Analysis of gene expression and methylation datasets identified ADAMTS9, FKBP5, and PFKBF3 as biomarkers for osteoarthritis

Glycolysis: an emerging regulator of osteoarthritis

Osteoarthritis (OA) has been a leading cause of disability in the elderly and there remains a lack of effective therapeutic approaches as the mechanisms of pathogenesis and progression have yet to be elucidated. As OA progresses, cellular metabolic profiles and energy production are altered, and emerging metabolic reprogramming highlights the importance of specific metabolic pathways in disease progression. As a crucial part of glucose metabolism, glycolysis bridges metabolic and inflammatory dysfunctions. Moreover, the glycolytic pathway is involved in different areas of metabolism and inflammation, and is associated with a variety of transcription factors. To date, it has not been fully elucidated whether the changes in the glycolytic pathway and its associated key enzymes are associated with the onset or progression of OA. This review summarizes the important role of glycolysis in mediating cellular metabolic reprogramming in OA and its role in inducing tissue inflammation and injury, with the aim of providing further insights into its pathological functions and proposing new targets for the treatment of OA.

Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages

Background

Osteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear.

Objective

The study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets.

Methods

To identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein-protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis.

Results

A total of 172 DEGs (differentially expressed genes) associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA.

Conclusion

Overall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis.

Exosomal miR-93-5p regulated the progression of osteoarthritis by targeting ADAMTS9

Osteoarthritis (OA) is a type of common degenerative joint disorder, in which adipose mesenchymal stem cells (ADSCs) and the secreted exosomes play an important role. The purpose of this study was to investigate the role and mechanism of exosomes derived from ADSCs (ADSC-exos) in OA. The gradient of IL-1β concentration was designed to construct the articular chondrocyte model of arthritic mice. The expression of miR-93-5p and ADAMTS9 in articular chondrocytes was detected by reverse transcription quantitative polymerase chain reaction. Dual luciferase reporter gene assay was performed to verify the interaction between them. Monodansylcadaverine staining was used to visualize the autophagosome formation and cell apoptosis was analyzed by flow cytometry. ADSC-exos were authenticated by transmission electron microscope and western blot assay. miR-93-5p was found to be downregulated in IL-1β-treated articular chondrocytes compared with OA cartilage while ADAMTS9 was upregulated, which was identified as a direct target gene of miR-93-5p. Silencing of ADAMTS9 attenuated the effects of miR-93-5p. Exosomal miR-93-5p can reduce the release of inflammatory factors in mouse arthritis cell models. This study first described the mechanism under that ADSC-exos inhibited inflammation and alleviated OA through the innovative targets miR-93-5p/ADAMTS9 signal axis. This provided a new method for the treatment of OA.

An integrative analysis of DNA methylation and gene expression to predict lung adenocarcinoma prognosis

Background: Abnormal DNA methylation of gene promoters is an important feature in lung adenocarcinoma (LUAD). However, the prognostic value of DNA methylation remains to be further explored. Objectives. We sought to explore DNA methylation characteristics and develop a quantifiable criterion related to DNA methylation to improve survival prediction for LUAD patients.

Methods: Illumina Human Methylation450K array data, level 3 RNA-seq data and corresponding clinical information were obtained from TCGA. Cox regression analysis and the Akaike information criterion were used to construct the best-prognosis methylation signature. Receiver operating characteristic curve analysis was used to validate the prognostic ability of the DNA methylation-related feature score. qPCR was used to measure the transcription levels of the identified genes upon methylation.

Results: We identified a set of DNA methylation features composed of 11 genes (MYEOV, KCNU1, SLC27A6, NEUROD4, HMGB4, TACR3, GABRA5, TRPM8, NLRP13, EDN3 and SLC34A1). The feature score, calculated based on DNA methylation features, was independent of tumor recurrence and TNM stage in predicting overall survival. Of note, the combination of this feature score and TNM stage provided a better overall survival prediction than either of them individually. The transcription levels of all the hypermethylated genes were significantly increased after demethylation, and the expression levels of 3 hypomethylated proteins were significantly higher in tumor tissues than in normal tissues, as indicated by immunohistochemistry data from the Human Protein Atlas. Our results suggested that these identified genes with prognostic features were regulated by DNA methylation of their promoters.

