Identifying a gene expression signature of frequent COPD exacerbations in peripheral blood using network methods

BMC Medical Genomics

Craig P Hersh³  Sreekumar G Pillai⁶  Anton Belousov⁴  Paula Belloni²  Stephen I Rennard¹  Divya Chhabra⁵  Weiliang Qiu³  Jarrett D Morrow³ 

[1] University of Nebraska Medical Center, Omaha, NE, USA;Genentech, Member of the Roche Group, South San Francisco, CA, USA;Channing Division of Network Medicine, Brigham and Women’s Hospital, 181 Longwood Avenue, Boston 02115, MA, USA;Roche Innovation Center, Penzberg, Germany;Division of Biomedical Informatics, University of California, San Diego, CA, USA;Current address: Eli Lilly and Company, Indianapolis, IN, USA

关键词: Biomarker;    Gene expression profiling;    Chronic obstructive pulmonary disease;    Network analysis;   

Others  :  1090016
DOI  :  10.1186/s12920-014-0072-y

 received in 2014-07-03, accepted in 2014-12-12,  发布年份 2015

【 摘 要 】

Background

Exacerbations of chronic obstructive pulmonary disease (COPD), characterized by acute deterioration in symptoms, may be due to bacterial or viral infections, environmental exposures, or unknown factors. Exacerbation frequency may be a stable trait in COPD patients, which could imply genetic susceptibility. Observing the genes, networks, and pathways that are up- and down-regulated in COPD patients with differing susceptibility to exacerbations will help to elucidate the molecular signature and pathogenesis of COPD exacerbations.

Methods

Gene expression array and plasma biomarker data were obtained using whole-blood samples from subjects enrolled in the Treatment of Emphysema With a Gamma-Selective Retinoid Agonist (TESRA) study. Linear regression, weighted gene co-expression network analysis (WGCNA), and pathway analysis were used to identify signatures and network sub-modules associated with the number of exacerbations within the previous year; other COPD-related phenotypes were also investigated.

Results

Individual genes were not found to be significantly associated with the number of exacerbations. However using network methods, a statistically significant gene module was identified, along with other modules showing moderate association. A diverse signature was observed across these modules using pathway analysis, marked by differences in B cell and NK cell activity, as well as cellular markers of viral infection. Within two modules, gene set enrichment analysis recapitulated the molecular signatures of two gene expression experiments; one involving sputum from asthma exacerbations and another involving viral lung infections. The plasma biomarker myeloperoxidase (MPO) was associated with the number of recent exacerbations.

Conclusion

A distinct signature of COPD exacerbations may be observed in peripheral blood months following the acute illness. While not predictive in this cross-sectional analysis, these results will be useful in uncovering the molecular pathogenesis of COPD exacerbations.

