Department ProMISE (Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties), University of Palermo, Palermo, Italy.
Department of Biopatologia e Biotecnologie Mediche e Forensi (DiBiMEF), AOUP "Paolo Giaccone" University of Palermo, Palermo, Italy.
Biochim Biophys Acta Mol Cell Biol Lipids. 2020 Jun;1865(6):158656. doi: 10.1016/j.bbalip.2020.158656. Epub 2020 Feb 8.
Untargeted lipidomics is a powerful tool to discover new biomarkers and to understand the physiology and pathology of lipids. The use of stable isotopes as tracers to investigate the kinetics of lipids is another tool able to supply dynamic information on lipid synthesis and catabolism. Coupling the two methodology is then very appealing in the study of lipid metabolism. The main issue to face is to perform thousands of calculations in order to obtain kinetic parameters starting from the MS raw data. An automated computerized routine able to do accomplish such task is presented in this paper. We analyzed the lipid kinetics of palmitic acid (PA) in hepatoma liver cells cultured in vitro in which insulin resistance has been induced by high glucose supplementation. The deuterated palmitate tracer (d5PA) was administered as a bolus and the cells were harvested daily for 48 h. 5dPA was incorporated into 326 monoisotopic compounds and in 84 of their [M + 1] isotopologues detected by high resolution orbitrap MS. The differences between the kinetics curves showed that at least four long chain triglycerides (TG) species incorporated more PA in glucose treated cells, while phosphocholines, sphingomyelins, mono- and di-glycerides and ceramides (Cer) incorporated less tracer under glucose treatment. Nevertheless, Cer amount was increased by glucose treatment. In conclusion we developed an automated powerful algorithm able to model simultaneously hundreds of kinetic curves obtained in a cell culture spiked with a stable isotope tracer, and to analyze the difference between the two different cell models.
非靶向脂质组学是一种发现新生物标志物和了解脂质生理学和病理学的强大工具。使用稳定同位素作为示踪剂来研究脂质的动力学是另一种能够提供脂质合成和分解代谢动态信息的工具。将这两种方法结合起来,在研究脂质代谢时非常有吸引力。主要问题是要从 MS 原始数据中进行数千次计算以获得动力学参数。本文提出了一种能够完成此任务的自动化计算机化例程。我们分析了体外培养的肝癌细胞中棕榈酸 (PA) 的脂质动力学,这些细胞通过高葡萄糖补充诱导了胰岛素抵抗。以脉冲方式给予氘代棕榈酸示踪剂 (d5PA),并在 48 小时内每天收获细胞。5dPA 被掺入 326 种单同位素化合物和 84 种通过高分辨轨道阱 MS 检测到的 [M+1] 同位素。动力学曲线的差异表明,至少有四种长链甘油三酯 (TG) 物种在葡萄糖处理的细胞中掺入了更多的 PA,而葡萄糖处理下磷酰胆碱、神经鞘磷脂、单甘油酯和二甘油酯以及神经酰胺 (Cer) 掺入的示踪剂较少。然而,葡萄糖处理会增加 Cer 的量。总之,我们开发了一种自动化的强大算法,能够同时对用稳定同位素示踪剂标记的细胞培养物中获得的数百条动力学曲线进行建模,并分析两种不同细胞模型之间的差异。