Introduction: The idiopathic inflammatory myopathies (IIM): dermatomyositis (DM) and polymyositis (PM) have been historically defined by broad clinical and pathological criteria. These conditions affect both adults and children with clinical features including muscle weakness, skin disease and internal organ involvement. Using a clinico-serological approach DM and PM can be defined into more homogeneous subsets. Myositis-specific autoantibodies (MSAs) are directed against cytoplasmic or nuclear components involved in key regulatory intra-cellular processes including protein synthesis, translocation and gene transcription. Over the last few years MSAs have been better characterised including autoantibodies directed against the aminoacyl tRNA-synthetase (ARS) enzymes, the signal recognition particle and the Mi-2 protein.
Aim: The overall aim of this thesis is to describe a comprehensive clinical and serological study of adult and juvenile IIM. Autoantigen targets including novel specificities were identified using protein immunoprecipitation.
Results: The first part of this thesis is a descriptive study on known myositis autoantibodies in adult IIM, confirming the significant association of interstitial pneumonia with anti-ARS, severe myopathy with anti-SRP, and classic DM with anti-Mi-2 serotype. In the next section, new autoantigen systems in adult IIM are described including a new anti-ARS (anti-Zo) in the anti-synthetase syndrome. Further autoantibodies directed against small ubiquitin-like modifier enzyme and a p155/140 autoantigen are major serological subsets in adult DM, the latter significantly associated with malignancy. The final section outlines a large serological study of juvenile DM (JDM) and JDM-overlap showing the frequency and clinical associations of MSAs and myositis-associated autoantibodies, including work on two new major subsets anti-p155/140 and anti-p140, which appear to define more severe disease.
Conclusion: The work in this thesis highlights the importance of autoimmunity in IIM and suggests a new approach where MSAs can classify patients into clinical syndromes, which predict outcomes and may as a result influence treatment strategies.
|Date of Award||1 Sep 2010|
|Supervisor||Clifford Stevens (Supervisor) & Neil McHugh (Supervisor)|