C-X-C motif chemokine 16, modulated by microRNA-545, aggravates myocardial damage and affects the inflammatory responses in myocardial infarction

Mechanism of immune infiltration in synovial tissue of osteoarthritis: a gene expression-based study

BACKGROUND

Osteoarthritis is a chronic degenerative joint disease, and increasing evidences suggest that the pathogenic mechanism involves immune system and inflammation.

AIMS

The aim of current study was to uncover hub genes linked to immune infiltration in osteoarthritis synovial tissue using comprehensive bioinformatics analysis and experimental confirmation.

METHODS

Multiple microarray datasets (GSE55457, GSE55235, GSE12021 and GSE1919) for osteoarthritis in Gene Expression Omnibus database were downloaded for analysis. Differentially expressed genes (DEGs) were identified using Limma package in R software, and immune infiltration was evaluated by CIBERSORT algorithm. Then weighted gene co-expression network analysis (WGCNA) was performed to uncover immune infiltration-associated gene modules. Protein-protein interaction (PPI) network was constructed to select the hub genes, and the tissue distribution of these genes was analyzed using BioGPS database. Finally, the expression pattern of these genes was confirmed by RT-qPCR using clinical samples.

RESULTS

Totally 181 DEGs between osteoarthritis and normal control were screened. Macrophages, mast cells, memory CD4 T cells and B cells accounted for the majority of immune cell composition in synovial tissue. Osteoarthritis synovial showed high abundance of infiltrating resting mast cells, B cells memory and plasma cells. WGCNA screened 93 DEGs related to osteoarthritis immune infiltration. These genes were involved in TNF signaling pathway, IL-17 signaling pathway, response to steroid hormone, glucocorticoid and corticosteroid. Ten hub genes including MYC, JUN, DUSP1, NFKBIA, VEGFA, ATF3, IL-6, PTGS2, IL1B and SOCS3 were selected by using PPI network. Among them, four genes (MYC, JUN, DUSP1 and NFKBIA) specifically expressed in immune system were identified and clinical samples revealed consistent change of these four genes in synovial tissue retrieved from patients with osteoarthritis.

CONCLUSION

A 4-gene-based diagnostic model was developed, which had well predictive performance in osteoarthritis. MYC, JUN, DUSP1 and NFKBIA might be biomarkers and potential therapeutic targets in osteoarthritis.

MicroRNAs (miRNAs) in Cardiovascular Complications of Rheumatoid Arthritis (RA): What Is New?

Rheumatoid Arthritis (RA) is among the most prevalent and impactful rheumatologic chronic autoimmune diseases (AIDs) worldwide. Within a framework that recognizes both immunological activation and inflammatory pathways, the exact cause of RA remains unclear. It seems however, that RA is initiated by a combination between genetic susceptibility, and environmental triggers, which result in an auto-perpetuating process. The subsequently, systemic inflammation associated with RA is linked with a variety of extra-articular comorbidities, including cardiovascular disease (CVD), resulting in increased mortality and morbidity. Hitherto, vast evidence demonstrated the key role of non-coding RNAs such as microRNAs (miRNAs) in RA, and in RA-CVD related complications. In this descriptive review, we aim to highlight the specific role of miRNAs in autoimmune processes, explicitly on their regulatory roles in the pathogenesis of RA, and its CV consequences, their main role as novel biomarkers, and their possible role as therapeutic targets.

Construction and Bioinformatics Analysis of circRNA-miRNA-mRNA Network in Acute Myocardial Infarction

Background: Acute myocardial infarction (AMI) is one of the main fatal diseases of cardiovascular diseases. Circular RNA (circRNA) is a non-coding RNA (ncRNA), which plays a role in cardiovascular disease as a competitive endogenous RNA (ceRNA). However, their role in AMI has not been fully clarified. This study aims to explore the mechanism of circRNA-related ceRNA network in AMI, and to identify the corresponding immune infiltration characteristics.

Materials and Methods: The circRNA (GSE160717), miRNA (GSE24548), and mRNA (GSE60993) microarray datasets of AMI were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified by the “limma” package. After integrating the circRNA, miRNA and mRNA interaction, we constructed a circRNA-miRNA-mRNA network. The “clusterProfiler” package and String database were used for functional enrichment analysis and protein-protein interaction (PPI) analysis, respectively. After that, we constructed a circRNA-miRNA-hub gene network and validated the circRNAs and mRNAs using an independent dataset (GSE61144) as well as qRT-PCR. Finally, we used CIBERSORTx database to analyze the immune infiltration characteristics of AMI and the correlation between hub genes and immune cells.

