An ex vivo tissue model of cartilage degradation suggests that cartilage state can be determined from secreted key protein patterns

Autoři: Michael Neidlin aff001;  Efthymia Chantzi aff002;  George Macheras aff003;  Mats G. Gustafsson aff002;  Leonidas G. Alexopoulos aff001
Působiště autorů: Department of Mechanical Engineering, National Technical University of Athens, Athens, Greece aff001;  Department of Medical Sciences, Uppsala University, Uppsala, Sweden aff002;  4th Orthopaedic Department, KAT Hospital, Athens, Greece aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224231


The pathophysiology of osteoarthritis (OA) involves dysregulation of anabolic and catabolic processes associated with a broad panel of proteins that ultimately lead to cartilage degradation. An increased understanding about these protein interactions with systematic in vitro analyses may give new ideas regarding candidates for treatment of OA related cartilage degradation. Therefore, an ex vivo tissue model of cartilage degradation was established by culturing tissue explants with bacterial collagenase II. Responses of healthy and degrading cartilage were analyzed through protein abundance in tissue supernatant with a 26-multiplex protein profiling assay, after exposing the samples to a panel of 55 protein stimulations present in synovial joints of OA patients. Multivariate data analysis including exhaustive pairwise variable subset selection identified the most outstanding changes in measured protein secretions. MMP9 response to stimulation was outstandingly low in degrading cartilage and there were several protein pairs like IFNG and MMP9 that can be used for successful discrimination between degrading and healthy samples. The discovered changes in protein responses seem promising for accurate detection of degrading cartilage. The ex vivo model seems interesting for drug discovery projects related to cartilage degradation, for example when trying to uncover the unknown interactions between secreted proteins in healthy and degrading tissues.

Klíčová slova:

Cartilage – Collagens – Cytokines – Histology – Osteoarthritis – Principal component analysis – Protein secretion – Collagenases


1. Goldring MB. Osteoarthritis and cartilage: the role of cytokines. Curr Rheumatol Rep. 2000;2(6):459–65. 11123098

2. Kapoor M, Martel-Pelletier J, Lajeunesse D, Pelletier J-P, Fahmi H. Role of proinflammatory cytokines in the pathophysiology of osteoarthritis. Nat Rev Rheumatol. 2011;7(1):33. doi: 10.1038/nrrheum.2010.196 21119608

3. Mariani E, Pulsatelli L, Facchini A. Signaling pathways in cartilage repair. Int J Mol Sci. 2014;15(5):8667–98. doi: 10.3390/ijms15058667 24837833

4. Palmer AW, Wilson CG, Baum EJ, Levenston ME. Composition-function relationships during IL-1-induced cartilage degradation and recovery. Osteoarthr Cartil. 2009;17(8):1029–39. doi: 10.1016/j.joca.2009.02.009 19281879

5. Karsdal MA, Michaelis M, Ladel C, Siebuhr AS, Bihlet AR, Andersen JR, et al. Disease-modifying treatments for osteoarthritis (DMOADs) of the knee and hip: lessons learned from failures and opportunities for the future. Osteoarthr Cartil. 2016;24(12):2013–21. doi: 10.1016/j.joca.2016.07.017 27492463

6. Melas IN, Chairakaki AD, Chatzopoulou EI, Messinis DE, Katopodi T, Pliaka V, et al. Modeling of signaling pathways in chondrocytes based on phosphoproteomic and cytokine release data. Osteoarthr Cartil. 2014;22(3):509–18. doi: 10.1016/j.joca.2014.01.001 24457104

7. Neidlin M, Korcari A, Macheras G, Alexopoulos LG. Cue-Signal-Response Analysis in 3D Chondrocyte Scaffolds with Anabolic Stimuli. Ann Biomed Eng. 2018;46(2):345–53. doi: 10.1007/s10439-017-1964-8 29147820

