Bidirectional Causal Association Between Chronic Obstructive Pulmonary Disease and Cardiovascular Diseases: A Mendelian Randomization Study.

Background: A large number of studies have demonstrated links between chronic obstructive pulmonary disease (COPD) and cardiovascular diseases (CVDs). However, the causal relationship between COPD and CVDs and the reverse causality remains divergent. Methods: Exposure and outcome data from the largest available genome-wide association studies were extracted for Mendelian randomization (MR) studies. Univariate MR analysis was performed using IVW as the primary analysis method, and multiple sensitivity analyses were used to enhance the robustness of the results. Furthermore, this was followed by mediation MR analysis of positive results after excluding confounding factors with multivariable MR analysis. Results: The MR estimation based on IVW method indicated a strong association between genetically determined COPD and heart failure (HF) (OR = 1.117, 95% CI: 1.066-1.170, p <0.001), coronary heart disease (CHD) (OR = 1.004, 95% CI: 1.002-1.006, p <0.001), essential hypertension (EH) (OR = 1.009, 95% CI: 1.005-1.013, p <0.001) as well as Stroke (OR = 1.003, 95% CI: 1.001-1.004, p <0.001). The results of multivariable MR analysis revealed that COPD is not significantly associated with CHD after adjusting for IL-6, LDL, or total cholesterol (p>0.05). Our findings indicated that BMI, smoking initiation, smoking status, obesity, and FEV1 played a role in the causal effect of COPD on HF, EH, and Stroke. Conclusion: We found positive causal relationships between COPD and HF, EH, and Stroke essentially unaffected by other confounding factors. The causal relationship exhibited between COPD and CHD was influenced by confounding factors. BMI, obesity, initiation of smoking, smoking status, and FEV1 were the mediators between COPD and CVDs.

Causal Effects of Gut Microbiota and Metabolites on Chronic Obstructive Pulmonary Disease: A Bidirectional Two Sample Mendelian Randomization Study.

Background: Recent evidence suggests that the gut microbiome and metabolites are intricately involved in Chronic Obstructive Pulmonary Disease (COPD) pathogenesis, yet the precise causal relationships remain unclear due to confounding factors and reverse causation. This study employs bidirectional two-sample Mendelian Randomization (MR) to clarify these connections. Methods: Summary data from publicly available Genome-Wide Association Studies (GWAS) concerning the gut microbiome, metabolites, and COPD were compiled. The selection of genetic instrumental variables (Single Nucleotide Polymorphisms, or SNPs) for MR analysis was conducted meticulously, primarily utilizing the Inverse Variance Weighting (IVW) method, supplemented by MR-Egger regression and the Weighted Median (WM) approach. The evaluation of heterogeneity and horizontal pleiotropy was performed using Cochran's Q test, the MR-Egger intercept test, and the MR-PRESSO global test. Sensitivity analyses, including leave-one-out tests, were conducted to verify the robustness of our results. And the mediation effect of gut microbiota-mediated changes in metabolites on the causal relationship with COPD was analyzed. Results: Our study identified nine significant gut microbiota taxa and thirteen known metabolites implicated in COPD pathogenesis. Moreover, associations between the onset of COPD and the abundance of five bacterial taxa, as well as the concentration of three known metabolites, were established. These findings consistently withstood sensitivity analyses, reinforcing their credibility. Additionally, our results revealed that gut microbiota contribute to the development of COPD by mediating changes in metabolites. Conclusion: Our bidirectional Two-Sample Mendelian Randomization analysis has revealed reciprocal causal relationships between the abundance of gut microbiota and metabolite concentrations in the context of COPD. This research holds promise for identifying biomarkers for early COPD diagnosis and monitoring disease progression, thereby opening new pathways for prevention and treatment. Further investigation into the underlying mechanisms is essential to improve our understanding of COPD onset.

KDELR2 is necessary for chronic obstructive pulmonary disease airway Mucin5AC hypersecretion via an IRE1α/XBP-1s-dependent mechanism.

