Genome-wide DNA methylation study of hip and knee cartilage reveals embryonic organ and skeletal system morphogenesis as major pathways involved in osteoarthritis

ETS2 overexpression ameliorates cartilage injury in osteoarthritis by the ETS2/miR-155/STAT1/DNMT1 feedback loop pathway

Osteoarthritis (OA) is the most common irreversible chronic joint dysfunction disease, which is pathologically characterized by disturbance of articular cartilage homeostasis leading to subsequent inflammatory response and cartilage extracellular matrix (ECM) degradation. Increasing evidence has demonstrated the dysregulation of transcription factors play crucial roles in the occurrence and development of osteoarthritis (OA), but the potential functions and mechanism of most transcription factors in OA has not been completely illuminated. In this study, we identified that transcription factor V-ets erythroblastosis virus E26 oncogene homolog 2 (ETS2) was significantly down-regulated in OA cartilage and IL-1β-induced OA chondrocytes. Functional experiments in vitro demonstrated that the overexpressed ETS2 strikingly enhanced proliferation, outstandingly suppressed apoptosis, and dramatically reduced inflammation and ECM degradation in IL-1β-induced OA chondrocytes, whereas the knockdown of ETS2 led to the opposite effects. Further in vivo studies have shown that up-regulated ETS2 dramatically ameliorates cartilage injury in DMM-induced OA mice. Mechanical studies have disclosed that DNMT1-mediated downregulation of ETS2 dramatically promotes STAT1 by inhibiting miR-155 transcription, and increased STAT1 initiates a feedback loop that may enhance DNMT1-mediated hypermethylation of ETS2 to inhibit ETS2 expression, thus forming a DNMT1/ETS2/miR-155/STAT1 feedback loop that inhibits MAPK signaling pathways and aggravates OA cartilage injury. In all, our results revealed that overexpression of ETS2 markedly ameliorated OA cartilage injury through the ETS2/miR-155/STAT1/DNMT1 feedback loop, providing a new perspective on the pathogenesis and therapeutic strategies for OA.

Downstream Assays for Variant Resolution: Epigenetics, RNA Sequnncing, and Metabolomics

As the availability of advanced molecular testing like whole exome and genome sequencing expands, it comes with the added complication of interpreting inconclusive results, including determining the relevance of variants of uncertain significance or failing to find a variant in an otherwise suspected specific genetic disorder. This complication necessitates the use of alternative testing methods to gather more information in support of, or against, a particular genetic diagnosis. Therefore, new genome-wide approaches, including DNA epigenetic testing, RNA sequencing, and metabolomics, are increasingly being used to increase the diagnostic yield when used in conjunction with more conventional genetic tests.

The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data

Introduction

Osteoarthritis (OA) refers to a commonly seen degenerative joint disorder and a major global public health burden. According to the existing literature, osteoarthritis is related to epigenetic changes, which are important for diagnosing and treating the disease early. Through early targeted treatment, costly treatments and poor prognosis caused by advanced osteoarthritis can be avoided.

Methods

This study combined gene differential expression analysis and weighted gene co-expression network analysis (WGCNA) of the transcriptome with epigenome microarray data to discover the hub gene of OA. We obtained 2 microarray datasets (GSE114007, GSE73626) in Gene Expression Omnibus (GEO). The R software was utilized for identifying differentially expressed genes (DEGs) and differentially methylated genes (DMGs). By using WGCNA to analyze the relationships between modules and phenotypes, it was discovered that the blue module (MEBlue) has the strongest phenotypic connection with OA (cor = 0.92, p = 4e-16). The hub genes for OA, also known as the hub methylated differentially expressed genes, were identified by matching the MEblue module to differentially methylated differentially expressed genes. Furthermore, this study used Gene set variation analysis (GSVA) to identify specific signal pathways associated with hub genes. qRT-PCR and western blotting assays were used to confirm the expression levels of the hub genes in OA patients and healthy controls.

Results

Three hub genes were discovered: HTRA1, P2RY6, and RCAN1. GSVA analysis showed that high HTRA1 expression was mainly enriched in epithelial-mesenchymal transition and apical junction; high expression of P2RY6 was mainly enriched in the peroxisome, coagulation, and epithelial-mesenchymal transition; and high expression of RCAN1 was mainly enriched in epithelial-mesenchymal-transition, TGF-β-signaling, and glycolysis. The results of the RT-qPCR and WB assay were consistent with the findings.

Discussion

The three genes tested may cause articular cartilage degeneration by inducing chondrocyte hypertrophy, regulating extracellular matrix accumulation, and improving macrophage pro-inflammatory response, resulting in the onset and progression of osteoarthritis. They can provide new ideas for targeted treatment of osteoarthritis.

A Pilot Analysis of Genome-Wide DNA Methylation Patterns in Mouse Cartilage Reveals Overlapping Epigenetic Signatures of Aging and Osteoarthritis

OBJECTIVE

Cartilage epigenetic changes are strongly associated with human osteoarthritis (OA). However, the influence of individual environmental OA risk factors on these epigenetic patterns has not been determined; herein we characterize cartilage DNA methylation patterns associated with aging and OA in a mouse model.

