Microbiomics have significantly advanced over the last decade, driven by the widespread availability of next-generation sequencing (NGS) and multi-omic technologies. Integration of NGS and multi-omic datasets allow for a holistic assessment of endophenotypes across a range of chronic respiratory disease states, including chronic obstructive pulmonary disease (COPD). Valuable insight has been attained into the nature, function, and significance of microbial communities in disease onset, progression, prognosis, and response to treatment in COPD. Moving beyond single-biome assessment, there now exists a growing literature on functional assessment and host–microbe interaction and, in particular, their contribution to disease progression, severity, and outcome. Identifying specific microbes and/or metabolic signatures associated with COPD can open novel avenues for therapeutic intervention and prognosis-related biomarkers. Despite the promise and potential of these approaches, the large amount of data generated by such technologies can be challenging to analyze and interpret, and currently, there remains a lack of standardized methods to address this. This review outlines the current use and proposes future avenues for the application of NGS and multi-omic technologies in the endophenotyping, prognostication, and treatment of COPD.

/pdf/applying-next-generation-sequencing-and-multi-omics-in-3c59tr1h.pdf

Applying Next-Generation Sequencing and Multi-Omics in Chronic Obstructive Pulmonary Disease

Combustible cigarettes produce many toxic substances that have been linked to diseases, such as lung cancer and chronic obstructive pulmonary disease. For those smokers unable or unwilling to quit, electronic cigarettes (e-cigarettes) could be used as an alternative to cigarettes. However, the effects and mechanisms of e-cigarette aerosol (ECA) on respiratory function have not been fully elucidated, and in vivo studies of its safety are limited compared to cigarette smoke (CS). In this article, we chose nicotine levels as dosing references and C57BL/6 mice for a 10-week subchronic inhalation toxicity study. A comprehensive set of toxicological endpoints was used to study the effect of exposure. Both CS (6 mg/kg) and ECA (6 or 12 mg/kg) inhalation had decreased the animal’s lung function and increased levels of inflammation markers, along with pathological changes in the airways and lungs, with ECA displaying a relatively small effect at the same dose. Proteomic analysis of lung tissue showed greater overall protein changes by CS than that of ECA, with more severe inflammatory network perturbations. Compared with ECA, KEGG analysis of CS revealed upregulation of more inflammatory and virus-related pathways. Protein–protein interactions (PPI) showed that both ECA and CS significantly changed ribosome and complement system-related proteins in mouse lung tissue. The results support that e-cigarette aerosol is less harmful to the respiratory system than cigarette smoke at the same dose using this animal model, thus providing additional evidence for the relative safety of e-cigarettes. 

/pdf/combined-biological-effects-and-lung-proteomics-analysis-in-2a5u14ng.pdf

Combined biological effects and lung proteomics analysis in mice reveal different toxic impacts of electronic cigarette aerosol and combustible cigarette smoke on the respiratory system

Potential Mechanisms Between HF and COPD: New Insights From Bioinformatics.

We aimed to study the molecular mechanisms of chronic obstructive pulmonary disease (COPD) caused by cigarette smoke more comprehensively and systematically through different perspectives and aspects and to explore the role of protein acetylation modification in COPD. We established the COPD model by exposing C57BL/6J mice to cigarette smoke for 24 weeks, then analyzed the transcriptomics, proteomics, and acetylomics data of mouse lung tissue by RNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and associated these omics data through unique algorithms. This study demonstrated that the differentially expressed proteins and acetylation modification in the lung tissue of COPD mice were co-enriched in pathways such as oxidative phosphorylation (OXPHOS) and fatty acid degradation. A total of 19 genes, namely, ENO3, PFKM, ALDOA, ACTN2, FGG, MYH1, MYH3, MYH8, MYL1, MYLPF, TTN, ACTA1, ATP2A1, CKM, CORO1A, EEF1A2, AKR1B8, MB, and STAT1, were significantly and differentially expressed at all the three levels of transcription, protein, and acetylation modification simultaneously. Then, we assessed the distribution and expression in different cell subpopulations of these 19 genes in the lung tissues of patients with COPD by analyzing data from single-cell RNA sequencing (scRNA-seq). Finally, we carried out the in vivo experimental verification using mouse lung tissue through quantitative real-time PCR (qRT-PCR), Western blotting (WB), immunofluorescence (IF), and immunoprecipitation (IP). The results showed that the differential acetylation modifications of mouse lung tissue are widely involved in cigarette smoke-induced COPD. ALDOA is significantly downregulated and hyperacetylated in the lung tissues of humans and mice with COPD, which might be a potential biomarker for the diagnosis and/or treatment of COPD.

