Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0

Respiratory Research

Ole Ammerpohl¹  Anne Kirsten²  Henrik Watz²  Torsten Goldmann³  Daniela Börnigen⁴  Klaus F. Rabe⁵  Thomas Bahmer⁶  Espen E. Groth⁷  Frauke Pedersen⁸  Melanie Weber⁹ 

[1] Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany;Institute of Human Genetics, University Medical Center Ulm, Ulm, Germany;Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany;Pulmonary Research Institute at LungenClinic Grosshansdorf, Großhansdorf, Germany;Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany;Research Center Borstel, Pathology, Borstel, Germany;Bioinformatics Core Unit, University Medical Center Hamburg-Eppendorf, Hamburg, Germany;LungenClinic Grosshansdorf, Großhansdorf, Germany;Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany;LungenClinic Grosshansdorf, Großhansdorf, Germany;Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany;Department of Internal Medicine I, Pneumology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany;LungenClinic Grosshansdorf, Großhansdorf, Germany;Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany;Department of Internal Medicine I, Pneumology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany;Department of Oncology, Hematology and BMT with Section Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany;LungenClinic Grosshansdorf, Großhansdorf, Germany;Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Großhansdorf, Germany;Pulmonary Research Institute at LungenClinic Grosshansdorf, Großhansdorf, Germany;Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA;

关键词: Sputum;    Omics;    Transcriptome;    Methylome;    Deconvolution;    RNA;    Degradation;    Biobanking;    Asthma;    COPD;   

DOI  :  10.1186/s12931-020-01544-4

来源: Springer

【 摘 要 】

BackgroundTo date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses.MethodsWhole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework.ResultsA linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma.ConclusionsIn this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.

【 授权许可】

CC BY