METHODS

Murine knee cartilage DNA was extracted from healthy young (16-week, n = 6), old (82-week, n = 6), and young 4-week post-destabilization of the medial meniscus (DMM) OA (n = 6) C57BL6/J mice. Genome-wide DNA methylation patterns were determined via Illumina BeadChip. Gene set enrichment analysis was performed by Ingenuity Pathway Analysis. The top seven most differentially methylated positions (DMPs) were confirmed by pyrosequencing in an independent animal set. Results were compared to previously published human OA methylation data.

RESULTS

Aging was associated with 20,940 DMPs, whereas OA was associated with 761 DMPs. Merging these two conditions revealed 279 shared DMPs. All demonstrated similar directionality and magnitude of change (Δβ 1.0% ± 0.2%, mean methylation change ± SEM). Shared DMPs were enriched in OA-associated pathways, including RhoA signaling (P = 1.57 × 10^-4 ), protein kinase A signaling (P = 3.38 × 10^-4 ), and NFAT signaling (P = 6.14 × 10^-4 ). Upstream regulators, including TET3 (P = 6.15 × 10^-4 ), immunoglobulin (P = 6.14 × 10^-4 ), and TLR7 (P = 7.53 × 10^-4 ), were also enriched. Pyrosequencing confirmed six of the seven top DMPs in an independent cohort.

CONCLUSION

Aging and early OA following DMM surgery induce similar DNA methylation changes within a murine OA model, suggesting that aging may induce pro-OA epigenetic "poising" within articular cartilage. Future research should focus on confirming and expanding these findings to other environmental OA risk factors, including obesity, as well as determining late OA changes in mice.

Multi-omics molecular biomarkers and database of osteoarthritis

Osteoarthritis (OA) is the most common form of arthritis in the adult population and is a leading cause of disability. OA-related genetic loci may play an important role in clinical diagnosis and disease progression. With the rapid development of diverse technologies and omics methods, many OA-related public data sets have been accumulated. Here, we retrieved a diverse set of omics experimental results from 159 publications, including genome-wide association study, differentially expressed genes and differential methylation regions, and 2405 classified OA-related gene markers. Meanwhile, based on recent single-cell RNA-seq data from different joints, 5459 cell-type gene markers of joints were collected. The information has been integrated into an online database named OAomics and molecular biomarkers (OAOB). The database (http://ibi.zju.edu.cn/oaobdb/) provides a web server for OA marker genes, omics features and so on. To our knowledge, this is the first database of molecular biomarkers for OA.

Making sense of the ageing methylome

Over time, the human DNA methylation landscape accrues substantial damage, which has been associated with a broad range of age-related diseases, including cardiovascular disease and cancer. Various age-related DNA methylation changes have been described, including at the level of individual CpGs, such as differential and variable methylation, and at the level of the whole methylome, including entropy and correlation networks. Here, we review these changes in the ageing methylome as well as the statistical tools that can be used to quantify them. We detail the evidence linking DNA methylation to ageing phenotypes and the longevity strategies aimed at altering both DNA methylation patterns and machinery to extend healthspan and lifespan. Lastly, we discuss theories on the mechanistic causes of epigenetic ageing.

Identification and development of the novel 7-genes diagnostic signature by integrating multi cohorts based on osteoarthritis

Background

A chronic progressive degenerative joint disease, such as osteoarthritis (OA) is positively related to age. The medical economy is facing a major burden, because of the high disability rate seen in patients with OA. Therefore, to prevent and treat OA, exploring the diagnostic biomarkers of OA will be of great significance.

Methods

Differentially expressed genes (DEGs) were obtained from the Gene Expression Omnibus database using the RobustRankAggreg R package, and a protein–protein interaction network was constructed. The module was obtained from Cytoscape, and the four algorithms of degree, MNC, closeness, and MCC in CytoHubba were used to identify the hub genes. A diagnostic model was constructed using Support Vector Machines (SVM), and the ability of the model to predict was evaluated by other cohorts.

Results

From normal and OA samples, 136 DEGs were identified, out of which 45 were downregulated in the normal group and 91 were upregulated in the OA group. These genes were associated with the extracellular matrix-receptor interactions, the PI3K-Akt signaling pathway, and the protein digestion and absorption pathway, as per a functional enrichment analysis. Finally, we identified the 7 hub genes (COL6A3, COL1A2, COL1A1, MMP2, COL3A1, POST, and FN1). These genes have important roles and are widely involved in the immune response, apoptosis, inflammation, and bone development. These 7 genes were used to construct a diagnostic model by SVM, and it performed well in different cohorts. Additionally, we verified the methylation expression of these hub genes.

Conclusions

The 7-genes signature can be used for the diagnosis of OA and can provide new ideas in the clinical decision-making for patients with OA.

Identification and development of a novel 5-gene diagnostic model based on immune infiltration analysis of osteoarthritis

Background

Osteoarthritis (OA), which is due to the progressive loss and degeneration of articular cartilage, is the leading cause of disability worldwide. Therefore, it is of great significance to explore OA biomarkers for the prevention, diagnosis, and treatment of OA.

Methods and materials

The GSE129147, GSE57218, GSE51588, GSE117999, and GSE98918 datasets with normal and OA samples were downloaded from the Gene Expression Omnibus (GEO) database. The GSE117999 and GSE98918 datasets were integrated, and immune infiltration was evaluated. The differentially expressed genes (DEGs) were analyzed using the limma package in R, and weighted gene co-expression network analysis (WGCNA) was used to explore the co-expression genes and co-expression modules. The co-expression module genes were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and hub genes were identified by the degree, MNC, closeness, and MCC algorithms. The hub genes were used to construct a diagnostic model based on support vector machines.

