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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| Format: | Article |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2021.760669/full |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T08:52:31Z |
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| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-19T08:52:31Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-e9fc995f62684ada87989a6a73fa43f82022-12-21T20:28:41ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2021-11-01810.3389/fmolb.2021.760669760669Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?Xiuqing Zhu0Xiuqing Zhu1Jiaxin Huang2Shanqing Huang3Yuguan Wen4Yuguan Wen5Xiaochang Lan6Xiaochang Lan7Xipei Wang8Chuanli Lu9Zhanzhang Wang10Zhanzhang Wang11Ni Fan12Ni Fan13Dewei Shang14Dewei Shang15Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, ChinaGuangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, ChinaDepartment of Substance Dependence, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, ChinaDepartment of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, ChinaDepartment of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, ChinaGuangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, ChinaGuangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, ChinaDepartment of Substance Dependence, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, ChinaDepartment of Medical Sciences, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangzhou Rely Medical Diagnostic Technology Co. Ltd., Guangzhou, ChinaDepartment of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, ChinaGuangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, ChinaGuangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, ChinaDepartment of Substance Dependence, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, ChinaDepartment of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, ChinaGuangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, ChinaAlcohol 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.https://www.frontiersin.org/articles/10.3389/fmolb.2021.760669/fullalcohol dependencemetabolic profilingbiological correlatemetabolomicsmachine learningtryptophan metabolism |
| spellingShingle | 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? Frontiers in Molecular Biosciences alcohol dependence metabolic profiling biological correlate metabolomics machine learning tryptophan metabolism |
| 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_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_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_sort | combining metabolomics and interpretable machine learning to reveal plasma metabolic profiling and biological correlates of alcohol dependent inpatients what about tryptophan metabolism regulation |
| topic | alcohol dependence metabolic profiling biological correlate metabolomics machine learning tryptophan metabolism |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2021.760669/full |
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