Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the pathogenesis of knee osteoarthritis

THEKNE-02937; No of Pages 10 The Knee xxx (xxxx) xxx

Contents lists available at ScienceDirect

The Knee

Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the pathogenesis of knee osteoarthritis S.M. Koh a, C.K. Chan b, S.H. Teo b, S. Singh b, A. Merican b, W.M. Ng b, A. Abbas b, T. Kamarul a,⁎ a b

Tissue Engineering Group (TEG), Department of Orthopaedic Surgery (NOCERAL), Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia Department of Orthopaedic Surgery (NOCERAL), Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

a r t i c l e

i n f o

Article history: Received 11 January 2019 Received in revised form 6 October 2019 Accepted 31 October 2019 Available online xxxx Keywords: Inflammation Osteoarthritis Blood Synovial fluid Pathway

a b s t r a c t Purpose: Osteoarthritis (OA) of the knee is a multifactorial degenerative disease typically defined as the ‘wear and tear’ of articular joint cartilage. However, recent studies suggest that OA is a disease arising from chronic low-grade inflammation. We conducted a study to investigate the relationship between chronic inflammatory mediators present in both the systemic peripheral blood system and localised inflammation in synovial fluid (SF) of OA and non-OA knees; and subsequently made direct comparative analyses to understand the mechanisms that may underpin the processes involved in OA. Methods: 20-Plex proteins were quantified using Human Magnetic Luminex® assay (R&D Systems, USA) from plasma and SF of OA (n = 14) and non-OA (n = 14) patients. Ingenuity Pathway Analysis (IPA) software was used to predict the relationship and possible interaction of molecules pertaining to OA. Results: There were significant differences in plasma level for matrix metalloproteinase (MMP)3, interleukin (IL)-27, IL-8, IL-4, tumour necrosis factor-alpha, MMP-1, IL-15, IL-21, IL-10, and IL-1 beta between the groups, as well as significant differences in SF level for IL-15, IL-8, vascular endothelial growth factor (VEGF), MMP-1, and IL-18. Our predictive OA model demonstrated that toll-like receptor (TLR) 2, macrophage migration inhibitory factor (MIF), TLR4 and IL-1 were the main regulators of IL-1B, IL-4, IL-8, IL-10, IL-15, IL-21, IL-27, MMP-1 and MMP-3 in the plasma system; whilst IL-1B, TLR4, IL-1, and basigin (BSG) were the regulators of IL-4, IL-8, IL-10, IL-15, IL-18, IL-21, IL-27, MMP-1, and MMP-3 in the SF system. Conclusion: The elevated plasma IL-8 and SF IL-18 may be associated with the pathogenesis of OA via the activation of MMP-3. © 2019 Published by Elsevier B.V.

1. Introduction Osteoarthritis (OA) of the knee is a progressive joint degenerative disease commonly diagnosed in individuals above 45 years of age. This disease is irreversible, and at present remains incurable. Some common factors that have been thought to result in OA have included advancing age, high body weight, female gender, genetic predisposition, mechanical stress, and trauma [1]. Tradi-

Abbreviations: ACL, anterior cruciate ligament; BMI, body mass index; BP, blood pressure; BSG, basigin; IL, interleukin; MIF, macrophage migration inhibitory factor; OARSI, Osteoarthritis Research Society International; PB, peripheral blood; SF, synovial fluid; TLR, toll-like receptor; WHR, waist hip ratio. ⁎ Corresponding author at: Tissue Engineering Group (TEG), Department of Orthopaedic Surgery (NOCERAL), Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia. E-mail address: [email protected]. (T. Kamarul).

https://doi.org/10.1016/j.knee.2019.10.028 0968-0160/© 2019 Published by Elsevier B.V.

Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

2

S.M. Koh et al. / The Knee xxx (xxxx) xxx

tionally, OA has been regarded as a disease of ‘wear and tear’ due to the increased prevalence in the elderly population. However, recent studies suggest that OA could be caused by chronic low-grade inflammation, also known as ‘cold inflammation’ [2]. In 2015, The Osteoarthritis Research Society International (OARSI) endorsed a new definition by Kraus et al. [3] for OA. This was done in view of the new definition's emphasis on the role of inflammation in this disease. It would therefore be prudent that a thorough investigation of the inflammatory pathway involving the pathogenesis of OA be carried out in order to achieve an indepth understanding of the disease, and subsequent development of more effective management modalities. The influx of pro-inflammatory mediators into joint spaces such as cytokines (interleukin (IL)-1, tumour necrosis factor (TNF), IL-6 and IL-8), matrix metalloproteinases (MMP)s, bioactive lipids, neuropeptides, and adipokines have been reported by Sellam et al. to be responsible for cartilage degradation in OA [4]. Kapoor et al. too have made various reports which supported the role of inflammatory cells from synovium releasing cytokines (IL-1β, IL-6, IL-17 and TNF) that induce the release of proteinase resulting in cartilage destruction. In addition, the chondrocytes' intrinsic/external stimuli could have led to the increase in MMPs, as well as reduced aggrecan and type II collagen [5]. Various studies have also reported that ageing-related inflammation in OA, particularly on IL-6, IL-8, IL-18 and TNF-α may be involved in this process [6–8]. Greene et al. stated that OA is a multifactorial condition with numerous risk factors such as ageing which promotes systemic and local chronic inflammation that serves as a contributing factor to OA progression; and such inflammation is also related to the development of chronic metabolic diseases of ageing [6]. In keeping with the Starling–Landis theory [9], which states that the fluid exchange between plasma and interstitial space is driven by the equilibrium of hydrostatic and oncotic pressure across the capillary wall, it was further suggested that changes in blood cytokines or pro-inflammatory levels could influence levels found in the synovial joint. To the best of our knowledge, no study has shown the association of systemic inflammation and localised inflammation as a direct relationship for the development OA. Therefore, we hypothesised that a correlation between selective inflammation (or inflammatory markers) and the degradative proteins found in peripheral blood (PB) and synovial fluid (SF) exists, which may be involved in the development of OA. Thus, to prove this hypothesis, we investigated the changes between non-OA and OA conditions by comparing their inflammatory cytokines in PB and SF, and based on these changes and by using known pathway mapping, we proposed a possible pathway that may describe the inflammatory process that occurs in OA. 2. Materials & methods Fourteen non-OA subjects (n = 14) and 14 knee OA subjects (n = 14) were recruited from the Department of Orthopaedic Surgery, University Malaya Medical Centre, Malaysia. Due to the ethical, logistical and legal issues of subjecting individuals with no knee pathology to invasive SF collection, we recruited patients less than 35 years old presenting with anterior cruciate ligament (ACL) injury without any cartilage injury to represent the non-OA subjects as our control group. These patients were without any clinical or radiological signs that suggested any presence of OA, and therefore were a good contrast to the opposing group. These recruits were patients who were planned for ACL reconstruction surgery with a minimal duration of 39 days to one year post-injury. In contrast, our subjects in the OA group were patients above the age of 65 years with end-stage OA and who were planned for total knee replacement(s). To ensure that our patients were patients of primary OA, none of our knee OA patients had reported previous knee injury or any medical history that may result in arthritis such as inflammatory arthritides, metabolic disease relating to joint damage or prolonged drug use such as steroids prior to OA manifestation. Subjects with known malignant haematological or oncologic conditions, chronic infection or inflammation were excluded from this study. This study protocol was approved by the Ethics Committee of University Malaya Medical Centre (Ethics Approval No.: 20156-1433 & 2016927-4288). We obtained written informed consents from all participants, and data collection was anonymised to maintain participants' confidentiality. Demographics data, physical assessment and medical history were obtained from all patients. During participants' pre-operative assessment 3 ml of PB was collected, whilst two millilitres of SF was collected from patients' knee joints during their ACL reconstruction (non-OA participants) and total knee arthroplasty (OA participants) surgical operation. Blood plasma and SF from each patient were aliquoted and stored at − 80 °C prior to undergoing multiplex enzyme-linked immunosorbent assay (ELISA) to quantify target analytes. Twenty analytes were quantified using Human Magnetic Luminex® assay (R&D Systems, USA). The analytes were IL-8, Granulocyte-macrophage colony-stimulating factor (GM-CSF), interferon (IFN)-γ, IL-1 beta, IL-10, IL-15, IL-17A, IL-17C, IL-17E, IL-18, IL-21, IL-23, IL-27, IL-4, IL-6, MMP-3, TNF-α, Receptor activator of nuclear factor kappa-Β ligand (RANK L), vascular endothelial growth factor (VEGF), and MMP-1 (20-plex). Statistical analysis was performed using SPSS Statistics 24 (IBM, USA) for data analysis and G*Power 3.1.9.2 [10] for determination of sample size, effect size and power of study. The present study focused mainly on the differences in IL-18 expression between patients with non-OA and patients with OA. Thus, based on reports such as that of Wang et al. [11] who reported controlled SF IL-18 (28.3 ± 11.6 pg/ml) and primary OA SF IL-18 (75.20 mean ± standard deviation 40.1 pg/ml), the effective sample size predicted to be sufficient for the present study was 13 data points per group to achieve 80% power of study. Kolmogorov–Smirnov test was used to determine data normality. Pearson's chi-squared test was used for categorical data, and Fisher's exact test was used for categorical data when expected values in more than 20% of the cells of a contingency table were below five. Mann–Whitney U-test was used for statistical evaluation of the differences between the two groups for which data were not normally distributed, whilst independent t-test was used for data that were normally distributed. Spearman's bivariate correlations were used to evaluate correlation between studied parameters. Differences were considered significant if P ≤ .05, and highly significant if P ≤ .01. The predicted proteins involved in this study were analysed and pathway modelling was designed using the IPA software (content version 26127183, released November 30, 2015, Ingenuity Systems). IPA was used to predict relationship and possible Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

S.M. Koh et al. / The Knee xxx (xxxx) xxx

3

interaction of molecules pertaining to OA based on Ingenuity Knowledge Base. IPA's prediction of these pathways and functions were ranked in P-values with right-tailed Fisher's exact test, which was the marginal statistical significance for the probability of its fitness in the predicted pathway networks and functions. 3. Results 3.1. Description of participants The demographic composition, physical assessment and medical history of participants for this study are described in Table 1. The median age for the non-OA group was 26 years whilst for the OA group it was 75.5 years. There were seven males and seven females in each group. The non-OA group (167.71 ± 6.47 cm) was significantly taller than the OA (156.11 ± 9.22 cm) group. There were significant differences in systolic blood pressure, hypertension and hypercholesterolaemia status, but not diabetes mellitus. OA patients were taking routine medications for their metabolic diseases which were of good control, whereas nonOA patients were not on any routine medication or regular follow up for any medical conditions. 3.2. Elevation in pro-inflammatory protein levels in OA blood plasma and SF The plasma protein levels for both non-OA and OA groups are described in Table 2. There were significant changes in plasma protein level for MMP-3, IL-27, IL-8, IL-4, TNF-α, MMP-1, IL-15, IL-21, IL-10, IL-1β in both groups. IL-17C and GM-CSF were not detectable in both OA and non-OA groups (data not shown). The SF protein levels for both non-OA and OA groups are described in Table 2. IL-17A, IL17C and IL-23 were not detectable in both OA and non-OA groups (data not shown), as well as 11 non-OA samples below the detection limit for IL-6 and 13 OA samples above the detection limit for MMP-3. There were significant changes in SF protein levels for IL-15, VEGF, IL-8, IL-6, MMP-1, IL-18, and MMP-3 in both groups. 3.3. Correlation between blood plasma and SF proteins Spearman correlation was performed for the statistically significant protein expressed between the OA and non-OA groups in plasma (MMP-3, IL-27, IL-8, IL-4, TNF-α, MMP-1, IL-15, IL-21, IL-10 and IL-1β) and SF (IL-15, VEGF, IL-8, IL-6, MMP-1, IL-18, and Table 1 The demographic, physical assessment and medical history of osteoarthritis and non-osteoarthritis patients.

