Búsqueda de biomarcadores de artrosis mediante técnicas proteómicas

[Resumen] La Artrosis (OA) es la enfermedad reumática más frecuente e invalidante a nivel mundial. Se caracteriza principalmente por una degradación progresiva del cartílago articular pero también por cambios en el hueso subcondral y la inflamación de la membrana sinovial. Pese a su elevada prevalencia, los métodos de diagnóstico actuales de la OA son poco sensibles y no existe tratamiento eficaz frente a la enfermedad. Estas limitaciones han originado un considerable interés por encontrar marcadores biológicos específicos que reflejen las variaciones cuantitativas y dinámicas de la remodelación de la articulación durante la artrosis. Teniendo en cuenta que el proceso de OA implica diferentes tejidos y complejos procesos biológicos, la estrategia diagnóstica más prometedora podría ser la combinación del estudio de diferentes biomarcadores con el fin de facilitar el diagnóstico precoz, evaluar la progresión de la enfermedad y posibilitar la monitorización de terapias alternativas. Así, en esta tesis se han empleado diferentes técnicas proteómicas de expresión diferencial basadas en espectrometría de masas y en arrays de proteínas para abordar el estudio de la búsqueda de potenciales biomarcadores de artrosis. En el primer trabajo presentado, se analizó el perfil de degradación del cartílago artrósico con la finalidad de identificar potenciales marcadores tempranos y de progresión de la enfermedad. Para ello, se utilizaron muestras de diferentes zonas del cartílago artrósico humano y también cartílago sano. En este estudio, se identificó un panel de 76 proteínas que estaban diferencialmente liberadas por el cartílago artrósico al compararlo con el cartílago sano. A continuación, se validó por inmunodetección el potencial de dos proteínas como marcadores de progresión de la degradación del cartílago artrósico. En el segundo trabajo, se utilizaron muestras de líquido sinovial de pacientes con OA y pacientes con artritis reumatoide (AR), con el objetivo de caracterizar el perfil proteico del líquido sinovial en la OA. Posteriormente, se validaron por inmunodetección los resultados obtenidos para 4 de las proteínas cuantificadas por espectrometría de masas. A continuación, utilizamos arrays de proteínas para buscar potenciales biomarcadores séricos de la OA. Así, en el tercer trabajo, se utilizaron muestras de suero de pacientes con artrosis, artritis reumatoide y controles sanos con el objetivo de identificar autoanticuerpos con potencial biomarcador de la artrosis empleando arrays de antígenos. En este trabajo, detectamos que los niveles de inmunorreactividad frente a 7 antígenos permitían distinguir los pacientes artrósicos de los individuos sanos mientras que los niveles de inmunorreactividad frente a otros 7 antígenos, permitían distinguir entre los pacientes con OA y con AR. Por último, se construyeron arrays de anticuerpos en suspensión con el fin de identificar, en un gran número de muestras, un panel de proteínas en el suero con potencial biomarcador para el diagnóstico de la artrosis. Esta metodología se utilizó para analizar muestras de suero de pacientes con diferentes enfermedades reumáticas (artrosis, artritis reumatoide y artritis psoriásica) e individuos sanos. Los resultados obtenidos proporcionaron un perfil proteico con potencial biomarcador para el diagnóstico de la artrosis de rodilla.[Resumo] A artrose (OA) é a enfermidade reumática mais frecuente e invalidante a nivel mundial. Caracterízase principalmente por unha degradación progresiva da cartilaxe articular pero tamén por cambios no óso subcondral e a inflamación da membrana sinovial. Pese a súa prevalencia, os métodos actuais de diagnose da OA son pouco sensibles e non existe tratamento eficaz frente a enfermidade. Estas limitacións orixinaron un considerable interés por encontrar marcadores biolóxicos específicos que reflexen variacións cuantitativas e dinámicas da remodelación da articulación durante a OA. Tendo en conta que o proceso artrósico implica a diferentes texidos e complexos procesos biolóxicos, a estratexia diagnóstica mais prometedora podería ser a combinación do estudio de diferentes biomarcadores coa finalidade de facilitar a diagnose temperá, evaluar a progresión da enfermidade e posibilitar a monitorización de terapias alternativas. Así, nesta tese se empregaron diferentes técnicas proteómicas de expresión diferencial basadas en