MiR-337-3p confers protective effect on facet joint osteoarthritis by targeting SKP2 to inhibit DUSP1 ubiquitination and inactivate MAPK pathway

Systems biology-based analysis exploring shared biomarkers and pathogenesis of myocardial infarction combined with osteoarthritis

Background

More and more evidence supports the association between myocardial infarction (MI) and osteoarthritis (OA). The purpose of this study is to explore the shared biomarkers and pathogenesis of MI complicated with OA by systems biology.

Methods

Gene expression profiles of MI and OA were downloaded from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-Expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify the common DEGs. The shared genes related to diseases were screened by three public databases, and the protein-protein interaction (PPI) network was built. GO and KEGG enrichment analyses were performed on the two parts of the genes respectively. The hub genes were intersected and verified by Least absolute shrinkage and selection operator (LASSO) analysis, receiver operating characteristic (ROC) curves, and single-cell RNA sequencing analysis. Finally, the hub genes differentially expressed in primary cardiomyocytes and chondrocytes were verified by RT-qPCR. The immune cell infiltration analysis, subtypes analysis, and transcription factors (TFs) prediction were carried out.

Results

In this study, 23 common DEGs were obtained by WGCNA and DEGs analysis. In addition, 199 common genes were acquired from three public databases by PPI. Inflammation and immunity may be the common pathogenic mechanisms, and the MAPK signaling pathway may play a key role in both disorders. DUSP1, FOS, and THBS1 were identified as shared biomarkers, which is entirely consistent with the results of single-cell RNA sequencing analysis, and furher confirmed by RT-qPCR. Immune infiltration analysis illustrated that many types of immune cells were closely associated with MI and OA. Two potential subtypes were identified in both datasets. Furthermore, FOXC1 may be the crucial TF, and the relationship of TFs-hub genes-immune cells was visualized by the Sankey diagram, which could help discover the pathogenesis between MI and OA.

Conclusion

In summary, this study first revealed 3 (DUSP1, FOS, and THBS1) novel shared biomarkers and signaling pathways underlying both MI and OA. Additionally, immune cells and key TFs related to 3 hub genes were examined to further clarify the regulation mechanism. Our study provides new insights into shared molecular mechanisms between MI and OA.

SKP2-mediated ubiquitination and degradation of KLF11 promotes osteoarthritis via modulation of JMJD3/NOTCH1 pathway

Osteoarthritis (OA) is the main cause of cartilage damage and disability. This study explored the biological function of S-phase kinase-associated protein 2 (SKP2) and Kruppel-like factor 11 (KLF11) in OA progression and its underlying mechanisms. C28/I2 chondrocytes were stimulated with IL-1β to mimic OA in vitro. We found that SKP2, Jumonji domain-containing protein D3 (JMJD3), and Notch receptor 1 (NOTCH1) were upregulated, while KLF11 was downregulated in IL-1β-stimulated chondrocytes. SKP2/JMJD3 silencing or KLF11 overexpression repressed apoptosis and extracellular matrix (ECM) degradation in chondrocytes. Mechanistically, SKP2 triggered the ubiquitination and degradation of KLF11 to transcriptionally activate JMJD3, which resulted in activation of NOTCH1 through inhibiting H3K27me3. What's more, the in vivo study found that KLF11 overexpression delayed OA development in rats via restraining apoptosis and maintaining the balance of ECM metabolism. Taken together, ubiquitination and degradation of KLF11 regulated by SKP2 contributed to OA progression by activation of JMJD3/NOTCH1 pathway. Our findings provide promising therapeutic targets for OA.

