title: Identifying a gene expression signature of frequent COPD exacerbations in peripheral blood using network methods.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA. jarrett.morrow@channing.harvard.edu.
authors.forename: Jarrett D
authors.initials: JD
authors.lastname: Morrow
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA. weiliang.qiu@channing.harvard.edu.
authors.forename: Weiliang
authors.initials: W
authors.lastname: Qiu
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA. redic@channing.harvard.edu.|Division of Biomedical Informatics, University of California, San Diego, CA, USA. redic@channing.harvard.edu.
authors.forename: Divya
authors.initials: D
authors.lastname: Chhabra
authors.affiliation: University of Nebraska Medical Center, Omaha, NE, USA. srennard@unmc.edu.
authors.forename: Stephen I
authors.initials: SI
authors.lastname: Rennard
authors.affiliation: Genentech, Member of the Roche Group, South San Francisco, CA, USA. belloni.paula@gene.com.
authors.forename: Paula
authors.initials: P
authors.lastname: Belloni
authors.affiliation: Roche Innovation Center, Penzberg, Germany. anton.belousov@roche.com.
authors.forename: Anton
authors.initials: A
authors.lastname: Belousov
authors.affiliation: Hoffman La Roche, Nutley, NJ, USA. pillaisreekumar@yahoo.com.|Current address: Eli Lilly and Company, Indianapolis, IN, USA. pillaisreekumar@yahoo.com.
authors.forename: Sreekumar G
authors.initials: SG
authors.lastname: Pillai
authors.affiliation: Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA. craig.hersh@channing.harvard.edu.
authors.forename: Craig P
authors.initials: CP
authors.lastname: Hersh
mesh_terms: D000368:Aged; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D019295:Computational Biology / Q000379:methods*; D003430:Cross-Sectional Studies; D004646:Emphysema / Q000097:blood; D005260:Female; D020869:Gene Expression Profiling; D020022:Genetic Predisposition to Disease; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D029424:Pulmonary Disease, Chronic Obstructive / Q000097:blood / Q000235:genetics*; D017423:Sequence Analysis, RNA; D012720:Severity of Illness Index; D059467:Transcriptome
chemical_list: D015415:Biomarkers