FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Equine Vet J / Identification and characterisation of temporal abundance of...
Equine veterinary journal2025; doi: 10.1111/evj.14456

Identification and characterisation of temporal abundance of microRNAs in synovial fluid from an experimental equine model of osteoarthritis.

Abstract: MicroRNAs, a class of small noncoding RNAs, serve as post-transcriptional regulators of gene expression and are present in a stable and quantifiable form in biological fluids. MicroRNAs may influence intra-articular responses and the course of disease, but very little is known about their temporal changes in osteoarthritis. Objective: To identify miRNAs and characterise the temporal changes in their abundance in SF from horses with experimentally induced osteoarthritis. We hypothesised that the abundance of miRNA would change during disease progression. Methods: In vivo experiments. Methods: RNA extracted from synovial fluid obtained sequentially (Day 0, 28 and 70) from nine horses with experimentally induced osteoarthritis was subjected to small RNA sequencing using the Illumina Hiseq 4000 sequencing platform. Differentially abundant miRNAs underwent further validation and mapping of temporal abundance (Day 0, 14, 17, 21, 28, 35, 42, 49, 56, 63 and 70 days after osteoarthritis induction) by microfluidic reverse transcription quantitative real-time PCR. Bioinformatic analyses were performed to predict potential biological associations and target genes of the differentially abundant microRNAs. Results: Small RNA sequencing revealed 61 differentially abundant microRNAs at an early osteoarthritis stage (Day 28), and subsequent reverse transcription quantitative real-time PCR analysis validated 20 of these. Significant biological functions of the differentially abundant microRNAs were apoptosis, necrosis, cell proliferation and cell invasion. Following validation, four microRNAs (miRNA-199b-3p, miRNA-139-5p, miRNA-1839 and miRNA-151-5p) were detected in more than 50% of the synovial fluid samples and had higher abundance in osteoarthritic than in control joints. Conclusions: Limited sample size. Conclusions: This is the first study to determine longitudinal changes in synovial fluid microRNA abundance in an equine model of osteoarthritis. Larger studies are needed in naturally occurring osteoarthritis to interrogate putative changes identified by this study.
© 2025 The Author(s). Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
Get Full Text
Publication Date: 2025-01-08 PubMed ID: 39775906DOI: 10.1111/evj.14456Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research defines the temporal evolution of microRNAs found in synovial fluid from horses that have been experimentally induced with osteoarthritis. This study provides new insights into potential biological associations and target genes of the differentially abundant microRNAs, contributing to our understanding of osteoarthritis progression in horses.

Objectives and Hypothesis

  • The primary objective of this research was to identify microRNAs and characterise the temporal changes in their abundance in synovial fluid from horses with experimentally induced osteoarthritis.
  • The researchers hypothesised that the abundance of microRNA would change during the progression of the disease.

Methods and Techniques

  • A total of nine horses with experimentally induced osteoarthritis provided synovial fluid samples on Day 0, 28, and 70.
  • The RNA was extracted from these samples and then subjected to small RNA sequencing using the Illumina Hiseq 4000 sequencing platform.
  • The differentially abundant microRNAs were further validated and mapped for their temporal abundance by performing reverse transcription quantitative real-time PCR.
  • Bioinformatic analyses were also executed to predict potential biological associations and target genes of these differentially abundant microRNAs.

Principal Findings

  • Small RNA sequencing revealed 61 differentially abundant microRNAs at an early stage of osteoarthritis (Day 28).
  • Out of these, 20 microRNAs were validated through reverse transcription quantitative real-time PCR analysis.
  • These differentially abundant microRNAs were found to perform significant biological functions such as apoptosis, necrosis, cell proliferation, and cell invasion.
  • Furthermore, four microRNAs (miRNA-199b-3p, miRNA-139-5p, miRNA-1839 and miRNA-151-5p) were detected in more than half of the synovial fluid samples and had a higher abundance in osteoarthritic joints than in control joints.

Conclusions and Recommendations

  • This research is pioneering in assessing longitudinal changes in synovial fluid microRNA abundance in an experimental equine model of osteoarthritis.
  • However, due to the limited sample size, larger studies are needed, particularly in naturally occurring osteoarthritis cases, to investigate the putative changes identified by this study.