MiR-337-3p improves metabolic-associated fatty liver disease through regulation of glycolipid metabolism

Epigenetic regulations play crucial roles in the pathogenesis of metabolic-associated fatty liver disease; therefore, elucidating the biological functions of differential miRNAs helps us to understand the pathogenesis. Herein, we discovered miR-337-3p was decreased in patients with NAFLD from Gene Expression Omnibus dataset, which was replicated in various cell and mouse models with lipid disorders. Subsequently, overexpression of miR-337-3p in vivo could ameliorate hepatic lipid accumulation, reduce fasting blood glucose, and improve insulin resistance. Meanwhile, we determined miR-337-3p might influence multiple genes involved in glycolipid metabolism through mass spectrometry detection, bioinformatics analysis, and experimental verification. Finally, we selected HMGCR as a representative example to investigate the molecular mechanism of miR-337-3p regulating these genes, where the seed region of miR-337-3p bound to 3'UTR of HMGCR to inhibit HMGCR translation. In conclusion, we discovered a new function of miR-337-3p in glycolipid metabolism and that might be a new therapeutic target of MAFLD.

Role of single-cell ferroptosis regulation in intercellular communication and skin cutaneous melanoma progression and immunotherapy

BACKGROUND

The involvement of ferroptosis in the pathogenesis and progression of various cancers has been well established. However, limited studies have investigated the role of ferroptosis-mediated tumor microenvironment (TME) in skin cutaneous melanoma (SKCM).

METHODS

By leveraging single-cell RNA sequencing data, the nonnegative matrix factorization (NMF) approach was employed to comprehensively characterize and identify distinct gene signatures within ferroptosis-associated TME cell clusters. Prognostic and treatment response analyses were conducted using both bulk datasets and external cancer cohort to evaluate the clinical implications of TME clusters.

RESULTS

This NMF-based analysis successfully delineated fibroblasts, macrophages, T cells, and B cells into multiple clusters, enabling the identification of unique gene expression patterns and the annotation of distinct TME clusters. Furthermore, pseudotime trajectories, enrichment analysis, cellular communication analysis, and gene regulatory network analysis collectively demonstrated significant intercellular communication between key TME cell clusters, thereby influencing tumor cell development through diverse mechanisms. Importantly, our bulk RNA-seq analysis revealed the prognostic significance of ferroptosis-mediated TME cell clusters in SKCM patients. Moreover, our analysis of immune checkpoint blockade highlighted the crucial role of TME cell clusters in tumor immunotherapy, facilitating the discovery of potential immunotherapeutic targets.

CONCLUSIONS

In conclusion, this pioneering study employing NMF-based analysis unravels the intricate cellular communication mediated by ferroptosis within the TME and its profound implications for the pathogenesis and progression of SKCM. We provide compelling evidence for the prognostic value of ferroptosis-regulated TME cell clusters in SKCM, as well as their potential as targets for immunotherapy.

The regulatory role and mechanism of lncTUG1 on cartilage apoptosis and inflammation in osteoarthritis

BACKGROUND

Long-stranded non-coding RNA TUG1 is lowly expressed in osteoarthritic chondrocytes. This study aimed to elucidate the role of TUG1 in osteoarthritic cartilage damage and the underlying mechanisms.

METHODS

Combined database analysis, using primary chondrocytes as well as the C28/I2 cell line, was performed by qRT-PCR, Western blotting, and immunofluorescence to determine the expression of TUG1, miR-144-3p, DUSP1, and other target proteins. Dual luciferase reporter gene and RIP to verify direct interaction of TUG1 with miR-144-3-p and miR-144-3-p with DUSP1, Annexin V-FITC/PI double staining to detect apoptosis. CCK-8 to detect cell proliferation. The biological significance of TUG1, miR-144-3p, and DUSP1 was assessed in vitro experiments using siRNA for TUG1, mimic and repressor for miR-144-3p, and overexpression plasmid for DUSP1. In this study, all data were subjected to a t-test or one-way analysis of variance with a p-value < 0.05 as the cutoff.

RESULTS

TUG1 expression was closely associated with osteoarthritic chondrocyte damage, and knockdown of TUG1 significantly promoted chondrocyte apoptosis and inflammation. In the present study, we found that TUG1 inhibited chondrocyte apoptosis and inflammation by competitively binding miR-144-3p, deregulating the negative regulatory effect of miR-144-3p on DUSP1, promoting DUSP1 expression, and inhibiting the p38 MAPK signaling pathway.

