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Involving research users in setting priorities for research is essential to ensure the outcomes are patient-centred and\ud maximise its value and impact. The Musculoskeletal Disorders Research Advisory Group Versus Arthritis led a\ud research priority setting exercise across musculoskeletal disorders. The Child Health and Nutrition Research Initiative\ud (CHNRI) method of setting research priorities with a range of stakeholders was used, involving four stages and two\ud surveys, to: (1) gather research uncertainties, (2) consolidate these, (3) score uncertainties against importance and\ud impact, and (4) analyse scoring for prioritisation. 213 people responded to the first survey and 285 people to the\ud second, representing clinicians, researchers, and people with musculoskeletal disorders. Key priorities included\ud developing and testing new treatments, better treatment targeting, early diagnosis, prevention, and better\ud understanding and management of pain, with an emphasis on understanding underpinning mechanisms. We\ud present a call to action to researchers and funders to target these priorities.

Summary 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.

Over 100 DNA variants have been associated with osteoarthritis (OA), including rs1046934, located within a linkage disequilibrium block encompassing part of COLGALT2 and TSEN15. Here, we used human foetal cartilage, cartilage from arthroplasty patients, and a chondrocyte cell model to determine the target gene(s) at the locus and the mechanism of action.Genotyping and methylation array data of cartilage DNA (n=87) were used to determine if rs1046934 genotype associated with differential DNA methylation at proximal CpGs. Results were replicated in arthroplasty (n=132) and foetal (n=77) cartilage DNA using pyrosequencing. Allelic expression imbalance (AEI) measured effect of genotype upon COLGALT2 and TSEN15 expression. Reporter gene assays and epigenetic editing determined the functional role of regions harbouring differentially methylated CpGs. In silico analyses complemented these experiments.Three differentially methylated CpGs residing within regulatory regions were detected, two of which replicated. AEI was detected for COLGALT2 and TSEN15, with associations between expression and methylation for COLGALT2. Reporter assays confirmed that the CpGs are in chondrocyte enhancers with epigenetic editing directly linking methylation with COLGALT2 expression.COLGALT2 is a target of this OA locus. We previously characterised another OA locus, marked by rs11583641, that independently targets COLGALT2. rs1046934, like rs11583641, mediates its effect by modulating expression of COLGALT2 via methylation changes to CpGs located in enhancers. The SNPs, CpGs and enhancers are distinct between the loci but the effect on COLGALT2 is the same. COLGALT2 is the target of independent OA risk loci sharing a common mechanism of action.

Osteoarthritis (OA) is a polygenic disease of older people resulting in the breakdown of cartilage within articular joints. Although it is a leading cause of disability, there are no disease-modifying therapies. Evidence is emerging to support the origins of OA in skeletogenesis. Whereas methylation quantitative trait loci (mQTLs) co-localizing with OA genome-wide association study signals have been identified in aged human cartilage and used to identify effector genes and variants, such analyses have never been conducted during human development. Here, for the first time, we have investigated the developmental origins of OA genetic risk at seven well-characterized OA risk loci, comprising 39 OA-mQTL CpGs, in human fetal limb (FL) and cartilage (FC) tissues using a range of molecular genetic techniques. We identified significant OA-mQTLs at 14 and 29 CpGs in FL and FC tissues, respectively, and compared our results with aged cartilage samples (AC). Differential methylation was observed at 26 sites between FC and AC, with the majority becoming actively hypermethylated in old age. Notably, 6/9 OA effector genes showed allelic expression imbalances during fetal development. Finally, we conducted ATAC-sequencing in cartilage from the developing and aged hip and knee to identify accessible chromatin regions and found enrichment for transcription factor binding motifs including SOX9 and FOS/JUN. For the first time, we have demonstrated the activity of OA-mQTLs and expression imbalance of OA effector genes during human skeletogenesis. We show striking differences in the spatiotemporal function of these loci, contributing to our understanding of OA aetiology, with implications for the timing and strategy of pharmacological interventions.

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.