Results

The Immune Score in the OA samples was significantly higher than in the normal samples, and a total of 2313 DEGs were identified. Through WGCNA, we found that the yellow module was significantly positively correlated with the OA samples and Immune Score and negatively correlated with the normal samples. The 142 DEGs of the yellow module were related to biological processes such as regulation of inflammatory response, positive regulation of inflammatory response, blood vessel morphogenesis, endothelial cell migration, and humoral immune response. The intersections of the genes obtained by the 4 algorithms resulted in 5 final hub genes, and the diagnostic model constructed with these 5 genes showed good performance in the training and validation cohorts.

Conclusions

The 5-gene diagnostic model can be used to diagnose OA and guide clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03183-9.

An evolutionary perspective of DNA methylation patterns in skeletal tissues using a baboon model of osteoarthritis

Epigenetic factors, such as DNA methylation, play an influential role in the development of the degenerative joint disease osteoarthritis (OA). These molecular mechanisms have been heavily studied in humans, and although OA affects several other animals in addition to humans, few efforts have taken an evolutionary perspective. This study explores the evolution of OA epigenetics by assessing the relationship between DNA methylation variation and knee OA development in baboons (Papio spp.) and by comparing these findings to human OA epigenetic associations. Genome-wide DNA methylation patterns were identified in bone and cartilage of the right distal femora from 56 pedigreed, adult baboons (28 with and 28 without knee OA) using the Illumina Infinium MethylationEPIC BeadChip. Several significantly differentially methylated positions (DMPs) and regions were found between tissue types. Substantial OA-related differential methylation was also identified in cartilage, but not in bone, suggesting that cartilage epigenetics may be more influential in OA than bone epigenetics. Additionally, some genes containing OA-related DMPs overlap with and display methylation patterns similar to those previously identified in human OA, revealing a mixture of evolutionarily conserved and divergent OA-related methylation patterns in primates. Overall, these findings reinforce the current etiological perspectives of OA and enhance our evolutionary understanding of epigenetic mechanisms associated with OA. This study further establishes baboons as a valuable nonhuman primate model of OA, and continued investigations in baboons will help to disentangle the molecular mechanisms contributing to OA and their evolutionary histories.

The Methylation Pattern for Knee and Hip Osteoarthritis

Osteoarthritis is one of the most prevalent chronic joint diseases for middle-aged and elderly people. But in recent years, the number of young people suffering from the disease increases quickly. It is known that osteoarthritis is a common degenerative disease caused by the combination and interaction of many factors such as natural and environmental factors. DNA methylations reflect the effects of environmental factors. Several researches on DNA methylation at specific genes in OA cartilage indicated the great potential roles of DNA methylation in OA. To systematically investigate the methylation pattern in knee and hip osteoarthritis, we analyzed the methylation profiles in cartilage of 16 OA hip samples, 19 control hip samples and 62 OA knee samples. 12 discriminative methylation sites were identified using advanced minimal Redundancy Maximal Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The SVM classifier of these 12 methylation sites from genes like MEIS1, GABRG3, RXRA, and EN1, can perfectly classify the OA hip samples, control hip samples and OA knee samples evaluated with LOOCV (Leave-One Out-Cross Validation). These 12 methylation sites can not only serve as biomarker, but also provide underlying mechanism of OA.

Glucose-induced, duration-dependent genome-wide DNA methylation changes in human endothelial cells

DNA methylation, a critical epigenetic mechanism, plays an important role in governing gene expressions during biological processes such as aging, which is well known to be accelerated in hyperglycemia (diabetes). In the present study, we investigated the effects of glucose on whole genome DNA methylation in small [human retinal microvascular endothelial cells (HRECs)] and large [human umbilical vein endothelial cells (HUVECs)] vessel endothelial cell (EC) lines exposed to basal or high glucose-containing media for variable lengths of time. Using the Infinium EPIC array, we obtained 773,133 CpG sites (probes) for analysis. Unsupervised clustering of the top 5% probes identified four distinct clusters within EC groups, with significant methylation differences attributed to EC types and the duration of cell culture rather than glucose stimuli alone. When comparing the ECs incubated for 2 days versus 7 days, hierarchical clustering analyses [methylation change >10% and false discovery rate (FDR) <0.05] identified 17,354 and 128 differentially methylated CpGs for HUVECs and HRECs, respectively. Predominant DNA hypermethylation was associated with the length of culture and was enriched for gene enhancer elements and regions surrounding CpG shores and shelves. We identified 88 differentially methylated regions (DMRs) for HUVECs and 8 DMRs for HRECs (all FDR <0.05). Pathway enrichment analyses of DMRs highlighted involvement of regulators of embryonic development (i.e., HOX genes) and cellular differentiation [transforming growth factor-β (TGF-β) family members]. Collectively, our findings suggest that DNA methylation is a complex process that involves tightly coordinated, cell-specific mechanisms. Such changes in methylation overlap genes critical for cellular differentiation and embryonic development.

