Comparative proteomic analysis of hypertrophic chondrocytes in osteoarthritis
© Tsolis et al.; licensee BioMed Central. 2015
Received: 17 February 2015
Accepted: 15 April 2015
Published: 25 April 2015
Osteoarthritis (OA) is a multi-factorial disease leading progressively to loss of articular cartilage and subsequently to loss of joint function. While hypertrophy of chondrocytes is a physiological process implicated in the longitudinal growth of long bones, hypertrophy-like alterations in chondrocytes play a major role in OA. We performed a quantitative proteomic analysis in osteoarthritic and normal chondrocytes followed by functional analyses to investigate proteome changes and molecular pathways involved in OA pathogenesis.
Chondrocytes were isolated from articular cartilage of ten patients with primary OA undergoing knee replacement surgery and six normal donors undergoing fracture repair surgery without history of joint disease and no OA clinical manifestations. We analyzed the proteome of chondrocytes using high resolution mass spectrometry and quantified it by label-free quantification and western blot analysis. We also used WebGestalt, a web-based enrichment tool for the functional annotation and pathway analysis of the differentially synthesized proteins, using the Wikipathways database. ClueGO, a Cytoscape plug-in, is also used to compare groups of proteins and to visualize the functionally organized Gene Ontology (GO) terms and pathways in the form of dynamical network structures.
The proteomic analysis led to the identification of a total of ~2400 proteins. 269 of them showed differential synthesis levels between the two groups. Using functional annotation, we found that proteins belonging to pathways associated with regulation of the actin cytoskeleton, EGF/EGFR, TGF-β, MAPK signaling, integrin-mediated cell adhesion, and lipid metabolism were significantly enriched in the OA samples (p ≤10−5). We also observed that the proteins GSTP1, PLS3, MYOF, HSD17B12, PRDX2, APCS, PLA2G2A SERPINH1/HSP47 and MVP, show distinct synthesis levels, characteristic for OA or control chondrocytes.
In this study we compared the quantitative changes in proteins synthesized in osteoarthritic compared to normal chondrocytes. We identified several pathways and proteins to be associated with OA chondrocytes. This study provides evidence for further testing on the molecular mechanism of the disease and also propose proteins as candidate markers of OA chondrocyte phenotype.
KeywordsOsteoarthritis Cartilage Chondrocytes Proteomics Mass spectrometry Pathway analysis PLS3 GSTP1
Osteoarthritis (OA) is the most common form of arthritis and a major cause of disability worldwide. OA affects the whole joint, leading to cartilage degradation, synovial inflammation and subchondral bone remodelling [1,2]. Disease progression is slow. Structural alterations start before middle age but can be diagnosed when they become symptomatic where at that time there is a severe damage in the joint. The aetiology of the disease is not completely defined [1-5]. Several patho-physiological processes are involved in the disease phenotype, however the triggering event for disease onset is unknown, as a result OA is considered a multi-factorial disease [1,3,6-8]. Several risk factors have been associated with OA, including age, obesity, mechanical load, genetic predisposition, sex, and prior joint injury [7,9].
Currently, there is not efficient treatment for OA. Recommended pharmacological interventions aim for the relief of pain and joint inflammation by administration of mild analgesics first and non-steroid anti-inflammatory drugs (NSAIDs), later [10,11]. Intra-articular injection of hyaluronic acid (HA) or corticosteroids was also shown to improve pain, whereas the HA administration showed more prolonged effect [10,11]. Another approach that is currently under research for the management of OA, is the use of disease-modifying osteoarthritis drugs (DMOADs) (e.g. bisphosphonates, doxycycline, strontium ranelate) [12,13]. This category of drugs slows down the cartilage degradation, stalling or reducing the disease progression. Other approaches aiming for the regeneration of cartilage, make use of growth factors or cytokines, either by direct injection to the infected areas or using platelet-rich plasma or gene therapy approaches [10,13-15]. All these methods were tested in animal models and clinical trials, however further studies are needed to verify their efficacy and the prerequisites in which these approaches could be beneficial. Collectively, the latest approaches for the management of OA highlight the importance of unravelling the molecular mechanism of OA progression, in order to design more targeted and effective therapeutic strategies.
Chondrocytes comprise the cell population responsible for cartilage homeostasis. During OA progression, chondrocytes acquire a hypertrophic phenotype, showing increased synthesis of several markers including COL10A1, MMP-13 and Runx2 . Even though there is a wealth of information about chondrocyte differentiation mechanisms, it is not completely understood how chondrocytes are affected during the progression of osteoarthritis [16-18]. Different -omics approaches have been used for the identification of the alterations that occur in articular chondrocytes during OA progression. Previous proteomics studies highlighted mitochondrial dysfunction and redox imbalance in osteoarthritic chondrocytes [19,20]. Another study, combining genomics and lipidomics analysis revealed that OA articular chondrocytes generate multiple eicosanoid products which show a complex role in cartilage homeostasis and pathophysiology of the disease .
The aim of this study was to improve our understanding of the molecular basis of OA by performing a comparative proteomic analysis of articular chondrocytes derived from patients with OA and from disease-free individuals, using high-resolution mass spectrometry and label-free quantification. 269 proteins showed significantly differential synthesis between patients and controls and these were functionally annotated and linked through network analysis, for the identification of substantially enriched pathways, related with the disease. Pathway analysis showed enrichment in those related to the cytoskeleton, adhesion and lipid metabolism. Also, we propose that eight of the identified proteins (GSTP1, PLS3, MYOF, HSD17B12, PRDX2, APCS, PLA2G2A SERPINH1/HSP47 and MVP) could be used as potential markers for chondrocyte differentiation, since they show high difference in abundance levels and are identified with a very high frequency in the sample population that was tested.
Identified and differentially synthesized proteins
Bioinformatic analysis for enriched terms
We next sought to determine biological processes and pathways that may be associated with OA pathogenesis in our experimental dataset. For this, we used in silico tools to determine which proteins or biological terms or pathways are enriched in the identified proteins. First, we used WebGestalt [29,30], which performs a hypergeometric test for the enrichment of GO terms and pathways in the selected proteins, followed by the Benjamini & Hochberg (BH) method for multiple test adjustment (adjP). We analyzed the 269 unique or differentially synthesized proteins in our dataset (Figure 1; Additional file 1 – “Unique IDs in OA”, “Over-synthesized in OA”, “Under-synthesized in OA”, “Unique IDs in Controls”), divided in two sets: the first consisted of 120 over-synthesized and 131 unique proteins in OA patients; the second consisted of seven under-synthesized proteins in the patients’ group and 11 unique proteins in the control subjects. Below we present a summary of the results of the WebGestalt analysis.
Enrichment analysis of GO-terms using WebGestalt
Enrichment analysis of GO-terms using ClueGO
To further probe the GO enrichment analysis presented above, we made use of the ClueGO plug-in of Cytoscape 3.1.0, which enables comparison of clusters/groups to highlight their functional specificity. For this, two protein sets were uploaded in ClueGO (Additional file 2 – Cluster 2 “Protein Set for normal chondrocytes”; Additional file 3 – Cluster 1 “Protein Set for OA chondrocytes”) and were compared to each other by applying appropriate settings (see Methods).