Osteoarthritis (OA) is a polygenic disease of older people resulting in the breakdown of cartilage within articular joints. Although a leading cause of disability, there are no disease-modifying therapies. Evidence is emerging to support the origins of OA in skeletogenesis. Whilst methylation QTLs (mQTLs) co-localizing with OA GWAS signals have been identified in aged human cartilage and used to identify effector genes and variants, such analyses have never been conducted during human development. Here, for the first time, we have investigated the developmental origins of OA genetic risk at seven well-characterized OA risk loci, comprising 39 OA-mQTL CpGs, in human fetal limb (FL) and cartilage (FC) tissues using a range of molecular genetic techniques. We compared our results to aged cartilage samples (AC) and identified significant OA-mQTLs at 14 CpGs and 29 CpGs in FL and FC tissues, respectively. Differential methylation was observed at 26 sites between fetal and aged cartilage, with the majority becoming actively hypermethylated in old age. Notably, 6/9 OA effector genes showed allelic expression imbalances during fetal development. Finally, we conducted ATAC-sequencing in cartilage from the developing and aged hip and knee to identify accessible chromatin regions, and found enrichment for transcription factor-binding motifs including SOX9 and FOS/JUN. For the first time, we have demonstrated the activity of OA-mQTLs and expression imbalance of OA effector genes during skeletogenesis. We show striking differences in the spatiotemporal function of these loci, contributing to our understanding of OA etiology, with implications for the timing and strategy of pharmacological interventions.

Objectives The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. Methods Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden’s index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. Results Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively). Conclusion The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors. Trial registration ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568.

BackgroundDespite recent advances in the understanding of the genetic architecture of osteoarthritis (OA), only two genetic loci have been identified for OA of the hand, in part explained by the complexity of the different hand joints and heterogeneity of OA pathology.MethodsWe used data from the Rotterdam Study (RSI, RSII and RSIII) to create three hand OA phenotypes based on clustering patterns of radiographic OA severity to increase power in our modest discovery genome-wide association studies in the RS (n=8700), and sought replication in an independent cohort, the Framingham Heart Study (n=1203). We used multiple approaches that leverage different levels of information and functional data to further investigate the underlying biological mechanisms and candidate genes for replicated loci. We also attempted to replicate known OA loci at other joint sites, including the hips and knees.ResultsWe found two novel genome-wide significant loci for OA in the thumb joints. We identifiedWNT9Aas a possible novel causal gene involved in OA pathogenesis. Furthermore, several previously identified genetic loci for OA seem to confer risk for OA across multiple joints:TGFa,RUNX2,COL27A1,ASTN2,IL11andGDF5loci.ConclusionsWe identified a robust novel genetic locus for hand OA on chromosome 1, of whichWNT9Ais the most likely causal gene. In addition, multiple genetic loci were identified to be associated with OA across multiple joints. Our study confirms the potential for novel insight into the genetic architecture of OA by using biologically meaningful stratified phenotypes.

ObjectivesOsteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component.MethodsBaseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component.ResultsOA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (pConclusionsOA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments.Trial registration numberThe study is registered under clinicaltrials.gov nr: NCT03883568.

The ultimate goal of molecular genetic studies of human diseases is to translate the discoveries for patient benefit. For diseases that lack licensed disease-modifying therapeutics, such as osteoarthritis (OA), the need is acute. OA is polygenic and affects older individuals, with a recent genome-wide study of over 800 000 individuals adding 52 novel association signals to those already reported on for this common arthritis. Many of the predicted effector genes of these signals encode proteins that are targets of drugs for other indications, highlighting repurposing opportunities. Here, the potential for OA genetic data to translate is discussed, including whether the developmental origin of OA will limit the application of genetic risk data for disease-modification purposes.

Publisher's version (útgefin grein)., Bone area is one measure of bone size that is easily derived from dual-energy X-ray absorptiometry (DXA) scans. In a GWA study of DXA bone area of the hip and lumbar spine (N ≥ 28,954), we find thirteen independent association signals at twelve loci that replicate in samples of European and East Asian descent (N = 13,608 – 21,277). Eight DXA area loci associate with osteoarthritis, including rs143384 in GDF5 and a missense variant in COL11A1 (rs3753841). The strongest DXA area association is with rs11614913[T] in the microRNA MIR196A2 gene that associates with lumbar spine area (P = 2.3 × 10−42, β = −0.090) and confers risk of hip fracture (P = 1.0 × 10−8, OR = 1.11). We demonstrate that the risk allele is less efficient in repressing miR-196a-5p target genes. We also show that the DXA area measure contributes to the risk of hip fracture independent of bone density., We thank all the study subjects for their valuable participation, the staff from all studies and the participating physicians. A part of this research has been conducted using the UK Biobank Resource under Application Number 23359. arcOGEN (http://www.arcogen.org.uk/) was funded by a special purpose grant from Arthritis Research UK (grant 18030).

