E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data.
E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data.
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DOI：
10.1371/journal.pone.0157101
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Lun DS
中科院分区：
文献类型：
--
作者：
Kim MK;Lane A;Kelley JJ;Lun DS
通过将转录数据与基因组规模的代谢模型相结合，已经开发出几种方法来预测系统范围和特定条件的细胞内代谢通量。虽然现有的方法在许多情况下都很强大，但也有几个缺点，而且由于与实验测量的细胞内通量的验证有限，目前尚不清楚哪种方法总体上具有最好的准确性。我们提出了一种通用的优化策略，用于从转录数据中推断细胞内代谢通量分布，并结合基因组规模的代谢重建。它包括两种不同的模板模型DC(确定的碳源模型)和AC(所有可能的碳源模型)，以及两种不同的新方法E-Flux方法(结合L2范数最小化的E-Flux方法)和SPOT(简化的Pearson相关与转译数据)，它们可以根据碳源知识或目标函数的可用性进行选择和组合。这使我们能够模拟广泛的实验条件。我们分别考察了具有代表性的原核和真核微生物--大肠杆菌和酿酒酵母。通过计算通量预测值与实测值之间的无中心皮尔逊相关系数，验证了该算法的预测精度。为此，我们汇编了20个实验条件(大肠杆菌中11个，酿酒酵母中9个)，转录组测量与13C代谢通量分析(13C-MFA)确定的相应中心碳代谢细胞内通量测量相结合，这是迄今为止收集的最大数据集，目的是验证预测细胞内通量的推断方法。在这两种生物中，我们的方法达到了从0.59到0.87的平均相关系数，超过了竞争方法中具有代表性的样本。作为开放源码包MOST(http://most.ccib.rutgers.edu/).)的一部分，提供了易于使用的E-flus2和Spot实现我们的方法比现有的从转录数据推断细胞内代谢通量的方法有了很大的进步。它不仅实现了更高的精度，而且还将许多其他理想的特征结合到一个方法中，包括适用于广泛的实验条件，产生唯一的解，运行时间快，以及用户友好的实施。
Several methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes. We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l2 norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by 13C metabolic flux analysis (13C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/). Our method represents a significant advance over existing methods for inferring intracellular metabolic flux from transcriptomic data. It not only achieves higher accuracy, but it also combines into a single method a number of other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, fast running time, and the availability of a user-friendly implementation.