ClueGO was used for the integration of GO terms as well as the generation and visualization of a functionally arranged GO term network that exposes the relations between the GO terms based on the similarity of their associated proteins. ClueGO utilizes kappa statistics, in a similar way as described by Huang et al. , to connect the GO terms in the network. Briefly, kappa statistics refers to the quantitative measurement of the degree of agreement on how proteins share similar annotation of GO terms.
Pathway enrichment analysis
Enriched pathways in OA chondrocytes
General metabolism and cellular processes
Parkin-Ubiquitin Proteasomal System pathway
TUBA1C PSMD4 HSPA1A PSMD2 PSMD6 HSPA2 PSMC1 TUBB
adjP = 1.47e-07
EIF3E EIF4A1 EEF1G EEF1A1 EEF2 EIF3B EEF1D
adjP = 2.60e-07
Electron Transport Chain
ATP5B COX6C SLC25A5 UQCRFS1 NDUFB4 ATP5A1
adjP = 0.0001
Cytoplasmic Ribosomal Proteins
RPL8 RPLP0 RPL35A RPL23 RPL10 RPL27
adjP = 8.18e-05
PSMD4 PSMD2 PSMD6 PSMC1
adjP = 0.0018
SUCLA2 IDH3G DLST
adjP = 0.0007
ATP5B NDUFB4 ATP5A1
adjP = 0.0098
Adhesion, cytoskeleton remodeling, cell-cell & cell-matrix interactions, endocytosis
ACTG1 RAC1 RAP1B FLNA VASP MAPK1 PDGFRB CAPN1
adjP = 8.18e-05
Regulation of Actin Cytoskeleton
ACTG1 IQGAP1 RAC1 ENAH GSN PFN1 MAPK1 PDGFRB
adjP = 3.12e-05
Integrin-mediated cell adhesion
RAC1 RAP1B CAPN2 VASP MAPK1 CAPN1
adjP = 0.0001
Synaptic Vesicle Pathway
AP2A2 AP2A1 CLTC AP2B1 NAPA
adjP = 8.74e-05
G13 Signaling Pathway
IQGAP1 RAC1 PFN1
adjP = 0.0034
RAC1 RRAGC PRKAG1
adjP = 0.0032
Senescence and Autophagy
GSN MMP14 CD44 MAPK1
adjP = 0.0100
EGF-EGFR Signaling Pathway
GJA1 IQGAP1 RAC1 HGS AP2A1 AP2B1 MAPK1
adjP = 0.0003
TGF beta Signaling Pathway
RAC1 HGS TRAP1 SPTBN1 SKP1 MAPK1 PRKAR2A
adjP = 0.0001
TNF alpha Signaling Pathway
RAC1 PSMD2 TRAP1 BAX SKP1 HSP90AA1 MAPK1
adjP = 2.36e-05
MAPK signaling pathway
RAC1 RAP1B FLNA MAPK1 PDGFRB
adjP = 0.0058
Wnt Signaling Pathway
GJA1 RAC1 CTNNB1
adjP = 0.0063
GJA1 RAP1B GNB2 GNAS CTNNB1 HSP90AA1 MAPK1 GNAI2
adjP = 6.10e-06
Androgen receptor signaling pathway
RAC1 FHL2 FLNA CTNNB1 CARM1
adjP = 0.0007
TSH signaling pathway
RAP1B GNAS MAPK1 GNAI2
adjP = 0.0021
TWEAK Signaling Pathway
RAC1 CTNNB1 MAPK1
adjP = 0.0083
RANKL-RANK Signaling Pathway
RAC1 FHL2 MAPK1
adjP = 0.0121
Lipid metabolism, eicosanoid metabolism, oxidative stress
CYP20A1 GSTT1 GSTP1 GSTK1 GPX4 CYP1B1 EPHX1
adjP = 0.0003
CYP20A1 CYB5R1 CYP1B1 CYB5R3
adjP = 0.0018
Fatty Acid Beta Oxidation
CHKB SLC25A20 DECR1
adjP = 0.0148
Prostaglandin Synthesis and Regulation
ANXA6 ANXA1 ANXA2
adjP = 0.0023
adjP = 0.0150
The second group includes pathways related with remodeling of cytoskeleton and endocytosis. Pathways mediating focal adhesion, integrin-mediated cell adhesion, G13 and TOR signaling are used in several biological processes including cell motility and proliferation, cell differentiation and gene expression or cell survival and cell cycle regulation, often linking extracellular signals with other intracellular signaling pathways. Several intracellular signaling pathways were also identified in pathway analysis and are listed in the third group. Proteins belonging to EGF-EGFR, TGF-beta, TNF-alpha, MAPK, Wnt, CRH, androgen receptor, senescence and autophagy and TSH signaling pathways showed altered synthesis in hypertrophic chondrocytes. Signaling through these pathways has been described in the literature during normal chondrocyte differentiation or development of the OA disease [32-39]. Two more signaling pathways, TWEAK and RANKL-RANK signaling pathways, which are associated with the production of pro-inflammatory cytokines and bone development were also enriched in OA chondrocytes [40,41].
The fourth group of enriched pathways includes those related to lipid metabolism and oxidative stress. Proteins participating in fatty acid beta oxidation and enzymes of the cytochrome p450, apart from their role in the metabolism of lipids and chemical compounds, are also used for the metabolism of eicosanoids and other secondary metabolites .
Proteins with synthesis characteristic to each group
We next searched for proteins that show distinct synthesis in each group, and potentially underscore the phenotypic shift in OA chondrocytes. Most in silico function annotation tools use public pathway databases to assign the identified proteins/genes to specific pathways. However, several proteins are not included in these databases, mainly because their interactions are not completely defined, and cannot be functionally annotated by the pathway enrichment tools. In our dataset, there were a few such proteins, which show distinct synthesis patterns between OA and normal chondrocytes, and are not included in the in silico pathway analysis described above, or their protein function is not completely described by the pathway analysis.
To corroborate the label-free quantification approach, we performed western blot analysis for two of the selected proteins (GSTP1 and PLS3), in four new samples of OA chondrocytes and equal number of controls (Figure 6B-E). For loading control and normalization between samples we probed for β-actin. Western blot analysis was in agreement with the mass spectrometric label-free quantification. The two proteins were found in 2.5 and 2 fold higher abundance in OA patients than in the controls, accordingly.
To probe the multifactorial nature of osteoarthritis, we used an –omics approach that offers a broad, unbiased view of the OA landscape, in order to improve our understanding on the molecular base of the disease. We performed a quantitative comparative proteomic analysis, of osteoarthritic and normal chondrocytes, combined with bioinformatics analysis.
