Basic Medical College, Harbin Medical University, Harbin, China.
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Life Sci. 2024 Sep 15;353:122923. doi: 10.1016/j.lfs.2024.122923. Epub 2024 Jul 18.
Sepsis pathophysiology is complex and identifying effective treatments for sepsis remains challenging. The study aims to identify effective drugs and targets for sepsis through transcriptomic analysis of sepsis patients, repositioning analysis of compounds, and validation by animal models.
GSE185263 obtained from the GEO database that includes gene expression profiles of 44 healthy controls and 348 sepsis patients categorized by severity. Bioinformatic algorithms revealed the molecular, function, and immune characteristics of the sepsis, and constructed sepsis-related protein-protein interaction networks. Subsequently, Random Walk with Restart analysis was applied to identify candidate drugs for sepsis, which were tested in animal models for survival, inflammation, coagulation, and multi-organ damage.
Our analysis found 1862 genes linked to sepsis development, enriched in functions like neutrophil extracellular trap formation (NETs) and complement/coagulation cascades. With disease progression, immune activation-associated cells were inhibited, while immune suppression-associated cells were activated. Next, the drug repositioning method identified candidate drugs, such as alpha-1 antitrypsin, that may play a therapeutic role by targeting neutrophil elastase (NE) to inhibit NETs. Animal experiments proved that alpha-1 antitrypsin treatment can improve the survival rate, reduce sepsis score, reduce the levels of inflammation markers in serum, and alleviate muti-organ morphological damage in mice with sepsis. The further results showed that α-1 antitrypsin can inhibit the NETs by suppressing the NE for the treatment of sepsis.
Alpha-1 antitrypsin acted on the NE to inhibit NETs thereby protecting mice from sepsis-induced inflammation and coagulation.
脓毒症发病机制复杂，寻找有效的脓毒症治疗方法仍然具有挑战性。本研究旨在通过对脓毒症患者的转录组分析、化合物重定位分析，并通过动物模型进行验证，来确定治疗脓毒症的有效药物和靶点。
从 GEO 数据库中获取 GSE185263，该数据集包含 44 名健康对照者和 348 名根据严重程度分类的脓毒症患者的基因表达谱。生物信息学算法揭示了脓毒症的分子、功能和免疫特征，并构建了脓毒症相关的蛋白质-蛋白质相互作用网络。随后，应用随机游走重启动分析识别脓毒症的候选药物，并在动物模型中进行生存、炎症、凝血和多器官损伤试验。
我们的分析发现了 1862 个与脓毒症发展相关的基因，这些基因富集在中性粒细胞胞外诱捕网（NETs）形成和补体/凝血级联等功能中。随着疾病的进展，免疫激活相关细胞受到抑制，而免疫抑制相关细胞被激活。接着，药物重定位方法确定了候选药物，如α-1 抗胰蛋白酶，通过靶向中性粒细胞弹性蛋白酶（NE）抑制 NETs 可能发挥治疗作用。动物实验证明，α-1 抗胰蛋白酶治疗可以提高脓毒症小鼠的存活率，降低脓毒症评分，降低血清中炎症标志物的水平，并减轻脓毒症小鼠多器官形态损伤。进一步的结果表明，α-1 抗胰蛋白酶通过抑制 NE 来抑制 NETs 从而治疗脓毒症。
α-1 抗胰蛋白酶通过作用于 NE 来抑制 NETs，从而保护小鼠免受脓毒症引起的炎症和凝血反应。