Airway mucus hypersecretion, a crucial pathological feature of chronic obstructive pulmonary disease (COPD), contributes to the initiation, progression, and exacerbation of this disease. As a macromolecular mucin, the secretory behaviour of Mucin5AC (MUC5AC) is highly dependent on a series of modifying and folding processes that occur in the endoplasmic reticulum (ER). In this study, we focused on the ER quality control protein KDEL receptor (KDELR) and demonstrated that KDELR2 and MUC5AC were colocalized in the airway epithelium of COPD patients and COPD model rats. In addition, knockdown of KDELR2 markedly reduced the expression of MUC5AC both in vivo and in vitro and knockdown of ATF6 further decreased the levels of KDELR2. Furthermore, pretreatment with 4µ8C, an IRE1α inhibitor, led to a partial reduction in the expression of KDELR2 and MUC5AC both in vivo and in vitro, which indicated the involvement of IRE1α/XBP-1s in the upstream signalling cascade. Our study revealed that KDELR2 plays a crucial role in airway MUC5AC hypersecretion in COPD, which might be dependent on ATF6 and IRE1α/XBP-1s upstream signalling.

Exploring the relationship between sarcopenia and 11 respiratory diseases: a comprehensive mendelian randomization analysis.

BACKGROUND: Sarcopenia (SP) is an aging-related loss of muscle mass and function, affecting the respiratory system. However, the causality of the association between sarcopenia on lung diseases remains elusive. METHODS: The bidirectional univariate Mendelian randomization (UVMR), multivariate MR (MVMR) analysis, and mediation MR were utilized to systematically investigate the genetic causal relationship of SP and 11 respiratory diseases. Independent genomic variants related to sarcopenia or respiratory diseases were identified as instrumental variables (IVs), and the summary level data of genome-wide associated studies (GWAS) were obtained from the UK biobank and FinnGen. MVMR analysis was conducted to explore the mediation effects of body mass index (BMI), Alcohol Use Disorders Identification Test (AUDIT), smoking, education attainment (EA), physical activity, and Type 2 Diabetes Mellitus (T2DM). RESULTS: Forward UVMR analysis based on the primary method revealed that pneumoconiosis was associated with a higher risk of appendicular lean mass (ALM) (OR = 1.01, p = 0.03), and BMI (10.65%), smoking (10.65%), and physical activity (17.70%) had a mediating role in the effect of pneumoconiosis on ALM. In reverse MR analysis, we found that genetically predicted ALM was significantly associated with an increased risk of pulmonary embolism (PE) (OR = 1.24, p = 7.21E-05). Chronic obstructive pulmonary disease (COPD) (OR = 0.98, p = 0.002) and sarcoidosis (OR = 1.01, p = 0.004) were identified to increase the loss of left-hand grip strength (HGS). Conversely, the increase in left- HGS presented a protective effect on chronic bronchitis (CB) (OR = 0.35, p = 0.03), (OR = 0.80, p = 0.02), and asthma (OR = 0.78, p = 0.04). Similarly, the loss of the right-HGS elevated the risk of low respiratory tract infection (LRTI) (OR = 0.97, p = 0.02) and bronchiectasis (OR = 1.01, p = 0.03), which is also an independent protective factor for LRTI and asthma. In the aspects of low HGS, the risk of LRTI was increased after MVMR analysis, and the risk of sarcoidosis and pneumoconiosis was elevated in the reverse analysis. Lastly, asthma was found to be related to the loss of the usual walking pace, and the reverse MR analysis suggested a causal relationship between the usual walking pace and LRTI (OR = 0.32, p = 2.79 × 10-5), asthma (OR = 0.24, p = 2.09 × 10-6), COPD (OR = 0.22, p = 6.64 × 10-4), and PE(OR = 0.35, p = 0.03). CONCLUSIONS: This data-driven MR analysis revealed SP was bidirectional causally associated with lung diseases, providing genetic evidence for further mechanistic and clinical studies to understand the crosstalk between SP and lung diseases.

Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes-EP300, MTOR, NFE2L1, TXN-exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

Identification of diagnostic biomarkers and immune cell profiles associated with COPD integrated bioinformatics and machine learning.

