1
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Zhang S, Qiu X, Zhang Y, Huang C, Lin D. Metabolomic Analysis of Trehalose Alleviating Oxidative Stress in Myoblasts. Int J Mol Sci 2023; 24:13346. [PMID: 37686153 PMCID: PMC10488301 DOI: 10.3390/ijms241713346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/10/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Trehalose, a naturally occurring non-toxic disaccharide, has attracted considerable attention for its potential in alleviating oxidative stress in skeletal muscle. In this study, our aim was to elucidate the metabolic mechanisms underlying the protective effects of trehalose against hydrogen peroxide (H2O2)-induced oxidative stress in C2C12 myoblasts. Our results show that both trehalose treatment and pretreatment effectively alleviate the H2O2-induced decrease in cell viability, reduce intracellular reactive oxygen species (ROS), and attenuate lipid peroxidation. Furthermore, using NMR-based metabolomics analysis, we observed that trehalose treatment and pretreatment modulate the metabolic profile of myoblasts, specifically regulating oxidant metabolism and amino acid metabolism, contributing to their protective effects against oxidative stress. Importantly, our results reveal that trehalose treatment and pretreatment upregulate the expression levels of P62 and Nrf2 proteins, thereby activating the Nrf2-NQO1 axis and effectively reducing oxidative stress. These significant findings highlight the potential of trehalose supplementation as a promising and effective strategy for alleviating oxidative stress in skeletal muscle and provide valuable insights into its potential therapeutic applications.
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Affiliation(s)
- Shuya Zhang
- Key Laboratory of Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (S.Z.); (X.Q.); (Y.Z.)
| | - Xu Qiu
- Key Laboratory of Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (S.Z.); (X.Q.); (Y.Z.)
| | - Yue Zhang
- Key Laboratory of Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (S.Z.); (X.Q.); (Y.Z.)
| | - Caihua Huang
- Research and Communication Center of Exercise and Health, Xiamen University of Technology, Xiamen 361021, China;
| | - Donghai Lin
- Key Laboratory of Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (S.Z.); (X.Q.); (Y.Z.)
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2
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Kurotani A, Kakiuchi T, Kikuchi J. Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN). ACS OMEGA 2021; 6:14278-14287. [PMID: 34124451 PMCID: PMC8190808 DOI: 10.1021/acsomega.1c01035] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/28/2021] [Indexed: 06/12/2023]
Abstract
Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log P are important values for understanding the physical properties of various substances. In this study, we succeeded at establishing a solubility prediction tool using a unique machine learning method called the in-phase deep neural network (ip-DNN), which starts exclusively from the analytical input data (e.g., NMR information, refractive index, and density) to predict solubility by predicting intermediate elements, such as molecular components and molecular descriptors, in the multiple-step method. For improving the level of accuracy of the prediction, intermediate regression models were employed when performing in-phase machine learning. In addition, we developed a website dedicated to the established solubility prediction method, which is freely available at "http://dmar.riken.jp/matsolca/".
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Affiliation(s)
- Atsushi Kurotani
- RIKEN
Center for Sustainable Resource Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Toshifumi Kakiuchi
- AGC
Yokohama Technical Center, 1-1 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- RIKEN
Center for Sustainable Resource Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Bioagricultural Sciences, Nagoya
University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
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3
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Ito K, Xu X, Kikuchi J. Improved Prediction of Carbonless NMR Spectra by the Machine Learning of Theoretical and Fragment Descriptors for Environmental Mixture Analysis. Anal Chem 2021; 93:6901-6906. [PMID: 33929838 DOI: 10.1021/acs.analchem.1c00756] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
As the first multidimensional NMR approach, 2D J-resolved (2DJ) spectroscopy is distinguished by signal resolution and detection sensitivity with remarkable advantages for the exhaustive evaluation of complex mixtures and environmental samples due to its carbonless feature without the requirement of 13C connectivity. Generally, the 2DJ signal assignment of metabolic mixtures is problematic in spite of references to experimental NMR databases, owing to the existence of metabolic "dark matter." In this study, a new method to predict 2DJ spectra was developed with a combination of quantum mechanical (QM) computation and machine learning (ML). The predictive accuracy of J-coupling constants was evaluated using validated data. The root-mean-square deviation (RMSD) for QM computation was 3.52 Hz, while the RMSD for QM + ML was 1.21 Hz, indicating a substantial increase in predictive accuracy. The proposed model was applied to predict the 2DJ spectra of 60 standard substances and 55 components of seawater. Furthermore, two practical environmental samples were used to evaluate the robustness of the constructed predictive model. A J-coupling tree and J-split spectra produced from QM + ML of aliphatic moieties had good consistency with the experimental data, as compared with the theoretical data produced by QM computation. The predicted J-coupling tree for the J-coupling multiplet analysis of freely rotating bonds in the complex mixture, which is traditionally difficult, was interpretable. In addition, in silico identification of the J-split 1H NMR signals, which was independent of experimental databases, aided in the discovery of new components in a mixture.