Age (years), median (IQR) Gender, n (%) Female Male Height (cm), mean (SD) Weight (kg), mean (SD) BMI (kg/m2), mean (SD) BMI Classification, n (%) Underweight, n (%) Healthy, n (%) Overweight, n (%) Obese, n (%) Waist (cm), mean (SD) Hip (cm), mean (SD) WHR, mean (SD) Systolic BP (mmHg), mean (SD) Diastolic BP (mmHg), mean (SD) BP Classification Normal, n (%) Pre-Hypertension, n (%) Stage 1 Hypertension, n (%) Stage 2 Hypertension, n (%) Hypertension, n (%) Yes No Diabetes, n (%) Yes No Hypercholesterolemia, n (%) Yes No

OA (n = 14)

Non-OA (n = 14)

P

Test

75.50 (8.75)

26.00 (13.75)

**b0.001

Mann–Whitney U-test

7 (50) 7 (50) 156.11 (9.22) 64.70 (13.11) 26.52 (4.58)

7 (50) 7 (50) 167.71 (6.47) 72.99 (14.57) 25.99 (5.25)

N0.999

Chi-squared test

**0.001 0.125 0.778

Independent t-test Independent t-test Independent t-test

1 (7.1) 5 (35.7) 4 (28.6) 4 (28.6) 91.29 (12.20) 96.54 (6.59) 0.94 (0.09) 150.50 (14.20) 72.21 (13.62)

1 (7.1) 4 (28.6) 7 (50.0) 2 (14.3) 87.79 (9.36) 99.89 (11.61) 0.88 (0.09) 116.00 (8.63) 67.50 (8.51)

0 (0) 4 (28.6) 8 (57.1) 2 (14.3)

10 (71.4) 4 (28.6) 0 (0) 0 (0)

14 (100) 0 (0)

0.797

Fisher's exact test

0.402 0.355 0.089 **b0.001 0.282

Independent t-test Independent t-test Independent t-test Independent t-test Independent t-test

**b0.001

Fisher's exact test

0 (0) 14 (100)

**b0.001

Fisher's exact test

4 (28.6) 10 (71.4)

0 (0) 14 (100)

0.098

Fisher's exact test

14 (100) 0 (0)

0 (0) 14 (0)

**b0.001

Fisher's exact test

BP, blood pressure; BMI, body mass index; IQR, interquartile range; SD, standard deviation; WHR, waist hip ratio. * P ≤ 0.05. ** P ≤ 0.01.

Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

4

S.M. Koh et al. / The Knee xxx (xxxx) xxx

Table 2 Protein expression level (pg/ml) for osteoarthritis and non-osteoarthritis groups. Analytes

n

Concentration (pg/ml), mean (SD)

P (independent t-test)

Plasma protein expression OPG OA Non-OA MMP-3 OA Non-OA IL-27 OA Non-OA IL-8 OA Non-OA IL-4 OA Non-OA TNF-α OA Non-OA

14 14 14 14 14 11 14 12 14 14 14 12

1756.39 (340.12) 858.03 (208.74) 24,670.52 (9748.89) 10,665.52 (6904.19) 409.93 (192.43) 189.05 (95.67) 5.64 (2.94) 2.65 (3.24) 180.30 (72.94) 121.34 (54.05) 11.32 (6.78) 6.14 (4.59)

**b0.001

Analytes

Group

n

Concentration (pg/ml), median (IQR)

MMP-1

OA Non-OA OA Non-OA OA Non-OA OA Non-OA OA Non-OA

13 13 14 13 14 14 8 8 14 12

234.59 (558.81) 105.69 (76.27) 4.25 (3.01) 2.24 (4.26) 42.50 (49.22) 29.55 (23.15) 0.21 (1.17) 0 5.91 (10.88) 0.02 (6.11)

P (Mann–Whitney U-test) **b0.001

OA Non-OA OA Non-OA OA Non-OA OA Non-OA OA Non-OA

14 14 14 13 14 12 14 13 13 3

13,621.14 (4942.38) 3319.86 (2482.81) 45.55 (10.98) 21.84 (13.31) 531.71 (215.58) 169.93 (117.70) 64.03 (37.82) 17.92 (16.72) 91.96 (65.89) 72.09 (56.76) (11 b DL)

**b0.001

Analytes

Group

N

Concentration (pg/ml), median (IQR)

MMP-1

OA Non-OA OA Non-OA OA Non-OA

13 13 14 9 1 9

36,101.33 (86,925.94) 3660.93 (58,424.19) 126.80 (134.66) 32.58 (64.53) 522,088 (13 N DL) 75,519.91 (114,094.56)

P (Mann–Whitney U-test) *0.016

IL-15 IL-21 IL-10 IL-1β

SF protein expression OPG IL-15 VEGF IL-8 IL-6

IL-18 MMP-3

Group

**b0.001 **0.002 *0.021 *0.022 *0.034

**0.017 *0.027 *0.038 *0.041

**b0.001 **b0.001 **0.001 0.639

**0.002 0.2

DL, detection limit; IL, interleukin; IQR, interquartile range; MMP, matrix metalloproteinase; OA, osteoarthritis; OPG, Osteoprotegerin; SD, standard deviation; SF, synovial fluid; TNF, tumour necrosis factor; VEGF, vascular endothelial growth factor.

