Discovery and analysis of methylation quantitative trait loci (mQTLs) mapping to novel osteoarthritis genetic risk signals

The Genetics and Functional Genomics of Osteoarthritis

Osteoarthritis is the most prevalent whole-joint degenerative disorder, and is characterized by the degradation of articular cartilage and the underlying bone structures. Almost 600 million people are affected by osteoarthritis worldwide. No curative treatments are available, and management strategies focus mostly on pain relief. Here, we provide a comprehensive overview of the available human genetic and functional genomics studies for osteoarthritis to date and delineate how these studies have helped shed light on disease etiopathology. We highlight genetic discoveries from genome-wide association studies and provide a detailed overview of molecular-level investigations in osteoarthritis tissues, including methylation-, transcriptomics-, and proteomics-level analyses. We review how functional genomics data from different molecular levels have helped to prioritize effector genes that can be used as drug targets or drug-repurposing opportunities. Finally, we discuss future directions with the potential to drive a step change in osteoarthritis research.

Evolution and advancements in genomics and epigenomics in OA research: How far we have come

OBJECTIVE

Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput "omic" technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics - including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease.

DESIGN

In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients.

RESULTS

Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease.

CONCLUSIONS

Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.

Osteoarthritis as an Enhanceropathy: Gene Regulation in Complex Musculoskeletal Disease

PURPOSE OF REVIEW

Osteoarthritis is a complex and highly polygenic disease. Over 100 reported osteoarthritis risk variants fall in non-coding regions of the genome, ostensibly conferring functional effects through the disruption of regulatory elements impacting target gene expression. In this review, we summarise the progress that has advanced our knowledge of gene enhancers both within the field of osteoarthritis and more broadly in complex diseases.

RECENT FINDINGS

Advances in technologies such as ATAC-seq have facilitated our understanding of chromatin states in specific cell types, bolstering the interpretation of GWAS and the identification of effector genes. Their application to osteoarthritis research has revealed enhancers as the principal regulatory element driving disease-associated changes in gene expression. However, tissue-specific effects in gene regulatory mechanisms can contribute added complexity to biological interpretation. Understanding gene enhancers and their altered activity in specific cell and tissue types is the key to unlocking the genetic complexity of osteoarthritis. The use of single-cell technologies in osteoarthritis research is still in its infancy. However, such tools offer great promise in improving our functional interpretation of osteoarthritis GWAS and the identification of druggable targets. Large-scale collaborative efforts will be imperative to understand tissue and cell-type specific molecular mechanisms underlying enhancer function in disease.

Epigenetic mechanisms of osteoarthritis risk in human skeletal development

The epigenome, including the methylation of cytosine bases at CG dinucleotides, is intrinsically linked to transcriptional regulation. The tight regulation of gene expression during skeletal development is essential, with ~1/500 individuals born with skeletal abnormalities. Furthermore, increasing evidence is emerging to link age-associated complex genetic musculoskeletal diseases, including osteoarthritis (OA), to developmental factors including joint shape. Multiple studies have shown a functional role for DNA methylation in the genetic mechanisms of OA risk using articular cartilage samples taken from aged patients. Despite this, our knowledge of temporal changes to the methylome during human cartilage development has been limited. We quantified DNA methylation at ~700,000 individual CpGs across the epigenome of developing human articular cartilage in 72 samples ranging from 7-21 post-conception weeks, a time period that includes cavitation of the developing knee joint. We identified significant changes in 8% of all CpGs, and >9400 developmental differentially methylated regions (dDMRs). The largest hypermethylated dDMRs mapped to transcriptional regulators of early skeletal patterning including MEIS1 and IRX1. Conversely, the largest hypomethylated dDMRs mapped to genes encoding extracellular matrix proteins including SPON2 and TNXB and were enriched in chondrocyte enhancers. Significant correlations were identified between the expression of these genes and methylation within the hypomethylated dDMRs. We further identified 811 CpGs at which significant dimorphism was present between the male and female samples, with the majority (68%) being hypermethylated in female samples. Following imputation, we captured the genotype of these samples at >5 million variants and performed epigenome-wide methylation quantitative trait locus (mQTL) analysis. Colocalization analysis identified 26 loci at which genetic variants exhibited shared impacts upon methylation and OA genetic risk. This included loci which have been previously reported to harbour OA-mQTLs (including GDF5 and ALDH1A2), yet the majority (73%) were novel (including those mapping to CHST3, FGF1 and TEAD1). To our knowledge, this is the first extensive study of DNA methylation across human articular cartilage development. We identify considerable methylomic plasticity within the development of knee cartilage and report active epigenomic mediators of OA risk operating in prenatal joint tissues.

Specific isoforms of the ubiquitin ligase gene WWP2 are targets of osteoarthritis genetic risk via a differentially methylated DNA sequence

BACKGROUND

Transitioning from a genetic association signal to an effector gene and a targetable molecular mechanism requires the application of functional fine-mapping tools such as reporter assays and genome editing. In this report, we undertook such studies on the osteoarthritis (OA) risk that is marked by single nucleotide polymorphism (SNP) rs34195470 (A > G). The OA risk-conferring G allele of this SNP associates with increased DNA methylation (DNAm) at two CpG dinucleotides within WWP2. This gene encodes a ubiquitin ligase and is the host gene of microRNA-140 (miR-140). WWP2 and miR-140 are both regulators of TGFβ signaling.

