Sequential depletion of human serum for the search of osteoarthritis biomarkers
© Fernández et al.; licensee BioMed Central Ltd. 2012
Received: 13 February 2012
Accepted: 6 September 2012
Published: 12 September 2012
The 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.
Following 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.
Altogether, 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.
In clinical proteomics, searching for protein biomarkers is currently done using mass spectrometry-based tools coupled to chromatography or to gel electrophoresis techniques. The samples most commonly used for biomarker discovery are plasma and serum, due to their easy availability. It is generally agreed that each of these samples have more than ten thousand different proteins. From such large amount, it is estimated that circa 20 proteins account for more than 95% of the bulk mass of all proteins [1, 2]. These large abundant proteins hinder the searching for putative disease biomarkers, as the signals belonging to such proteins mask those from other less abundant, yet more important proteins in terms of information related to diseases. Therefore, removal of the most abundant proteins helps to overcome the aforementioned problem. However, to date there is no universal method to accomplish this task. Therefore, many different protocols to deplete high abundant proteins can be found in literature comprising, but not limited to: (i) HPLC columns containing antibodies to the most abundant proteins, (ii) spin columns, (iii) the use of chemical reagents, (iv) or even sophisticated kits used to compress the dynamic range of the proteins rather than to deplete them [3–5].
Recently, the use of non-expensive chemical reagents as a way to deplete high abundance proteins has attracted the attention of the proteomics community [6, 7]. Furthermore, it has been suggested to link the use of such methods to the resulting proteome after depletion. For instance, serum depleted with DTT was found rich in IgG-type proteins and for this reason it has been proposed as an economic method in studies of diseases that are characterized by modifications in the IgG-type protein content, such as multiple myeloma . After depletion, the resulting proteome is investigated for biomarker discovery using different methodologies, including gel electrophoresis. Two-dimensional difference in gel electrophoresis, 2D-DIGE, has emerged has a powerful tool in proteomics. In 2D-DIGE, complex protein samples are labeled with different fluorescent dyes, mixed together and separated in the same gel. This method solves the problem of gel-to-gel variation , and allows for high sample throughput as only one gel is needed to perform the comparisons and spot intensities can be normalized against a unique internal standard.
The present work reports for the first time the combination of a chemical sequential depletion method combined with 2D-DIGE for the search of osteoarthritis biomarkers. Osteoarthritis is a degenerative joint disease characterized by cartilage destruction and bone changes, occasionally accompanied by synovial inflammation . It is the most common rheumatic pathology, and estimates are that the number of people with some degree of OA will double over the next three decades . Despite this high prevalence, existing diagnostic tests are very limited and ultimately rely on the subjective description of pain symptoms, stiffness in the affected joints, and radiography . These limitations have increased the interest in identifying new specific biological markers for cartilage degradation, both to facilitate early diagnosis of joint destruction and to improve the prognosis and evaluation of disease progression. Besides the promising results obtained with some cartilage-specific proteins, still useful biomarkers are needed for clinical practice, which could be effective for the development of early diagnostic tools and/or alternative therapies . The method proposed of sequential depletion for biomarker search in serum samples is non-expensive, of easy implementation and allows fast sample throughput.
Results and discussion
This work illustrates the application of a chemical sequential depletion method in quantitative proteomics studies for biomarker discovery. The search of novel biomarkers is an area in which proteomics has recently emerged as a powerful approach for the detection of new proteins with diagnostic, prognostic or therapy evaluation utilities. In this field, a number of proteomics experiments have been followed in the last years with the aim of characterizing novel molecules with biomarker power for joint diseases, including osteoarthritis (OA) . In the present study, we report the combination of a chemical sequential depletion method combined with 2D-DIGE for the search of OA biomarkers. This method reduces the dynamic range of serum proteins, thus allowing the identification of a higher number of proteins in the samples.
