Comprehensive Analysis of Transcriptome Sequencing Data in the Lung Tissues of COPD Subjects

SGCP: a spectral self-learning method for clustering genes in co-expression networks

BACKGROUND

A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules.

RESULTS

We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines.

CONCLUSION

Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Potential marker genes for chronic obstructive pulmonary disease revealed based on single-cell sequencing and Mendelian randomization analysis

BACKGROUND

Progress is being made in the prevention and treatment of chronic obstructive pulmonary disease (COPD), but it is still unsatisfactory. With the development of genetic technology, validated genetic information can better explain COPD.

OBJECTIVE

The study utilized scRNA-seq and Mendelian randomization analysis of eQTLs to identify crucial genes and potential mechanistic pathways underlying COPD pathogenesis.

MEHODS

Single-cell sequencing data were used to identify marker genes for immune cells in the COPD process. Data on eQTLs for immune cell marker genes were obtained from the eQTLGen consortium. To estimate the causal effect of marker genes on COPD, we selected an independent cohort (ukb-b-16751) derived from the UK Biobank database for two-sample Mendelian randomization analysis. Subsequently, we performed immune infiltration analysis, gene set enrichment analysis (GSEA), and co-expression network analysis on the key genes.

RESULTS

The 154 immune cell-associated marker genes identified were mainly involved in pathways such as vacuolar cleavage, positive regulation of immune response and regulation of cell activation. Mendelian randomization analysis screened four pairs of marker genes (GZMH, COTL1, CSTA and CD14) were causally associated with COPD. These four key genes were significantly associated with immune cells. In addition, we have identified potential transcription factors associated with these key genes using the Cistrome database, thus contributing to a deeper understanding of the regulatory network of these gene expressions.

CONCLUSIONS

This eQTLs Mendelian randomization study identified four key genes (GZMH, COTL1, CSTA, and CD14) causally associated with COPD, providing new insights for prevention and treatment of COPD.

Downregulation of Mirlet7 miRNA family promotes Tc17 differentiation and emphysema via de-repression of RORγt

Environmental air irritants including nanosized carbon black (nCB) can drive systemic inflammation, promoting chronic obstructive pulmonary disease (COPD) and emphysema development. The let-7 microRNA (Mirlet7 miRNA) family is associated with IL-17-driven T cell inflammation, a canonical signature of lung inflammation. Recent evidence suggests the Mirlet7 family is downregulated in patients with COPD, however, whether this repression conveys a functional consequence on emphysema pathology has not been elucidated. Here, we show that overall expression of the Mirlet7 clusters, Mirlet7b/Mirlet7c2 and Mirlet7a1/Mirlet7f1/Mirlet7d, are reduced in the lungs and T cells of smokers with emphysema as well as in mice with cigarette smoke (CS)- or nCB-elicited emphysema. We demonstrate that loss of the Mirlet7b/Mirlet7c2 cluster in T cells predisposed mice to exaggerated CS- or nCB-elicited emphysema. Furthermore, ablation of the Mirlet7b/Mirlet7c2 cluster enhanced CD8+IL17a+ T cells (Tc17) formation in emphysema development in mice. Additionally, transgenic mice overexpressing Mirlet7g in T cells are resistant to Tc17 and CD4+IL17a+ T cells (Th17) development when exposed to nCB. Mechanistically, our findings reveal the master regulator of Tc17/Th17 differentiation, RAR-related orphan receptor gamma t (RORγt), as a direct target of Mirlet7 in T cells. Overall, our findings shed light on the Mirlet7/RORγt axis with Mirlet7 acting as a molecular brake in the generation of Tc17 cells and suggest a novel therapeutic approach for tempering the augmented IL-17-mediated response in emphysema.

Transcriptomic Insights into Different Stimulation Intensity of Electroacupuncture in Treating COPD in Rat Models

Background

Electroacupuncture (EA), with varying stimulation intensities, has demonstrated therapeutic potentials in both animal and clinical studies for the treatment of chronic obstructive pulmonary disease (COPD). However, a comprehensive investigation of the intensity-related effects, particularly 1mA and 3mA of EA, and the underlying mechanisms remains lacking.

Methods

A COPD rat model was established by prolonged exposure to cigarette smoke and intermittent intratracheal instillation of lipopolysaccharide. EA treatment was administered at acupoints BL13 (Feishu) and ST36 (Zusanli), 20 minutes daily for 2 weeks, with intensities of 1mA and 3mA. EA effectiveness was evaluated by pulmonary function, histopathological change, serum level of inflammatory cytokines, and level of oxidative stress markers in serum and lung tissues. Transcriptome profiling and weighted gene co-expression network analysis (WGCNA) were performed to reveal gene expression patterns and identify hub genes. Real-time quantitative PCR (RT-qPCR) and Western blot (WB) were performed to detect the mRNA and protein expression levels, respectively.

Results

EA at both 1mA and 3mA exerted differing therapeutic effects by improving lung function and reducing inflammation and oxidative stress in COPD rats. Transcriptome analysis revealed distinct expression patterns between the two groups, functionally corresponding to shared and intensity-specific (1mA and 3mA) enriched pathways. Eight candidate genes were identified, including Aqp9, Trem1, Mrc1, and Gpnmb that were downregulated by EA and upregulated in COPD. Notably, Msr1 and Slc26a4 exclusively downregulated in EA-1mA, while Pde3a and Bmp6 upregulated solely in EA-3mA. WGCNA constructed 5 key modules and elucidated the module-trait relationship, with the aforementioned 8 genes being highlighted. Additionally, their mRNA and protein levels were validated by RT-qPCR and WB.

Conclusion

Our results demonstrated that 1mA and 3mA intensities induce distinct gene expression patterns at the transcriptional level, associated with shared and 1mA vs 3mA-specific enriched pathways. Genes Mrc1, Gpnmb, Trem1, and Aqp9 emerge as promising targets, and further studies are needed to elucidate their functional consequences in COPD.

Downregulation of Let-7 miRNA promotes Tc17 differentiation and emphysema via de-repression of RORγt

Environmental air irritants including nanosized carbon black (nCB) can drive systemic inflammation, promoting chronic obstructive pulmonary disease (COPD) and emphysema development. The let-7 family of miRNAs is associated with IL-17-driven T cell inflammation, a canonical signature of lung inflammation. Recent evidence suggests the let-7 family is downregulated in patients with COPD, however, whether this repression conveys a functional consequence on emphysema pathology has not been elucidated. Here we show that overall expression of the let-7 miRNA clusters, let-7b/let-7c2 and let-7a1/let-7f1/let-7d, are reduced in the lungs and T cells of smokers with emphysema as well as in mice with cigarette smoke (CS)- or nCB-elicited emphysema. We demonstrate that loss of the let-7b/let-7c2-cluster in T cells predisposed mice to exaggerated CS- or nCB-elicited emphysema. Furthermore, ablation of the let-7b/let-7c2-cluster enhanced CD8+IL17a+ T cells (Tc17) formation in emphysema development in mice. Additionally, transgenic mice overexpressing let-7 in T cells are resistant to Tc17 and CD4+IL17a+ T cells (Th17) development when exposed to nCB. Mechanistically, our findings reveal the master regulator of Tc17/Th17 differentiation, RAR-related orphan receptor gamma t (RORγt), as a direct target of let-7 miRNA in T cells. Overall, our findings shed light on the let-7/RORγt axis with let-7 acting as a molecular brake in the generation of Tc17 cells and suggests a novel therapeutic approach for tempering the augmented IL-17-mediated response in emphysema.

Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema

Rationale: Emphysema is a chronic obstructive pulmonary disease phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. Objectives: To discover blood omics biomarkers for chest computed tomography-quantified emphysema and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training sample of 2,370 COPDGene participants with available blood RNA sequencing, plasma proteomics, and clinical data. Internal validation was conducted in a COPDGene testing sample (n = 1,016), and external validation was done in the ECLIPSE study (n = 526). Because low body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models with bootstrapping were also developed in the training sample sequentially using clinical, blood cell proportions, RNA-sequencing, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed by the area under the receiver operating characteristic curves for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: Totals of 3,829 genes, 942 isoforms, 260 exons, and 714 proteins were significantly associated with emphysema (false discovery rate, 5%) and yielded 11 biological pathways. Seventy-four percent of these genes and 62% of these proteins showed mediation by BMI. Our prediction models demonstrated reasonable predictive performance in both COPDGene and ECLIPSE. The highest-performing model used clinical, blood cell, and protein data (area under the receiver operating characteristic curve in COPDGene testing, 0.90; 95% confidence interval, 0.85-0.90). Conclusions: Blood transcriptome and proteome-wide analyses revealed key biological pathways of emphysema and enhanced the prediction of emphysema.

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

Background

This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment.

Methods

In this study, the "CIBERSORT" algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The "limma" package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients.

Results

M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P < 0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the "Boruta" and "LASSO" algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients.

Conclusion

According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD.

Exploration of the Mechanism of Shengxian Decoction Against Chronic Obstructive Pulmonary Disease Based on Network Pharmacology and Experimental Verification

Shengxian decoction (SXT) is clinically used in chronic obstructive pulmonary disease (COPD) treatment. This study aimed to explore the mechanism and target genes of SXT acting on COPD. Differentially expressed genes (DEGs) between COPD and controls were identified and then performed enrichment analysis. The effective active compounds and corresponding target genes were obtained from the traditional Chinese medicine systems pharmacology database. We also compiled COPD related genes from the GeneCards database. Through the protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) regression was performed to identify key genes. Molecular docking was used for docking of key genes and compounds. The expression of key genes was detected by quantitative real-time PCR in COPD patients and bronchial epithelial cells stimulated with cigarette stroke extract (CSE). We identified 1,458 intersected DEGs from GSE47460 and GSE57148 datasets. Compared with intersected DEGs, we obtained 33 SXT target COPD-related genes. PI3K-Akt signaling pathway, MAPK signaling pathway, and focal adhesion were enriched by these 33 genes, as well as intersected DEGs. According to LASSO regression, there were 12 genes considered as signature genes. Then we constructed active compounds and corresponding six target genes. Finally, HIF1A and IL1B were selected as key genes by combining PPI network. HIF1A and IL1B were all upregulated expression in COPD and CSE stimulated cells and recovered in SXT treated CSE stimulated cells. This study provides a scientific basis for the identification of active compounds and target genes of SXT in the treatment of COPD.

NRF2 Activation Reprograms Defects in Oxidative Metabolism to Restore Macrophage Function in Chronic Obstructive Pulmonary Disease

Rationale: Chronic obstructive pulmonary disease (COPD) is a disease characterized by persistent airway inflammation and disordered macrophage function. The extent to which alterations in macrophage bioenergetics contribute to impaired antioxidant responses and disease pathogenesis has yet to be fully delineated. Objectives: Through the study of COPD alveolar macrophages (AMs) and peripheral monocyte-derived macrophages (MDMs), we sought to establish if intrinsic defects in core metabolic processes drive macrophage dysfunction and redox imbalance. Methods: AMs and MDMs from donors with COPD and healthy donors underwent functional, metabolic, and transcriptional profiling. Measurements and Main Results: We observed that AMs and MDMs from donors with COPD display a critical depletion in glycolytic- and mitochondrial respiration-derived energy reserves and an overreliance on glycolysis as a source for ATP, resulting in reduced energy status. Defects in oxidative metabolism extend to an impaired redox balance associated with defective expression of the NADPH-generating enzyme, ME1 (malic enzyme 1), a known target of the antioxidant transcription factor NRF2 (nuclear factor erythroid 2-related factor 2). Consequently, selective activation of NRF2 resets the COPD transcriptome, resulting in increased generation of TCA cycle intermediaries, improved energetic status, favorable redox balance, and recovery of macrophage function. Conclusions: In COPD, an inherent loss of metabolic plasticity leads to metabolic exhaustion and reduced redox capacity, which can be rescued by activation of the NRF2 pathway. Targeting these defects, via NRF2 augmentation, may therefore present an attractive therapeutic strategy for the treatment of the aberrant airway inflammation described in COPD.

IL-1β Induces a Proinflammatory Fibroblast Microenvironment that Impairs Lung Progenitors' Function

Chronic obstructive pulmonary disease (COPD) is characterized by a persistent inflammatory state in the lungs and defective tissue repair. Although the inflammatory response in patients with COPD is well characterized and known to be exaggerated during exacerbations, its contribution to lung injury and abnormal repair is still unclear. In this study, we aimed to investigate how the inflammatory microenvironment affects the epithelial progenitors and their supporting mesenchymal niche cells involved in tissue repair of the distal lung. We focused on IL-1β, a key inflammatory mediator that is increased during exacerbations of COPD, and used an organoid model of lung epithelial cells and fibroblasts to assess the effect of IL-1β treatment on these cells' transcriptome and secreted factors. Whereas direct treatment of the lung organoids with IL-1β promoted organoid growth, this switched toward inhibition when it was added as fibroblast pretreatment followed by organoid treatment. We then investigated the IL-1β-driven mechanisms in the fibroblasts and found an inflammatory response related to (C-X-C motif) ligand (CXCL) chemokines; we confirmed that these chemokines were responsible for the impaired organoid growth and found that targeting their C-X-C chemokine receptors 1/2 (CXCR1/2) receptors or the IL-1β intracellular signaling reduced the proinflammatory response and restored organoid growth. These data demonstrate that IL-1β alters the fibroblasts' state by promoting a distinct inflammatory response, switching their supportive function on epithelial progenitors toward an inhibitory one in an organoid assay. These results imply that chronic inflammation functions as a shift toward inhibition of repair, thereby contributing to chronic inflammatory diseases like COPD.

RNA-seq preprocessing and sample size considerations for gene network inference

Background

Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are different from microarray data, it is an unanswered question what preprocessing methods for RNA-seq data should be applied prior to GNI to attain optimal performance, or what the required sample size for RNA-seq data is to obtain reliable GNI estimates.

