Gene expression profiling as a window into idiopathic pulmonary fibrosis pathogenesis: can we identify the right target genes?

Expression microarrays that provide genome-level, transcriptional, high-resolution profiles have been applied successfully to multiple diseases. Although microarrays provide information regarding thousands of genes, many investigators prefer to focus on a single gene and validate its role, an approach often supported by grant and journal reviewers. Only a minority of investigators focus on global changes in gene expression. Here, we describe and contrast two general approaches to the use of microarray data: the reductionist "cherry picking" approach and the more global, quantitative "systems" approach. We describe microarray analysis experiments relevant to idiopathic pulmonary fibrosis (IPF) in the context of these two approaches. Although it seems that the cherry-picking approaches have been successful in identifying new relevant genes in IPF, we suggest that to fulfill the discovery potential of microarrays in IPF and to create a working model of IPF, unbiased integrative systems approaches are required.

[1]  J. Downing,et al.  Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.

[2]  Naftali Kaminski,et al.  Gene expression profiles distinguish idiopathic pulmonary fibrosis from hypersensitivity pneumonitis. , 2006, American journal of respiratory and critical care medicine.

[3]  M. Nasu,et al.  High plasma concentrations of osteopontin in patients with interstitial pneumonia. , 2005, Respiratory medicine.

[4]  D. Koller,et al.  From signatures to models: understanding cancer using microarrays , 2005, Nature Genetics.

[5]  Naftali Kaminski,et al.  Analysis of microarray experiments for pulmonary fibrosis. , 2005, Methods in molecular medicine.

[6]  L. Staudt,et al.  The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. , 2002, The New England journal of medicine.

[7]  Ash A. Alizadeh,et al.  Distinct IL-4-induced gene expression, proliferation, and intracellular signaling in germinal center B-cell-like and activated B-cell-like diffuse large-cell lymphomas. , 2005, Blood.

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

[9]  Naftali Kaminski,et al.  Gene expression analysis reveals matrilysin as a key regulator of pulmonary fibrosis in mice and humans , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[11]  N. Kaminski,et al.  The integrin alpha v beta 6 binds and activates latent TGF beta 1: a mechanism for regulating pulmonary inflammation and fibrosis. , 1999, Cell.

[12]  S. P. Fodor,et al.  Using oligonucleotide probe arrays to access genetic diversity. , 1995, BioTechniques.

[13]  F. Martinez,et al.  Enhanced monocyte chemoattractant protein-3/CC chemokine ligand-7 in usual interstitial pneumonia. , 2004, American journal of respiratory and critical care medicine.

[14]  W. Gerald,et al.  Distinct organ-specific metastatic potential of individual breast cancer cells and primary tumors. , 2005, The Journal of clinical investigation.

[15]  Andy J. Minn,et al.  Genes that mediate breast cancer metastasis to lung , 2005, Nature.

[16]  Ash A. Alizadeh,et al.  Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. , 2004, The New England journal of medicine.

[17]  A. Nicholson,et al.  Gene expression profiling reveals novel TGFβ targets in adult lung fibroblasts , 2004, Respiratory research.

[18]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[19]  David G. Morris,et al.  Global analysis of gene expression in pulmonary fibrosis reveals distinct programs regulating lung inflammation and fibrosis. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Naftali Kaminski,et al.  Up-Regulation and Profibrotic Role of Osteopontin in Human Idiopathic Pulmonary Fibrosis , 2005, PLoS medicine.

[21]  David E. Misek,et al.  Gene-expression profiles predict survival of patients with lung adenocarcinoma , 2002, Nature Medicine.

[22]  T. King,et al.  International consensus statement on idiopathic pulmonary fibrosis. , 2001, The European respiratory journal.

[23]  L. Liaw,et al.  Altered bleomycin-induced lung fibrosis in osteopontin-deficient mice. , 2004, American journal of physiology. Lung cellular and molecular physiology.

[24]  N. Kaminski Microarray analysis of idiopathic pulmonary fibrosis. , 2003, American journal of respiratory cell and molecular biology.

[25]  Kazuhisa Takahashi,et al.  Role of osteopontin in the pathogenesis of bleomycin-induced pulmonary fibrosis. , 2001, American journal of respiratory cell and molecular biology.

[26]  Wei He,et al.  Breast cancer bone metastasis mediated by the Smad tumor suppressor pathway. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

[28]  K. Brown,et al.  Overexpression of matrix metalloproteinase-7 in pulmonary fibrosis. , 2002, Chest.

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

[30]  Douglas A. Hosack,et al.  Identifying biological themes within lists of genes with EASE , 2003, Genome Biology.

[31]  J. Mesirov,et al.  An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis , 2005, Nature Genetics.

[32]  S. Dhanasekaran,et al.  FIZZ1 stimulation of myofibroblast differentiation. , 2004, The American journal of pathology.