Conclusion: Our studies demonstrated the potential application of DNA methylation markers in the prognosis of LUAD.

Potential of a Novel Chemical Compound Targeting Matrix Metalloprotease-13 for Early Osteoarthritis: An In Vitro Study

Osteoarthritis is a progressive disease characterized by cartilage destruction in the joints. Matrix metalloproteinases (MMPs) and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTSs) play key roles in osteoarthritis progression. In this study, we screened a chemical compound library to identify new drug candidates that target MMP and ADAMTS using a cytokine-stimulated OUMS-27 chondrosarcoma cells. By screening PCR-based mRNA expression, we selected 2-(8-methoxy-2-methyl-4-oxoquinolin-1(4H)-yl)-N-(3-methoxyphenyl) acetamide as a potential candidate. We found that 2-(8-methoxy-2-methyl-4-oxoquinolin-1(4H)-yl)-N-(3-methoxyphenyl) acetamide attenuated IL-1β-induced MMP13 mRNA expression in a dose-dependent manner, without causing serious cytotoxicity. Signaling pathway analysis revealed that 2-(8-methoxy-2-methyl-4-oxoquinolin-1(4H)-yl)-N-(3-methoxyphenyl) acetamide attenuated ERK- and p-38-phosphorylation as well as JNK phosphorylation. We then examined the additive effect of 2-(8-methoxy-2-methyl-4-oxoquinolin-1(4H)-yl)-N-(3-methoxyphenyl) acetamide in combination with low-dose betamethasone on IL-1β-stimulated cells. Combined treatment with 2-(8-methoxy-2-methyl-4-oxoquinolin-1(4H)-yl)-N-(3-methoxyphenyl) acetamide and betamethasone significantly attenuated MMP13 and ADAMTS9 mRNA expression. In conclusion, we identified a potential compound of interest that may help attenuate matrix-degrading enzymes in the early osteoarthritis-affected joints.

An integrative analysis of DNA methylation and transcriptome showed the dysfunction of MAPK pathway was involved in the damage of human chondrocyte induced by T-2 toxin

Background

T-2 toxin is thought to induce the growth plate and articular cartilage damage of Kashin-Beck disease (KBD), an endemic osteochondropathy in China. This study aims to explore the potential underlying mechanism of such toxic effects by integrating DNA methylation and gene expression profiles.

Methods

In this study, C28/I2 chondrocytes were treated with T-2 toxin (5 ng/mL) for 24 h and 72 h. Global DNA methylation level of chondrocyte was tested by Enzyme-Linked Immuno Sorbent Assay. Genome-wide DNA methylation and expression profiles were detected using Illumina Infinium HumanMethylation850 BeadChip and RNA-seq technique, respectively. Differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were identified mainly for two stages including 24 h group versus Control group and 72 h group versus 24 h group. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed by Metascape. DMGs and DEGs were further validated by Sequenom MassARRAY system and quantitative real-time polymerase chain reaction.

Results

The global DNA methylation levels of chondrocytes exposed to T-2 toxin were significantly increased (P < 0.05). For 24 h group versus Control group (24 VS C), 189 DEGs and 590 DMGs were identified, and 4 of them were overlapping. For 72 h group versus 24 h group (72 VS 24), 1671 DEGs and 637 DMGs were identified, and 45 of them were overlapping. The enrichment analysis results of DMGs and DEGs both showed that MAPK was the one of the mainly involved signaling pathways in the regulation of chondrocytes after T-2 toxin exposure (DEGs: P_24VSc = 1.62 × 10^− 7; P_72VS24 = 1.20 × 10^− 7; DMGs: P_24VSc = 0.0056; P_72VS24 = 3.80 × 10^− 5).

