Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer
Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer
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DOI：
10.1038/s41416-019-0659-3
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发表时间：
2020-01-01
影响因子：
8.8
通讯作者：
Cramer, Thorsten
中科院分区：
文献类型：
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作者：
Berndt, Nikolaus;Egners, Antje;Cramer, Thorsten
背景代谢改变可作为诊断和治疗癌症的靶点。由于细胞代谢的高度复杂的调节，代谢途径改变的明确鉴定仍然具有挑战性，需要复杂的实验。方法应用中心碳代谢（CCM）的综合动力学模型研究小鼠肝癌细胞的代谢重编程。结果利用质谱分析得到的代谢酶蛋白质丰度的相对差异，可以估算代谢酶的最大速率值。模型模拟预测了CCM的各种组分的肿瘤特异性改变，其中选定的一些随后通过体外和体内实验进行了验证。此外，我们证明了动力学模型的能力，以确定代谢途径，其抑制结果在选择性肿瘤细胞杀伤。结论我们的系统生物学方法确立了将细胞实验与基于生理学的代谢模型的计算机模拟相结合，能够全面了解癌症中的能量失调。我们建议，用我们的方法对人类HCC的蛋白质组学数据进行建模，将能够对肿瘤进行个性化的代谢分析，并预测针对特定代谢途径的药物治疗的疗效。
Background Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. Methods We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer. Results We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing. Conclusions Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.