Cite This Article

APA
Walters M, Skovgaard K, Heegaard PMH, Fang Y, Kharaz YA, Bundgaard L, Skovgaard LT, Jensen HE, Andersen PH, Peffers MJ, Jacobsen S. (2025). Identification and characterisation of temporal abundance of microRNAs in synovial fluid from an experimental equine model of osteoarthritis. Equine Vet J. https://doi.org/10.1111/evj.14456

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English

Researcher Affiliations

Walters, Marie
  • Department of Veterinary Clinical Sciences, University of Copenhagen, Taastrup, Denmark.
Skovgaard, Kerstin
  • Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark.
Heegaard, Peter M H
  • Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
Fang, Yongxiang
  • Centre for Genomic Research, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK.
Kharaz, Yalda A
  • Department of Musculoskeletal Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.
Bundgaard, Louise
  • Department of Veterinary Clinical Sciences, University of Copenhagen, Taastrup, Denmark.
  • Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark.
Skovgaard, Lene T
  • Department of Public Health, University of Copenhagen, Copenhagen K, Denmark.
Jensen, Henrik E
  • Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark.
Andersen, Pia H
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Peffers, Mandy J
  • Department of Musculoskeletal Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.
Jacobsen, Stine
  • Department of Veterinary Clinical Sciences, University of Copenhagen, Taastrup, Denmark.

Grant Funding

  • DFF-7017-00066 / Independent Research Fund Denmark, Technology and Production Sciences
  • Horse Levy Foundation
  • Gerda and Aage Haensch's Foundation
  • Lu00e6gefonden-AP Mu00f8ller Foundation
  • Kuustos Foundation
  • Toosbuys Foundation
  • University of Copenhagen
  • Technical University of Denmark
  • Swedish University of Agricultural Science
  • 107u2009471/Z/15/Z / Wellcome Trust Clinical Intermediate Fellowship
  • Versus Arthritis