Results

We ran 9144 analysis of 7 different RNA-seq datasets to evaluate 300 different preprocessing combinations that include data transformations, normalizations and association estimators. We found that there was no single best performing preprocessing combination but that there were several good ones. The performance varied widely over various datasets, which emphasized the importance of choosing an appropriate preprocessing configuration before GNI. Two preprocessing combinations appeared promising in general: First, Log-2 TPM (transcript per million) with Variance-stabilizing transformation (VST) and Pearson Correlation Coefficient (PCC) association estimator. Second, raw RNA-seq count data with PCC. Along with these two, we also identified 18 other good preprocessing combinations. Any of these algorithms might perform best in different datasets. Therefore, the GNI performances of these approaches should be measured on any new dataset to select the best performing one for it. In terms of the required biological sample size of RNA-seq data, we found that between 30 to 85 samples were required to generate reliable GNI estimates.

Conclusions

This study provides practical recommendations on default choices for data preprocessing prior to GNI analysis of RNA-seq data to obtain optimal performance results.

Recent evidence from omic analysis for redox signalling and mitochondrial oxidative stress in COPD

COPD is driven by exogenous and endogenous oxidative stress derived from inhaled cigarette smoke, air pollution and reactive oxygen species from dysregulated mitochondria in activated inflammatory cells within the airway and lung. This is compounded by the loss in antioxidant defences including FOXO and NRF2 and other antioxidant transcription factors together with various key enzymes that attenuate oxidant effects. Oxidative stress enhances inflammation; airway remodelling including fibrosis and emphysema; post-translational protein modifications leading to autoantibody generation; DNA damage and cellular senescence. Recent studies using various omics technologies in the airways, lungs and blood of COPD patients has emphasised the importance of oxidative stress, particularly that derived from dysfunctional mitochondria in COPD and its role in immunity, inflammation, mucosal barrier function and infection. Therapeutic interventions targeting oxidative stress should overcome the deleterious pathologic effects of COPD if targeted to the lung. We require novel, more efficacious antioxidant COPD treatments among which mitochondria-targeted antioxidants and Nrf2 activators are promising.

SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure

A challenge in bulk gene differential expression analysis is to differentiate changes due to cell type-specific gene expression and cell type proportions. SCADIE is an iterative algorithm that simultaneously estimates cell type-specific gene expression profiles and cell type proportions, and performs cell type-specific differential expression analysis at the group level. Through its unique penalty and objective function, SCADIE more accurately identifies cell type-specific differentially expressed genes than existing methods, including those that may be missed from single cell RNA-Seq data. SCADIE has robust performance with respect to the choice of deconvolution methods and the sources and quality of input data.

Macrophage programming is regulated by a cooperative interaction between fatty acid binding protein 5 and peroxisome proliferator-activated receptor γ

Resolution of inflammation is an active process that is tightly regulated to achieve repair and tissue homeostasis. In the absence of resolution, persistent inflammation underlies the pathogenesis of chronic lung disease such as chronic obstructive pulmonary disease (COPD) with recurrent exacerbations. Over the course of inflammation, macrophage programming transitions from pro-inflammatory to pro-resolving, which is in part regulated by the nuclear receptor Peroxisome Proliferator-Activated Receptor γ (PPARγ). Our previous work demonstrated an association between Fatty Acid Binding Protein 5 (FABP5) expression and PPARγ activity in peripheral blood mononuclear cells of healthy and COPD patients. However, a role for FABP5 in macrophage programming has not been examined. Here, using a combination of in vitro and in vivo approaches, we demonstrate that FABP5 is necessary for PPARγ activation. In turn, PPARγ acts directly to increase FABP5 expression in primary human alveolar macrophages. We further illustrate that lack of FABP5 expression promotes a pro-inflammatory macrophage programming with increased secretion of pro-inflammatory cytokines and increased chromatin accessibility for pro-inflammatory transcription factors (e.g., NF-κB and MAPK). And finally, real-time cell metabolic analysis using the Seahorse technology shows an inhibition of oxidative phosphorylation in FABP5-deficient macrophages. Taken together, our data indicate that FABP5 and PPARγ reciprocally regulate each other's expression and function, consistent with a novel positive feedback loop between the two factors that mediates macrophage pro-resolving programming. Our studies highlight the importance of defining targets and regulatory mechanisms that control the resolution of inflammation and may serve to inform novel interventional strategies directed towards COPD.

Hsa-Mir-320c, Hsa-Mir-200c-3p, and Hsa-Mir-449c-5p as Potential Specific miRNA Biomarkers of COPD: A Pilot Study

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease commonly induced by cigarette smoke. The expression of miRNAs can be altered in patients with COPD and could be used as a biomarker. We aimed to identify a panel of miRNAs in bronchoalveolar lavage (BAL) to differentiate COPD patients from smokers and non-smokers with normal lung function. Accordingly, forty-five subjects classified as COPD, smokers, and non-smokers (n = 15 per group) underwent clinical, functional characterization and bronchoscopy with BAL. The mean age of the studied population was 61.61 ± 12.95 years, BMI 25.72 ± 3.82 Kg/m2, FEV1/FVC 68.37 ± 12.00%, and FEV1 80.07 ± 23.63% predicted. According to microarray analysis, three miRNAs of the most upregulated were chosen: miR-320c, miR-200c-3p, and miR-449c-5p. These miRNAs were validated by qPCR and were shown to be differently expressed in COPD patients. ROC analysis showed that these three miRNAs together had an area under the curve of 0.89 in differentiating COPD from controls. Moreover, in silico analysis of candidate miRNAs by DIANA-miRPath showed potential involvement in the EGFR and Hippo pathways. These results suggest a specific 3-miRNA signature that could be potentially used as a biomarker to distinguish COPD patients from smokers and non-smoker subjects.

Perplexity: evaluating transcript abundance estimation in the absence of ground truth

Background

There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best.

Results

We derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. Furthermore, we demonstrate theoretically and experimentally that perplexity can be computed for arbitrary transcript abundance estimation models.

Conclusions

Alongside the derivation and implementation of perplexity for transcript abundance estimation, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.

Impact of Smoking on COVID-19 Symptoms in Non-Vaccinated Patients: A Matched Observational Study from Qatar

Purpose

Predisposition to acute illness from COVID-19 is suggested to correlate with cigarette smoking as it augments the risk of developing cardiovascular and respiratory illnesses, including infections. However, the effects of smoking on COVID-19 symptoms are not well described and controversial. In this study, we aim to explore the associations between smoking and COVID-19 symptoms.

Subjects and Methods

A cross-sectional study using the Ministry of Public Health (MoPH), Qatar database was administered to a Qatari population with confirmed COVID-19 disease who filled in pre-defined phone-call questionnaire between 27th February 2020 and 31st December 2020. We analyzed 11,701 non-vaccinated COVID-19 individuals (2952 smokers and 8749 non-smokers) with confirmed RT-PCR test results. The association of smoking and the presence of symptoms as well as patient characteristics was calculated using Pearson’s Chi-square and Fisher’s exact tests, adjusting for potential covariates.

Results

Compared with the non-smokers, symptomatic COVID-19 infection is significantly higher in smokers. In addition, we found fever as the most common symptom developed in COVID-19 patients followed by cough, headache, muscle ache, and sore throat. As compared to other symptoms, association of smoking with chills and abdominal pain was less evident (P < 0.05 and P < 0.001, respectively). However, both groups showed similar rates of developing cough.