During OA progression, chondrocytes acquire a hypertrophic phenotype, characterized by altered synthesis of many proteins related to normal development and cartilage turnover. Hypertrophy is a physiological stage in chondrocyte differentiation, and an essential step during bone development [17,18,46,47]. In this study, we showed that hypertrophic osteoarthritic chondrocytes possess a distinct synthesized proteome compared to that of the control group. Major biological processes that are affected during disease progression, are related to the cytoskeleton, cell-cell and cell-ECM interactions. In agreement with this, several proteins over-synthesized in OA chondrocytes are related to cell localization and transport (Figure 4). Network analysis for GO terms, using ClueGO (Figure 5), showed that a significant number of the differentially synthesized proteins cluster together having biological functions related to the cytoskeleton, whereas several pathways, such as regulation of actin cytoskeleton, integrin-mediated adhesion and the G13 and TOR signaling pathways where found enriched in OA chondrocytes (Table 1). The interaction of chondrocytes with ECM molecules has been shown to affect cytoskeletal organization, proliferation, differentiation, and gene expression in chondrocytes [48-54]. Also, it is noted that the micro-environment of cartilage possibly triggers an autophagy response in the hypoxic chondrocytes . Collectively, these data demonstrate a global view of cytoskeleton-related changes associated with OA chondrocytes.
Several signaling pathways are activated during chondrocyte differentiation [16,46]. Apart from cytoskeleton-related pathways, we also found that other signaling pathways are enriched in OA chondrocytes. These include EGF-EGFR, TGF-beta, TNF-alpha, MAPK and Wnt canonical and non-canonical signaling pathways, TWEAK and RANKL-RANK signaling pathways (Table 1). Signaling through those pathways has been reported to occur during normal chondrocyte differentiation or OA development in arthritis models, whereas the RANKL-RANK pathway was also proposed as a target for treatment of OA [16,32-35,40,41,46,56-58].
In addition, pathway enrichment analysis showed that signaling through corticotrophin releasing hormone (CRH), androgen receptor (AR) and thryroid stimulating hormone (TSH) is also affected in OA chondrocytes. CRH signaling plays a role in inflammatory responses and its enrichment could possibly reflect joint inflammation . Expression of AR in articular chondrocytes was assessed in human and mouse biopsies. The percentage of cells showing increased expression of AR is increased in resting and hypertrophic zones compared to the proliferative zone, suggesting that signaling through AR might be used during normal chondrocyte differentiation [36,59]. Also, differentiation of mesenchymal stem cells into chondrocyte-like cells was observed after treatment with TSH . However, the authors conclude that further studies are needed in order to determine the exact role of TSH in the chondrocyte differentiation process and whether this pathway may promote osteogenic differentiation, as well.
Another group of pathways that is enriched in OA chondrocytes includes those related to lipid metabolism, prostaglandin synthesis, glutathione metabolism and metabolism through the cytochrome p450 (Table 1). Enzymes of those pathways are also used for the metabolism of eicosanoids . Several secondary metabolites, including prostaglandins (PG), epoxyeicosanoic acids (EET), hydroxyeicosatetranoic acids (HETE) are synthesized from eicosanoids, and regulate a plethora of biological functions, including inflammation, cell growth, survival, migration and invasion [42,61-66]. Altered eicosanoid metabolism was reported before in OA articular chondrocytes . Elevated synthesis of proteins of glutathione metabolism could be induced by increased oxidative stress in the OA cells, a finding that is corroborated by another proteomic study . In addition, we found several proteins, which are related with previously described biological processes, but which have not yet been related to OA or shown to be over-synthesized in OA chondrocytes. These include mainly GSTP1 and HSD17B12 (Figure 6A). The levels of GSTP1 protein were also verified using WB (Figure 6B). GSTP1 is important for metabolite detoxification . Over-expression of the GSTP1 gene was observed in several cancer types and is considered to be a marker for cancer development . Additionally, GSTP interacts directly and can inhibit c-Jun N-terminal kinase (JNK), affecting that way processes like apoptosis, differentiation and proliferation [68-70]. The expression of HSD17B12 was associated with the metastatic phenotype in tumor cell lines, through its function in Arachinoid Acid (AA) metabolism, linking the metabolism of eicosanoids and cytoskeleton remodeling [71,72]. Thus, activation of lipid metabolism pathways, might lead to altered production of secondary metabolites which in turn support cytoskeleton reorganization during synovial inflammation and chondrocyte differentiation, phenotypes well defined during OA.
Two more proteins which are over-synthesized in OA chondrocytes, and are associated with the observed phenotype are PLS3 and MYOF. PLS3 synthesis levels were also verified with WB (Figure 6B). Pathogenic variants of this protein were found in families with X-linked osteoporosis and fractures, suggesting that PLS3 might be important in human bone health . The synthesis levels and function of PLS3 has not been studied in chondrocytes. Over-synthesis of PLS3 was found in cancer cells and is proposed as a marker of circulating tumor cells (CTCs) . The exact function of PLS3 is not defined. However, it seems to function as a key downstream molecule in the TGF-β pathway . In addition, we observed significantly increased synthesis of MYOF in OA chondrocytes, a protein that is related to membrane repair by an unknown mechanism [76,77]. Over-synthesis of MYOF in breast cancer cell lines leads to an increased invasion phenotype and secretion of matrix metalloproteinases (MMPs), while silencing of the gene restores the normal phenotype, possibly by blocking the EGFR signaling pathway [78,79]. Both proteins seem to have a key functional role, since silencing of their genes can reverse the phenotype induced by the initial stimuli.
Additionally, we identified five more proteins with distinct synthesis between OA and control chondrocytes, and for which we do not have any knowledge about the function in relation to the disease. From these proteins PRDX2, SERPINH1/HSP47 and MVP are over-synthesized in OA chondrocytes, whereas APCS and PLA2G2A are mainly synthesized in control chondrocytes (Figure 6A). Peroxiredoxin-2 (PRDX2) is a member of a class of thiol peroxidases and is hypothesized to act as a redox sensor . SERPINH1/HSP47 is a collagen specific molecular chaperone, essential for the maturation of type I and type III collagens . Major vault protein (MVP) is an evolutionally conserved, high abundant protein that seems to play a role in drug resistance and in signaling pathways, however its role is not understood [82,83]. Serum amyloid P component (APCS) is a member of pentraxin family of proteins, that plays a role in innate immune responses . Its presence in cartilage was previously reported, however nothing is known about the role of this protein in OA . Finally, the membrane-associated form of phospholipase A2 (PLA2G2A), is also highly synthesized in control chondrocytes compared to the OA, but the function of this protein is unknown .
In summary, we observed that OA chondrocytes display molecular signatures of the hypertrophic phenotype, where several pathways including adhesion, proliferation, growth, and lipid metabolism are enriched. We propose that the expression of the proteins GSTP1, PLS3, MYOF, HSD17B12, PRDX2, APCS, PLA2G2A SERPINH1/HSP47 and MVP also reflect the altered phenotype of OA chondrocytes. Our study provides a number of testable hypotheses to further probe the role of various proteins and biological process pathways in osteoarthritis.