This retrospective transcriptomic study leveraged bioinformatics and machine learning algorithms to identify novel gene biomarkers and explore immune cell infiltration profiles associated with chronic obstructive pulmonary disease (COPD). Utilizing an integrated analysis of metadata encompassing six gene expression omnibus (GEO) microarray datasets, 987 differentially expressed genes were identified. Further gene ontology and pathway enrichment analyses revealed the enrichment of these genes across various biological processes and pathways. Moreover, a systematic integration of two machine learning algorithms along with pathway-gene correlations identified six candidate biomarkers, which were validated in a separate cohort comprising six additional microarray datasets, ultimately identifying ADD3 and GNAS as diagnostic biomarkers for COPD. Subsequently, the diagnostic efficacy of ADD3 and GNAS was assessed, and the impact of their expression levels on overall survival was further evaluated and quantified in the validation cohort. Examination of immune cell subtype infiltration found increased proportions of cytotoxic CD8+ T cells, resting and activated NK cells, along with decreased M0 and M2 macrophages, in COPD versus control samples. Correlation analyses also uncovered significant associations between ADD3 and GNAS expression and infiltration of various immune cell types. In conclusion, this study elucidates crucial COPD diagnostic biomarkers and immune cell profiles which may illuminate the immunopathological drivers of COPD progression, representing personalized therapeutic targets warranting further investigation.

HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.

Epidemiologic and genetic studies in many complex diseases suggest subgroup disparities (e.g. by sex, race) in disease course and patient outcomes. We consider this from the standpoint of integrative analysis where we combine information from different views (e.g. genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. We propose Heterogeneity in Integration and Prediction (HIP), a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to a subgroup. We apply HIP to proteomics and gene expression data pertaining to chronic obstructive pulmonary disease (COPD) to identify proteins and genes shared by, and unique to, males and females, contributing to the variation in COPD, measured by airway wall thickness. Our COPD findings have identified proteins, genes, and pathways that are common across and specific to males and females, some implicated in COPD, while others could lead to new insights into sex differences in COPD mechanisms. HIP accounts for subgroup heterogeneity in multi-view data, ranks variables based on importance, is applicable to univariate or multivariate continuous outcomes, and incorporates covariate adjustment. With the efficient algorithms implemented using PyTorch, this method has many potential scientific applications and could enhance multiomics research in health disparities. HIP is available at https://github.com/lasandrall/HIP, a video tutorial at https://youtu.be/O6E2OLmeMDo and a Shiny Application at https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/ for users with limited programming experience.

Single-cell transcriptomics reveals e-cigarette vapor-induced airway epithelial remodeling and injury.

BACKGROUND: In recent years, e-cigarettes have been used as alternatives among adult smokers. However, the impact of e-cigarette use on human bronchial epithelial (HBE) cells remains controversial. METHODS: We collected primary HBE cells of healthy nonsmokers and chronic obstructive pulmonary disease (COPD) smokers, and analyzed the impact of e- cigarette vapor extract (ECE) or cigarette smoke extract (CSE) on HBE cell differentiation and injury by single-cell RNA sequencing, immunostaining, HE staining, qPCR and ELISA. We obtained serum and sputum from healthy non- smokers, smokers and e-cigarette users, and analyzed cell injury markers and mucin proteins. RESULTS: ECE treatment led to a distinct differentiation program of ciliated cells and unique patterns of their cell-cell communications compared with CSE. ECE treatment caused increased Notch signaling strength in a ciliated cell subpopulation, and HBE cell remodeling and injury including hypoplasia of ciliated cells and club cells, and shorter cilia. ECE-induced hypoplasia of ciliated cells and shorter cilia were ameliorated by the Notch signaling inhibition. CONCLUSIONS: This study reveals distinct characteristics in e-cigarette vapor-induced airway epithelial remodeling, pointing to Notch signaling pathway as a potential targeted intervention for e-cigarette vapor-caused ciliated cell differentiation defects and cilia injury. In addition, a decrease in SCGB1A1 proteins is associated with e- cigarette users, indicating a potential lung injury marker for e-cigarette users.

Screening COPD-Related Biomarkers and Traditional Chinese Medicine Prediction Based on Bioinformatics and Machine Learning.

