血红素
代谢组学
鹅去氧胆酸
代谢组
胆汁酸
卟啉
尿
胆酸
新陈代谢
化学
内科学
生物化学
医学
生理学
色谱法
酶
作者
Thibaud Lefèbvre,Thibaut Eguether,Etienne Thévenot,Antoine Poli,Emeline Chu‐Van,Pranvera Krasniqi,Caroline Schmitt,Neila Talbi,Gaël Nicolas,Hervé Puy,Christophe Junot,Antonin Lamazière,Florence Castelli,Laurent Gouya,François Fenaille
摘要
Acute intermittent porphyria is an inherited error of heme synthesis. The underlying pathophysiology, involving mainly hepatic heme synthesis, is poorly understood despite its occurrence, and the severity of acute porphyria attack is still difficult to control. A better understanding of the interactions between heme synthesis and global metabolism would improve the management of AIP patients. An untargeted metabolomic analysis was performed on the urine of 114 patients with overt AIP and asymptomatic carriers using liquid chromatography coupled to high-resolution mass spectrometry. The collected data were analyzed by combining univariate and multivariate analyses. A total of 239 metabolites were annotated in urine samples by matching chromatographic and mass spectral characteristics with those from our chemical library. Twenty-six metabolites, including porphyrin precursors, intermediates of tryptophan or glycine metabolism and, unexpectedly, bile acids, showed significant concentration differences between the phenotypic groups. Dysregulation of bile acid metabolism was confirmed by targeted quantitative analysis, which revealed an imbalance in favor of hydrophobic bile acids associated with changes in conjugation, which was more pronounced in the severe phenotype. Using a random forest model, the cholic acid/chenodeoxycholic acid ratio enables the differential classification of severe patients from other patients with a diagnostic accuracy of 84%. The analysis of urine samples revealed significant modifications in the metabolome of AIP patients. Alteration in bile acids provides new insights into the pathophysiology of chronic complications, such as primary liver cancer, while also providing new biomarker candidates for predicting the most severe phenotypes.
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