A Clinical Model Including Protein Biomarkers Predicts Radiographic Knee Osteoarthritis: A Prospective Study Using Data From the Osteoarthritis Initiative

The Osteoarthritis Initiative (OAI) is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Pfizer, Inc.; Novartis Pharmaceuticals Corporation; Merck Research Laboratories; and GlaxoSmithKline. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This work has been supported by grants from Fondo Investigación Sanitaria (PI16/02124, PI17/00404, PI19/01206, DTS17/00200, CIBER-CB06/01/0040 and RETIC-RIER-RD16/0012/0002), integrated in the National Plan for Scientific Program, Development and Technological Innovation 2013–2016 and funded by the ISCIII-General Subdirection of Assessment and Promotion of Research-European Regional Development Fund (FEDER) “A way of making Europe”. This study has been also supported by grants IN607A2017/11, IN607D2020/10 and AE CICA-INIBIC (ED431E 2018/03) from Xunta de Galicia. The Proteomics Unit of GIR belongs to ProteoRed, PRB3- ISCIII (PT17/0019/0014). L.L. is supported by Xunta de Galicia (IN606B-2016/2005) and Contrato Sara Borrell (CD19/00229) Fondo de Investigación Sanitaria, ISCIII. I.R.P. is supported by Contrato Miguel Servet-II Fondo de Investigación Sanitaria (CPII17/00026). This work was also supported by the KTH Center for Applied Precision Medicine funded by the Ehrling Persson foundation, as well as the Human Protein Atlas project funded by Knut and Alice Wallenberg FoundationXunta de Galicia; IN607A2017/11Xunta de Galicia; IN607D2020/10Xunta de Galicia; ED431E 2018/03Xunta de Galicia; IN606B-2016/200

Autophagy activation by resveratrol reduces severity of experimental rheumatoid arthritis

Instituto de Salud Carlos III; RETIC‐RIER RD16/0012/0002Instituto de Salud Carlos III; PI12/02771Instituto de Salud carlos III; AGRUP2015/05Instituto de Salud Carlos III; AGRUP2018/0

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

[Abstract] 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 (MAT2β) and verify the results in 327 participants from the same subcohorts. The association of MAT2β-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 MAT2β-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 MAT2β in serum might be highly useful to improve the prediction of OA development, and also to estimate the time to incidence.Instituto de Salud Carlos III; PT17/0019/0014Instituto de Salud Carlos III; PI14/01707Instituto de Salud Carlos III; PI16/02124Instituto de Salud Carlos III; PI17/00404Instituto de Salud Carlos III; DTS17/00200Instituto de Salud Carlos III; CIBER-BBN CB06/01/0040Insituto de Salud Carlos III; CIBER-ONC CB16/12/00400Instituto de Salud Carlos III; RETIC-RIER-RD12/0009/0018Xunta de Galicia; IN606A-2016/012Instituto de Salud Carlos III; CPII17/0026Insituto de Salud Carlos III; CPII15/0001

Analysis of endogenous peptides released from osteoarthritic articular cartilage unravels novel pathogenic markers

[Abstract] 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.Instituto de Salud Carlos III; PI14/01707Instituto de Salud Carlos III; PI16/02124Instituto de Salud Carlos III; PI17/00404Instituto de Salud Carlos III; CIBER-CB06/01/0040Instituto de Salud Carlos III; DTS17/00200Instituto de Salud Carlos III; RETIC-RIER-RD16/0012/0002Instituto de Salud Carlos III; PT17/0019/001

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

[Abstract] 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.Insituto de Salud Carlos III; PI14/01707Instituto de Salud Carlos III; PI16/02124Insituto de Salud Carlos III; PI17/00404Instituto de Salud Carlos III; DTS17/00200Instituto de Salud Carlos III; CIBER-CB06/01/0040Insituto de Salud Carlos III; RETIC-RIER-RD16/0012/000

