Zhang Yu-Zhu, Jia Xiu-Juan, Xu Wen-Juan, Ding Xiao-Qian, Wang Xiao-Meng, Chi Xiao-Sa, Hu Yi, Yang Xiao-Hui
Department of Geriatrics, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
Front Med (Lausanne). 2024 Aug 7;11:1410051. doi: 10.3389/fmed.2024.1410051. eCollection 2024.
Alterations in metabolites and metabolic pathways are thought to be important triggers of idiopathic pulmonary fibrosis (IPF), but our lack of a comprehensive understanding of this process has hampered the development of IPF-targeted drugs.
To fully understand the metabolic profile of IPF, C57BL/6 J male mice were injected intratracheally with bleomycin so that it could be used to construct a mouse model of IPF, and lung tissues from 28-day and control IPF mice were analyzed by pathology and immunohistochemistry. In addition, serum metabolites from IPF mice were examined using LC-ESI-MS/MS, and the differential metabolites were analyzed for KEGG metabolic pathways and screened for biomarkers using machine learning algorithms.
In total, the levels of 1465 metabolites were detected, of which 104 metabolites were significantly altered after IPF formation. In IPF mouse serum, 52% of metabolite expression was downregulated, with lipids (e.g., GP, FA) and organic acids and their derivatives together accounting for more than 70% of the downregulated differentially expressed metabolites. In contrast, FA and oxidised lipids together accounted for 60% of the up-regulated differentially expressed metabolites. KEGG pathway enrichment analyses of differential metabolites were mainly enriched in the biosynthesis of unsaturated fatty acids, pentose phosphate pathway, and alanine, aspartate, and glutamate metabolism. Seven metabolites were screened by machine learning LASSO models and evaluated as ideal diagnostic tools by receiver operating characteristic curves (ROCs).
In conclusion, the serum metabolic disorders found to be associated with pulmonary fibrosis formation will help to deepen our understanding of the pathogenesis of pulmonary fibrosis.
代谢物和代谢途径的改变被认为是特发性肺纤维化(IPF)的重要触发因素,但我们对这一过程缺乏全面了解,阻碍了针对IPF的药物开发。
为全面了解IPF的代谢特征,对C57BL/6 J雄性小鼠进行气管内注射博来霉素,以构建IPF小鼠模型,并通过病理学和免疫组织化学分析28天龄IPF小鼠和对照小鼠的肺组织。此外,使用液相色谱-电喷雾串联质谱法(LC-ESI-MS/MS)检测IPF小鼠的血清代谢物,并对差异代谢物进行KEGG代谢途径分析,使用机器学习算法筛选生物标志物。
共检测到1465种代谢物的水平,其中104种代谢物在IPF形成后发生显著改变。在IPF小鼠血清中,52%的代谢物表达下调,其中脂质(如甘油磷脂、脂肪酸)以及有机酸及其衍生物占下调差异表达代谢物的70%以上。相比之下,脂肪酸和氧化脂质占上调差异表达代谢物的60%。差异代谢物的KEGG通路富集分析主要富集在不饱和脂肪酸的生物合成、磷酸戊糖途径以及丙氨酸、天冬氨酸和谷氨酸代谢。通过机器学习LASSO模型筛选出7种代谢物,并通过受试者工作特征曲线(ROC)评估为理想的诊断工具。
总之,发现与肺纤维化形成相关的血清代谢紊乱将有助于加深我们对肺纤维化发病机制的理解。