Cartilage tissue samples
Primary cultures of human articular chondrocytes, normal and osteoarthritic
Articular cartilage was transported from the surgical room to the laboratory in HBSS medium (Hanks Balanced Salt Solution). It was then immediately dissected and subjected to sequential digestion with pronase (1 mg/ml; 90 min; Roche Applied Science, Germany) and collagenase P (1 mg/ml; 3 h; Roche Applied Science, Germany) at 37°C. Chondrocytes were cultured in Dulbecco’s Modified Eagles Medium/ Ham’s F-12 (DMEM/F-12) (GIBCO BRL, UK) plus 5% fetal bovine serum (FBS) and 100 U/ml penicillin-streptomycin and were incubated at 37°C under a humidified 5% CO2 atmosphere. Chondrocytes were kept in culture for maximum of two passages. Trypsin was used for the detachment of chondrocytes during the primary culture, while type II collagen and type I collagen ratio was screened in all samples to exclude dedifferentiation events.
Protein extraction from chondrocytes
OA and normal chondrocytes were trypsinized, collected and centrifuged for 10 min at 543xg. The cell pellet was washed with PBS and then centrifuged again for 10 min at 543xg at 4°C. The cell pellet was resuspended in RIPA lysis buffer (50 mM Tris/HCl pH 7.2, 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS) and incubated on ice for 30 min. Remaining cells where lysed after sonication (3 rounds at 30% amplitute), using a tip sonicator (VCX130, Sonics & Materials, Newtown, USA). Samples were clarified via centrifugation (15 min; 13,000xg; 4°C), in a bench top centrifuge, and total protein content was calculated using the BCA method .
1D-SDS-PAGE and in-gel digestion
Proteins from each sample (50 μg) were precipitated using trichloroacetic acid/acetone (final concentration of 25% TCA w/v; 4°C; 30 min). Proteins were precipitated after centrifugation (15,000 g; 30 min; 4°C), in a bench-top centrifuge. Protein pellets were washed twice with ice-cold acetone, and precipitated via centrifugation, as previously. Excess acetone was aspirated and the protein pellet was air-dried. Precipitated proteins were re-solubilized in a solution containing 1.5 M Tris/HCl pH 8.8 and 6x sample buffer in a ratio of 2:1 and analyzed by 1D-SDS-PAGE 4-10% (29:1 acrylamide/bisacrylamide). Gels were stained with colloidal coomassie blue (Coomassie G250, 10% phosphoric acid, 10% ammonium sulfate, 20% methanol). Each lane was cut in 10 slices, and de-stained after three washes with 50% acetonitrile/water and 50 mM ammonium bicarbonate. Samples were reduced in the presence of 10 mM DTT (45 min; 56°C) and then washed and alkylated in the presence of 55 mM IAA (45 min; 22oC, shaking, in the dark). Gel slices were washed with 50 mM ABS and proteins were digested with 0.1 μg trypsin (trypsin Gold, Promega, Fitchburg, Wisconsin), overnight. Generated peptides were collected, after repeated washes with nanopure-H2O, 50% ACN in low binding tubes (Axygen, Union City, CA) and trypsin was quenched by acidifying the sample with 1–2 μL trifluoroacetic acid (TFA), until pH < 2. Peptides were dried under vacuum, during centrifugation (Speedvac; Savant) and desalted using STAGE tips . The desalted peptides were then lyophilized (Speedvac) and stored at −20°C until use.
Peptide samples were analyzed using nano-Reverse Phase (RP) LC coupled to an LTQ-Orbitrap XL or an Orbitrap QE instrument. In the first case, peptides were separated by an EASY-nLC 1000 HPLC (Proxeon, Thermo Fisher Scientific, Odense, Denmark) system on a pre-packed column (Thermo, OD 360 μm, ID 50 μm, 15 cm, C18, 2 μm). A linear gradient was used 5-30% from buffer A (99.9% water, 0.1% FA) to buffer B (9.9% water, 0.1% FA, 90% ACN) run for 165 min (flow rate: 300 nL/min), and peptides were analyzed in an LTQ-Orbitrap XL, at a resolution of 60,000 (FWHM) at m/z 400. CID fragmentation was performed on the 10 most intense precursor ions using 60 seconds exclusion time. For the analysis in the Orbitrap QE mass spectrometer, peptides were separated in a Dionex UltiMate 3000 UHPLC system. The samples were separated using an EasySpray C18 column (Thermo Scientific) in a linear gradient using as buffer A (water 99.9%, FA 0.1%) and buffer B (ACN 80%, water 20%, FA 0.1%). Gradient from buffer A to buffer B was separated in five steps, 4% to 10% B (12 min) followed by 10-35% B (20 minutes), 35- 65% B (5 min) and a final elution and re-equilibration step at 95% and 5% B respectively. The flow-rate was set at 300 nL/min. The Q Exactive was operated in positive ion mode (nanospray voltage 1.5 kV, source temperature 250°C). The instrument was operated in data-dependent acquisition (DDA) mode with a survey MS scan at a resolution of 70,000 for the mass range of m/z 400–1600 for precursor ions, followed by MS/MS scans of the top 10 most intense peaks with +2, +3 and +4 charged ions above a threshold ion count of 16,000 at 35,000 resolution using normalized collision energy (NCE) of 25 eV with an isolation window of 3.0 m/z, an apex trigger 5–15 sec and a dynamic exclusion of 10 s. All data were acquired with Xcalibur 2.2 software (Thermo Scientific).
MS data analysis
Raw files were processed in Proteome Discoverer v1.4 using the algorithms SEQUEST v184.108.40.2063 and Mascot v2.3.2. Spectra were searched against the Uniprot Human reference database, without isoforms (Oct 2012, 20,505 entries) . Precursor mass error was set to 10 ppm for both Orbitrap mass spectrometers, and fragment mass error to 800 mmu or 20 mmu, for the LTQ Orbitrap XL and Orbitrap QE, respectively. Enzyme cleavage was set to trypsin, and maximally two missed cleavages were allowed. Variable modifications selected were methionine oxidation and N-terminal acetylation; constant modification selected was S-Carbamidomethylation of cysteine residues. All files were merged in Scaffold v3.4.5 (Proteome Software, Portland, OR) for the comparative analysis. Scores from both Mascot and SEQUEST algorithms was used through the PeptideProphet and ProteinProphet algorithms for the identification of proteins [92-94]. Thresholds for protein and peptide identification through ProteinProphet and PeptideProphet algorithms were set to 99% and 95% accordingly, for proteins with minimum 2 different peptides identified, resulting in a protein false discovery rate (FDR) of <0.1%. For the label-free quantification of the identified proteins, we used spectral counting quantification through Scaffold software, which sums all the spectra associated with a specific protein within the sample and also includes these spectra that are shared with other proteins .
Statistical and enrichment analyses
Protein lists were exported from Scaffold to Microsoft Excel for further processing. To ensure robustness of identification, we filtered-out proteins identified with a low frequency. Specifically, we accepted as “uniquely identified” proteins those, which were identified in at least half of the samples of each group (n ≥ 3 in the control group and n ≥ 5 in OA group). From the commonly identified proteins between OA and control conditions, we discarded proteins with a frequency of identification < 4 and <3, accordingly.
