Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?

Alcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic p...

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Autores principales:	Xiuqing Zhu, Jiaxin Huang, Shanqing Huang, Yuguan Wen, Xiaochang Lan, Xipei Wang, Chuanli Lu, Zhanzhang Wang, Ni Fan, Dewei Shang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	alcohol dependence metabolic profiling biological correlate metabolomics machine learning tryptophan metabolism Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e9fc995f62684ada87989a6a73fa43f8
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spelling	oai:doaj.org-article:e9fc995f62684ada87989a6a73fa43f82021-11-08T17:55:02ZCombining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?2296-889X10.3389/fmolb.2021.760669https://doaj.org/article/e9fc995f62684ada87989a6a73fa43f82021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmolb.2021.760669/fullhttps://doaj.org/toc/2296-889XAlcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic pathways. This study aimed to: i) compare the plasma metabolic profiling between healthy and AD-diagnosed individuals to reveal the altered metabolic profiles in AD, and ii) identify potential biological correlates of alcohol-dependent inpatients based on metabolomics and interpretable machine learning. Plasma samples were obtained from healthy (n = 42) and AD-diagnosed individuals (n = 43). The plasma metabolic differences between them were investigated using liquid chromatography-tandem mass spectrometry (AB SCIEX® QTRAP 4500 system) in different electrospray ionization modes with scheduled multiple reaction monitoring scans. In total, 59 and 52 compounds were semi-quantitatively measured in positive and negative ionization modes, respectively. In addition, 39 metabolites were identified as important variables to contribute to the classifications using an orthogonal partial least squares-discriminant analysis (OPLS-DA) (VIP > 1) and also significantly different between healthy and AD-diagnosed individuals using univariate analysis (p-value < 0.05 and false discovery rate < 0.05). Among the identified metabolites, indole-3-carboxylic acid, quinolinic acid, hydroxy-tryptophan, and serotonin were involved in the tryptophan metabolism along the indole, kynurenine, and serotonin pathways. Metabolic pathway analysis revealed significant changes or imbalances in alanine, aspartate, glutamate metabolism, which was possibly the main altered pathway related to AD. Tryptophan metabolism interactively influenced other metabolic pathways, such as nicotinate and nicotinamide metabolism. Furthermore, among the OPLS-DA-identified metabolites, normetanephrine and ascorbic acid were demonstrated as suitable biological correlates of AD inpatients from our model using an interpretable, supervised decision tree classifier algorithm. These findings indicate that the discriminatory metabolic profiles between healthy and AD-diagnosed individuals may benefit researchers in illustrating the underlying molecular mechanisms of AD. This study also highlights the approach of combining metabolomics and interpretable machine learning as a valuable tool to uncover potential biological correlates. Future studies should focus on the global analysis of the possible roles of these differential metabolites and disordered metabolic pathways in the pathophysiology of AD.Xiuqing ZhuXiuqing ZhuJiaxin HuangShanqing HuangYuguan WenYuguan WenXiaochang LanXiaochang LanXipei WangChuanli LuZhanzhang WangZhanzhang WangNi FanNi FanDewei ShangDewei ShangFrontiers Media S.A.articlealcohol dependencemetabolic profilingbiological correlatemetabolomicsmachine learningtryptophan metabolismBiology (General)QH301-705.5ENFrontiers in Molecular Biosciences, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	alcohol dependence metabolic profiling biological correlate metabolomics machine learning tryptophan metabolism Biology (General) QH301-705.5
spellingShingle	alcohol dependence metabolic profiling biological correlate metabolomics machine learning tryptophan metabolism Biology (General) QH301-705.5 Xiuqing Zhu Xiuqing Zhu Jiaxin Huang Shanqing Huang Yuguan Wen Yuguan Wen Xiaochang Lan Xiaochang Lan Xipei Wang Chuanli Lu Zhanzhang Wang Zhanzhang Wang Ni Fan Ni Fan Dewei Shang Dewei Shang Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?
description	Alcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic pathways. This study aimed to: i) compare the plasma metabolic profiling between healthy and AD-diagnosed individuals to reveal the altered metabolic profiles in AD, and ii) identify potential biological correlates of alcohol-dependent inpatients based on metabolomics and interpretable machine learning. Plasma samples were obtained from healthy (n = 42) and AD-diagnosed individuals (n = 43). The plasma metabolic differences between them were investigated using liquid chromatography-tandem mass spectrometry (AB SCIEX® QTRAP 4500 system) in different electrospray ionization modes with scheduled multiple reaction monitoring scans. In total, 59 and 52 compounds were semi-quantitatively measured in positive and negative ionization modes, respectively. In addition, 39 metabolites were identified as important variables to contribute to the classifications using an orthogonal partial least squares-discriminant analysis (OPLS-DA) (VIP > 1) and also significantly different between healthy and AD-diagnosed individuals using univariate analysis (p-value < 0.05 and false discovery rate < 0.05). Among the identified metabolites, indole-3-carboxylic acid, quinolinic acid, hydroxy-tryptophan, and serotonin were involved in the tryptophan metabolism along the indole, kynurenine, and serotonin pathways. Metabolic pathway analysis revealed significant changes or imbalances in alanine, aspartate, glutamate metabolism, which was possibly the main altered pathway related to AD. Tryptophan metabolism interactively influenced other metabolic pathways, such as nicotinate and nicotinamide metabolism. Furthermore, among the OPLS-DA-identified metabolites, normetanephrine and ascorbic acid were demonstrated as suitable biological correlates of AD inpatients from our model using an interpretable, supervised decision tree classifier algorithm. These findings indicate that the discriminatory metabolic profiles between healthy and AD-diagnosed individuals may benefit researchers in illustrating the underlying molecular mechanisms of AD. This study also highlights the approach of combining metabolomics and interpretable machine learning as a valuable tool to uncover potential biological correlates. Future studies should focus on the global analysis of the possible roles of these differential metabolites and disordered metabolic pathways in the pathophysiology of AD.
format	article
author	Xiuqing Zhu Xiuqing Zhu Jiaxin Huang Shanqing Huang Yuguan Wen Yuguan Wen Xiaochang Lan Xiaochang Lan Xipei Wang Chuanli Lu Zhanzhang Wang Zhanzhang Wang Ni Fan Ni Fan Dewei Shang Dewei Shang
author_facet	Xiuqing Zhu Xiuqing Zhu Jiaxin Huang Shanqing Huang Yuguan Wen Yuguan Wen Xiaochang Lan Xiaochang Lan Xipei Wang Chuanli Lu Zhanzhang Wang Zhanzhang Wang Ni Fan Ni Fan Dewei Shang Dewei Shang
author_sort	Xiuqing Zhu
title	Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?
title_short	Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?
title_full	Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?
title_fullStr	Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?
title_full_unstemmed	Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?
title_sort	combining metabolomics and interpretable machine learning to reveal plasma metabolic profiling and biological correlates of alcohol-dependent inpatients: what about tryptophan metabolism regulation?
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e9fc995f62684ada87989a6a73fa43f8
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Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?

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