title: Noninvasive analysis of the sputum transcriptome discriminates clinical phenotypes of asthma.
abstract: BACKGROUND
  The airway transcriptome includes genes that contribute to the pathophysiologic heterogeneity seen in individuals with asthma.
  
  
  OBJECTIVE
  We analyzed sputum gene expression for transcriptomic endotypes of asthma (TEA), gene signatures that discriminate phenotypes of disease.
  
  
  METHODS
  Gene expression in the sputum and blood of patients with asthma was measured using Affymetrix microarrays. Unsupervised clustering analysis based on pathways from the Kyoto Encyclopedia of Genes and Genomes was used to identify TEA clusters. Logistic regression analysis of matched blood samples defined an expression profile in the circulation to determine the TEA cluster assignment in a cohort of children with asthma to replicate clinical phenotypes.
  
  
  RESULTS
  Three TEA clusters were identified. TEA cluster 1 had the most subjects with a history of intubation (P = 0.05), a lower prebronchodilator FEV1 (P = 0.006), a higher bronchodilator response (P = 0.03), and higher exhaled nitric oxide levels (P = 0.04) compared with the other TEA clusters. TEA cluster 2, the smallest cluster, had the most subjects that were hospitalized for asthma (P = 0.04). TEA cluster 3, the largest cluster, had normal lung function, low exhaled nitric oxide levels, and lower inhaled steroid requirements. Evaluation of TEA clusters in children confirmed that TEA clusters 1 and 2 are associated with a history of intubation (P = 5.58 × 10(-6)) and hospitalization (P = 0.01), respectively.
  
  
  CONCLUSIONS
  There are common patterns of gene expression in the sputum and blood of children and adults that are associated with near-fatal, severe, and milder asthma.
authors.affiliation: 1 Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine.
authors.forename: Xiting
authors.initials: X
authors.lastname: Yan
authors.forename: Jen-Hwa
authors.initials: JH
authors.lastname: Chu
authors.forename: Jose
authors.initials: J
authors.lastname: Gomez
authors.forename: Maria
authors.initials: M
authors.lastname: Koenigs
authors.forename: Carole
authors.initials: C
authors.lastname: Holm
authors.forename: Xiaoxuan
authors.initials: X
authors.lastname: He
authors.forename: Mario F
authors.initials: MF
authors.lastname: Perez
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
authors.forename: Shrikant
authors.initials: S
authors.lastname: Mane
authors.forename: Fernando D
authors.initials: FD
authors.lastname: Martinez
authors.forename: Carole
authors.initials: C
authors.lastname: Ober
authors.forename: Dan L
authors.initials: DL
authors.lastname: Nicolae
authors.forename: Kathleen C
authors.initials: KC
authors.lastname: Barnes
authors.forename: Stephanie J
authors.initials: SJ
authors.lastname: London
authors.forename: Frank
authors.initials: F
authors.lastname: Gilliland
authors.forename: Scott T
authors.initials: ST
authors.lastname: Weiss
authors.forename: Benjamin A
authors.initials: BA
authors.lastname: Raby
authors.forename: Lauren
authors.initials: L
authors.lastname: Cohn
authors.forename: Geoffrey L
authors.initials: GL
authors.lastname: Chupp
mesh_terms: D000293:Adolescent; D000328:Adult; D017668:Age of Onset; D001249:Asthma / Q000097:blood / Q000235:genetics* / Q000503:physiopathology; D001774:Blood Chemical Analysis; D016022:Case-Control Studies; D002648:Child; D003430:Cross-Sectional Studies; D005260:Female; D020869:Gene Expression Profiling; D005819:Genetic Markers; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D012313:RNA / Q000097:blood / Q000235:genetics; D013183:Sputum; D011795:Surveys and Questionnaires; D059467:Transcriptome / Q000235:genetics*; D055815:Young Adult
chemical_list: D005819:Genetic Markers; D012313:RNA
keywords: RNA; genomic; molecular endotyping; pathway analysis; severe asthma