Characterisation of Equine Synovial Fluid Derived Extracellular Vesicles from Young and Old Horses.

Equine osteoarthritis is a disease that impacts the welfare and performance of horses from all disciplines. Similar to its effect on human joints, osteoarthritis in horses is a painful joint condition that leads to lameness and decreased range of movement. This study compared the lubricating synovial joint fluid from eight young and seven old horses, specifically looking for differences in the extracellular vesicles (EVs). EVs are small nanoparticles present in the joint synovial fluid. EVs contain a type of ribonucleic acid (RNA) called microRNAs (miRNA), which can alter expression of genes and therefore influence the environment of a joint as some gene changes may promote or prevent osteoarthritic changes. This study was investigating whether there was a difference in the miRNAs present between old and young horses, as well as whether other characteristics of the EVs differ. Our study aimed to add to ongoing research into the role of EVs in the progression of osteoarthritis, and whether they could be used as biomarkers to diagnose joint changes. Our study did not find any significant differences in the size, concentration or miRNA expression of three miRNAs tested which suggests that their characteristics remain similar as a horse ages. However, a trend of decreased expression of these miRNAs was found, and while not statistically significant, this suggests that older horses may have lower levels of expression of certain osteoarthritis and inflammation related miRNAs. Additional work is required to confirm these findings

Donor age affects proteome composition of tenocyte-derived engineered tendon

All proteins identified by PEAKS in young and old tendon-derived TEC with correpsonding cellular sublocations defined by IPA and Matrisome Project. (XLSX 57 kb

Editorial: Epigenetic regulation of the musculoskeletal system in health, disease, and aging.

From PubMed via Jisc Publications RouterHistory: received 2023-01-12, accepted 2023-01-16Publication status: epublis

The non-coding RNA interactome in joint health and disease

Non-coding RNAs have distinct regulatory roles in the pathogenesis of joint diseases including osteoarthritis (OA) and rheumatoid arthritis (RA). As the amount of high-throughput profiling studies and mechanistic investigations of microRNAs, long non-coding RNAs and circular RNAs in joint tissues and biofluids has increased, data have emerged that suggest complex interactions among non-coding RNAs that are often overlooked as critical regulators of gene expression. Identifying these non-coding RNAs and their interactions is useful for understanding both joint health and disease. Non-coding RNAs regulate signalling pathways and biological processes that are important for normal joint development but, when dysregulated, can contribute to disease. The specific expression profiles of non-coding RNAs in various disease states support their roles as promising candidate biomarkers, mediators of pathogenic mechanisms and potential therapeutic targets. This Review synthesizes literature published in the past 2 years on the role of non-coding RNAs in OA and RA with a focus on inflammation, cell death, cell proliferation and extracellular matrix dysregulation. Research to date makes it apparent that \u27non-coding\u27 does not mean \u27non-essential\u27 and that non-coding RNAs are important parts of a complex interactome that underlies OA and RA

A Network Biology Approach to Understanding the Tissue-Specific Roles of Non-Coding RNAs in Arthritis

Discovery of non-coding RNAs continues to provide new insights into some of the key molecular drivers of musculoskeletal diseases. Among these, microRNAs have received widespread attention for their roles in osteoarthritis and rheumatoid arthritis. With evidence to suggest that long non-coding RNAs and circular RNAs function as competing endogenous RNAs to sponge microRNAs, the net effect on gene expression in specific disease contexts can be elusive. Studies to date have focused on elucidating individual long non-coding-microRNA-gene target axes and circular RNA-microRNA-gene target axes, with a paucity of data integrating experimentally validated effects of non-coding RNAs. To address this gap, we curated recent studies reporting non-coding RNA axes in chondrocytes from human osteoarthritis and in fibroblast-like synoviocytes from human rheumatoid arthritis. Using an integrative computational biology approach, we then combined the findings into cell- and disease-specific networks for in-depth interpretation. We highlight some challenges to data integration, including non-existent naming conventions and out-of-date databases for non-coding RNAs, and some successes exemplified by the International Molecular Exchange Consortium for protein interactions. In this perspective article, we suggest that data integration is a useful in silico approach for creating non-coding RNA networks in arthritis and prioritizing interactions for further in vitro and in vivo experimentation in translational research

Ex-Vivo Equine Cartilage Explant Osteoarthritis Model - A Metabolomics and Proteomics Study.