Conclusion

In conclusion, smoking is associated with COVID-19 symptoms frequency in non-vaccinated patients; nevertheless, further investigations are necessary to understand the mechanism of this association which could generate new targets for the management of COVID-19 in smoker patients.

Identification of the Key Immune-Related Genes in Chronic Obstructive Pulmonary Disease Based on Immune Infiltration Analysis

Purpose

Chronic obstructive pulmonary disease (COPD) is a major cause of death and morbidity worldwide. A better understanding of new biomarkers for COPD patients and their complex mechanisms in the progression of COPD are needed.

Methods

An algorithm was conducted to reveal the proportions of 22 subsets of immune cells in COPD samples. Differentially expressed immune-related genes (DE-IRGs) were obtained based on the differentially expressed genes (DEGs) of the GSE57148 dataset, and 1509 immune-related genes (IRGs) were downloaded from the ImmPort database. Functional enrichment analyses of DE-IRGs were conducted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and Ingenuity Pathway Analysis (IPA). We defined the DE-IRGs that had correlations with immune cells as hub genes. The potential interactions among the hub genes were explored by a protein-protein interaction (PPI) network.

Results

The CIBERSORT results showed that lung tissue of COPD patients contained a greater number of resting NK cells, activated dendritic cells, and neutrophils than normal samples. However, the fractions of follicular helper T cells and resting dendritic cells were relatively lower. Thirty-eight DE-IRGs were obtained for further analysis. Functional enrichment analysis revealed that these DE-IRGs were significantly enriched in several immune-related biological processes and pathways. Notably, we also observed that DE-IRGs were associated with the coronavirus disease COVID-19 in the progression of COPD. After correlation analysis, six DE-IRGs associated with immune cells were considered hub genes, including AHNAK, SLIT2 TNFRRSF10C, CXCR1, CXCR2, and FCGR3B.

Conclusion

In the present study, we investigated immune-related genes as novel diagnostic biomarkers and explored the potential mechanism for COPD based on CIBERSORT analysis, providing a new understanding for COPD treatment.

Mitochondrial ribosomal stress in lung diseases

Mitochondria are involved in a variety of critical cellular functions, and their impairment drives cell injury. The mitochondrial ribosome (mitoribosome) is responsible for the protein synthesis of mitochondrial DNA encoded genes. These proteins are involved in oxidative phosphorylation, respiration, and ATP production required in the cell. Mitoribosome components originate from both mitochondrial and nuclear genomes. Their dysfunction can be caused by impaired mitochondrial protein synthesis or mitoribosome misassembly, leading to a decline in mitochondrial translation. This decrease can trigger mitochondrial ribosomal stress and contribute to pulmonary cell injury, death, and diseases. This review focuses on the contribution of the impaired mitoribosome structural components and function to respiratory disease pathophysiology. We present recent findings in the fields of lung cancer, chronic obstructive pulmonary disease, interstitial lung disease, and asthma. We also include reports on the mitoribosome dysfunction in pulmonary hypertension, high altitude pulmonary edema, bacterial and viral infections. Studies of the mitoribosome alterations in respiratory diseases can lead to novel therapeutic targets.

Identification of Potential Key Genes in the Pathogenesis of Chronic Obstructive Pulmonary Disease Through Bioinformatics Analysis

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease with high morbidity and mortality. The etiology of COPD is complex, and the pathogenesis mechanisms remain unclear. In this study, we used rat and human COPD gene expression data from our laboratory and the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between individuals with COPD and healthy individuals. Then, protein–protein interaction (PPI) networks were constructed, and hub genes were identified. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. A total of 198 DEGs were identified, and a PPI network with 144 nodes and 355 edges was constructed. Twelve hub genes were identified by the cytoHubba plugin in Cytoscape. Of these genes, CCR3, CCL2, COL4A2, VWF, IL1RN, IL2RA, and CCL13 were related to inflammation or immunity, or tissue-specific expression in lung tissue, and their messenger RNA (mRNA) levels were validated by qRT-PCR. COL4A2, VWF, and IL1RN were further verified by the GEO dataset GSE76925, and the ceRNA network was constructed with Cytoscape. These three genes were consistent with COPD rat model data compared with control data, and their dysregulation direction was reversed when the COPD rat model was treated with effective-component compatibility of Bufei Yishen formula III. This bioinformatics analysis strategy may be useful for elucidating novel mechanisms underlying COPD. We pinpointed three key genes that may play a role in COPD pathogenesis and therapy, which deserved to be further studied.

Alternative poly-adenylation modulates α1-antitrypsin expression in chronic obstructive pulmonary disease

α1-anti-trypsin (A1AT), encoded by SERPINA1, is a neutrophil elastase inhibitor that controls the inflammatory response in the lung. Severe A1AT deficiency increases risk for Chronic Obstructive Pulmonary Disease (COPD), however, the role of A1AT in COPD in non-deficient individuals is not well known. We identify a 2.1-fold increase (p = 2.5x10-6) in the use of a distal poly-adenylation site in primary lung tissue RNA-seq in 82 COPD cases when compared to 64 controls and replicate this in an independent study of 376 COPD and 267 controls. This alternative polyadenylation event involves two sites, a proximal and distal site, 61 and 1683 nucleotides downstream of the A1AT stop codon. To characterize this event, we measured the distal ratio in human primary tissue short read RNA-seq data and corroborated our results with long read RNA-seq data. Integrating these results with 3' end RNA-seq and nanoluciferase reporter assay experiments we show that use of the distal site yields mRNA transcripts with over 50-fold decreased translation efficiency and A1AT expression. We identified seven RNA binding proteins using enhanced CrossLinking and ImmunoPrecipitation precipitation (eCLIP) with one or more binding sites in the SERPINA1 3' UTR. We combined these data with measurements of the distal ratio in shRNA knockdown experiments, nuclear and cytoplasmic fractionation, and chemical RNA structure probing. We identify Quaking Homolog (QKI) as a modulator of SERPINA1 mRNA translation and confirm the role of QKI in SERPINA1 translation with luciferase reporter assays. Analysis of single-cell RNA-seq showed differences in the distribution of the SERPINA1 distal ratio among hepatocytes, macrophages, αβ-Tcells and plasma cells in the liver. Alveolar Type 1,2, dendritic cells and macrophages also vary in their distal ratio in the lung. Our work reveals a complex post-transcriptional mechanism that regulates alternative polyadenylation and A1AT expression in COPD.

