What did we learn from 'omics' studies in osteoarthritis

Therapeutic Controlled Release Strategies for Human Osteoarthritis

Osteoarthritis is a progressive, irreversible debilitating whole joint disease that affects millions of people worldwide. Despite the availability of various options (non-pharmacological and pharmacological treatments and therapy, orthobiologics, and surgical interventions), none of them can definitively cure osteoarthritis in patients. Strategies based on the controlled release of therapeutic compounds via biocompatible materials may provide powerful tools to enhance the spatiotemporal delivery, expression, and activities of the candidate agents as a means to durably manage the pathological progression of osteoarthritis in the affected joints upon convenient intra-articular (injectable) delivery while reducing their clearance, dissemination, or side effects. The goal of this review is to describe the current knowledge and advancements of controlled release to treat osteoarthritis, from basic principles to applications in vivo using therapeutic recombinant molecules and drugs and more innovatively gene sequences, providing a degree of confidence to manage the disease in patients in a close future.

Exploring the Early Molecular Pathogenesis of Osteoarthritis Using Differential Network Analysis of Human Synovial Fluid

The molecular mechanisms that drive the onset and development of osteoarthritis (OA) remain largely unknown. In this exploratory study, we used a proteomic platform (SOMAscan assay) to measure the relative abundance of more than 6000 proteins in synovial fluid (SF) from knees of human donors with healthy or mildly degenerated tissues, and knees with late-stage OA from patients undergoing knee replacement surgery. Using a linear mixed effects model, we estimated the differential abundance of 6251 proteins between the three groups. We found 583 proteins upregulated in the late-stage OA, including MMP1, collagenase 3 and interleukin-6. Further, we selected 760 proteins (800 aptamers) based on absolute fold changes between the healthy and mild degeneration groups. To those, we applied Gaussian Graphical Models (GGMs) to analyze the conditional dependence of proteins and to identify key proteins and subnetworks involved in early OA pathogenesis. After regularization and stability selection, we identified 102 proteins involved in GGM networks. Notably, network complexity was lost in the protein graph for mild degeneration when compared to controls, suggesting a disruption in the regular protein interplay. Furthermore, among our main findings were several downregulated (in mild degeneration versus healthy) proteins with unique interactions in the healthy group, one of which, SLCO5A1, has not previously been associated with OA. Our results suggest that this protein is important for healthy joint function. Further, our data suggests that SF proteomics, combined with GGMs, can reveal novel insights into the molecular pathogenesis and identification of biomarker candidates for early-stage OA.

An osteoarthritis subtype characterized by synovial lipid metabolism disorder and fibroblast-like synoviocyte dysfunction

Background

The heterogeneity of osteoarthritis (OA) significantly limits the effectiveness of pharmacological treatments in an unselected patient population. In this context, the identification of OA subtypes is meaningful for the development of therapies that target specific types of OA pathogenesis.

Methods

Expression array profiles of 70 OA and 36 control synovial samples were extracted from the GEO database. Unsupervised consensus clustering was performed based on the most variable genes to identify OA subclusters. Next, Joint samples from OA patients were obtained. We divided the OA patient into two subpopulations according to synovial ADCY7 levels. Synovium and cartilage samples from different OA subpopulations were evaluated. In addition, we established a high-fat diet (HFD)-induced rat OA model. We evaluated OA progression, lipid metabolism, synovitis and fibroblast-like synoviocytes (FLS) function in this HFD-induced OA model.

Results

70 OA patients were categorized into three distinct subclusters. We noted that one subcluster was characterized by synovial lipid metabolism disorder GO terms. We further identified the most noticeable KEGG pathway “Regulation of lipolysis in adipocytes” in this subcluster as well as the most significantly differentially expressed gene, ADCY7. We found that the ADCY7 high expressing group (32.6%) exhibited features of synovial inflammatory lipolysis epithelial-mesenchymal transition (EMT) tendency, as well as faster join space narrowing. The HFD induced OA-like degeneration in rat joints. We observed similar synovial inflammatory lipolysis and EMT in FLS, characterized by higher proliferative and invasive activity and elevated proinflammatory and procatabolic properties. ADCY7 was highly expressed in the synovium of the HFD-OA model rats and the inhibition of ADCY7 effectively attenuated these HFD-induced degenerative changes as well as synovial inflammatory lipolysis and FLS dysfunction. In HFD-FLSs, ADCY7 promoted the phosphorylation of PKA as well as its downstream lipid droplet-associated protein PLIN1 and hormone-sensitive lipase (HSL). The inhibition of PKA largely alleviated ADCY7-mediated HFD-FLS dysfunction.