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.Ministerio of Educación (Spain). M.C.-E. is supported by the Xunta de Galicia and the European Union (European Social Fund - ESF) through a predoctoral fellowship (IN606A-2016/012). C.R.-R. has been supported by the Miguel Servet II program from Fondo Investigacón Sanitaria-Spain (CPII15/00013

Validación de biomarcadores de artrosis mediante microarrays de proteínas

Programa Oficial de Doctorado en Ciencias de la Salud. 5007V01[Resumen] Aunque la artrosis (OA) es una patología reumática caracterizada por una larga fase inicial clínicamente silente de deterioro articular, generalmente la enfermedad no se diagnostica hasta etapas muy avanzadas, donde la única solución posible es un reemplazamiento protésico. Esto es mayormente debido a las limitaciones y a la baja sensibilidad que presentan las técnicas de diagnóstico actuales, basadas en la descripción subjetiva de los síntomas del paciente y en pruebas radiológicas. En los últimos años, el uso de técnicas proteómicas ha dado lugar a una larga lista de marcadores solubles asociados con la patología artrósica, que podrían tener cierto potencial para el diagnóstico precoz y/o la predicción de la enfermedad. Sin embargo, ninguno de ellos ha sido suficientemente validado para su uso en la rutina clínica, debido principalmente a la falta de estudios prospectivos en un gran número de muestras procedentes de pacientes que hayan sido seguidos durante largos períodos de tiempo. En esta tesis se han empleado técnicas proteómicas rápidas y económicas, basadas en microarrays de proteínas en suspensión, para la validación y posterior cualificación como marcadores de predicción de incidencia de OA de un panel de seis proteínas seleccionadas en base a resultados previos de nuestro grupo de investigación. Así mismo, a pesar de que no se conoce la causa exacta que inicia el proceso artrósico, es bien sabido que el sistema inmune, entre otros, tiene un papel fundamental. La producción de anticuerpos frente a antígenos propios del cuerpo, o autoanticuerpos (AAbs) es una de las principales características de la actuación de dicho sistema. Por ello, durante la realización de esta tesis doctoral, se llevó a cabo una fase de descubrimiento de biomarcadores mediante arrays de proteínas en formato plano, con el fin de definir un perfil de inmunoreactividad propio de las etapas clínicamente silentes de la OA que permitiera identificar un panel de AAbs con posible potencial como marcadores de predicción. Los resultados obtenidos mediante el análisis de un amplio set de muestras de suero a tiempo cero de individuos sin evidencia ni radiográfica ni sintomática de OA de rodilla, procedentes de la cohorte de la Osteoartrhritis Initiative, han demostrado la asociación de distintos biomarcadores protéicos con la futura aparición de la enfermedad. Por otro lado, el uso de pruebas estadísticas multivariantes ha dado lugar a la generación de dos posibles modelos para predecir la incidencia de OA radiográfica de rodilla, formados por la combinación de marcadores protéicos y clínicos asociados con el desarrollo de la enfermedad. Además, mediante el empleo de análisis de supervivencia, se ha demostrado que los niveles en suero a tiempo cero de estos biomarcadores solubles se asocian con el tiempo de aparición de la misma: a mayores niveles del biomarcador, antes se desarrolla la OA.[Resumo] Aínda que a artrose (OA) é unha patoloxía reumática caracterizada por unha longa fase inicial clinicamente silente de deterioración articular, xeralmente a enfermidade non se diagnostica ata etapas moi avanzadas, onde a única solución posible é un reemplazamiento protésico. Isto, é maiormente debido ás limitacións e á baixa sensibilidade que presentan as técnicas de diagnóstico actuais, baseadas na descrición subxectiva dos síntomas do paciente e en probas radiolóxicas. Nos últimos anos, o uso de técnicas proteómicas deu lugar a unha longa lista de marcadores solubles asociados coa patoloxía artrósica, que poderían ter certo potencial para o diagnóstico precoz e/ou a predición da enfermidade. Con todo, ningún deles foi suficientemente validado para o seu uso na rutina clínica, debido principalmente á falta de estudos prospectivos nun gran número de mostras procedentes de pacientes que fosen seguidos durante longos períodos de tempo. Nesta tese empregáronse técnicas proteómicas rápidas e económicas, baseadas en microarrays de proteínas en suspensión, para a validación e posterior cualificación como marcadores de predición de incidencia de OA dun panel