Evaluation of DNA Methylation Episignatures for Diagnosis and Phenotype Correlations in 42 Mendelian Neurodevelopmental Disorders

Genetic syndromes frequently present with overlapping clinical features and inconclusive or ambiguous genetic findings which can confound accurate diagnosis and clinical management. An expanding number of genetic syndromes have been shown to have unique genomic DNA methylation patterns (called "episignatures"). Peripheral blood episignatures can be used for diagnostic testing as well as for the interpretation of ambiguous genetic test results. We present here an approach to episignature mapping in 42 genetic syndromes, which has allowed the identification of 34 robust disease-specific episignatures. We examine emerging patterns of overlap, as well as similarities and hierarchical relationships across these episignatures, to highlight their key features as they are related to genetic heterogeneity, dosage effect, unaffected carrier status, and incomplete penetrance. We demonstrate the necessity of multiclass modeling for accurate genetic variant classification and show how disease classification using a single episignature at a time can sometimes lead to classification errors in closely related episignatures. We demonstrate the utility of this tool in resolving ambiguous clinical cases and identification of previously undiagnosed cases through mass screening of a large cohort of subjects with developmental delays and congenital anomalies. This study more than doubles the number of published syndromes with DNA methylation episignatures and, most significantly, opens new avenues for accurate diagnosis and clinical assessment in individuals affected by these disorders.

Genome-wide analysis of aberrant methylation of enhancer DNA in human osteoarthritis

Background

Osteoarthritis is a chronic musculoskeletal disease characterized by age-related gradual thinning and a high risk in females. Recent studies have shown that DNA methylation plays important roles in osteoarthritis. However, the genome-wide pattern of methylation in enhancers in osteoarthritis remains unclear.

Methods

To explore the function of enhancers in osteoarthritis, we quantified CpG methylation in human enhancers based on a public dataset that included methylation profiles of 470,870 CpG probes in 108 samples from patients with hip and knee osteoarthritis and hip tissues from healthy individuals. Combining various bioinformatics analysis tools, we systematically analyzed aberrant DNA methylation of the enhancers throughout the genome in knee osteoarthritis and hip osteoarthritis.

Results

We identified 16,816 differentially methylated CpGs, and nearly half (8111) of them were from enhancers, suggesting major DNA methylation changes in both types of osteoarthritis in the enhancer regions. A detailed analysis of hip osteoarthritis identified 2426 differentially methylated CpGs in enhancers between male and female patients, and 84.5% of them were hypomethylated in female patients and enriched in phenotypes related to hip osteoarthritis in females. Next, we explored the enhancer methylation dynamics among patients with knee osteoarthritis and identified 280 differentially methylated enhancer CpGs that were enriched in the human phenotypes and disease ontologies related to osteoarthritis. Finally, a comparison of enhancer methylation between knee osteoarthritis and hip osteoarthritis revealed organ source-dependent differences in enhancer methylation.

Conclusion

Our findings indicate that aberrant methylation of enhancers is related to osteoarthritis phenotypes, and a comprehensive atlas of enhancer methylation is useful for further analysis of the epigenetic regulation of osteoarthritis and the development of clinical drugs for treatment of osteoarthritis.

A pilot study of peripheral blood DNA methylation models as predictors of knee osteoarthritis radiographic progression: data from the Osteoarthritis Initiative (OAI)

Knee osteoarthritis (OA) is a leading cause of chronic disability worldwide, but no diagnostic or prognostic biomarkers are available. Increasing evidence supports epigenetic dysregulation as a contributor to OA pathogenesis. In this pilot study, we investigated epigenetic patterns in peripheral blood mononuclear cells (PBMCs) as models to predict future radiographic progression in OA patients enrolled in the longitudinal Osteoarthritis Initiative (OAI) study. PBMC DNA was analyzed from baseline OAI visits in 58 future radiographic progressors (joint space narrowing at 24 months, sustained at 48 months) compared to 58 non-progressors. DNA methylation was quantified via Illumina microarrays and beta- and M-values were used to generate linear classification models. Data were randomly split into a 60% development and 40% validation subsets, models developed and tested, and cross-validated in a total of 40 cycles. M-value based models outperformed beta-value based models (ROC-AUC 0.81 ± 0.01 vs. 0.73 ± 0.02, mean ± SEM, comparison p = 0.002), with a mean classification accuracy of 73 ± 1% (mean ± SEM) for M- and 69 ± 1% for beta-based models. Adjusting for covariates did not significantly alter model performance. Our findings suggest that PBMC DNA methylation-based models may be useful as biomarkers of OA progression and warrant additional evaluation in larger patient cohorts.

Analysis of "old" proteins unmasks dynamic gradient of cartilage turnover in human limbs

Unlike highly regenerative animals, such as axolotls, humans are believed to be unable to counteract cumulative damage, such as repetitive joint use and injury that lead to the breakdown of cartilage and the development of osteoarthritis. Turnover of insoluble collagen has been suggested to be very limited in human adult cartilage. The goal of this study was to explore protein turnover in articular cartilage from human lower limb joints. Analyzing molecular clocks in the form of nonenzymatically deamidated proteins, we unmasked a position-dependent gradient (distal high, proximal low) of protein turnover, indicative of a gradient of tissue anabolism reflecting innate tissue repair capacity in human lower limb cartilages that is associated with expression of limb-regenerative microRNAs. This association shows a potential link to a capacity, albeit limited, for regeneration that might be exploited to enhance joint repair and establish a basis for human limb regeneration.

