Uncovering in vivo biochemical patterns from time-series metabolic dynamics.
Uncovering in vivo biochemical patterns from time-series metabolic dynamics.
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DOI：
10.1371/journal.pone.0268394
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
系统生物学依赖于整体的生物分子测量，解开生物化学网络需要时间序列代谢组学分析。利用目前的代谢组学方法，可以对数百个代谢特征进行时间序列测量，从而解码潜在的代谢调节。这样的代谢组学数据集是非靶向的，其中大多数特征未经注释并且无法进行统计分析和计算建模。代谢空间的高维度也导致机械建模在计算上相当麻烦。我们实施了一个更快的探索性工作流程，以可视化和提取化学和生化依赖性。时间序列代谢特征（每个数据集约300个）提取脊跟踪为基础的提取（RTExtract）的测量从连续在体内监测代谢的NMR（CIVM-NMR）在粗糙脉孢菌在不同条件下。然后将代谢谱平滑并投影到较低的维度，从而能够比较培养物中的代谢趋势。接下来，我们使用相关网络扩展了不完整的代谢物注释。最后，我们通过估计平滑代谢曲线之间的依赖关系发现了有意义的代谢簇。因此，我们避开了耗时的机械建模，困难的全局优化和劳动密集型注释的过程。多个集群引导人们深入了解中心能量代谢和膜合成。与葡萄糖1-磷酸的紧密连接表明其在N.粗鲁。我们的方法是模拟随机网络动力学的基准，并提供了一种新的探索性方法来分析高维代谢动力学。
System biology relies on holistic biomolecule measurements, and untangling biochemical networks requires time-series metabolomics profiling. With current metabolomic approaches, time-series measurements can be taken for hundreds of metabolic features, which decode underlying metabolic regulation. Such a metabolomic dataset is untargeted with most features unannotated and inaccessible to statistical analysis and computational modeling. The high dimensionality of the metabolic space also causes mechanistic modeling to be rather cumbersome computationally. We implemented a faster exploratory workflow to visualize and extract chemical and biochemical dependencies. Time-series metabolic features (about 300 for each dataset) were extracted by Ridge Tracking-based Extract (RTExtract) on measurements from continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) in Neurospora crassa under different conditions. The metabolic profiles were then smoothed and projected into lower dimensions, enabling a comparison of metabolic trends in the cultures. Next, we expanded incomplete metabolite annotation using a correlation network. Lastly, we uncovered meaningful metabolic clusters by estimating dependencies between smoothed metabolic profiles. We thus sidestepped the processes of time-consuming mechanistic modeling, difficult global optimization, and labor-intensive annotation. Multiple clusters guided insights into central energy metabolism and membrane synthesis. Dense connections with glucose 1-phosphate indicated its central position in metabolism in N. crassa. Our approach was benchmarked on simulated random network dynamics and provides a novel exploratory approach to analyzing high-dimensional metabolic dynamics.
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影响因子：
4
作者：
Bingol, Kerem;Li, Da-Wei;Bruschweiler-Li, Lei;Cabrera, Oscar A.;Megraw, Timothy;Zhang, Fengli;Brueschweiler, Rafael
通讯作者：
Brueschweiler, Rafael
影响因子：
4.3
作者：
Dreyfuss, Jonathan M.;Zucker, Jeremy D.;Galagan, James E.
通讯作者：
Galagan, James E.
DOI：
10.1073/pnas.262658899
发表时间：
2002-12-24
影响因子：
11.1
作者：
Battogtokh, D;Asch, DK;Schüttler, HB
通讯作者：
Schüttler, HB
影响因子：
4.3
作者：
Klimovskaia, Anna;Ganscha, Stefan;Claassen, Manfred
通讯作者：
Claassen, Manfred
影响因子：
7.4
作者：
Alves, Alexessander Couto;Rantalainen, Mattias;Ebbels, Timothy M. D.
通讯作者：
Ebbels, Timothy M. D.