MMP-3). Bivariate correlation studies were represented using Spearman's rank correlation coefficient. For ease of presentation, the tables were merged and a simplified version was attached in Table 3, where the correlation in non-OA was represented in yellow, in OA – red, and in both – orange. In non-OA, there were positive correlations amongst plasma IL-4, IL-8, IL-15, IL-27, MMP-1, MMP-3, TNF-α, with SF IL-8, IL-15, IL-18, MMP-1, MMP-3, and VEGF. In OA, there were positive correlations amongst plasma IL-1β, IL-4, IL-8, IL-10, IL-15, IL-21, IL-27, MMP-1, MMP-3 with SF IL-8 and IL-18, as well as a negative correlation between plasma IL-10 and SF MMP-1. There were some positive correlations that appeared in both non-OA and OA, i.e. amongst plasma IL-1β, IL-4, IL-8, IL-15, IL-21, MMP-1, MMP-3, and TNF-α, and also between SF IL-15 and MMP-1. 3.4. Predicted pathway in OA The changes in plasma OA were linked to the following top five canonical pathways: role of cytokines in mediating communication between immune cells (P = 2.40E−19), role of macrophages, fibroblasts and endothelial cells in rheumatoid arthritis (P = 4.82E−13), hepatic fibrosis/hepatic stellate cell activation (P = 1.70E−12), altered T cell and B cell signalling in rheumatoid arthritis (P = 2.26E−12) and communication between innate and adaptive immune cells (P = 3.02E−12). The top five upstream regulators (toll-like receptor (TLR) 4 (P = 4.27E−18), TLR2 (P = 8.82E−16), TLR6 (P = 2.20E−14), macrophage migration Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

S.M. Koh et al. / The Knee xxx (xxxx) xxx

5

Table 3 The Spearman correlation for cytokine expressions in osteoarthritis and non-osteoarthritis.

Plasma IL-4

Plasma IL-8

Plasma TNF-α

Plasma IL-27

Plasma MMP-1

Plasma IL-15

Plasma IL-10

Plasma IL-21

Plasma IL-1β

SF OPG SF VEGF

SF MMPSF MMPSF IL-8 SF IL-18 1 3

Plasma OPG Plasma MMP-3 Plasma IL-4 Plasma IL-8 Plasma TNF-α

0.850** 0.591* 0.728**

0.582*

0.591* 0.631*

0.582* 0.697**

0.794** 0.863**

0.798** 0.766**

0.838**

0.952** 0.933**

0.864**

0.771**

0.805**

0.705** 0.737**

0.589*

0.650*

0.860** 0.832**

0.591*

0.639*

0.736** 0.808**

0.630*

Plasma IL-27 Plasma MMP-1 Plasma IL-15 Plasma IL-10

0.651* 0.740** 0.572* 0.781** 0.612*

0.763* 0.754**

0.728* 0.584*

0.937** 0.655*

0.767** 0.881**

0.852*

0.629*

0.548*

0.538* 0.534*

0.703** -0.746*

IL, interleukin; MMP, matrix metalloproteinase; OPG, Osteoprotegerin; SF, synovial fluid; TNF, tumour necrosis factor; VEGF, vascular endothelial growth factor.

inhibitory factor (MIF) (P = 3.42E−14), IL-1 (P = 1.46E−13)) were incorporated into the protein pathway analysis to visualise regulatory effects and relationships. The changes in OA SF were linked to the following top five canonical pathways: role of macrophages, fibroblasts and endothelial cells in rheumatoid arthritis (P = 1.32E−11), IL-6 signalling (P = 2.64E−08), granulocyte adhesion and diapedesis (P = 7.53E−08), hepatic fibrosis/hepatic stellate cell activation (P = 1.07E−07), and role of hypercytokinemia/hyperchemokinemia in the pathogenesis of influenza (P = 1.72E−07). The top five upstream regulators (IL-1β (P = 5.19E−10), TLR4 (P = 2.70E−09), basigin (BSG) (P = 1.01E−08), IL-1 (P = 1.21E−08), TNF (P = 3.67E−08)) were incorporated into the protein pathway analysis to visualise regulatory effect and relationship. Solid purple lines (Figures 1–3) indicated relationships found in the Spearman correlation, but had no known connection in IPA. The OA model in the plasma system (Figure 1) demonstrated that TLR2, MIF, TLR4 and IL-1 were the regulators of IL-1B, IL4, IL-8, IL-10, IL-15, IL-21, IL-27, MMP-1 and MMP-3. Our IPA model predicted that TLR2, MIF, TLR4 and IL-27 increased IL-10 expression, and IL-10 subsequently increased MMP-1 and inhibited IL-1 expression. IL-15 was predicted to increase the expression of IL4, IL-8 and IL-21. Despite elevation of IL-4's activity on IL-1B inhibition, IL-1B expression was predicted to be activated by TLR4 and IL-18, whereby IL-1B resulted in MMP-1 and IL-21 elevation. IL-21 also could be increased by IL-1 activation. IL-18 was also predicted to increase MMP-3 production; and MMP-1 elevation was also predicted to increase IL-8 expression. The OA model in the SF system (Figure 2), conversely, demonstrated that IL-1B, TLR4, IL-1, and BSG were the regulators of IL-4, IL-8, IL-10, IL-15, IL-18, IL-21, IL-27, MMP-1, and MMP-3. IPA model in SF for OA model predicted that IL-15, IL-18 and MMP-1 upregulation on IL-8 expression. In contrast to the IL-4 in pathway in plasma OA, IL-4 expression was significantly reduced, and resulted in failed inhibition of IL-1B activity. Unique in the SF OA pathway, we predicted IL-21 increased BSG expression which in turn increased IL-10 expression. When we compared the changes in protein network in IPA from OA blood plasma and SF, IPA predicted the suppression of IL4 signalling and increased expression of IL-8, MMP-3 and IL-18 in SF. Compared to our IPA prediction, we observed a contrasting decrement in IL-18, and similarly IL-4 decreased whilst IL-8 and MMP-3 were increased. In addition, we designed another pathway which illustrated the cross-boundary relationships between plasma and SF proteins in Figure 3. Our Spearman correlation analysis showed positive correlation between plasma IL-10 with SF MMP-1, plasma MMP-3 and SF IL-18, plasma IL-8 with SF IL-18, plasma IL-15 with SF IL-18, plasma IL-21 with SF IL-18, plasma IL-1B with SF IL-18, and plasma MMP-1 with SF IL-8. Our predictive model suggested that SF IL-18 regulated the expressions of MMP-3, IL-8 and IL-1B in Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

