Open Access

Proteomic analysis of synovial fluid in osteoarthritis using SWATH‑mass spectrometry

  • Authors:
    • Weixiong Liao
    • Zhongli Li
    • Tanshi Li
    • Qiang Zhang
    • Heng Zhang
    • Xinzheng Wang
  • View Affiliations

  • Published online on: December 11, 2017     https://doi.org/10.3892/mmr.2017.8250
  • Pages: 2827-2836
  • Copyright: © Liao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The lack of early diagnostic maneuvers and effective pharmacotherapy for osteoarthritis (OA) is predominantly attributed to current limited understanding of its pathogenesis. In the present study, the alteration of synovial fluid (SF) proteome in OA were analyzed using SWATH‑mass spectrometry (SWATH‑MS) to further elucidate the pathogenesis of OA. SF samples were collected from 10 OA and 10 rheumatoid arthritis (RA) patients undergoing arthroplasty and 10 patients undergoing arthroscopy for traumatic arthritis (meniscus injury without cartilage lesion). According to the Kellgren‑Lawrence (KL) radiographic grading criteria, all the OA and RA patients were classified as KL grade 4. SWATH‑MS was applied to identify differentially expressed proteins specifically regulated in OA. Differentially expressed proteins identified by SWATH‑MS were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Proteins of interest were quantified by enzyme‑linked immunosorbent assay (ELISA) following identification. With the use of SWATH‑MS, 131 proteins were identified to be differentially expressed in OA, of which 93 corresponded to upregulation and 38 to downregulation. Complement C1r was the most significantly upregulated protein in OA. Twenty‑eight out of the 131 proteins were specifically regulated in OA, of which 17 were upregulated and 11 were downregulated. Dickkopf‑related protein 2 (DKK2) was one of the proteins specifically upregulated in OA. Furthermore, KEGG pathway annotation indicated that differentially expressed proteins in OA were enriched in complement and coagulation cascades. ELISA indicated that OA severity was positively correlated with the levels of complement C1r (r=0.549; P<0.001) and DKK2 (r=0.79; P<0.001) in the SF. The results indicate that complement and coagulation cascades are involved in the pathogenesis of OA. Differentially expressed proteins, including complement C1r and DKK2 may be used as potential biomarkers or drug targets, which may facilitate with intervention of OA.

Introduction

Osteoarthritis (OA) is a degenerative joint disorder characterized by articular cartilage degradation, osteophyte formation, synovium hyperplasia, subchondral bone cysts and sclerosis. It is one of the leading causes of pain and dysfunction in the joints among the aging population (1). Despite the high prevalence of OA, its pathogenesis remains unclear. At present, the diagnosis of OA primarily depends on clinical symptoms and radiographic findings. However, the majority of individuals are clinically asymptomatic at the early stages of OA, and pathological degradation of the articular cartilage already exists before the symptoms arise. Furthermore, radiographs may appear normal at this stage due to lack of sensitivity in visualizing minimal cartilage lesions. Thus, the diagnostic methods currently in use have limitations in detecting preclinical OA and provide an inaccurate assessment of disease progression. Although multiple translational techniques detect early OA processes, it remains unknown as to whether they truly represent the initial events. Therefore, the clinical diagnosis of early OA is one of the greatest challenges in this field (2). Currently, there is no definitely effective medication for the treatment of early OA. Traditional medications, such as glucosamine and hyaluronic acid, once thought of as effective, have no longer been recommended by the latest American Academy of Orthopaedic Surgeons guideline due to evidence of a lack of efficacy (3). The medications currently used for the treatment of OA are predominantly aimed at relieving pain, but do not prevent disease progression. These limitations give rise to the interest in proteomic studies to identify candidate biomarkers and drug targets, which may facilitate early diagnosis and intervention of OA. Additionally, proteomic studies provide greater insight into OA pathogenesis and original insights into the molecular mechanisms involved in disease progression.

Advances in proteomic technologies have facilitated extensive proteomic characterization of various body fluids, including serum (4) and urine (5) in OA. However, evaluating the synovial fluid (SF) proteome is more likely to yield a higher concentration of potential biomarkers than serum and urine, as SF is in direct contact with articular cartilage, synovium, ligament, meniscus and joint capsule. Alterations in metabolism of any of these tissues during disease progression are reflected as alterations in the proteomic profile of SF, which is also less influenced by systematic conditions. Therefore, it is considered more appropriate to detect the proteomic profile in SF than in other body fluids. There are also certain studies designed to investigate the expression of certain proteins in SF from OA patients (6). These studies focused on evaluating the expression of certain proteins, but failed to screen novel proteins that are differentially expressed in SF. With the use of quantitative proteomic technologies, a series of novel proteins differentially expressed in SF from OA patients have been identified in previous studies (7,8). However, the majority of these investigations were performed using low resolution mass spectrometry (MS) and with minimal fractionation of the samples, which limited the depth of coverage. Isobaric tags for relative and absolute quantification is a labeling method with high throughput, easy automation, high resolution and quantification accuracy, which enables quantitative proteomics analysis of four samples simultaneously under the same experimental conditions (9). This technology has also been applied to investigate alterations of the proteomic profile in OA in previous studies (10,11). Although a greater number of novel proteins were identified to be differentially expressed using this method (10,11), whether these proteins are specifically regulated in OA remains unclear due to a lack of effective standard controls or inter-controls.

