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. 2015 Feb 18;17(1):33.
doi: 10.1186/s13075-015-0544-2.

Transcriptome analysis of ageing in uninjured human Achilles tendon

Mandy Jayne Peffers  1 , Yongxiang Fang  2 , Kathleen Cheung  3 , Tim Koh Jia Wei  4 , Peter David Clegg  5 , Helen Lucy Birch  6
Affiliations

Transcriptome analysis of ageing in uninjured human Achilles tendon

Mandy Jayne Peffers et al. Arthritis Res Ther. .

Abstract

Introduction: The risk of tendon injury and disease increases significantly with increasing age. The aim of the study was to characterise transcriptional changes in human Achilles tendon during the ageing process in order to identify molecular signatures that might contribute to age-related degeneration.

Methods: RNA for gene expression analysis using RNA-Seq and quantitative real-time polymerase chain reaction analysis was isolated from young and old macroscopically normal human Achilles tendon. RNA sequence libraries were prepared following ribosomal RNA depletion, and sequencing was undertaken by using the Illumina HiSeq 2000 platform. Expression levels among genes were compared by using fragments per kilobase of exon per million fragments mapped. Differentially expressed genes were defined by using Benjamini-Hochberg false discovery rate approach (P<0.05, expression ratios 1.4 log2 fold change). Alternative splicing of exon variants were also examined by using Cufflinks. The functional significance of genes that showed differential expression between young and old tendon was determined by using ingenuity pathway analysis.

Results: 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 with young tendon (±1.4 log2 fold change, P<0.05). Of these, 191 were at higher levels in older tendon and 134 were at lower levels in older tendon. The top networks for genes differentially expressed with tendon age were from cellular function, cellular growth, and cellular cycling pathways. Notable differential transcriptome changes were also observed in alternative splicing patterns. Several of the top gene ontology terms identified in downregulated isoforms in old tendon related to collagen and post-translational modification of collagen.

Conclusions: This study demonstrates dynamic alterations in RNA with age at numerous genomic levels, indicating changes in the regulation of transcriptional networks. The results suggest that ageing is not primarily associated with loss of ability to synthesise matrix proteins and matrix-degrading enzymes. In addition, we have identified non-coding RNA genes and differentially expressed transcript isoforms of known matrix components with ageing which require further investigation.

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Figures

Figure 1
Figure 1
Principal component analysis (PCA) and volcano plot of differentially abundant transcripts. (A) PCA plot of log2 gene expression data showed the greatest variability in RNA-Seq data was due to the age of the donor. The second and third components are also shown as changes in the first and second components are due to differing sample sizes between young and old groups. (B) Volcano plot represents log2 FC × log2 CPM plot (counts per million mapped reads) CPM plot. Low expression genes (log2 CPM of less than −5) are coloured orange. Significant differentially expressed genes (DEGs) are coloured in red. A set of DEGs between young and old tendon was identified. With the common dispersion in edgeR [30], 325 DEGs were identified with a P value of less than 0.05 (red).
Figure 2
Figure 2
Volcano plot showing significant differentially expressed (DE) isoforms between young and old tendon. Following assembly with Cufflinks of alternative splicing transcripts [36], significant DE transcripts were identified with q values (P value adjusted to false discovery rate) of less than 0.05. Red spots represent significant DE isoforms, and black spots are for non-DE isoforms.
Figure 3
Figure 3
Top-scoring networks derived from the 325 genes differentially expressed in ageing. (A) Ingenuity pathway analysis (IPA) identified from cellular function and maintenance as the principal associated network functions with scores of 43. (B) The second top-scoring network was a cellular growth and proliferation, with scores of 32. (C) IPA identified cell cycle and skeletal and muscular system development function with a score of 32. (D) The fourth top-scoring network was cellular development, with a score of 28. Figures are graphical representations between molecules identified in our data in their respective networks. Green nodes indicate upregulated gene expression in older tendon; red nodes indicate downregulated gene expression in older tendon. Intensity of colour is related to higher fold change. The key to the main features in the networks is shown.
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