METHODS

Nucleic acids were extracted from adult OA (arthroplasty) and foetal cartilage. Samples were genotyped and DNAm quantified by pyrosequencing at the two CpGs plus 14 flanking CpGs. CpGs were tested for transcriptional regulatory effects using a chondrocyte cell line and reporter gene assay. DNAm was altered using epigenetic editing, with the impact on gene expression determined using RT-qPCR. In silico analysis complemented laboratory experiments.

RESULTS

rs34195470 genotype associates with differential methylation at 14 of the 16 CpGs in OA cartilage, forming a methylation quantitative trait locus (mQTL). The mQTL is less pronounced in foetal cartilage (5/16 CpGs). The reporter assay revealed that the CpGs reside within a transcriptional regulator. Epigenetic editing to increase their DNAm resulted in altered expression of the full-length and N-terminal transcript isoforms of WWP2. No changes in expression were observed for the C-terminal isoform of WWP2 or for miR-140.

CONCLUSIONS

As far as we are aware, this is the first experimental demonstration of an OA association signal targeting specific transcript isoforms of a gene. The WWP2 isoforms encode proteins with varying substrate specificities for the components of the TGFβ signaling pathway. Future analysis should focus on the substrates regulated by the two WWP2 isoforms that are the targets of this genetic risk.

E3 ubiquitin ligase WWP2 as a promising therapeutic target for diverse human diseases

Mammalian E3 ubiquitin ligases have emerged in recent years as critical regulators of cellular homeostasis due to their roles in targeting substrate proteins for ubiquitination and triggering subsequent downstream signals. In this review, we describe the multiple roles of WWP2, an E3 ubiquitin ligase with unique and important functions in regulating a wide range of biological processes, including DNA repair, gene expression, signal transduction, and cell-fate decisions. As such, WWP2 has evolved to play a key role in normal physiology and diseases, such as tumorigenesis, skeletal development and diseases, immune regulation, cardiovascular disease, and others. We attempt to provide an overview of the biochemical, physiological, and pathophysiological roles of WWP2, as well as open questions for future research, particularly in the context of putative therapeutic opportunities.

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

BACKGROUND

Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course.

METHODS

We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data.

RESULTS

We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922).

CONCLUSIONS

The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.

The identification of distinct protective and susceptibility mechanisms for hip osteoarthritis: findings from a genome-wide association study meta-analysis of minimum joint space width and Mendelian randomisation cluster analyses

BACKGROUND

Hip minimum joint space width (mJSW) provides a proxy for cartilage thickness. This study aimed to conduct a genome-wide association study (GWAS) of mJSW to (i) identify new genetic determinants of mJSW and (ii) identify which mJSW loci convey hip osteoarthritis (HOA) risk and would therefore be of therapeutic interest.

METHODS

GWAS meta-analysis of hip mJSW derived from plain X-rays and DXA was performed, stratified by sex and adjusted for age and ancestry principal components. Mendelian randomisation (MR) and cluster analyses were used to examine causal effect of mJSW on HOA.

FINDINGS

50,745 individuals were included in the meta-analysis. 42 SNPs, which mapped to 39 loci, were identified. Mendelian randomisation (MR) revealed little evidence of a causal effect of mJSW on HOA (ORIVW 0.98 [95% CI 0.82-1.18]). However, MR-Clust analysis suggested the null MR estimates reflected the net effect of two distinct causal mechanisms cancelling each other out, one of which was protective, whereas the other increased HOA susceptibility. For the latter mechanism, all loci were positively associated with height, suggesting mechanisms leading to greater height and mJSW increase the risk of HOA in later life.

INTERPRETATIONS

One group of mJSW loci reduce HOA risk via increased mJSW, suggesting possible utility as targets for chondroprotective therapies. The second group of mJSW loci increased HOA risk, despite increasing mJSW, but were also positively related to height, suggesting they contribute to HOA risk via a growth-related mechanism.

FUNDING

Primarily funded by the Medical Research Council and Wellcome Trust.

Elucidating the role of ubiquitination and deubiquitination in osteoarthritis progression

Osteoarthritis is non-inflammatory degenerative joint arthritis, which exacerbates disability in elder persons. The molecular mechanisms of osteoarthritis are elusive. Ubiquitination, one type of post-translational modifications, has been demonstrated to accelerate or ameliorate the development and progression of osteoarthritis via targeting specific proteins for ubiquitination and determining protein stability and localization. Ubiquitination process can be reversed by a class of deubiquitinases via deubiquitination. In this review, we summarize the current knowledge regarding the multifaceted role of E3 ubiquitin ligases in the pathogenesis of osteoarthritis. We also describe the molecular insight of deubiquitinases into osteoarthritis processes. Moreover, we highlight the multiple compounds that target E3 ubiquitin ligases or deubiquitinases to influence osteoarthritis progression. We discuss the challenge and future perspectives via modulation of E3 ubiquitin ligases and deubiquitinases expression for enhancement of the therapeutic efficacy in osteoarthritis patients. We conclude that modulating ubiquitination and deubiquitination could alleviate the osteoarthritis pathogenesis to achieve the better treatment outcomes in osteoarthritis patients.

Mediation of the Same Epigenetic and Transcriptional Effect by Independent Osteoarthritis Risk-Conferring Alleles on a Shared Target Gene, COLGALT2

OBJECTIVE

Over 100 DNA variants have been associated with osteoarthritis (OA), including rs1046934, located within a linkage disequilibrium block encompassing part of COLGALT2 and TSEN15. The present study was undertaken to determine the target gene(s) and the mechanism of action of the OA locus using human fetal cartilage, cartilage from OA and femoral neck fracture arthroplasty patients, and a chondrocyte cell model.