For the identification of differentially synthesized proteins, we tested for statistical (t-test) and biological (fold-change) significance. A two tail t-test was performed in the filtered “common sub-proteome” described before (n ≥ 3 in the control group and n ≥ 4 in OA group) . Missing values were excluded. Also for the same sub-proteome, the fold change of protein levels was calculated by dividing the mean spectral counting quantitative value in OA samples with the mean value of the control samples for each of the proteins. Proteins that were significantly “over-synthesized” or “under-synthesized” were these with p-value ≤0.05 and a fold change ≥ 2.
For the enrichment analysis we divided these proteins in two groups. The first group contains proteins that are over-synthesized and uniquely identified in OA chondrocytes and the second group contains proteins which are under-synthesized in OA chondrocytes, compared to control samples, or were uniquely identified in control chondrocytes. The two protein groups were submitted separately to WebGestalt as lists of their respective Entrez gene IDs in order to enable a distinct enrichment analysis after comparison with the existing list of the human genome, which is generated from prior knowledge sorted into functionally related gene/protein groups. WebGestalt performs the hypergeometric test for the enrichment of GO terms and pathways in the selected proteins, followed by the Benjamini & Hochberg (BH) method for multiple test adjustment (adjP) [29,30]. As we are testing multiple gene sets at the same time, the p-values need to be adjusted. BH is implemented to control for false positive (i.e. Type I) errors. Traditional multiple-testing corrections, adjust p-values derived from multiple statistical tests to correct for the occurrence of false positives. BH ranks p-values in an ascending order, multiplies them by the number of features, and divides them by their corresponding rank . Using WebGestalt, we conducted a functional enrichment analysis for Gene Ontology (GO) terms, as well as a pathway enrichment analysis using the Wikipathways database (http://www.wikipathways.org).
Cluster/Group comparison by ClueGO
ClueGO, a Cytoscape plug-in was used to visualize the non-redundant GO terms and pathways in functionally organized networks, which reflect the relations between the biological terms based on the similarity of their linked gene/proteins . For the cluster/group comparison by ClueGO, we used the two protein groups, as mentioned above, in order to illustrate their functional differences. Using the Cytoscape environment , the two protein groups were uploaded in ClueGO as two separate clusters (Additional file 2 – Cluster 2 “Protein Set for normal chondrocytes”; Additional file 3 – Cluster 1 “Protein Set for OA chondrocytes”) from the text files, and were compared with each other by applying the following settings.
For the enrichment of biological terms and groups, we used the two-sided (Enrichment/Depletion) tests based on the hyper-geometric distribution. We set the statistical significance to 0.05 (p ≤ 0.05), and we used the Bonferroni adjustment to correct the p-value for the terms and the groups created by ClueGO. We used fusion criteria to diminish the redundancy of the terms shared by similar associated proteins, which allows one to maintain the most representative parent or child term. The Kappa-statistics score threshold was set to 0.3. Other analysis parameters include: GO level intervals: (3–15); number of associated proteins for cluster 1: (nine); number of associated proteins for cluster 2: (one); OR 50% specific; GO Term Fusion; Leading Group: Highest Significance; Group Merge: (50%).
Western blot analysis
Chondrocytes were lysed as previously described and protein concentration was quantified using the Bradford protein assay (Bio-Rad Protein Assay, BioRad, Hercules, CA) with bovine serum albumin as standard. Cell lysates from chondrocytes were electrophoresed and separated on a 10% acrylamide gels and transferred to PVDF membranes (Millipore) that were probed with polyclonal rabbit anti-GSTP1 and anti-PLS3 antibodies (Sigma-Aldrich, Missouri, USA). Polyclonal rabbit Anti-β-actin antibody was used as loading control (Sigma-Aldrich, Missouri, USA). Signals were detected using suitable immunoglobulin IgG conjugated with horseradish peroxidase. Nitrocellulose membranes were exposed to photographic film, which was scanned and intensities of protein bands were determined using ImageJ software.
Images and vectors were processed using Adobe Illustrator CS5 and Graphs were designed in GraphPad Prism.
Ammonium bicarbonate solution
Aggrecan core protein
Serum amyloid P-component
Collision induced dissociation
Collagen type II alpha 1 chain
Corticotropin releasing hormone
Data dependent acquisition
Epoxide hydrolase 1
False discovery rate
Glutathione S-transferase P1
Estradiol 17-beta-dehydrogenase 12
Kellgren-Lawrence grading scale
Major vault protein
Normalized collision energy
Phospholipase A2, membrane associated
Thyroid stimulating hormone
We are grateful to: G. Orfanoudaki for bioinformatics help; M. Aivaliotis, M. Papanastasiou and N. Kountourakis (Proteomics Facility IMBB) and S. Carpentier, W. Vermaelen and K. Arat (SyBioMa proteomics facility at KU Leuven) for support in proteomics experiments. Our research was funded by the OASYS (09SYN-13-705; to AT, MZ and AE), SYMPA (09SYΝ-13-832; to AE) and an Excellence grant (#1473 to AE). from the Greek Ministry of Education.
