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Rational design of 13C-labeling experiments for metabolic flux analysis in mammalian cells

机译：哺乳动物细胞代谢通量分析的13C标记实验的合理设计

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Background 13C-Metabolic flux analysis (13C-MFA) is a standard technique to probe cellular metabolism and elucidate in vivo metabolic fluxes. 13C-Tracer selection is an important step in conducting 13C-MFA, however, current methods are restricted to trial-and-error approaches, which commonly focus on an arbitrary subset of the tracer design space. To systematically probe the complete tracer design space, especially for complex systems such as mammalian cells, there is a pressing need for new rational approaches to identify optimal tracers. Results Recently, we introduced a new framework for optimal 13C-tracer design based on elementary metabolite units (EMU) decomposition, in which a measured metabolite is decomposed into a linear combination of so-called EMU basis vectors. In this contribution, we applied the EMU method to a realistic network model of mammalian metabolism with lactate as the measured metabolite. The method was used to select optimal tracers for two free fluxes in the system, the oxidative pentose phosphate pathway (oxPPP) flux and anaplerosis by pyruvate carboxylase (PC). Our approach was based on sensitivity analysis of EMU basis vector coefficients with respect to free fluxes. Through efficient grouping of coefficient sensitivities, simple tracer selection rules were derived for high-resolution quantification of the fluxes in the mammalian network model. The approach resulted in a significant reduction of the number of possible tracers and the feasible tracers were evaluated using numerical simulations. Two optimal, novel tracers were identified that have not been previously considered for 13C-MFA of mammalian cells, specifically [2,3,4,5,6-13C]glucose for elucidating oxPPP flux and [3,4-13C]glucose for elucidating PC flux. We demonstrate that 13C-glutamine tracers perform poorly in this system in comparison to the optimal glucose tracers. Conclusions In this work, we have demonstrated that optimal tracer design does not need to be a pure simulation-based trial-and-error process; rather, rational insights into tracer design can be gained through the application of the EMU basis vector methodology. Using this approach, rational labeling rules can be established a priori to guide the selection of optimal 13C-tracers for high-resolution flux elucidation in complex metabolic network models.
机译：背景 13 C代谢通量分析（ 13 C-MFA）是检测细胞代谢并阐明体内代谢通量的标准技术。 13 C-Tracer的选择是进行 13 C-MFA的重要步骤，但是，当前的方法仅限于反复试验方法，该方法通常侧重于任意跟踪器设计空间的子集。为了系统地探测完整的示踪剂设计空间，特别是对于诸如哺乳动物细胞之类的复杂系统，迫切需要新的合理方法来鉴定最佳示踪剂。结果最近，我们引入了一种基于基本代谢物单元（EMU）分解的优化 13 C-示踪剂设计的新框架，其中将测量的代谢物分解为所谓的EMU基向量的线性组合。在这项贡献中，我们将EMU方法应用于哺乳动物代谢的实际网络模型，其中乳酸作为测量的代谢产物。该方法用于为系统中的两个自由通量选择最佳示踪剂，即氧化戊糖磷酸途径（oxPPP）通量和丙酮酸羧化酶（PC）的过失。我们的方法是基于EMU基本矢量系数对自由通量的敏感性分析。通过系数敏感性的有效分组，导出了简单的示踪剂选择规则，用于对哺乳动物网络模型中的通量进行高分辨率量化。该方法大大减少了可能的示踪剂，并使用数值模拟对可行的示踪剂进行了评估。确定了两个最佳的新型示踪剂，它们以前没有用于哺乳动物细胞的 13 C-MFA，特别是[2,3,4,5,6- 13 C ]葡萄糖用于阐明oxPPP通量，[3,4- 13 C]葡萄糖用于阐明PC通量。我们证明，与最佳葡萄糖示踪剂相比， 13 C-谷氨酰胺示踪剂在该系统中的性能较差。结论在这项工作中，我们证明了最佳跟踪器设计不必是纯粹的基于仿真的反复试验过程。相反，可以通过使用EMU基矢量方法来获得对示踪剂设计的合理见解。使用这种方法，可以先验地建立合理的标记规则，以指导为复杂代谢网络模型中的高分辨率通量阐明选择最佳的 13 C示踪剂。

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