1
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Shima H, Asakura T, Sakata K, Koiso M, Kikuchi J. Feed Components and Timing to Improve the Feed Conversion Ratio for Sustainable Aquaculture Using Starch. Int J Mol Sci 2024; 25:7921. [PMID: 39063163 PMCID: PMC11276616 DOI: 10.3390/ijms25147921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/08/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
Aquaculture contributes to the sustainable development of food security, marine resource conservation, and economy. Shifting aquaculture feed from fish meal and oil to terrestrial plant derivatives may result in cost savings. However, many carnivorous fish cannot be sustained on plant-derived materials, necessitating the need for the identification of important factors for farmed fish growth and the identification of whether components derived from terrestrial plants can be used in feed. Herein, we focused on the carnivorous fish leopard coral grouper (P. leopardus) to identify the essential growth factors and clarify their intake timing from feeds. Furthermore, we evaluated the functionality of starch, which are easily produced by terrestrial plants. Results reveal that carbohydrates, which are not considered essential for carnivorous fish, can be introduced as a major part of an artificial diet. The development of artificial feed using starch offers the possibility of increasing the growth of carnivorous fish in aquaculture.
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Affiliation(s)
- Hideaki Shima
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
| | - Taiga Asakura
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
| | - Kenji Sakata
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
| | - Masahiko Koiso
- Research Center for Subtropical Fisheries, Seikai National Fisheries Research Institute, Japan Fishery Research and Education Agency, 148 Fukaiota, Ishigaki 907-0451, Okinawa, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
- Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8601, Aichi, Japan
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2
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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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3
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Yamada S, Ito K, Kurotani A, Yamada Y, Chikayama E, Kikuchi J. InterSpin: Integrated Supportive Webtools for Low- and High-Field NMR Analyses Toward Molecular Complexity. ACS OMEGA 2019; 4:3361-3369. [PMID: 31459550 PMCID: PMC6648201 DOI: 10.1021/acsomega.8b02714] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/24/2018] [Indexed: 05/06/2023]
Abstract
InterSpin (http://dmar.riken.jp/interspin/) comprises integrated, supportive, and freely accessible preprocessing webtools and a database to advance signal assignment in low- and high-field NMR analyses of molecular complexities ranging from small molecules to macromolecules for food, material, and environmental applications. To support handling of the broad spectra obtained from solid-state NMR or low-field benchtop NMR, we have developed and evaluated two preprocessing tools: sensitivity improvement with spectral integration, which enhances the signal-to-noise ratio by spectral integration, and peaks separation, which separates overlapping peaks by several algorithms, such as non-negative sparse coding. In addition, the InterSpin Laboratory Information Management System (SpinLIMS) database stores numerous standard spectra ranging from small molecules to macromolecules in solid and solution states (dissolved in polar/nonpolar solvents), and can be searched under various conditions using the following molecular assignment tools. SpinMacro supports easy assignment of macromolecules in natural mixtures via solid-state 13C peaks and dimethyl sulfoxide-dissolved 1H-13C correlation peaks. InterAnalysis improves the accuracy of molecular assignment by integrated analysis of 1H-13C correlation peaks and 1H-J correlation peaks of small molecules dissolved in D2O or deuterated methanol, which supports easy narrowing down of metabolite candidates. Finally, by enabling database interoperability, SpinLIMS's client software will ultimately support scientific discovery by facilitating sharing and reusing of NMR data.
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Affiliation(s)
- Shunji Yamada
- Graduate
School of Bioagricultural Sciences, Nagoya
University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kengo Ito
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Atsushi Kurotani
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yutaka Yamada
- RIKEN
Center for Sustainable Resource Science, 1-7-22 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
| | - Jun Kikuchi
- Graduate
School of Bioagricultural Sciences, Nagoya
University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
- 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
- E-mail: . Phone/Fax: +81-544039439
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4
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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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5
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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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6
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Wei F, Sakata K, Asakura T, Date Y, Kikuchi J. Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem. Sci Rep 2018; 8:3478. [PMID: 29472553 PMCID: PMC5823927 DOI: 10.1038/s41598-018-20120-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 01/09/2018] [Indexed: 01/07/2023] Open
Abstract
Data-driven approaches were applied to investigate the temporal and spatial changes of 1,022 individuals of wild yellowfin goby and its potential interaction with the estuarine environment in Japan. Nuclear magnetic resonance (NMR)-based metabolomics revealed that growth stage is a primary factor affecting muscle metabolism. Then, the metabolic, elemental and microbial profiles of the pooled samples generated according to either the same habitat or sampling season as well as the river water and sediment samples from their habitats were measured using NMR spectra, inductively coupled plasma optical emission spectrometry and next-generation 16 S rRNA gene sequencing. Hidden interactions in the integrated datasets such as the potential role of intestinal bacteria in the control of spawning migration, essential amino acids and fatty acids synthesis in wild yellowfin goby were further extracted using correlation clustering and market basket analysis-generated networks. Importantly, our systematic analysis of both the seasonal and latitudinal variations in metabolome, ionome and microbiome of wild yellowfin goby pointed out that the environmental factors such as the temperature play important roles in regulating the body homeostasis of wild fish.
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Affiliation(s)
- Feifei Wei
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan
| | - Kenji Sakata
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, Japan
| | - Taiga Asakura
- 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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7
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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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8
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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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9
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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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10
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Affiliation(s)
- G. A. Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine and
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine and
- Department of Chemistry, University of Washington, Seattle, Washington 98109, United States
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
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11
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Kikuchi J, Yamada S. NMR window of molecular complexity showing homeostasis in superorganisms. Analyst 2017; 142:4161-4172. [DOI: 10.1039/c7an01019b] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.
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Affiliation(s)
- Jun Kikuchi
- RIKEN Center for Sustainable Resource Science
- Yokohama
- Japan
- Graduate School of Bioagricultural Sciences
- Nagoya University
| | - Shunji Yamada
- RIKEN Center for Sustainable Resource Science
- Yokohama
- Japan
- Graduate School of Bioagricultural Sciences
- Nagoya University
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12
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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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13
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Misawa T, Wei F, Kikuchi J. Application of Two-Dimensional Nuclear Magnetic Resonance for Signal Enhancement by Spectral Integration Using a Large Data Set of Metabolic Mixtures. Anal Chem 2016; 88:6130-4. [DOI: 10.1021/acs.analchem.6b01495] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Takuma Misawa
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan
| | - Feifei Wei
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22 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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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. [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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15
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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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16
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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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