- Scanzello CR, Goldring SR. The role of synovitis in osteoarthritis pathogenesis. Bone. 2012;51:249–57.View ArticlePubMed CentralPubMedGoogle Scholar
- Loeser RF, Goldring SR, Scanzello CR, Goldring MB. Osteoarthritis: A disease of the joint as an organ. Arthritis Rheum. 2012;64:1697–707.View ArticlePubMed CentralPubMedGoogle Scholar
- Haseeb A, Haqqi TM. Immunopathogenesis of osteoarthritis. Clin Immunol. 2013;146:185–96.View ArticlePubMed CentralPubMedGoogle Scholar
- Vincent TL. Targeting mechanotransduction pathways in osteoarthritis: a focus on the pericellular matrix. Curr Opin Pharmacol. 2013;13:449–54.View ArticlePubMedGoogle Scholar
- Castañeda S, Roman-Blas JA, Largo R, Herrero-Beaumont G. Subchondral bone as a key target for osteoarthritis treatment. Biochem Pharmacol. 2012;83:315–23.View ArticlePubMedGoogle Scholar
- Wieland HA, Michaelis M, Kirschbaum BJ, Rudolphi KA. Osteoarthritis — an untreatable disease? Nat Rev Drug Discov. 2005;4:331–44.View ArticlePubMedGoogle Scholar
- Zhuo Q, Yang W, Chen J, Wang Y. Metabolic syndrome meets osteoarthritis. Nat Rev Rheumatol. 2012;8:729–37.View ArticlePubMedGoogle Scholar
- Little CB, Hunter DJ. Post-traumatic osteoarthritis: from mouse models to clinical trials. Nat Rev Rheumatol. 2013;9:485–97.View ArticlePubMedGoogle Scholar
- Felson DT, Lawrence RC, Dieppe PA, Hirsch R, Helmick CG, Jordan JM, et al. Osteoarthritis: new insights. Part 1: the disease and its risk factors. Ann Intern Med. 2000;133:635–46.View ArticlePubMedGoogle Scholar
- Fibel KH. State-of-the-Art management of knee osteoarthritis. World J Clin Cases. 2015;3:89.View ArticlePubMed CentralPubMedGoogle Scholar
- Jarcho JA, Hunter DJ. Viscosupplementation for osteoarthritis of the knee. N Engl J Med. 2015;372:1040–7.View ArticleGoogle Scholar
- Mobasheri A. The future of osteoarthritis therapeutics: targeted pharmacological therapy. Curr Rheumatol Rep. 2013;15:364.View ArticlePubMed CentralPubMedGoogle Scholar
- Tonge DP, Pearson MJ, Jones SW. The hallmarks of osteoarthritis and the potential to develop personalised disease-modifying pharmacological therapeutics. Osteoarthr Cartil OARS Osteoarthr Res Soc. 2014;22:609–21.View ArticleGoogle Scholar
- Evans CH, Huard J. Gene therapy approaches to regenerating the musculoskeletal system. Nat Rev Rheumatol. 2015;11:234–42.View ArticlePubMedGoogle Scholar
- Harmon KG, Rao AL. The use of platelet-rich plasma in the nonsurgical management of sports injuries: hype or hope? ASH Educ Program Book. 2013;2013:620–6.Google Scholar
- Michigami T. Current understanding on the molecular basis of chondrogenesis. Clin Pediatr Endocrinol. 2014;23:1.View ArticlePubMed CentralPubMedGoogle Scholar
- Carroll SH, Ravid K. Differentiation of mesenchymal stem cells to osteoblasts and chondrocytes: a focus on adenosine receptors. Expert Rev Mol Med. 2013;15:e1.View ArticlePubMedGoogle Scholar
- Goldring MB. Chondrogenesis, chondrocyte differentiation, and articular cartilage metabolism in health and osteoarthritis. Ther Adv Musculoskelet Dis. 2012;4:269–85.View ArticlePubMed CentralPubMedGoogle Scholar
- Ruiz-Romero C, Carreira V, Rego I, Remeseiro S, López-Armada MJ, Blanco FJ. Proteomic analysis of human osteoarthritic chondrocytes reveals protein changes in stress and glycolysis. Proteomics. 2008;8:495–507.View ArticlePubMedGoogle Scholar
- Ruiz-Romero C, Calamia V, Mateos J, Carreira V, Martínez-Gomariz M, Fernández M, et al. Mitochondrial dysregulation of osteoarthritic human articular chondrocytes analyzed by proteomics a decrease in mitochondrial superoxide dismutase points to a redox imbalance. Mol Cell Proteomics. 2009;8:172–89.View ArticlePubMed CentralPubMedGoogle Scholar
- Attur M, Dave M, Abramson SB, Amin A. Activation of diverse eicosanoid pathways in osteoarthritic cartilage. Bull NYU Hosp Jt Dis. 2012;70:99–108.PubMedGoogle Scholar
- Zubarev RA. The challenge of the proteome dynamic range and its implications for in-depth proteomics. Proteomics. 2013;13:723–6.View ArticlePubMedGoogle Scholar
- Frazer A, Bunning RA, Thavarajah M, Seid JM, Russell RG. Studies on type II collagen and aggrecan production in human articular chondrocytes in vitro and effects of transforming growth factor-beta and interleukin-1beta. Osteoarthr Cartil OARS Osteoarthr Res Soc. 1994;2:235–45.View ArticleGoogle Scholar
- Searle BC. Scaffold: A bioinformatic tool for validating MS/MS-based proteomic studies. Proteomics. 2010;10:1265–9.View ArticlePubMedGoogle Scholar
- Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, Samatova NF. Detecting differential and correlated protein expression in label-free shotgun proteomics. J Proteome Res. 2006;5:2909–18.View ArticlePubMedGoogle Scholar
- Beck M, Schmidt A, Malmstroem J, Claassen M, Ori A, Szymborska A, et al. The quantitative proteome of a human cell line. Mol Syst Biol. 2014;7:549.View ArticleGoogle Scholar
- Geiger T, Wehner A, Schaab C, Cox J, Mann M. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol Cell Proteomics. 2012;11:M111.014050.View ArticlePubMed CentralPubMedGoogle Scholar
- Pontén F, Gry M, Fagerberg L, Lundberg E, Asplund A, Berglund L, et al. A global view of protein expression in human cells, tissues, and organs. Mol Syst Biol. 2009;5:337.View ArticlePubMed CentralPubMedGoogle Scholar
- Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41:W77–83.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhang B, Kirov S, Snoddy J. WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 2005;33(Web Server issue):W741–8.View ArticlePubMed CentralPubMedGoogle Scholar
- Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, et al. The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007;8:R183.View ArticlePubMed CentralPubMedGoogle Scholar
- Kong D, Zheng T, Zhang M, Wang D, Du S, Li X, et al. Static mechanical stress induces apoptosis in rat endplate chondrocytes through MAPK and mitochondria-dependent caspase activation signaling pathways. PloS One. 2013;8:e69403.View ArticlePubMed CentralPubMedGoogle Scholar
- Allen JL, Cooke ME, Alliston T. ECM stiffness primes the TGFβ pathway to promote chondrocyte differentiation. Mol Biol Cell. 2012;23:3731–42.View ArticlePubMed CentralPubMedGoogle Scholar
- Phornphutkul C, Wu K-Y, Auyeung V, Chen Q, Gruppuso PA. mTOR signaling contributes to chondrocyte differentiation. Dev Dyn Off Publ Am Assoc Anat. 2008;237:702–12.Google Scholar
