J.R. Smith

High throughput proteomic analysis of the secretome in an explant model of articular cartilage inflammation

This study employed a targeted high-throughput proteomic approach to identify the major proteins present in the secretome of articular cartilage. Explants from equine metacarpophalangeal joints were incubated alone or with interleukin-1beta (IL-1β, 10 ng/ml), with or without carprofen, a non-steroidal anti-inflammatory drug, for six days. After tryptic digestion of culture medium supernatants, resulting peptides were separated by HPLC and detected in a Bruker amaZon ion trap instrument. The five most abundant peptides in each MS scan were fragmented and the fragmentation patterns compared to mammalian entries in the Swiss-Prot database, using the Mascot search engine. Tryptic peptides originating from aggrecan core protein, cartilage oligomeric matrix protein (COMP), fibronectin, fibromodulin, thrombospondin-1 (TSP-1), clusterin (CLU), cartilage intermediate layer protein-1 (CILP-1), chondroadherin (CHAD) and matrix metalloproteinases MMP-1 and MMP-3 were detected. Quantitative western blotting confirmed the presence of CILP-1, CLU, MMP-1, MMP-3 and TSP-1. Treatment with IL-1β increased MMP-1, MMP-3 and TSP-1 and decreased the CLU precursor but did not affect CILP-1 and CLU levels. Many of the proteins identified have well-established extracellular matrix functions and are involved in early repair/stress responses in cartilage. This high throughput approach may be used to study the changes that occur in the early stages of osteoarthritis.

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

BackgroundOsteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions.MethodsThis study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified.ResultsBioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein.ConclusionsUsing this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation.

Label-free proteomic analysis of the hydrophobic membrane protein complement in articular chondrocytes: a technique for identification of membrane biomarkers

Abstract Context: There is insufficient knowledge about the chondrocyte membranome and its molecular composition. Objective: To develop a Triton X-114 based separation technique using nanoLC-MS/MS combined with shotgun proteomics to identify chondrocyte membrane proteins. Materials and methods: Articular chondrocytes from equine metacarpophalangeal joints were separated into hydrophobic and hydrophilic fractions; trypsin-digested proteins were analysed by nanoLC-MS/MS. Results: A total of 315 proteins were identified. The phase extraction method yielded a high proportion of membrane proteins (56%) including CD276, S100-A6 and three VDAC isoforms. Discussion: Defining the chondrocyte membranome is likely to reveal new biomarker targets for conventional and biological drug discovery.

High-throughput proteomic analysis of the cartilage secretome for identification of inflammatory biomarkers

digestion by trypsin and reversed phase separations coupled to tandem mass spectrometry for the identification and targeted quantification of peptides by multiple reaction monitoring (MRM). The tissue was sliced from synovial surface to bone in 10mm thin sections and the full thickness cartilage was divided into 100mm pools including superficial, intermediate and deep zones. Results: Previous known distribution of proteoglycan 4 (PRG4, lubricin, superficial zone protein) was confirmed but also novel findings were observed e.g. asporin which was predominantly present in the superficial layers. In total 40 proteins were quantified showing distinct patterns in normal tissue. In addition we observed altered protein distribution patterns in preclinical (macroscopically normal) tissue from a joint with early fibrillation (OA-lesions) on the opposite surface. Conclusions: As an alternative to immunohistochemistry we used proteomics technology to study the protein abundance across full thickness articular cartilage. The advantages of this approach are that it allows multiple targets to be studied simultaneously, it is independent of antibody availability and circumvent some antibody-related artifacts. Other advantages include unambiguous identifications and improved quantifications. The work shows novel information on the differences that exist in different layers of cartilage that is of value in understanding changes in early pathology.