. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Dalia Cohen,et al.  Functional genomics to new drug targets , 2004, Nature Reviews Drug Discovery.

[31]  Doron Lancet,et al.  Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification , 2005, Bioinform..

[32]  M. Gerstein,et al.  Interrelating different types of genomic data, from proteome to secretome: 'oming in on function. , 2001, Genome research.

[33]  Thomas Seidl,et al.  Rules for Gene Usage Inferred from a Comparison of Large-Scale Gene Expression Profiles of T and B Lymphocyte Development 1 , 2003, The Journal of Immunology.

[34]  A. Hsueh,et al.  Discovering new hormones, receptors, and signaling mediators in the genomic era. , 2000, Molecular endocrinology.

[35]  S. Mundlos,et al.  Cbfa1, a Candidate Gene for Cleidocranial Dysplasia Syndrome, Is Essential for Osteoblast Differentiation and Bone Development , 1997, Cell.

[36]  S. Hayward,et al.  Expression profiling of a human cell line model of prostatic cancer reveals a direct involvement of interferon signaling in prostate tumor progression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Rosamonde E Banks,et al.  Housekeeping proteins: A preliminary study illustrating some limitations as useful references in protein expression studies , 2005, Proteomics.

[38]  F. Klis,et al.  Parallel and comparative analysis of the proteome and transcriptome of sorbic acid‐stressed Saccharomyces cerevisiae , 2001, Yeast.

[39]  J. Drews Drug discovery: a historical perspective. , 2000, Science.

[40]  Mark Gerstein,et al.  Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts , 2002, Bioinform..

[41]  E. Brown,et al.  Identification and validation of novel androgen-regulated genes in prostate cancer. , 2004, Endocrinology.

[42]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Genomics versus orphan nuclear receptors--a half-time report. , 2002, Molecular endocrinology.

[44]  A. Aszódi,et al.  Chondromodulin I Is Dispensable during Enchondral Ossification and Eye Development , 2002, Molecular and Cellular Biology.

[45]  S. Kaesler,et al.  The chemokine receptor CCR1 is strongly up‐regulated after skin injury but dispensable for wound healing , 2004, Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society.

[46]  G. Robinson,et al.  Gene Expression Profiles in the Brain Predict Behavior in Individual Honey Bees , 2003, Science.

[47]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[48]  P. Schultz,et al.  A role for chemistry in stem cell biology , 2004, Nature Biotechnology.

[49]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[50]  P. Lorenz,et al.  From transcriptome to proteome: Differentially expressed proteins identified in synovial tissue of patients suffering from rheumatoid arthritis and osteoarthritis by an initial screen with a panel of 791 antibodies , 2003, Proteomics.