METHODS

Genotyping and methylation array data of DNA from human OA cartilage samples (n = 87) were used to determine whether the rs1046934 genotype is associated with differential DNA methylation at proximal CpGs. Results were replicated in DNA from human arthroplasty (n = 132) and fetal (n = 77) cartilage samples using pyrosequencing. Allelic expression imbalance (AEI) measured the effects of genotype on COLGALT2 and TSEN15 expression. Reporter gene assays and epigenetic editing determined the functional role of regions harboring differentially methylated CpGs. In silico analyses complemented these experiments.

RESULTS

Three differentially methylated CpGs residing within regulatory regions were detected in the human OA cartilage array data, and 2 of these were replicated in human arthroplasty and fetal cartilage. AEI was detected for COLGALT2 and TSEN15, with associations between expression and methylation for COLGALT2. Reporter gene assays confirmed that the CpGs are in chondrocyte enhancers, with epigenetic editing results directly linking methylation with COLGALT2 expression.

CONCLUSION

COLGALT2 is a target of this OA locus. We previously characterized another OA locus, marked by rs11583641, that independently targets COLGALT2. The genotype of rs1046934, like rs11583641, mediates its effect by modulating expression of COLGALT2 via methylation changes to CpGs located in enhancers. Although the single-nucleotide polymorphisms, CpGs, and enhancers are distinct between the 2 independent OA risk loci, their effect on COLGALT2 is the same. COLGALT2 is the target of independent OA risk loci sharing a common mechanism of action.

Epigenetics as a mediator of genetic risk in osteoarthritis: role during development, homeostasis, aging, and disease progression

The identification of genomic loci that are associated with osteoarthritis (OA) has provided a starting point for understanding how genetic variation activates catabolic processes in the joint. However, genetic variants can only alter gene expression and cellular function when the epigenetic environment is permissive to these effects. In this review, we provide examples of how epigenetic shifts at distinct life stages can alter the risk for OA, which we posit is critical for the proper interpretation of genome-wide association studies (GWAS). During development, intensive work on the growth and differentiation factor 5 (GDF5) locus has revealed the importance of tissue-specific enhancer activity in controlling both joint development and the subsequent risk for OA. During homeostasis in adults, underlying genetic risk factors may help establish beneficial or catabolic "set points" that dictate tissue function, with a strong cumulative effect on OA risk. During aging, methylation changes and the reorganization of chromatin can "unmask" the effects of genetic variants. The destructive function of variants that alter aging would only mediate effects after reproductive competence and thus avoid any evolutionary selection pressure, as consistent with larger frameworks of biological aging and its relationship to disease. A similar "unmasking" may occur during OA progression, which is supported by the finding of distinct expression quantitative trait loci (eQTLs) in chondrocytes depending on the degree of tissue degradation. Finally, we propose that massively parallel reporter assays (MPRAs) will be a valuable tool to test the function of putative OA GWAS variants in chondrocytes from different life stages.

Osteoarthritis year in review: genetics, genomics and epigenetics

This "year in review" provides a summary of the research findings on the topic of genetics, genomics and epigenetics for osteoarthritis (OA) between Mar 2021-Apr 2022. A search routine of the literature in PubMed for the keyword, osteoarthritis, together with topics on genetics, genomics, epigenetics, polymorphism, DNA methylation, noncoding RNA, lncRNA, proteomics, and single cell RNA sequencing, returned key research articles and relevant reviews. Following filtering of duplicates across search routines, 695 unique research articles and 112 reviews were identified. We manually curated these articles and selected 90 as references for this review. However, we were unable to refer to all these articles, and only used selected articles to highlight key outcomes and trends. The trend in genetics is on the meta-analysis of existing cohorts with comparable genetic and phenotype characterisation of OA; in particular, clear definition of endophenotypes to enhance the genetic power. Further, many researchers are realizing the power of big data and multi-omics approaches to gain molecular insights for OA, and this has opened innovative approaches to include transcriptomics and epigenetics data as quantitative trait loci (QTLs). Given that most of the genetic loci for OA are not located within coding regions of genes, implying the impact is likely to be on gene regulation, epigenetics is a hot topic, and there is a surge in studies relating to the role of miRNA and long non-coding RNA on cartilage biology and pathology. The findings are exciting and new insights are provided in this review to summarize a year of research and the road map to capture all new innovations to achieve the desired goal in OA prevention and treatment.

Intergenic Interactions of SBNO1, NFAT5 and GLT8D1 Determine the Susceptibility to Knee Osteoarthritis among Europeans of Russia

This study was conducted to examine the associations between genome-wide association studies (GWAS)-important single nucleotide polymorphisms (SNPs) and knee osteoarthritis (KOA) among Europeans of Russia. The present replicative study ("patient-control" design has been used) was carried out on 1000 DNA samples from KOA (n = 500) and KOA-free (n = 500) participants. Ten GWAS-important for KOA SNPs of eight candidate genes (LYPLAL1, GNL3, GLT8D1, SBNO1, WWP2, NFAT5, TGFA, GDF5) were studied. To assess the link between SNPs and KOA susceptibility, logistic regression (to establish independent SNP effects) and MB-MDR (to identify SNP-SNP interactions) were used. As a result of this genetic analysis, the associations of individual SNPs with KOA have not been proven. Eight loci out of ten tested SNPs interacted with each other (within twelve genetic models) and determined susceptibility to KOA. The greatest contribution to the disease development were made by three polymorphisms/genes such as rs6976 (C>T) GLT8D1, rs56116847 (G>A) SBNO1, rs6499244 (T>A) NFAT5 (each was included in 2/3 [8 out 12] KOA-responsible genetic interaction models). A two-locus epistatic interaction of rs56116847 (G >A) SBNO1 × rs6499244 (T>A) NFAT5 determined the maximum percentage (0.86%) of KOA entropy. KOA-associated SNPs are regulatory polymorphisms that affect the expression/splicing level, epigenetic modification of 72 genes in KOA-pathogenetically significant organs such as skeletal muscles, tibial arteries/nerves, thyroid, adipose tissue, etc. These putative KOA-effector genes are mainly involved in the organization/activity of the exoribonuclease complex and antigen processing/presentation pathways. In conclusion, KOA susceptibility among Europeans of Russia is mediated by intergenic interactions (but not the main effects) of GWAS-important SNPs.