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Affiliation(s)
- Kengo Ito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Xiangru Xu
- Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
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4
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Zhang J, Terayama K, Sumita M, Yoshizoe K, Ito K, Kikuchi J, Tsuda K. NMR-TS: de novo molecule identification from NMR spectra. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2020; 21:552-561. [PMID: 32939179 PMCID: PMC7476483 DOI: 10.1080/14686996.2020.1793382] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/05/2020] [Accepted: 07/05/2020] [Indexed: 05/09/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS.
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Affiliation(s)
- Jinzhe Zhang
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Kei Terayama
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
- RIKEN Medical Sciences Innovation Hub Program (MIH), Yokohama, Japan
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
| | - Masato Sumita
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Japan
| | - Kazuki Yoshizoe
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Kengo Ito
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Jun Kikuchi
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan
| | - Koji Tsuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Japan
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5
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Ito K, Tsuboi Y, Kikuchi J. Spatial molecular-dynamically ordered NMR spectroscopy of intact bodies and heterogeneous systems. Commun Chem 2020; 3:80. [PMID: 36703472 PMCID: PMC9814264 DOI: 10.1038/s42004-020-0330-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 05/29/2020] [Indexed: 01/29/2023] Open
Abstract
Noninvasive evaluation of the spatial distribution of chemical composition and diffusion behavior of materials is becoming possible by advanced nuclear magnetic resonance (NMR) pulse sequence editing. However, there is room for improvement in the spectral resolution and analytical method for application to heterogeneous samples. Here, we develop applications for comprehensively evaluating compounds and their dynamics in intact bodies and heterogeneous systems from NMR data, including spatial z-position, chemical shift, and diffusion or relaxation. This experiment is collectively named spatial molecular-dynamically ordered spectroscopy (SMOOSY). Pseudo-three-dimensional (3D) SMOOSY spectra of an intact shrimp and two heterogeneous systems are recorded to evaluate this methodology. Information about dynamics is mapped onto two-dimensional (2D) chemical shift imaging spectra using a pseudo-spectral imaging method with a processing tool named SMOOSY processor. Pseudo-2D SMOOSY spectral images can non-invasively assess the different dynamics of the compounds at each spatial z-position of the shrimp's body and two heterogeneous systems.
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Affiliation(s)
- Kengo Ito
- grid.7597.c0000000094465255RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan ,grid.268441.d0000 0001 1033 6139Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan
| | - Yuuri Tsuboi
- grid.7597.c0000000094465255RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan
| | - Jun Kikuchi
- grid.7597.c0000000094465255RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan ,grid.268441.d0000 0001 1033 6139Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan ,grid.27476.300000 0001 0943 978XGraduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810 Japan
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6
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Asakura T, Date Y, Kikuchi J. Application of ensemble deep neural network to metabolomics studies. Anal Chim Acta 2018; 1037:230-236. [DOI: 10.1016/j.aca.2018.02.045] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 02/05/2018] [Accepted: 02/10/2018] [Indexed: 10/18/2022]
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7
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Ito K, Obuchi Y, Chikayama E, Date Y, Kikuchi J. Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals. Chem Sci 2018; 9:8213-8220. [PMID: 30542569 PMCID: PMC6240814 DOI: 10.1039/c8sc03628d] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 08/23/2018] [Indexed: 12/30/2022] Open
Abstract
Various chemical shift predictive methodologies have been studied and developed, but there remains the problem of prediction accuracy. Assigning the NMR signals of metabolic mixtures requires high predictive performance owing to the complexity of the signals. Here we propose a new predictive tool that combines quantum chemistry and machine learning. A scaling factor as the objective variable to correct the errors of 2355 theoretical chemical shifts was optimized by exploring 91 machine learning algorithms and using the partial structure of 150 compounds as explanatory variables. The optimal predictive model gave RMSDs between experimental and predicted chemical shifts of 0.2177 ppm for δ 1H and 3.3261 ppm for δ 13C in the test data; thus, better accuracy was achieved compared with existing empirical and quantum chemical methods. The utility of the predictive model was demonstrated by applying it to assignments of experimental NMR signals of a complex metabolic mixture.