This conclusion ties together the various contributions to the Special Issue from the perspective of the introductory framework. Based on this framework, the conclusion shows that: (i) domestic mediating factors are responsible for nuanced state rescaling outcomes in small unitary states, (ii) the EU has led to decentralization and recentralization in both phases of the EU decision-making process and (iii) the level of authority is the best predictor of SNAs’ empowerment, although this is intertwined with secondary mediating domestic factors. This concluding article sheds new light on the hybrid nature of the European polity and demonstrates that states remain the most important pieces of the European ‘puzzle’. The Special Issue arrives at two conclusions: first, we are witnessing a ‘transformation of the state’ rather than its demise, and second, the notion of a ‘Europe of the Regions’ in small unitary states is nothing more than a ‘damp squib’., This research project was supported by the Portuguese National Funding Agency for Science, Research and Technology (FCT).

We provide a detailed account of the origin and establishment of the Osteoarthritis Research Society International (OARSI) and celebrate its history from inception to the current day. We discuss the mission, vision and strategic objectives of OARSI and how these have developed and evolved over the last 3 decades. We celebrate the achievements of the society as we approach its 30th birthday, honor the entire presidential line and respectfully pay tribute to the past presidents who are no longer with us. We reflect on the strong foundations of our society, OARSI’s efforts to disseminate understanding of the health, disability and economic burdens of osteoarthritis (OA) to policymakers, and the exciting initiatives to make the society inclusive and international. We thank our corporate and industrial sponsors, who have supported us over many years, without whom our annual congresses would not have been possible. We celebrate our longstanding strategic partnership with our publisher, Elsevier, and the successful launch of our new journal Osteoarthritis and Cartilage Open, the most significant new development in our dissemination toolbox. For the first time in the history of the organization, our annual congress was cancelled in April 2020 and the 2021 meeting will be virtual. Despite the numerous challenges posed by the ongoing COVID-19 pandemic and the need to adapt quickly to a rapidly changing landscape, we must remain optimistic about the future. We will take advantage of new exciting opportunities to advance our mission and vision to enhance the quality of life of persons with OA.

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.

Summary Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation., Graphical abstract, Highlights • A multicohort study identifies 52 previously unknown osteoarthritis genetic risk variants • Similarities and differences in osteoarthritis genetic risk depend on joint sites • Osteoarthritis genetic components are associated with pain-related phenotypes • High-confidence effector genes highlight potential targets for drug intervention, A multicohort genome-wide association meta-analysis of osteoarthritis highlights the impact of joint site types on the features of genetic risk variants and the link between osteoarthritis genetics and pain-related phenotypes, pointing toward potential targets for therapeutic intervention.

Background Despite recent advances in the understanding of the genetic architecture of osteoarthritis (OA), only two genetic loci have been identified for OA of the hand, in part explained by the complexity of the different hand joints and heterogeneity of OA pathology. Methods We used data from the Rotterdam Study (RSI, RSII and RSIII) to create three hand OA phenotypes based on clustering patterns of radiographic OA severity to increase power in our modest discovery genome-wide association studies in the RS (n=8700), and sought replication in an independent cohort, the Framingham Heart Study (n=1203). We used multiple approaches that leverage different levels of information and functional data to further investigate the underlying biological mechanisms and candidate genes for replicated loci. We also attempted to replicate known OA loci at other joint sites, including the hips and knees. Results We found two novel genome-wide significant loci for OA in the thumb joints. We identified WNT9A as a possible novel causal gene involved in OA pathogenesis. Furthermore, several previously identified genetic loci for OA seem to confer risk for OA across multiple joints: TGFa, RUNX2, COL27A1, ASTN2, IL11 and GDF5 loci. Conclusions We identified a robust novel genetic locus for hand OA on chromosome 1, of which WNT9A is the most likely causal gene. In addition, multiple genetic loci were identified to be associated with OA across multiple joints. Our study confirms the potential for novel insight into the genetic architecture of OA by using biologically meaningful stratified phenotypes.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials., Comment: 22 pages, 12 figures, 10 tables

© 2020, Springer Nature Limited. 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.