espectrometría de masas e en arrays de proteínas para abordar o estudo da búsqueda de biomarcadores de artrose. No primeiro traballo presentado, se analizou o perfil de degradación proteica da cartilaxe artrósica co obxectivo de indentificar potenciais marcadores temperáns e de progresión da enfermidade. Para isto, se utilizaron mostras de diferentes zonas da cartilaxe artrósica humana e tamén cartilaxe sana. En este estudio, se identificou un panel de 76 proteínas que estaban diferencialmente liberadas pola cartilaxe artrósica en comparación coa cartilaxe sana. A continuación, validamos por inmunodetección o potencial de dúas proteínas como marcadores da degradación progresiva da cartilaxe artrósica. No segundo traballo, se empregaron mostras de líquido sinovial de pacientes con artrose e pacientes con artritis reumatoide (AR), co obxectivo de caracterizar o perfil proteico do líquido sinovial na OA. Posteriormente, se validaron por inmunodetección os resultados obtidos para dúas das proteínas cuantificadas por espectrometría de masas. A continuación, utilizamos arrays de proteínas para buscar potenciais biomarcadores séricos da OA. Así, no terceiro traballo, se utilizaron mostras de soro de pacientes con OA, pacientes con AR e individuos sanos para buscar autoanticorpos con potencial biomarcador da artrose empregando arrays de antíxenos. Neste traballo, detectamos que os niveis de inmurreactividade frente a 7 antíxenos permitían distinguir entre pacientes con OA e individuos sanos, mentres que os niveis de inmurreactividade frente a outros 7 antíxenos permitían distinguir entre pacientes con OA e AR. Por último, se construíron arrays de anticorpos en suspensión coa finalidade de identificar, nun gran número de mostras, un panel de proteínas no soro con potencial biomarcador para a diagnose da artrose. Así, se analizaron mostras de soro de doentes con diferentes enfermidades reumáticas (artrose, artritis reumatoide e artritis psoriásica) e individuos sanos. Os resultados obtidos proporcionaron un perfil proteico con potencial biomarcador para a diagnose da artrose de xeonllo.[Abstract] Osteoarthritis (OA) is the most common and disabling rheumatic disease worldwide. It is characterized by the progressive loss of cartilage, subchondral bone remodelling and synovial inflammation. Despite its high prevalence, current diagnosis methods are poor sensitive and there is no efficient treatment for the disease. These limitations have prompted a considerable interest in finding specific biological markers to reflect quantitative and dynamic joint changes that occur in OA. Taking in account that OA process involves different tissues and complex biological process, the most promising strategy for early diagnosis and monitoring of the disease, could be the combination of the study of different biomarkers. Therefore, in this thesis, different proteomic technologies based on mass spectrometry and protein arrays were employed to address the research of potential OA biomarkers. Firstly, we aimed to study the protein profile of OA cartilage degradation to find potential early and progression OA biomarkers. For this, we used OA and healthy human cartilage explants. A panel of 76 differentially secreted proteins from OA cartilage compared to normal cartilage was found. Then, two proteins with potential value as progression biomarkers were validated. Next, we used synovial fluid from OA patients and rheumatoid arthritis (RA) patients to find a characteristic protein profile of OA process using mass spectrometry. Four modulated proteins were validated. Then we used protein arrays to find potential serum OA biomarkers. Therefore, antigen arrays were used to search for serum autoantibodies in OA comparing the autoantibody profile from OA patients, healthy donors as well as RA patients. We observed immunoreactivity levels towards 7 antigens allowed to distinguish between OA patients and healthy controls, whereas the immunoreactivity towards other 7 antigens enables to discriminate between OA and RA patients. Finally, we used antibody suspension bead arrays to identify, in a large set of samples, a panel of serum proteins with potential value for OA diagnosis. This methodology was used to analyse serum samples from patients with different rheumatic diseases (OA, RA and psoriatic arthritis) and healthy donors. The results obtained provide a serum protein profile with a great potential for knee OA diagnosis