References

This article includes 58 references
  1. De Guire V, Robitaille R, Tetreault N, Guerin R, Menard C, Bambace N. Circulating miRNAs as sensitive and specific biomarkers for the diagnosis and monitoring of human diseases: promises and challenges.. Clin Biochem 2013;46(10–11):846–860.
  2. O'Brien J, Hayder H, Zayed Y, Peng C. Overview of microRNA biogenesis, mechanisms of actions, and circulation.. Front Endocrinol (Lausanne) 2018;9:402.
  3. Miyaki S, Asahara H. Macro view of microRNA function in osteoarthritis.. Nat Rev Rheumatol 2012;8(9):543–552.
  4. McIlwraith CW, Frisbie DD, Kawcak CE. The horse as a model of naturally occurring osteoarthritis.. Bone Joint Res 2012;1(11):297–309.
  5. Loeser RF, Goldring SR, Scanzello CR, Goldring MB. Osteoarthritis: a disease of the joint as an organ.. Arthritis Rheum 2012;64(6):1697–1707.
  6. Barrey E, Bonnamy B, Barrey EJ, Mata X, Chaffaux S, Guerin G. Muscular microRNA expressions in healthy and myopathic horses suffering from polysaccharide storage myopathy or recurrent exertional rhabdomyolysis.. Equine Vet J 2010;42(S38):303–310.
  7. Unger L, Abril C, Gerber V, Jagannathan V, Koch C, Hamza E. Diagnostic potential of three serum microRNAs as biomarkers for equine sarcoid disease in horses and donkeys.. J Vet Intern Med 2021;35(1):610–619.
  8. Unger L, Gerber V, Pacholewska A, Leeb T, Jagannathan V. MicroRNA fingerprints in serum and whole blood of sarcoid‐affected horses as potential non‐invasive diagnostic biomarkers.. Vet Comp Oncol 2019;17(1):107–117.
  9. Unger L, Jagannathan V, Pacholewska A, Leeb T, Gerber V. Differences in miRNA differential expression in whole blood between horses with sarcoid regression and progression.. J Vet Intern Med 2019;33(1):241–250.
  10. Hulliger MF, Pacholewska A, Vargas A, Lavoie JP, Leeb T, Gerber V. An integrative miRNA–mRNA analysis reveals striking transcriptomic similarities between severe equine asthma and specific asthma Endotypes in humans.. Genes (Basel) 2020;11(10):1143.
  11. Desjardin C, Vaiman A, Mata X, Legendre R, Laubier J, Kennedy SP. Next‐generation sequencing identifies equine cartilage and subchondral bone miRNAs and suggests their involvement in osteochondrosis physiopathology.. BMC Genomics 2014;15(1):798.
  12. Peffers M, Liu X, Clegg P. Transcriptomic signatures in cartilage ageing.. Arthritis Res Ther 2013;15(4):R98.
  13. Castanheira C, Balaskas P, Falls C, Ashraf‐Kharaz Y, Clegg P, Burke K. Equine synovial fluid small non‐coding RNA signatures in early osteoarthritis.. BMC Vet Res 2021;17(1):26.
  14. Baker ME, Lee S, Clinton M, Hackl M, Castanheira C, Peffers MJ. Investigation of microRNA biomarkers in equine distal interphalangeal joint osteoarthritis.. Int J Mol Sci 2022;23(24):15526.
  15. Anderson JR, Jacobsen S, Walters M, Bundgaard L, Diendorfer A, Hackl M. Small non‐coding RNA landscape of extracellular vesicles from a post‐traumatic model of equine osteoarthritis.. Front Vet Sci 2022;9:901269.
  16. Xu JF, Zhang SJ, Zhao C, Qiu BS, Gu HF, Hong JF. Altered microRNA expression profile in synovial fluid from patients with knee osteoarthritis with treatment of hyaluronic acid.. Mol Diagn Ther 2015;19(5):299–308.
  17. Kung LH, Zaki S, Ravi V, Rowley L, Smith MM, Bell KM. Utility of circulating serum miRNAs as biomarkers of early cartilage degeneration in animal models of post‐traumatic osteoarthritis and inflammatory arthritis.. Osteoarthr Cartil 2017;25(3):426–434.
  18. Castanheira C, Anderson JR, Fang Y, Milner PI, Goljanek‐Whysall K, House L. Mouse microRNA signatures in joint ageing and post‐traumatic osteoarthritis.. Osteoarthr Cartil Open 2021;3(4):100186.
  19. Ali SA, Peffers MJ, Ormseth MJ, Jurisica I, Kapoor M. The non‐coding RNA interactome in joint health and disease.. Nat Rev Rheumatol 2021;17(11):692–705.
