质谱探针与机器学习相结合,在全细胞水平上捕捉代谢物与线粒体复合物活性之间的关系。

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Analytical Chemistry Pub Date : 2024-11-12 Epub Date: 2024-11-01 DOI:10.1021/acs.analchem.4c04376
Jia-Yi Zheng, Xiao-Yuan Ji, An-Qi Zhao, Fang-Yuan Sun, Li-Fang Liu, Gui-Zhong Xin
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引用次数: 0

摘要

线粒体复合活性通过调节细胞新陈代谢来控制多种生理过程。目前评估线粒体复合活性的方法主要集中于线粒体内的单一代谢反应。这些方法通常需要大量新鲜样本用于线粒体纯化,或需要完整的线粒体膜用于实时监测。面对这些局限性,我们将分析视角转向全细胞水平的交互式代谢网络,以反映线粒体的复合活动。为此,我们编制了一个线粒体呼吸链映射代谢物(MRCMs)小组,从理论上讲,这些代谢物的扰动可以全面反映线粒体复合体的活动。通过引入 N-二甲基对苯二胺和 N-甲基对苯二胺作为一对质谱探针,我们开发了一种具有高灵敏度(LLOQ 低至 0.2 fmol)的超高效液相色谱-串联质谱方法,以获得 MRCMs 的精确定量数据。然后结合机器学习捕捉 MRCMs 与线粒体复合物活性之间的关系。以复合体 I 为概念验证,我们确定了 NADH、丙氨酸和磷酸烯醇丙酮酸是基于全细胞水平的与复合体 I 活性相关的代谢物。利用它们的浓度来反映复合体 I 活性的有效性在外部数据集中得到了进一步验证。因此,通过在全细胞水平捕捉代谢物与线粒体复合体活性之间的关系,本研究为线粒体复合体活性的检测探索了一种新的分析范式,为现有方法提供了有利的补充,尤其是在样本数量、类型和处理时效性构成挑战的情况下。更重要的是,该研究将重点从线粒体内的单个代谢反应转移到了更全面的交互式代谢网络上,为未来研究线粒体复合体与代谢物之间的功能结构指明了方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mass Spectrometry Probe Combined with Machine Learning to Capture the Relationship between Metabolites and Mitochondrial Complex Activity at the Whole-Cell Level.

Mass Spectrometry Probe Combined with Machine Learning to Capture the Relationship between Metabolites and Mitochondrial Complex Activity at the Whole-Cell Level.

Mitochondrial complex activity controls a multitude of physiological processes by regulating the cellular metabolism. Current methods for evaluating mitochondrial complex activity mainly focus on single metabolic reactions within mitochondria. These methods often require fresh samples in large quantities for mitochondria purification or intact mitochondrial membranes for real-time monitoring. Confronting these limitations, we shifted the analytical perspective toward interactive metabolic networks at the whole-cell level to reflect mitochondrial complex activity. To this end, we compiled a panel of mitochondrial respiratory chain-mapped metabolites (MRCMs), whose perturbations theoretically provide an overall reflection on mitochondrial complex activity. By introducing N-dimethyl-p-phenylenediamine and N-methyl-p-phenylenediamine as a pair of mass spectrometry probes, an ultraperformance liquid chromatography-tandem mass spectrometry method with high sensitivity (LLOQ as low as 0.2 fmol) was developed to obtain accurate quantitative data of MRCMs. Machine learning was then combined to capture the relationship between MRCMs and mitochondrial complex activity. Using Complex I as a proof-of-concept, we identified NADH, alanine, and phosphoenolpyruvate as metabolites associated with Complex I activity based on the whole-cell level. The effectiveness of using their concentrations to reflect Complex I activity was further validated in external data sets. Hence, by capturing the relationship between metabolites and mitochondrial complex activity at the whole-cell level, this study explores a novel analytical paradigm for the interrogation of mitochondrial complex activity, offering a favorable complement to existing methods particularly when sample quantities, type, and treatment timeliness pose challenges. More importantly, it shifts the focus from individual metabolic reactions within mitochondria to a more comprehensive view of an interactive metabolic network, which should serve as a promising direction for future research into the functional architecture between mitochondrial complexes and metabolites.

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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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