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Lung fibroblasts are implicated in abnormal tissue repair in chronic obstructive pulmonary disease COPD The exact mechanisms are unknown and comprehensive analysis comparing COPD and control fibroblasts is lacking The aim of this study is to gain insight into the role of lung fibroblasts in COPD pathology using unbiased proteomic and transcriptomic analysis Protein and RNA were isolated from cultured parenchymal lung fibroblasts of 17 patients with stage IV COPD and 16 non COPD controls Proteins were analyzed using LC MS MS and RNA through RNA sequencing Differential protein and gene expression in COPD was assessed via linear regression followed by pathway enrichment correlation analysis and immunohistological staining in lung tissue Proteomic and transcriptomic data were compared to investigate the overlap and correlation between both levels of data We identified 40 differentially expressed DE proteins and zero DE genes between COPD and control fibroblasts The most significant DE proteins were HNRNPA2B1 and FHL1 Thirteen of the 40 proteins were previously associated with COPD including FHL1 and GSTP1 Six of the 40 proteins were related to telomere maintenance pathways and were positively correlated with the senescence marker LMNB1 No significant correlation between gene and protein expression was observed for the 40 proteins We hereby describe 40 DE proteins in COPD fibroblasts including previously described COPD proteins FHL1 GSTP1 and new COPD research targets like HNRNPA2B1 Lack of overlap and correlation between gene and protein data supports the use of unbiased proteomics analysis and indicates that different types of information are generated with both methods

Studying the effects of asthma SNPs on alternative splicing can lead to new insights into asthma pathophysiology. More specifically, a 17q12 SNP is associated to alternative splicing of GSDMB. https://bit.ly/3W49oTs.

INTRODUCTION: A subset of COPD patients develops advanced disease with severe airflow obstruction, hyperinflation and extensive emphysema. We propose that the pathogenesis in these patients differs from mild-moderate COPD and is reflected by bronchial gene expression. The aim of the present study was to identify a unique bronchial epithelial gene signature for severe COPD patients. METHODS: We obtained RNA sequencing data from bronchial brushes from 123 ex-smokers with severe COPD, 23 with mild-moderate COPD and 23 non-COPD controls. We identified genes specific to severe COPD by comparing severe COPD to non-COPD controls, followed by removing genes that were also differentially expressed between mild-moderate COPD and non-COPD controls. Next, we performed a pathway analysis on these genes and evaluated whether this signature is retained in matched nasal brushings. RESULTS: We identified 219 genes uniquely differentially expressed in severe COPD. Interaction network analysis identified VEGFA and FN1 as the key genes with the most interactions. Genes were involved in extracellular matrix regulation, collagen binding and the immune response. Of interest were 10 genes (VEGFA, DCN, SPARC, COL6A2, MGP, CYR61, ANXA6, LGALS1, C1QA and C1QB) directly connected to fibronectin 1 (FN1). Most of these genes were lower expressed in severe COPD and showed the same effect in nasal brushings. CONCLUSIONS: We found a unique severe COPD bronchial gene signature with key roles for VEGFA and FN1, which was retained in the upper airways. This supports the hypothesis that severe COPD, at least partly, comprises a different pathology and supports the potential for biomarker development based on nasal brushes in COPD.

After more than two years the COVID-19 pandemic, that is caused by infection with the respiratory SARS-CoV-2 virus, is still ongoing. The risk to develop severe COVID-19 upon SARS-CoV-2 infection is increased in individuals with a high age, high body mass index, and who are smoking. The SARS-CoV-2 virus infects cells of the upper respiratory tract by entering these cells upon binding to the Angiotensin-converting enzyme 2 (ACE2) receptor. ACE2 is expressed in various cell types in the lung but the expression is especially high in goblet and ciliated cells. Recently, it was shown that next to its full-length isoform, ACE2 also has a short isoform. The short isoform is unable to bind SARS-CoV-2 and does not facilitate viral entry. In the current study we investigated whether active cigarette smoking increases the expression of the long or the short ACE2 isoform. We showed that in active smokers the expression of the long, active isoform, but not the short isoform of ACE2 is higher compared to never smokers. Additionally, it was shown that the expression of especially the long, active isoform of ACE2 was associated with secretory, club and goblet epithelial cells. This study increases our understanding of why current smokers are more susceptible to SARS-CoV-2 infection, in addition to the already established increased risk to develop severe COVID-19.

Peripartum cardiomyopathy (PPCM) is a rare form of acute onset heart failure that presents in otherwise healthy pregnant women around the time of delivery. While most of these women respond to early intervention, about 20% progress to end-stage heart failure that symptomatically resembles dilated cardiomyopathy (DCM). In this study, we examined two independent RNAseq datasets from the left ventricle of end-stage PPCM patients and compared gene expression profiles to female DCM and non-failing donors. Differential gene expression, enrichment analysis and cellular deconvolution were performed to identify key processes in disease pathology. PPCM and DCM display similar enrichment in metabolic pathways and extracellular matrix remodeling suggesting these are similar processes across end-stage systolic heart failure. Genes involved in golgi vesicles biogenesis and budding were enriched in PPCM left ventricles compared to healthy donors but were not found in DCM. Furthermore, changes in immune cell populations are evident in PPCM but to a lesser extent compared to DCM, where the latter is associated with pronounced pro-inflammatory and cytotoxic T cell activity. This study reveals several pathways that are common to end-stage heart failure but also identifies potential targets of disease that may be unique to PPCM and DCM.

An amendment to this paper has been published and can be accessed via the original article.

Current knowledge about the respiratory microbiome is mainly based on 16S ribosomal RNA gene sequencing. Newer sequencing approaches, such as metatranscriptomics, offer the technical ability to measure the viable microbiome response to environmental conditions such as smoking as well as to explore its functional role by investigating host-microbiome interactions. However, knowledge about its feasibility in respiratory microbiome research, especially in lung biopsies, is still very limited. RNA sequencing was performed in bronchial biopsies from clinically stable smokers (n = 5) and ex-smokers (n = 6) with COPD not using (inhaled) steroids. The Trinity assembler was used to assemble non-human reads in order to allow unbiased taxonomical and microbial transcriptional analyses. Subsequently, host-microbiome interactions were analyzed based on associations with host transcriptomic data. Ultra-low levels of microbial mass (0.009%) were identified in the RNA-seq data. Overall, no differences were identified in microbiome diversity or transcriptional profiles of microbial communities or individual microbes between COPD smokers and ex-smokers in the initial test dataset as well as a larger replication dataset. We identified an upregulated host gene set, related to the simultaneous presence of Bradyrhizobium, Roseomonas, Brevibacterium.spp., which were related to PERK-mediated unfolded protein response (UPR) and expression of the microRNA-155-5p. Our results show that metatranscriptomic profiling in bronchial biopsy samples from stable COPD patients yields ultra-low levels of microbial mass. Further, this study illustrates the potential of using transcriptional profiling of the host and microbiome to gain more insight into their interaction in the airways.

The COPD susceptibility SNP rs2070600 affects the levels of the COPD biomarker sRAGE in sputum as well as splicing of AGER. Moreover, @PouwelsScience et al. demonstrate large differences in sRAGE levels between serum and sputum. https://bit.ly/3t0pJtK.

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.

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/2N8sY3s.

Contains fulltext : 218166.pdf (publisher's version ) (Open Access), The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.