WWP2 confers risk to osteoarthritis by affecting cartilage matrix deposition via hypoxia associated genes

OBJECTIVE

To explore the co-expression network of the osteoarthritis (OA) risk gene WWP2 in articular cartilage and study cartilage characteristics when mimicking the effect of OA risk allele rs1052429-A on WWP2 expression in a human 3D in vitro model of cartilage.

METHOD

Co-expression behavior of WWP2 with genes expressed in lesioned OA articular cartilage (N = 35 samples) was explored. By applying lentiviral particle mediated WWP2 upregulation in 3D in vitro pellet cultures of human primary chondrocytes (N = 8 donors) the effects of upregulation on cartilage matrix deposition was evaluated. Finally, we transfected primary chondrocytes with miR-140 mimics to evaluate whether miR-140 and WWP2 are involved in similar pathways.

RESULTS

Upon performing Spearman correlations in lesioned OA cartilage, 98 highly correlating genes (|ρ| > 0.7) were identified. Among these genes, we identified GJA1, GDF10, STC2, WDR1, and WNK4. Subsequent upregulation of WWP2 on 3D chondrocyte pellet cultures resulted in a decreased expression of COL2A1 and ACAN and an increase in EPAS1 expression. Additionally, we observed a decreased expression of GDF10, STC2, and GJA1. Proteomics analysis identified 42 proteins being differentially expressed with WWP2 upregulation, which were enriched for ubiquitin conjugating enzyme activity. Finally, upregulation of miR-140 in 2D chondrocytes resulted in significant upregulation of WWP2 and WDR1.

CONCLUSIONS

Mimicking the effect of OA risk allele rs1052429-A on WWP2 expression initiates detrimental processes in the cartilage shown by a response in hypoxia associated genes EPAS1, GDF10, and GJA1 and a decrease in anabolic markers, COL2A1 and ACAN.

Insights from multi-omics integration in complex disease primary tissues

Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer's disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations.

Allelic expression imbalance in articular cartilage and subchondral bone refined genome-wide association signals in osteoarthritis

OBJECTIVES

To present an unbiased approach to identify positional transcript single nucleotide polymorphisms (SNPs) of osteoarthritis (OA) risk loci by allelic expression imbalance (AEI) analyses using RNA sequencing of articular cartilage and subchondral bone from OA patients.

METHODS

RNA sequencing from 65 articular cartilage and 24 subchondral bone from OA patients was used for AEI analysis. AEI was determined for all genes present in the 100 regions reported by the GWAS catalog that were also expressed in cartilage or bone. The count fraction of the alternative allele (φ) was calculated for each heterozygous individual with the risk-SNP or with the SNP in linkage disequilibrium (LD) with it (r2 > 0.6). Furthermore, a meta-analysis was performed to generate a meta-φ (null hypothesis median φ = 0.49) and P-value for each SNP.

RESULTS

We identified 30 transcript SNPs (28 in cartilage and 2 in subchondral bone) subject to AEI in 29 genes. Notably, 10 transcript SNPs were located in genes not previously reported in the GWAS catalog, including two long intergenic non-coding RNAs (lincRNAs), MALAT1 (meta-φ = 0.54, FDR = 1.7x10-4) and ILF3-DT (meta-φ = 0.6, FDR = 1.75x10-5). Moreover, 12 drugs were interacting with 7 genes displaying AEI, of which 7 drugs have been already approved.

CONCLUSIONS

By prioritizing proxy transcript SNPs that mark AEI in cartilage and/or subchondral bone at loci harboring GWAS signals, we present an unbiased approach to identify the most likely functional OA risk-SNP and gene. We identified 10 new potential OA risk genes ready for further, translation towards underlying biological mechanisms.

Identification of TMEM129, encoding a ubiquitin-protein ligase, as an effector gene of osteoarthritis genetic risk

BACKGROUND

Osteoarthritis is highly heritable and genome-wide studies have identified single nucleotide polymorphisms (SNPs) associated with the disease. One such locus is marked by SNP rs11732213 (T > C). Genotype at rs11732213 correlates with the methylation levels of nearby CpG dinucleotides (CpGs), forming a methylation quantitative trait locus (mQTL). This study investigated the regulatory activity of the CpGs to identify a target gene of the locus.

METHODS

Nucleic acids were extracted from the articular cartilage of osteoarthritis patients. Samples were genotyped, and DNA methylation was quantified by pyrosequencing at 14 CpGs within a 259-bp interval. CpGs were tested for enhancer effects in immortalised chondrocytes using a reporter gene assay. DNA methylation at the locus was altered using targeted epigenome editing, with the impact on gene expression determined using quantitative polymerase chain reaction.

