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博来霉素致幼年小鼠肺纤维化模型血清脂质代谢物及潜在生物标志物的改变。

Disturbance of serum lipid metabolites and potential biomarkers in the Bleomycin model of pulmonary fibrosis in young mice.

机构信息

Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.

Department of Geriatrics, Affiliated Hospital of Qingdao University, Qingdao, 266000, People's Republic of China.

出版信息

BMC Pulm Med. 2022 May 4;22(1):176. doi: 10.1186/s12890-022-01972-6.

Abstract

BACKGROUND

Altered metabolic pathways have recently been considered as potential drivers of idiopathic pulmonary fibrosis (IPF) for the study of drug therapeutic targets. However, our understanding of the metabolite profile during IPF formation is lacking.

METHODS

To comprehensively characterize the metabolic disorders of IPF, a mouse IPF model was constructed by intratracheal injection of bleomycin into C57BL/6J male mice, and lung tissues from IPF mice at 7 days, 14 days, and controls were analyzed by pathology, immunohistochemistry, and Western Blots. Meanwhile, serum metabolite detections were conducted in IPF mice using LC-ESI-MS/MS, KEGG metabolic pathway analysis was applied to the differential metabolites, and biomarkers were screened using machine learning algorithms.

RESULTS

We analyzed the levels of 1465 metabolites and found that more than one-third of the metabolites were altered during IPF formation. There were 504 and 565 metabolites that differed between M7 and M14 and controls, respectively, while 201 differential metabolites were found between M7 and M14. In IPF mouse sera, about 80% of differential metabolite expression was downregulated. Lipids accounted for more than 80% of the differential metabolite species with down-regulated expression. The KEGG pathway enrichment analysis of differential metabolites was mainly enriched to pathways such as the metabolism of glycerolipids and glycerophospholipids. Eight metabolites were screened by a machine learning random forest model, and receiver operating characteristic curves (ROC) assessed them as ideal diagnostic tools.

CONCLUSIONS

In conclusion, we have identified disturbances in serum lipid metabolism associated with the formation of pulmonary fibrosis, contributing to the understanding of the pathogenesis of pulmonary fibrosis.

摘要

背景

最近,人们认为改变的代谢途径可能是特发性肺纤维化(IPF)的潜在驱动因素,以便研究药物治疗靶点。然而,我们对 IPF 形成过程中代谢物谱的理解还很缺乏。

方法

为了全面描述 IPF 的代谢紊乱,通过气管内注射博来霉素构建了一个小鼠 IPF 模型,并通过病理、免疫组织化学和 Western Blot 分析了 IPF 小鼠的肺组织,同时采用 LC-ESI-MS/MS 对 IPF 小鼠的血清代谢物进行检测,应用 KEGG 代谢途径分析差异代谢物,并采用机器学习算法筛选生物标志物。

结果

我们分析了 1465 种代谢物的水平,发现有三分之一以上的代谢物在 IPF 形成过程中发生了改变。M7 和 M14 与对照组之间分别有 504 和 565 种代谢物不同,而 M7 和 M14 之间有 201 种差异代谢物。在 IPF 小鼠的血清中,约 80%的差异代谢物表达下调。下调表达的差异代谢物中,脂质占 80%以上。差异代谢物的 KEGG 途径富集分析主要富集到甘油磷脂和甘油脂代谢等途径。通过机器学习随机森林模型筛选出 8 种代谢物,ROC 评估它们是理想的诊断工具。

结论

总之,我们发现与肺纤维化形成相关的血清脂质代谢紊乱,有助于了解肺纤维化的发病机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89d6/9066762/82758d2c1ec2/12890_2022_1972_Fig1_HTML.jpg

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