Objective Osteoarthritis (OA) is polygenic, with more than 90 risk loci currently mapped, including at the single-nucleotide polymorphism rs6516886. Previous analysis of OA cartilage DNA identified 6 CpG dinucleotides whose methylation levels correlated with the rs6516886 genotype, forming methylation quantitative trait loci (mQTLs). We undertook this study to investigate these mQTLs and to map expression quantitative trait loci (eQTLs) across joint tissues in order to prioritize a particular gene as a target of the rs6516886 association effect. Methods Nucleic acids were extracted from the cartilage, fat pad, synovium, and peripheral blood from OA patients. Methylation of CpGs and allelic expression imbalance of potential target genes were assessed by pyrosequencing. A chondrocyte cell line expressing deactivated Cas9 (dCas9)-TET1 was used to directly alter CpG methylation levels, with effects on gene expression quantified by polymerase chain reaction. Results Multiple mQTLs were detected, with effects strongest in joint tissues and with methylation at CpG cg20220242 correlating most significantly with the rs6516886 genotype. CpG cg20220242 is located upstream of RWDD2B. Significant rs6516886 eQTLs were observed for this gene, with the OA risk-conferring allele of rs6516886 correlating with reduced expression CpG methylation also correlated with allelic expression of RWDD2B, forming methylation-expression QTLs (meQTLs). Deactivated Cas9-TET1 reduction in the methylation of cg20220242 increased expression of RWDD2B. Conclusion The rs6516886 association signal is a multi-tissue meQTL involving cg20220242 and acting on RWDD2B. Modulating CpG methylation reverses the impact of the risk allele. RWDD2B codes for a protein about which little is currently known. Its further analysis as a target of OA genetic risk will provide novel insight into this complex disease.

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.

ObjectiveOsteoarthritis (OA) associated single nucleotide polymorphism (SNP) rs11780978 correlates with differential expression of PLEC, and methylation quantitative trait loci (mQTLs) at PLEC CpGs in cartilage. This implies that methylation links chondrocyte genotype and phenotype, thus driving the functional effect. PLEC encodes plectin, a cytoskeletal protein that enables tissues to respond to mechanical forces. We sought to assess whether PLEC functional effects were cartilage specific.MethodCartilage, 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. We focussed on previously reported mQTL clusters neighbouring cg19405177 and cg14598846. Plectin was knocked down in a mesenchymal stem cell (MSC) line using CRISPR/Cas9 and cells phenotyped by RNA-sequencing.ResultsNovel mQTLs were discovered in fat pad, synovium and peripheral blood at both clusters. The genotype-methylation effect of rs11780978 was stronger in cg14598846 than in cg19405177 and stronger in joint tissues than in peripheral blood. We observed AEI in synovium in the same direction as for cartilage. Knocking-down plectin impacted on pathways reported to have a role in OA, including Wnt signalling, glycosaminoglycan biosynthesis and immune regulation.ConclusionsSynovium 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.

PurposeThe Applied Public-Private Research enabling OsteoArthritis Clinical Headway (APPROACH) consortium intends to prospectively describe in detail, preselected patients with knee osteoarthritis (OA), using conventional and novel clinical, imaging, and biochemical markers, to support OA drug development.ParticipantsAPPROACH is a prospective cohort study including 297 patients with tibiofemoral OA, according to the American College of Rheumatology classification criteria. Patients were (pre)selected from existing cohorts using machine learning models, developed on data from the CHECK cohort, to display a high likelihood of radiographic joint space width (JSW) loss and/or knee pain progression.Findings to dateSelection appeared logistically feasible and baseline characteristics of the cohort demonstrated an OA population with more severe disease: age 66.5 (SD 7.1) vs 68.1 (7.7) years, min-JSW 2.5 (1.3) vs 2.1 (1.0) mm and Knee injury and Osteoarthritis Outcome Score pain 31.3 (19.7) vs 17.7 (14.6), except for age, all: pFuture plansPatients will visit the hospital again at 6, 12 and 24 months for physical examination, pain and general health questionnaires, collection of blood and urine, MRI scans, radiographs of knees and hands, CT scan of the knee, low radiation whole-body CT, HandScan, motion analysis and performance-based tests.After two years, data will show whether those patients with the highest probabilities for progression experienced disease progression as compared to those wit lower probabilities (model validation) and whether phenotypes/endotypes can be identified and predicted to facilitate targeted drug therapy.Trial registration numberNCT03883568

This special issue addresses the impact of the European Union (EU) on subnational mobilization in small unitary states. Located at the intersection between varied contributions from the literatures on multilevel governance and Europeanization, it offers a new theoretical framework to account for state rescaling processes in small unitary states. By means of a comparative analysis of eight small unitary states, this collection shows that the impact of the EU on state rescaling processes is filtered through domestic mediating factors which can lead to three possible outcomes: (i) decentralization, (ii) recentralization or (iii) no change. It concludes that ‘hybridity’ is the most appropriate concept for capturing the compound nature of the European polity, in which local and regional tiers of government have secured new opportunities for influencing policies and making autonomous decisions. These impacts are conditioned by nuanced domestic mediating factors without challenging the overall dominance of the nation-state., FCT -Fundação para a Ciência e a Tecnologia(UID/CPO/00758/2013)