Analysis of methylation datasets identified significantly changed genes and functional pathways in osteoarthritis

BACKGROUND

Researches indicate that epigenetics was involved in osteoarthritis (OA). The purpose of this study was to describe the alterations of DNA methylation in hip and knee OA by comparing DNA methylome of OA cartilage and non-OA samples and to identify novel genes and pathways associated with OA.

METHODS

We gained two expression profiling datasets (GSE73626 and GSE63695) from the GEO dataset. The RnBeads in R package was used to identify differentially methylated CpG sites. Genes that showed significant differences in DNA methylation between OA and normal control groups underwent functional annotation analysis using the online tool of GeneCodis. Furthermore, we used the Sequenom MassARRAY platform (CapitalBio, Beijing, China) to perform the quantitative methylation analysis.

RESULTS

A total of 249 hypermethylated sites and 96 hypomethylated sites were obtained from OA samples compared with normal control samples. Functional analysis of differentially methylated genes obtained that embryonic skeletal system morphogenesis, cartilage development, and skeletal system development may be involved in the pathogenesis of OA. Eight genes including HOXB3, HOXB4, HOXB6, HOXC4, HOXC10, HOXD3, TBX3, and TBX5 were identified as potential novel biomarkers for OA.

CONCLUSION

Taken together, our study found different molecular characteristics between OA patients and normal controls. This may provide new clues to elucidate the pathogenesis of OA.Key Points• Embryonic skeletal system morphogenesis, cartilage development, skeletal system development may be involved in the pathogenesis of OA.• Eight genes are identified as potential novel markers for OA.• Our future in vivo molecular intervention experiments will extend our current findings.

Assessment of DNA Methylation Patterns in the Bone and Cartilage of a Nonhuman Primate Model of Osteoarthritis

OBJECTIVE

Osteoarthritis (OA) affects humans and several other animals. Thus, the mechanisms underlying this disorder, such as specific skeletal tissue DNA methylation patterns, may be evolutionary conserved. However, associations between methylation and OA have not been readily studied in nonhuman animals. Baboons serve as important models of disease and develop OA at rates similar to those in humans. Therefore, this study investigated the associations between methylation and OA in baboons to advance the evolutionary understanding of OA.

DESIGN

Trabecular bone and cartilage was collected from the medial condyles of adult female baboon femora, 5 with and 5 without knee OA. The Infinium HumanMethylation450 BeadChip (450K array) was used to identify DNA methylation patterns in these tissues.

RESULTS

Approximately 44% of the 450K array probes reliably align to the baboon genome, contain a CpG site of interest, and maintain a wide distribution throughout the genome. Of the 2 filtering methods tested, both identified significantly differentially methylated positions (DMPs) between healthy and OA individuals in cartilage tissues, and some of these patterns overlap with those previously identified in humans. Conversely, no DMPs were found between tissue types or between disease states in bone tissues.

CONCLUSIONS

Overall, the 450K array can be used to measure genome-wide DNA methylation in baboon tissues and identify significant associations with complex traits. The results of this study indicate that some DNA methylation patterns associated with OA are evolutionarily conserved, while others are not. This warrants further investigation in a larger and more phylogenetically diverse sample set.

Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers

Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design.

Role of Epigenomics in Bone and Cartilage Disease

Phenotypic variation in skeletal traits and diseases is the product of genetic and environmental factors. Epigenetic mechanisms include information-containing factors, other than DNA sequence, that cause stable changes in gene expression and are maintained during cell divisions. They represent a link between environmental influences, genome features, and the resulting phenotype. The main epigenetic factors are DNA methylation, posttranslational changes of histones, and higher-order chromatin structure. Sometimes non-coding RNAs, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), are also included in the broad term of epigenetic factors. There is rapidly expanding experimental evidence for a role of epigenetic factors in the differentiation of bone cells and the pathogenesis of skeletal disorders, such as osteoporosis and osteoarthritis. However, different from genetic factors, epigenetic signatures are cell- and tissue-specific and can change with time. Thus, elucidating their role has particular difficulties, especially in human studies. Nevertheless, epigenomewide association studies are beginning to disclose some disease-specific patterns that help to understand skeletal cell biology and may lead to development of new epigenetic-based biomarkers, as well as new drug targets useful for treating diffuse and localized disorders. Here we provide an overview and update of recent advances on the role of epigenomics in bone and cartilage diseases. © 2019 American Society for Bone and Mineral Research.

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

BACKGROUND

Osteoarthritis (OA) is a kind of chronic osteoarthropathy and degenerative joint disease. Epigenetic regulation in the gene expression dynamics has become increasingly important in OA. We performed a combined analysis of two types of microarray datasets (gene expression and DNA methylation) to identify methylation-based key biomarkers to provide a better understanding of molecular biological mechanisms of OA.

METHODS

We obtained two expression profiling datasets (GSE55235, GSE55457) and one DNA methylation profiling data set (GSE63695) from the Gene Expression Omnibus. First, differentially expressed genes (DEGs) between patients with OA and controls were identified using the Limma package in R(v3.4.4). Then, function enrichment analysis of DEGs was performed using a DAVID database. For DNA methylation datasets, ChAMP methylation analysis package was used to identify differential methylation genes (DMGs). Finally, a comprehensive analysis of DEGs and DMGs was conducted to identify genes that exhibited differential expression and methylation simultaneously.

