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.

肌炎由一组异质性骨骼肌疾病组成，这些疾病集中表现为慢性肌肉炎症和无力。我们对肌炎发病机制的了解有限，许多肌炎患者缺乏有效的治疗方法。利用 119 名肌炎患者（涵盖主要临床和血清学疾病亚型）和 20 名正常对照的肌肉活检转录组图谱，我们生成了 8101 个动态调节转录本的共表达网络。该网络将肌炎转录组组织成代表相互关联的生物过程和疾病特征的基因表达模块图。肌炎普遍上调的网络模块包括肌肉再生、特定细胞因子特征、急性期反应和中性粒细胞脱颗粒。普遍的肌炎抑制途径包括肌丝的特定子集、线粒体包膜和抗凋亡护脑蛋白的核亚型。肌炎亚型特异性模块包括 1 型干扰素信号传导和肌联蛋白（皮肌炎）、RNA 加工（抗合成酶综合征）和血管发生（包涵体肌炎）。重要的是，现有的治疗方法针对许多肌炎失调模块中的有影响的蛋白质，几乎所有模块都含有待研究的蛋白质和非编码RNA——其中许多在肌炎中异常失调，可能代表新的治疗靶点。最后，我们将我们的网络应用于患者分类，发现在患者级网络“图像”上训练的深度学习算法成功地将患者分配到临床组，并进一步分配到分子子簇。总而言之，我们提供了一个全球资源来探索肌炎的差异基因表达并对其进行背景分析。