Open Access Research article

Degradome expression profiling in human articular cartilage

Tracey E Swingler1, Jasmine G Waters1, Rosemary K Davidson1, Caroline J Pennington1, Xose S Puente2, Clare Darrah3, Adele Cooper3, Simon T Donell3, Geoffrey R Guile4, Wenjia Wang4 and Ian M Clark1*

Author Affiliations

1 School of Biological Sciences, University of East Anglia, Earlham Road, Norwich NR4 7TJ, UK

2 Departamento de Bioquímica y Biología Molecular, Facultad de Medicina, Universidad de Oviedo, 33006 Oviedo, Spain

3 Institute of Orthopaedics, Norfolk & Norwich University Hospital, Colney Lane, Norwich NR4 7UY, UK

4 School of Computing Sciences, University of East Anglia, Earlham Road, Norwich NR4 7TJ, UK

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Arthritis Research & Therapy 2009, 11:R96  doi:10.1186/ar2741

Published: 23 June 2009

Abstract

Introduction

The molecular mechanisms underlying cartilage destruction in osteoarthritis are poorly understood. Proteolysis is a key feature in the turnover and degradation of cartilage extracellular matrix where the focus of research has been on the metzincin family of metalloproteinases. However, there is strong evidence to indicate important roles for other catalytic classes of proteases, with both extracellular and intracellular activities. The aim of this study was to profile the expression of the majority of protease genes in all catalytic classes in normal human cartilage and that from patients with osteoarthritis (OA) using a quantitative method.

Methods

Human cartilage was obtained from femoral heads at joint replacement for either osteoarthritis or following fracture to the neck of femur (NOF). Total RNA was purified, and expression of genes assayed using Taqman® low-density array quantitative RT-PCR.

Results

A total of 538 protease genes were profiled, of which 431 were expressed in cartilage. A total of 179 genes were differentially expressed in OA versus NOF cartilage: eight aspartic proteases, 44 cysteine proteases, 76 metalloproteases, 46 serine proteases and five threonine proteases. Wilcoxon ranking as well as the LogitBoost-NR machine learning approach were used to assign significance to each gene, with the most highly ranked genes broadly similar using each method.

Conclusions

This study is the most complete quantitative analysis of protease gene expression in cartilage to date. The data help give direction to future research on the specific function(s) of individual proteases or protease families in cartilage and may help to refine anti-proteolytic strategies in OA.