Prioritization of PLEC and GRINA as Osteoarthritis Risk Genes Through the Identification and Characterization of Novel Methylation Quantitative Trait Loci

Identification of AGO2 and PLEC genes polymorphisms in Hu sheep and their relationship with body size traits

The body size traits are major traits in livestock, which intuitively displays the development of the animal's bones and muscles. This study used PCR amplification, Sanger sequencing, KASPar genotyping, and quantitative real-time reverse transcription PCR (qRT-PCR) to analyze the Single-nucleotide polymorphism and expression characteristics of Argonaute RISC catalytic component 2 (AGO2) and Plectin (PLEC) genes in Hu sheep. Two intron mutations were found in Hu sheep, which were AGO2 g.51700 A > C and PLEC g.23157 C > T, respectively. Through association analysis of two mutation sites and body size traits, it was found that AGO2 g.51700 A > C mainly affects the chest and cannon circumference of Hu sheep of while PLEC g.23157 C mainly affects body height and body length. The combined genotypes of AGO2 and PLEC genes with body size traits showed SNPs at the AGO2 g.51700 A > C and PLEC g.23157 C > T loci significantly improved the body size traits of Hu sheep. In addition, the AGO2 gene has the highest expression levels in the heart, rumen, and tail fat, and the PLEC gene is highly expressed in the heart. These two loci can provide new research ideas for improving the body size traits of Hu sheep.

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.

Cartilage progenitor cells derived extracellular vesicles-based cell-free strategy for osteoarthritis treatment by efficient inflammation inhibition and extracellular matrix homeostasis restoration

Osteoarthritis (OA) is a common degenerative joint disease which currently lacks of effective agents. It is therefore urgent and necessary to seek an effective approach that can inhibit inflammation and promote cartilage matrix homeostasis. Cartilage progenitor cells (CPCs) are identified as a cell population of superficial zone in articular cartilage which possess strong migration ability, proliferative capacity, and chondrogenic potential. Recently, the application of CPCs may represent a novel cell therapy strategy for OA treatment. There is growing evidence that extracellular vesicles (EVs) are primary mediators of the benefits of stem cell-based therapy. In this study, we explored the protective effects of CPCs-derived EVs (CPCs-EVs) on IL-1β-induced chondrocytes. We found CPCs-EVs exhibited chondro-protective effects in vitro. Furthermore, our study demonstrated that CPCs-EVs promoted matrix anabolism and inhibited inflammatory response at least partially via blocking STAT3 activation. In addition, liquid chromatography-tandem mass spectrometry analysis identified 991 proteins encapsulated in CPCs-EVs. By bioinformatics analysis, we showed that STAT3 regulatory proteins were enriched in CPCs-EVs and could be transported to chondrocytes. To promoting the protective function of CPCs-EVs in vivo, CPCs-EVs were modified with cationic peptide ε-polylysine-polyethylene-distearyl phosphatidylethanolamine (PPD) for surface charge reverse. In posttraumatic OA mice, our results showed PPD modified CPCs-EVs (PPD-EVs) effectively inhibited extracellular matrix catabolism and attenuated cartilage degeneration. Moreover, PPD-EVs down-regulated inflammatory factors expressions and reduced OA-related pain in OA mice. In ex-vivo cultured OA cartilage explants, PPD-EVs successfully promoted matrix anabolism and inhibited inflammation. Collectively, CPCs-EVs-based cell-free therapy is a promising strategy for OA treatment.

Epigenome-wide association study identifies DNA methylation loci associated with handgrip strength in Chinese monozygotic twins