Estudio comparativo del secretoma de condrocitos articulares humanos analizados mediante las técnicas iTRAQ e SILAC

Comunicaciones a congreso

Determinación del perfil proteico diferencial de líquido sinovial en pacientes con artrosis y artritis reumatoide mediante nanoLC acoplada a MALDI-TOF7TOF

Comunicaciones a congreso

Cuantificación por SILAC del efecto de la nicotina sobre condrocitos articulares

Comunicaciones a congreso

Quantitative proteomics analysis of chondrogenic differentiation of mesenchymal stem cells by SILAC

Comunicaciones a congreso

Association of the serological status of rheumatoid arthritis patients with two circulating protein biomarkers: a useful tool for precision medicine strategies

[Abstract] Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints and presence of systemic autoantibodies, with a great clinical and molecular heterogeneity. Rheumatoid Factor (RF) and anti-citrullinated protein antibodies (ACPA) are routinely used for the diagnosis of RA. However, additional serological markers are needed to improve the clinical management of this disease, allowing for better patient stratification and the desirable application of precision medicine strategies. In the present study, we investigated those systemic molecular changes that are associated with the RF and ACPA status of RA patients. To achieve this objective, we followed a proteomic biomarker pipeline from the discovery phase to validation. First, we performed an iTRAQ-based quantitative proteomic experiment on serum samples from the RA cohort of the Hospital of Santiago de Compostela (CHUS). In this discovery phase, serum samples from the CHUS cohort were pooled according to their RF/ACPA status. Shotgun analysis revealed that, in comparison with the double negative group (RF-/ACPA-), the abundance of 12 proteins was altered in the RF+/ACPA+ pool, 16 in the RF+/ACPA- pool and 10 in the RF-/ACPA+ pool. Vitamin D binding protein and haptoglobin were the unique proteins increased in all the comparisons. For the verification phase, 80 samples from the same cohort were analyzed individually. To this end, we developed a Multiple Reaction Monitoring (MRM) method that was employed in a comprehensive targeted analysis with the aim of verifying the results obtained in the discovery phase. Thirty-one peptides belonging to 12 proteins associated with RF and/or ACPA status were quantified by MRM. In a final validation phase, the serum levels of alpha-1-acid glycoprotein 1 (A1AG1), haptoglobin (HPT) and retinol-binding protein 4 (RET4) were measured by immunoassays in the RA cohort of the Hospital of A Coruña (HUAC). The increase of two of these putative biomarkers in the double seropositive group was validated in 260 patients from this cohort (p = 0.009 A1AG1; p = 0.003 HPT). The increased level of A1AG1 showed association with RF rather than ACPA (p = 0.023), whereas HPT showed association with ACPA rather than RF (p = 0.013). Altogether, this study has allowed a further classification of the RA seropositive patients into two novel clusters: RF+A1AG+ and ACPA+HPT+. The determination of A1AG1 and HPT in serum would provide novel information useful for RA patient stratification, which could facilitate the effective implementation of personalized medicine in routine clinical practice.Instituto de Salud Carlos III; PI16/02124Instituto de Salud Carlos III; PI17/00404Instituto de Salud Carlos III; PI19/01206Instituto de Salud Carlos III; PI20/00793Instituto de Salud Carlos III; RD16/0012/0002Instituto de Salud Carlos III; RD21/0002/0009Xunta de Galicia; IN607A2021/07Xunta de Galicia; IN607D2020/1

An atlas of the knee joint proteins and their role in osteoarthritis defined by literature mining

[Abstract] 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.PI19/01206; Instituto de Salud Carlos IIIPI20/00793; Instituto de Salud Carlos IIIPI20/01409; Instituto de Salud Carlos IIIPI22/01155; Instituto de Salud Carlos IIIRICORS-REIRD21/0002/0009; Instituto de Salud Carlos IIIED431E2018/03; Xunta de GaliciaIN607A2021/07; Xunta de GaliciaIN607D2020/10; Xunta de Galici

Variable effect of 3 different chondroitin sulfate compounds on chondrocytes secreted proteins profile revealed by silac techinique

Comunicaciones a congreso

Secretome analysis of chondroitin sulfate-treated chondrocytes reveals anti-angiogenic, anti-inflammatory and anti-catabolic properties

