Transcriptome profiling and longitudinal cohort studies of myositis subsets
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 disea...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/673293 |
| Acceso en línea: | http://hdl.handle.net/10803/673293 |
| Access Level: | acceso abierto |
| Palabra clave: | miositis myositis dermatomyositis dermatomiositis polimiositis polymyositis miopatia per cossos d'inclusió miopatía por cuerpos de inclusión inclusion body myositis seqüenciació d'ARN secuenciación de ARN sequence analysis, RNA bioinformàtica bioinformática bioinformatics Bioinformatics 004 311 57 6 61 616.7 618 62 |
| Sumario: | 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. |
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