- Wu Q, Zhu M, Rosier RN, Zuscik MJ, O’Keefe RJ, Chen D. Beta-catenin, cartilage, and osteoarthritis. Ann N Y Acad Sci. 2010;1192:344–50.View ArticlePubMed CentralPubMedGoogle Scholar
- Nilsson O, Chrysis D, Pajulo O, Boman A, Holst M, Rubinstein J, et al. Localization of estrogen receptors-alpha and-beta and androgen receptor in the human growth plate at different pubertal stages. J Endocrinol. 2003;177:319–26.View ArticlePubMedGoogle Scholar
- Intekhab-Alam NY, White OB, Getting SJ, Petsa A, Knight RA, Chowdrey HS, et al. Urocortin protects chondrocytes from NO-induced apoptosis: a future therapy for osteoarthritis? Cell Death Dis. 2013;4, e717.View ArticlePubMed CentralPubMedGoogle Scholar
- Srinivas V, Shapiro IM. Addendum chondrocytes embedded in the epiphyseal growth plates of long bones undergo autophagy prior to the induction of osteogenesis. Autophagy. 2006;2:215–6.View ArticlePubMedGoogle Scholar
- Xing W, Cheng S, Wergedal J, Mohan S. Epiphyseal chondrocyte secondary ossification centers require thyroid hormone activation of indian hedgehog and osterix signaling. J Bone Miner Res. 2014;29:2262–75.View ArticlePubMedGoogle Scholar
- Chicheportiche Y, Chicheportiche R, Sizing I, Thompson J, Benjamin CB, Ambrose C, et al. Proinflammatory activity of TWEAK on human dermal fibroblasts and synoviocytes: blocking and enhancing effects of anti-TWEAK monoclonal antibodies. Arthritis Res. 2002;4:126–33.View ArticlePubMed CentralPubMedGoogle Scholar
- Wada T, Nakashima T, Hiroshi N, Penninger JM. RANKL-RANK signaling in osteoclastogenesis and bone disease. Trends Mol Med. 2006;12:17–25.View ArticlePubMedGoogle Scholar
- Buczynski MW, Dumlao DS, Dennis EA. Thematic review series: proteomics. An integrated omics analysis of eicosanoid biology. J Lipid Res. 2009;50:1015–38.View ArticlePubMed CentralPubMedGoogle Scholar
- Tarantino U, Ferlosio A, Arcuri G, Spagnoli LG, Orlandi A. Transglutaminase 2 as a biomarker of osteoarthritis: an update. Amino Acids. 2013;44:199–207.View ArticlePubMedGoogle Scholar
- Lai W-FT, Chang C-H, Tang Y, Bronson R, Tung C-H. Early diagnosis of osteoarthritis using cathepsin B sensitive near-infrared fluorescent probes. Osteoarthr Cartil OARS Osteoarthr Res Soc. 2004;12:239–44.View ArticleGoogle Scholar
- Papathanasiou I, Malizos KN, Tsezou A. Bone morphogenetic protein-2-induced Wnt/β-catenin signaling pathway activation through enhanced low-density-lipoprotein receptor-related protein 5 catabolic activity contributes to hypertrophy in osteoarthritic chondrocytes. Arthritis Res Ther. 2012;14:R82.View ArticlePubMed CentralPubMedGoogle Scholar
- Hojo H, Ohba S, Yano F, Chung U. Coordination of chondrogenesis and osteogenesis by hypertrophic chondrocytes in endochondral bone development. J Bone Miner Metab. 2010;28:489–502.View ArticlePubMedGoogle Scholar
- Kronenberg HM. Developmental regulation of the growth plate. Nature. 2003;423:332–6.View ArticlePubMedGoogle Scholar
- Hirsch MS, Lunsford LE, Trinkaus-Randall V, Svoboda KK. Chondrocyte survival and differentiation in situ are integrin mediated. Dev Dyn. 1997;210:249–63.View ArticlePubMedGoogle Scholar
- Woods A, Wang G, Beier F. Regulation of chondrocyte differentiation by the actin cytoskeleton and adhesive interactions. J Cell Physiol. 2007;213:1–8.View ArticlePubMedGoogle Scholar
- Gao Y, Liu S, Huang J, Guo W, Chen J, Zhang L, et al. The ECM-cell interaction of cartilage extracellular matrix on chondrocytes. BioMed Res Int. 2014;2014:1–8.Google Scholar
- Wozniak MA, Modzelewska K, Kwong L, Keely PJ. Focal adhesion regulation of cell behavior. Biochim Biophys Acta BBA - Mol Cell Res. 2004;1692:103–19.View ArticleGoogle Scholar
- Knudson W, Loeser RF. CD44 and integrin matrix receptors participate in cartilage homeostasis. Cell Mol Life Sci CMLS. 2002;59:36–44.View ArticleGoogle Scholar
- Ishida O, Tanaka Y, Morimoto I, Takigawa M, Eto S. Chondrocytes are regulated by cellular adhesion through CD44 and hyaluronic acid pathway. J Bone Miner Res. 1997;12:1657–63.View ArticlePubMedGoogle Scholar
- Knudson CB, Knudson W. Hyaluronan and CD44: modulators of chondrocyte metabolism. Clin Orthop. 2004;(427 Suppl):S152–62.
- Shapiro IM, Adams CS, Freeman T, Srinivas V. Fate of the hypertrophic chondrocyte: Microenvironmental perspectives on apoptosis and survival in the epiphyseal growth plate. Birth Defects Res Part C Embryo Today Rev. 2005;75:330–9.View ArticleGoogle Scholar
- Sassi N, Laadhar L, Allouche M, Zandieh-Doulabi B, Hamdoun M, Klein-Nulend J, et al. The roles of canonical and non-canonical Wnt signaling in human de-differentiated articular chondrocytes. Biotech Histochem Off Publ Biol Stain Comm. 2014;89:53–65.View ArticleGoogle Scholar
- Perper SJ, Browning B, Burkly LC, Weng S, Gao C, Giza K, et al. TWEAK is a novel arthritogenic mediator. J Immunol. 2006;177:2610–20.View ArticlePubMedGoogle Scholar
- Tat SK, Pelletier J-P, Velasco CR, Padrines M, Martel-Pelletier J. New perspective in osteoarthritis: the OPG and RANKL system as a potential therapeutic target? Keio J Med. 2009;58:29–40.View ArticlePubMedGoogle Scholar
- Van der Eerden BCJ, Van Til NP, Brinkmann AO, Lowik C, Wit JM, Karperien M. Gender differences in expression of androgen receptor in tibial growth plate and metaphyseal bone of the rat. Bone. 2002;30:891–6.View ArticlePubMedGoogle Scholar
- Bagriacik EU, Yaman M, Haznedar R, Sucak G, Delibasi T. TSH-induced gene expression involves regulation of self-renewal and differentiation-related genes in human bone marrow-derived mesenchymal stem cells. J Endocrinol. 2012;212:169–78.View ArticlePubMedGoogle Scholar
- Masuko K, Murata M, Suematsu N, Okamoto K, Yudoh K, Nakamura H, et al. A metabolic aspect of osteoarthritis: lipid as a possible contributor to the pathogenesis of cartilage degradation. Clin Exp Rheumatol. 2009;27:347–53.PubMedGoogle Scholar
- Schneider C, Pozzi A. Cyclooxygenases and lipoxygenases in cancer. Cancer Metastasis Rev. 2011;30:277–94.View ArticlePubMed CentralPubMedGoogle Scholar
- Panigrahy D, Greene ER, Pozzi A, Wang DW, Zeldin DC. EET signaling in cancer. Cancer Metastasis Rev. 2011;30:525–40.View ArticlePubMed CentralPubMedGoogle Scholar
- Panigrahy D, Kaipainen A, Greene ER, Huang S. Cytochrome P450-derived eicosanoids: the neglected pathway in cancer. Cancer Metastasis Rev. 2010;29:723–35.View ArticlePubMed CentralPubMedGoogle Scholar
