Identifying miRNA-mRNA Networks Associated With COPD Phenotypes

Exploring Molecular Mechanisms and Biomarkers in COPD: An Overview of Current Advancements and Perspectives

Chronic obstructive pulmonary disease (COPD) plays a significant role in global morbidity and mortality rates, typified by progressive airflow restriction and lingering respiratory symptoms. Recent explorations in molecular biology have illuminated the complex mechanisms underpinning COPD pathogenesis, providing critical insights into disease progression, exacerbations, and potential therapeutic interventions. This review delivers a thorough examination of the latest progress in molecular research related to COPD, involving fundamental molecular pathways, biomarkers, therapeutic targets, and cutting-edge technologies. Key areas of focus include the roles of inflammation, oxidative stress, and protease-antiprotease imbalances, alongside genetic and epigenetic factors contributing to COPD susceptibility and heterogeneity. Additionally, advancements in omics technologies-such as genomics, transcriptomics, proteomics, and metabolomics-offer new avenues for comprehensive molecular profiling, aiding in the discovery of novel biomarkers and therapeutic targets. Comprehending the molecular foundation of COPD carries substantial potential for the creation of tailored treatment strategies and the enhancement of patient outcomes. By integrating molecular insights into clinical practice, there is a promising pathway towards personalized medicine approaches that can improve the diagnosis, treatment, and overall management of COPD, ultimately reducing its global burden.

SmCCNet 2.0: A Comprehensive Tool for Multi-omics Network Inference with Shiny Visualization

Summary

Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience.

Availability

This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/.

Analysis of Key Genes and miRNA-mRNA Networks Associated with Glucocorticoids Treatment in Chronic Obstructive Pulmonary Disease

Background

Some patients with chronic obstructive pulmonary disease (COPD) benefit from glucocorticoid (GC) treatment, but its mechanism is unclear.

Objective

With the help of the Gene Expression Omnibus (GEO) database, the key genes and miRNA-mRNA related to the treatment of COPD by GCs were discussed, and the potential mechanism was explained.

Methods

The miRNA microarray dataset (GSE76774) and mRNA microarray dataset (GSE36221) were downloaded, and differential expression analysis were performed. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the differentially expressed genes (DEGs). The protein interaction network of the DEGs in the regulatory network was constructed with the STRING database, and the key genes were screened through Cytoscape. Potential downstream target genes regulated by differentially expressed miRNAs (DEMs) were predicted by the miRWalk3.0 database, and miRNA-mRNA regulatory networks were constructed. Finally, some research results were validated.

Results

① Four DEMs and 83 DEGs were screened; ② GO and KEGG enrichment analysis mainly focused on the PI3K/Akt signalling pathway, ECM receptor interaction, etc.; ③ CD2, SLAMF7, etc. may be the key targets of GC in the treatment of COPD; ④ 18 intersection genes were predicted by the mirwalk 3.0 database, and 9 pairs of miRNA-mRNA regulatory networks were identified; ⑤ The expression of miR-320d-2 and TFCP2L1 were upregulated by dexamethasone in the COPD cell model, while the expression of miR-181a-2-3p and SLAMF7 were downregulated.

Conclusion

In COPD, GC may mediate the expression of the PI3K/Akt signalling pathway through miR-181a-2-3p, miR-320d-2, miR-650, and miR-155-5p, targeting its downstream signal factors. The research results provide new ideas for RNA therapy strategies of COPD, and also lay a foundation for further research.

RNA-Binding Proteins as a Molecular Link between COPD and Lung Cancer

Chronic obstructive pulmonary disease (COPD) represents an independent risk factor for lung cancer development. Accelerated cell senescence, induced by oxidative stress and inflammation, is a common pathogenic determinant of both COPD and lung cancer. The post transcriptional regulation of genes involved in these processes is finely regulated by RNA-binding proteins (RBPs), which regulate mRNA turnover, subcellular localization, splicing and translation. Multiple pro-inflammatory mediators (including cytokines, chemokines, proteins, growth factors and others), responsible of lung microenvironment alteration, are regulated by RBPs. Several mouse models have shown the implication of RBPs in multiple mechanisms that sustain chronic inflammation and neoplastic transformation. However, further studies are required to clarify the role of RBPs in the pathogenic mechanisms shared by lung cancer and COPD, in order to identify novel biomarkers and therapeutic targets. This review will therefore focus on the studies collectively indicating the role of RBPs in oxidative stress and chronic inflammation as common pathogenic mechanisms shared by lung cancer and COPD.

PTD-FGF2 Attenuates Elastase Induced Emphysema in Mice and Alveolar Epithelial Cell Injury

