Journal of Clinical Bioinformatics


Open Access Research

Peripheral blood gene expression profiles in COPD subjects

Soumyaroop Bhattacharya1, Shivraj Tyagi2, Sorachai Srisuma4, Dawn L DeMeo2, Steven D Shapiro5, Raphael Bueno3, Edwin K Silverman2, John J Reilly5 and Thomas J Mariani1*

Author Affiliations

1 Neonatology Division and Center for Pediatric Biomedical Research, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, 14642, NY

2 Pulmonary and Critical Care Division, Department of Medicine, The Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood avenue, Boston, 02115, MA

3 Thoracic Surgery, Brigham and Women's Hospital, Harvard Medical School, 15 Francis Street, Boston, 02115, MA

4 Department of Physiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Prannok Road, Bangkok Noi, Bangkok, 10700, THAILAND

5 Department of Medicine, University of Pittsburgh Medical Center, 3550 Terrace StreetPittsburgh, 15261, PA

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Journal of Clinical Bioinformatics 2011, 1:12 doi:10.1186/2043-9113-1-12

Published: 24 April 2011

Abstract

To identify non-invasive gene expression markers for chronic obstructive pulmonary disease (COPD), we performed genome-wide expression profiling of peripheral blood samples from 12 subjects with significant airflow obstruction and an equal number of non-obstructed controls. RNA was isolated from Peripheral Blood Mononuclear Cells (PBMCs) and gene expression was assessed using Affymetrix U133 Plus 2.0 arrays.

Tests for gene expression changes that discriminate between COPD cases (FEV1< 70% predicted, FEV1/FVC < 0.7) and controls (FEV1> 80% predicted, FEV1/FVC > 0.7) were performed using Significance Analysis of Microarrays (SAM) and Bayesian Analysis of Differential Gene Expression (BADGE). Using either test at high stringency (SAM median FDR = 0 or BADGE p < 0.01) we identified differential expression for 45 known genes. Correlation of gene expression with lung function measurements (FEV1 & FEV1/FVC), using both Pearson and Spearman correlation coefficients (p < 0.05), identified a set of 86 genes. A total of 16 markers showed evidence of significant correlation (p < 0.05) with quantitative traits and differential expression between cases and controls. We further compared our peripheral gene expression markers with those we previously identified from lung tissue of the same cohort. Two genes, RP9and NAPE-PLD, were identified as decreased in COPD cases compared to controls in both lung tissue and blood. These results contribute to our understanding of gene expression changes in the peripheral blood of patients with COPD and may provide insight into potential mechanisms involved in the disease.

Keywords:
Microarray; Biomarkers; PBMC