RESULTS

We identified 112 DEGs and 2,896 DMGs in patients with OA compared with controls. Functional analysis of DEGs obtained that inflammatory responses, immune responses, and positive regulation of apoptosis, tumor necrosis factor (TNF) signaling pathway, and osteoclast differentiation may be involved in the pathogenesis of OA. Cross-analysis revealed 26 genes that exhibited differential expression and methylation in OA. Among them, ADAMTS9, FKBP5, and PFKBF3 were identified as valuable methylation-based biomarkers for OA.

CONCLUSION

In summary, our study identified different molecular features between patients with OA and controls. This may provide new clues for clarifying the pathogenetic mechanisms of OA.

Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage

Aim

Osteoarthritis (OA) is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control the expression of genes and are likely to regulate the OA transcriptome. We performed integrative genomic analyses to define methylation-gene expression relationships in osteoarthritic cartilage.

Patients and Methods

Genome-wide DNA methylation profiling of articular cartilage from five patients with OA of the knee and five healthy controls was conducted using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, California). Other independent genome-wide mRNA expression profiles of articular cartilage from three patients with OA and three healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Integrative pathway enrichment analysis of DNA methylation and mRNA expression profiles was performed using integrated analysis of cross-platform microarray and pathway software. Gene ontology (GO) analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID).

Results

We identified 1265 differentially methylated genes, of which 145 are associated with significant changes in gene expression, such as DLX5, NCOR2 and AXIN2 (all p-values of both DNA methylation and mRNA expression < 0.05). Pathway enrichment analysis identified 26 OA-associated pathways, such as mitogen-activated protein kinase (MAPK) signalling pathway (p = 6.25 × 10-4), phosphatidylinositol (PI) signalling system (p = 4.38 × 10-3), hypoxia-inducible factor 1 (HIF-1) signalling pathway (p = 8.63 × 10-3 pantothenate and coenzyme A (CoA) biosynthesis (p = 0.017), ErbB signalling pathway (p = 0.024), inositol phosphate (IP) metabolism (p = 0.025), and calcium signalling pathway (p = 0.032).

Conclusion

We identified a group of genes and biological pathwayswhich were significantly different in both DNA methylation and mRNA expression profiles between patients with OA and controls. These results may provide new clues for clarifying the mechanisms involved in the development of OA.

Cite this article: A. He, Y. Ning, Y. Wen, Y. Cai, K. Xu, Y. Cai, J. Han, L. Liu, Y. Du, X. Liang, P. Li, Q. Fan, J. Hao, X. Wang, X. Guo, T. Ma, F. Zhang. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018;7:343–350. DOI: 10.1302/2046-3758.75.BJR-2017-0284.R1.

Combined bioinformatics analysis reveals gene expression and DNA methylation patterns in osteoarthritis

Osteoarthritis (OA) is a common type of arthritis, which may cause pain and disability. Alterations in gene expression and DNA methylation have been proven to be associated with the development of OA. The aim of the present study was to identify potential therapeutic targets and associated processes for OA via the combined analysis of gene expression and DNA methylation datasets. The gene expression and DNA methylation profiles were obtained from the Gene Expression Omnibus, and differentially expressed genes (DEGs) and differentially methylated sites (DMSs) were identified in the present study, using R programming software. The enriched functions of DEGs and DMSs were obtained via the Database for Annotation, Visualization and Integrated Discovery. Finally, cross analysis of DEGs and DMSs was performed to identify genes that exhibited differential expression and methylation simultaneously. The protein‑protein interaction (PPI) network of overlaps between DEGs and DMSs was obtained using the Human Protein Reference Database; the topological properties of PPI network overlaps were additionally obtained. Hub genes in the PPI network were further confirmed via reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). The results of the present study revealed that the majority of DEGs and DMSs were upregulated and hypomethylated in patients with OA, respectively. DEGs and DMSs were primarily involved in inflammatory, immune and gene expression regulation‑associated processes and pathways. Cross analysis revealed 30 genes that exhibited differential expression and methylation in OA simultaneously. Topological analysis of the PPI network revealed that numerous genes, including G protein subunit α1 (GNAI1), runt related transcription factor 2 (RUNX2) and integrin subunit β2 (ITGB2), may be involved in the development of OA. Additionally, RT‑qPCR analysis of GNAI1, RUNX2 and ITGB2 provided further confirmation. Numerous known and novel therapeutic targets were obtained via network analysis. The results of the present study may be beneficial for the diagnosis and treatment of OA.

DNA Methylation in Osteoarthritis: Current Status and Therapeutic Implications

Background:

Primary Osteoarthritis (OA) is a multifactorial disease in which genetic factors are strongly associated with its development; however, recently it has been observed that epigenetic modifications are also involved in the pathogenesis of OA. DNA methylation is related to gene silencing, and several studies have investigated its role in the loci of different pathways or molecules associated to OA.

Objective:

This review is focused on the current status of DNA methylation studies related to OA pathogenesis.

Method:

A review of the literature was conducted on searching in PUBMED for original papers on DNA methylation in OA.

Conclusion:

The DNA methylation research of loci related to OA pathogenesis has shown a correlation between methylation and gene repression; however, there are some exceptions to this rule. Recently, the development of genome-wide methylation and genome-wide hydroxymethylation profiles has demonstrated that several genes previously associated with OA can have changes in their methylation status, favoring the development of the disease, and these have even shown the role of other epigenetic markers.

