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22 Transcriptomic Signatures Of Tendon Ageing
  1. Mandy Peffers1,
  2. Yongxiang Fang1,
  3. Helen Birch2,
  4. Timothy Koh Jia Pei3,
  5. Kathleen Cheung4,
  6. Peter Clegg1
  1. 1University of Liverpool, UK
  2. 2University College London, UK
  3. 3Ngee Ann Polytechnic, Singapore
  4. 4University of Newcastle, UK


Introduction Ageing represents a huge challenge for society as whilst lifespan increases the quality of life is often poor, in part due to a progressive loss of mobility. Alterations in tendon properties contribute to muscle weakness and thus reduced mobility in old age as well as an increased risk of injury. The cellular and molecular mechanisms behind these changes in tendons are not understood but are generally considered to result from a failure of cell metabolism and proteostasis. RNA-Seq has been demonstrated to be an effective approach for gene expression profiling in ageing tissues1 with a greater dynamic range than microarrays and the ability to detect non-coding RNAs. Therefore we undertook an RNA-Seq experiment on young and old human Achilles to characterise molecular mechanisms associated with age-related changes in gene signatures.

Methods RNA was extracted from young (n = 4) and old (n = 5) macroscopically normal Achilles tendons collected from amputated limbs with appropriate ethical approval. Ribosomal RNA depleted samples were used for RNA-Seq library preparation and sequencing using the Illumina HiSeq 2000 platform and 100bp paired-end reads. The Bioconductor package edgeR was used to identify differential gene expression (DGE) and genes deemed differentially expressed between age groups with a Benjamini-Hochberg false discovery rate corrected P-value < 0.05 and a log2fold change ≥ 1.4. Splice variant analysis was undertaken using a number of software packages in R. Gene ontology investigations were undertaken using Ingenuity Pathway Analysis.

Results Analysis identified 42,830 genes expressed in tendon. In total, the expression of 325 transcribed elements including protein-coding transcripts and non-coding transcripts; small non-coding RNAs, pseudogenes, long non-coding RNAs and a single microRNA was significantly different in old compared to young tendon (±1.4 log2 fold change, p < 0.05). Of these, 191 were at higher levels in the older tendon and 134 were at lower levels in the older tendon. IPA of this data indicated the top scoring network from DGE was from cellular function and maintenance (Figure 1). The top canonical pathways demonstrated were hepatic fibrosis and transcriptional regulatory network in stem cells. Notable differential transcriptome changes were also observed in alternative splicing patterns. In total, 183660 isoforms were detected in young and 191673 isoforms were detected in old tendon. Among these, 21193 isoforms were detected only in young and 29206 isoforms only in old. There were 63 known isoforms up regulated in old tendon, with 80 down regulated. A number of long and short non-coding RNAs were differentially expressed.

Abstract 22 Figure 1

Top-scoring network was identified as cellular function and Figures are graphical representations between molecules identified in our data in their respective networks. Green nodes, upregulated; red nodes, downregulated in older tendon. Intensity of colour is related to higher fold-change

Discussion This study demonstrates dynamic alterations in RNA with age at numerous genomic levels which indicate changes in the regulation of transcriptional networks. Age-related isoform gene expression changes identified an over-representation of genes with reduced expression relating to the ECM, degradative proteases, cytokines and growth factors in tendon derived from older donors compared with young donors. These results suggest that age related decline in tendon integrity might result from changes in protein chemistry (isoforms) rather than absolute levels of matrix protein and protease expression.

Reference 1 Peffers, et al. Transcriptomic signatures in cartilage ageing. Arthritis Res Ther. 2013;15(4):R98

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