Another step in COPD-endotyping:transcriptomic profiling of the host and respiratory microbiome

The work presented in this thesis comprises various chapters applying gene expression profiling in bronchial biopsy and sputum samples for the molecular characterization of COPD patients. Based on these results, various conclusions can be drawn: (1) RNA-Seq allows to study expression profiles of genes in much higher detail compared to microarrays, but available microarray datasets could still be utilized to study bulk tissue deconvolution in lung biopsies, without the need to resequence samples. (2) Smoke-induced changes in the transcriptome of the bronchial mucus barrier might be reversible after 1-year smoking cessation in healthy and COPD patients. (3) The sputum gene signature PRISE suggests that eosinophils and macrophages as innate effector cells are associated with the likelihood to exacerbate in COPD patients after ICS withdrawal. PRISE might also be a better predictor for ICS-response in COPD patients, compared to eosinophilia, however, this hypothesis needs to be validated in independent cohorts. (4) The sputum microbiome exhibits a promising potential to contribute to endotyping in COPD, but still faces important limitations and challenges on its path to becoming an important biomarker. (5) Investigating the microbiome in bronchial biopsy samples in stable COPD patients using RNA-seq, yields ultra-low levels of microbial biomass, indicating that other lung specimens might be preferable to study the viable respiratory microbiome

Sputum microbiome profiling in COPD:beyond singular pathogen detection

Culture-independent microbial sequencing techniques have revealed that the respiratory tract harbours a complex microbiome not detectable by conventional culturing methods. The contribution of the microbiome to chronic obstructive pulmonary disease (COPD) pathobiology and the potential for microbiome-based clinical biomarkers in COPD are still in the early phases of investigation. Sputum is an easily obtainable sample and has provided a wealth of information on COPD pathobiology, and thus has been a preferred sample type for microbiome studies. Although the sputum microbiome likely reflects the respiratory microbiome only in part, there is increasing evidence that microbial community structure and diversity are associated with disease severity and clinical outcomes, both in stable COPD and during the exacerbations. Current evidence has been limited to mainly cross-sectional studies using 16S rRNA gene sequencing, attempting to answer the question 'who is there?' Longitudinal studies using standardised protocols are needed to answer outstanding questions including differences between sputum sampling techniques. Further, with advancing technologies, microbiome studies are shifting beyond the examination of the 16S rRNA gene, to include whole metagenome and metatranscriptome sequencing, as well as metabolome characterisation. Despite being technically more challenging, whole-genome profiling and metabolomics can address the questions 'what can they do?' and 'what are they doing?' This review provides an overview of the basic principles of high-throughput microbiome sequencing techniques, current literature on sputum microbiome profiling in COPD, and a discussion of the associated limitations and future perspectives

Comparison of genome-wide gene expression profiling by RNA Sequencing versus microarray in bronchial biopsies of COPD patients before and after inhaled corticosteroid treatment:does it provide new insights?

More DEGs are detected by RNA-Seq than microarrays in COPD lung biopsies and are associated with immunological pathways. Performing bulk tissue cell-type deconvolution in microarray lung samples, using the SVR method, reflects RNA-Seq results. https://bit.ly/2N8sY3

The sputum transcriptome better predicts COPD exacerbations after the withdrawal of inhaled corticosteroids than sputum eosinophils

INTRODUCTION: Continuing inhaled corticosteroid (ICS) use does not benefit all patients with COPD, yet it is difficult to determine which patients may safely sustain ICS withdrawal. Although eosinophil levels can facilitate this decision, better biomarkers could improve personalised treatment decisions. METHODS: We performed transcriptional profiling of sputum to explore the molecular biology and compared the predictive value of an unbiased gene signature versus sputum eosinophils for exacerbations after ICS withdrawal in COPD patients. RNA-sequencing data of induced sputum samples from 43 COPD patients were associated with the time to exacerbation after ICS withdrawal. Expression profiles of differentially expressed genes were summarised to create gene signatures. In addition, we built a Bayesian network model to determine coregulatory networks related to the onset of COPD exacerbations after ICS withdrawal. RESULTS: In multivariate analyses, we identified a gene signature (LGALS12, ALOX15, CLC, IL1RL1, CD24, EMR4P) associated with the time to first exacerbation after ICS withdrawal. The addition of this gene signature to a multiple Cox regression model explained more variance of time to exacerbations compared to a model using sputum eosinophils. The gene signature correlated with sputum eosinophil as well as macrophage cell counts. The Bayesian network model identified three coregulatory gene networks as well as sex to be related to an early versus late/nonexacerbation phenotype. CONCLUSION: We identified a sputum gene expression signature that exhibited a higher predictive value for predicting COPD exacerbations after ICS withdrawal than sputum eosinophilia. Future studies should investigate the utility of this signature, which might enhance personalised ICS treatment in COPD patients