Sequential depletion of human serum for proteomics analyses
We randomly picked 54 spots from these 2D gels and identified the protein forms present in each of them, which correspond to 16 different proteins (Additional file 1: Figure S1 and Additional file 2: Table S1). Although many of them belong to the most abundant plasma proteins, as expected for a gel-based technique (which has an important bias towards abundant proteins), we were also able to identify two proteins that are reported to be present at less than 1 μmol/L in plasma, such as serum amyloid A (SAA1) or angiotensinogen (ANGT) . These results support the usefulness of the strategy for the study of medium-abundant serum proteins.
2D-DIGE quantitative proteomics analysis on the sequentially depleted serum samples
The sequential depletion method was then applied to study the osteoarthritis-dependent modulation of proteins in serum. Those proteins that are altered in samples from diseased patients might be useful as OA biomarkers for diagnostic or prognostic purposes, and also for the evaluation of alternative therapies.
Characteristics of the osteoarthritis (OA) patients and controls included in this study
Number of patients
67.75 ± 9.08
75.85 ± 5.00
57.68 ± 5.06
56.38 ± 4.92
Number of patients
66.20 ± 8.83
76.10 ± 4.79
57.69 ± 5.15
54.69 ± 4.50
Number of patients
69.30 ± 9.52
75.60 ± 5.44
57.66 ± 5.25
58.07 ± 3.83
DIGE experimental design
Novel putative serum osteoarthritis biomarkers identified in this work
Proteins identified in this work as altered in Osteoarthritis vs Control sera
Protein ID a)
Ratio OA:N b)
Sequence tags f)
Inhibitor of serine proteases
Negative regulation of bone mineralization
Essential component of the renin-angiotensin system (RAS)
Lipid degradation and transport
Haptoglobin (β chain)
Cellular iron ion homeostasis
Haptoglobin (α 1 chain)
Cellular iron ion homeostasis
Haptoglobin (α 2 chain)
Cellular iron ion homeostasis
Hemoglobin subunit beta
Involved in oxygen transport from the lung to peripheral tissues
Ig kappa chain C region
Ig lambda-2 chain C regions
Complement activation, classical pathway
Protein binding (secreted)
Serum amyloid A protein
Thyroid hormone-binding protein
Interestingly, this altered panel differs substantially from that obtained in a previous work from our group based on serum immunodepletion and subsequent LC-MS/MS analysis . In that work, several relatively high abundant proteins (such as complement components) were found increased in the sera from OA patients. On the contrary, most complement proteins are removed by the different depletion approach in the present study, while several apolipoproteins are identified. This demonstrates the complementarity of different depletion steps. Furthermore, although the gel-based strategy performed in this study did not allowed the identification of less abundant proteins, it demonstrates its usefulness of for characterizing disease-specific patterns of protein modifications, which are impossible to obtain from peptide/MS-based strategies. This includes not only the identification of a specific isoform of HPT beta chain as increased in OA, but also different forms of serum amyloid A, transthyretin or some apolipoproteins.
Nevertheless, both proteomic approaches led to the identification of several ubiquitous proteins related with lipid transport, immune response or protein binding. Although the molecules that had been employed typically as OA biomarkers are proteins directly or indirectly involved in cartilage degradation, or proteins synthesized in an attempt at cartilage repair (including different type II collagen fragments or other cartilage extracellular matrix components) , increasing evidences suggest that the most promising strategy in OA would be the combination of different biomarkers into panels. These might include proteins highly specific to the pathology, but also ever-present proteins that can be altered during the progression of several unspecific processes such as inflammation, immune response, cellular death/proliferation, etc. The possible biomarker value of these proteins is exemplified by a recent work unravelling the key role of complement dysregulation in OA pathogenesis . Furthermore, the identification in the present study of several proteins related with the lipid metabolism might be relevant for the definition of a new phenotype termed ‘metabolic OA’, which is recently acquiring more attention in the research community .
Osteoarthritis patients display an altered haptoglobin protein profile in serum
In summary, we show in this work the application of a novel sequential depletion procedure for the search of osteoarthritis biomarkers in serum. This strategy has been coupled to a DIGE-based quantitative proteomic analysis, in order to find protein isoforms or fragments that are specific of the disease. By these means, we were able to identify 16 protein forms altered in the disease. Among these, we verified for the first time the OA-dependent increase of an haptoglobin chain. These data demonstrate the usefulness of the approach for protein biomarker discovery, and provide a list of potential OA biomarker candidates that might be subject of further validation studies.