Purpose: To employ bioinformatics and machine learning to predict the characteristics of immune cells and genes associated with the inflammatory response and ferroptosis in chronic obstructive pulmonary disease (COPD) patients and to aid in the development of targeted traditional Chinese medicine (TCM). Mendelian randomization analysis elucidates the causal relationships among immune cells, genes, and COPD, offering novel insights for the early diagnosis, prevention, and treatment of COPD. This approach also provides a fresh perspective on the use of traditional Chinese medicine for treating COPD. Methods: R software was used to extract COPD-related data from the Gene Expression Omnibus (GEO) database, differentially expressed genes were identified for enrichment analysis, and WGCNA was used to pinpoint genes within relevant modules associated with COPD. This analysis included determining genes linked to the inflammatory response in COPD patients and analyzing their correlation with ferroptosis. Further steps involved filtering core genes, constructing TF-miRNA‒mRNA network diagrams, and employing three types of machine learning to predict the core miRNAs, key immune cells, and characteristic genes of COPD patients. This process also delves into their correlations, single-gene GSEA, and diagnostic model predictions. Reverse inference complemented by molecular docking was used to predict compounds and traditional Chinese medicines for treating COPD; Mendelian randomization was applied to explore the causal relationships among immune cells, genes, and COPD. Results: We identified 2443 differential genes associated with COPD through the GEO database, along with 8435 genes relevant to WGCNA and 1226 inflammation-related genes. A total of 141 genes related to the inflammatory response in COPD patients were identified, and 37 core genes related to ferroptosis were selected for further enrichment analysis and analysis. The core miRNAs predicted for COPD include hsa-miR-543, hsa-miR-181c, and hsa-miR-200a, among others. The key immune cells identified were plasma cells, activated memory CD4 T cells, gamma delta T cells, activated NK cells, M2 macrophages, and eosinophils. Characteristic genes included EGF, PLG, PTPN22, and NR4A1. A total of 78 compounds and 437 traditional Chinese medicines were predicted. Mendelian randomization analysis revealed a causal relationship between 36 types of immune cells and COPD, whereas no causal relationship was found between the core genes and COPD. Conclusion: A definitive causal relationship exists between immune cells and COPD, while the prediction of core miRNAs, key immune cells, characteristic genes, and targeted traditional Chinese medicines offers novel insights for the early diagnosis, prevention, and treatment of COPD.

The association between cumulative exposure to PM2.5 and DNA methylation measured using methyl-capture sequencing among COPD patients.

BACKGROUND: Particulate matter with a diameter of < 2.5 µm (PM2.5) influences gene regulation via DNA methylation; however, its precise mechanism of action remains unclear. Thus, this study aimed to examine the connection between personal PM2.5 exposure and DNA methylation in CpG islands as well as explore the associated gene pathways. METHODS: A total of 95 male patients with chronic obstructive pulmonary disease (COPD) were enrolled in this study. PM2.5 concentrations were measured for 12 months, with individual exposure recorded for 24 h every 3 months. Mean indoor and estimated individual PM2.5 exposure levels were calculated for short-term (7 days), mid-term (35 days), and long-term (90 days). DNA methylation analysis was performed on the blood samples, which, after PCR amplification and hybridization, were finally sequenced using an Illumina NovaSeq 6000 system. Correlation between PM2.5 exposure and CpG methylation sites was confirmed via a mixed-effects model. Functional enrichment analysis was performed on unique CpG methylation sites associated with PM2.5 exposure to identify the relevant biological functions or pathways. RESULTS: The number of CpG sites showing differential methylation was 36, 381, and 182 for the short-, mid-, and long-term indoor models, respectively, and 3, 98, and 28 for the short-, mid-, and long-term estimated exposure models, respectively. The representative genes were TMTC2 (p = 1.63 × 10-3, R2 = 0.656), GLRX3 (p = 1.46 × 10-3, R2 = 0.623), DCAF15 (p = 2.43 × 10-4, R2 = 0.623), CNOT6L (p = 1.46 × 10-4, R2 = 0.609), BSN (p = 2.21 × 10-5, R2 = 0.606), and SENP6 (p = 1.59 × 10-4, R2 = 0.604). Functional enrichment analysis demonstrated that the related genes were mostly associated with pathways related to synaptic transmission in neurodegenerative diseases and cancer. CONCLUSION: A significant association was observed between PM2.5 exposure and DNA methylation upon short-term exposure, and the extent of DNA methylation was the highest upon mid-term exposure. Additionally, various pathways related to neurodegenerative diseases and cancer were associated with patients with COPD. GOV IDENTIFIER: NCT04878367.

Extracellular Vesicle microRNA: A Promising Biomarker and Therapeutic Target for Respiratory Diseases.

Respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, lung cancer, and coronavirus pneumonia, present a major global health challenge. Current diagnostic and therapeutic options for these diseases are limited, necessitating the urgent development of novel biomarkers and therapeutic strategies. In recent years, microRNAs (miRNAs) within extracellular vesicles (EVs) have received considerable attention due to their crucial role in intercellular communication and disease progression. EVs are membrane-bound structures released by cells into the extracellular environment, encapsulating a variety of biomolecules such as DNA, RNA, lipids, and proteins. Specifically, miRNAs within EVs, known as EV-miRNAs, facilitate intercellular communication by regulating gene expression. The expression levels of these miRNAs can reflect distinct disease states and significantly influence immune cell function, chronic airway inflammation, airway remodeling, cell proliferation, angiogenesis, epithelial-mesenchymal transition, and other pathological processes. Consequently, EV-miRNAs have a profound impact on the onset, progression, and therapeutic responses of respiratory diseases, with great potential for disease management. Synthesizing the current understanding of EV-miRNAs in respiratory diseases such as COPD, asthma, lung cancer, and novel coronavirus pneumonia, this review aims to explore the potential of EV-miRNAs as biomarkers and therapeutic targets and examine their prospects in the diagnosis and treatment of these respiratory diseases.

Causal Relationship Between Gut Microbiota and Chronic Obstructive Pulmonary Disease: A Bidirectional Two-Sample Mendelian Randomization Study.

Background: The associations between gut microbiota and chronic obstructive pulmonary disease (COPD) have gained increasing attention and research interest among scholars. However, it remains unclear whether gut microbiota serves as a causal factor for COPD or if it is a consequence of the disease. Therefore, we investigated the causal relationship between COPD and gut microbiota, with intention of providing novel insights and references for clinical diagnosis and treatment. Methods: Based on the genome-wide association study (GWAS) data, we employed MR-Egger regression, random-effects inverse variance-weighted (IVW) method, and weighted median method for bidirectional Mendelian randomization (MR) analysis. We conducted Cochran's Q test for heterogeneity assessment and performed multivariable analysis, sensitivity analysis, and heterogeneity testing to validate the reliability and stability of results. Results: Utilizing MR analysis, mainly employing the IVW method, we detected a collective of 11 gut microbiota species that exhibited associations with COPD. Among them, Bacteroidia, family XIII, Clostridium innocuum group, Barnesiella, Collinsella, Lachnospiraceae NK4A136 group, Lachnospiraceae UCG004, Lachnospiraceae UCG010, and Bacteroidales were found to be protective factors for COPD. On the other hand, Holdemanella and Marvinbryantia were identified as risk factors for COPD. Individuals with elevated levels of Holdemanella exhibited a 1.141-fold higher risk of developing COPD compared to their healthy counterparts, and those with increased levels of Marvinbryantia had a 1.154-fold higher risk. Reverse MR analysis yielded no evidence indicating a causal relationship between gut microbiota and COPD occurrence. Conclusion: Our study established a causal link between 11 specific gut microbiota species and COPD, offering novel insights and valuable references for targeted therapies in the clinical management of COPD. However, our results were mainly based on the analysis of database, and further clinical studies are needed to clarify the effects of gut microbiota on COPD and its specific protective mechanism.

Proteomic networks and related genetic variants associated with smoking and chronic obstructive pulmonary disease.

BACKGROUND: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features. METHODS: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS. RESULTS: We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts. CONCLUSIONS: In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

ZCCHC8 p.P410A disrupts nucleocytoplasmic localization, promoting idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a special kind of chronic interstitial lung disease with insidious onset. Previous studies have revealed that mutations in ZCCHC8 may lead to IPF. The aim of this study is to explore the ZCCHC8 mutations in Chinese IPF patients. METHODS: Here, we enrolled 124 patients with interstitial lung disease from 2017 to 2023 in our hospital. Whole exome sequencing and Sanger sequencing were employed to explore the genetic lesions of these patients. RESULTS: Among these 124 patients, a novel mutation (NM_017612: c.1228 C > G/p.P410A) of Zinc Finger CCHC-Type Containing 8 (ZCCHC8)was identified in a family with IPF and chronic obstructive lung disease. As a component of the nuclear exosome-targeting complex that regulates the turnover of human telomerase RNA, ZCCHC8 mutations have been reported may lead to IPF in European population and American population. Functional study confirmed that the novel mutation can disrupt the nucleocytoplasmic localization of ZCCHC8, which further decreased the expression of DKC1 and RTEL1, and finally reduced the length of telomere and led to IPF and related disorders. CONCLUSIONS: We may first report the ZCCHC8 mutation in Asian population with IPF. Our study broadens the mutation, phenotype, and population spectrum of ZCCHC8 deficiency.