Background: The decline in muscle strength and function with aging is well recognized, but remains poorly characterized at the molecular level. Here, we report the epigenetic relationship between genome-wide DNA methylation and handgrip strength (HGS) among Chinese monozygotic (MZ) twins. Methods: DNA methylation (DNAm) profiling was conducted in whole blood samples through Reduced Representation Bisulfite Sequencing method. Generalized estimating equation was applied to regress the DNAm of each CpG with HGS. The Genomic Regions Enrichment of Annotations Tool was used to perform enrichment analysis. Differentially methylated regions (DMRs) were detected using comb-p. Causal inference was performed using Inference about Causation through Examination of Familial Confounding method. Finally, we validated candidate CpGs in community residents. Results: We identified 25 CpGs reaching genome-wide significance level. These CpGs located in 9 genes, especially FBLN1, RXRA, and ABHD14B. Many enriched terms highlighted calcium channels, neuromuscular junctions, and skeletal muscle organ development. We identified 21 DMRs of HGS, with several DMRs within FBLN1, SLC30A8, CST3, and SOCS3. Causal inference indicated that the DNAm of 16 top CpGs within FBLN1, RXRA, ABHD14B, MFSD6, and TYW1B might influence HGS, while HGS influenced DNAm at two CpGs within FBLN1 and RXRA. In validation analysis, methylation levels of six CpGs mapped to FLBN1 and one CpG mapped to ABHD14B were negatively associated with HGS weakness in community population. Conclusion: Our study identified multiple DNAm variants potentially related to HGS, especially CpGs within FBLN1 and ABHD14B. These findings provide new clues to the epigenetic modification underlying muscle strength decline.

HSPB1 as an RNA-binding protein mediates the pathological process of osteoarthritis

Heat-shock protein beta1 (HSPB1) is a member of the small HSP family, downregulated in osteoarthritis (OA) chondrocytes and demonstrated the capacity to serve as an RNA-binding protein (RBP). This work aimed to explore the profile of HSPB1 bound RNA and reveal the potential regulation mechanism of HSPB1 in OA. In this work, we captured an unbiased HSPB1-RNA interaction map in Hela cells using the iRIP-seq. The results demonstrated that HSPB1 interacted with plentiful of mRNAs and genomic location toward the CDS region. Functional enrichment of HSPB1-related peaks showed the involvement in gene expression, translation initiation, cellular protein metabolic process, and nonsense-mediated decay. HOMER software analysis showed that HSPB1 bound peaks were over-represented in GAGGAG sequences. In addition, ABLIRC and CIMS algorithm indicated that HSPB1 bound to AU-rich motifs and the proportion of AU-rich peaks in 3' UTR were slightly higher than that in other regions. Moreover, HSPB1-binding targets analysis revealed several gens were associated with OA including EGFR, PLEC, COL5A1, and ROR2. The association of OA-related mRNAs to HSPB1 was additionally confirmed in OA tissues by the quantitative RIP-PCR experiments. Further experiment demonstrated the downregulation of HSPB1 in OA tissues. In conclusion, our current study confirmed HSPB1 as an RNA-binding protein and revealed its potential function in the pathological process of OA, providing a reliable insight to further investigate the molecular regulation mechanism of HSPB1 in OA.

[Plectin Promotes the Migration of Hepatocellular Carcinoma Cells Through Inducing F-actin Polymerization]

Objective

To explore the relationship between the expression of plectin and the migration of hepatocellular carcinoma (HCC) cells and to elucidate the molecular mechanisms by which plectin expression affects the migration of HCC cells.

Methods

First of all, Western blot was performed to determine the expression of plectin in normal hepatocytes and HCC cells. Secondly, a plectin-downregulated HCC cell strain was established and the control group (shNC group) and shPLEC group were set up. Each group was divided into a vehicle control group (shNC+DMSO group or shPLEC+DMSO group) and a F-actin cytoskeleton polymerization inducer Jasplakinolide group (shNC+Jasp group or shPLEC+Jasp group). Western blot was performed to determine the expression of plectin and epithelial-mesenchymal transition (EMT)-related proteins, including N-cadherin, vimentin, and E-cadherin. HCC cell migration was evaluated by Transwell assay. KEGG (Kyoto Encyclopedia of Genes and Genomes) was used to analyze the signaling pathways related to plectin gene. The polymerization of F-actin was analyzed by immunofluorescence assay.

Results

Compared with the normal hepatocytes, HCC cells showed high expression of plectin. Compared with those in the shNC group, the expression of plectin in the shPLEC group was decreased (P<0.05), the migration ability of HCC cells was weakened (P<0.05), and the EMT process was inhibited (with the expression of N-cadherin and vimentin being decreased and the expression of E-cadherin being increased) (P<0.05). KEGG analysis showed that the regulation of cytoskeletal F-actin was most closely associated with plectin and cytoskeletal F-actin depolymerized in the shPLEC group. After treatment with Jasplakinolide, an inducer of F-actin cytoskeleton polymerization, the migration ability of HCC cells in the shPLEC+Jasp group was enhanced compared with that of shPLEC+DMSO group (P<0.05) and the EMT process was restored (with the expression of N-cadherin and vimentin being increased and the expression of E-cadherin being decreased) (P<0.05). In addition, the polymerization of cytoskeletal F-actin in HCC cells was also restored.