SWATH-MS is an emerging data-independent acquisition method, whichgenerates a single file containing fragment ion spectra of all ionized species of a sample (12). When combined with a targeted data analysis strategy, SWATH-MS was demonstrated to achieve favorable accuracy, dynamic range, and reproducibility of selected reaction monitoring, which is considered to be the golden standard in quantitative proteomic technology (13). In order to identify differentially expressed proteins specifically regulated in OA and further elucidate the disease pathogenesis, SWATH-MS was performed to compare the SF proteome in OA, rheumatoid arthritis (RA) and traumatic arthritis (meniscus injury without cartilage defect). Subsequently, proteins of interest were quantified by enzyme-linked immunosorbent assay (ELISA). To the best of our knowledge, no comparative analysis with respect to the SF proteome has been conducted in these three types of knee disorders using SWATH-MS.

Materials and methods

Subjects

The present study was initiated following approval from the Ethical Committee of the Chinese PLA General Hospital and informed consent was obtained from all the subjects. SF samples were acquired from 30 patients who had received surgical treatment for a knee disorder between September 2014 and January 2015 in the Orthopedics Department of the Chinese PLA General Hospital (Beijing, China). A total of 10 knee OA patients (4 men and 6 women; age, 64–72 years; mean age, 67 years) undergoing arthroplasty were defined as the study group, 10 knee RA patients (4 men and 6 women; age, 45–69 years; mean age, 60 years) undergoing arthroplasty served as the inter-control group, and 10 patients (7 men and 3 women; age, 23–47 years; mean age, 29 years) undergoing arthroscopy for traumatic knee arthritis (meniscus injury without cartilage defect) within 7 days after injury served as the standard control group. The diagnosis of knee OA and RA was based upon the criteria of the American College of Rheumatology (14). The severity of knee OA and RA was evaluated by the Kellgren-Lawrence (KL) radiographic grading criteria (Fig. 1), according to which all the knee OA and RA patients recruited were classified as KL grade 4. All the patient data were retrieved from our institutional database through chart review.

Sample preparation and protein digestion

SF samples (1–2 ml) were aspirated from the affected knee using a sterile puncture needle prior to surgery and 50 µl proteinase inhibitor cocktail (Roche Molecular Diagnostics, Pleasanton, CA, USA) was immediately added to the sample after collection. The mixture was centrifuged at 19,228 × g for 20 min at 4°C (Hettich MIKRO 22R; Hettich Benelux, Tuttlingen, Germany) to remove cells and debris. For the comprehensive shotgun analysis, all the SF samples were depleted of the 14 most abundant proteins with the multiple affinity removal system (MARS Hu-14 spin cartridge; Agilent Technologies, Inc., Santa Clara, CA, USA) according to the manufacturer's instruction. After reduction and alkylation with 10 mM tris (2-carboxyethyl) phosphine (Sigma-Aldrich; Merck KGaA, Darmstadt, Germany) and 20 mM iodoacetamide (Sigma-Aldrich; Merck KGaA), the samples were subjected to protein quantification using the Bradford Assay according to the manufacturer's instructions with a commercial Bradford reagent (Bio-Rad Laboratories, Hercules, CA, USA). Subsequently, the samples were diluted with 1 M urea and digested with sequencing-grade trypsin (Promega Corporation, Madison, WI, USA) at a protease/protein ratio of 1:30 overnight at 37°C. Following digestion, the samples were stored at −80°C until use.

Shotgun proteomic measurement

The processed peptides were measured using a Triple TOF 5600 Plus mass spectrometer (SCIEX, Framingham, MA, USA) operated in data-dependent acquisition (DDA) mode. The mass spectrometer was interfaced with a NanoLC Ultra 2D Plus HPLC system (Eksigent Technologies, Dublin, CA, USA) as previously described (12,13). Peptides were directly injected onto a 20-cm PicoFrit emitter and separated using a 120-min gradient from 2–35% (buffer A: 0.1% Formic acid and 2% acetonitrile; buffer B: 0.1% Formic acid and 90% acetonitrile) at a flow rate of 300 nl/min. MS1 spectra were collected in the range 350–1,250 m/z for 250 msec. The 20 most intense precursors with charge state 2–5, which exceeded 250 counts per sec were selected for fragmentation, and MS2 spectra were collected in the range 100–1,500 m/z for 50 msec. The precursor ions were dynamically excluded from reselection for 20 sec.