Aberrant communication in alveolar epithelium is a major feature of inflammatory response for the airway remodeling leading to chronic obstructive pulmonary disease (COPD). In this study, we investigated the effect of protein transduction domains (PTD) conjugated Basic Fibroblast Growth Factor (FGF2) (PTD-FGF2) in response to cigarette smoke extract (CSE) in MLE-12 cells and porcine pancreatic elastase (PPE)-induced emphysematous mice. When PPE-induced mice were intraperitoneally treated with 0.1-0.5 mg/kg PTD-FGF2 or FGF2, the linear intercept, infiltration of inflammatory cells into alveoli and pro-inflammatory cytokines were significantly decreased. In western blot analysis, phosphorylated protein levels of c-Jun N-terminal Kinase 1/2 (JNK1/2), extracellular signal-regulated kinase (ERK1/2) and p38 mitogen-activated protein kinases (MAPK) were decreased in PPE-induced mice treated PTD-FGF2. In MLE-12 cells, PTD-FGF2 treatment decreased reactive oxygen species (ROS) production and further decreased Interleukin-6 (IL-6) and IL-1b cytokines in response to CSE. In addition, phosphorylated protein levels of ERK1/2, JNK1/2 and p38 MAPK were reduced. We next determined microRNA expression in the isolated exosomes of MLE-12 cells. In reverse transcription-polymerase chain reaction (RT-PCR) analysis, level of let-7c miRNA was significantly increased while levels of miR-9 and miR-155 were decreased in response to CSE. These data suggest that PTD-FGF2 treatment plays a protective role in regulation of let-7c, miR-9 and miR-155 miRNA expressions and MAPK signaling pathways in CSE-induced MLE-12 cells and PPE-induced emphysematous mice.

Molecular beacon decorated silver nanowires for quantitative miRNA detection by a SERS approach

Advantages of biosensors based on surface enhanced Raman scattering (SERS) rely on improved sensitivity and specificity, and suited reproducibility in detecting a target molecule that is localized in close proximity to a SERS-active surface. Herein, a comprehensive study on the realization of a SERS biosensor designed for detecting miRNA-183, a miRNA biomarker that is specific for chronic obstructive pulmonary disease (COPD), is presented. The used strategy exploits a signal-off mechanism by means of a labelled molecular beacon (MB) as the oligonucleotide biorecognition element immobilized on a 2D SERS substrate, based on spot-on silver nanowires (AgNWs) and a multi-well low volume cell. The MB was properly designed by following a dedicated protocol to recognize the chosen miRNA. A limit of detection down to femtomolar concentration (3 × 10-16 M) was achieved and the specificity of the biosensor was proved. Furthermore, the possibility to regenerate the sensing system through a simple procedure is shown: with regeneration by using HCl 1 mM, two detection cycles were performed with a good recovery of the initial MB signal (83%) and a reproducible signal after hybridization.

Non-Coding RNAs in Human Cancer and Other Diseases: Overview of the Diagnostic Potential

Non-coding RNAs (ncRNAs) are abundant single-stranded RNA molecules in human cells, involved in various cellular processes ranging from DNA replication and mRNA translation regulation to genome stability defense. MicroRNAs are multifunctional ncRNA molecules of 18-24 nt in length, involved in gene silencing through base-pair complementary binding to target mRNA transcripts. piwi-interacting RNAs are an animal-specific class of small ncRNAs sized 26-31 nt, responsible for the defense of genome stability via the epigenetic and post-transcriptional silencing of transposable elements. Long non-coding RNAs are ncRNA molecules defined as transcripts of more than 200 nucleotides, their function depending on localization, and varying from the regulation of cell differentiation and development to the regulation of telomere-specific heterochromatin modifications. The current review provides recent data on the several forms of small and long non-coding RNA's potential to act as diagnostic, prognostic or therapeutic target for various human diseases.

Non-Coding RNAs in Airway Diseases: A Brief Overview of Recent Data

Inflammation of the human lung is mediated in response to different stimuli (e.g., physical, radioactive, infective, pro-allergenic, or toxic) such as cigarette smoke and environmental pollutants. These stimuli often promote an increase in different inflammatory activities in the airways, manifesting themselves as chronic diseases (e.g., allergic airway diseases, asthma chronic bronchitis/chronic obstructive pulmonary disease, or even lung cancer). Non-coding RNA (ncRNAs) are single-stranded RNA molecules of few nucleotides that regulate the gene expression involved in many cellular processes. ncRNA are molecules typically involved in the reduction of translation and stability of the genes of mRNAs s. They regulate many biological aspects such as cellular growth, proliferation, differentiation, regulation of cell cycle, aging, apoptosis, metabolism, and neuronal patterning, and influence a wide range of biologic processes essential for the maintenance of cellular homeostasis. The relevance of ncRNAs in the pathogenetic mechanisms of respiratory diseases has been widely established and in the last decade many papers were published. However, once their importance is established in pathogenetic mechanisms, it becomes important to further deepen the research in this direction. In this review we describe several of most recent knowledge concerning ncRNA (overall miRNAs) expression and activities in the lung.

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.

New drugs under development for COPD

The characteristic features of chronic obstructive pulmonary disease (COPD) include inflammation and remodelling of the lower airways and lung parenchyma together with activation of inflammatory and immune processes. Due to the increasing habit of cigarette smoking worldwide COPD prevalence is increasing globally. Current therapies are unable to prevent COPD progression in many patients or target many of its hallmark characteristics which may reflect the lack of adequate biomarkers to detect the heterogeneous clinical and molecular nature of COPD. In this chapter we review recent molecular data that may indicate novel pathways that underpin COPD subphenotypes and indicate potential improvements in the classes of drugs currently used to treat COPD. We also highlight the evidence for new drugs or approaches to treat COPD identified using molecular and other approaches including kinase inhibitors, cytokine- and chemokine-directed biologicals and small molecules, antioxidants and redox signalling pathway inhibitors, inhaled anti-infectious agents and senolytics. It is important to consider the phenotypes/molecular endotypes of COPD patients together with specific outcome measures to target new therapies to particular COPD subtypes. This will require greater understanding of COPD molecular pathologies and a focus on biomarkers of predicting disease subsets and responder/non-responder populations.