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Affiliation(s)
- Kengo Ito
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku , Yokohama , Kanagawa 230-0045 , Japan .
- Graduate School of Medical Life Science , Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku , Yokohama , Kanagawa 230-0045 , Japan
| | - Yuka Obuchi
- Graduate School of Medical Life Science , Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku , Yokohama , Kanagawa 230-0045 , Japan
| | - Eisuke Chikayama
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku , Yokohama , Kanagawa 230-0045 , Japan .
- Department of Information Systems , Niigata University of International and Information Studies , 3-1-1 Mizukino, Nishi-ku , Niigata-shi , Niigata 950-2292 , Japan
| | - Yasuhiro Date
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku , Yokohama , Kanagawa 230-0045 , Japan .
- Graduate School of Medical Life Science , Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku , Yokohama , Kanagawa 230-0045 , Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku , Yokohama , Kanagawa 230-0045 , Japan .
- Graduate School of Medical Life Science , Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku , Yokohama , Kanagawa 230-0045 , Japan
- Graduate School of Bioagricultural Sciences , Nagoya University , 1 Furo-cho, Chikusa-ku , Nagoya , Aichi 464-0810 , Japan
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8
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Shiokawa Y, Date Y, Kikuchi J. Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet. Sci Rep 2018; 8:3426. [PMID: 29467421 PMCID: PMC5821832 DOI: 10.1038/s41598-018-20121-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 01/08/2018] [Indexed: 12/13/2022] Open
Abstract
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general “linear” multivariate analysis alone limits interpretations due to “non-linear” variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input–output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.
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Affiliation(s)
- Yuka Shiokawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
| | - Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan. .,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan. .,Graduate School of Bioagricultural Sciences and School of Agricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
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9
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Kikuchi J, Ito K, Date Y. Environmental metabolomics with data science for investigating ecosystem homeostasis. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 104:56-88. [PMID: 29405981 DOI: 10.1016/j.pnmrs.2017.11.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 11/19/2017] [Accepted: 11/19/2017] [Indexed: 05/08/2023]
Abstract
A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.
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Affiliation(s)
- Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan.
| | - Kengo Ito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
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10
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Date Y, Kikuchi J. Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables. Anal Chem 2018; 90:1805-1810. [DOI: 10.1021/acs.analchem.7b03795] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
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11
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[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Visualizing Individual and Region-specific Microbial–metabolite Relations by Important Variable Selection Using Machine Learning Approaches. JOURNAL OF COMPUTER AIDED CHEMISTRY 2017. [DOI: 10.2751/jcac.18.31] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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12
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Bingol K, Brüschweiler R. Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods. Curr Opin Biotechnol 2016; 43:17-24. [PMID: 27552705 DOI: 10.1016/j.copbio.2016.07.006] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 07/26/2016] [Accepted: 07/28/2016] [Indexed: 01/10/2023]
Abstract
Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.
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Affiliation(s)
- Kerem Bingol
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Rafael Brüschweiler
- Campus Chemical Instrument Center, The Ohio State University, Columbus, OH 43210, United States; Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, United States; Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, OH 43210, United States.
