Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133 Milan, Italy.
Department of Human Science and Quality of Life Promotion, Telematic University San Raffaele, 00166 Rome, Italy.
Int J Mol Sci. 2022 Aug 12;23(16):9025. doi: 10.3390/ijms23169025.
Advanced quantitative bioanalytical approaches in combination with network analyses allow us to answer complex biological questions, such as the description of changes in protein profiles under disease conditions or upon treatment with drugs. In the present work, three quantitative proteomic approaches-either based on labelling or not-in combination with network analyses were applied to a new in vitro cellular model of nonalcoholic fatty liver disease (NAFLD) for the first time. This disease is characterized by the accumulation of lipids, inflammation, fibrosis, and insulin resistance. Hepatic G2 cells were used as model, and NAFLD was induced by a complex of oleic acid and bovine albumin. The development of the disease was verified by lipid vesicle staining and by the increase in the expression of perilipin-2-a protein constitutively present in the vesicles during NAFLD. The nLC-MS/MS analyses of peptide samples obtained from three different proteomic approaches resulted in accurate and reproducible quantitative data of protein fold-change expressed in NAFLD versus control cells. The differentially regulated proteins were used to evaluate the involved and statistically enriched pathways. Network analyses highlighted several functional and disease modules affected by NAFLD, such as inflammation, oxidative stress defense, cell proliferation, and ferroptosis. Each quantitative approach allowed the identification of similar modulated pathways. The combination of the three approaches improved the power of statistical network analyses by increasing the number of involved proteins and their fold-change. In conclusion, the application of advanced bioanalytical approaches in combination with pathway analyses allows the in-depth and accurate description of the protein profile of an in vitro cellular model of NAFLD by using high-resolution quantitative mass spectrometry data. This model could be extremely useful in the discovery of new drugs to modulate the equilibrium NAFLD health state.
先进的定量生物分析方法与网络分析相结合，使我们能够回答复杂的生物学问题，例如描述疾病状态下或药物治疗下蛋白质谱的变化。在本工作中，首次将三种定量蛋白质组学方法（一种基于标记，另一种不基于标记）与网络分析相结合，应用于一种新的非酒精性脂肪性肝病（NAFLD）体外细胞模型。这种疾病的特征是脂质积累、炎症、纤维化和胰岛素抵抗。使用肝 G2 细胞作为模型，通过油酸和牛血清白蛋白的复合物诱导 NAFLD。通过脂质囊泡染色和 perilipin-2 蛋白的表达增加来验证疾病的发展，perilipin-2 蛋白在 NAFLD 期间在囊泡中持续存在。从三种不同蛋白质组学方法获得的肽样品的 nLC-MS/MS 分析产生了准确且可重现的蛋白质倍数变化的定量数据，表达在 NAFLD 与对照细胞中。差异调节的蛋白质用于评估涉及和统计学上富集的途径。网络分析突出了几个受 NAFLD 影响的功能和疾病模块，如炎症、氧化应激防御、细胞增殖和铁死亡。每种定量方法都允许识别类似的调节途径。三种方法的结合通过增加涉及的蛋白质数量及其倍数变化，提高了统计网络分析的能力。总之，先进的生物分析方法与途径分析相结合的应用，允许使用高分辨率定量质谱数据深入准确地描述 NAFLD 体外细胞模型的蛋白质谱。该模型对于发现新的药物来调节 NAFLD 健康状态的平衡可能非常有用。