SWATH-MS measurement

SWATH-MS measurements were performed with peptide mixtures generated by digesting SF samples. The unfractionated, total peptide samples were analyzed to minimize confounding factors introduced by sample handling. The same liquid chromatography-MS/MS system used for DDA measurements was used for SWATH analysis (12,13). In SWATH-MS mode, the instrument was specifically tuned to optimize the quadrupole settings for the selection of 25-m/z wide precursor ion selection windows. Using an isolation width of 25 m/z, a set of overlapping windows was constructed, covering the precursor mass range of 400–1,200 m/z. The effective isolation windows were considered as being 400–425, 424–449, 448–473 m/z, and so on. SWATH-MS2 spectra were collected from 100–1,500 m/z. The collision energy was optimized for each window according to the calculation for a charge 2+ion centered upon the window with a spread of 15 eV. An accumulation time of 50 msec was used for all fragment ion scans in high-sensitivity mode, and for each SWATH-MS cycle, a survey scan in high-resolution mode was acquired for 50 msec. SWATH-MS measurements were repeated three times on each sample to access the technical variability. Differentially expressed proteins identified by SWATH-MS were subjected to gene ontology (GO) and KEGG pathway annotation.

ELISA

Another 90 SF samples were obtained from 36 knee OA patients (17 men and 19 women, aged 57–74 years; mean age, 65 years), 36 knee RA patients (15 men and 21 women, aged 41–70 years; mean age, 58 years) and 18 patients (12 men and 6 women, aged 21–47 years;mean age, 28 years) with traumatic knee arthritis (meniscus injury without cartilage defect) to detect the level of complement C1r and that of Dickkopf-related protein 2 (DKK2). According to the KL radiographic grading criteria, 18 out of the 36 knee OA patients were classified as KL grade 2, and the other 18 as KL grade 4. Similarly, 18 out of the 36 knee RA patients were categorized as KL grade 2, and the other 18 as KL grade 4. The levelsof complement C1r and that of DKK2 in SF were measured by ELISA (MyBioSource; R&D Systems, Inc., Minneapolis, MN, USA) according to the manufacturer's instructions.

Statistical analysis

Statistical analysis was performed with SPSS software (Version 17.0; SPSS Inc, Chicago, IL, USA). Data were expressed as the mean ± standard deviation. One way analysis of variance followed by a post hoc Tukey test for multiple comparisons was performed to compare the quantitative data from different groups. Pearson's correlation coefficient was used to analyze the correlation between protein level and disease severity. P<0.05 was considered to indicate a statistically significant difference.

Results

SWATH-MS

To evaluate technical variability, each sample was analyzed in triplicate. As presented in Fig. 2, total ion current chromatograms from triplicates of the same sample exhibited high similarity, whereas those from different samples displayed obvious variation. The results revealed small technical variability and excellent reproducibility of SWATH-MS measurements and great variation in proteomic profiles of SF from the different groups.

Proteins were regarded to be differentially expressed when ion intensity between groups was significantly different (P<0.05) and fold change between groups was >1.5. Based on these criteria, the comparison between OA and traumatic arthritis resulted in the identification of 131 significantly different proteins, of which 93 corresponded to upregulation and 38 to downregulation in OA. Complement C1r was the most significantly upregulated protein in OA (Fig. 3). The comparison between RA and traumatic arthritis led to the identification of 185 significantly different proteins, of which 152 corresponded to upregulation and 33 to downregulation in RA. The comparison between OA and RA gave rise to the identification of 139 significantly different proteins, of which 22 corresponded to upregulation and 117 to downregulation in OA.

Proteins with a fold change >1.5 in OA and a fold change <1.5 in RA were defined as differentially expressed proteins specifically regulated in OA. On the basis of these criteria, 28 proteins were considered to be specifically regulated in OA, of which 17 were upregulated and 11 were downregulated (Table I). DKK2 was one of the proteins specifically upregulated in OA (Fig. 4). Proteins up-/downregulated with a fold change >1.5 in OA and down-/upregulated with a fold change >1.5 in RA were defined as differentially expressed proteins conversely regulated in OA and RA. According to these criteria, 8 proteins were identified to be conversely regulated in OA and RA, of which 3 were upregulated in OA and downregulated in RA, 5 were downregulated in OA and upregulated in RA (Table II).

Table I.

Differentially expressed proteins specifically regulated in osteoarthritis identified by SWATH-mass spectrometry.

Table I.

Differentially expressed proteins specifically regulated in osteoarthritis identified by SWATH-mass spectrometry.