Conclusions

We described a synovial EMT and lipid metabolism disorder in the pathogenesis of OA. This novel mechanism may represent a currently undefined OA subtype. ADCY7 is a potential molecular marker of this pathomechanism.

The Translational potential of this article

Utilizing synovial samples from OA patients, we identified a subpopulation with high ADCY7 expression. This may represent a currently undefined OA subtype and explain the clinical phenomenon of more severe synovial inflammation in obese OA patients. In addition, we established an HFD-induced OA rat model and found an upregulation of ADCY7 in the synovium. We confirmed that the inhibition of ADCY7 could effectively attenuate HFD-induced degenerative changes as well as the inflammatory lipolysis and FLS dysfunction observed in the rat model. This suggests that ADCY7 and its downstream pathways are potential pharmacological targets for treating this lipid-metabolism-disorder-related OA mechanism.

Proteomics profiling of human synovial fluid suggests increased protein interplay in early-osteoarthritis (OA) that is lost in late-stage OA

The underlying molecular mechanisms in osteoarthritis (OA) development are largely unknown. This study explores the proteome and the pairwise interplay of proteins in synovial fluid from patients with late-stage knee OA (arthroplasty), early knee OA (arthroscopy due to degenerative meniscal tear), and from deceased controls without knee OA. Synovial fluid samples were analyzed using state-of-the-art mass spectrometry with data-independent acquisition. The differential expression of the proteins detected was clustered and evaluated with data mining strategies and a multilevel model. Group-specific slopes of associations were estimated between expressions of each pair of identified proteins to assess the co-expression (i.e., interplay) between the proteins in each group. More proteins were increased in early-OA versus controls than late-stage OA versus controls. For most of these proteins, the fold changes between late-stage OA versus controls and early-stage OA versus controls were remarkably similar suggesting potential involvement in the OA process. Further, for the first time, this study illustrated distinct patterns in protein co-expression suggesting that the interplay between the protein machinery is increased in early-OA and lost in late-stage OA. Further efforts should focus on earlier stages of the disease than previously considered.

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Synovial fluid proteomics study of different stages of osteoarthritis (OA).

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Higher catabolic activity is found in both early- and late-stage OA.

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Imbalance of the metabolic homeostasis in late-stage OA.

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Understanding early-stage OA may lead to finding better effective therapies.

The future of deep phenotyping in osteoarthritis: How can high throughput omics technologies advance our understanding of the cellular and molecular taxonomy of the disease?

Osteoarthritis (OA) is the most common form of musculoskeletal disease with significant healthcare costs and unmet needs in terms of early diagnosis and treatment. Many of the drugs that have been developed to treat OA failed in phase 2 and phase 3 clinical trials or produced inconclusive and ambiguous results. High throughput omics technologies are a powerful tool to better understand the mechanisms of the development of OA and other arthritic diseases. In this paper we outline the strategic reasons for increasingly applying deep phenotyping in OA for the benefit of gaining a better understanding of disease mechanisms and developing targeted treatments. This editorial is intended to launch a special themed issue of Osteoarthritis and Cartilage Open addressing the timely topic of "Advances in omics technologies for deep phenotyping in osteoarthritis". High throughput omics technologies are increasingly being applied in mechanistic studies of OA and other arthritic diseases. Applying multi-omics approaches in OA is a high priority and will allow us to gather new information on disease pathogenesis at the cellular level, and integrate data from diverse omics technology platforms to enable deep phenotyping. We anticipate that new knowledge in this area will allow us to harness the power of Big Data Analytics and resolve the extremely complex and overlapping clinical phenotypes into molecular endotypes, revealing new information about the cellular taxonomy of OA and "druggable pathways", thus facilitating future drug development.

Comparative transcriptomics and network pharmacology analysis to identify the potential mechanism of celastrol against osteoarthritis

INTRODUCTION

Celastrol is a promising therapeutic agent for the treatment of osteoarthritis (OA). However, the mechanism of action of celastrol is unclear. This study was aiming to identify the potential function of celastrol on OA and determine its underlying mechanism.

METHOD

Celastrol targets were collected from web database searches and literature review, while pathogenic OA targets were obtained from Online Mendelian Inheritance in Man (OMIM) and GeneCards databases. Transcriptomics data was sequenced using an Illumina HiSeq 4000 platform. Celastrol-OA overlapping genes were then identified followed by prediction of the potential function and signaling pathways associated with celastrol using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A celastrol-target network was constructed to identify the candidate core targets of celastrol. The predictions were then validated by performing molecular docking and molecular dynamics simulation studies.