- Miyata N, Roman RJ. Role of 20-hydroxyeicosatetraenoic acid (20-HETE) in vascular system. J Smooth Muscle Res Nihon Heikatsukin Gakkai Kikanshi. 2005;41:175–93.View ArticleGoogle Scholar
- Morisseau C. Role of epoxide hydrolases in lipid metabolism. Biochimie. 2013;95:91–5.View ArticlePubMed CentralPubMedGoogle Scholar
- Vasieva O. The many faces of glutathione transferase pi. Curr Mol Med. 2011;11:129–39.View ArticlePubMedGoogle Scholar
- Laborde E. Glutathione transferases as mediators of signaling pathways involved in cell proliferation and cell death. Cell Death Differ. 2010;17:1373–80.View ArticlePubMedGoogle Scholar
- Wang T, Arifoglu P, Ronai Z, Tew KD. Glutathione S-transferase P1-1 (GSTP1-1) inhibits c-Jun N-terminal kinase (JNK1) signaling through interaction with the C terminus. J Biol Chem. 2001;276:20999–1003.View ArticlePubMedGoogle Scholar
- Weston CR, Davis RJ. The JNK signal transduction pathway. Curr Opin Cell Biol. 2007;19:142–9.View ArticlePubMedGoogle Scholar
- Szajnik M, Szczepanski MJ, Elishaev E, Visus C, Lenzner D, Zabel M, et al. 17β hydroxysteroid dehydrogenase type 12 (HSD17B12) is a marker of poor prognosis in ovarian carcinoma. Gynecol Oncol. 2012;127:587–94.View ArticlePubMed CentralPubMedGoogle Scholar
- Rickman DS, Millon R, De Reynies A, Thomas E, Wasylyk C, Muller D, et al. Prediction of future metastasis and molecular characterization of head and neck squamous-cell carcinoma based on transcriptome and genome analysis by microarrays. Oncogene. 2008;27:6607–22.View ArticlePubMedGoogle Scholar
- Van Dijk FS, Zillikens MC, Micha D, Riessland M, Marcelis CLM, de Die-Smulders CE, et al. PLS3 mutations in X-linked osteoporosis with fractures. N Engl J Med. 2013;369:1529–36.View ArticlePubMedGoogle Scholar
- Yokobori T, Iinuma H, Shimamura T, Imoto S, Sugimachi K, Ishii H, et al. Plastin3 is a novel marker for circulating tumor cells undergoing the epithelial-mesenchymal transition and is associated with colorectal cancer prognosis. Cancer Res. 2013;73:2059–69.View ArticlePubMedGoogle Scholar
- Sugimachi K, Yokobori T, Iinuma H, Ueda M, Ueo H, Shinden Y, et al. Aberrant expression of plastin-3 via copy number gain induces the epithelial-mesenchymal transition in circulating colorectal cancer cells. Ann Surg Oncol. 2014;21:3680–90.View ArticlePubMedGoogle Scholar
- Bernatchez PN, Sharma A, Kodaman P, Sessa WC. Myoferlin is critical for endocytosis in endothelial cells. Am J Physiol Cell Physiol. 2009;297:C484–92.View ArticlePubMed CentralPubMedGoogle Scholar
- Cipta S, Patel HH. Molecular bandages: inside-out, outside-in repair of cellular membranes. Focus on “Myoferlin is critical for endocytosis in endothelial cells.”. Am J Physiol Cell Physiol. 2009;297:C481–3.View ArticlePubMed CentralPubMedGoogle Scholar
- Li R, Ackerman 4th WE, Mihai C, Volakis LI, Ghadiali S, Kniss DA. Myoferlin depletion in breast cancer cells promotes mesenchymal to epithelial shape change and stalls invasion. PloS One. 2012;7:e39766.View ArticlePubMed CentralPubMedGoogle Scholar
- Turtoi A, Blomme A, Bellahcène A, Gilles C, Hennequière V, Peixoto P, et al. Myoferlin is a key regulator of EGFR activity in breast cancer. Cancer Res. 2013;73:5438–48.View ArticlePubMedGoogle Scholar
- Poynton RA, Hampton MB. Peroxiredoxins as biomarkers of oxidative stress. Biochim Biophys Acta BBA - Gen Subj. 1840;2014:906–12.Google Scholar
- Taguchi T, Razzaque MS. The collagen-specific molecular chaperone HSP47: is there a role in fibrosis? Trends Mol Med. 2007;13:45–53.View ArticlePubMedGoogle Scholar
- Berger W, Steiner E, Grusch M, Elbling L, Micksche M. Vaults and the major vault protein: Novel roles in signal pathway regulation and immunity. Cell Mol Life Sci. 2009;66:43–61.View ArticlePubMedGoogle Scholar
- Tanaka H, Tsukihara T. Structural studies of large nucleoprotein particles, vaults. Proc Jpn Acad Ser B Phys Biol Sci. 2012;88:416–33.View ArticlePubMed CentralPubMedGoogle Scholar
- De Ceuninck F, Marcheteau E, Berger S, Caliez A, Dumont V, Raes M, et al. Assessment of some tools for the characterization of the human osteoarthritic cartilage proteome. J Biomol Tech JBT. 2005;16:256.Google Scholar
- Pilling D, Buckley CD, Salmon M, Gomer RH. Inhibition of fibrocyte differentiation by serum amyloid P. J Immunol. 2003;171:5537–46.View ArticlePubMedGoogle Scholar
- Murakami M, Taketomi Y, Sato H, Yamamoto K. Secreted phospholipase A2 revisited. J Biochem (Tokyo). 2011;150:233–55.View ArticleGoogle Scholar
- Kostopoulou F, Gkretsi V, Malizos KN, Iliopoulos D, Oikonomou P, Poultsides L, et al. Central role of SREBP-2 in the pathogenesis of osteoarthritis. PLoS ONE. 2012;7:e35753.View ArticlePubMed CentralPubMedGoogle Scholar
- Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16:494–502.View ArticlePubMed CentralPubMedGoogle Scholar
- Smith PK, Krohn RI, Hermanson GT, Mallia AK, Gartner FH, Provenzano MD, et al. Measurement of protein using bicinchoninic acid. Anal Biochem. 1985;150:76–85.View ArticlePubMedGoogle Scholar
- Rappsilber J, Ishihama Y, Mann M. Stop and Go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal Chem. 2003;75:663–70.View ArticlePubMedGoogle Scholar
- The UniProt Consortium. The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Res. 2010;38(Database):D142–8.View ArticlePubMed CentralGoogle Scholar
- Keller A, Nesvizhskii AI, Kolker E, Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem. 2002;74:5383–92.View ArticlePubMedGoogle Scholar
- Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003;75:4646–58.View ArticlePubMedGoogle Scholar
- Searle BC, Turner M, Nesvizhskii AI. Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies. J Proteome Res. 2008;7:245–53.View ArticlePubMedGoogle Scholar
- Gokce E, Shuford CM, Franck WL, Dean RA, Muddiman DC. Evaluation of normalization methods on GeLC-MS/MS label-free spectral counting data to correct for variation during proteomic workflows. J Am Soc Mass Spectrom. 2011;22:2199–208.View ArticlePubMedGoogle Scholar
- Camargo A, Azuaje F, Wang H, Zheng H. Permutation - based statistical tests for multiple hypotheses. Source Code Biol Med. 2008;3:15.View ArticlePubMed CentralPubMedGoogle Scholar
- Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25:1091–3.View ArticlePubMed CentralPubMedGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.View ArticlePubMed CentralPubMedGoogle Scholar
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