Conclusion

Plectin is highly expressed in HCC cells. Plectin promotes the migration and the EMT of HCC cells through inducing F-actin polymerization.

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.

Gene Expression in Glenoid Articular Cartilage Varies Across Acute Instability, Chronic Instability, and Osteoarthritis

BACKGROUND

Shoulder instability is a common pathology associated with an elevated risk of osteoarthritis (OA). Little is known about gene expression in the cartilage of the glenohumeral joint after dislocation events, particularly as it relates to the risk of posttraumatic OA. This study tested the hypothesis that gene expression in glenoid cartilage varies among acute instability (<3 dislocations), chronic instability (≥3 dislocations), and OA.

METHODS

Articular cartilage was collected from the anteroinferior glenoid of consenting patients undergoing shoulder stabilization surgery (n = 17) or total shoulder arthroplasty (n = 16). Digital quantitative polymerase chain reaction was used to assess the relative expression of 57 genes (36 genes from OA risk allele studies, 21 genes from differential expression studies), comparing (1) OA versus instability (acute and chronic combined), (2) acute versus chronic instability, (3) OA versus acute instability, and (4) OA versus chronic instability.

RESULTS

The expression of 11 genes from OA risk allele studies and 9 genes from differential expression studies was significantly different between cartilage from patients with instability and those with OA. Pro-inflammatory genes from differential expression studies and genes from OA risk allele studies were more highly expressed in cartilage in the OA group compared with the instability group, which expressed higher levels of extracellular matrix and pro-anabolic genes. The expression of 14 genes from OA risk allele studies and 4 genes from differential expression studies, including pro-inflammatory genes, anti-anabolic genes, and multiple genes from OA risk allele studies, was higher in the acute instability group compared with the chronic instability group. Cartilage in the OA group displayed higher expression of CCL3, CHST11, GPR22, PRKAR2B, and PTGS2 than cartilage in the group with acute or chronic instability. Whereas cartilage in both the acute and chronic instability groups had higher expression of collagen genes, cartilage in the OA group had expression of a subset of genes from OA risk allele studies or from differential expression studies that was lower than in the acute group and higher than in the chronic group.

CONCLUSIONS

Glenoid cartilage has an inflammatory and catabolic phenotype in shoulders with OA but an anabolic phenotype in shoulders with instability. Cartilage from shoulders with acute instability displayed greater (cellular) metabolic activity compared with shoulders with chronic instability.

CLINICAL RELEVANCE

This exploratory study identified genes of interest, such as CCL3, CHST11, GPR22, PRKAR2B, and PTGS2, that have elevated expression in osteoarthritic glenoid cartilage. These findings provide new biological insight into the relationship between shoulder instability and OA, which could lead to strategies to predict and potentially modify patients' risk of degenerative arthritis due to shoulder instability.

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.

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.

Genetic analysis of DNA methylation in dyslipidemia: a case-control study

Background

Coronary heart disease has become the leading cause of death in developed countries, and dyslipidemia is closely associated with the risk of cardiovascular disease. Dyslipidemia is caused by the abnormal regulation of several genes and signaling pathways, and dyslipidemia is influenced mainly by genetic variation. AMFR, FBXW7, INSIG1, INSIG2, and MBTPS1 genes are associated with lipid metabolism. In a recent GWAS study, the GRINA gene has been reported to be associated with dyslipidemia, but its molecular mechanism has not been thoroughly investigated. The correlation between the DNA methylation of these genes and lipid metabolism has not been studied. This study aimed to examine the relationship between the DNA methylation of these genes and the risk of dyslipidemia by comparing the methylation levels of dyslipidemia and control samples.

Methods

A case-control research method was used in this study. The patient's blood samples were collected at the Heart Center of the First Affiliated Hospital of Xinjiang Medical University. In the Xinjiang Han population, 100 cases of hyperlipidemia and 80 cases of the control group were selected. The two groups were age and gender-matched. Quantitative methylation analysis of CpG sites in the gene promoter regions of six genes was performed by Solexa high-throughput sequencing.