Cholinergic neuroplasticity in asthma driven by TrkB signaling

Parasympathetic neurons in the airways control bronchomotor tone. Increased activity of cholinergic neurons are mediators of airway hyperresponsiveness (AHR) in asthma, however, mechanisms are not elucidated. We describe remodeling of the cholinergic neuronal network in asthmatic airways driven by brain-derived neurotrophic factor (BDNF) and Tropomyosin receptor kinase B (TrkB). Human bronchial biopsies were stained for cholinergic marker vesicular acetylcholine transporter (VAChT). Human lung gene expression and single nucleotide polymorphisms (SNP) in neuroplasticity-related genes were compared between asthma and healthy patients. Wild-type (WT) and mutated TrkB knock-in mice (Ntrk2tm1Ddg/J) with impaired BDNF signaling were chronically exposed to ovalbumin (OVA). Neuronal VAChT staining and airway narrowing in response to electrical field stimulation in precision cut lung slices (PCLS) were assessed. Increased cholinergic fibers in asthmatic airway biopsies was found, paralleled by increased TrkB gene expression in human lung tissue, and SNPs in the NTRK2 [TrkB] and BDNF genes linked to asthma. Chronic allergen exposure in mice resulted in increased density of cholinergic nerves, which was prevented by inhibiting TrkB. Increased nerve density resulted in AHR in vivo and in increased nerve-dependent airway reactivity in lung slices mediated via TrkB. These findings show cholinergic neuroplasticity in asthma driven by TrkB signaling and suggest that the BDNF-TrkB pathway may be a potential target

Smoking induces shifts in cellular composition and transcriptome within the bronchial mucus barrier

Background and Objective: Smoking disturbs the bronchial-mucus-barrier. This study assesses the cellular composition and gene expression shifts of the bronchial-mucus-barrier with smoking to understand the mechanism of mucosal damage by cigarette smoke exposure. We explore whether single-cell-RNA-sequencing (scRNA-seq) based cellular deconvolution (CD) can predict cell-type composition in RNA-seq data. Methods: RNA-seq data of bronchial biopsies from three cohorts were analysed using CD. The cohorts included 56 participants with chronic obstructive pulmonary disease [COPD] (38 smokers; 18 ex-smokers), 77 participants without COPD (40 never-smokers; 37 smokers) and 16 participants who stopped smoking for 1 year (11 COPD and 5 non-COPD-smokers). Differential gene expression was used to investigate gene expression shifts. The CD-derived goblet cell ratios were validated by correlating with staining-derived goblet cell ratios from the COPD cohort. Statistics were done in the R software (false discovery rate p-value < 0.05). Results: Both CD methods indicate a shift in bronchial-mucus-barrier cell composition towards goblet cells in COPD and non-COPD-smokers compared to ex- and never-smokers. It shows that the effect was reversible within a year of smoking cessation. A reduction of ciliated and basal cells was observed with current smoking, which resolved following smoking cessation. The expression of mucin and sodium channel (ENaC) genes, but not chloride channel genes, were altered in COPD and current smokers compared to never smokers or ex-smokers. The goblet cell-derived staining scores correlate with CD-derived goblet cell ratios. Conclusion: Smoking alters bronchial-mucus-barrier cell composition, transcriptome and increases mucus production. This effect is partly reversible within a year of smoking cessation. CD methodology can predict goblet-cell percentages from RNA-seq