A Systematic Analysis Identifies Key Regulators Involved in Cell Proliferation and Potential Drugs for the Treatment of Human Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common and malignant cancer types. Abnormal cell proliferation, exemplified by cell cycle and cell division dysregulation, is one of the most prominent hallmarks of cancer and is responsible for recurrence, metastasis, and resistance to cancer therapy. However, LUAD-specific gene regulation and clinical significance remain obscure. Here, by using both tissues and cells from LUAD and normal lung samples, 434 increased and 828 decreased genes of biological significance were detected, including 127 cell cycle-associated genes (95 increased and 32 decreased), 66 cell division-associated genes (56 increased and 10 decreased), and 81 cell proliferation-associated genes (34 increased and 47 decreased). Among them, 12 increased genes (TPX2, CENPF, BUB1, PLK1, KIF2C, AURKB, CDKN3, BUB1B, HMGA2, CDK1, ASPM, and CKS1B) and 2 decreased genes (TACC1 and MYH10) were associated with all the three above processes. Importantly, 2 (CDKN3 and CKS1B) out of the 11 increased genes (except HMGA2) are previously uncharacterized ones in LUAD and can potentially be prognostic markers. Moreover, PLK1 could be a promising therapeutic target for LUAD. Besides, protein–protein interaction network analysis showed that CDK1 and CDC20 were the hub genes, which might play crucial roles in cell proliferation of LUAD. Furthermore, transcriptional regulatory network analysis suggested that the transcription factor E2F1 could be a key regulator in controlling cell proliferation of LUAD via expression modulation of most cell cycle-, cell division-, and cell proliferation-related DEGs. Finally, trichostatin A, hycanthone, vorinostat, and mebeverine were identified as four potential therapeutic agents for LUAD. This work revealed key regulators contributing to cell proliferation in human LUAD and identified four potential therapeutic agents for treatment strategy.

Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein-protein interaction networks

Background

Identifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment.

Methods

In this paper, a novel method was proposed to Prioritize genes using Expression information in Protein–protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order.

Results

Further analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD.

Conclusions

The proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01646-9.

Functional genomics of GPR126 in airway smooth muscle and bronchial epithelial cells

GPR126 is an adhesion G protein-coupled receptor which lies on chromosome 6q24. Genetic variants in this region are reproducibly associated with lung function and COPD in genome wide association studies (GWAS). The aims of this study were to define the role of GPR126 in the human lung and in pulmonary disease and identify possible casual variants. Online tools (GTEx and LDlink) identified SNPs which may have effects on GPR126 function/ expression, including missense variant Ser123Gly and an intronic variant that shows eQTL effects on GPR126 expression. GPR126 signaling via cAMP-mediated pathways was identified in human structural airway cells when activated with the tethered agonist, stachel. RNA-seq was used to identify downstream genes/ pathways affected by stachel-mediated GPR126 activation in human airway smooth muscle cells. We identified ~350 differentially expressed genes at 4 and 24 hours post stimulation with ~20% overlap. We identified that genes regulated by GPR126 activation include IL33, CTGF, and SERPINE1, which already have known roles in lung biology. Pathways altered by GPR126 included those involved in cell cycle progression and cell proliferation. Here, we suggest a role for GPR126 in airway remodeling.

Gender specific airway gene expression in COPD sub-phenotypes supports a role of mitochondria and of different types of leukocytes

Chronic obstructive pulmonary disease (COPD) is a destructive inflammatory disease and the genes expressed within the lung are crucial to its pathophysiology. We have determined the RNAseq transcriptome of bronchial brush cells from 312 stringently defined ex-smoker patients. Compared to healthy controls there were for males 40 differentially expressed genes (DEGs) and 73 DEGs for females with only 26 genes shared. The gene ontology (GO) term “response to bacterium” was shared, with several different DEGs contributing in males and females. Strongly upregulated genes TCN1 and CYP1B1 were unique to males and females, respectively. For male emphysema (E)-dominant and airway disease (A)-dominant COPD (defined by computed tomography) the term “response to stress” was found for both sub-phenotypes, but this included distinct up-regulated genes for the E-sub-phenotype (neutrophil-related CSF3R, CXCL1, MNDA) and for the A-sub-phenotype (macrophage-related KLF4, F3, CD36). In E-dominant disease, a cluster of mitochondria-encoded (MT) genes forms a signature, able to identify patients with emphysema features in a confirmation cohort. The MT-CO2 gene is upregulated transcriptionally in bronchial epithelial cells with the copy number essentially unchanged. Both MT-CO2 and the neutrophil chemoattractant CXCL1 are induced by reactive oxygen in bronchial epithelial cells. Of the female DEGs unique for E- and A-dominant COPD, 88% were detected in females only. In E-dominant disease we found a pronounced expression of mast cell-associated DEGs TPSB2, TPSAB1 and CPA3. The differential genes discovered in this study point towards involvement of different types of leukocytes in the E- and A-dominant COPD sub-phenotypes in males and females.

Benchmarking association analyses of continuous exposures with RNA-seq in observational studies

Large datasets of hundreds to thousands of individuals measuring RNA-seq in observational studies are becoming available. Many popular software packages for analysis of RNA-seq data were constructed to study differences in expression signatures in an experimental design with well-defined conditions (exposures). In contrast, observational studies may have varying levels of confounding transcript-exposure associations; further, exposure measures may vary from discrete (exposed, yes/no) to continuous (levels of exposure), with non-normal distributions of exposure. We compare popular software for gene expression-DESeq2, edgeR and limma-as well as linear regression-based analyses for studying the association of continuous exposures with RNA-seq. We developed a computation pipeline that includes transformation, filtering and generation of empirical null distribution of association P-values, and we apply the pipeline to compute empirical P-values with multiple testing correction. We employ a resampling approach that allows for assessment of false positive detection across methods, power comparison and the computation of quantile empirical P-values. The results suggest that linear regression methods are substantially faster with better control of false detections than other methods, even with the resampling method to compute empirical P-values. We provide the proposed pipeline with fast algorithms in an R package Olivia, and implemented it to study the associations of measures of sleep disordered breathing with RNA-seq in peripheral blood mononuclear cells in participants from the Multi-Ethnic Study of Atherosclerosis.

Assessment of the Association of COPD and Asthma with In-Hospital Mortality in Patients with COVID-19. A Systematic Review, Meta-Analysis, and Meta-Regression Analysis

Together, chronic obstructive pulmonary disease (COPD) and asthma account for the most common non-infectious respiratory pathologies. Conflicting preliminary studies have shown varied effect for COPD and asthma as prognostic factors for mortality in coronavirus disease 2019 (COVID-19). The aim of this study was to explore the association of COPD and asthma with in-hospital mortality in patients with COVID-19 by systematically reviewing and synthesizing with a meta-analysis the available observational studies. MEDLINE, Scopus, and medRxiv databases were reviewed. A random-effects model meta-analysis was used, and I-square was utilized to assess for heterogeneity. In-hospital mortality was defined as the primary endpoint. Sensitivity and meta-regression analyses were performed. Thirty studies with 21,309 patients were included in this meta-analysis (1465 with COPD and 633 with asthma). Hospitalized COVID-19 patients with COPD had higher risk of death compared to those without COPD (OR: 2.29; 95% CI: 1.79–2.93; I² 59.6%). No significant difference in in-hospital mortality was seen in patients with and without asthma (OR: 0.87; 95% CI: 0.68–1.10; I² 0.0%). The likelihood of death was significantly higher in patients with COPD that were hospitalized with COVID-19 compared to patients without COPD. Further studies are needed to assess whether this association is independent or not. No significant difference was demonstrated in COVID-19-related mortality between patients with and without asthma.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein–protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors–genes interaction, protein–drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

