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

• Published in: Analytical chemistry (Washington) 2024-11, Vol.96 (45), p.18195-18203
• Main Authors: Zheng, Jia-Yi, Ji, Xiao-Yuan, Zhao, An-Qi, Sun, Fang-Yuan, Liu, Li-Fang, Xin, Gui-Zhong
• Format: Article
• Language: English
• Subjects: Alanine; Animals; Concept learning; Electron transport chain; Humans; Interactive control; Interrogation; Learning algorithms; Liquid chromatography; Machine Learning; Mass spectrometry; Mass spectroscopy; Metabolic networks; Metabolism; Metabolites; Mitochondria; Mitochondria - metabolism; Nicotinamide adenine dinucleotide; Phenylenediamine; Phenylenediamines - chemistry; Phenylenediamines - metabolism; Real time; Scientific imaging; Tandem Mass Spectrometry - methods
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/acs.analchem.4c04376

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.