Conclusions

The findings depicted a landscape of genomic methylation and transcriptome changes of chondrocytes after T-2 toxin exposure and suggested that dysfunction of MAPK pathway may play important roles in the chondrocytes damage induced by T-2 toxin, which could provide new clues for understanding the potential biological mechanism of KBD cartilage damage induced by T-2 toxin.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12860-021-00404-3.

Effect of DNA methylation on gene transcription is associated with the distribution of methylation sites across the genome in osteoarthritis

Genetics and epigenetics are important subjects in the field of osteoarthritis (OA) research. DNA methylation may affect gene transcription, but the specific mechanisms have remained to be fully elucidated. In the present study, the ChAMP methylation analysis package was used to identify differentially methylated genes (DMGs) from the dataset GSE63695 from the Gene Expression Omnibus (GEO) database. The distribution of differentially methylated sites (DMS) and the total array sites across the genome were analyzed by enrichment analysis. Subsequently, two mRNA expression profiling datasets, GSE114007 and GSE113825, were obtained from the GEO database and common differentially expressed genes (DEGs) were identified using the Limma package. Key genes were screened by analyzing the distribution of DMS across the genome consisting of DEGs and DMGs. A total of 1,662 and 1,986 DEGs were identified between OA and normal human cartilage from the GSE113825 and GSE114007 dataset, respectively. A further screening revealed 292 genes with common differences between the two datasets. A total of 574 DMS containing 394 DMGs were observed between OA and normal cartilage. Integrative analysis revealed a corresponding subset of 15 genes. Of these, 6 genes were verified by reverse transcription-quantitative PCR, confirming that the mRNA expression of 5 genes (MAP1B, FNDC1, ANLN, SCNN1A and STC2) in OA cartilage was consistent with the mRNA expression from the analysis of the datasets. Upon treatment with the DNA methylation inhibitor 5-aza-2'-deoxycytidine, the mRNA levels of FNDC1 and SCNN1A were decreased, and no significant alteration in the mRNA levels of MAP1B, ANLN, KCNN4 and STC2 was observed. The incidence of differential methylation varied in subregions of the genome and the effects on transcription were associated with the distribution of DEGs across the genome. The regulation of this appears more complex than initially postulated. Combining the data on epigenetic differences of OA with the genome or transcriptome data for analysis may improve the understanding of the pathophysiological processes of OA. FNDC1 and SCNN1A may potentially be valuable biomarkers for OA.

Identification of differentially expressed genes in synovial tissue of osteoarthritis based on a more robust integrative analysis method

OBJECTIVE

This study aimed to identify osteoarthritis (OA) related genes based on microarray data in synovium with a more robust integrative analysis method.

METHODS

Four series GSE55457, GSE12021, GSE55235, and GSE55584 (36 OA and 29 normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) of GSE55457, GSE12021, and GSE55235 were identified using the LIMMA package. Overlapping DEGs from the intersection of the three series were detected. Simultaneously, samples in the four series were pooled to identify DEGs with integrated analysis using the Sva package.

RESULTS

In total, 74 overlapping DEGs and 242 DEGs by integrating four series were detected. Based on them, 70 common DEGs were used to construct a protein-protein interaction (PPI) network, involving 61 nodes and 206 edges. Also, three gene modules and five hub genes, named JUN, IL6, VEGFA, MYC, and EGR1, were identified.

CONCLUSIONS

Seventy DEGs were finally identified with a more robust integrative analysis method. JUN, IL6, VEGFA, MYC, and EGR1 were identified as hub genes in the development of OA. Key Points • 76 overlapping DEGs were detected from the intersection of DEGs in GSE55457, GSE12021, and GSE55235. • 242 DEGs were identified by integrating four series using Sva package. • 72 common DEGs were finally identified based on the overlapping DEGs and the integrated DEGs. • JUN, IL6, VEGFA, MYC, and EGR1 were identified as hub genes in the development of OA.

Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

Background

N6-methyladenosine (m6A), the most abundant chemical modification on eukaryotic messenger RNA (mRNA), is modulated by three class of regulators namely "writers," "erasers," and "readers." Increasing studies have shown that aberrant expression of m6A regulators plays broad roles in tumorigenesis and progression. However, it is largely unknown regarding the expression regulation for RNA m6A regulators in human cancers.

Results

Here we characterized the expression profiles of RNA m6A regulators in 13 cancer types with The Cancer Genome Atlas (TCGA) data. We showed that METTL14, FTO, and ALKBH5 were down-regulated in most cancers, whereas YTHDF1 and IGF2BP3 were up-regulated in 12 cancer types except for thyroid carcinoma (THCA). Survival analysis further revealed that low expression of several m6A regulators displayed longer overall survival times. Then, we analyzed microRNA (miRNA)-regulated and DNA methylation-regulated expression changes of m6A regulators in pan-cancer. In total, we identified 158 miRNAs and 58 DNA methylation probes (DMPs) involved in expression regulation for RNA m6A regulators. Furthermore, we assessed the survival significance of those regulatory pairs. Among them, 10 miRNAs and 7 DMPs may promote cancer initiation and progression; conversely, 3 miRNA/mRNA pairs in kidney renal clear cell carcinoma (KIRC) may exert tumor-suppressor function. These findings are indicative of their potential prognostic values. Finally, we validated two of those miRNA/mRNA pairs (hsa-miR-1307-3p/METTL14 and hsa-miR-204-5p/IGF2BP3) that could serve a critical role for potential clinical application in KIRC patients.

Conclusions

Our findings highlighted the importance of upstream regulation (miRNA and DNA methylation) governing m6A regulators' expression in pan-cancer. As a result, we identified several informative regulatory pairs for prognostic stratification. Thus, our study provides new insights into molecular mechanisms of m6A modification in human cancers.

Three-dimensional (3D) hydrogel serves as a platform to identify potential markers of chondrocyte dedifferentiation by combining RNA sequencing

Dedifferentiation of chondrocyte greatly restricts its function and application, however, it is poorly understood except a small number of canonical markers. The non-cell-adhesive property endows polysaccharide hydrogel with the ability to maintain chondrocyte phenotype, which can serve as a platform to identify new molecular markers and therapeutic targets of chondrocyte dedifferentiation. In this study, the high-throughput RNA sequencing (RNA-seq) was first performed on articular chondrocytes at primary (P0) and passage 1 (P1) stages to explore the global alteration of gene expression along with chondrocyte dedifferentiation. Significantly, several potential marker genes, such as PFKFB3, KDM6B, had been identified via comparatively analyzing their expression in P0 and P1 chondrocytes as well as in 3D constructs (i.e. chondrocyte-laden alginate hydrogel and HA-MA hydrogel) at both mRNA and protein level. Besides, the changes in cellular morphology and enriched pathway of differentially expressed genes during chondrocyte dedifferentiation was studied in detail. This study developed the use of hydrogel as a platform to investigate chondrocyte dedifferentiation; the results provided new molecular markers and potential therapeutic targets of chondrocyte dedifferentiation.

A Genome-Wide Integrative Association Study of DNA Methylation and Gene Expression Data and Later Life Cognitive Functioning in Monozygotic Twins