Results: Using the “limma” package of the R, 83 DEcircRNAs, 54 DEmiRNAs, and 754 DEmRNAs were identified in the microarray datasets of AMI. Among 83 DEcircRNAs, there are 55 exonic DEcircRNAs. Then, a circRNA-miRNA-mRNA network consists of 21 DEcircRNAs, 11 DEmiRNAs, and 106 DEmRNAs were predicted by the database. After that, 10 hub genes from the PPI network were identified. Then, a new circRNA-miRNA-hub gene network consists of 14 DEcircRNAs, 7 DEmiRNAs, and 9 DEmRNAs was constructed. After that, three key circRNAs (hsa_circ_0009018, hsa_circ_0030569 and hsa_circ_0031017) and three hub genes (BCL6, PTGS2 and PTEN) were identified from the network by qRT-PCR. Finally, immune infiltration analysis showed that hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI.

Conclusion: Our study constructed a circRNA-related ceRNA networks in AMI, consists of hsa_circ_0031017/hsa-miR-142-5p/PTEN axis, hsa_circ_0030569/hsa-miR-545/PTGS2 axis and hsa_circ_0009018/hsa-miR-139-3p/BCL6 axis. These three hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. It helps improve understanding of AMI mechanism and provides future potential therapeutic targets.

Comprehensive Analysis of N6-Methyladenosine RNA Methylation Regulators Expression Identify Distinct Molecular Subtypes of Myocardial Infarction

Background: Myocardial infarction (MI) is one of the leading threats to human health. N6-methyladenosine (m6A) modification, as a pivotal regulator of messenger RNA stability, protein expression, and cellular processes, exhibits important roles in the development of cardiac remodeling and cardiomyocyte contractile function.

Methods: The expression levels of m6A regulators were analyzed using the GSE5406 database. We analyzed genome-wide association study data and single-cell sequencing data to confirm the functional importance of m6A regulators in MI. Three molecular subtypes with different clinical characteristics were established to tailor treatment strategies for patients with MI. We applied pathway analysis and differentially expressed gene (DEG) analysis to study the changes in gene expression and identified four common DEGs. Furthermore, we constructed the protein–protein interaction network and confirmed several hub genes in three clusters of MI. To lucubrate the potential functions, we performed a ClueGO analysis of these hub networks.

Results: In this study, we identified that the levels of FTO, YTHDF3, ZC3H13, and WTAP were dramatically differently expressed in MI tissues compared with controls. Bioinformatics analysis showed that DEGs in MI were significantly related to modulating calcium signaling and chemokine signaling, and m6A regulators were related to regulating glucose measurement and elevated blood glucose levels. Furthermore, genome-wide association study data analysis showed that WTAP single-nucleotide polymorphism was significantly related to the progression of MI. In addition, single-cell sequencing found that WTAP is widely expressed in the heart tissues. Moreover, we conducted consensus clustering for MI in view of the dysregulated m6A regulators’ expression in MI. According to the expression levels, we found MI patients could be clustered into three subtypes. Pathway analysis showed the DEGs among different clusters in MI were assigned to HIF-1, IL-17, MAPK, PI3K-Akt signaling pathways, etc. The module analysis detected several genes, including BAG2, BAG3, MMP2, etc. We also found that MI-related network was significantly related to positive and negative regulation of angiogenesis and response to heat. The hub networks in MI clusters were significantly related to antigen processing and ubiquitin-mediated proteolysis, RNA splicing, and stability, indicating that these processes may contribute to the development of MI.

Conclusion: Collectively, our study could provide more information for understanding the roles of m6A in MI, which may provide a novel insight into identifying biomarkers for MI treatment and diagnosis.

The Mechanism Action of German Chamomile (Matricaria recutita L.) in the Treatment of Eczema: Based on Dose-Effect Weight Coefficient Network Pharmacology

To determine the active ingredients in German chamomile volatile oil and the mechanism of action in the treatment of eczema, this study used two parameters (ingredient content and oil–water partition coefficient) and established a new network pharmacology method based on the dose–effect weight coefficient. Through the new network pharmacology method, we found that German chamomile volatile oil regulated T-cell lymphatic subpopulations to inhibit the Th17 cell differentiation signaling pathway. This resulted in a reduction of interleukin 17 (IL-17), thereby inhibiting the activation of the nuclear factor kappa beta (NF-κB) and MAPK pathways, decreasing the secretion of the pro-inflammatory factors (tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6)), and reducing inflammation. In this study, a new dose–effect relationship synergistic network pharmacology method was established to provide a new method for the screening of effective ingredients and pathways of drugs, and to provide a basis for the follow-up studies of German chamomile volatile oil in the treatment of eczema.