Author: Iago Pinal-Fernandez
Programme: Doctoral Programme in Computer Science, Technology and Multimedia
Language: English
Supervision: Carme Carrion, Andrew Mammen
Faculty / Institute: Doctoral School UOC
Subjects: Bioinformatics
Key words: Myositis, Dermatomyositis, Polymyositis, Inclusion body myositis, sequence analysis, RNA, Bioinformatics
Area of knowledge: Computer Science, Technology and Multimedia
Abstract:
Inflammatory myopathies are a heterogeneous family of rare autoimmune diseases affecting multiple organs and systems, including the skin, the lungs, the muscles and/or the joints. Accurately defining their pathogenesis and classifying them correctly are key for understanding and managing these diseases. In this doctoral thesis we explored specific autoantibody-defined myositis subsets and quantitatively compared the ability of autoantibodies to the 2017 EULAR/ACR classification standard to predict the phenotype of patients with myositis. We also performed RNA sequencing on 119 muscle biopsies of patients with different types of myositis and 20 controls. We studied the differential expression, performed pathway analysis and developed exploratory machine learning pipelines to define the specific expression profiles and pathogenic pathways in each disease subgroup. With these studies we determined that the autoantibodies outperform current clinical criteria to predict the phenotype of myositis patients and discovered unique expression profiles in the muscle tissue of patients with different types of myositis.