The sera used for this study were obtained from OA patients and controls with no history of joint disease, which were characterized radiographically. Inclusion criteria are fully described in a previous publication from our group . The patients meeting these criteria were diagnosed with OA according to the American College of Rheumatology (ACR) criteria, and knee and hip radiographs from the participants were classified from grade I to grade IV according to the Kellgren and Lawrence (K/L) scoring system . The patients were of both genders and ages ranged from 58 to 90 years. A population of 20 samples from K/L grade IV and 20 non-symptomatic controls was utilized for this study. Prior to proteomic analysis, the serum samples were grouped into four pools of 10 samples each to reduce individual and biological variability. Table 1 summarizes the characteristics of each pool.
Sequential depletion of high abundant proteins in serum
The sera were subjected to a sequential depletion protocol involving two precipitation steps (Figure 1): protein depletion was first performed with DTT according to the protocol described by Warder et al.  with minor modifications, and then further depleted with ACN according to Kay et al. . With this aim, 2.2 μl of 500 mM DTT were added to 20 μl of pooled serum and incubated for 1 h at room temperature until a viscous precipitate persisted. This precipitate was pelleted by centrifugation 2 × 20 min at 14000 × g. Subsequently, the supernatant was transferred to a clean LoBind tube and diluted with water prior to the addition of 57% ACN . Samples were briefly vortexed and sonicated twice for 10 min. Then, ACN-precipitated proteins were pelleted during 10 min at 14000 × g. Finally, the supernatant was transferred to clean LoBind tubes and evaporated to dryness in a Savant SPD121P SpeedVac (Thermo, Waltham, USA) for further analysis.
Precipitated proteins were solubilized in an isolectric focusing-compatible lysis buffer containing 8 M urea, 2 M thiourea, 30 mM Tris and 4% CHAPS. The proteins present in the pools were quantified (n = 5) using the Bradford method (modified with HCl) . A BSA standard curve (0 to 4 mg/ml) and the samples were analyzed in triplicate by reading at 570 nm in a microplate reader (Multiskan® Plus, Labsystems, Quesada, Argentina).
Protein labeling and two-dimensional electrophoresis
The proteomics comparison between osteoarthritic and control sera was performed across four DIGE gels using 20 μg of total protein fraction per CyDye™ gel and two biological replicates for each condition. Proteins in each sample were fluorescently tagged with a set of matched fluorescent dyes according to the manufacturer’s protocol for minimal labeling. To ensure that there were no dye-specific labeling artifacts, Cy3 and Cy5 labels were swapped between two technical replicates of the same sample, whereas the pooled standard sample was labeled with Cy2. The standard pool was prepared by pooling 20 μg of proteins from each sample prior to labeling. In every case, 160 pmol of dye was used for 20 μg of proteins (8 pmol/μg). Labeling was performed for 30 min on ice in darkness, and the reaction was quenched with 1 μl of 10 mM L-lysine for 10 min under the same conditions.
The four pairs of Cy3- and Cy5-labeled samples (each containing 20 μg of protein) were combined and mixed with a 20-μg aliquot of the Cy2-labeled standard pool. The mixtures, containing 60 μg of protein, were diluted to 125 μl with rehydration buffer (7 M urea, 2 M thiourea, 4% CHAPS, 0.002% bromphenol blue, 2% ampholytes (pH 3–11), and 97 mM Destreak reagent). Samples were loaded onto IPG strips (7 cm, pH 4–7 non-linear) by passive overnight rehydration. Isoelectric focusing was carried out on a IPGphor II IEF system (GE Healthcare) using the following conditions: 333 Vhr at 500 V, 40 min at 1000 V and finally 9166 Vhr at 5000 V. Prior to the second dimension run, the strips were equilibrated first for 15 min in equilibration buffer (100 mM Tris–HCl (pH 8.0), 6 M urea, 30% glycerol, and 2% SDS) with 2% DTT and then for another 15 min in the same buffer supplemented with 2.5% iodoacetamide and 0.002% bromophenol blue. The equilibrated strips were transferred onto 12.5% homogenous polyacrylamide gels (2.6% C) casted in low fluorescence glass plates. Electrophoresis was run at 150 V at 20°C.