Protein IDAccessionProtein namet-valueP-valueFold change
  1E7ES66Glycocalicin−9.8350.00065.251
  2P02748Complement component C941.185   2.08E-063.898
  3Q15848Adiponectin   7.9330.001373.575
  4P01833Polymeric immunoglobulin receptor−3.6820.021173.056
  5P10643Complement component C712.3080.000252.900
  6F8W1Q3Biotinidase−54.461   6.81E-072.676
  7O95497Pantetheinase−17.427   6.36E-052.391
  8F6SYF8Dickkopf-related protein 2−19.531   4.05E-052.178
  9P01024Complement C320.642   3.25E-051.981
10P04208Ig λ chain V–I region WAH−3.7300.02031.918
11P01019 Angiotensinogen−43.998   1.60E-061.846
12Q5VY30Plasma retinol-binding protein (1–182)−5.8670.004211.830
13F5H6I0 β2-microglobulin30.320   7.05E-061.781
14P02768Serum albumin−51.884   8.26E-071.723
15P02760Protein AMBP−11.9340.000281.709
16P10599Thioredoxin   3.9410.016941.647
17P07355-2Annexin A226.293   1.24E-051.622
18P01857Ig γ-1 chain C region   6.7900.002460.546
19P04207Ig κ chain V–III region CLL   8.9970.000840.534
20P01861Ig γ-4 chain C region   8.3290.001140.505
21P02647Apolipoprotein A-I66.441   3.07E-070.484
22P58546Myotrophin   4.8050.008620.456
23C9JIZ6Saposin-D   7.4740.001710.340
24E9PGN7Plasma protease C1 inhibitor14.4220.000130.327
25P60174Triosephosphate isomerase15.2630.000110.322
26Q9H299SH3 domain-binding glutamic acid-rich-like protein 38.8930.000880.268
27B7Z6Z4Myosin light polypeptide 624.641   1.61E-050.151
28Q14624-2Isoform 2 of Inter-α-trypsin inhibitor heavy chain H475.790   1.82E-070.076

Table II.

Differentially expressed proteins conversely regulated in OA and RA identified by SWATH-MS.

Table II.

Differentially expressed proteins conversely regulated in OA and RA identified by SWATH-MS.

Protein IDAccessionProtein nameP-value (OA)Fold change (OA)P-value (RA)Fold change (RA)
1P09211Glutathione S-transferase0.010972.051   2.87E-060.527
2P13611-2Isoform V1 of Versican core protein   5.08E-051.985   2.34E-050.380
3P10915Hyaluronan and proteoglycan link protein   1.28E-051.636   4.36E-060.212
4P37837Transaldolase0.008260.5770.000225.286
5P07737Profilin-10.030040.572   3.57E-054.777
6P6310414-3-3 protein zeta/delta   1.82E-070.501   1.28E-052.567
7P06727Apolipoprotein A-IV   1.73E-060.463   1.24E-061.536
8P01880Ig delta chain C region0.028840.261   3.47E-052.521

[i] OA, osteoarthritis; RA, rheumatoid arthritis.

GO annotation with respect to the 131 differentially expressed proteins in OA demonstrated that the cellular component of these proteins was predominantly derived from the extracellular matrix (ECM), cytoplasmic membrane, cytoplasm, secretory granule and lipoprotein particle. The molecular function of these proteins primarily included binding, transporter, enzyme activator and inhibitor activity. The biological process these proteins participated in predominantly included the acute inflammatory response, complement activation, immune response, response to stimulus, regulation of apoptosis and homeostatic process. KEGG pathway annotation indicated that these proteins were enriched in complement and coagulation cascades (Fig. 5), ECM-receptor interaction and the peroxisome proliferator activated receptor signaling pathway.

ELISA

As presented in Fig. 6, the mean level of complement C1r in SF from OA was 2,063.1±1,238.1 ng/ml, that from RA was 2,726.4±1,627.8 ng/ml and that from traumatic arthritis was 180.6±102.4 ng/ml. The mean level of complement C1r in SF from RA was significantly higher than those from OA (P=0.033) and traumatic arthritis (P<0.001), and that from OA was also significantly higher than that from traumatic arthritis (P<0.001). According to the KL radiographic grading criteria, the mean level of complement C1r in SF from OA with KL grade 2 was 2,147.8±1,337.2 ng/ml, and that from OA with KL grade 4 was 1,978.4±1,163.1 ng/ml. The mean level of complement C1r in SF from traumatic arthritis was significantly lower than those from OA with KL grade 2 (P<0.001) and KL grade 4 (P<0.001); however, no significant difference was identified between the latter two (P=0.698). The levels of complement C1r in SF from OA were positively correlated with disease severity (r=0.549, P<0.001). Similarly, the mean level of complement C1r in SF from RA with KL grade 2 was 2,920.9±1,743.0 ng/ml, and that from RA with KL grade 4 was 2,531.8±1,528.7 ng/ml. In addition, the mean level of complement C1r in SF from traumatic arthritis was significantly lower than those from RA with KL grade 2 (P<0.001) and KL grade 4 (P<0.001); however, no significant difference was identified between the latter two (P=0.374). The levels of complement C1r in SF from RA were also positively correlated with disease severity (r=0.540, P<0.001).