CONCLUSIONS

In conclusion, our study clarifies the role of the ceRNA regulatory network of TUG1/miR-144-3p/DUSP1/P38 MAPK in OA cartilage injury and provides an experimental and theoretical basis for genetic engineering tools to promote articular cartilage repair.

Overview of distinct N6-Methyladenosine profiles of messenger RNA in osteoarthritis

Although N6-methyladenosine (m6A) modification is closely associated with the pathogenesis of osteoarthritis (OA), the mRNA profile of m6A modification in OA remains unknown. Therefore, our study aimed to identify common m6A features and novel m6A-related therapeutic targets in OA. In the present study, we identified 3962 differentially methylated genes (DMGs) and 2048 differentially expressed genes (DEGs) using methylated RNA immunoprecipitation next-generation sequencing (MeRIP-seq) and RNA-sequencing. A co-expression analysis of DMGs and DEGs showed that the expression of 805 genes was significantly affected by m6A methylation. Specifically, we obtained 28 hypermethylated and upregulated genes, 657 hypermethylated and downregulated genes, 102 hypomethylated and upregulated genes, and 18 hypomethylated and downregulated genes. The differential gene expression analysis based on GSE114007 revealed 2770 DEGs. The Weighted Gene Co-expression Network Analysis (WGCNA) based on GSE114007 identified 134 OA-related genes. By taking the intersection of these results, ten novel aberrantly expressed, m6A-modified and OA-related key genes were identified, including SKP2, SULF1, TNC, ZFP36, CEBPB, BHLHE41, SOX9, VEGFA, MKNK2 and TUBB4B. The present study may provide valuable insight into identifying m6A-related pharmacological targets in OA.

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.

DUSP1 promotes muscle atrophy by inhibiting myocyte differentiation in cachectic patients

BACKGROUND

Skeletal muscle atrophy is the major hallmark of cancer cachexia. The mechanisms underlying muscle wasting remain elusive in cachectic patients. Our research seeks to identify differentially expressed genes (DEGs) between non-cachectic and cachectic cancer patients and elucidate their functions.

METHODS

We screened the DEGs of skeletal muscle between patients with and without cachexia from microarray data. Biological function of DEGs is analyzed through gene enrichment analysis, while an interaction network is constructed to visualize how genes are related. A Spearman's correlation analysis demonstrated the clinical significance of DUSP1 related to cancer cachexia. Skeletal muscle samples were collected and histomorphology studies were conducted. Function of DUSP1 on myogenesis was clarified by qPCR, western blotting, and immunofluorescence.

RESULTS

We screened 324 DEGs in skeletal muscle from patients with and without cachexia. The results of the gene enrichment analysis indicated that inflammatory cytokines and immune responses contribute significantly to the pathological condition of cachexia. DUSP1 was one of the key genes in the regulating network. DUSP1 protein and mRNA levels were increased significantly in skeletal muscle tissues from patients with cancer cachexia. DUSP1 expression in cachectic group was found to have negative correlation with SMA, prealbumin and BMI and positive correlation with TNFα, IL6 and weight loss. Significant changes of myogenesis related genes were observed in myocyte after DUSP1 was overexpressed and knocked down.

CONCLUSION

In skeletal muscle of cachectic patients, DUSP1 expression was observed to be higher and thus DUSP1 promote muscle atrophy by inhibiting myogenesis. DUSP1 is expected to be a specific target in cancer cachexia for preventing and treating muscle atrophy.

LncRNA TM1-3P Regulates Proliferation, Apoptosis and Inflammation of Fibroblasts in Osteoarthritis through miR-144-3p/ONECUT2 Axis

OBJECTIVE

This study explores LncRNA TM1-3P effects on the proliferation, apoptosis, and inflammatory response of fibroblasts in osteoarthritis (OA) and its underlying mechanism.

