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.

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.

Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative.

OBJECTIVE: To find autoantibodies (AAbs) in serum that could be useful to predict incidence of radiographic knee osteoarthritis (KOA). DESIGN: A Nucleic-acid Programmable Protein Arrays (NAPPA) platform was used to screen AAbs against 2125 human proteins in sera at baseline from participants free of radiographic KOA belonging to the incidence and non-exposed subcohorts of the Osteoarthritis Initiative (OAI) who developed or not, radiographic KOA during a follow-up period of 96 months. NAPPA-ELISA were performed to analyse reactivity against methionine adenosyltransferase two beta (MAT2ß) and verify the results in 327 participants from the same subcohorts. The association of MAT2ß-AAb levels with KOA incidence was assessed by combining several robust biostatistics analysis (logistic regression, Receiver Operating Characteristic and Kaplan-Meier curves). The proposed prognostic model was replicated in samples from the progression subcohort of the OAI. RESULTS: In the screening phase, six AAbs were found significantly different at baseline in samples from incident compared with non-incident participants. In the verification phase, high levels of MAT2ß-AAb were significantly associated with the future incidence of KOA and with an earlier development of the disease. The incorporation of this AAb in a clinical model for the prognosis of incident radiographic KOA significantly improved the identification/classification of patients who will develop the disorder. The usefulness of the model to predict radiographic KOA was confirmed on a different OAI subcohort. CONCLUSIONS: The measurement of AAbs against MAT2ß in serum might be highly useful to improve the prediction of OA development, and also to estimate the time to incidence.

Molecular Detection of Lymph Node Metastases in Lung Cancer Patients Using the One-Step Nucleic Acid Amplification Method:Clinical Significance and Prognostic Value.

The one-step nucleic acid amplification (OSNA) method allows for the quantitative evaluation of the tumor burden in resected lymph nodes (LNs) in patients with lung cancer. This technique enables to detect macro and micrometastases, facilitating the correct classification of patients for appropriate follow-up of the disease after surgery. Of 160 patients with resectable lung cancer whose LNs were examined by OSNA, H&E and CK19 IHC between July 2015 and December 2018, 110 patients with clinical stages from IA1 to IIIB were selected for follow-up. LN staging in lung cancer by pathological study led to understaging in 13.64% of the cases studied. OSNA allowed to quantify the tumor burden and establish a prognostic value. Patients with a total tumor load of ≥1650 cCP/uL were associated with a significantly increased likelihood of recurrence. Moreover, the survival of patients with <4405 cCP/uL was significantly higher than patients with ≥4405 cCP/uL. The OSNA assay is a rapid and accurate technique for quantifying the tumor burden in the LNs of lung cancer patients and OSNA quantitative data could allow to establish prognostic values for recurrence-free survival and overall survival in this type of malignancy.

Predictive modeling of therapeutic response to chondroitin sulfate/glucosamine hydrochloride in knee osteoarthritis.

BACKGROUND: In the present study, we explored potential protein biomarkers useful to predict the therapeutic response of knee osteoarthritis (KOA) patients treated with pharmaceutical grade Chondroitin sulfate/Glucosamine hydrochloride (CS+GH; Droglican, Bioiberica), in order to optimize therapeutic outcomes. METHODS: A shotgun proteomic analysis by iTRAQ labelling and liquid chromatography-mass spectrometry (LC-MS/MS) was performed using sera from 40 patients enrolled in the Multicentre Osteoarthritis interVEntion trial with Sysadoa (MOVES). The panel of proteins potentially useful to predict KOA patient's response was clinically validated in the whole MOVES cohort at baseline (n = 506) using commercially available enzyme-linked immunosorbent assays kits. Logistic regression models and receiver-operating-characteristics (ROC) curves were used to analyze the contribution of these proteins to our prediction models of symptomatic drug response in KOA. RESULTS: In the discovery phase of the study, a panel of six putative predictive biomarkers of response to CS+GH (APOA2, APOA4, APOH, ITIH1, C4BPa and ORM2) were identified by shotgun proteomics. Data are available via ProteomeXchange with identifier PXD012444. In the verification phase, the panel was verified in a larger set of KOA patients (n = 262). Finally, ITIH1 and ORM2 were qualified by a blind test in the whole MOVES cohort at baseline. The combination of these biomarkers with clinical variables predict the patients' response to CS+GH with a specificity of 79.5% and a sensitivity of 77.1%. CONCLUSIONS: Combining clinical and analytical parameters, we identified one biomarker that could accurately predict KOA patients' response to CS+GH treatment. Its use would allow an increase in response rates and safety for the patients suffering KOA.