Image acquisition and DIGE data analysis
The differentially labeled co-resolved proteins within each gel were imaged at 100 dots/inch resolution using a DIGE Imager scanner (GE Healthcare). Cy2-, Cy3-, and Cy5-labeled images of each gel were acquired at excitation/emission values of 488/520, 523/580, and 633/670 nm, respectively. Gels were scanned directly between the glass plates, and the 16-bit image file format images were exported for data analysis. After imaging for CyDyes, the gels were removed from the plates and stained with colloidal Coomassie following standard procedures.
Semi-automated image analysis was performed with Progenesis SameSpots V3.2 software (Nonlinear Dynamics). Multiplexed analysis was selected for DIGE experiments and a representative gel image was chosen as reference. Spots were detected and their normalized volumes were ranked on the basis of ANOVA p-values and fold changes.
Ultrasonic in-gel enzymatic digestion
The gel spots of interest were manually excised and transferred to microcentrifuge tubes. Samples selected for analysis were subjected to ultrasonic in-gel enzymatic digestion, according to the ultrafast proteolytic digestion protocol previously described [34, 35]. Briefly, protein bands were washed with water and acetonitrile, reduced with DTT and alkylated with IAA in an ultrasonic bath (Sonorex RK 31 H, Bandelin, Berlin, Germany) operating at 35 kHz (100% amplitude). Then, the gel was washed again, dried and trypsin (375 ng in 25 μl) was added to the dried protein bands. The in-gel protein digestion was performed in a sonicator model SONOPULS HD 2200 (Bandelin, Berlin, Germany) operating at 50% amplitude for 2 min. Finally, supernatants were collected and further peptide extraction was performed on the remaining gel piece with acetonitrile 50% V/V, trifluoroacetic acid 0.1%V/V during 2 min at 50% amplitude in the sonicator. Supernatants were pooled, evaporated to dryness and reconstituted in 5 μl of formic acid 0.3% V/V.
Mass spectrometry (MS) analysis
The samples were analyzed using the Matrix-assisted laser desorption/ionization (MALDI)-Time of Flight (TOF)/TOF mass spectrometer 4800 Proteomics Analyzer (ABSCIEX, MA, USA) and 4000 Series Explorer™ Software (ABSCIEX). Data Explorer version 4.2 (ABSCIEX) was used for spectra analyses and generating peak-picking lists. All mass spectra were internally calibrated using autoproteolytic trypsin fragments and externally calibrated using a standard peptide mixture (Sigma-Aldrich). TOF/TOF fragmentation spectra were acquired by selecting the 20 most abundant ions of each MALDI-TOF peptide mass map (excluding trypsin autolytic peptides and other known background ions). An average of 2000 laser shots were employed per fragmentation spectrum.
The amino acid sequence tags obtained from each peptide fragmentation in MS/MS analyses were used to search for protein candidates using Mascot version 2.2 from Matrix Science (http://www.matrixscience.com). Peak intensity was used to select up to 50 peaks per precursor for MS/MS identification. Tryptic autolytic fragments, keratin and matrix-derived peaks were removed from the dataset used for the database search. The searches for peptide mass fingerprints and tandem MS spectra were performed in the SwissProt knowledgebase (2011_05 release version, June 2011), by searching in the UniProtKB/Swiss-Prot (http://www.expasy.ch/sprot) database, containing 528048 sequences and 186939477 residues, with taxonomy restriction (Homo sapiens, 20239 sequences). Fixed and variable modifications were considered (Cys as S-carbamidomethyl derivate and Met as oxidized methionine, respectively), allowing one trypsin missed cleavage site and a mass tolerance of 50 ppm. MS identifications were accepted as positive when at least five peptides matched and at least 20% of the peptide coverage of the theoretical sequences matched within a mass accuracy of 50 or 25 ppm with internal calibration. For MS/MS identifications, a precursor tolerance of 50 ppm and MS/MS fragment tolerance of 0.3 Da were used. Probability scores were significant at p < 0.01 for all matches.