As shown Fig. 7, the mean level of DKK2 in SF from OA was 186.3±41.0 ng/ml, that from RA was 93.0±34.5 ng/ml and that from traumatic arthritis was 74.2±22.9 ng/ml. The mean level of DKK2 in SF from OA was significantly higher when compared with those from RA (P<0.001) and traumatic arthritis (P<0.001); however, no significant difference was identified between the latter two (P=0.069). According to the KL radiographic grading criteria, the mean level of DKK2 in SF from OA with KL grade 2 was 174.4±42.5 ng/ml, and that from OA with KL grade 4 was 198.2±36.8 ng/ml. The mean level of DKK2 in SF from OA with KL grade 4 was significantly higher than those from OA with KL grade 2 (P=0.045) and traumatic arthritis (P<0.001), and that from OA with KL grade 2 was also significantly higher than that from traumatic arthritis (P<0.001). The levels of DKK2 in SF from OA were positively correlated with disease severity (r=0.796, P<0.001). Similarly, the mean level of DKK2 in SF from RA with KL grade 2 was 89.4±35.4 ng/ml, and that from RA with KL grade 4 was 96.6±34.3 ng/ml. No significant difference was detected between the mean levels of DKK2 in SF from RA with KL grade 2 and traumatic arthritis (P=0.195), between those from RA with KL grade 4 and traumatic arthritis (P=0.057) and between those from RA with KL grade 2 and KL grade 4 (P=0.537). In addition, the levels of DKK2 in SF from RA were positively correlated with disease severity (r=0.287, P=0.035).

Discussion

OA exerts a great influence on physical and psychological health of the elderly due to its high morbidity and disability rate (1). The lack of early diagnostic maneuvers and effective pharmacotherapy for OA is primarily attributed to the limitations in current understanding of its pathogenesis, which may be further elucidated by investigating the SF proteome. However, for a variety of reasons, previous studies with regard to proteomic analysis of OA were not consistent in demonstrating the pathogenesis and failed to detect effective biomarkers or drug targets to facilitate early diagnosis and intervention of OA. Therefore, this provided the rationale for conducting the present study. In the current study, with the use of SWATH-MS, a total of 131 proteins were identified to be differentially expressed in SF from OA, of which 28 were specifically regulated in OA and eight were conversely regulated in OA and RA. The quantity of differentially expressed proteins identified in the current study is markedly more than in previous studies analyzing SF proteome using other quantitative proteomic technologies (7,8). With the use of two-dimensional polyacrylamide gel electrophoresis, Liu et al (15) identified 10 differential protein spots between the synovial membranes of the bilateral and unilateral knee OA in surgery-induced rabbit models. Similarly, Fernandez-Puente et al (16) developed a multiple reaction monitoring method with which a panel of 14 protein biomarker candidates for OA was identified. Differential proteins identified in the above-mentioned studies, such as lumican (15) and haptoglobin (16) were also identified in the current study. These indicate that, as a novel proteomic technology, SWATH-MS provides a more comprehensive quantification result in delineating the SF proteome and markedly benefits the screening of biomarkers due to its higher resolution and coverage rate.

The hypothesis that the complement system may participate in OA pathogenesis is substantiated by the identification of complement C1r and enrichment of differentially expressed proteins in complement and coagulation cascades. The results are consistent with those reported by Wang et al (17), which indicates abnormally high expression levels and activation of complement components in SF and synovial membranes from OA. Complement activation in turn results in the formation of membrane attack complex (MAC) on chondrocytes, which either kills the cells or causes them to produce matrix-degrading enzymes, inflammatory mediators and other complement effectors, all of which promote joint pathology. A previous study indicates that MAC mediates collagenase expression and cartilage destruction primarily via extracellular signal-regulated kinase-dependent c-Fos induction (18). In addition, complement activation and MAC assembly also stimulate the cell cycle by inducing expression of c-Jun, JunD, and c-Fos mRNAs (19), which appears to have a potential correlation with the pathology of hyperosteogeny and synovial hyperplasia in OA. These findings provide a rationale for targeting the complement system as a disease-modifying therapeutic strategy for OA.