Osteoarthritis is an age-related degenerative musculoskeletal disease characterised by loss of articular cartilage, synovitis and subchondral bone sclerosis. Osteoarthritis pathogenesis is yet to be fully elucidated with no osteoarthritis specific biomarkers in clinical use. Ex-vivo equine cartilage explants (n=5) were incubated in TNF-α/IL-1β supplemented culture media for 8 days, with media removed and replaced at 2, 5 and 8 days. Acetonitrile metabolite extractions of 8 day cartilage explants and media samples at all time points underwent 1D 1H nuclear magnetic resonance metabolomic analysis with media samples also undergoing mass spectrometry proteomic analysis. Within the cartilage, glucose and lysine were elevated following TNF-α/IL-1β treatment whilst adenosine, alanine, betaine, creatine, myo-inositol and uridine decreased. Within the culture media, four, four and six differentially abundant metabolites and 154, 138 and 72 differentially abundant proteins were identified at 1-2 days, 3-5 days and 6-8 days respectively, including reduced alanine and increased isoleucine, enolase 1, vimentin and lamin A/C following treatment. Nine potential novel osteoarthritis neopeptides were elevated in treated media. Implicated pathways were dominated by those involved in cellular movement. Our innovative study has provided insightful information on early osteoarthritis pathogenesis, enabling potential translation for clinical markers and possible new therapeutic targets

Proteomic Analyses of Autologous Chondrocyte Implantation Plasma Highlight Cartilage Acidic Protein 1 as a Candidate for Preclinical Screening.

BackgroundStratification is required to ensure that only patients likely to benefit receive autologous chondrocyte implantation (ACI). It would be advantageous to identify biomarkers to predict ACI outcome that are measurable in blood, avoiding the need for an invasive synovial fluid harvest.PurposeTo assess if proteomic analyses can be used to identify novel candidate blood biomarkers in individuals who respond well or poorly to ACI.Study designControlled laboratory study.MethodsIsobaric tagging for relative and absolute quantitation (iTRAQ) mass spectrometry was used to assess the proteome in plasma pooled from ACI responders (mean Lysholm improvement after ACI, 33; n = 10) or nonresponders (mean, -13; n = 10), collected at the time of surgery for cartilage harvest (stage 1) or implantation of culture-expanded chondrocytes (stage 2). An alternative proteomic method, label-free quantitation liquid chromatography-tandem mass spectrometry, was used to analyze plasma samples (majority matched to iTRAQ) individually. Differentially abundant proteins (±2.0-fold) were analyzed from both proteomic data sets, and markers of interest identified via pooled iTRAQ were validated via immunoassay of individual samples.ResultsProtein differences could be detected in the plasma preoperatively between ACI responders and nonresponders (16 proteins; ≥±2.0-fold change; P ConclusionsThis study is the first to use proteomic techniques to profile the plasma of individuals treated with ACI. Despite iTRAQ analysis of pooled plasmas indicating that there are differences in the plasma proteome between responders and nonresponders to ACI, these findings were not replicated when assessed using an alternative nonpooled technique. This study highlights some of the difficulties in profiling the plasma proteome in an attempt to identify novel biomarkers. Regardless, cartilage acidic protein 1 has been identified as a protein candidate, which is detectable in plasma and can predict outcome to ACI before treatment.Clinical relevanceCandidate plasma protein biomarkers identified in this study have the potential to help determine which patients will be best suited to treatment with ACI

Synovial Fluid Metabolites Differentiate between Septic and Nonseptic Joint Pathologies

Osteoarthritis (OA), osteochondrosis (OC), and synovial sepsis in horses cause loss of function and pain. Reliable biomarkers are required to achieve accurate and rapid diagnosis, with synovial fluid (SF) holding a unique source of biochemical information. Nuclear magnetic resonance (NMR) spectroscopy allows global metabolite analysis of a small volume of SF, with minimal sample preprocessing using a noninvasive and nondestructive method. Equine SF metabolic profiles from both nonseptic joints (OA and OC) and septic joints were analyzed using 1D 1H NMR spectroscopy. Univariate and multivariate statistical analyses were used to identify differential metabolite abundance between groups. Metabolites were annotated via 1H NMR using 1D NMR identification software Chenomx, with identities confirmed using 1D 1H and 2D 1H 13C NMR. Multivariate analysis identified separation between septic and nonseptic groups. Acetate, alanine, citrate, creatine phosphate, creatinine, glucose, glutamate, glutamine, glycine, phenylalanine, pyruvate, and valine were higher in the nonseptic group, while glycylproline was higher in sepsis. Multivariate separation was primarily driven by glucose; however, partial-least-squares discriminant analysis plots with glucose excluded demonstrated the remaining metabolites were still able to discriminate the groups. This study demonstrates that a panel of synovial metabolites can distinguish between septic and nonseptic equine SF, with glucose the principal discriminator