Monozygotic twins are genetically identical but rarely phenotypically identical. Epigenetic and transcriptional variation could influence this phenotypic discordance. Investigation of intra-pair differences in molecular markers and a given phenotype in monozygotic twins controls most of the genetic contribution, enabling studies of the molecular features of the phenotype. This study aimed to identify genes associated with cognition in later life using integrated enrichment analyses of the results of blood-derived intra-pair epigenome-wide and transcriptome-wide association analyses of cognition in 452 middle-aged and old-aged monozygotic twins (56–80 years). Integrated analyses were performed with an unsupervised approach using KeyPathwayMiner, and a supervised approach using the KEGG and Reactome databases. The supervised approach identified several enriched gene sets, including “neuroactive ligand receptor interaction” (p-value = 1.62^∗10-2), “Neurotrophin signaling” (p-value = 2.52^∗10-3), “Alzheimer’s disease” (p-value = 1.20^∗10-2), and “long-term depression” (p-value = 1.62^∗10-2). The unsupervised approach resulted in a 238 gene network, including the Alzheimer’s disease gene APP (Amyloid Beta Precursor Protein) as an exception node, and several novel candidate genes. The strength of the unsupervised method is that it can reveal previously uncharacterized sub-pathways and detect interplay between biological processes, which remain undetected by the current supervised methods. In conclusion, this study identified several previously reported cognition genes and pathways and, additionally, puts forward novel candidates for further verification and validation.

Integrative analysis of ceRNA network and DNA methylation associated with gene expression in malignant pheochromocytomas: a study based on The Cancer Genome Atlas

Background

Competitive endogenous RNAs (ceRNAs) have revealed a new mechanism of interaction between RNAs. Epigenetic regulation in the gene expression dynamics has become increasingly important in malignant pheochromocytomas (PCCs). We performed an integrative analysis of ceRNA networks and DNA methylation to identify key biomarkers and contribute to the understanding of the molecular biological mechanisms of malignant PCCs.

Methods

Differentially expressed genes in malignant PCCs and controls were identified from The Cancer Genome Atlas database by using the Limma package in R (v3.4.4). An abnormal lncRNA-miRNA-mRNA ceRNA network was constructed for malignant PCCs, and function enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery. For DNA methylation datasets, the methylation analysis package was used in identifying differential methylation genes, and potential prognostic genes were identified by Kaplan-Meier survival analysis.

Results

A total of 447 lncRNAs, 26 miRNAs, and 1,607 mRNAs were found to be differentially expressed in malignant PCCs as compared with those in normal samples. We then constructed an abnormal lncRNA-miRNA-mRNA ceRNA network for malignant PCCs. The network consisted of 12 lncRNAs, 6 miRNAs, and 220 mRNAs. Functional enrichment analysis showed that differentially expressed mRNAs were particularly enriched in the biological process, cellular component, and molecular function. Furthermore, four differentially expressed mRNAs from ceRNAs were identified through the cross-analysis of gene expression and DNA methylation profiles. LncRNA C9orf147 and 6 out of 220 mRNAs were indicated as prognostic biomarkers for patients with malignant PCCs (P<0.05).

Conclusions

Our research increases the understanding of the pathogenesis of malignant PCCs and offers potential genes as underlying therapeutic targets or prognostic biomarkers.

Identification of Key Genes and Pathways Associated with Sex Differences in Osteoarthritis Based on Bioinformatics Analysis

Sex differences have been suggested to play critical roles in the pathophysiology of osteoarthritis (OA), resulting in sex-specific prevalence and incidence. However, their roles in the development of OA remain largely unknown. The aim of this study was to screen out key genes and pathways mediating biological differences between OA females after menopause and OA males. First, the gene expression data of GSE36700 and GSE55457 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between sexes were identified using R software, respectively. The overlapping DEGs were obtained. Then, protein-protein interactive (PPI) network was constructed to further analyze interactions between the overlapping DEGs. Finally, enrichment analyses were separately performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes tools. In our results, a total of 278 overlapping DEGs were identified between OA females after menopause and OA males, including 219 upregulated and 59 downregulated genes. In the PPI network, seven hub genes were identified, including EGF, ERBB2, CDC42, PIK3R2, LCK, CBL, and STAT1. Functional enrichment analysis revealed that these genes were mainly enriched in PI3K-Akt signaling pathway, osteoclast differentiation, and focal adhesion. In conclusion, the results in the current study suggest that pathways of PI3K-Akt, osteoclast differentiation, and focal adhesion may play important roles in the development of OA females after menopause. EGFR, ERBB2, CDC42, and STAT1 may be key genes related to OA progression in postmenopausal women and may be promising therapeutic targets for OA.