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13
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Komatsu T, Ohishi R, Shino A, Kikuchi J. Structure and Metabolic‐Flow Analysis of Molecular Complexity in a
13
C‐Labeled Tree by 2D and 3D NMR. Angew Chem Int Ed Engl 2016. [DOI: 10.1002/ange.201600334] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Takanori Komatsu
- RIKEN Center for Sustainable Resource Science Graduate School of Medical Life Science Yokohama City University and Graduate School of Bioagricultural Sciences Nagoya University 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 235-0045 Japan
| | - Risa Ohishi
- RIKEN Center for Sustainable Resource Science Graduate School of Medical Life Science Yokohama City University and Graduate School of Bioagricultural Sciences Nagoya University 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 235-0045 Japan
| | - Amiu Shino
- RIKEN Center for Sustainable Resource Science Graduate School of Medical Life Science Yokohama City University and Graduate School of Bioagricultural Sciences Nagoya University 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 235-0045 Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science Graduate School of Medical Life Science Yokohama City University and Graduate School of Bioagricultural Sciences Nagoya University 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 235-0045 Japan
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14
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Komatsu T, Ohishi R, Shino A, Kikuchi J. Structure and Metabolic-Flow Analysis of Molecular Complexity in a (13) C-Labeled Tree by 2D and 3D NMR. Angew Chem Int Ed Engl 2016; 55:6000-3. [PMID: 27060701 DOI: 10.1002/anie.201600334] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Indexed: 01/05/2023]
Abstract
Improved signal identification for biological small molecules (BSMs) in a mixture was demonstrated by using multidimensional NMR on samples from (13) C-enriched Rhododendron japonicum (59.5 atom%) cultivated in air containing (13) C-labeled carbon dioxide for 14 weeks. The resonance assignment of 386 carbon atoms and 380 hydrogen atoms in the mixture was achieved. 42 BSMs, including eight that were unlisted in the spectral databases, were identified. Comparisons between the experimental values and the (13) C chemical shift values calculated by density functional theory supported the identifications of unlisted BSMs. Tracing the (13) C/(12) C ratio by multidimensional NMR spectra revealed faster and slower turnover ratios of BSMs involved in central metabolism and those categorized as secondary metabolites, respectively. The identification of BSMs and subsequent flow analysis provided insight into the metabolic systems of the plant.
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Affiliation(s)
- Takanori Komatsu
- RIKEN Center for Sustainable Resource Science, Graduate School of Medical Life Science, Yokohama City University and, Graduate School of Bioagricultural Sciences, Nagoya University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan
| | - Risa Ohishi
- RIKEN Center for Sustainable Resource Science, Graduate School of Medical Life Science, Yokohama City University and, Graduate School of Bioagricultural Sciences, Nagoya University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan
| | - Amiu Shino
- RIKEN Center for Sustainable Resource Science, Graduate School of Medical Life Science, Yokohama City University and, Graduate School of Bioagricultural Sciences, Nagoya University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, Graduate School of Medical Life Science, Yokohama City University and, Graduate School of Bioagricultural Sciences, Nagoya University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan.
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Chikayama E, Shimbo Y, Komatsu K, Kikuchi J. The Effect of Molecular Conformation on the Accuracy of Theoretical (1)H and (13)C Chemical Shifts Calculated by Ab Initio Methods for Metabolic Mixture Analysis. J Phys Chem B 2016; 120:3479-87. [PMID: 26963288 DOI: 10.1021/acs.jpcb.5b12748] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
NMR spectroscopy is a powerful method for analyzing metabolic mixtures. The information obtained from an NMR spectrum is in the form of physical parameters, such as chemical shifts, and construction of databases for many metabolites will be useful for data interpretation. To increase the accuracy of theoretical chemical shifts for development of a database for a variety of metabolites, the effects of sets of conformations (structural ensembles) and the levels of theory on computations of theoretical chemical shifts were systematically investigated for a set of 29 small molecules in the present study. For each of the 29 compounds, 101 structures were generated by classical molecular dynamics at 298.15 K, and then theoretical chemical shifts for 164 (1)H and 123 (13)C atoms were calculated by ab initio quantum chemical methods. Six levels of theory were used by pairing Hartree-Fock, B3LYP (density functional theory), or second order Møller-Plesset perturbation with 6-31G or aug-cc-pVDZ basis set. The six average fluctuations in the (1)H chemical shift were ±0.63, ± 0.59, ± 0.70, ± 0.62, ± 0.75, and ±0.66 ppm for the structural ensembles, and the six average errors were ±0.34, ± 0.27, ± 0.32, ± 0.25, ± 0.32, and ±0.25 ppm. The results showed that chemical shift fluctuations with changes in the conformation because of molecular motion were larger than the differences between computed and experimental chemical shifts for all six levels of theory. In conclusion, selection of an appropriate structural ensemble should be performed before theoretical chemical shift calculations for development of an accurate database for a variety of metabolites.
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Affiliation(s)
- Eisuke Chikayama
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Information Systems, Niigata University of International and Information Studies , 3-1-1 Mizukino, Nishi-ku, Niigata, Niigata 950-2292, Japan
| | - Yudai Shimbo
- NEC Solution Innovators, Ltd. , 2-2-41 Ekimae, Kashiwazaki, Niigata 945-0055, Japan
| | - Keiko Komatsu
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Bioagricultural Sciences, Nagoya University , 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
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