RESULTS

rs11732213 genotype correlated with DNA methylation at nine CpGs, which formed a differentially methylated region (DMR), with the osteoarthritis risk allele T corresponding to reduced levels of methylation. The DMR acted as an enhancer and demethylation of the CpGs altered expression of TMEM129. Allelic imbalance in TMEM129 expression was identified in cartilage, with under-expression of the risk allele.

CONCLUSIONS

TMEM129 is a target of osteoarthritis genetic risk at this locus. Genotype at rs11732213 impacts DNA methylation at the enhancer, which, in turn, modulates TMEM129 expression. TMEM129 encodes an enzyme involved in protein degradation within the endoplasmic reticulum, a process previously implicated in osteoarthritis. TMEM129 is a compelling osteoarthritis susceptibility target.

Lycium barbarum polysaccharide modulates gut microbiota to alleviate rheumatoid arthritis in a rat model

Rheumatoid arthritis (RA) seriously impairs the quality of life of sufferers. It has been shown that Lycium barbarum polysaccharide (LBP), a natural active indigestible ingredient with medicinal and edible functions, can effectively relieve RA, however, whether this effect is related to gut microbiota is not known. This study aimed to explore the RA alleviating mechanism of LBP mediated by gut microbiota using a collagen-induced arthritis rat model. The results showed that LBP significantly changed the gut microflora structure accompanied with the RA alleviation. Specifically, a LBP intervention reduced the relative abundance of Lachnospiraceae_NK4A136_group and uncultured_bacterium_f_Ruminococcaceae and significantly increased the abundance of Romboutsia, Lactobacillus, Dubosiella and Faecalibaculum. The mRNA contents of several colonic epithelial genes including Dpep3, Gstm6, Slc27a2, Col11a2, Sycp2, SNORA22, Tnni1, Gpnmb, Mypn and Acsl6, which are potentially associated to RA, were down-regulated due to the DNA hypermethylation, possibly caused by the elevating content of a bacterial metabolite S-adenosyl methionine (SAM). In conclusion, our current study suggests that LBP alleviated RA by reshaping the composition of intestinal microflora which may generate SAM, inducing DNA hypermethylation of RA-related genes in the host intestinal epithelium and subsequently reducing their expression.

Epigenetic Regulation in Knee Osteoarthritis

Osteoarthritis (OA) is a complicated disease with both hereditary and environmental causes. Despite an increase in reports of possible OA risk loci, it has become clear that genetics is not the sole cause of osteoarthritis. Epigenetics, which can be triggered by environmental influences and result in transcriptional alterations, may have a role in OA pathogenesis. The majority of recent research on the epigenetics of OA has been focused on DNA methylation, histone modification, and non-coding RNAs. However, this study will explore epigenetic regulation in OA at the present stage. How genetics, environmental variables, and epigenetics interact will be researched, shedding light for future studies. Their possible interaction and control processes open up new avenues for the development of innovative osteoarthritis treatment and diagnostic techniques.

An epigenome-wide view of osteoarthritis in primary tissues

Osteoarthritis is a complex degenerative joint disease. Here, we investigate matched genotype and methylation profiles of primary chondrocytes from macroscopically intact (low-grade) and degraded (high-grade) osteoarthritis cartilage and from synoviocytes collected from 98 osteoarthritis-affected individuals undergoing knee replacement surgery. We perform an epigenome-wide association study of knee cartilage degeneration and report robustly replicating methylation markers, which reveal an etiologic mechanism linked to the migration of epithelial cells. Using machine learning, we derive methylation models of cartilage degeneration, which we validate with 82% accuracy in independent data. We report a genome-wide methylation quantitative trait locus (mQTL) map of articular cartilage and synovium and identify 18 disease-grade-specific mQTLs in osteoarthritis cartilage. We resolve osteoarthritis GWAS loci through causal inference and colocalization analyses and decipher the epigenetic mechanisms that mediate the effect of genotype on disease risk. Together, our findings provide enhanced insights into epigenetic mechanisms underlying osteoarthritis in primary tissues.

Limb Mesoderm and Head Ectomesenchyme Both Express a Core Transcriptional Program During Chondrocyte Differentiation

To explain how cartilage appeared in different parts of the vertebrate body at discrete times during evolution, we hypothesize that different embryonic populations co-opted expression of a core gene regulatory network (GRN) driving chondrocyte differentiation. To test this hypothesis, laser-capture microdissection coupled with RNA-seq was used to reveal chondrocyte transcriptomes in the developing chick humerus and ceratobranchial, which are mesoderm- and neural crest-derived, respectively. During endochondral ossification, two general types of chondrocytes differentiate. Immature chondrocytes (IMM) represent the early stages of cartilage differentiation, while mature chondrocytes (MAT) undergo additional stages of differentiation, including hypertrophy and stimulating matrix mineralization and degradation. Venn diagram analyses generally revealed a high degree of conservation between chondrocyte transcriptomes of the limb and head, including SOX9, COL2A1, and ACAN expression. Typical maturation genes, such as COL10A1, IBSP, and SPP1, were upregulated in MAT compared to IMM in both limb and head chondrocytes. Gene co-expression network (GCN) analyses of limb and head chondrocyte transcriptomes estimated the core GRN governing cartilage differentiation. Two discrete portions of the GCN contained genes that were differentially expressed in limb or head chondrocytes, but these genes were enriched for biological processes related to limb/forelimb morphogenesis or neural crest-dependent processes, respectively, perhaps simply reflecting the embryonic origin of the cells. A core GRN driving cartilage differentiation in limb and head was revealed that included typical chondrocyte differentiation and maturation markers, as well as putative novel "chondrocyte" genes. Conservation of a core transcriptional program during chondrocyte differentiation in both the limb and head suggest that the same core GRN was co-opted when cartilage appeared in different regions of the skeleton during vertebrate evolution.