Results

The DNA methylation levels of 23 CpG sites in six genes were shown to be associated with hyperlipidemia, and a total of 20 DNA methylation haplotypes showed statistically significant differences between the two groups. When compared with the control group, the dyslipidemia group had significantly higher levels of methylation in the GRINA gene (2.68 vs 2.36, P = 0.04). Additionally, we also discovered a significant methylation haplotype of GRINA (P = 0.017).

Conclusion

The findings of this study reveal that the DNA methylation of GRINA increases the risk for dyslipidemia in humans.

A single-cell mass cytometry platform to map the effects of preclinical drugs on cartilage homeostasis

No disease-modifying drug exists for osteoarthritis (OA). Despite success in animal models, candidate drugs continue to fail in clinical trials owing to the unmapped interpatient heterogeneity and disease complexity. We used a single-cell platform based on cytometry by time-of-flight (cyTOF) to precisely outline the effects of candidate drugs on human OA chondrocytes. OA chondrocytes harvested from patients undergoing total knee arthroplasty were treated with 2 drugs, an NF-κB pathway inhibitor, BMS-345541, and a chondroinductive small molecule, kartogenin, that showed preclinical success in animal models for OA. cyTOF conducted with 30 metal isotope-labeled antibodies parsed the effects of the drugs on inflammatory, senescent, and chondroprogenitor cell populations. The NF-κB pathway inhibition decreased the expression of p-NF-κB, HIF2A, and inducible NOS in multiple chondrocyte clusters and significantly depleted 4 p16ink4a-expressing senescent populations, including NOTCH1+STRO1+ chondroprogenitor cells. While kartogenin also affected select p16ink4a-expressing senescent clusters, there was a less discernible effect on chondroprogenitor cell populations. Overall, BMS-345541 elicited a uniform drug response in all patients, while only a few responded to kartogenin. These studies demonstrate that a single-cell cyTOF-based drug screening platform can provide insights into patient response assessment and patient stratification.

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.

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.

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.

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.

Insights into the molecular landscape of osteoarthritis in human tissues

PURPOSE OF REVIEW

To provide an overview of recent developments in the field of osteoarthritis research with a focus on insights gleaned from the application of different -omic technologies.

RECENT FINDINGS

We searched for osteoarthritis-relevant studies focusing on transcriptomics, epigenomics, proteomics and metabolomics, published since November of 2019. Study designs showed a trend towards characterizing the genomic profile of osteoarthritis-relevant tissues with high resolution, for example either by using single-cell technologies or by considering several -omic levels and disease stages.

SUMMARY

Multitissue interactions (cartilage-subchondral bone; cartilage-synovium) are prevalent in the pathophysiology of osteoarthritis, which is characterized by substantial matrix remodelling in an inflammatory milieu. Subtyping approaches using -omic technologies have contributed to the identification of at least two osteoarthritis endotypes. Studies using data integration approaches have provided molecular maps that are tissue-specific for osteoarthritis and pave the way for expanding these data integration approaches towards a more comprehensive view of disease aetiopathogenesis.

Identifying Plectin Isoform Functions through Animal Models

Plectin, a high-molecular-weight cytoskeletal linker protein, binds with high affinity to intermediate filaments of all types and connects them to junctional complexes, organelles, and inner membrane systems. In addition, it interacts with actomyosin structures and microtubules. As a multifunctional protein, plectin has been implicated in several multisystemic diseases, the most common of which is epidermolysis bullosa simplex with muscular dystrophy (EBS-MD). A great part of our knowledge about plectin’s functional diversity has been gained through the analysis of a unique collection of transgenic mice that includes a full (null) knockout (KO), several tissue-restricted and isoform-specific KOs, three double KOs, and two knock-in lines. The key molecular features and pathological phenotypes of these mice will be discussed in this review. In summary, the analysis of the different genetic models indicated that a functional plectin is required for the proper function of striated and simple epithelia, cardiac and skeletal muscle, the neuromuscular junction, and the vascular endothelium, recapitulating the symptoms of humans carrying plectin mutations. The plectin-null line showed severe skin and muscle phenotypes reflecting the importance of plectin for hemidesmosome and sarcomere integrity; whereas the ablation of individual isoforms caused a specific phenotype in myofibers, basal keratinocytes, or neurons. Tissue-restricted ablation of plectin rendered the targeted cells less resilient to mechanical stress. Studies based on animal models other than the mouse, such as zebrafish and C. elegans, will be discussed as well.