The Epigenomic Landscape in Osteoarthritis

PURPOSE OF REVIEW

Epigenomics has emerged as a key player in our rapidly evolving understanding of osteoarthritis. Historical studies implicated epigenetic alterations, particularly DNA methylation, in OA pathogenesis; however, recent technological advances have resulted in numerous epigenome-wide studies examining in detail epigenetic modifications in OA. The purpose of this article is to introduce basic concepts in epigenetics and their recent applications to the study of osteoarthritis development and progression.

RECENT FINDINGS

Epigenetics describes three major phenomena: DNA modification via methylation, histone sidechain modifications, and short noncoding RNA sequences which work in concert to regulate gene transcription in a heritable fashion. Cartilage has been the most widely studied tissue in OA, and differential methylation of genes involved in inflammation, cell cycle, TGFβ, and HOX genes have been confirmed several times. Bone studies suggest similar findings, and the intriguing possibility of epigenetic changes in subchondral bone during many OA processes. Multiple studies have demonstrated the involvement of certain noncoding RNAs, particularly miR-140, in OA development via modulation of key catabolic factors. Although much work has been done, much is still unknown. Future epigenomic studies will no doubt continue to widen our understanding of extraarticular tissues and OA pathogenesis, and studies in animal models may offer glimpses into epigenome alterations in the earliest stages of OA.

Transcriptome comparison of meniscus from patients with and without osteoarthritis

OBJECTIVE

To assess the impact of osteoarthritis (OA) on the meniscus by comparing transcripts and biological processes in the meniscus between patients with and without OA.

DESIGN

RNA microarrays were used to identify transcripts differentially expressed (DE) in meniscus obtained from 12 OA and 12 non-OA patients. The non-OA specimens were obtained at the time of arthroscopic partial meniscectomy. Real-time PCR was performed on selected transcripts. Biological processes and gene-networking was examined computationally. Transcriptome signatures were mapped with 37 OA-related transcripts to evaluate how meniscus gene expression relates to that of OA cartilage.

RESULTS

We identified 168 transcripts significantly DE between OA (75 elevated, 93 repressed) and non-OA samples (≥1.5-fold). Among these, CSN1S1, COL10A1, WIF1, and SPARCL1 were the most prominent transcripts elevated in OA meniscus, POSTN and VEGFA were most highly repressed in OA meniscus. Transcripts elevated in OA meniscus represented response to external stimuli, cell migration and cell localization while those repressed in OA meniscus represented histone deacetylase activity (related to epigenetics) and skeletal development. Numerous long non-coding RNAs (lncRNAs) were DE between the two groups. When segregated by OA-related transcripts, two distinct clustering patterns appeared: OA meniscus appeared to be more inflammatory while non-OA meniscus exhibited a "repair" phenotype.

CONCLUSIONS

Numerous transcripts with potential relevance to the pathogenesis of OA are DE in OA and non-OA meniscus. These data suggest an involvement of epigenetically regulated histone deacetylation in meniscus tears as well as expression of lncRNAs. Patient clustering based on transcripts related to OA in articular cartilage confirmed distinct phenotypes between injured (non-OA) and OA meniscus.

Specific premature epigenetic aging of cartilage in osteoarthritis

Osteoarthritis (OA) is a disease affecting multiple tissues of the joints in the elderly, but most notably articular cartilage. Premature biological aging has been described in this tissue and in blood cells, suggesting a systemic component of premature aging in the pathogenesis of OA. Here, we have explored epigenetic aging in OA at the local (cartilage and bone) and systemic (blood) levels. Two DNA methylation age-measures (DmAM) were used: the multi-tissue age estimator for cartilage and bone; and a blood-specific biomarker for blood. Differences in DmAM between OA patients and controls showed an accelerated aging of 3.7 years in articular cartilage (95% CI = 1.1 to 6.3, P = 0.008) of OA patients. By contrast, no difference in epigenetic aging was observed in bone (0.04 years; 95% CI = -1.8 to 1.9, P = 0.3) and in blood (-0.6 years; 95% CI = -1.5 to 0.3, P = 0.2) between OA patients and controls. Therefore, premature epigenetic aging according to DNA methylation changes was specific of OA cartilage, adding further evidence and insight on premature aging of cartilage as a component of OA pathogenesis that reflects damage and vulnerability.

Genomic approaches to diagnose rare bone disorders

Skeletal dysplasias are Mendelian disorders with a prevalence of approximatively 1 in every 5000 individuals and can usually be diagnosed based on clinical and radiological findings. However, given that some diseases can be caused by several different genes, and that some genes can cause a variety of different phenotypes, achieving a molecular diagnosis can be challenging. We review here different approaches, from single gene sequencing to genomic approaches using next-generation sequencing, to reach a molecular diagnosis for skeletal dysplasias. We will further describe the overall advantages and limitations of first, second and third-generation sequencing, including single gene sequencing, whole-exome and genome sequencing (WES and WGS), multiple gene panel sequencing and single molecule sequencing. We also provide a brief overview of potential future applications of emerging technologies.