Loss of JCAD/KIAA1462 Protects the Lung from Acute and Chronic Consequences of Chronic Obstructive Pulmonary Disease.

Even with recent advances in pathobiology and treatment options, chronic obstructive pulmonary disease (COPD) remains a major contributor to morbidity and mortality. To develop new ways of combating this disease, breakthroughs in our understanding of its mechanisms are sorely needed. Investigating the involvement of underanalyzed lung cell types, such as endothelial cells (ECs), is one way to further our understanding of COPD. JCAD is a junctional protein in endothelial cells (ECs) arising from the KIAA1462 gene, and a mutation in this gene has been implicated in the risk of developing COPD. In our study, we induced inflammation and emphysema in mice via the global knockout of KIAA1462/JCAD (JCAD-KO) and confirmed it in HPMECs and A549 to examine how the loss of JCAD could affect COPD development. We found that KIAA1462/JCAD loss reduced acute lung inflammation after elastase treatment. Even after 3 weeks of elastase, JCAD-KO mice demonstrated a preserved lung parenchymal structure and vasculature. In vitro, after KIAA1462 expression is silenced, both endothelial and epithelial cells showed alterations in pro-inflammatory gene expression after TNF-α treatment. We concluded that JCAD loss could ameliorate COPD through its anti-inflammatory and anti-angiogenic effects, and that KIAA1462/JCAD could be a novel target for COPD therapy.

Differentiation and Growth-Arrest-Related lncRNA (DAGAR): Initial Characterization in Human Smooth Muscle and Fibroblast Cells.

Vascular smooth muscle cells (SMCs) can transition between a quiescent contractile or "differentiated" phenotype and a "proliferative-dedifferentiated" phenotype in response to environmental cues, similar to what in occurs in the wound healing process observed in fibroblasts. When dysregulated, these processes contribute to the development of various lung and cardiovascular diseases such as Chronic Obstructive Pulmonary Disease (COPD). Long non-coding RNAs (lncRNAs) have emerged as key modulators of SMC differentiation and phenotypic changes. In this study, we examined the expression of lncRNAs in primary human pulmonary artery SMCs (hPASMCs) during cell-to-cell contact-induced SMC differentiation. We discovered a novel lncRNA, which we named Differentiation And Growth Arrest-Related lncRNA (DAGAR) that was significantly upregulated in the quiescent phenotype with respect to proliferative SMCs and in cell-cycle-arrested MRC5 lung fibroblasts. We demonstrated that DAGAR expression is essential for SMC quiescence and its knockdown hinders SMC differentiation. The treatment of quiescent SMCs with the pro-inflammatory cytokine Tumor Necrosis Factor (TNF), a known inducer of SMC dedifferentiation and proliferation, elicited DAGAR downregulation. Consistent with this, we observed diminished DAGAR expression in pulmonary arteries from COPD patients compared to non-smoker controls. Through pulldown experiments followed by mass spectrometry analysis, we identified several proteins that interact with DAGAR that are related to cell differentiation, the cell cycle, cytoskeleton organization, iron metabolism, and the N-6-Methyladenosine (m6A) machinery. In conclusion, our findings highlight DAGAR as a novel lncRNA that plays a crucial role in the regulation of cell proliferation and SMC differentiation. This paper underscores the potential significance of DAGAR in SMC and fibroblast physiology in health and disease.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases.

It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by "Harmony" algorithm. The data was standardized and normalized by using "Seurat" package, and "SingleR" algorithm was used for cell grouping annotation. The "Findmarker" function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.

Lipolysis engages CD36 to promote ZBP1-mediated necroptosis-impairing lung regeneration in COPD.

Lung parenchyma destruction represents a severe condition commonly found in chronic obstructive pulmonary disease (COPD), a leading cause of morbidity and mortality worldwide. Promoting lung regeneration is crucial for achieving clinical improvement. However, no therapeutic drugs are approved to improve the regeneration capacity due to incomplete understanding of the underlying pathogenic mechanisms. Here, we identify a positive feedback loop formed between adipose triglyceride lipase (ATGL)-mediated lipolysis and overexpression of CD36 specific to lung epithelial cells, contributing to disease progression. Genetic deletion of CD36 in lung epithelial cells and pharmacological inhibition of either ATGL or CD36 effectively reduce COPD pathogenesis and promote lung regeneration in mice. Mechanistically, disruption of the ATGL-CD36 loop rescues Z-DNA binding protein 1 (ZBP1)-induced cell necroptosis and restores WNT/ß-catenin signaling. Thus, we uncover a crosstalk between lipolysis and lung epithelial cells, suggesting the regenerative potential for therapeutic intervention by targeting the ATGL-CD36-ZBP1 axis in COPD.