Associative analysis of multi-omics data indicates that acetylation modification is widely involved in cigarette smoke-induced chronic obstructive pulmonary disease

Abstract Alzheimer’s disease (AD) is a primary cause of dementia. The complement system is closely related to AD pathology and may be a potential target for the prevention and treatment of AD. In our study, we conducted a bioinformatics analysis to analyze the role of the complement system and its related factors in AD using Gene Expression Omnibus (GEO) data. We also conducted a functional analysis. Our study verified that 23 genes were closely related to differentially expressed complement system genes in diseases after intersecting the disease-related complement system module genes and differentially expressed genes. The STRING database was used to predict the interactions between the modular gene proteins of the differential complement system. A total of 21 gene proteins and 44 interaction pairs showed close interactions. We screened key genes and created a diagnostic model. The predictive effect of the model was constructed using GSE5281 and our study indicated that the predictive effect of the model was good. Our study also showed enriched negative regulation of Notch signaling, cytokine secretion involved in the immune response pathway, and cytokine secretion involved in immune response hormone-mediated apoptotic signaling pathway. We hope that our study provides a promising target to prevent and delay the onset, diagnosis, and treatment of AD. 

Analysis of complement system and its related factors in Alzheimer’s disease

Background and Aims Alveolar macrophages (AM) play a crucial role in the development of chronic obstructive pulmonary disease (COPD). The role that AM plays in the molecular pathways and clinical phenotypes associated with tobacco-related emphysema remain poorly understood. Thus, we investigated the transcriptomic profile of AM in COPD patients with a history of smoking and explored the molecular mechanisms associated with enriched pathways and hub genes. Methods Four data sets (GSE2125, GSE8823, GSE13896 and GSE130928) were retrieved from the GEO Database. A total of 203 GEO samples (GSM) were collated for this study. About 125 of these cases were classified as smokers (91 as healthy non-COPD smokers and 34 as COPD smokers). Based on the bioinformatics obtained using the R3.6.1 program, the data were successively adopted for differential genetic expression analysis, enrichment analysis (EA), and then protein-protein interaction analysis (PPI) in a STRING database. Finally, Cytoscape 3.8 software was used to screen the hub genes. A further data analysis was performed using a set of 154 cases, classified as 64 healthy non-smokers and 91 as healthy smokers. The same procedures were used as for the COPD dataset. Results When comparing the data pertaining to COPD-smokers and non-COPD smokers, the top ten genes with the greatest transcriptional differences were found to be NADK, DRAP1, DEDD, NONO, KLHL12, PRKAR1A, ITGAL, GLE1, SLC8A1, SVIL. A GSEA (Gene Set Enrichment Analysis) revealed that these genes manifested an up-regulated ribosomal pathway in contrast with other genes that exhibited an extensive down-regulated pathway. The hub genes were mainly genes encoding ribosomal subunits through PPI. Furthermore, it was found that there is a narrow transcriptional difference between healthy non-smokers and non-COPD smokers and the hub genes identified here are mainly members of the chemokines, including CCL5, CCR5, CXCL9 and CXCL11. Conclusion An elevated activity of the ribosome pathway in addition to the increased expression of ribosomal housekeeping genes (also known as hub genes) were identified with COPD-smokers, and these have the potential to cause a wide range of downstream pathogenetic effects. As for the preclinical phase, non-COPD smokers were found to be characterized by enriched pathways of several chemokines in AM.

/pdf/transcriptomics-analysis-identifies-the-presence-of-5drpg9k3b4.pdf

Transcriptomics Analysis Identifies the Presence of Upregulated Ribosomal Housekeeping Genes in the Alveolar Macrophages of Patients with Smoking-Induced Chronic Obstructive Pulmonary Disease

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Transcriptomics Analysis Identifies the Presence of Upregulated Ribosomal Housekeeping Genes in the Alveolar Macrophages of Patients with Smoking-Induced Chronic Obstructive Pulmonary Disease