Genetics of osteoarthritis

Osteoarthritis genetics has been transformed in the past decade through the application of large-scale genome-wide association scans. So far, over 100 polymorphic DNA variants have been associated with this common and complex disease. These genetic risk variants account for over 20% of osteoarthritis heritability and the vast majority map to non-protein coding regions of the genome where they are presumed to act by regulating the expression of target genes. Statistical fine mapping, in silico analyses of genomics data, and laboratory-based functional studies have enabled the identification of some of these targets, which encode proteins with diverse roles, including extracellular signaling molecules, intracellular enzymes, transcription factors, and cytoskeletal proteins. A large number of the risk variants correlate with epigenetic factors, in particular cartilage DNA methylation changes in cis, implying that epigenetics may be a conduit through which genetic effects on gene expression are mediated. Some of the variants also appear to have been selected as humans adapted to bipedalism, suggesting that a proportion of osteoarthritis genetic susceptibility results from antagonistic pleiotropy, with risk variants having a positive role in joint formation but a negative role in the long-term health of the joint. Although data from an osteoarthritis genetic study has not yet directly led to a novel treatment, some of the osteoarthritis associated genes code for proteins that have available therapeutics. Genetic investigations are therefore revealing fascinating fundamental insights into osteoarthritis and can expose options for translational intervention.

Insight into the potential pathogenesis of human osteoarthritis via single-cell RNA sequencing data on osteoblasts

Osteoarthritis (OA) is the most common degenerative joint disease caused by osteoblastic lineage cells. However, a comprehensive molecular program for osteoblasts in human OA remains underdeveloped. The single-cell gene expression of osteoblasts and microRNA array data were from human. After processing the single-cell RNA sequencing (scRNA-seq) data, it was subjected to principal component analysis (PCA) and T-Stochastic neighbor embedding analysis (TSNE). Differential expression analysis was aimed to find marker genes. Gene-ontology (GO) enrichment, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis and Gene set enrichment analysis (GSEA) were applied to characterize the molecular function of osteoblasts with marker genes. Protein-protein interaction (PPI) networks and core module were established for marker genes by using the STRING database and Cytoscape software. All nodes in the core module were considered to be hub genes. Subsequently, we predicted the potential miRNA of hub genes through the miRWalk, miRDB and TargetScan database and experimentally verified the miRNA by GSE105027. Finally, miRNA-mRNA regulatory network was constructed using the Cytoscape software. We characterized the single-cell expression profiling of 4387 osteoblasts from normal and OA sample. The proportion of osteoblasts subpopulations changed dramatically in the OA, with 70.42% of the pre-osteoblasts. 117 marker genes were included and the results of GO analysis show that up-regulated marker genes enriched in collagen-containing extracellular matrix were highly expressed in the pre-osteoblasts cluster. Both KEGG and GSEA analyses results indicated that IL-17 and NOD-like receptor signaling pathways were enriched in down-regulated marker genes. We visualize the weight of marker genes and constructed the core module in PPI network. In potential mRNA-miRNA regulatory network, hsa-miR-449a and hsa-miR-218-5p may be involved in the development of OA. Our study found that alterations in osteoblasts state and cellular molecular function in the subchondral bone region may be involved in the pathogenesis of osteoarthritis.

Associations between adipokines gene polymorphisms and knee osteoarthritis: a meta-analysis

Background

Adipokines gene polymorphisms are speculated to be associated with the risk of knee osteoarthritis (OA), but evidence remains conflicting. This study therefore aimed to examine whether associations exist between adipokines gene polymorphisms and knee OA by considering the evidence collected from eligible studies through a meta-analysis.

Methods

A systematic search was performed on PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang up to March 31, 2020. Meta-analysis was carried out by focusing on the associations between adipokines gene polymorphisms and knee OA with the allele model, dominant model, and recessive model.

Results

The present meta-analysis included 5 eligible studies for ADIPOQ rs1501299 with 1,021 cases and 1,097 controls, 3 eligible studies for ADIPOQ rs2241766 with 549 cases and 544 controls, 3 eligible studies for LEPR rs1137101 with 808 cases and 856 controls, 2 eligible studies for VISFATIN rs4730153 with 339 cases and 680 controls and 2 eligible studies for VISFATIN rs16872158 with 339 cases and 680 controls. Significant association was observed between LEPR rs1137101 and knee OA in the overall population (recessive: OR = 0.40, 95% CI 0.21–0.79). Limited data revealed that associations may exist between ADIPOQ rs2241766 and knee OA in Asians (dominant: OR = 1.35, 95% CI 1.03–1.78), between VISFATIN rs4730153 and knee OA in Asians (allele: OR = 0.58, 95% CI 0.41–0.83; dominant: OR = 0.57, 95% CI 0.39–0.83), and between VISFATIN rs16872158 and knee OA in Asians (allele: OR = 1.84, 95% CI 1.26–2.68; dominant: OR = 1.94, 95% CI 1.31–2.89).

Conclusions

Adipokines gene polymorphisms may be associated with knee OA. The association was observed in LEPR rs1137101 in the present study. In addition, limited data revealed that associations may also exist in ADIPOQ rs2241766, VISFATIN rs4730153 and VISFATIN rs16872158.

Prospero registration

CRD42020187664.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05111-4.