Western blot analysis
One-dimensional Western blot tests were performed according to standard procedures. Briefly, 40 μg of serum proteins were loaded and resolved on standard 15% polyacrylamide SDS-PAGE gels. Separated proteins were then electroblotted onto PVDF membranes (Immobilon P, Millipore, Bedford, MA). Equivalent loadings were verified by Ponceau Red staining after transference. Membranes were blocked in Tris-buffered saline (pH 7.4) containing 0.1% Tween 20 (TBST) and 5% non-fat dried milk for 60 min at room temperature. The blots were then hybridized overnight at 4°C with monoclonal antibodies against the beta chain of HPT (ab13429, 1:500 dilution, Abcam, Cambridge, UK), diluted in TBST with 2% nonfat milk. After thorough washing with TBST, immunoreactive bands were detected by chemiluminescence using the corresponding horseradish peroxidase-conjugated secondary antibodies and ECL detection reagents (GE Healthcare), and then digitized using an LAS 3000 image analyzer. Quantitative changes in band intensities were evaluated with ImageQuant 5.2 software (GE Healthcare). The densitometry values of the Western blot bands containing the HPT were normalized against those of Ponceau staining obtained from the same membranes. Then, the relative abundance of HPT was calculated by obtaining the ratio of the normalized densitometric values between normal and OA samples. Statistical p values of the densitometry data were obtained by application of Mann–Whitney U test using SPSS version 15.0 program.
We are grateful to Natividad Oreiro and Carlos Fernández, from the Rheumatology Division of Hospital Universitario A Coruña, for providing serum samples. This study was supported by grants from Fondo Investigación Sanitaria-Spain (CIBER-CB06/01/0040; PI08/2028; PI11/2397); and Secretaria I + D + I Xunta de Galicia (09CSA043383PR, 10CSA916058PR). C. F. and V. C. are supported by Foundation of the Hospital Universitario A Coruña. J.-L. C.-M. is grateful to the Xunta de Galicia (Spain) for the Isidro Parga Pondal program. C.R.-R. is supported by Fondo Investigación Sanitaria-Spain (CP09/00114).
- Anderson NL, Anderson NG: The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 2002,1(11):845–867. 10.1074/mcp.R200007-MCP200View ArticleGoogle Scholar
- Roche S, Tiers L, Provansal M, Piva MT, Lehmann S: Interest of major serum protein removal for Surface-Enhanced Laser Desorption/Ionization - Time Of Flight (SELDI-TOF) proteomic blood profiling. In: Proteome Sci 2006, 4: 20. 10.1186/1477-5956-4-20Google Scholar
- Echan LA, Tang HY, Ali-Khan N, Lee K, Speicher DW: Depletion of multiple high-abundance proteins improves protein profiling capacities of human serum and plasma. Proteomics 2005,5(13):3292–3303. 10.1002/pmic.200401228View ArticleGoogle Scholar
- Righetti PG, Castagna A, Antonioli P, Boschetti E: Prefractionation techniques in proteome analysis: the mining tools of the third millennium. Electrophoresis 2005,26(2):297–319. 10.1002/elps.200406189View ArticleGoogle Scholar
- Roche S, Tiers L, Provansal M, Seveno M, Piva MT, Jouin P, Lehmann S: Depletion of one, six, twelve or twenty major blood proteins before proteomic analysis: the more the better? In: J Proteomics. vol. 72. Netherlands 2009, 72: 945–951.Google Scholar
- Kay R, Barton C, Ratcliffe L, Matharoo-Ball B, Brown P, Roberts J, Teale P, Creaser C: Enrichment of low molecular weight serum proteins using acetonitrile precipitation for mass spectrometry based proteomic analysis. Rapid Commun Mass Spectrom 2008,22(20):3255–3260. 10.1002/rcm.3729View ArticleGoogle Scholar