【 授权许可】

   
2015 Morrow et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Vestbo J, Hurd SS, Agustí AG, Jones PW, Vogelmeier C, Anzueto A, et al.: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2013, 187:347-365.
• [2]Alfredo P, Gaetano C, Paolo C, Marco C, Johnston SL, Alberto P: Pathophysiology of viral-induced exacerbations of COPD. Int J Chron Obstruct Pulmon Dis 2007, 2:477-483.
• [3]Foreman MG, DeMeo DL, Hersh CP, Reilly JJ, Silverman EK: Clinical determinants of exacerbations in severe, early-onset COPD. Eur Respir J 2007, 30:1124-1130.
• [4]Hurst JR, Vestbo J, Anzueto A, Locantore N, Müllerova H, Tal-Singer R, et al.: Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med 2010, 363:1128-1138.
• [5]Foreman MG, DeMeo DL, Hersh CP, Carey VJ, Fan VS, Reilly JJ, et al.: Polymorphic variation in surfactant protein B is associated with COPD exacerbations. Eur Respir J 2008, 32:938-944.
• [6]Takabatake N, Shibata Y, Abe S, Wada T, Machiya J, Igarashi A, et al.: A single nucleotide polymorphism in the CCL1 gene predicts acute exacerbations in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2006, 174:875-885.
• [7]Hansel NN, Hilmer SC, Georas SN, Cope LM, Guo J, Irizarry RA, et al.: Oligonucleotide-microarray analysis of peripheral-blood lymphocytes in severe asthma. J Lab Clin Med 2005, 145:263-274.
• [8]Herazo-Maya JD, Noth I, Duncan SR, Kim S, Ma S-F, Tseng GC, et al. Peripheral blood mononuclear cell gene expression profiles predict poor outcome in idiopathic pulmonary fibrosis. Sci Transl Med. 2013;5:205ra136.
• [9]Yang IV, Luna LG, Cotter J, Talbert J, Leach SM, Kidd R, et al. The peripheral blood transcriptome identifies the presence and extent of disease in idiopathic pulmonary fibrosis. PLoS One. 2012;7:e37708.
• [10]Poliska S, Csanky E, Szanto A, Szatmari I, Mesko B, Szeles L, et al.: Chronic obstructive pulmonary disease-specific gene expression signatures of alveolar macrophages as well as peripheral blood monocytes overlap and correlate with lung function. Respiration 2011, 81:499-510.
• [11]Bhattacharya S, Tyagi S, Srisuma S, Demeo DL, Shapiro SD, Bueno R, et al. Peripheral blood gene expression profiles in COPD subjects. J Clin Bioinforma. 2011;1:12.
• [12]Bahr TM, Hughes GJ, Armstrong M, Reisdorph R, Coldren CD, Edwards MG, et al.: Peripheral blood mononuclear cell gene expression in chronic obstructive pulmonary disease. Am J Respir Cell Mol Biol 2013, 49:316-323.
• [13]Silverman E, Loscalzo J: Network medicine approaches to the genetics of complex diseases. Discov Med 2012, 14:143-152.
• [14]Haas BE, Horvath S, Pietiläinen KH, Cantor RM, Nikkola E, Weissglas-Volkov D, et al. Adipose co-expression networks across Finns and Mexicans identify novel triglyceride-associated genes. BMC Med Genomics. 2012;5:61.
• [15]Xue J, Schmidt SV, Sander J, Draffehn A, Krebs W, Quester I, et al.: Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity 2014, 40:274-288.
• [16]Jones P, Rames A. TESRA (Treatment of Emphysema with a Selective Retinoid Agonist) study results. Am J Respir Crit Care Med. 2011;A6418.
• [17]Cheng DT, Kim DK, Cockayne DA, Belousov A, Bitter H, Cho MH, et al.: Systemic soluble receptor for advanced glycation endproducts is a biomarker of emphysema and associated with AGER genetic variants in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2013, 188:948-957.
• [18]Gautier L, Cope L, Bolstad BM, Irizarry RA: Affy–analysis of affymetrix GeneChip data at the probe level. Bioinforma Oxf Engl 2004, 20:307-315.
• [19]Smyth GK: Limma: Linear Models for Microarray Data. In Bioinforma Comput Biol Solut Using R Bioconductor. Edited by Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. Springer, New York; 2005:397-420.
• [20]Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995, 57:289-300.
• [21]Horvath S: Weighted Network Analysis: Applications in Genomics and Systems Biology. 2011 edition. Springer, New York; 2011.
• [22]Feng G, Du P, Krett NL, Tessel M, Rosen S, Kibbe WA, et al. A collection of bioconductor methods to visualize gene-list annotations. BMC Res Notes. 2010;3:10.
• [23]Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102:15545-15550.
• [24]Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010;5:e13984.
• [25]Hodge G, Mukaro V, Holmes M, Reynolds PN, Hodge S: Enhanced cytotoxic function of natural killer and natural killer T-like cells associated with decreased CD94 (Kp43) in the chronic obstructive pulmonary disease airway. Respirology 2013, 18:369-376.
• [26]Veljkovic E, Jiricny J, Menigatti M, Rehrauer H, Han W: Chronic exposure to cigarette smoke condensate in vitro induces epithelial to mesenchymal transition-like changes in human bronchial epithelial cells, BEAS-2B. Toxicol In Vitro 2011, 25:446-453.
• [27]Doyle I, Ratcliffe M, Walding A, Vanden Bon E, Dymond M, Tomlinson W, et al.: Differential gene expression analysis in human monocyte-derived macrophages: impact of cigarette smoke on host defence. Mol Immunol 2010, 47:1058-1065.
• [28]Bosco A, Ehteshami S, Stern DA, Martinez FD: Decreased activation of inflammatory networks during acute asthma exacerbations is associated with chronic airflow obstruction. Mucosal Immunol 2010, 3:399-409.
• [29]Ioannidis I, McNally B, Willette M, Peeples ME, Chaussabel D, Durbin JE, et al.: Plasticity and virus specificity of the airway epithelial cell immune response during respiratory virus infection. J Virol 2012, 86:5422-5436.
• [30]Fiorini G, Crespi S, Rinaldi M: Serum ECP and MPO are increased during exacerbations of chronic bronchitis with airway obstruction. Biomed Pharmacother 2000, 54:274-278.
• [31]Gompertz S, Bayley DL, Hill SL, Stockley RA: Relationship between airway inflammation and the frequency of exacerbations in patients with smoking related COPD. Thorax 2001, 56:36-41.
• [32]Tumkaya M, Atis S, Ozge C, Delialioglu N, Polat G, Kanik A: Relationship between airway colonization, inflammation and exacerbation frequency in COPD. Respir Med 2007, 101:729-737.
• [33]Gompertz S, O’Brien C, Bayley DL, Hill SL, Stockley RA: Changes in bronchial inflammation during acute exacerbations of chronic bronchitis. Eur Respir J 2001, 17:1112-1119.
• [34]Cockayne DA, Cheng DT, Waschki B, Sridhar S, Ravindran P, Hilton H, et al. Systemic biomarkers of neutrophilic inflammation, tissue injury and repair in COPD patients with differing levels of disease severity. PLoS One. 2012;7:e38629.