de seis proteínas seleccionadas en base a resultados previos do noso grupo de investigación. Así mesmo, a pesar de que non se coñece a causa exacta que inicia o proceso artrósico, é ben sabido que o sistema inmune, entre outros, ten un papel fundamental. A produción de anticorpos fronte a antíxenos propios do corpo, ou autoanticorpos (AAbs) é unha das principais características da actuación devandito sistema. Por iso, durante a realización desta tese doutoral, levou a cabo unha fase de descubrimento de biomarcadores mediante arrays de proteínas en formato plano, co fin de definir un perfil de inmunoreactividad propio das etapas clinicamente silentes da OA que permitise identificar un panel de AAbs con posible potencial como marcadores de predición. Os resultados obtidos mediante a análise dun amplo set de mostras de soro a tempo cero de individuos sen evidencia nin radiográfica nin sintomática de OA de xeonllo, procedentes da cohorte da Osteoartrhritis Initiative, demostraron a asociación de distintos biomarcadores protéicos coa futura aparición da enfermidade. Doutra banda, o uso de probas estatísticas multivariantes deu lugar á xeración de dous posibles modelos para predicir a incidencia de OA radiográfica de xeonllo, formados pola combinación de marcadores protéicos e clínicos asociados co desenvolvemento da enfermidade. Ademais, mediante o emprego de análise de supervivencia, demostrouse que os niveis en soro a tempo cero destes biomarcadores solubles asócianse co tempo de aparición da mesma: A maiores niveis do biomarcador, antes desenvólvese a OA[Abstract] Although osteoarthritis (OA) is a rheumatic pathology characterized by a long clinically silent early phase of joint degeneration, the disease is generally diagnosed at advanced stages, when the only possible solution is prosthetic replacement. This is mainly due to the limitations and low sensitivity of the actual diagnostic techniques, which are based on the patient’s subjective description of the symptoms and radiological tests. In the last years, the use of proteomic technologies have defined a large list of potential OA soluble biomarkers, which may have a putative utility for the early diagnosis and/or prediction of the disease. However, none of them has been sufficiently validated for their use in the daily clinical routine, mostly due to the lack of prospective studies in a large number of individuals who have been followed for long periods of time. In this thesis project, a high-throughput proteomic technique based on suspension protein arrays has been employed for the validation and subsequent qualification as prognostic markers of incident OA of a panel of six proteins selected based on previous findings from our research group. Likewise, despite the exactly mechanism involving the onset of the osteoarthritic pathogenesis remains still unknown, the fundamental role of the immune system, among others, is well documented. The production of antibodies against self-antigens, or autoantibodies (AAbs) is one of the main features of the humoral response. Therefore, as part of this thesis, a biomarker discovery phase using planar protein arrays was carried out in order to detect a specific immunoreactivity signature of the very early stage of the disorder, which might determine a panel of OA-associated AAbs with potential use as prognostic biomarkers. The results obtained by the analysis of a large set of sera at baseline from participants without evident radiological or symptomatic knee OA, belonging to the Osteoarthritis Initiative cohort, have proved the association of different protein biomarkers with the future appearance of radiographic knee OA. On the other hand, the use of multivariable logistic regression analysis has resulted in the generation of two potential prognostic models to predict the incidence of knee OA, which combine protein and clinical markers associated with the development of the disease. In addition, using survival analysis, it has been demonstrated that the baseline serum levels of these biochemical markers are associated with the time of occurrence of the disease: the higher the levels, the sooner the disease appears

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 (MAT2beta) and verify the results in 327 participants from the same subcohorts. The association of MAT2beta-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 MAT2beta-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 MAT2beta in serum might be highly useful to improve the prediction of OA development, and also to estimate the time to incidence