  20. Kobayashi T, Lu J, Cobb BS, Rodda SJ, McMahon AP, Schipani E. Dicer‐dependent pathways regulate chondrocyte proliferation and differentiation.. Proc Natl Acad Sci U S A 2008;105(6):1949–1954.
  21. Ntoumou E, Tzetis M, Braoudaki M, Lambrou G, Poulou M, Malizos K. Serum microRNA array analysis identifies miR‐140‐3p, miR‐33b‐3p and miR‐671‐3p as potential osteoarthritis biomarkers involved in metabolic processes.. Clin Epigenetics 2017;9:127.
  22. Li X, Gibson G, Kim JS, Kroin J, Xu S, van Wijnen AJ. MicroRNA‐146a is linked to pain‐related pathophysiology of osteoarthritis.. Gene 2011;480(1–2):34–41.
  23. Chang ZK, Meng FG, Zhang ZQ, Mao GP, Huang ZY, Liao WM. MicroRNA‐193b‐3p regulates matrix metalloproteinase 19 expression in interleukin‐1beta‐induced human chondrocytes.. J Cell Biochem 2018;119(6):4775–4782.
  24. Yamasaki K, Nakasa T, Miyaki S, Ishikawa M, Deie M, Adachi N. Expression of MicroRNA‐146a in osteoarthritis cartilage.. Arthritis Rheum 2009;60(4):1035–1041.
  25. Li YH, Tavallaee G, Tokar T, Nakamura A, Sundararajan K, Weston A. Identification of synovial fluid microRNA signature in knee osteoarthritis: differentiating early‐ and late‐stage knee osteoarthritis.. Osteoarthr Cartil 2016;24(9):1577–1586.
  26. Yassin AM, AbuBakr HO, Abdelgalil AI, Farid OA, El‐Behairy AM, Gouda EM. Circulating miR‐146b and miR‐27b are efficient biomarkers for early diagnosis of Equidae osteoarthritis.. Sci Rep 2023;13(1):7966.
  27. Frisbie DD, Kawcak CE, McIlwraith CW. Evaluation of the effect of extracorporeal shock wave treatment on experimentally induced osteoarthritis in middle carpal joints of horses.. Am J Vet Res 2009;70(4):449–454.
  28. McIlwraith CW, Frisbie DD, Kawcak CE, Fuller CJ, Hurtig M, Cruz A. The OARSI histopathology initiative—recommendations for histological assessments of osteoarthritis in the horse.. Osteoarthr Cartil 2010;18(Suppl 3):S93–S105.
  29. Balcells I, Cirera S, Busk PK. Specific and sensitive quantitative RT‐PCR of miRNAs with DNA primers.. BMC Biotechnol 2011;11(1):70.
  30. Busk PK. A tool for design of primers for microRNA‐specific quantitative RT‐qPCR.. BMC Bioinform 2014;15(1):29.
  31. Brogaard L, Heegaard PM, Larsen LE, Mortensen S, Schlegel M, Dürrwald R. Late regulation of immune genes and microRNAs in circulating leukocytes in a pig model of influenza A (H1N2) infection.. Sci Rep 2016;6:21812.
  32. Kim D, Salzberg SL. TopHat‐fusion: an algorithm for discovery of novel fusion transcripts.. Genome Biol 2011;12(8):R72.
  33. Ali SA, Gandhi R, Potla P, Keshavarzi S, Espin‐Garcia O, Shestopaloff K. Sequencing identifies a distinct signature of circulating microRNAs in early radiographic knee osteoarthritis.. Osteoarthr Cartil 2020;28(11):1471–1481.
  34. Borgonio Cuadra VM, Gonzalez‐Huerta NC, Romero‐Cordoba S, Hidalgo‐Miranda A, Miranda‐Duarte A. Altered expression of circulating microRNA in plasma of patients with primary osteoarthritis and in silico analysis of their pathways.. PLoS One 2014;9(6):e97690.
  35. Beyer C, Zampetaki A, Lin NY, Kleyer A, Perricone C, Iagnocco A. Signature of circulating microRNAs in osteoarthritis.. Ann Rheum Dis 2015;74(3):e18.
  36. Diaz‐Prado S, Cicione C, Muinos‐Lopez E, Hermida‐Gomez T, Oreiro N, Fernandez‐Lopez C. Characterization of microRNA expression profiles in normal and osteoarthritic human chondrocytes.. BMC Musculoskelet Disord 2012;13:144.
  37. Andersen C, Walters M, Bundgaard L, Berg LC, Vonk LA, Lundgren‐Åkerlund E. Intraarticular treatment with integrin α10β1‐selected mesenchymal stem cells affects microRNA expression in experimental post‐traumatic osteoarthritis in horses.. Front Vet Sci 2024;11:1374681.F.
  38. Murata K, Furu M, Yoshitomi H, Ishikawa M, Shibuya H, Hashimoto M. Comprehensive microRNA analysis identifies miR‐24 and miR‐125a‐5p as plasma biomarkers for rheumatoid arthritis.. PLoS One 2013;8(7):e69118.
