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

Abstract

Background: To 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.

Methods: Whole-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.

Results: A 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.

Conclusions: In 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.

Keywords: Asthma; Biobanking; COPD; Deconvolution; Degradation; Methylome; Omics; RNA; Sputum; Transcriptome.

Fig. 1

Graphical abstract. Induced sputum, containing a variety of inflammatory cells, exhibits potential to directly reflect inflammatory processes in the lower airways. Progress in understanding the underlying mechanisms has been made by supplying sputum samples to high-throughput molecular analyses, primarily transcriptomics. To date, these have provided valuable insights to disease mechanisms and have led to differentiation of molecular endotypes (1) that are associated with distinct clinical presentations. However, most high-throughput analyses of sputum samples are prone to substantial bias by variation in cellular composition. Here, we introduce an unbiased deconvolution approach to sputum omics analysis in order to improve the identification of molecular patterns and dysregulation (2). Furthermore, were provide an example that sputum analysis can be extended by whole-genome methylation profiling to broaden the view on molecular mechanisms of pulmonary inflammation. Created with BioRender.com

Fig. 2

Mean cellular composition of sputum samples. AM alveolar macrophages, NG neutrophil granulocytes, EO eosinophils, LY lymphocytes, MO monocytes, CC ciliated cells (respiratory epithelium), SC squamous cells

Fig. 3

Principal component analysis of the gene expression data. Before correction for RNA degradation (a), after correlation filtering (b) and after correction by linear regression (c)

Fig. 4

Venn diagram visualizations of differentially expressed genes (DEGs). Asthma vs. controls (a) and COPD vs. controls (b). Analyses were performed on the uncorrected, mixed-cell transcriptome dataset (white/black circle), after correction for RNA degradation by correlation filtering (yellow) and after correction by linear regression (blue)

Fig. 5

Principal component analysis of the whole-genome methylation data