- Warder SE, Tucker LA, Strelitzer TJ, McKeegan EM, Meuth JL, Jung PM, Saraf A, Singh B, Lai-Zhang J, Gagne G, et al.: Reducing agent-mediated precipitation of high-abundance plasma proteins. In: Anal Biochem. vol. 387. United States 2009, 387: 184–193.Google Scholar
- Fernández C, Santos HM, Ruiz-Romero C: Blanco FJ. Capelo JL: A comparison of depletion vs. equalization for reducing high abundance proteins in human serum. Electrophoresis; 2011.Google Scholar
- Marouga R, David S, Hawkins E: The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal Bioanal Chem 2005,382(3):669–678. 10.1007/s00216-005-3126-3View ArticleGoogle Scholar
- Pritzker K, In: Osteoarthritis: Pathology of osteoarthritis. Edited by: Brandt K, Doherty M, Lohmander L. New York: Oxford University Press; 1998:50–61.Google Scholar
- Woolf A, Pfleger B: Burden of major musculoskeletal conditions. Bulletin of the World Health Organization 2003, 81: 10.Google Scholar
- Peat G, Thomas E, Duncan R, Wood L, Wilkie R, Hill J, Hay EM, Croft P: Estimating the probability of radiographic osteoarthritis in the older patient with knee pain. Arthritis Rheum 2007,57(5):794–802. 10.1002/art.22785View ArticleGoogle Scholar
- Kraus VB, Burnett B, Coindreau J, Cottrell S, Eyre D, Gendreau M, Gardiner J, Garnero P, Hardin J, Henrotin Y, et al.: Application of biomarkers in the development of drugs intended for the treatment of osteoarthritis. Osteoarthritis Cartilage 2011,19(5):515–542. 10.1016/j.joca.2010.08.019PubMed CentralView ArticleGoogle Scholar
- Ruiz-Romero C, Blanco FJ, In: Osteoarthritis Cartilage: Proteomics role in the search for improved diagnosis, prognosis and treatment of osteoarthritis. vol. 18. England: 2010 Osteoarthritis Research Society International: Published by Elsevier Ltd; 2010:500–509.Google Scholar
- Hortin GL, Sviridov D, Anderson NL: High-abundance polypeptides of the human plasma proteome comprising the top 4 logs of polypeptide abundance. Clin Chem 2008,54(10):1608–1616. 10.1373/clinchem.2008.108175View ArticleGoogle Scholar
- Nedelkov D, Kiernan UA, Niederkofler EE, Tubbs KA, Nelson RW: Investigating diversity in human plasma proteins. Proc Natl Acad Sci U S A 2005,102(31):10852–10857. 10.1073/pnas.0500426102PubMed CentralView ArticleGoogle Scholar
- Alsaif M, Guest PC, Schwarz E, Reif A, Kittel-Schneider S, Spain M, Rahmoune H, Bahn S: Analysis of serum and plasma identifies differences in molecular coverage, measurement variability, and candidate biomarker selection. Proteomics Clin Appl 2012,6(5–6):297–303.View ArticleGoogle Scholar
- Unlu M, Morgan ME, Minden JS: Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis 1997,18(11):2071–2077. 10.1002/elps.1150181133View ArticleGoogle Scholar
- Lilley KS, Friedman DB: All about DIGE: quantification technology for differential-display 2D-gel proteomics. Expert Rev Proteomics 2004,1(4):401–409. 10.1586/147894126.96.36.1991View ArticleGoogle Scholar
- Minden J: Comparative proteomics and difference gel electrophoresis. Biotechniques 2007,43(6):739–741. 743 passim 10.2144/000112653View ArticleGoogle Scholar
- Fernandez-Puente P, Mateos J, Fernandez-Costa C, Oreiro N, Fernandez-Lopez C, Ruiz-Romero C, Blanco FJ: Identification of a panel of novel serum osteoarthritis biomarkers. J Proteome Res 2011,10(11):5095–5101. 10.1021/pr200695pView ArticleGoogle Scholar
