Combined analysis of gene expression and clinical data in the severe asthma U-BIOPRED cohorts
B. De Meulder, D. Lefaudeux, M. Saqi, C. Auffray, I. Adcock, F. Chung, J. Bigler, M. Loza, N. Rao, A. Rowe, F. Baribaud, G. Roberts, A. Sousa, A. Bansal, C. Compton, D. Myles, R. Djukanovic, D. Shaw, I. Horvath, J. Musial, J. Corfield, P. Chanez, N. Krug, P. Montuschi, P. Bakke, P. Sterk, R. Polosa, S. Wager, F. Singer, S. Fowler, S. E. Dahlen, T. Sandström, T. Higenbottam, U. Nihlén, U. Frey, W. Seibold (Lyon, Marseille, France; London, Southampton, Uxbridge, Cambridge, Redhill, Nottingham, Manchester, Cheadle, United Kingdom; Seattle, Springhouse, United States Of America; Budapest, Hungary; Krakow, Poland; Hannover, Ingelheim am Rhein, Germany; Roma, Catania, Italy; Bergen, Stockholm, Umea, Södertälje, Sweden; Amsterdam, Netherlands; Maasmechelen, Belgium; Bern, Switzerland)
Source: International Congress 2014 – Novel approaches in transcriptomics and epigenomics in inflammatory lung diseases and lung cancer
Disease area: Airway diseases
Abstract IntroductionIn order to gain new insights into the biology of severe asthma, correlation analysis was performed between clinical and transcriptome data obtained from biopsies and bronchial brushings in the U-BIOPRED cohorts.ObjectiveDiscovering transcriptomic fingerprints associated with clinically relevant traits of severe asthma.MethodsGene expression data obtained from bronchial biopsies and brushing samples from severe asthma subjects and normal healthy volunteers (52 vs 41 and 60 vs 46 samples respectively) were compared using the R package WGCNA (Langfelder & Horvath, BMC Bioinformatics, 2008; 9, 559) and correlated to the clinical data. Groups of genes (modules) were determined by topological overlap matrix analysis.ResultsSignificant correlations were found between specific modules and severe asthma clinical traits in both biopsies (see table) and brushings. Interestingly, 214 genes (module 1) were correlated to the number of exacerbations in the previous year. In addition, 19 significant correlations were identified between gene modules and clinical traits from analysis of the brushing transcriptome.Module (gene number) Clinical trait Correlation Pvalue 1 (214) Number of exacerbations previous year 37.5 % 3.46E-03 2 (254) Atopy Skin Prick Test -28.3 % 2.96E-02 3 (14153) Pack/years 31.7 % 1.44E-02
ConclusionsA cutting-edge bioinformatics methodology was adapted for the analysis of severe asthma transcriptomes. Several gene modules were found to be correlated with specific clinical traits and will be presented.Funded by the Innovative Medicines Initiative (U-BIOPRED: IMI n°115010; eTRIKS: IMI n°115446).
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B. De Meulder, D. Lefaudeux, M. Saqi, C. Auffray, I. Adcock, F. Chung, J. Bigler, M. Loza, N. Rao, A. Rowe, F. Baribaud, G. Roberts, A. Sousa, A. Bansal, C. Compton, D. Myles, R. Djukanovic, D. Shaw, I. Horvath, J. Musial, J. Corfield, P. Chanez, N. Krug, P. Montuschi, P. Bakke, P. Sterk, R. Polosa, S. Wager, F. Singer, S. Fowler, S. E. Dahlen, T. Sandström, T. Higenbottam, U. Nihlén, U. Frey, W. Seibold (Lyon, Marseille, France; London, Southampton, Uxbridge, Cambridge, Redhill, Nottingham, Manchester, Cheadle, United Kingdom; Seattle, Springhouse, United States Of America; Budapest, Hungary; Krakow, Poland; Hannover, Ingelheim am Rhein, Germany; Roma, Catania, Italy; Bergen, Stockholm, Umea, Södertälje, Sweden; Amsterdam, Netherlands; Maasmechelen, Belgium; Bern, Switzerland). Combined analysis of gene expression and clinical data in the severe asthma U-BIOPRED cohorts. Eur Respir J 2014; 44: Suppl. 58, 399
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