  39. Murata K, Yoshitomi H, Tanida S, Ishikawa M, Nishitani K, Ito H. Plasma and synovial fluid microRNAs as potential biomarkers of rheumatoid arthritis and osteoarthritis.. Arthritis Res Ther 2010;12(3):R86.
  40. Churov AV, Oleinik EK, Knip M. MicroRNAs in rheumatoid arthritis: altered expression and diagnostic potential.. Autoimmun Rev 2015;14(11):1029–1037.
  41. Feng L, Feng C, Wang CX, Xu DY, Chen JJ, Huang JF. Circulating microRNA let‐7e is decreased in knee osteoarthritis, accompanied by elevated apoptosis and reduced autophagy.. Int J Mol Med 2020;45(5):1464–1476.
  42. Kurowska‐Stolarska M, Alivernini S, Ballantine LE, Asquith DL, Millar NL, Gilchrist DS. MicroRNA‐155 as a proinflammatory regulator in clinical and experimental arthritis.. Proc Natl Acad Sci U S A 2011;108(27):11193–11198.
  43. Li X, Kroin JS, Kc R, Gibson G, Chen D, Corbett GT. Altered spinal microRNA‐146a and the microRNA‐183 cluster contribute to osteoarthritic pain in knee joints.. J Bone Miner Res 2013;28(12):2512–2522.
  44. Zhang L, Yang M, Marks P, White LM, Hurtig M, Mi QS. Serum non‐coding RNAs as biomarkers for osteoarthritis progression after ACL injury.. Osteoarthr Cartil 2012;20(12):1631–1637.
  45. Akhtar N, Haqqi TM. MicroRNA‐199a* regulates the expression of cyclooxygenase‐2 in human chondrocytes.. Ann Rheum Dis 2012;71(6):1073–1080.
  46. Ukai T, Sato M, Akutsu H, Umezawa A, Mochida J. MicroRNA‐199a‐3p, microRNA‐193b, and microRNA‐320c are correlated to aging and regulate human cartilage metabolism.. J Orthop Res 2012;30(12):1915–1922.
  47. Hu W, Zhang W, Li F, Guo F, Chen A. miR‐139 is up‐regulated in osteoarthritis and inhibits chondrocyte proliferation and migration possibly via suppressing EIF4G2 and IGF1R.. Biochem Biophys Res Commun 2016;474(2):296–302.
  48. Makki MS, Haqqi TM. miR‐139 modulates MCPIP1/IL‐6 expression and induces apoptosis in human OA chondrocytes.. Exp Mol Med 2015;47(10):e189.
  49. Hwang HS, Kim HA. Chondrocyte apoptosis in the pathogenesis of osteoarthritis.. Int J Mol Sci 2015;16(11):26035–26054.
  50. Sharif M, Whitehouse A, Sharman P, Perry M, Adams M. Increased apoptosis in human osteoarthritic cartilage corresponds to reduced cell density and expression of caspase‐3.. Arthritis Rheum 2004;50(2):507–515.
  51. Zamli Z, Sharif M. Chondrocyte apoptosis: a cause or consequence of osteoarthritis?. Int J Rheum Dis 2011;14(2):159–166.
  52. Sellam J, Berenbaum F. The role of synovitis in pathophysiology and clinical symptoms of osteoarthritis.. Nat Rev Rheumatol 2010;6(11):625–635.
  53. Berenbaum F. The quest for the Holy Grail: a disease‐modifying osteoarthritis drug.. Arthritis Res Ther 2007;9(6):111.
  54. Bertuglia A, Pagliara E, Grego E, Ricci A, Brkljaca‐Bottegaro N. Pro‐inflammatory cytokines and structural biomarkers are effective to categorize osteoarthritis phenotype and progression in Standardbred racehorses over five years of racing career.. BMC Vet Res 2016;12(1):246.
  55. Kamm JL, Nixon AJ, Witte TH. Cytokine and catabolic enzyme expression in synovium, synovial fluid and articular cartilage of naturally osteoarthritic equine carpi.. Equine Vet J 2010;42(8):693–699.
  56. Kok MGM, de Ronde MWJ, Moerland PD, Ruijter JM, Creemers EE, Pinto‐Sietsma SJ. Small sample sizes in high‐throughput miRNA screens: a common pitfall for the identification of miRNA biomarkers.. Biomol Detect Quantif 2018;15:1–5.
  57. Pritchard CC, Cheng HH, Tewari M. MicroRNA profiling: approaches and considerations.. Nat Rev Genet 2012;13(5):358–369.
  58. Van Spil WE, Kubassova O, Boesen M, Bay‐Jensen AC, Mobasheri A. Osteoarthritis phenotypes and novel therapeutic targets.. Biochem Pharmacol 2019;165:41–48.

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.