Mayo Clinic, Rochester, Minnesota.
Arthritis Rheumatol. 2022 Aug;74(8):1376-1386. doi: 10.1002/art.42138. Epub 2022 Jun 20.
To identify hallmark genes and biomolecular processes in aortitis using high-throughput gene expression profiling, and to provide a range of potentially new drug targets (genes) and therapeutics from a pharmacogenomic network analysis.
Bulk RNA sequencing was performed on surgically resected ascending aortic tissues from inflammatory aneurysms (giant cell arteritis [GCA] with or without polymyalgia rheumatica, n = 8; clinically isolated aortitis [CIA], n = 17) and noninflammatory aneurysms (n = 25) undergoing surgical aortic repair. Differentially expressed genes (DEGs) between the 2 patient groups were identified while controlling for clinical covariates. A protein-protein interaction model, drug-gene target information, and the DEGs were used to construct a pharmacogenomic network for identifying promising drug targets and potentially new treatment strategies in aortitis.
Overall, tissue gene expression patterns were the most associated with disease state than with any other clinical characteristic. We identified 159 and 93 genes that were significantly up-regulated and down-regulated, respectively, in inflammatory aortic aneurysms compared to noninflammatory aortic aneurysms. We found that the up-regulated genes were enriched in immune-related functions, whereas the down-regulated genes were enriched in neuronal processes. Notably, gene expression profiles of inflammatory aortic aneurysms from patients with GCA were no different than those from patients with CIA. Finally, our pharmacogenomic network analysis identified genes that could potentially be targeted by immunosuppressive drugs currently approved for other inflammatory diseases.
We performed the first global transcriptomics analysis in inflammatory aortic aneurysms from surgically resected aortic tissues. We identified signature genes and biomolecular processes, while finding that CIA may be a limited presentation of GCA. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions and suggests future biomarker discovery directions for the precise diagnosis and treatment of aortitis.
通过高通量基因表达谱分析，确定大动脉炎的标志性基因和生物分子过程，并从药物基因组网络分析中提供一系列潜在的新药物靶点（基因）和治疗方法。
对接受手术修复的升主动脉组织中炎症性动脉瘤（巨细胞动脉炎伴或不伴多发性肌痛，n=8；临床孤立性大动脉炎，n=17）和非炎症性动脉瘤（n=25）的手术切除标本进行批量 RNA 测序。在控制临床协变量的情况下，鉴定两组患者之间差异表达的基因（DEGs）。利用蛋白质-蛋白质相互作用模型、药物-基因靶点信息和 DEGs，构建药物基因组网络，以鉴定大动脉炎中潜在的有前途的药物靶点和潜在的新治疗策略。
总的来说，组织基因表达模式与疾病状态的相关性大于任何其他临床特征。与非炎症性主动脉瘤相比，我们发现炎症性主动脉瘤中有 159 个和 93 个基因分别显著上调和下调。我们发现上调的基因富集在免疫相关功能中，而下调的基因富集在神经元过程中。值得注意的是，巨细胞动脉炎患者和临床孤立性大动脉炎患者的炎症性主动脉瘤基因表达谱无差异。最后，我们的药物基因组网络分析确定了一些基因，这些基因可能成为目前批准用于其他炎症性疾病的免疫抑制剂的潜在靶点。
我们对手术切除的主动脉组织中的炎症性主动脉瘤进行了首次全基因组转录组分析。我们确定了特征基因和生物分子过程，同时发现 CIA 可能是 GCA 的一种有限表现。此外，我们的计算网络分析揭示了潜在的新的药物干预策略，并为大动脉炎的精确诊断和治疗提供了未来的生物标志物发现方向。