RESULTS

In total, 96 genes were identified as the putative celastrol targets for treatment of OA. These genes were possibly involved in cell phenotype changes including response to lipopolysaccharide and oxidative stress as well as in cell apoptosis and aging. The genes also induced the mTOR pathway and AGE-RAGE signaling pathway at the intracellular level. Additionally, results indicated that 13 core targets including mTOR, TP53, MMP9, EGFR, CCND1, MAPK1, STAT3, VEGFA, CASP3, TNF, MYC, ESR1, and PTEN were likely direct targets of celastrol in OA. Finally, mTOR was determined as the most likely therapeutic target of celastrol in OA.

CONCLUSION

This study provides a basic understanding and novel insight into the potential mechanism of celastrol against OA. Key Points • Our study provides a strong indication that further study of celastrol therapy in OA is required. • mTOR is the most likely therapeutic target of celastrol in OA.

Mass Spectrometry Imaging as a Potential Tool to Investigate Human Osteoarthritis at the Tissue Level

Osteoarthritis (OA) is the most common degenerative joint disease, predicted to increase in incidence year by year due to an ageing population. Due to the biological complexity of the disease, OA remains highly heterogeneous. Although much work has been undertaken in the past few years, underlying molecular mechanisms leading to joint tissue structural deterioration are not fully understood, with only few validated markers for disease diagnosis and progression being available. Discovery and quantitation of various OA-specific biomarkers is still largely focused on the bodily fluids which does not appear to be reliable and sensitive enough. However, with the advancement of spatial proteomic techniques, several novel peptides and proteins, as well as N-glycans, can be identified and localised in a reliable and sensitive manner. To summarise the important findings from OA biomarker studies, papers published between 2000 and 2020 were searched via Google Scholar and PubMed. Medical subject heading (MeSH) terms ‘osteoarthritis’, ‘biomarker’, ‘synovial fluid’, ‘serum’, ‘urine’, ’matrix-assisted laser desorption/ionisation’, ‘mass spectrometry imaging’, ‘proteomic’, ‘glycomic’, ‘cartilage’, ‘synovium’ AND ‘subchondral bone’ were selectively used. The literature search was restricted to full-text original research articles and written only in English. Two main areas were reviewed for OA biomarker studies: (1) an overview of disease-specific markers detected from different types of OA bio-samples, and (2) an up-to-date summary of the tissue-specific OA studies that have utilised matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI). Overall, these OA biomarkers could provide clinicians with information for better the diagnosis, and prognosis of individual patients, and ultimately help facilitate the development of disease-modifying treatments.

Proteomic comparison of osteoarthritic and reference human menisci using data-independent acquisition mass spectrometry

OBJECTIVE

Recent research in knee osteoarthritis (OA) highlights the role of the meniscus in OA pathology. Our aim was to compare the proteomes of medial and lateral menisci from end-stage medial compartment knee OA patients, with reference menisci from knee-healthy deceased donors, using mass spectrometry.

DESIGN

Tissue plugs of Ø3 mm were obtained from the posterior horns of the lateral and medial menisci from one knee of 10 knee-healthy deceased donors and 10 patients undergoing knee replacement. Proteins were extracted and prepared for mass spectrometric analysis. Statistical analysis was conducted on abundance data that was log₂-transformed, using a linear mixed effects model and evaluated using pathway analysis.

RESULTS

We identified a total of 835 proteins in all samples, of which 331 were included in the statistical analysis. The largest differences could be seen between the medial menisci from OA patients and references, with most proteins showing higher intensities in the medial menisci from OA patients. Several matrix proteins, e.g., matrix metalloproteinase 3 (MMP3) (4.3 times higher values [95%CI 1.8, 10.6]), TIMP1 (3.5 [1.4, 8.5]), asporin (4.1 [1.7, 10.0]) and versican (4.4 [1.8, 10.9]), all showed higher abundance in medial menisci from OA patients compared to medial reference menisci. OA medial menisci also showed increased activation of several pathways involved in inflammation.

CONCLUSION

An increase in protein abundance for proteins such as MMP and TIMP1 in the medial menisci from OA patients suggests simultaneous activation of both catabolic and anabolic processes that warrants further attention.

