Analysis of sputum gene expression data in Asthma and COPD

In addition to analyzing whole-genome methylation, we concomitantly evaluated sputum cell gene expression in the context of chronic inflammatory lung disease. Nucleic acids were purified from sputum samples of subjects with Asthma, COPD as well as healthy controls. Gene expression was analyzed on the Agilent Human GE 4x44k v2 platform. Induced sputum samples were sourced from the German cohort studies ALLIANCE and COSYCONET. We selected a total of 9 samples from non-smoking subjects with controlled (as indicated by Asthma Control Test score, ACT), eosinophilic/Type-2 Asthma (based on blood/sputum eosinophil counts). A total of 10 samples from subjects with COPD (>12 months after smoking cessation) as well as 10 samples from healthy controls (w/o any pulmonary or systemic-inflammatory disorder) were included in the analysis. Intentionally, we selected sputum samples that were stored/preserved in two different ways in order to explore effects on downstream analysis and simulate challenges in larger-scale biobanking studies (mainly regarding RNA quality in downstream transcriptome analysis): 1) storage in RLT lysis buffer (Qiagen) at -80 °C following sputum processing. 2) storage in HOPE medium (HEPES-glutamic acid buffer-mediated organic solvent protection effect; DCS innovative diagnostic systems, Hamburg, Germany) after sputum processing, followed by paraffin-embedding and storage at 4°C. Total RNA was extracted from the samples, amplified, labeled and hybridized to the Agilent Human GE 4x44k v2 array. Additionally, methylome analysis was performed on extracted DNA. From the initial sample pool, gene expression analysis was successfully performed on all Asthma samples as well as 7/9 COPD and 9/10 control samples. Downstream in silico analysis focused on methylation/transcription differences between Asthma/COPD and healthy controls, respectively. Information availbale about sputum differential cell counts (consisting of alveolar macrophages, neutrophils, eosinophils, lymphocytes, monocytes, ciliated cells and squamous cells) was used to implement and evaluate an in silico model correcting for variation in cell counts across disease conditions.