Simultaneous determination of methionine cycle metabolites, urea cycle intermediates and polyamines in serum, urine and intestinal tissue by using UHPLC-MS/MS

化学 尿素循环 尿 腐胺 衍生化 色谱法 精胺 尿素 亚精胺 甲酸 鸟氨酸 蛋氨酸 氯化丹酯 代谢物 代谢途径 多胺 高效液相色谱法 新陈代谢 生物化学 氨基酸 精氨酸
作者
Xiuli Su,Xiaona Li,Haojiang Wang,Zongwei Cai
出处
期刊:Talanta [Elsevier]
卷期号:224: 121868-121868 被引量:22
标识
DOI:10.1016/j.talanta.2020.121868
摘要

Metabolites of methionine cycle, urea cycle and polyamine metabolism play important roles in regulating the metabolic processes and the development of diseases. It is rewarding and interesting to monitor the levels of the above metabolites in biological matrices to investigate pathological mechanisms. However, their quantitation is still unsatisfactory due to the poor retention behavior of the analytes on the traditional reversed-phase column. And never a single analytical method simultaneously quantify these three classes of metabolites. Besides, the concentrations of some metabolites are too low to be detected in the biological samples. In this study, we developed a UHPLC-ESI-MS/MS method to simultaneously determine the levels of 14 metabolites, including 4 methionine metabolism metabolites (methionine, homocysteine, S-adenosylmethionine and S-adenosylhomocysteine), 3 urea cycle intermediates (arginine, citrulline and ornithine) and 7 polyamines (putrescine, spermidine, spermine, N1-acetylputrescine, N1-acetylspermidine, N1-acetylspermine and N1,N12-diacetylspermine). The chromatographic separation was performed on the BEH amide column within 14 min using water and acetonitrile (both with 0.1% formic acid) as the mobile phases. The results of method validation showed good selectivity, linearity (r2 > 0.99), recovery (93.1%–112.1%), inter-day and intra-day precision (RSD < 13.6% and RSD < 11.0%, respectively), stability (RSD < 15.1%) and matrix effect (76.0%–113.2%). The method is simple, quick and sensitive without derivatization processes and the use of ion-pairing reagents. This approach was successfully applied in urine, serum and tissue matrices, as well as in identifying potential biomarkers for hyperthyroidism and hypothyroidism. The method is promising to provide more information on pathophysiological mechanisms in metabolomics study.
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