DKK2 is one of the proteins that is specifically upregulated in OA, the alteration of which has been confirmed by subsequent ELISA in the present study. As a Wnt signaling antagonist, the overexpression of DKK2 contributes to the abnormal phenotype and poor mineralization of OA osteoblasts by altering the Wnt signaling pathway, which is crucial for osteogenesis and regulates terminal osteoblast differentiation (20). The upregulation of DKK2 in OA osteoblasts may be associated with the hypoxic condition and ischemic episode caused by modification of subchondral bone vascularization (21). Besides DKK2, there are many other proteins specifically regulated in OA. These proteins and their possible underlying mechanism in OA pathogenesis are as follows: i) Adiponectin is a protein hormone secreted predominantly by differentiated adipocytes, it increases intercellular adhesion molecule-1 (ICAM-1) expression levels in OA synovial fibroblasts via the liver kinase B1/calmodulin-dependent protein kinase II, AMP-activated protein kinase, c-Jun, and activator protein 1 signaling pathway. Adiponectin-induced ICAM-1 expression promotes the adhesion of monocytes to OA synovial fibroblasts, which may be involved in the pathogenesis of OA (22). Furthermore, adiponectin enhances production of nitric oxide, interleukin (IL)-6, matrix metalloproteinase (MMP)-1 and MMP-3 in OA chondrocytes in a mitogen-activated protein kinase-dependent manner (23). ii) The mechanisms by which β2-microglobulin induces joint destruction is to induce the release of MMP-1 without concomitant release of tissue inhibitor of metalloproteinase-1 (TIMP-1) from OA synovial fibroblasts, leading to an increase in the MMP-1/TIMP-1 ratio, which is indicative of uncontrolled collagenolysis. The release of MMP-1 induced by β2-microglobulin may be inhibited by doxycycline (24). iii) Thioredoxin, known as a cellular reducing catalyst induced by oxidative stress, is involved in the redox regulation of transcription factors, such as nuclear factor (NF)-κB. It appears to accelerate the nuclear translocation of NF-κB, a major transcriptional regulator for production of IL-6 and IL-8 on stimulation with tumor necrosis factor-a (25). iv) As for annexin A2, a discoidin domain receptor 2 (DDR-2) binding protein in the DDR-2/MMP signaling pathway, its phosphorylation by phosphorylated-DDR-2 leads to MMP-13 secretion (26).

Glutathione S-transferase (GST) is one of the proteins conversely regulated in OA and RA identified in the present study. The differential expression of GST in OA coincides with the results documented by Ostalowska et al (27), which indicate aberrant changes in the activities of antioxidant enzymes, including superoxide dismutase and GST in SF from OA. GST is critically important for cellular defense against oxidative stress-induced apoptosis in OA chondrocytes, possibly by elimination of 4-hydroxynonenal, one of the most abundant and reactive aldehydes of lipid peroxidation products (28). Hyaluronan and proteoglycan link protein is another protein conversely regulated in OA and RA. It has been reported that LP stabilizes the proteoglycan aggregates towards dissociation, and helps to protect them from degradation under conditions where tissue catabolism is promoted (29).

There were certain limitations of the current study. Due to ethical restrictions, patients undergoing arthroscopy for traumatic knee arthritis were selected, rather than healthy individuals, and served as the standard control group. However, as SF samples were acquired within 7 days after injury and patients in this group all exhibited intact articular cartilage under arthroscopy, it is reasonable for us to consider that these samples are most similar to those from healthy individuals in the proteomic profile. Furthermore, to investigate proteins specifically regulated in OA, the design of the inter-control group requires more diversification. In addition to RA, other arthropathies due to systemic lupus erythematosus, ankylosing spondylitis, gout, psoriasis or haemochromatosis should be included as inter-control groups.

In conclusion, the findings of the present study provide an improved understanding of the pathogenesis of OA, and may facilitate the early diagnosis and treatment of OA following the identification of various potential biomarkers and drug targets.

Acknowledgements

The authors would like to thank Dr Yang Liu for assistance in acquisition of SF samples, and Dr Can Huang for assistance with collection of patient data.

Glossary

Abbreviations

Abbreviations:

DDA

data-dependent acquisition

DDR-2

discoidin domain receptor 2

DKK2

Dickkopf-related protein 2

ECM

extracellular matrix

ELISA

enzyme-linked immunosorbent assay

GO

gene ontology

GST

glutathione S-transferase

ICAM-1

intercellular adhesion molecule-1

KEGG

Kyoto Encyclopedia of Genes and Genomes

LP

link protein

MAC

membrane attack complex

OA

osteoarthritis

RA

rheumatoid arthritis

SF

synovial fluid

SWATH-MS

SWATH-mass spectrometry

TIMP-1

tissue inhibitor of metalloproteinase-1

References

1 

Neogi T: The epidemiology and impact of pain in osteoarthritis. Osteoarthritis Cartilage. 21:1145–1153. 2013. View Article : Google Scholar : PubMed/NCBI

2 

Arnscheidt C, Meder A and Rolauffs B: Early diagnosis of osteoarthritis: Clinical reality and promising experimental techniques. Z Orthop Unfall. 154:254–268. 2016.PubMed/NCBI

3 

Jevsevar DS: Treatment of osteoarthritis of the knee: Evidence-based guideline, 2nd edition. J Am Acad Orthop Surg. 21:571–576. 2013. View Article : Google Scholar : PubMed/NCBI