Inhibition of angiogenesis by arsenic trioxide via TSP-1-TGF-β1-CTGF-VEGF functional module in rheumatoid arthritis

Angiogenesis is a critical factor for rheumatoid arthritis (RA). Although anti-TNF biologics work effectively on some RA patients, concerns have been raised about the possible increased development of malignancies alongside such treatments. Arsenic trioxide (As₂O₃) has attracted worldwide attention and has been reported to treat some cancers. However, the effects of As₂O₃ on angiogenesis in the RA synovium remain unclear. Here, we report a systematic increased expression of TSP-1, TGF-β1, CTGF and VEGF in supernatants of a RA fibroblast-like synoviocytes (RA-FLS) and human dermal microvascular endothelial cells (HDMECs) co-culture compared with those from a normal human fibroblast-like synoviocytes (NH-FLS) and HDMECs co-culture. This increased expression may up-regulate endothelial tube formation and transwell migration, as well as microvessel sprouting in ex vivo aortic ring assay. These networked angiogenic factors mainly form a functional module regulating angiogenesis in the RA synovium. We show that As₂O₃ inhibits angiogenesis in the collagen-induced arthritis (CIA) synovium and consequently arthritis severity via significant suppression of TSP-1, TGF-β1, CTGF and VEGF expression in the CIA synovium, plus in the RA-FLS and HDMECs co-culture as well as NH-FLS and HDMECs co-culture system along with the presence or absence of TNF-α treatment. Thus As₂O₃ has a significant anti-angiogenesis effect on the RA-FLS and CIA synovium via its inhibition of the RA angiogenic functional module of TSP-1, TGF-β1, CTGF and VEGF and may have a potential for treating RA beyond cancer therapy.

Why location matters - site-specific factors in rheumatic diseases

Rheumatic diseases follow a characteristic anatomical pattern of joint and organ involvement. This Review explores three interconnected mechanisms that might be involved in the predilection of specific joints for developing specific forms of arthritis: site-specific local cell types that drive disease; systemic triggers that affect local cell types; and site-specific exogenous factors, such as focal mechanical stress, that activate cells locally. The embryonic development of limbs and joints is also relevant to the propensity of certain joints to develop arthritis. Additionally, location-specific homeostasis and disease occurs in skin and blood vessels, thereby extending the concept of site-specificity in human diseases beyond rheumatology. Acknowledging the importance of site-specific parameters increases the complexity of current disease paradigms and brings us closer to understanding why particular disease processes manifest at a particular location.

Osteoarthritis year in review 2016: genetics, genomics and epigenetics

The purpose of this narrative review is to provide an overview of last year's publications in the field of genetics, genomics and epigenetics in the osteoarthritis (OA) field. Major themes arising from a Pubmed search on (epi)genetics in OA were identified. In addition, general developments in the fast evolving field of (epi)genetics are reviewed and relevance for the OA field is summarized. In the last 5 years, a number of genome-wide association studies have identified a modest number of genetic loci associated to OA. Continued functional research into these DNA variants is showing putative biological mechanisms underlying these associations. Over the last year, no additional large genome-wide association studies were published, but there clearly remains much to be discovered in the OA genetic field. A lot of research has been done into the epigenetics of OA over the last year. Several genome-wide screens examining the methylome of osteoarthritic cartilage were done. Pathway analysis confirmed deregulation of developmental and extracellular pathways in OA cartilage. Over the last year many microRNAs (miRNAs) have been identified that potentially play important roles in cartilage homeostasis and/or OA process. Continued research will learn whether these identified miRNAs are truly causal and can be used in clinical applications. Many of the epigenetic findings need further confirmation, but they highlight potential novel pathways involved in cartilage biology and OA.

Candidate methylated genes in osteoarthritis explored by bioinformatics analysis

BACKGROUND

This study aimed to explore potential novel genes correlated with osteoarthritis (OA).

METHODS

The gene expression profile of GSE48422 was downloaded from the Gene Expression Omnibus (GEO) database. This dataset included five arthritic cartilage samples and five non-arthritic cartilage samples from five female OA patients. Differentially methylated genes (DMGs) between the two kinds of samples were identified, followed by their functional analysis and protein-protein interaction (PPI) analysis. Furthermore, the Comparative Toxicogenomics Database (CTD) was used to further identify OA-related genes among these DMGs.

RESULTS

In total, 965 hypermethylated genes and 112 hypomethylated genes were identified in the arthritic cartilage samples. The hypermethylated genes (e.g., ADCY4 and ADCY6) were significantly related to the calcium signaling pathway and gonadotropin-releasing hormone signaling pathway, while the hypomethylated genes were implicated in the mammalian target of rapamycin signaling pathway. In the PPI network, several genes had a higher degree, such as ADCY4, ADCY6 and GPR17, and they interacted with each other. Additionally, 565 DMGs were predicted to be associated with OA, and five of them (e.g., COMP and EDIL3) were previously identified as OA markers.

CONCLUSIONS

The methylation of genes ADCY4, ADCY6 and GPR17, as well as the gonadotropin-releasing hormone signaling pathway, was newly found to be potentially associated with OA. They may be novel OA markers.

Analysis of genetics and DNA methylation in osteoarthritis: What have we learnt about the disease?

Osteoarthritis (OA) is a chronic musculoskeletal disease characterised by the destruction of articular cartilage, synovial inflammation and bone remodelling. Disease aetiology is complex and highly heritable, with genetic variation estimated to contribute to 50% of OA occurrence. Epigenetic alterations, including DNA methylation changes, have also been implicated in OA pathophysiology. This review examines what genetic and DNA methylation studies have taught us about the genes and pathways involved in OA pathology. The influence of DNA methylation on the molecular mechanisms underlying OA genetic risk and the consequence of this interaction on disease susceptibility and penetrance are also discussed.