Versatile workflow for cell type-resolved transcriptional and epigenetic profiles from cryopreserved human lung

Complexity of lung microenvironment and changes in cellular composition during disease make it exceptionally hard to understand molecular mechanisms driving development of chronic lung diseases. Although recent advances in cell type-resolved approaches hold great promise for studying complex diseases, their implementation relies on local access to fresh tissue, as traditional tissue storage methods do not allow viable cell isolation. To overcome these hurdles, we developed a versatile workflow that allows storage of lung tissue with high viability, permits thorough sample quality check before cell isolation, and befits sequencing-based profiling. We demonstrate that cryopreservation enables isolation of multiple cell types from both healthy and diseased lungs. Basal cells from cryopreserved airways retain their differentiation ability, indicating that cellular identity is not altered by cryopreservation. Importantly, using RNA sequencing and EPIC Array, we show that gene expression and DNA methylation signatures are preserved upon cryopreservation, emphasizing the suitability of our workflow for omics profiling of lung cells. Moreover, we obtained high-quality single-cell RNA-sequencing data of cells from cryopreserved human lungs, demonstrating that cryopreservation empowers single-cell approaches. Overall, thanks to its simplicity, our workflow is well suited for prospective tissue collection by academic collaborators and biobanks, opening worldwide access to viable human tissue.

Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples

Mucus hypersecretion contributes to lung function impairment observed in COPD (chronic obstructive pulmonary disease), a tobacco smoking-related disease. A detailed mucus hypersecretion adverse outcome pathway (AOP) has been constructed from literature reviews, experimental and clinical data, mapping key events (KEs) across biological organisational hierarchy leading to an adverse outcome. AOPs can guide the development of biomarkers that are potentially predictive of diseases and support the assessment frameworks of nicotine products including electronic cigarettes. Here, we describe a method employing manual literature curation supported by a focused automated text mining approach to identify genes involved in 5 KEs contributing to decreased lung function observed in tobacco-related COPD. KE genesets were subsequently confirmed by unsupervised clustering against 3 different transcriptomic datasets including (1) in vitro acute cigarette smoke and e-cigarette aerosol exposure, (2) in vitro repeated incubation with IL-13, and (3) lung biopsies from COPD and healthy patients. The 5 KE genesets were demonstrated to be predictive of cigarette smoke exposure and mucus hypersecretion in vitro, and less conclusively predict the COPD status of lung biopsies. In conclusion, using a focused automated text mining and curation approach with experimental and clinical data supports the development of risk assessment strategies utilising AOPs.

NOD-like receptors mediate inflammatory lung injury during plateau hypoxia exposure

Background

The lung is an important target organ for hypoxia treatment, and hypoxia can induce several diseases in the body.

Methods

We performed transcriptome sequencing for the lungs of rats exposed to plateau hypoxia at 0 day and 28 days. Sequencing libraries were constructed, and enrichment analysis of the differentially expressed genes (DEGs) was implemented using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, experimental validation was executed by quantitative real-time PCR (qRT-PCR) and western blot.

Results

The results showed that the nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) signaling pathway that was involved in immunity may play a crucial function in lung injury caused by plateau hypoxia. And the expressions of NOD1, NOD2, IL-1β, TNF-α, IL-6, and IL-18 were higher at 28 days of exposure to plateau hypoxia than that at 0 day. Similarly, CARD9, MYD88, p38 MAPK, and NF-κB p65, which are related to the NF-κB and MAPK signaling pathways, also demonstrated increased expression at 28 days exposure to plateau hypoxia than at 0 day.

Conclusions

Our study suggested that the NF­κBp65 and p38 MAPK signaling pathways may be activated in the lungs of rats during plateau hypoxia. Upregulated expression of NF­κBp65 and p38 MAPK can promote the transcription of downstream inflammatory factors, thereby aggravating the occurrence and development of lung tissue remodeling.

SARS-CoV-2 Infection and Lung Cancer: Potential Therapeutic Modalities

Human coronaviruses, especially SARS-CoV-2, are emerging pandemic infectious diseases with high morbidity and mortality in certain group of patients. In general, SARS-CoV-2 causes symptoms ranging from the common cold to severe conditions accompanied by lung injury, acute respiratory distress syndrome in addition to other organs' destruction. The main impact upon SARS-CoV-2 infection is damage to alveolar and acute respiratory failure. Thus, lung cancer patients are identified as a particularly high-risk group for SARS-CoV-2 infection and its complications. On the other hand, it has been reported that SARS-CoV-2 spike (S) protein binds to angiotensin-converting enzyme 2 (ACE-2), that promotes cellular entry of this virus in concert with host proteases, principally transmembrane serine protease 2 (TMPRSS2). Today, there are no vaccines and/or effective drugs against the SARS-CoV-2 coronavirus. Thus, manipulation of key entry genes of this virus especially in lung cancer patients could be one of the best approaches to manage SARS-CoV-2 infection in this group of patients. We herein provide a comprehensive and up-to-date overview of the role of ACE-2 and TMPRSS2 genes, as key entry elements as well as therapeutic targets for SARS-CoV-2 infection, which can help to better understand the applications and capacities of various remedial approaches for infected individuals, especially those with lung cancer.

ACE2 Expression Is Increased in the Lungs of Patients With Comorbidities Associated With Severe COVID-19

Patients who died from COVID-19 often had comorbidities, such as hypertension, diabetes, and chronic obstructive lung disease. Although angiotensin-converting enzyme 2 (ACE2) is crucial for SARS-CoV2 to bind and enter host cells, no study has systematically assessed the ACE2 expression in the lungs of patients with these diseases. Here, we analyzed over 700 lung transcriptome samples of patients with comorbidities associated with severe COVID-19 and found that ACE2 was highly expressed in these patients, compared to control individuals. This finding suggests that patients with such comorbidities may have higher chances of developing severe COVID-19. Correlation and network analyses revealed many potential regulators of ACE2 in the human lung, including genes related to histone modifications, such as HAT1, HDAC2, and KDM5B. Our systems biology approach offers a possible explanation for increase of COVID-19 severity in patients with certain comorbidities.

Cigarette Smoke Exposure and Inflammatory Signaling Increase the Expression of the SARS-CoV-2 Receptor ACE2 in the Respiratory Tract

The factors mediating fatal SARS-CoV-2 infections are poorly understood. Here, we show that cigarette smoke causes a dose-dependent upregulation of angiotensin converting enzyme 2 (ACE2), the SARS-CoV-2 receptor, in rodent and human lungs. Using single-cell sequencing data, we demonstrate that ACE2 is expressed in a subset of secretory cells in the respiratory tract. Chronic smoke exposure triggers the expansion of this cell population and a concomitant increase in ACE2 expression. In contrast, quitting smoking decreases the abundance of these secretory cells and reduces ACE2 levels. Finally, we demonstrate that ACE2 expression is responsive to inflammatory signaling and can be upregulated by viral infections or interferon treatment. Taken together, these results may partially explain why smokers are particularly susceptible to severe SARS-CoV-2 infections. Furthermore, our work identifies ACE2 as an interferon-stimulated gene in lung cells, suggesting that SARS-CoV-2 infections could create positive feedback loops that increase ACE2 levels and facilitate viral dissemination.

