The role of the meniscus in osteoarthritis


The menisci are specialised tissues that perform important mechanical roles in the knee joint including weight bearing(1).  Their inner regions are avascular and non-healing in adults with injuries to the inner regions experiencing disrupted function predisposing the knee to early osteoarthritis development(2).  Meniscal injuries are common with an incidence of 12-14%(3). Ageing is the most prominent risk factor for the development of osteoarthritis (OA)(4) , which affects knees and causes major health burdens. Meniscal degeneration contributes to the development and progression of knee OA. Little is known about age-related changes in meniscus as most reports only demonstrate the effects of OA. Furthermore, there is a lack of research to investigate the specific mechanisms underlying meniscal pathology and OA.

Progressive changes to epigenetic information accompany ageing. Small non-coding RNAs including microRNAs and snoRNAs have the ability to post-transcriptionally control the expression of multiple genes. We have previously demonstrated that their altered expression occurs in ageing and age-related musculoskeletal pathologies in cartilage(5-12), ligament(13), tendon (14, 15) and synovium(16).

This studentship will perform mRNA and small non-coding RNA (sncRNA) sequencing of meniscus in ageing and following induction of osteoarthritis determining mRNA and sncRNA expression profiles. You will undertake bioinformatics analysis of differentially expressed RNAs identifying key molecules and functional pathways in both aged meniscus and osteoarthritis.

Overall ai

You will use a combined small RNA and transcriptome sequencing analysis on meniscus in ageing and following osteoarthritis induction to determine identify key molecules and functional pathways in the aged meniscus and osteoarthritis. This will be undertaken by realising the following objectives:

  1. Determine the transcriptome and small non-coding RNA transcriptome of the ageing meniscus using RNASeq.
  2. Predict and annotate targets of differentially expressed miRNAs in the ageing meniscus using transcriptome data and bioinformatics tools.
  3. Determine the transcriptome small non-coding RNA transcriptome of the meniscus following induction of osteoarthritis in vivo using RNASeq.
  4. Predict and annotate targets of differentially expressed miRNAs in the meniscus following osteoarthritis induction using transcriptome data and bioinformatics tools.


Meniscus were dissected from young and old healthy sheep identified on the absence of musculoskeletal disease (knee joint palpation, lameness assessment and gross scoring on joint opening). Joints macroscopically assessed(18). In additional sheep with osteoarthritis will be analysed. All menisci will be histologically assessed(19).

Following RNA extraction small RNA libraries will be prepared using the NEBNext small RNA library preparation kit. Sequencing will be undertaken on the Illumina NovaSeq using SP chemistry. For mRNA dual-indexed, strand-specific RNASeq libraries prepared using NEBNext polyA selection and Ultra II Directional RNA library preparation kits and sequenced on the Illumina NovaSeq using SP chemistry.

Differential gene expression analysis following alignment of reads to reference genome with splice-aware short-read following alignment, counting of reads aligning to genome features in annotation file, using htseq-count. Quality assessment of mapping data and assessment of intra- and inter- group variance. Detection of significantly differentially expressed genes between sample groups, using DESeq2. Bioinformatics using our previous methods including STRING(21), PANTHER(22), IPA(23), to include identification of hub genes, including functional and pathway enrichment, protein–protein interaction network, hub genes screening, and construction of a lncRNA– miRNA–mRNA networks.



Open to students worldwide

Funding information

Self-funded project

This is a project open to national and international students who must have funds for living costs, university PhD fees and consumables for the project.




  • Makris EA, Hadidi P, Athanasiou KA. The knee meniscus: structure-function, pathophysiology, current repair techniques, and prospects for regeneration. Biomaterials. 2011;32(30):7411-31.


