An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells
An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells
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DOI：
10.1038/s41540-019-0103-6
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发表时间：
2019-07-23
影响因子：
4
通讯作者：
Betenbaugh, Michael J.
中科院分区：
文献类型：
--
作者：
Chen, Yiqun;McConnell, Brian O.;Betenbaugh, Michael J.
基于约束的建模已被应用于通过通量平衡分析和基因组尺度代谢模型来分析许多生物体的代谢，包括哺乳动物细胞，如中国仓鼠卵巢（CHO）细胞-用于治疗性蛋白质生产的主要细胞工厂平台。不幸的是，使用传统的生物量目标函数的基因组规模的模型方法的应用受到了过度限制性约束的存在的挑战，包括必需氨基酸交换通量，这可能导致不正确的预测的生长速率和细胞内通量分布。在这项研究中，这些限制被认为是可靠的预测“必需营养素最小化”的方法。在用预测的最小摄取值修改这些约束之后，应用一系列非常规目标函数来最小化每个个体的非必需营养素摄取率，从而揭示关于代谢交换率和跨不同细胞系和培养条件的流动的有用见解。这种非常规的吸收率目标函数（UOFs）方法能够区分使用常规生物质生长最大化解决方案无法直接观察到的三种不同CHO细胞系（CHO-K1、-DG 44和-5）之间的代谢差异。此外，模型预测与来自文献的实验数据的比较正确地与实验中使用的特定CHO-DG 44衍生细胞系相关，并且相应的双重价格提供了关于营养素之间的偶联关系的丰富信息。UOFs方法可能特别适合于哺乳动物细胞和其他含有多种不同必需营养素输入的复杂生物体，并且可以为表征细胞代谢和生理学以及培养基优化和生物制造控制提供增强的适用性。
Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genomescale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells-the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an "essential nutrient minimization" approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -5) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.