Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Department of Mathematics, University of Trento, Trento, Italy.
Nat Commun. 2019 Nov 18;10(1):5215. doi: 10.1038/s41467-019-13208-z.
Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.
代谢综合征是一种病理状态，其特征为肥胖、高血糖、高血压、甘油三酯水平升高和高密度脂蛋白胆固醇水平降低，这些都会增加心血管疾病和 2 型糖尿病的风险。尽管已经确定了许多易患遗传风险因素，但这种复杂表型的生物学机制尚未完全阐明。在这里，我们介绍了一种基于网络分析的系统生物学方法，用于研究失调的生物学过程，并随后确定药物再利用的候选药物。通过结合拓扑和功能相似性来定义描述药物和途径之间相互作用的接近分数。该计算框架的结果突出了免疫系统在代谢综合征中的重要作用，并表明 BTK 抑制剂伊布替尼可能作为一种新的药理治疗方法。使用斑马鱼幼虫高脂肪饮食诱导的肥胖模型进行的实验验证表明，伊布替尼可有效降低由于巨噬细胞积累引起的炎症负荷。