Plectin-Mediated Intermediate Filament Functions: Why Isoforms Matter

This essay focuses on the role of plectin and its various isoforms in mediating intermediate filament (IF) network functions. It is based on previous studies that provided comprehensive evidence for a concept where plectin acts as an IF recruiter, and plectin-mediated IF networking and anchoring are key elements in IF function execution. Here, plectin’s global role as modulator of IF functionality is viewed from different perspectives, including the mechanical stabilization of IF networks and their docking platforms, contribution to cellular viscoelasticity and mechanotransduction, compartmentalization and control of the actomyosin machinery, connections to the microtubule system, and mechanisms and specificity of isoform targeting. Arguments for IF networks and plectin acting as mutually dependent partners are also given. Lastly, a working model is presented that describes a unifying mechanism underlying how plectin–IF networks mechanically control and propagate actomyosin-generated forces, affect microtubule dynamics, and contribute to mechanotransduction.

Genetic impacts on DNA methylation: research findings and future perspectives

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

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.

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.

Osteoarthritis year in review 2019: genetics, genomics and epigenetics

Although osteoarthritis (OA) aetiology is complex, genetic, genomic and epigenetic studies published within the last decade have advanced our understanding of the molecular processes underlying this common musculoskeletal disease. The purpose of this narrative review is to highlight the key research articles within the OA genetics, genomics and epigenetics fields that were published between April 2018 and April 2019. The review focuses on the identification of new OA genetic risk loci, genomics techniques that have been used for the first time in human cartilage and new publicly available databases, and datasets that will aid OA functional studies. Fifty-six new OA susceptibility loci were identified by two large scale genome wide association study meta-analyses, increasing the number of genome-wide significant risk loci to 90. OA risk variants are enriched near genes involved in skeletal development and morphology, and show genetic overlap with height, hip shape, bone area and developmental dysplasia of the hip. Several functional studies of OA loci were published, including a genome-wide analysis of genetic variation on cartilage gene expression. A specialised data portal for exploring cross-species skeletal transcriptomic datasets has been developed, and the first use of cartilage single cell RNAseq analysis reported. This year also saw the systematic identification of all microRNAs, long non-coding RNAs and circular RNAs expressed in human OA cartilage. Putative transcriptional regulatory regions have been mapped in human chondrocytes genome-wide, providing a dataset that will facilitate the prioritisation and characterisation of OA genetic and epigenetic loci.

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

OBJECTIVE

Osteoarthritis (OA) is polygenic with over 90 independent genome-wide association loci so far reported. A key next step is the identification of target genes and the molecular mechanisms through which this genetic risk operates. The majority of OA risk-conferring alleles are predicted to act by modulating gene expression. DNA methylation at CpG dinucleotides may be a functional conduit through which this occurs and is detectable by mapping methylation quantitative trait loci, or mQTLs. This approach can therefore provide functional insight into OA risk and will prioritize genes for subsequent investigation. That was our goal, with a focus on the largest set of OA loci yet to be reported.

METHOD

We investigated DNA methylation, genotype and RNA sequencing data derived from the cartilage of patients who had undergone arthroplasty and combined this with in silico analyses of expression quantitative trait loci, epigenomes and chromatin interactions.

RESULTS

We investigated 42 OA risk loci and in ten of these we identified 24 CpGs in which methylation correlated with genotype (false discovery rate (FDR) P-values ranging from 0.049 to 1.73x10^-25). In silico analyses of these mQTLs prioritised genes and regulatory elements at the majority of the ten loci, with COLGALT2 (encoding a collagen galactosyltransferase), COL11A2 (encoding a polypeptide chain of type XI collagen) and WWP2 (encoding a ubiquitin ligase active during chondrogenesis) emerging as particularly compelling target genes.

CONCLUSION

We have highlighted the pivotal role of DNA methylation as a link between genetic risk and OA and prioritized genes for further investigation.