ACE2 Expression is Increased in the Lungs of Patients with Comorbidities Associated with Severe COVID-19

The pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in several thousand deaths worldwide in just a few months. Patients who died from Coronavirus disease 2019 (COVID-19) often had comorbidities, such as hypertension, diabetes, and chronic obstructive lung disease. The angiotensin-converting enzyme 2 (ACE2) was identified as a crucial factor that facilitates SARS-CoV2 to bind and enter host cells. To date, no study has assessed the ACE2 expression in the lungs of patients with these diseases. Here, we analyzed over 700 lung transcriptome samples of patients with comorbidities associated with severe COVID-19 and found that ACE2 was highly expressed in these patients, compared to control individuals. This finding suggests that patients with such comorbidities may have higher chances of developing severe COVID-19. We also found other genes, such as RAB1A, that can be important for SARS-CoV-2 infection in the lung. Correlation and network analyses revealed many potential regulators of ACE2 in the human lung, including genes related to histone modifications, such as HAT1, HDAC2, and KDM5B. In fact, epigenetic marks found in ACE2 locus were compatible to with those promoted by KDM5B. Our systems biology approach offers a possible explanation for increase of COVID-19 severity in patients with certain comorbidities.

Integrated proteogenomic approach identifying a protein signature of COPD and a new splice variant of SORBS1

Translation of genomic alterations to protein changes in chronic obstructive pulmonary disease (COPD) is largely unexplored. Using integrated proteomic and RNA sequencing analysis of COPD and control lung tissues, we identified a protein signature in COPD characterised by extracellular matrix changes and a potential regulatory role for SUMO2. Furthermore, we identified 61 differentially expressed novel, non-reference, peptides in COPD compared with control lungs. This included two peptides encoding for a new splice variant of SORBS1, of which the transcript usage was higher in COPD compared with control lungs. These explorative findings and integrative proteogenomic approach open new avenues to further unravel the pathology of COPD.

Eosinophilic COPD Patients Display a Distinctive Serum miRNA Profile From Asthma and Non-eosinophilic COPD

BACKGROUND

Asthma and chronic obstructive pulmonary disease (COPD) are common chronic airway diseases that may overlap in some individuals. Asthma COPD overlap (ACO) is a heterogeneous conditions that includes smoking-asthma (SA) and COPD with eosinophilia (COPDe). MicroRNAs (miRNA) are regulators of gene expression with a great potential as biomarkers.

OBJECTIVES

The objective of this study was to identify distinctive miRNA signatures in patients from the whole spectrum of chronic obstructive bronchial disease (SA, COPDe, non-smoking asthmatics (NSA), and COPD) that could serve as diagnostic biomarkers or describe differential molecular mechanisms with potential therapeutic implications.

METHODS

From a previously characterized cohort of ACO, COPD and asthma patients, we selected a discovery group of 40 patients for miRNA expression profiling by means of microarray technology. Differential expression of miRNAs were validated by quantitative PCR in the complete cohort (n=274).

RESULTS

Thirty differentially expressed miRNAs (eBAYES p<0.05, fold change ≥2) were found among the different groups of patients regarding COPDe: 19 COPD-vs-COPDe, 13 NSA-vs-COPDe, 11 SA-vs-COPDe. A characteristic down-regulated miRNA expression pattern was identified in COPDe patients. Differential expression of miR-619-5p and miR-4486 in COPDe patients were validated in the complete cohort (n=274).

CONCLUSIONS

We postulate that COPDe patients show a characteristic expression profile of miRNAs distinctive from asthma and COPD. Also that SA and COPDe patients, which have been typically clustered in the ACO group, display distinct molecular events.

Chronic obstructive pulmonary disease (COPD) and lung cancer: common pathways for pathogenesis

Chronic obstructive pulmonary disease (COPD) and lung cancer comprise the leading causes of lung disease-related mortality worldwide. Exposure to tobacco smoke is a mutual aetiology underlying the two diseases, accounting for almost 90% of cases. There is accumulating evidence supporting the role of immune dysfunction, the lung microbiome, extracellular vesicles and underlying genetic susceptibility in the development of COPD and lung cancer. Further, epigenetic factors, involving DNA methylation and microRNA expression, have been implicated in both diseases. Chronic inflammation is a key feature of COPD and could be a potential driver of lung cancer development. Using next generation technologies, further studies investigating the genomics, epigenetics and gene-environment interaction in key molecular pathways will continue to elucidate the pathogenic mechanisms underlying the development of COPD and lung cancer, and contribute to the development of novel diagnostic and prognostic tools for early intervention and personalised therapeutic strategies.

Allele-specific expression identified rs2509956 as a novel long-distance cis-regulatory SNP for SCGB1A1, an important gene for multiple pulmonary diseases

SCGB1A1 (secretoglobin family 1A member 1) is an important protein for multiple pulmonary diseases, especially asthma, chronic obstructive pulmonary disease, and lung cancer. One single-nucleotide polymorphism (SNP) at 5'-untranslated region of SCGB1A1, rs3741240, has been suggested to be associated with reduced protein expression and further asthma susceptibility. However, it was still unclear whether there were other cis-regulatory elements for SCGB1A1 that might further contribute to pulmonary diseases. Allele-specific expression (ASE) is a novel approach to identify the functional region in human genome. In the present study, we measured ASE on rs3741240 in lung tissues and observed a consistent excess of G allele over A (P < 10^-6), which indicated that this SNP or the one(s) in linkage disequilibrium (LD) could regulate SCGB1A1 expression. By analyzing 1000 Genomes Project data for Chinese, one SNP locating ~10.2 kb away and downstream of SCGB1A1, rs2509956, was identified to be in strong LD with rs3741240. Reporter gene assay confirmed that both SNPs could regulate gene expression in the lung cell. By chromosome conformation capture, it was verified that the region surrounding rs2509956 could interact with SCGB1A1 promoter region and act as an enhancer. Through chromatin immunoprecipitation and overexpression assay, the related transcription factor RELA (RELA proto-oncogene, NF-kB subunit) was recognized to bind the region spanning rs2509956. Our work identified a novel long-distance cis-regulatory SNP for SCGB1A1, which might contribute to multiple pulmonary diseases.

Identification of Putative Regulatory Alterations Leading to Changes in Gene Expression in Chronic Obstructive Pulmonary Disease

Various genetic and environmental factors are known to be associated with chronic obstructive pulmonary disease (COPD). We identified COPD-related differentially expressed genes (DEGs) using 189 samples accompanying either adenocarcinoma (AC) or squamous cell carcinoma (SC), comprising 91 normal and 98 COPD samples. DEGs were obtained from the intersection of two DEG sets separately identified for AC and SC to exclude the influence of different cancer backgrounds co-occurring with COPD. We also measured patient samples named group 'I', which were unable to be determined as normal or COPD based on alterations in gene expression. The Gene Ontology (GO) analysis revealed significant alterations in the expression of genes categorized with the 'cell adhesion', 'inflammatory response', and 'mitochondrial functions', i.e., well-known functions related to COPD, in samples from patients with COPD. Multi-omics data were subsequently integrated to decipher the upstream regulatory changes linked to the gene expression alterations in COPD. COPD-associated expression quantitative trait loci (eQTLs) were located at the upstream regulatory regions of 96 DEGs. Additionally, 45 previously identified COPD-related miRNAs were predicted to target 66 of the DEGs. The eQTLs and miRNAs might affect the expression of 'respiratory electron transport chain' genes and 'cell proliferation' genes, respectively, while both eQTLs and miRNAs might affect the expression of 'apoptosis' genes. We think that our present study will contribute to our understanding of the molecular etiology of COPD accompanying lung cancer.