Mechanism underlying the therapeutic effects of effective component compatibility of Bufei Yishen formula III combined with exercise rehabilitation on chronic obstructive pulmonary disease.

OBJECTIVE: To explore the mechanism underlying the therapeutic effect of Bufei Yishen Formula III combined with exercise rehabilitation (ECC-BYF III + ER) on chronic obstructive pulmonary disease (COPD) and further identify hub genes. MATERIALS AND METHODS: Gene Set Enrichment Analysis was used to identify the COPD-associated pathways and reversal pathways after ECC-BYF III + ER treatment. Protein-protein interaction network analysis and cytoHubba were used to identify the hub genes. These genes were verified using independent datasets, molecular docking and quantitative real-time polymerase chain reaction experiment. RESULTS: Using the high-throughput sequencing data of COPD rats from our laboratory, 49 significantly disturbed pathways were identified in COPD model compared with control group via gene set enrichment analysis (false discovery rate < 0.05). The 34 pathways were reversed after ECC-BYF III + ER treatment. In the 2306 genes of these 34 pathways, 121 of them were differentially expressed in COPD rats compared with control samples. A protein-protein interaction network comprising 111 nodes and 274 edges was created, and 34 candidate genes were identified. Finally, seven COPD hub genes (Il1b, Ccl2, Cxcl1, Apoe, Ccl7, Ccl12, and Ccl4) were well identified and verified in independent COPD rat data from our laboratory and the public dataset GSE178513. The area under the receiver operating characteristic curve values ranged from 0.86 to 1 and from 0.67 to 1, respectively. The reliability of the mentioned genes, which can bind to the active ingredients of ECC-BYF III through molecular docking, were further verified through qRT-PCR experiments. CONCLUSION: Thirty-four COPD-related pathways and seven hub genes that may be regulated by ECC-BYF III + ER were identified and well verified. The findings of this study may provide insights into the treatment and mechanism underlying COPD.

GSEA method can circumvent the limitations of the preacquisition of DEGs for ORA and is suitable for small sample data.34 COPD-related pathways that can be regulated by ECC-BYF III + ER were identified.Seven COPD hub genes were identified and well verified in independent RNA-seq data and PCR experiment, and they may play a crucial role in TCM treatment.

Increased Frequency of Angiotensin-Converting Enzyme D Allele in Asian Patients With Chronic Obstructive Pulmonary Disease: An Updated Meta-Analysis.

At present, the angiotensin-converting enzyme (ACE) I/D polymorphism was considered to be associated to the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the association between it and the risk of COPD in different ethnic groups is still unclear. The purpose of this study is to conduct an updated meta-analysis of the association between them; collect literatures published before 10 February 2023 by searching PubMed, Embase, MEDLINE, CBM, CNKI, Wanfang, and VIP Chinese scientific databases; and display the analysis results by drawing forest plots. At the same time, publication bias, sensitivity analysis, and trial sequential analysis (TSA) were performed to evaluate the stability and reliability of the results. In the overall population, the result of the DD versus II model showed the association with the risk of COPD ([OR] = 1.30, 95% CI [1.08, 1.56]), and there were no associations in other genetic models (p > 0.05). In Caucasians, the results of all genetic models showed no associations (p > 0.05). In Asians, the results of D versus I, DD versus II, and DD versus II + ID models showed the associations with the risk of COPD (D vs. I: [OR] = 1.48, 95% CI [1.14, 1.93]; DD vs. II: [OR] = 2.04, 95% CI [1.53, 2.72]; DD vs. II + ID: [OR] = 2.19, 95% CI [1.45, 3.29]), while the results of ID versus II and DD + ID versus II models showed no associations (p > 0.05). Therefore, the D allele and "DD" genotype variation of the ACE I/D gene polymorphism are associated with susceptibility to COPD in Asians but not in Caucasians.