4 

Jiao Q, Wei L, Chen C, Li P, Wang X, Li Y, Guo L, Zhang C and Wei X: Cartilage oligomeric matrix protein and hyaluronic acid are sensitive serum biomarkers for early cartilage lesions in the knee joint. Biomarkers. 21:146–151. 2016. View Article : Google Scholar : PubMed/NCBI

5 

Nemirovskiy O, Li WW and Szekely-Klepser G: Design and validation of an immunoaffinity LC-MS/MS assay for the quantification of a collagen type II neoepitope peptide in human urine: Application as a biomarker of osteoarthritis. Methods Mol Biol. 641:253–270. 2010. View Article : Google Scholar : PubMed/NCBI

6 

Duan Y, Hao D, Li M, Wu Z, Li D, Yang X and Qiu G: Increased synovial fluid visfatin is positively linked to cartilage degradation biomarkers in osteoarthritis. Rheumatol Int. 32:985–990. 2012. View Article : Google Scholar : PubMed/NCBI

7 

Yamagiwa H, Sarkar G, Charlesworth MC, McCormick DJ and Bolander ME: Two-dimensional gel electrophoresis of synovial fluid: Method for detecting candidate protein markers for osteoarthritis. J Orthop Sci. 8:482–490. 2003. View Article : Google Scholar : PubMed/NCBI

8 

Gobezie R, Kho A, Krastins B, Sarracino DA, Thornhill TS, Chase M, Millett PJ and Lee DM: High abundance synovial fluid proteome: Distinct profiles in health and osteoarthritis. Arthritis Res Ther. 9:R362007. View Article : Google Scholar : PubMed/NCBI

9 

Wu WW, Wang G, Baek SJ and Shen RF: Comparative study of three proteomic quantitative methods, DIGE, cICAT and iTRAQ, using 2D gel- or LC-MALDI TOF/TOF. J Proteome Res. 5:651–658. 2006. View Article : Google Scholar : PubMed/NCBI

10 

Balakrishnan L, Bhattacharjee M, Ahmad S, Nirujogi RS, Renuse S, Subbannayya Y, Marimuthu A, Srikanth SM, Raju R and Dhillon M: Differential proteomic analysis of synovial fluid from rheumatoid arthritis and osteoarthritis patients. Clin Proteomics. 11:12014. View Article : Google Scholar : PubMed/NCBI

11 

Lourido L, Calamia V, Mateos J, Fernández-Puente P, Fernández-Tajes J, Blanco FJ and Ruiz-Romero C: Quantitative proteomic profiling of human articular cartilage degradation in osteoarthritis. J Proteome Res. 13:6096–6106. 2014. View Article : Google Scholar : PubMed/NCBI

12 

Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, Bonner R and Aebersold R: Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: A new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 11:O111.0167172012. View Article : Google Scholar : PubMed/NCBI

13 

Collins BC, Gillet LC, Rosenberger G, Röst HL, Vichalkovski A, Gstaiger M and Aebersold R: Quantifying protein interaction dynamics by SWATH mass spectrometry: Application to the 14-3-3 system. Nat Methods. 10:1246–1253. 2013. View Article : Google Scholar : PubMed/NCBI

14 

Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO III, Birnbaum NS, Burmester GR, Bykerk VP and Cohen MD: 2010 Rheumatoid arthritis classification criteria: An american college of rheumatology/european league against rheumatism collaborative initiative. Arthritis Rheum. 62:2569–2581. 2010. View Article : Google Scholar : PubMed/NCBI

15 

Liu W, He J, Lin R, Liang J and Luo Q: Differential proteomics of the synovial membrane between bilateral and unilateral knee osteoarthritis in surgery–induced rabbit models. Mol Med Rep. 14:2243–2249. 2016. View Article : Google Scholar : PubMed/NCBI

16 

Fernandez-Puente P, Calamia V, González-Rodríguez L, Lourido L, Camacho-Encina M, Oreiro N, Ruiz-Romero C and Blanco FJ: Multiplexed mass spectrometry monitoring of biomarker candidates for osteoarthritis. J Proteomics. 152:216–225. 2017. View Article : Google Scholar : PubMed/NCBI

17 

Wang Q, Rozelle AL, Lepus CM, Scanzello CR, Song JJ, Larsen DM, Crish JF, Bebek G, Ritter SY and Lindstrom TM: Identification of a central role for complement in osteoarthritis. Nat Med. 17:1674–1679. 2011. View Article : Google Scholar : PubMed/NCBI

18 

Litherland GJ, Elias MS, Hui W, Macdonald CD, Catterall JB, Barter MJ, Farren MJ, Jefferson M and Rowan AD: Protein kinase C isoforms zeta and iota mediate collagenase expression and cartilage destruction via STAT3- and ERK-dependent c-fos induction. J Biol Chem. 285:22414–22425. 2010. View Article : Google Scholar : PubMed/NCBI

