Carolina Fernández-Costa

Identification of a panel of novel serum osteoarthritis biomarkers.

Osteoarthritis (OA) is the most common rheumatic pathology. Because currently available diagnostic methods are limited and lack sensitivity, the identification of new specific biological markers for OA has become a focus. The purpose of this study was to identify novel protein biomarkers for moderate and severe OA in serum. Sera were obtained from 50 moderate OA patients, 50 severe OA patients, and 50 nonsymptomatic controls. Serum protein levels were analyzed using isobaric tags for relative and absolute quantitation (iTRAQ) and matrix-assisted laser desorption/ionization (MALDI)-TOF/TOF mass spectrometry. We identified 349 different proteins in the sera, 262 of which could be quantified by calculation of their iTRAQ ratios. Three sets of proteins were significantly (p < 0.05) changed in OA samples compared to controls. Of these, 6 were modulated only in moderate OA, 13 only in severe OA and 7 in both degrees. Although some of these proteins, such as cartilage oligomeric matrix protein, have a previously reported putative biomarker value for OA, most are novel biomarker candidates for the disease. These include some complement components, lipoproteins, von Willebrand factor, tetranectin, and lumican. The specificity and selectivity of these candidates need to be validated before new molecular diagnostic or prognostic tests for OA can be developed.

Sequential depletion of human serum for the search of osteoarthritis biomarkers

BackgroundThe field of biomarker discovery, development and application has been the subject of intense interest and activity, especially with the recent emergence of new technologies, such as proteomics-based approaches. In proteomics, search for biomarkers in biological fluids such as human serum is a challenging issue, mainly due to the high dynamic range of proteins present in these types of samples. Methods for reducing the content of most highly abundant proteins have been developed, including immunodepletion or protein equalization. In this work, we report for the first time the combination of a chemical sequential depletion method based in two protein precipitations with acetonitrile and DTT, with a subsequent two-dimensional difference in-gel electrophoresis (2D-DIGE) analysis for the search of osteoarthritis (OA) biomarkers in human serum. The depletion method proposed is non-expensive, of easy implementation and allows fast sample throughput.ResultsFollowing this workflow, we have compared sample pools of human serum obtained from 20 OA patients and 20 healthy controls. The DIGE study led to the identification of 16 protein forms (corresponding to 14 different proteins) that were significantly (p < 0.05) altered in OA when compared to controls (8 increased and 7 decreased). Immunoblot analyses confirmed for the first time the increase of an isoform of Haptoglobin beta chain (HPT) in sera from OA patients.ConclusionsAltogether, these data demonstrate the utility of the proposed chemical sequential depletion for the analysis of sera in protein biomarker discovery approaches, exhibit the usefulness of quantitative 2D gel-based strategies for the characterization of disease-specific patterns of protein modifications, and also provide a list of OA biomarker candidates for validation.

A pharmacoproteomic study confirms the synergistic effect of chondroitin sulfate and glucosamine

Osteoarthritis (OA) is the most common age-related rheumatic disease. Chondrocytes play a primary role in mediating cartilage destruction and extracellular matrix (ECM) breakdown, which are main features of the OA joint. Quantitative proteomics technologies are demonstrating a very interesting power for studying the molecular effects of some drugs currently used to treat OA patients, such as chondroitin sulfate (CS) and glucosamine (GlcN). In this work, we employed the iTRAQ (isobaric tags for relative and absolute quantitation) technique to assess the effect of CS and GlcN, both alone and in combination, in modifying cartilage ECM metabolism by the analysis of OA chondrocytes secretome. 186 different proteins secreted by the treated OA chondrocytes were identified. 36 of them presented statistically significant differences (p ≤ 0.05) between untreated and treated samples: 32 were increased and 4 decreased. The synergistic chondroprotective effect of CS and GlcN, firstly reported by our group at the intracellular level, is now demonstrated also at the extracellular level.

PACOM: A Versatile Tool for Integrating, Filtering, Visualizing, and Comparing Multiple Large Mass Spectrometry Proteomics Data Sets

Mass-spectrometry-based proteomics has evolved into a high-throughput technology in which numerous large-scale data sets are generated from diverse analytical platforms. Furthermore, several scientific journals and funding agencies have emphasized the storage of proteomics data in public repositories to facilitate its evaluation, inspection, and reanalysis. (1) As a consequence, public proteomics data repositories are growing rapidly. However, tools are needed to integrate multiple proteomics data sets to compare different experimental features or to perform quality control analysis. Here, we present a new Java stand-alone tool, Proteomics Assay COMparator (PACOM), that is able to import, combine, and simultaneously compare numerous proteomics experiments to check the integrity of the proteomic data as well as verify data quality. With PACOM, the user can detect source of errors that may have been introduced in any step of a proteomics workflow and that influence the final results. Data sets can be easily compared and integrated, and data quality and reproducibility can be visually assessed through a rich set of graphical representations of proteomics data features as well as a wide variety of data filters. Its flexibility and easy-to-use interface make PACOM a unique tool for daily use in a proteomics laboratory. PACOM is available at https://github.com/smdb21/pacom .