6 S.M. Koh et al. / The Knee xxx (xxxx) xxx

Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

Figure 1. The relationship network of our target biomarkers and upstream regulators for the correlation of osteoarthritis in plasma. Red and orange colours in the nodes indicate activation and upregulation of expression, respectively, whilst the colour intensity is reflective of its predicted and confidence level. Solid lines indicate direct interactions between nodes, whilst dotted lines represent indirect interactions. Orange lines represent predicted activation of the interaction, purple lines represent novel interaction not reported in any literature, whilst yellow and grey lines represent interaction with inconsistent findings in literature. IL, interleukin; MIF, macrophage migration inhibitory factor; MMP, matrix metalloproteinase; TLR, Toll-like receptor. CXCL8 = IL-8. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

S.M. Koh et al. / The Knee xxx (xxxx) xxx

7

Figure 2. The relationship network of our target biomarkers and upstream regulators for the correlation of osteoarthritis in synovial fluid. Red and orange colours in the nodes indicate activation and upregulation of expression, respectively, whilst the colour intensity is reflective of its predicted and confidence level. Solid lines indicate direct interactions between nodes, whilst dotted lines represent indirect interactions. Orange lines represent predicted activation of the interaction, purple lines represent novel interaction not reported in any literature, whilst yellow and grey lines represent interaction with inconsistent findings to that reported in literature. BSG, basigin; IL, interleukin; MMP, matrix metalloproteinase; TLR, Toll-like receptor. CXCL8 = IL-8. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

plasma, as well as the regulatory association between SF IL-18 with plasma IL-15 and plasma IL-21. Besides that, we also observed plasma IL-10 in increasing SF MMP-1, and also plasma MMP-1 regulating association with SF IL-8. 4. Discussion 4.1. Regulatory pathways for OA in plasma and the SF system The present study investigated the potential inflammatory changes that occurred between systemic circulation and local SF by comparing the levels in plasma and SF of non-OA and OA participants. We further investigated the possible pathways involved by tabulating the possible pathway leading to OA in plasma and the SF system. The present study demonstrated significant changes in plasma MMP-3, IL-27, IL-8, IL-4, TNF-α, MMP-1, IL-15, IL-21, IL-10, and IL-1β, as well as SF IL-15, VEGF, IL-8, IL-6, MMP-1, IL-18, and MMP-3 between the older OA and younger non-OA patients. The association of plasma IL-8 and SF IL-18 in orchestration of inflammatory signalling in OA was apparent. These changes suggested that there was correlation between inflammation and OA, as well as the association between protein expression in plasma and SF. In the plasma system for OA (Figure 1), previous literatures have reported that TLR2 [12,13], MIF [14], TLR4 [15] and IL-27 [16] increased IL-10 expression. Several studies have reported IL-10 to increase the expression of MMP-1 [17] and to inhibit the expression of IL-1 [18,19]. IL-15 has also been reported to involve in the increment of IL-4 [20], IL-8 [21] and IL-21 [22] expressions. Despite inhibitory effect from activated IL-4 on IL-1B [23], IL-1B expression was predicted to be elevated by TLR4 [24,25] and IL-18 [26,27]. Increased IL-1B expression was predicted to result in MMP-1 [28,29] and IL-21 [30,31] elevation, and further increase of IL-21 expression by IL-1 [32]. A study by Dai et al. reported that MMP-3 [33] production is increased by IL-18 whilst Agarwal et al. reported that MMP-1 protein increased secretion of human IL-8 protein [34]. The activated IL-4 and IL-10 signified the increment in anti-inflammatory activity [35], which could explain the reduction in IL-1 pro-inflammatory activity [18,19]. However, other pro-inflammatory mediators (IL-8, IL-15, IL-18 and IL-21) were activated in OA, wherein such inflammation especially IL-10 and IL-18 increased the production of MMP-1 and MMP-3, respectively. Elevated MMP-1 in return increased the expression of IL-8, thus creating a vicious cycle of increasing chronic inflammation. In our SF system for OA (Figure 2), we observed that IL-21 increased BSG expression, which in turn increased IL-10 expression. This prediction appears to be supported by findings of other groups such as Lindner et al. [36], who reported that IL-21 protein Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

8

S.M. Koh et al. / The Knee xxx (xxxx) xxx

Figure 3. The relationship network of our target biomarkers and upstream regulators for the correlation of OA cross boundary between plasma and SF. Red and orange colours in the nodes indicate activation and upregulation of identified selected proteins respectively, whilst the colour intensity is reflective of its predicted and confidence level. Solid lines indicate direct interactions between nodes, whilst dotted lines represent indirect interactions. Orange lines represent predicted activation of the interaction, purple lines represent novel interaction not reported in any literature, whilst yellow and grey lines represent interaction with inconsistent findings to that reported in literature. BSG, basigin; IL, interleukin; MIF, macrophage migration inhibitory factor; MMP, matrix metalloproteinase; TLR, Tolllike receptor. CXCL8 = IL-8. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