Pathway centrality in protein interaction networks identifies putative functional mediating pathways in pulmonary disease

Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We define the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes. Using pathway centrality, we identify mediating pathways in three pulmonary diseases (asthma; bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)). We systematically evaluate the significance of all identified central pathways using genetic interactions. Mediating pathways shared by all three pulmonary disorders favor innate immune and inflammation-related processes, including toll-like receptor (TLR) signaling, PDGF- and angiotensin-regulated airway remodeling, the JAK-STAT signaling pathway, and interferon gamma. Disease-specific mediators, such as neurodevelopmental processes in BPD or adhesion molecules in COPD, are also highlighted. Some of our findings implicate pathways already in development as drug targets, while others may suggest new therapeutic approaches.

RNA-sequencing across three matched tissues reveals shared and tissue-specific gene expression and pathway signatures of COPD

BACKGROUND

Multiple gene expression studies have been performed separately in peripheral blood, lung, and airway tissues to study COPD. We performed RNA-sequencing gene expression profiling of large-airway epithelium, alveolar macrophage and peripheral blood samples from the same subset of COPD cases and controls from the COPDGene study who underwent bronchoscopy at a single center. Using statistical and gene set enrichment approaches, we sought to improve the understanding of COPD by studying gene sets and pathways across these tissues, beyond the individual genomic determinants.

METHODS

We performed differential expression analysis using RNA-seq data obtained from 63 samples from 21 COPD cases and controls (includes four non-smokers) via the R package DESeq2. We tested associations between gene expression and variables related to lung function, smoking history, and CT scan measures of emphysema and airway disease. We examined the correlation of differential gene expression across the tissues and phenotypes, hypothesizing that this would reveal preserved and private gene expression signatures. We performed gene set enrichment analyses using curated databases and findings from prior COPD studies to provide biological and disease relevance.

RESULTS

The known smoking-related genes CYP1B1 and AHRR were among the top differential expression results for smoking status in the large-airway epithelium data. We observed a significant overlap of genes primarily across large-airway and macrophage results for smoking and airway disease phenotypes. We did not observe specific genes differentially expressed in all three tissues for any of the phenotypes. However, we did observe hemostasis and immune signaling pathways in the overlaps across all three tissues for emphysema, and amyloid and telomere-related pathways for smoking. In peripheral blood, the emphysema results were enriched for B cell related genes previously identified in lung tissue studies.

CONCLUSIONS

Our integrative analyses across COPD-relevant tissues and prior studies revealed shared and tissue-specific disease biology. These replicated and novel findings in the airway and peripheral blood have highlighted candidate genes and pathways for COPD pathogenesis.

Evaluation and Interpretation of Transcriptome Data Underlying Heterogeneous Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a type of progressive lung disease, featured by airflow obstruction. Recently, a comprehensive analysis of the transcriptome in lung tissue of COPD patients was performed, but the heterogeneity of the sample was not seriously considered in characterizing the mechanistic dysregulation of COPD. Here, we established a new transcriptome analysis pipeline using a deconvolution process to reduce the heterogeneity and clearly identified that these transcriptome data originated from the mild or moderate stage of COPD patients. Differentially expressed or co-expressed genes in the protein interaction subnetworks were linked with mitochondrial dysfunction and the immune response, as expected. Computational protein localization prediction revealed that 19 proteins showing changes in subcellular localization were mostly related to mitochondria, suggesting that mislocalization of mitochondria-targeting proteins plays an important role in COPD pathology. Our extensive evaluation of COPD transcriptome data could provide guidelines for analyzing heterogeneous gene expression profiles and classifying potential candidate genes that are responsible for the pathogenesis of COPD.

High-Throughput Sequencing in Respiratory, Critical Care, and Sleep Medicine Research. An Official American Thoracic Society Workshop Report

High-throughput, "next-generation" sequencing methods are now being broadly applied across all fields of biomedical research, including respiratory disease, critical care, and sleep medicine. Although there are numerous review articles and best practice guidelines related to sequencing methods and data analysis, there are fewer resources summarizing issues related to study design and interpretation, especially as applied to common, complex, nonmalignant diseases. To address these gaps, a single-day workshop was held at the American Thoracic Society meeting in May 2017, led by the American Thoracic Society Section on Genetics and Genomics. The aim of this workshop was to review the design, analysis, interpretation, and functional follow-up of high-throughput sequencing studies in respiratory, critical care, and sleep medicine research. This workshop brought together experts in multiple fields, including genetic epidemiology, biobanking, bioinformatics, and research ethics, along with physician-scientists with expertise in a range of relevant diseases. The workshop focused on application of DNA and RNA sequencing research in common chronic diseases and did not cover sequencing studies in lung cancer, monogenic diseases (e.g., cystic fibrosis), or microbiome sequencing. Participants reviewed and discussed study design, data analysis and presentation, interpretation, functional follow-up, and reporting of results. This report summarizes the main conclusions of the workshop, specifically addressing the application of these methods in respiratory, critical care, and sleep medicine research. This workshop report may serve as a resource for our research community as well as for journal editors and reviewers of sequencing-based manuscript submissions in our research field.

Gene expression profile of human lung in a relatively early stage of COPD with emphysema

Purpose

As only some smokers develop COPD with emphysema, we explored the molecular pathogenesis of early-stage COPD with emphysema using gene expression profiling of human lung tissues.

Patients and methods

First, 110 subjects who had smoked more than ten pack-years were classified into three groups: COPD with emphysema, COPD without emphysema, and healthy smokers. COPD and emphysema were confirmed by post-bronchodilator forced expiratory volume in 1 second/forced vital capacity <0.7 and by chest computed tomography. Lung tissues obtained surgically from the 110 subjects were processed and used for RNA-Seq analysis.

Results

Among the 110 subjects, 29 had COPD with emphysema, 21 had COPD without emphysema, and 60 were healthy smokers; their mean post-bronchodilator forced expiratory volume in 1 second values were 78%, 80%, and 94%, respectively. Using RNA-Seq, we evaluated 16,676 genes expressed in lung tissues. Among them, 1,226 genes in the COPD with emphysema group and 434 genes in the COPD without emphysema group were differentially expressed genes compared to the expression in healthy smokers. In the COPD with emphysema group, ACER2 and LMAN2L were markedly increased and decreased, respectively. In the COPD without emphysema group, the CHRM3 gene, previously reported to be associated with COPD, and HDAC10 were markedly increased and decreased, respectively.

Conclusion

Our study identified differences in gene expression in subjects with COPD according to emphysema status using RNA-Seq transcriptome analysis. These findings may have mechanistic implications in COPD.