8. Tsuchida AI, Beekhuizen M, Ct Hart M, Radstake TRDJ, Dhert WJA, Saris DBF, et al. Cytokine profiles in the joint depend on pathology, but are different between synovial fluid, cartilage tissue and cultured chondrocytes. Arthritis Res Ther. 2014;16(5):441. doi: 10.1186/s13075-014-0441-0 25256035

9. Johnson CI, Argyle DJ, Clements DN. In vitro models for the study of osteoarthritis. Vet J. 2016;209:40–9. doi: 10.1016/j.tvjl.2015.07.011 26831151

10. Grenier S, Bhargava MM, Torzilli PA. An in vitro model for the pathological degradation of articular cartilage in osteoarthritis. J Biomech. 2014;47(3):645–52. doi: 10.1016/j.jbiomech.2013.11.050 24360770

11. Alexopoulos LG, Saez-Rodriguez J, Espelin CW. High throughput protein-based technologies and computational models for drug development, efficacy and toxicity. Drug Effic Safety, Biol Discov Emerg Technol Tools Wiley. 2009;29–52.

12. Schmitz N, Laverty S, Kraus VB, Aigner T. Basic methods in histopathology of joint tissues. Osteoarthr Cartil. 2010;18:S113—S116. doi: 10.1016/j.joca.2010.05.026 20864017

13. Mow VC, Kuei SC, Lai WM, Armstrong CG. Biphasic creep and stress relaxation of articular cartilage in compression: theory and experiments. J Biomech Eng. 1980;102(1):73–84. doi: 10.1115/1.3138202 7382457

14. Enobakhare BO, Bader DL, Lee DA. Quantification of sulfated glycosaminoglycans in chondrocyte/alginate cultures, by use of 1, 9-dimethylmethylene blue. Anal Biochem. 1996;243(1):189–91. doi: 10.1006/abio.1996.0502 8954546

15. Reddy GK, Enwemeka CS. A simplified method for the analysis of hydroxyproline in biological tissues. Clin Biochem. 1996;29(3):225–9. doi: 10.1016/0009-9120(96)00003-6 8740508

16. Andridge RR, Little RJA. A review of hot deck imputation for survey non-response. Int Stat Rev. 2010;78(1):40–64. doi: 10.1111/j.1751-5823.2010.00103.x 21743766

17. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria; 2018. Available from:

18. Jolliffe I. Principal component analysis. In: International encyclopedia of statistical science. Springer; 2011. p. 1094–6.

19. Hartigan JA, Wong MA. Algorithm AS 136: A k-means clustering algorithm. J R Stat Soc Ser C (Applied Stat. 1979;28(1):100–8.

20. Okada T, Tomita S. An optimal orthonormal system for discriminant analysis. Pattern Recognit. 1985;18(2):139–44.

21. Bishop CM, others. Pattern recognition and machine learning (information science and statistics). 2006;

22. Darmanis S, Nong RY, Vänelid J, Siegbahn A, Ericsson O, Fredriksson S, et al. ProteinSeq: high-performance proteomic analyses by proximity ligation and next generation sequencing. PLoS One. 2011;6(9):e25583. doi: 10.1371/journal.pone.0025583 21980495

23. Herman S, Khoonsari PE, Aftab O, Krishnan S, Strömbom E, Larsson R, et al. Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions. Metabolomics. 2017;13(7):79. doi: 10.1007/s11306-017-1213-z 28596718

24. Maas SA, Ellis BJ, Ateshian GA, Weiss JA. FEBio: finite elements for biomechanics. J Biomech Eng. 2012;134 1:11005.

25. Ateshian GA, Rajan VSP, Chahine NO, Canal CE, Hung CT. Modeling the matrix of articular cartilage using a continuous fiber angular distribution predicts many observed phenomena. J Biomech Eng. 2009;131 6:61003.