Introduction: Chondroitin sulfate (CS) is a symptomatic slow-acting drug for osteoarthritis (OA) widely used in the clinic. The aim of this work is to find proteins whose secretion from cartilage cells under proinflammatory stimuli (IL-1β) is regulated by CS, employing a novel quantitative proteomic approach. Methods: Human articular chondrocytes released from three normal cartilages were grown in SILAC medium. When complete incorporation of the heavy isotope was achieved, chondrocytes were stimulated with IL-1β 5 ng/ml with or without CS pretreatment (200 µg/ml). Forty-eight hours later, chondrocyte secretomes were analyzed by nano-scale liquid chromatography-mass spectrometry. Real-time PCR, western blot and immunohistochemistry analyses were employed to confirm some of the results. Results: We could identify 75 different proteins in the secretome of human articular chondrocytes. Eighteen of these were modulated by CS with statistical significance (six increased and 12 decreased). In normal chondrocytes stimulated with IL-1β, CS reduces inflammation directly by decreasing the presence of several complement components (CFAB, C1S, CO3, and C1R) and also indirectly by increasing proteins such as TNFα-induced protein (TSG6). TSG6 overexpression correlates with a decrease in pro-matrix metalloproteinase activation (observed in MMP1 and MMP3 levels). Finally, we observed a strong CS-dependent increase of an angiogenesis inhibitor, thrombospondin-1. Conclusion: We have generated a quantitative profile of chondrocyte extracellular protein changes driven by CS in the presence of IL-1β. We have also provided novel evidences of its anti-angiogenic, anti-inflammatory, and anti-catabolic properties. Demonstration of the anti-angiogenic action of CS might provide a novel therapeutic approach for OA targeting

Prognostic model to predict the incidence of radiographic knee osteoarthritis

[Abstract] 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.This work has been funded by Instituto de Salud Carlos III (ISCIII) through the projects PI19/01206, PI20/00793, PI20/01409 and PI22/01155, and co-funded by the European Union, and also by the grant RD21/0002/0009 financed by Instituto de Salud Carlos III–European Union-NextGenerationEU-Plan de Recuperación transformación y resiliencia. This study has been also supported by grants IN607A2021/07 and IN607D2020/10 from Xunta de Galicia. The Biomedical Research Networking Center (CIBER) is an initiative from Instituto de Salud Carlos III (ISCIII). LL is supported by Contrato Sara Borrell (CD19/00229), Fondo de Investigación Sanitaria, ISCIII. VC is supported by RICORS-REI RD21/0002/0009.info:eu-repo/grantAgreement/ISCIII/Programa Estatal de Generación de Conocimiento y Fortalecimiento del Sistema Español de I+D+I/PI19%2F01206/ES/VALIDACION CLINICA DE NUEVOS BIOMARCADORES PREDICTIVOS DE DIAGNOSTICO Y PRONOSTICO EN ARTROSIS: EL PROYECTO HPPinfo:eu-repo/grantAgreement/ISCIII/Programa Estatal de Generación de Conocimiento y Fortalecimiento del Sistema Español de I+D+I/PI20%2F00793/ES/DESARROLLO DE SOLUCIONES INTEGRADAS DE ANALITICA PREDICTIVA PARA PERSONALIZAR LA FARMACOTERAPIA EN PACIENTES CON ARTRITIS REUMATOIDEinfo:eu-repo/grantAgreement/ISCIII/Programa Estatal de Generación de Conocimiento y Fortalecimiento del Sistema Español de I+D+I/PI20%2F01409/ES/NUEVAS METODOLOGIAS PARA LA ESTRATIFICACION DE PACIENTES ARTROSICOS (OA) MEDIANTE TECNICAS PROTEOMICA, APRENDIZAJE AUTOMATICO Y BIG DATAinfo:eu-repo/grantAgreement/ISCIII/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PI22%2F01155/ES/MEDICINA PERSONALIZADA EN LA ARTROSIS: INTELIGENCIA ARTIFICIAL APLICADA AL DIAGNÓSTICO DE OA DE RODILLA RAPIDAMENTE PROGRESIVAXunta de Galicia; IN607A2021/07Xunta de Galicia; IN607D2020/1