Genetic biomarkers in osteoarthritis: a quick overview

Osteoarthritis (OA) is a chronic musculoskeletal disease with a polygenic and heterogeneous nature. In addition, when clinical manifestations appear, the evolution of the disease is usually already irreversible. Therefore, the efforts on OA research are focused mainly on the discovery of therapeutic targets and reliable biomarkers that permit the early identification of different OA-related parameters such as diagnosis, prognosis, or phenotype identification. To date, potential candidate protein biomarkers have been associated with different aspects of the disease; however, there is currently no gold standard. In this sense, genomic data could act as complementary biomarkers of diagnosis and prognosis or even help to identify therapeutic targets of the disease. In this review, we will describe the most recent advances in genetic biomarkers in OA over the past three years.

Transcriptomic and epigenomic analyses uncovered Lrrc15 as a contributing factor to cartilage damage in osteoarthritis

In osteoarthritis (OA), articular chondrocytes display phenotypic and functional changes associated with epigenomic alterations. These changes contribute to the disease progression, which is characterized by dysregulated reparative processes and abnormal extracellular matrix remodeling leading to cartilage degradation. Recent studies using a murine model of posttraumatic OA highlighted the contribution of changes in DNA hydroxymethylation (5hmC) to OA progression. Here, we integrated transcriptomic and epigenomic analyses in cartilage after induction of OA to show that the structural progression of OA is accompanied by early transcriptomic and pronounced DNA methylation (5mC) changes in chondrocytes. These changes accumulate over time and are associated with recapitulation of developmental processes, including cartilage development, chondrocyte hypertrophy, and ossification. Our integrative analyses also uncovered that Lrrc15 is differentially methylated and expressed in OA cartilage, and that it may contribute to the functional and phenotypic alterations of chondrocytes, likely coordinating stress responses and dysregulated extracellular matrix remodeling.

Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

Background

Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects.

Results

Here, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements.

Conclusions

Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-021-00415-6.

Genetic and Epigenetic Interplay Within a COLGALT2 Enhancer Associated With Osteoarthritis

OBJECTIVE

The osteoarthritis (OA)-associated single-nucleotide polymorphism (SNP) rs11583641 is located in COLGALT2, encoding a posttranslational modifier of collagen. In cartilage, the SNP genotype correlates with DNA methylation in a putative enhancer. This study was undertaken to characterize the mechanistic relationship between rs11583641, the putative enhancer, and COLGALT2 expression using cartilage samples from human patients and a chondrocyte cell model.

METHODS

Nucleic acids were extracted from articular cartilage samples obtained from patients with OA (n = 137). Samples were genotyped, and DNA methylation was quantified at 12 CpGs using pyrosequencing. The putative enhancer was deleted in Tc28a2 chondrocytes using clustered regularly interspaced short palindromic repeat/Cas9, and the impact on nearby gene expression was determined using real-time quantitative polymerase chain reaction. Targeted modulation of the epigenome using catalytically dead Cas9 (dCas9) constructs fused to DNA methyltransferase 3a or ten-eleven translocase 1 allowed for the investigation of a causal relationship between DNA methylation and enhancer activity.

RESULTS

The genotype at rs11583641 correlated with DNA methylation at 3 CpGs, and the presence of the OA risk allele, C, corresponded to reduced levels of methylation. Deletion of the enhancer resulted in a 2.7-fold reduction in COLGALT2 expression. Targeted methylation and demethylation of the CpGs had antagonistic effects on COLGALT2 expression. An allelic imbalance in the expression of COLGALT2 was identified in the cartilage from patients with OA, with relative overexpression of the OA risk allele. Allelic expression ratios correlated with DNA methylation at 4 CpGs.

CONCLUSION

COLGALT2 is a target of OA genetic risk at this locus. The genotype at rs11583641 impacts DNA methylation in a gene enhancer, which, in turn, modulates COLGALT2 expression. COLGALT2 encodes an enzyme that initiates posttranslational glycosylation of collagens and is therefore a compelling OA susceptibility target.

Genetic and Epigenetic Fine-Tuning of TGFB1 Expression Within the Human Osteoarthritic Joint

OBJECTIVE

Osteoarthritis (OA) is an age-related disease characterized by articular cartilage degeneration. It is largely heritable, and genetic screening has identified single-nucleotide polymorphisms (SNPs) marking genomic risk loci. One such locus is marked by the G>A SNP rs75621460, downstream of TGFB1. This gene encodes transforming growth factor β1, the correct expression of which is essential for cartilage maintenance. This study investigated the regulatory activity of rs75621460 to characterize its impact on TGFB1 expression in disease-relevant patient samples (n = 319) and in Tc28a2 immortalized chondrocytes.

METHODS

Articular cartilage samples from human patients were genotyped, and DNA methylation levels were quantified using pyrosequencing. Gene reporter and electrophoretic mobility shift assays were used to determine differential nuclear protein binding to the region. The functional impact of DNA methylation on TGFB1 expression was tested using targeted epigenome editing.

RESULTS

The analyses showed that SNP rs75621460 was located within a TGFB1 enhancer region, and the OA risk allele A altered transcription factor binding, with decreased enhancer activity. Protein complexes binding to A (but not G) induced DNA methylation at flanking CG dinucleotides. Strong correlations between patient DNA methylation levels and TGFB1 expression were observed, with directly opposing effects in the cartilage and the synovium at this locus. This demonstrated biologic pleiotropy in the impact of the SNP within different tissues of the articulating joint.