increased BSG protein on the cell surface, and Dear et al. [37] who found that an interference of BSG protein was associated with decreased IL-10. The reduction in IL-4 expression in synovial space signified the reduction in anti-inflammatory properties in the knee joint, which could result in the failure of the suppression of IL-1B pro-inflammatory activity. However, IL-10 attempted to counteract the detrimental activity of catabolism via pro-inflammatory cytokines, and IL-10's activity being regulated by IL-21 and BSG. In the cross-boundary relationships between plasma and SF protein in OA (Figure 3), we predicted that SF IL-18 may be increased by MMP-3 [33], IL-8 [26] and IL-1B [26,27] signalling in plasma. Our pathway modelling suggested a novel finding on the possible association between SF IL-18 with plasma IL-15 and IL-21. There may also be associations between IL-8 with increased MMP-1 in plasma of OA, and also SF MMP-1 may be further increased by plasma IL-10. As discussed previously, these findings appear to be corroborated by studies conducted by other groups such as Agarwal et al. who reported that MMP-1 protein increased secretion of human IL-8 protein [34], and Friedman et al. who reported the elevation of MMP-1 by IL-10 [17]. This could be potentially useful as an indicator for the early inflammation occurring in joint space which may further lead to OA degradation. Because our OA pathway models suggested that increased IL-8 expression in blood plasma may be associated with the increment of IL-18 and MMP-3 expressions in the knee joint, it is worth noting that further studies should consider investigating the roles of IL-8 and IL-18 in OA in the hope of finding an early, non-invasive diagnostic plasma marker for OA. It is worth noting that, in the present study, the participants recruited were patients with knee OA specifically; keeping in mind that the knee is the most common joint affected by this disease. In addition, the present study and the discussion that follow involve the pathogenesis of knee OA exclusively and not of OA in general. It should be highlighted here that the fluid exchange between plasma and interstitial space in the knee joint is rather unique [38,39] due to its weight bearing and physiological positioning [40,41], and hence it would be unwise to equate this condition to that of other anatomic joint OAs, such as OA of the hand. It would thus be prudent to consider that the changes observed in the present study may be specific to changes in the knee, or perhaps selected weight-bearing joints but not synovial joints in general. 4.2. Limitations One limitation of this study that is worth highlighting was the difficulty in recruiting a significant number of OA patients with no comorbidities such as hypertension and hypercholesteroalemia. This limitation led us to compare inflammation between nonOA and OA having other conditions with potential systemic inflammation. Whilst we believed that our OA participants were on routine medication and had their diseases in good control, there could be some degree of inflammation which should not be ignored. However, given the fact that OA is a chronic disease with increasing prevalence with ageing, it is difficult to dissociate ageing and age-related metabolic changes from OA, particularly in human samples. The risks of developing hypertension or diabetes in individuals above 65 years of age are 70% [38] and 26.9% [39], respectively. As ageing is a common factor for OA, metabolic Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

S.M. Koh et al. / The Knee xxx (xxxx) xxx

9

changes due to ageing may contribute to the chronic inflammation related to OA. Because the majority of OA patients also present with potentially several metabolic diseases mentioned earlier, patients recruited for the present study will be truly reflective of the common clinical conditions seen in patients with OA. Such findings should be viewed as the collective inflammation of various factors naturally expected of the populations of this age, which may pose as a strong contributor to the development of OA. In view of the ethical and legal restrictions in recruiting individuals with no knee pathology to undergo invasive SF collection as our control group, we recruited patients with non-OA related cases (ACL injury) who had been planned for surgery. This was done in order to compare the data obtained from young healthy individuals to older OA patients. Whilst ACL injury may manifest with an acute inflammatory response and a transient post-injury swelling, samples from these donors were collected after a minimum duration of 39 days to 1 year post-injury where no signs of local inflammation were observed prior to their ACL reconstruction surgery. More importantly, these patients did not have clinical, radiological or arthroscopic investigated findings that would indicate that they had developed OA, even at the very early stages of this disease. Whilst transient elevation of inflammatory markers may exist in the early stages of ACL injury [40], these are minimal at best and, more specifically, only involved acute inflammatory markers; which is in contrast to the chronic inflammatory markers being investigated. We believed that, on this basis, the recruitment of these patients would not lead to major discrepancies in our study [42,43]. We would like to emphasise that the bivariate correlation analyses presented in our study were performed following the results from our Students' t-test. This was carried out in order to limit the correlation analyses to only include markers that are significantly different between OA and non-OA groups. Although this method may not appear to be particularly robust as compared to more complex analytical tools such as multivariate analysis, it was deemed appropriate and adequate for the purpose of this study. However, future studies should include larger sample sizes that would enable the use of such tools. The advantage is that they produce more refined results, and in addition are able to remove the effects of any confounding factors that may have produced anomalous results in our study. Whilst we are confident in the predictive modelling created based on the results obtained using our Luminex technique, there are limitations here that are worth noting. As IPA served only as a simulation tool to predict the ageing inflammatory pathway and non-OA/OA pathways, the exact inhibition/activation pathways require further validation through knock-in/out model or through other supplementing/blocking treatment studies. As the validation studies required additional funding (which was not available at that time), studies incorporating such models should be considered in the future. 4.3. Summary IL-8 appeared consistently in ageing and OA pathways, whereas IL-8 could be involved in chronic inflammation in ageing. This in turn leads to OA via the activation of IL-18, and suggests that IL-18 plays a major role in the pathogenesis of OA. This finding provides useful insight that will allow us to consider the use of anti-IL-8 and anti-IL-18 factors as potential targeted applications to prevent OA progression. However, further investigations are warranted prior to clinical application to ensure that the balance between inflammation control and a functional immune system is achieved. 5. Conclusion The present study suggested that the elevation of pro-inflammatory cytokines, in particular IL-8 in OA blood plasma and IL-18 in OA SF may be associated with the pathogenesis of OA, although further investigation may be warranted. Acknowledgements The authors are grateful for the financial support provided by the University of Malaya High Impact Research (Ref: UM.c/625/ 1/HIR/MOHE/CHAN/03; Account A000003-50001), University of Malaya Research Grant (UMRG-RP005A-13HTM) and Fundamental Research Grant Scheme (FP031-2015A and FRGS - FP046-2016). The authors are also grateful to the following bodies/individuals for their contributions to this study: University of Malaya High Impact Research for the courtesy in granting the access to Ingenuity Pathway Analysis software; S.H.S., H.K.Y. and L.S.K. from Biomed Global for technical support in Luminex assay; all medical staffs at UMMC for their support for patient liaison and sample acquisition; and all patients for donating their samples to this study. References [1] Francisco V, Pérez T, Pino J, López V, Franco E, Alonso A, et al. Biomechanics, obesity, and osteoarthritis. The role of adipokines: When the levee breaks. J Orthop Res 2018. https://doi.org/10.1002/jor.23788. [2] Calay ES, Hotamisligil GS. Turning off the inflammatory, but not the metabolic, flames. Nat Med 2013. https://doi.org/10.1038/nm.3114. [3] Kraus VB, Blanco FJ, Englund M, Karsdal MA, Lohmander LS. Call for standardized definitions of osteoarthritis and risk stratification for clinical trials and clinical use. Osteoarthr Cartil 2015. https://doi.org/10.1016/j.joca.2015.03.036. [4] Sellam J, Berenbaum F. The role of synovitis in pathophysiology and clinical symptoms of osteoarthritis. Nat Rev Rheumatol 2010. https://doi.org/10.1038/ nrrheum.2010.159. [5] Kapoor M, Martel-Pelletier J, Lajeunesse D, Pelletier JP, Fahmi H. Role of proinflammatory cytokines in the pathophysiology of osteoarthritis. Nat Rev Rheumatol 2011. https://doi.org/10.1038/nrrheum.2010.196. [6] Greene MA, Loeser RF. Aging-related inflammation in osteoarthritis. Osteoarthr Cartil 2015. https://doi.org/10.1016/j.joca.2015.01.008.

Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028

10

S.M. Koh et al. / The Knee xxx (xxxx) xxx

[7] Penninx BWJH, Abbas H, Ambrosius W, Nicklas BJ, Davis C, Messier SP, et al. Inflammatory markers and physical function among older adults with knee osteoarthritis. J Rheumatol 2004. [8] Goekoop RJ, Kloppenburg M, Kroon HM, Frölich M, Huizinga TWJ, Westendorp RGJ, et al. Low innate production of interleukin-1β and interleukin-6 is associated with the absence of osteoarthritis in old age. Osteoarthr Cartil 2010. https://doi.org/10.1016/j.joca.2010.03.016. [9] Starling EH. On the Absorption of Fluids from the Connective Tissue Spaces. J Physiol 1896. https://doi.org/10.1113/jphysiol.1896.sp000596. [10] Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007. https://doi.org/10.3758/BF03193146. [11] Wang Y, Xu D, Long L, Deng X, Tao R, Huang G. Correlation between plasma, synovial fluid and articular cartilage Interleukin-18 with radiographic severity in 33 patients with osteoarthritis of the knee. Clin Exp Med 2014. https://doi.org/10.1007/s10238-013-0251-8. [12] Suurmond J, Stoop JN, Rivellese F, Bakker AM, Huizinga TWJ, Toes REM. Activation of human basophils by combined toll-like receptor- and FcεRI-triggering can promote Th2 skewing of naive T helper cells. Eur J Immunol 2014. https://doi.org/10.1002/eji.201343617. [13] Shirey KA, Cole LE, Keegan AD, Vogel SN. Francisella tularensis Live Vaccine Strain Induces Macrophage Alternative Activation as a Survival Mechanism. J Immunol 2008. https://doi.org/10.4049/jimmunol.181.6.4159. [14] Vasanthakumar A, Kallies A. IL-27 paves different roads to Tr1. Eur J Immunol 2013. https://doi.org/10.1002/eji.201343479. [15] Pore D, Matsui K, Parameswaran N, Gupta N. Cutting Edge: Ezrin Regulates Inflammation by Limiting B Cell IL-10 Production. J Immunol 2016. https://doi.org/10. 4049/jimmunol.1502098. [16] Wang H, Meng R, Li Z, Yang B, Liu Y, Huang F, et al. IL-27 induces the differentiation of Tr1-like cells from human naive CD4+ T cells via the phosphorylation of STAT1 and STAT3. Immunol Lett 2011. https://doi.org/10.1016/j.imlet.2010.11.007. [17] Friedman SL. Molecular regulation of hepatic fibrosis, an integrated cellular response to tissue injury. J Biol Chem 2000. https://doi.org/10.1074/jbc.275.4.2247. [18] Van Meegeren MER, Roosendaal G, Jansen NWD, Wenting MJG, Van Wesel ACW, Van Roon JAG, et al. IL-4 alone and in combination with IL-10 protects against blood-induced cartilage damage. Osteoarthr Cartil 2012. https://doi.org/10.1016/j.joca.2012.04.002. [19] Schmid-Hempel P. Immune defence, parasite evasion strategies and their relevance for “macroscopic phenomena” such as virulence. Philos Trans R Soc B Biol Sci 2009. https://doi.org/10.1098/rstb.2008.0157. [20] Dunne J, Lynch S, O’Farrelly C, Todryk S, Hegarty JE, Feighery C, et al. Selective Expansion and Partial Activation of Human NK Cells and NK Receptor-Positive T Cells by IL-2 and IL-15. J Immunol 2001. https://doi.org/10.4049/jimmunol.167.6.3129. [21] El-Shazly AE, Lefebvre PP. Modulation of NK cell autocrine-induced eosinophil chemotaxis by interleukin-15 and vitamin D 3: A possible NK-eosinophil crosstalk via IL-8 in the pathophysiology of allergic rhinitis. Mediators Inflamm 2011. https://doi.org/10.1155/2011/373589. [22] Sarra M, Cupi ML, Monteleone I, Franzè E, Ronchetti G, Di Sabatino A, et al. IL-15 positively regulates IL-21 production in celiac disease mucosa. Mucosal Immunol 2013. https://doi.org/10.1038/mi.2012.65. [23] Onderdijk AJ, Baerveldt EM, Kurek D, Kant M, Florencia EF, Debets R, et al. IL-4 Downregulates IL-1β and IL-6 and Induces GATA3 in Psoriatic Epidermal Cells: Route of Action of a Th2 Cytokine. J Immunol 2015. https://doi.org/10.4049/jimmunol.1401740. [24] Wu TT, Chen TL, Chen RM. Lipopolysaccharide triggers macrophage activation of inflammatory cytokine expression, chemotaxis, phagocytosis, and oxidative ability via a toll-like receptor 4-dependent pathway: Validated by RNA interference. Toxicol Lett 2009. https://doi.org/10.1016/j.toxlet.2009.08.025. [25] Wi SM, Park J, Shim JH, Chun E, Lee KY. Ubiquitination of ECSIT is crucial for the activation of p65/p50 NF-κBs in Toll-like receptor 4 signaling. Mol Biol Cell 2015. https://doi.org/10.1091/mbc.E14-08-1277. [26] Boraschi D, Tagliabue A. The Interleukin-1 Receptor Family. Vitam Horm 2006. https://doi.org/10.1016/S0083-6729(06)74009-2. [27] Biet F, Locht C, Kremer L. Immunoregulatory functions of interleukin 18 and its role in defense against bacterial pathogens. J Mol Med 2002. https://doi.org/10. 