Proteomic characterization of the normal human medial meniscus body using data-independent acquisition mass spectrometry

Recent research suggests an important role of the meniscus in the development of knee osteoarthritis. We, therefore, aimed to analyze the proteome of the normal human meniscus body, and specifically to gain new knowledge on global protein expression in the different radial zones. Medial menisci were retrieved from the right knees of 10 human cadaveric donors, from which we cut a 2 mm radial slice from the mid-portion of the meniscal body. This slice was further divided into three zones: inner, middle, and peripheral. Proteins were extracted and prepared for mass spectrometric analysis using data-independent acquisition. We performed subsequent data searches using Spectronaut Pulsar and used fixed-effect linear regression models for statistical analysis. We identified 638 proteins and after statistical analysis, we observed the greatest number of differentially expressed proteins between the inner and peripheral zones (163 proteins) and the peripheral and middle zones (136 proteins), with myocilin being the protein with the largest fold-change in both comparisons. Chondroadherin was one of eight proteins that differed between the inner and middle zones. Functional enrichment analyses showed that the peripheral one-third of the medial meniscus body differed substantially from the two more centrally located zones, which were more similar to each other. This is probably related to the higher content of cells and vascularization in the peripheral zone, whereas the middle and inner zones of the meniscal body appear to be more similar to hyaline cartilage, with high levels of extracellular matrix proteins such as aggrecan and collagen type II.

Spatially resolved endogenous improved metabolite detection in human osteoarthritis cartilage by matrix assisted laser desorption ionization mass spectrometry imaging

Osteoarthritis (OA) is one of the most common musculoskeletal diseases, characterized by the progressive deterioration of articular cartilage. Although the disease has been well studied in the past few years, the endogenous metabolic composition and more importantly the spatial information of these molecules in cartilage is still poorly understood. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has been previously used for the investigation of the bimolecular distribution of proteins and lipids through the in situ analysis of cartilage tissue sections. MALDI-MSI as a tool to detect metabolites remains challenging, as these species have low abundance and degrade rapidly. In this work, we present a complete methodology, from sample preparation to data analysis for the detection of endogenous metabolites on cartilage by MSI. Our results demonstrate for the first time the ability to detect small molecules in fragile, challenging tissues through an optimized protocol, and render MSI as a tool towards a better understanding of OA.

Osteoarthritis phenotypes and novel therapeutic targets

The success of disease-modifying osteoarthritis drug (DMOAD) development is still elusive. While there have been successes in preclinical and early clinical studies, phase 3 clinical trials have failed so far and there is still no approved, widely available DMOAD on the market. The latest research suggests that, among other causes, poor trial outcomes might be explained by the fact that osteoarthritis (OA) is a heterogeneous disease with distinct phenotypes. OA trials might be more successful if they would address and target a specific phenotype. The increasing availability of advanced techniques to detect particular OA characteristics expands the possibilities to distinguish between such potential OA phenotypes. Magnetic resonance imaging is among the key imaging techniques to stratify and monitor patients with changes in bone, cartilage and inflammation. Biochemical markers have mainly used as secondary parameters and could further delineate phenotypes. Moreover, post-hoc analyses of trial data have suggested the existence of distinct pain phenotypes and their relevance in the design of clinical trials. Although ongoing work in the field supports the concept of OA heterogeneity, this has not yet resulted in more effective treatment options. This paper reviews the current knowledge about potential OA phenotypes and suggests that combining patient clinical data, quantitative imaging, biochemical markers and utilizing data-driven approaches in patient selection and efficacy assessment will allow for more successful development of effective DMOADs.

Soluble biochemical markers of osteoarthritis: Are we close to using them in clinical practice?

Osteoarthritis (OA) is the most common form of arthritis and a major cause of pain and disability. Recent work suggests that the global burden of OA is increasing, and costs associated with treatment are expected to increase dramatically as the aging human population expands. OA is currently diagnosed using radiography, but this technique is an indirect and insensitive measure of alterations in articular cartilage and fails to measure dynamic inflammatory processes in the joint. Radiographic changes detected overtime are small and occur in only a subset (progressors) of patients with OA. Therefore, we diagnose patients with OA on the basis of a diagnostic classification that is outdated. We also use the same tools and approaches for assessing the efficacy of new pharmacological and nonpharmacological interventions. In this review, we discuss the utility of soluble biochemical markers as biomarkers of OA and discuss whether we are close to using them in clinical practice. Combining patient information, functional imaging and carefully selected panels of biomarkers can help in achieving enhanced patient stratification and lead to better designed clinical trials. Biomarkers can be used for molecular endotyping and for developing more effective and more personalized treatments that will enhance clinical care for patients with OA.