19 

Rus HG, Niculescu F and Shin ML: Sublytic complement attack induces cell cycle in oligodendrocytes. J Immunol. 156:4892–4900. 1996.PubMed/NCBI

20 

Chan TF, Couchourel D, Abed E, Delalandre A, Duval N and Lajeunesse D: Elevated Dickkopf-2 levels contribute to the abnormal phenotype of human osteoarthritic osteoblasts. J Bone Miner Res. 26:1399–1410. 2011. View Article : Google Scholar : PubMed/NCBI

21 

Bouvard B, Abed E, Yéléhé-Okouma M, Bianchi A, Mainard D, Netter P, Jouzeau JY, Lajeunesse D and Reboul P: Hypoxia and vitamin D differently contribute to leptin and dickkopf-related protein 2 production in human osteoarthritic subchondral bone osteoblasts. Arthritis Res Ther. 16:4592014. View Article : Google Scholar : PubMed/NCBI

22 

Chen HT, Tsou HK, Chen JC, Shih JM, Chen YJ and Tang CH: Adiponectin enhances intercellular adhesion molecule-1 expression and promotes monocyte adhesion in human synovial fibroblasts. PLoS One. 9:e927412014. View Article : Google Scholar : PubMed/NCBI

23 

Koskinen A, Juslin S, Nieminen R, Moilanen T, Vuolteenaho K and Moilanen E: Adiponectin associates with markers of cartilage degradation in osteoarthritis and induces production of proinflammatory and catabolic factors through mitogen-activated protein kinase pathways. Arthritis Res Ther. 13:R1842011. View Article : Google Scholar : PubMed/NCBI

24 

Moe SM, Singh GK and Bailey AM: beta2-microglobulin induces MMP-1 but not TIMP-1 expression in human synovial fibroblasts. Kidney Int. 57:2023–2034. 2000. View Article : Google Scholar : PubMed/NCBI

25 

Yoshida S, Katoh T, Tetsuka T, Uno K, Matsui N and Okamoto T: Involvement of thioredoxin in rheumatoid arthritis: Its costimulatory roles in the TNF-alpha-induced production of IL-6 and IL-8 from cultured synovial fibroblasts. J Immunol. 163:351–358. 1999.PubMed/NCBI

26 

Zhao W, Zhang C, Shi M, Zhang J, Li M, Xue X, Zhang Z, Shu Z, Zhu J and Mu N: The discoidin domain receptor 2/annexin A2/matrix metalloproteinase 13 loop promotes joint destruction in arthritis through promoting migration and invasion of fibroblast-like synoviocytes. Arthritis Rheumatol. 66:2355–2367. 2014. View Article : Google Scholar : PubMed/NCBI

27 

Ostalowska A, Birkner E, Wiecha M, Kasperczyk S, Kasperczyk A, Kapolka D and Zon-Giebel A: Lipid peroxidation and antioxidant enzymes in synovial fluid of patients with primary and secondary osteoarthritis of the knee joint. Osteoarthritis Cartilage. 14:139–145. 2006. View Article : Google Scholar : PubMed/NCBI

28 

Vaillancourt F, Fahmi H, Shi Q, Lavigne P, Ranger P, Fernandes JC and Benderdour M: 4-Hydroxynonenal induces apoptosis in human osteoarthritic chondrocytes: The protective role of glutathione-S-transferase. Arthritis Res Ther. 10:R1072008. View Article : Google Scholar : PubMed/NCBI

29 

Rodriguez E and Roughley P: Link protein can retard the degradation of hyaluronan in proteoglycan aggregates. Osteoarthritis Cartilage. 14:823–829. 2006. View Article : Google Scholar : PubMed/NCBI

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February 2018
Volume 17 Issue 2

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Online ISSN:1791-3004

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APA
Liao, W., Li, Z., Li, T., Zhang, Q., Zhang, H., & Wang, X. (2018). Proteomic analysis of synovial fluid in osteoarthritis using SWATH‑mass spectrometry. Molecular Medicine Reports, 17, 2827-2836. https://doi.org/10.3892/mmr.2017.8250
MLA
Liao, W., Li, Z., Li, T., Zhang, Q., Zhang, H., Wang, X."Proteomic analysis of synovial fluid in osteoarthritis using SWATH‑mass spectrometry". Molecular Medicine Reports 17.2 (2018): 2827-2836.
Chicago
Liao, W., Li, Z., Li, T., Zhang, Q., Zhang, H., Wang, X."Proteomic analysis of synovial fluid in osteoarthritis using SWATH‑mass spectrometry". Molecular Medicine Reports 17, no. 2 (2018): 2827-2836. https://doi.org/10.3892/mmr.2017.8250