  1. Murphy CA, Garg AK, Silva-Correia J, Reis RL, Oliveira JM, Collins MN. The Meniscus in Normal and Osteoarthritic Tissues: Facing the Structure Property Challenges and Current Treatment Trends. Annu Rev Biomed Eng. 2019;21:495-521.
  2. Logerstedt DS, Snyder-Mackler L, Ritter RC, Axe MJ, Orthopedic Section of the American Physical Therapy A. Knee pain and mobility impairments: meniscal and articular cartilage lesions. J Orthop Sports Phys Ther. 2010;40(6):A1-A35.
  1. Loeser RF. Age-related changes in the musculoskeletal system and the development of osteoarthritis. Clin Geriatr Med. 2010;26(3):371-86.
  2. Castanheira C, Anderson JR, Fang Y, Milner PI, Goljanek-Whysall K, House L, et al. Mouse microRNA signatures in joint ageing and post-traumatic osteoarthritis. Osteoarthr Cartil Open. 2021;3(4):100186.
  1. Peffers MJ, Cremers A, Welting TJM. Small Nucleolar RNA Expression Profiling in Cartilage. Methods Mol Biol. 2021;2245:135-49.
  2. Balaskas P, Green JA, Haqqi TM, Dyer P, Kharaz YA, Fang Y, et al. Small Non-Coding RNAome of Ageing Chondrocytes. Int J Mol Sci. 2020;21(16).
  1. Ripmeester EGJ, Caron MMJ, van den Akker GGH, Surtel DAM, Cremers A, Balaskas P, et al. Impaired chondrocyte U3 snoRNA expression in osteoarthritis impacts the chondrocyte protein translation apparatus. Sci Rep. 2020;10(1):13426.
  2. Peffers MJ, Chabronova A, Balaskas P, Fang Y, Dyer P, Cremers A, et al. SnoRNA signatures in cartilage ageing and osteoarthritis. Sci Rep. 2020;10(1):10641.
  1. Balaskas P, Goljanek-Whysall K, Clegg P, Fang Y, Cremers A, Emans P, et al. MicroRNA Profiling in Cartilage Ageing. Int J Genomics. 2017;2017:2713725.
  2. Steinbusch MM, Fang Y, Milner PI, Clegg PD, Young DA, Welting TJ, et al. Serum snoRNAs as biomarkers for joint ageing and post traumatic osteoarthritis. Sci Rep. 2017;7:43558.
  1. Peffers MJ, Liu X, Clegg PD. Transcriptomic profiling of cartilage ageing. Genom Data. 2014;2:27-8.
  2. Kharaz YA, Fang Y, Welting TM, Peffers MJ, Comerford E, J,. Small RNA signatures of the anterior cruciate ligament from patients with knee joint osteoarthritis. MedRiV. 2020.
  1. Peffers MJ, Fang Y, Cheung K, Wei TK, Clegg PD, Birch HL. Transcriptome analysis of ageing in uninjured human Achilles tendon. Arthritis Res Ther. 2015;17:33.
  2. Riasat K, Bardell D, Goljanek-Whysall K, Clegg PD, Peffers MJ. Epigenetic mechanisms in Tendon Ageing. Br Med Bull. 2020;135(1):90-107.
  1. Wijesinghe SN, Anderson J, Brown TJ, Nanus DE, Housmans B, Green JA, et al. The role of extracellular vesicle miRNAs and tRNAs in synovial fibroblast senescence. Front Mol Biosci. 2022;9:971621.
  2. Vandeweerd JM, Hontoir F, Kirschvink N, Clegg P, Nisolle JF, Antoine N, et al. Prevalence of naturally occurring cartilage defects in the ovine knee. Osteoarthritis Cartilage. 2013;21(8):1125-31.
  1. Little CB, Smith MM, Cake MA, Read RA, Murphy MJ, Barry FP. The OARSI histopathology initiative - recommendations for histological assessments of osteoarthritis in sheep and goats. Osteoarthritis Cartilage. 2010;18 Suppl 3:S80-92.
  2. Pauli C, Grogan SP, Patil S, Otsuki S, Hasegawa A, Koziol J, et al. Macroscopic and histopathologic analysis of human knee menisci in aging and osteoarthritis. Osteoarthritis Cartilage. 2011;19(9):1132-41.
  1. Peffers MJ, Collins J, Fang Y, Goljanek-Whysall K, Rushton M, Loughlin J, et al. Age-related changes in mesenchymal stem cells identified using a multi-omics approach. Eur Cell Mater. 2016;31:136-59.
  2. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2022.
  1. Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 2019;47(D1):D419-D26.
  2. Yu F, Shen XY, Fan L, Yu ZC. Genome-wide analysis of genetic variations assisted by Ingenuity Pathway Analysis to comprehensively investigate potential genetic targets associated with the progression of hepatocellular carcinoma. Eur Rev Med Pharmacol Sci. 2014;18(15):2102-8.