1007/s00109-001-0307-1. [28] Ahmed S, Wang N, Lalonde M, Goldberg VM, Haqqi TM. Green Tea Polyphenol Epigallocatechin-3-gallate (EGCG) Differentially Inhibits Interleukin-1β-Induced Expression of Matrix Metalloproteinase-1 and -13 in Human Chondrocytes. J Pharmacol Exp Ther 2004. https://doi.org/10.1124/jpet.103.059220. [29] Kour S, Garimella MG, Shiroor DA, Mhaske ST, Joshi SR, Singh K, et al. IL-3 Decreases Cartilage Degeneration by Downregulating Matrix Metalloproteinases and Reduces Joint Destruction in Osteoarthritic Mice. J Immunol 2016. https://doi.org/10.4049/jimmunol.1500907. [30] Kattah MG, Wong MT, Yocum MD, Utz PJ. Cytokines secreted in response to toll-like receptor ligand stimulation modulate differentiation of human Th17 cells. Arthritis Rheum 2008. https://doi.org/10.1002/art.23497. [31] Volpe E, Servant N, Zollinger R, Bogiatzi SI, Hupé P, Barillot E, et al. A critical function for transforming growth factor-β, interleukin 23 and proinflammatory cytokines in driving and modulating human TH-17 responses. Nat Immunol 2008. https://doi.org/10.1038/ni.1613. [32] Maue AC, Eaton SM, Lanthier PA, Sweet KB, Blumerman SL, Haynes L. Proinflammatory Adjuvants Enhance the Cognate Helper Activity of Aged CD4 T Cells. J Immunol 2009. https://doi.org/10.4049/jimmunol.0804226. [33] Dai SM, Shan ZZ, Nishioka K, Yudoh K. Implication of interleukin 18 in production of matrix metalloproteinases in articular chondrocytes in arthritis: Direct effect on chondrocytes may not be pivotal. Ann Rheum Dis 2005. https://doi.org/10.1136/ard.2004.026088. [34] Agarwal A, Tressel SL, Kaimal R, Balla M, Lam FH, Covic L, et al. Identification of a metalloprotease-chemokine signaling system in the ovarian cancer microenvironment: Implications for antiangiogenic therapy. Cancer Res 2010. https://doi.org/10.1158/0008-5472.CAN-09-4341. [35] Wojdasiewicz P, Poniatowski ŁA, Szukiewicz D. The role of inflammatory and anti-inflammatory cytokines in the pathogenesis of osteoarthritis. Mediators Inflamm 2014. https://doi.org/10.1155/2014/561459. [36] Lindner S, Dahlke K, Sontheimer K, Hagn M, Kaltenmeier C, Barth TFE, et al. Interleukin 21-induced granzyme b-expressing b cells infiltrate tumors and regulate t cells. Cancer Res 2013. https://doi.org/10.1158/0008-5472.CAN-12-3450. [37] Dear JW, Leelahavanichkul A, Aponte A, Hu X, Constant SL, Hewitt SM, et al. Liver proteomics for therapeutic drug discovery: Inhibition of the cyclophilin receptor CD147 attenuates sepsis-induced acute renal failure. Crit Care Med 2007. https://doi.org/10.1097/01.CCM.0000281858.44387.A2. [38] Barreto G, Sandelin J, Salem A, Nordström DC, Waris E. Toll-like receptors and their soluble forms differ in the knee and thumb basal osteoarthritic joints. Acta Orthop 2017. https://doi.org/10.1080/17453674.2017.1281058. [39] Aurich M, Hofmann GO, Rolauffs B. Differences in type II collagen turnover of osteoarthritic human knee and ankle joints. Int Orthop 2017. https://doi.org/10. 1007/s00264-017-3414-5. [40] Thijssen E, Van Caam A, Van Der Kraan PM. Obesity and osteoarthritis, more than just wear and tear: Pivotal roles for inflamed adipose tissue and dyslipidaemia in obesity-induced osteoarthritis. Rheumatol (United Kingdom) 2014. https://doi.org/10.1093/rheumatology/keu464. [41] Funck-Brentano T, Nethander M, Movérare-Skrtic S, Richette P, Ohlsson C. Causal Factors for Knee, Hip, and Hand Osteoarthritis: A Mendelian Randomization Study in the UK Biobank. Arthritis Rheumatol 2019. https://doi.org/10.1002/art.40928. [42] Roemer FW, Englund M, Turkiewicz A, Struglics A, Guermazi A, Lohmander LS, et al. Molecular and Structural Biomarkers of Inflammation at Two Years After Acute Anterior Cruciate Ligament Injury Do Not Predict Structural Knee Osteoarthritis at Five Years. Arthritis Rheumatol 2019. https://doi.org/10.1002/art.40687. [43] Irie K, Uchiyama E, Iwaso H. Intraarticular inflammatory cytokines in acute anterior cruciate ligament injured knee. Knee 2003. https://doi.org/10.1016/S09680160(02)00083-2.

Please cite this article as: S.M. Koh, C.K. Chan, S.H. Teo, et al., Elevated plasma and synovial fluid interleukin-8 and interleukin-18 may be associated with the patho..., The Knee, https://doi.org/10.1016/j.knee.2019.10.028