- Olsen AK, Sondergaard BC, Byrjalsen I, Tanko LB, Christiansen C, Muller A, Hein GE, Karsdal MA, Qvist P: Anabolic and catabolic function of chondrocyte ex vivo is reflected by the metabolic processing of type II collagen. Osteoarthritis Cartilage 2007,15(3):335–342. 10.1016/j.joca.2006.08.015View ArticleGoogle Scholar
- Wang Q, Rozelle AL, Lepus CM, Scanzello CR, Song JJ, Larsen DM, Crish JF, Bebek G, Ritter SY, Lindstrom TM, et al.: Identification of a central role for complement in osteoarthritis. In: Nat Med. vol. 17. United States 2011, 17: 1674–1679.Google Scholar
- Blanco FJ, Ruiz-Romero C: Osteoarthritis: Metabolomic characterization of metabolic phenotypes in OA. Nat Rev Rheumatol 2012,8(3):130–132. 10.1038/nrrheum.2012.11View ArticleGoogle Scholar
- Zhang H, Li XJ, Martin DB, Aebersold R: Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. In: Nat Biotechnol. vol. 21. United States 2003, 21: 660–666.Google Scholar
- Sinz A, Bantscheff M, Mikkat S, Ringel B, Drynda S, Kekow J, Thiesen HJ, Glocker MO: Mass spectrometric proteome analyses of synovial fluids and plasmas from patients suffering from rheumatoid arthritis and comparison to reactive arthritis or osteoarthritis. Electrophoresis 2002,23(19):3445–3456. 10.1002/1522-2683(200210)23:19<3445::AID-ELPS3445>3.0.CO;2-JView ArticleGoogle Scholar
- Yamagiwa H, Sarkar G, Charlesworth MC, McCormick DJ, Bolander ME: Two-dimensional gel electrophoresis of synovial fluid: method for detecting candidate protein markers for osteoarthritis. J Orthop Sci 2003,8(4):482–490. 10.1007/s00776-003-0657-3View ArticleGoogle Scholar
- Gutteridge JM: The antioxidant activity of haptoglobin towards haemoglobin-stimulated lipid peroxidation. Biochim Biophys Acta 1987,917(2):219–223. 10.1016/0005-2760(87)90125-1View ArticleGoogle Scholar
- Jue DM, Shim BS, Kang YS: Inhibition of prostaglandin synthase activity of sheep seminal vesicular gland by human serum haptoglobin. Mol Cell Biochem 1983,51(2):141–147.View ArticleGoogle Scholar
- Arredouani M, Matthijs P, Van Hoeyveld E, Kasran A, Baumann H, Ceuppens JL, Stevens E: Haptoglobin directly affects T cells and suppresses T helper cell type 2 cytokine release. Immunology 2003,108(2):144–151. 10.1046/j.1365-2567.2003.01569.xPubMed CentralView ArticleGoogle Scholar
- Rego I, Fernandez-Moreno M, Fernandez-Lopez C, Gomez-Reino JJ, Gonzalez A, Arenas J, Blanco FJ: Role of European mitochondrial DNA haplogroups in the prevalence of hip osteoarthritis in Galicia, Northern Spain. In: Ann Rheum Dis. vol. 69. England 2010, 69: 210–213.Google Scholar
- Kessler S, Guenther KP, Puhl W: Scoring prevalence and severity in gonarthritis: the suitability of the Kellgren & Lawrence scale. Clin Rheumatol 1998,17(3):205–209. 10.1007/BF01451048View ArticleGoogle Scholar
- Kruger NJ: The Bradford method for protein quantitation. Methods Mol Biol 1994, 32: 9–15.Google Scholar
- Rial-Otero R, Carreira RJ, Cordeiro FM, Moro AJ, Fernandes L, Moura I, Capelo JL: Sonoreactor-based technology for fast high-throughput proteolytic digestion of proteins. J Proteome Res 2007,6(2):909–912. 10.1021/pr060508mView ArticleGoogle Scholar
- Galesio M, Vieira DV, Rial-Otero R, Lodeiro C, Moura I, Capelo JL: Influence of the protein staining in the fast ultrasonic sample treatment for protein identification through peptide mass fingerprint and matrix-assisted laser desorption ionization time of flight mass spectrometry. J Proteome Res 2008,7(5):2097–2106. 10.1021/pr700850wView ArticleGoogle Scholar
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