METHODS

Bioinformatics was performed to analyze OA disease-related genes, miRNA profiles, and function. The targeted regulation of LncRNA TM1-3P and miR-144-3p, ONECUT2 and miR-144-3p were analyzed by dual luciferase reporter gene assay, RNA Binding Protein Immunoprecipitation (RIP), and RNA pull down. Histopathological morphology of the knee joint was observed by hematoxylin-eosin (HE) and Annona Red O/Fast Green. The expressions of mRNAs and proteins were detected by RT-qPCR, Western blot, and immunohistochemistry. Unpaired T test was used between groups, and the one-way analysis of variance of repeated measurement data was applied for multi-group comparison, following Tukey's post-test.

RESULTS

ONECUT2 and Smurf2 genes were significantly elevated in the osteoarthritis group compared with the normal group (P < 0.001, P < 0.001). Expressions of ONECUT2 and LncRNA TM1-3P were increased, and expression of miR-144-3p was decreased in interleukin (IL)-1β-induced human fibroblast synovial cells (hFSCs) (mRNA: 1.06 ± 0.24 vs. 3.29 ± 0.73, proteins: 0.22 ± 0.03 vs. 0.46 ± 0.22, 1.23 ± 0.22 vs. 3.76 ± 0.73, 1.06 ± 0.25 vs. 0.37 ± 0.13, P < 0.01, P < 0.001, P < 0.01, P < 0.05). Overexpression of miR-144-3p down-regulated the ONECUT2 expression, reduced cell proliferation, promoted apoptosis in hFSCs induced by IL-1β (mRNA: 0.89 ± 0.14 vs. 0.15 ± 0.01, P < 0.05; proteins: 0.46 ± 0.01 vs. 0.23 ± 0.01, P < 0.001; CCK8: 1.88 ± 0.07 vs. 1.65 ± 0.07; P < 0.05; EDU: 55.82 ± 1.44 vs 40.57 ± 2.24, P < 0.05; apoptosis: 10.57 ± 0.79 vs 16.36 ± 0.35, P < 0.0001). Overexpression of LncRNA TM1-3P up-regulated the expression of ONECUT2, promoted cell proliferation, and inhibited apoptosis (mRNA: 0.9 ± 0.09 vs 1.94 ± 0.12, P < 0.05; proteins: 0.61 ± 0.05 vs 0.76 ± 0.03, P > 0.05; CCK8: 2.07 ± 0.05 vs 2.47 ± 0.06; P < 0.01; EDU: 52.67 ± 1.17 vs 60.06 ± 3.24, P < 0.05; apoptosis: 10.57 ± 0.79 vs 16.36 ± 0.35, P < 0.001), which were reversed by the overexpression of miR-144-3p treatment (mRNA: 1.82 ± 0.07 vs 0.31 ± 0.07, P < 0.0001; proteins: 0.74 ± 0.02 vs 0.35 ± 0.01, P < 0.01; CCK8: 2.41 ± 0.01 vs 1.67 ± 0.02; P < 0.0001; EDU: 66.85 ± 2.86 vs 44.68 ± 1.97, P < 0.0001; apoptosis: 7.19 ± 0.19 vs 13.36 ± 0.53, P < 0.0001). Silencing LncRNA TM1-3P attenuated the injury of knee joint tissue, down-regulated the expression of ONECUT2, Smurf2, IL-1β, IL-6, TNF-α, and improved the expression of Rap1 in rats (0.71 ± 0.04 vs 0.48 ± 0.02, 0.68 ± 0.06 vs 0.36 ± 0.02, 0.74 ± 0.03 vs 0.49 ± 0.04, 0.78 ± 0.01 vs 0.54 ± 0.03, 0.68 ± 0.02 vs 0.4 ± 0.04, 0.24 ± 0.01 vs 0.4 ± 0.03, P < 0.05, P < 0.05, P < 0.05, P < 0.01, P < 0.01, P < 0.05).

CONCLUSION

LncRNA TM1-3P improved inflammation and damage of knee joints in OA rats through miR-144-3p/ONECUT2 axis, providing a new theoretical basis for gene therapy of OA.