Myositis comprises a heterogeneous group of skeletal muscle disorders which converge on chronic muscle inflammation and weakness. Our understanding of myositis pathogenesis is limited, and many myositis patients lack effective therapies. Using muscle biopsy transcriptome profiles from 119 myositis patients (spanning major clinical and serological disease subtypes) and 20 normal controls, we generated a co-expression network of 8101 dynamically regulated transcripts. This network organized the myositis transcriptome into a map of gene expression modules representing interrelated biological processes and disease signatures. Universally myositis-upregulated network modules included muscle regeneration, specific cytokine signatures, the acute phase response, and neutrophil degranulation. Universally myositis-suppressed pathways included a specific subset of myofilaments, the mitochondrial envelope, and nuclear isoforms of the anti-apoptotic humanin protein. Myositis subtype-specific modules included type 1 interferon signaling and titin (dermatomyositis), RNA processing (antisynthetase syndrome), and vasculogenesis (inclusion body myositis). Importantly, therapies exist to target influential proteins in many myositis-dysregulated modules, and nearly all modules contained understudied proteins and non-coding RNAs – many of which were extraordinarily dysregulated in myositis and may represent novel therapeutic targets. Finally, we apply our network to patient classification, finding that a deep learning algorithm trained on patient-level network “images” successfully assigned patients to clinical groups and further into molecular subclusters. Altogether, we provide a global resource to probe and contextualize differential gene expression in myositis.
- Deep learning
ASJC Scopus subject areas
- Pathology and Forensic Medicine
- Clinical Neurology
- Cellular and Molecular Neuroscience