An Atlas of the Knee Joint Proteins and Their Role in Osteoarthritis Defined by Literature Mining.

Osteoarthritis (OA) is the most prevalent rheumatic pathology. However, OA is not simply a process of wear and tear affecting articular cartilage but rather a disease of the entire joint. One of the most common locations of OA is the knee. Knee tissues have been studied using molecular strategies, generating a large amount of complex data. As one of the goals of the Rheumatic and Autoimmune Diseases initiative of the Human Proteome Project, we applied a text-mining strategy to publicly available literature to collect relevant information and generate a systematically organized overview of the proteins most closely related to the different knee components. To this end, the PubPular literature-mining software was employed to identify protein-topic relationships and extract the most frequently cited proteins associated with the different knee joint components and OA. The text-mining approach searched over eight million articles in PubMed up to November 2022. Proteins associated with the six most representative knee components (articular cartilage, subchondral bone, synovial membrane, synovial fluid, meniscus, and cruciate ligament) were retrieved and ranked by their relevance to the tissue and OA. Gene ontology analyses showed the biological functions of these proteins. This study provided a systematic and prioritized description of knee-component proteins most frequently cited as associated with OA. The study also explored the relationship of these proteins to OA and identified the processes most relevant to proper knee function and OA pathophysiology.

Prognostic model to predict the incidence of radiographic knee osteoarthritis.

OBJECTIVE: Early diagnosis of knee osteoarthritis (KOA) in asymptomatic stages is essential for the timely management of patients using preventative strategies. We develop and validate a prognostic model useful for predicting the incidence of radiographic KOA (rKOA) in non-radiographic osteoarthritic subjects and stratify individuals at high risk of developing the disease. METHODS: Subjects without radiographic signs of KOA according to the Kellgren and Lawrence (KL) classification scale (KL=0 in both knees) were enrolled in the OA initiative (OAI) cohort and the Prospective Cohort of A Coruña (PROCOAC). Prognostic models were developed to predict rKOA incidence during a 96-month follow-up period among OAI participants based on clinical variables and serum levels of the candidate protein biomarkers APOA1, APOA4, ZA2G and A2AP. The predictive capability of the biomarkers was assessed based on area under the curve (AUC), and internal validation was performed to correct for overfitting. A nomogram was plotted based on the regression parameters. Model performance was externally validated in the PROCOAC. RESULTS: 282 participants from the OAI were included in the development dataset. The model built with demographic, anthropometric and clinical data (age, sex, body mass index and WOMAC pain score) showed an AUC=0.702 for predicting rKOA incidence during the follow-up. The inclusion of ZA2G, A2AP and APOA1 data significantly improved the model's sensitivity and predictive performance (AUC=0.831). The simplest model, including only clinical covariates and ZA2G and A2AP serum levels, achieved an AUC=0.826. Both models were internally cross-validated. Predictive performance was externally validated in an independent dataset of 100 individuals from the PROCOAC (AUC=0.713). CONCLUSION: A novel prognostic model based on common clinical variables and protein biomarkers was developed and externally validated to predict rKOA incidence over a 96-month period in individuals without any radiographic signs of disease. The resulting nomogram is a useful tool for stratifying high-risk populations and could potentially lead to personalised medicine strategies for treating OA.

Analysis of Endogenous Peptides Released from Osteoarthritic Cartilage Unravels Novel Pathogenic Markers.

Osteoarthritis (OA) is a pathology characterized by the loss of articular cartilage. In this study, we performed a peptidomic strategy to identify endogenous peptides (neopeptides) that are released from human osteoarthritic tissue, which may serve as disease markers. With this aim, secretomes of osteoarthritic and healthy articular cartilages obtained from knee and hip were analyzed by shotgun peptidomics. This discovery step led to the identification of 1175 different peptides, corresponding to 101 proteins, as products of the physiological or pathological turnover of cartilage extracellular matrix. Then, a targeted multiple reaction monitoring-mass spectrometry method was developed to quantify the panel of best marker candidates on a larger set of samples (n = 62). Statistical analyses were performed to evaluate the significance of the observed differences and the ability of the neopeptides to classify the tissue. Eight of them were differentially abundant in the media from wounded zones of OA cartilage compared with the healthy tissue (p < 0.05). Three neopeptides belonging to Clusterin and one from Cartilage Oligomeric Matrix Protein showed a disease-dependent decrease specifically in hip OA, whereas two from Prolargin (PRELP) and one from Cartilage Intermediate Layer Protein 1 were significantly increased in samples from knee OA. The release of one peptide from PRELP showed the best metrics for tissue classification (AUC = 0.834). The present study reveals specific neopeptides that are differentially released from knee or hip human osteoarthritic cartilage compared with healthy tissue. This evidences the intervention of characteristic pathogenic pathways in OA and provides a novel panel of peptidic candidates for biomarker development.

Proteomic profiling of human menisci from mild joint degeneration and end-stage osteoarthritis versus healthy controls.

Objective: To gain new insight into the molecular changes of the meniscus by comparing the proteome profiles of healthy controls with mild degeneration and end-stage osteoarthritis (OA). Method: We obtained tissue plugs from lateral and medial menisci of 37 individuals (central part of the posterior horn) classified as healthy (n â€‹= â€‹12), mild signs of joint damage (n â€‹= â€‹13) and end-stage OA (n â€‹= â€‹12). The protein profile was analysed by nano-liquid chromatography-mass spectrometry using data-independent acquisition and quantified by Spectronaut. Linear-mixed effects modelling was applied to extract the between-group comparisons. Results: A similar protein profile was observed for the mild group as compared to healthy controls while the most different group was end-stage OA mainly for the medial compartment. When a pattern of gradual change in protein levels from healthy to end-stage OA was required, a 42-proteins panel was identified, suggesting a potential role in OA development. The levels of QSOX1 were lower and G6PD higher in the mild group following the proposed protein abundance pattern. Qualitative protein changes suggest lower levels of CYTL1 as a potential biomarker of early joint degradation. Conclusion: For future targeted proteomic approaches, we propose a candidate panel of 42 proteins based on gradually altered meniscal posterior horn protein abundance patterns associated with joint degradation.

Serum Proteomic Profiling in Rheumatoid Arthritis by Antibody Suspension Bead Arrays.

The versatility of protein microarrays provides researchers with a wide variety of possibilities to address proteomic studies. Therefore, protein microarrays are becoming very useful tools to identify candidate biomarkers in human body fluids for disease states such as rheumatoid arthritis (RA). In RA serum, there is a high prevalence of rheumatoid factor (RF), which is an antibody with high specificity against Fc portion of IgG. The presence of RF, in particular RF-IgM, has the great potential to interfere with antibody-based immunoassays by nonspecifically binding capture antibodies. Because of this concern, we describe a procedure to reduce the interference of RF-IgM on RA serum protein profiling approaches based on multiplexed antibody suspension bead arrays.