26. Holmes MH, Mow VC. The nonlinear characteristics of soft gels and hydrated connective tissues in ultrafiltration. J Biomech. 1990;23 11:1145–56. doi: 10.1016/0021-9290(90)90007-p 2277049

27. de Lange-Brokaar BJE, Ioan-Facsinay A, Van Osch G, Zuurmond A-M, Schoones J, Toes REM, et al. Synovial inflammation, immune cells and their cytokines in osteoarthritis: a review. Osteoarthr Cartil. 2012;20(12):1484–99. doi: 10.1016/j.joca.2012.08.027 22960092

28. Yang P, Tan J, Yuan Z, Meng G, Bi L, Liu J. Expression profile of cytokines and chemokines in osteoarthritis patients: proinflammatory roles for CXCL8 and CXCL11 to chondrocytes. Int Immunopharmacol. 2016;40:16–23. doi: 10.1016/j.intimp.2016.08.005 27567247

29. Vrgoc G, Vrbanec J, Eftedal RK, Dembic PL, Balen S, Dembic Z, et al. Interleukin-17 and Toll-like Receptor 10 genetic polymorphisms and susceptibility to large joint osteoarthritis. J Orthop Res. 2017;

30. Park J-S, Park M-K, Lee S-Y, Oh H-J, Lim M-A, Cho W-T, et al. TWEAK promotes the production of Interleukin-17 in rheumatoid arthritis. Cytokine. 2012;60(1):143–9. doi: 10.1016/j.cyto.2012.06.285 22819243

31. Rösler S, Haase T, Claassen H, Schulze U, Schicht M, Riemann D, et al. Trefoil factor 3 is induced during degenerative and inflammatory joint disease, activates matrix metalloproteinases, and enhances apoptosis of articular cartilage chondrocytes. Arthritis Rheumatol. 2010;62(3):815–25.

32. Nakamura DS, Hollander JM, Uchimura T, Nielsen HC, Zeng L. Pigment Epithelium-Derived Factor (PEDF) mediates cartilage matrix loss in an age-dependent manner under inflammatory conditions. BMC Musculoskelet Disord. 2017;18(1):39. doi: 10.1186/s12891-017-1410-y 28122611

33. Tang C-H, Hsu C-J, Fong Y-C. The CCL5/CCR5 axis promotes interleukin-6 production in human synovial fibroblasts. Arthritis Rheumatol. 2010;62(12):3615–24.

34. Lourido L, Ayoglu B, Fernández-Tajes J, Oreiro N, Henjes F, Hellström C, et al. Discovery of circulating proteins associated to knee radiographic osteoarthritis. Sci Rep. 2017;7(1):137. doi: 10.1038/s41598-017-00195-8 28273936

35. Galasso O, Familiari F, De Gori M, Gasparini G. Recent Findings on the Role of Gelatinases (Matrix Metalloproteinase-2 and -9) in Osteoarthritis. Adv Orthop. 2012:834208. doi: 10.1155/2012/834208 22900195

36. Naito K, Takahashi M, Kushida K, Suzuki M, Ohishi T, Miura M, et al. "Measurement of matrix metalloproteinases (MMPs) and tissue inhibitor of metalloproteinases-1 (TIMP-1) in patients with knee osteoarthritis: comparison with generalized osteoarthritis." Rheumatology (Oxford, England) 38, no. 6 (1999): 510–515.

37. Masuhara K, Nakai T, Yamaguchi K, Yamasaki S, Sasaguri Y. (2002). Significant increases in serum and plasma concentrations of matrix metalloproteinases 3 and 9 in patients with rapidly destructive osteoarthritis of the hip. Arthritis & Rheumatism, 46(10), 2625–2631.

38. Peck Y, Ng LY, Goh JYL, Gao C, Wang D-A. A three-dimensionally engineered biomimetic cartilaginous tissue model for osteoarthritic drug evaluation. Mol Pharm. 2014;11(7):1997–2008. doi: 10.1021/mp500026x 24579704

39. Westerlund B, Korhonen TK. Bacterial proteins binding to the mammalian extracellular matrix. Mol Microbiol. 1993;9 4:687–94. doi: 10.1111/j.1365-2958.1993.tb01729.x 7901732

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