CONCLUSION

The OA risk SNP rs75621460 impacts TGFB1 expression by modulating the function of a gene enhancer. We propose a mechanism by which the SNP impacts enhancer function, providing novel biologic insight into one mechanism of OA genetic risk, which may facilitate the development of future pharmacologic therapies.

Osteoarthritis year in review: genetics, genomics, epigenetics

OBJECTIVE

In this review, we have highlighted advances in genetics, genomics and epigenetics in the field of osteoarthritis (OA) over the past year.

METHODS

A literature search was performed using PubMed and the criteria: "osteoarthritis" and one of the following terms "genetic(s), genomic(s), epigenetic(s), epigenomic(s), noncoding RNA, microRNA, long noncoding RNA, lncRNA, circular RNA, RNA sequencing, single cell sequencing, or DNA methylation between April 1, 2019 and April 30, 2020.

RESULTS

We identified 653 unique publications, many studies spanned multiple search terms. We summarized advances relating to evolutionary genetics, pain, ethnicity specific risk factors, functional studies of gene variants, and interactions between coding and non-coding RNAs in OA pathogenesis.

CONCLUSIONS

Studies have identified variants contributing to OA susceptibility, candidate biomarkers for diagnosis and prognosis, as well as promising therapeutic candidates. Validation in multiple cohorts, multi-omics strategies, and machine learning aided computational analyses have all contributed to the strength of published literature. Open access data-sets, greater sample sizes to capture broader populations and understanding disease mechanisms by investigating the interactions between multiple tissue types will further aid in progress towards understanding and curing OA.

Understanding osteoarthritis pathogenesis: a multiomics system-based approach

PURPOSE OF REVIEW

Osteoarthritis is a heterogeneous, multifactorial condition regulated by complex biological interactions at multiple levels. Comprehensive understanding of these regulatory interactions is required to develop feasible advances to improve patient outcomes. Improvements in technology have made extensive genomic, transcriptomic, epigenomic, proteomic, and metabolomic profiling possible. This review summarizes findings over the past 20 months related to omics technologies in osteoarthritis and examines how using a multiomics approach is necessary for advancing our understanding of osteoarthritis as a disease to improve precision osteoarthritis treatments.

RECENT FINDINGS

Using the search terms 'genomics' or 'transcriptomics' or 'epigenomics' or 'proteomics' or 'metabolomics' and 'osteoarthritis' from January 1, 2018 to August 31, 2019, we identified advances in omics approaches applied to osteoarthritis. Trends include untargeted whole genome, transcriptome, proteome, and metabolome analyses leading to identification of novel molecular signatures, cell subpopulations and multiomics validation approaches.

SUMMARY

To address the complexity of osteoarthritis, integration of multitissue analyses by multiomics approaches with the inclusion of longitudinal clinical data is necessary for a comprehensive understanding of the disease process, and for appropriate development of efficacious diagnostics, prognostics, and biotherapeutics.

Multi-tissue epigenetic analysis of the osteoarthritis susceptibility locus mapping to the plectin gene PLEC

OBJECTIVE

In cartilage, the osteoarthritis (OA) associated single nucleotide polymorphism (SNP) rs11780978 correlates with differential expression of PLEC, and with differential methylation of PLEC CpG dinucleotides, forming eQTLs and mQTLs respectively. This implies that methylation links chondrocyte genotype and phenotype, thus driving the functional effect of this genetic risk signal. PLEC encodes plectin, a cytoskeletal protein that enables tissues to respond to mechanical forces. We sought to assess whether these PLEC functional effects were cartilage specific.

METHOD

Cartilage, fat pad, synovium and peripheral blood were collected from patients undergoing arthroplasty. PLEC CpGs were analysed for mQTLs and allelic expression imbalance (AEI) was performed to test for eQTLs. Plectin was knocked down in a mesenchymal stem cell (MSC) line using CRISPR/Cas9 and cells phenotyped by RNA-sequencing.

RESULTS

mQTLs were discovered in fat pad, synovium and blood. Their effects were however stronger in the joint tissues and of comparable effect between these tissues. We observed AEI in synovium in the same direction as for cartilage and correlations between methylation and PLEC expression. Knocking-down plectin impacted on pathways reported to have a role in OA, including Wnt signalling, glycosaminoglycan biosynthesis and immune regulation.

CONCLUSIONS

Synovium is also a target of the rs11780978 OA association functionally operating on PLEC. In fat pad, mQTLs were identified but these did not correlate with PLEC expression, suggesting the functional effect is not joint-wide. Our study highlights interplay between genetic risk, DNA methylation and gene expression in OA, and reveals clear differences between tissues from the same diseased joint.

Interplay between genetics and epigenetics in osteoarthritis

Research into the molecular genetics of osteoarthritis (OA) has been substantially bolstered in the past few years by the implementation of powerful genome-wide scans that have revealed a large number of novel risk loci associated with the disease. This refreshing wave of discovery has occurred concurrently with epigenetic studies of joint tissues that have examined DNA methylation, histone modifications and regulatory RNAs. These epigenetic analyses have involved investigations of joint development, homeostasis and disease and have used both human samples and animal models. What has become apparent from a comparison of these two complementary approaches is that many OA genetic risk signals interact with, map to or correlate with epigenetic mediators. This discovery implies that epigenetic mechanisms, and their effect on gene expression, are a major conduit through which OA genetic risk polymorphisms exert their functional effects. This observation is particularly exciting as it provides mechanistic insight into OA susceptibility. Furthermore, this knowledge reveals avenues for attenuating the negative effect of risk-conferring alleles by exposing the epigenome as an exploitable target for therapeutic intervention in OA.