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Anjitha KS, Sarath NG, Sameena PP, Janeeshma E, Shackira AM, Puthur JT. Plant response to heavy metal stress toxicity: the role of metabolomics and other omics tools. FUNCTIONAL PLANT BIOLOGY : FPB 2023; 50:965-982. [PMID: 37995340 DOI: 10.1071/fp23145] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/31/2023] [Indexed: 11/25/2023]
Abstract
Metabolomic investigations offers a significant foundation for improved comprehension of the adaptability of plants to reconfigure the key metabolic pathways and their response to changing climatic conditions. Their application to ecophysiology and ecotoxicology help to assess potential risks caused by the contaminants, their modes of action and the elucidation of metabolic pathways associated with stress responses. Heavy metal stress is one of the most significant environmental hazards affecting the physiological and biochemical processes in plants. Metabolomic tools have been widely utilised in the massive characterisation of the molecular structure of plants at various stages for understanding the diverse aspects of the cellular functioning underlying heavy metal stress-responsive mechanisms. This review emphasises on the recent progressions in metabolomics in plants subjected to heavy metal stresses. Also, it discusses the possibility of facilitating effective management strategies concerning metabolites for mitigating the negative impacts of heavy metal contaminants on the growth and productivity of plants.
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Affiliation(s)
- K S Anjitha
- Plant Physiology and Biochemistry Division, Department of Botany, University of Calicut, C. U. Campus P.O., Malappuram, Kerala 673635, India
| | - Nair G Sarath
- Department of Botany, Mar Athanasius College, Kothamangalam, Ernakulam, Kerala 686666, India
| | - P P Sameena
- Department of Botany, PSMO College, Tirurangadi, Malappuram, Kerala 676306, India
| | - Edappayil Janeeshma
- Department of Botany, MES KEVEEYAM College, Valanchery, Malappuram, Kerala 676552, India
| | - A M Shackira
- Department of Botany, Sir Syed College, Kannur University, Kannur, Kerala 670142, India
| | - Jos T Puthur
- Plant Physiology and Biochemistry Division, Department of Botany, University of Calicut, C. U. Campus P.O., Malappuram, Kerala 673635, India
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2
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Dollinger J, Pétriacq P, Flandin A, Samouelian A. Soil metabolomics: A powerful tool for predicting and specifying pesticide sorption. CHEMOSPHERE 2023:139302. [PMID: 37385484 DOI: 10.1016/j.chemosphere.2023.139302] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023]
Abstract
Sorption regulates the dispersion of pesticides from cropped areas to surrounding water bodies as well as their persistence. Assessing the risk of water contamination and evaluating the efficiency of mitigation measures, requires fine-resolution sorption data and a good knowledge of its drivers. This study aimed to assess the potential of a new approach combining chemometric and soil metabolomics to estimate the adsorption and desorption coefficients of a range of pesticides. It also aims to identify and characterise key components of soil organic matter (SOM) driving the sorption of these pesticides. We constituted a dataset of 43 soils from Tunisia, France and Guadeloupe (West Indies), covering extensive ranges of texture, organic carbon and pH. We performed untargeted soil metabolomics by liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS). We measured the adsorption and desorption coefficients of three pesticides namely glyphosate, 2,4-D and difenoconazole for these soils. We developed Partial Least Square Regression (PLSR) models for the prediction of the sorption coefficients from the RT-m/z matrix and conducted further ANOVA analyses to identify, annotate and characterise the most significant constituents of SOM in the PLSR models. The curated metabolomics matrix yielded 1213 metabolic markers. The prediction performance of the PLSR models was generally high for the adsorption coefficients Kdads (0.3 < R2 < 0.8) and for the desorption coefficients Kfdes (0.6 < R2 < 0.8) but low for ndes (0.03 < R2 < 0.3). The most significant features in the predictive models were annotated with a confidence level of 2 or 3. The molecular descriptors of these putative compounds suggest that the pool of SOM compounds driving glyphosate sorption is reduced compared to 2,4-D and difenoconazole, and these compounds are generally more polar. This approach can provide estimates of the adsorption and desorption coefficients of pesticides, including polar pesticide, for contrasted pedoclimates.
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Affiliation(s)
- Jeanne Dollinger
- UMR LISAH, Université Montpellier, INRAE, IRD, Institut Agro, 34060, Montpellier, France.
| | - Pierre Pétriacq
- Univ. Bordeaux, INRAE, UMR1332, BFP, 33882, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Amélie Flandin
- Univ. Bordeaux, INRAE, UMR1332, BFP, 33882, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Anatja Samouelian
- UMR LISAH, Université Montpellier, INRAE, IRD, Institut Agro, 34060, Montpellier, France
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3
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Yamada S, Tsuboi Y, Yokoyama D, Kikuchi J. Polymer composition optimization approach based on feature extraction of bound and free water using time-domain nuclear magnetic resonance. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 351:107438. [PMID: 37084520 DOI: 10.1016/j.jmr.2023.107438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 03/30/2023] [Accepted: 04/05/2023] [Indexed: 05/03/2023]
Abstract
As global environmental sustainability becomes increasingly emphasized, the development of eco-friendly materials, including solutions to the issue of marine plastics, is thriving. However, the material parameter space is vast, making efficient search a challenge. Time-domain nuclear magnetic resonance offers material property information through the complex T2 relaxation curves resulting from multiple mobilities. In this research, we used the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence to evaluate the binding state of water (water affinity) in polymers synthesized with various monomer compositions, which were immersed in seawater. We also assessed the T2 relaxation property of the polymers using the magic sandwich echo, double quantum filter, and magic-and-polarization echo filter techniques. We separated the T2 relaxation curves of CPMG into free and bound water for polymers by employing semisupervized nonnegative matrix factorization. By employing the features of separated bound water and polymer properties, a polymer composition optimization method offered crucial factors to monomers through random forests, predicted the components of the polymer using generative topography mapping regression, and determined expected values using Bayesian optimization for polymer composition candidates with the desired high water affinity and high rigidity.
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Affiliation(s)
- Shunji Yamada
- RIKEN Center for Sustainable Resource Science, 1-7-22, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Yuuri Tsuboi
- RIKEN Center for Sustainable Resource Science, 1-7-22, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Daiki Yokoyama
- RIKEN Center for Sustainable Resource Science, 1-7-22, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22, Tsurumi-ku, Yokohama 230-0045, Japan; Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, 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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4
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Miyamoto H, Kikuchi J. An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach. Comput Struct Biotechnol J 2023; 21:869-878. [PMID: 36698969 PMCID: PMC9860287 DOI: 10.1016/j.csbj.2023.01.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/05/2023] Open
Abstract
The natural world is constantly changing, and planetary boundaries are issuing severe warnings about biodiversity and cycles of carbon, nitrogen, and phosphorus. In other views, social problems such as global warming and food shortages are spreading to various fields. These seemingly unrelated issues are closely related, but it can be said that understanding them in an integrated manner is still a step away. However, progress in analytical technologies has been recognized in various fields and, from a microscopic perspective, with the development of instruments including next-generation sequencers (NGS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS), various forms of molecular information such as genome data, microflora structure, metabolome, proteome, and lipidome can be obtained. The development of new technology has made it possible to obtain molecular information in a variety of forms. From a macroscopic perspective, the development of environmental analytical instruments and environmental measurement facilities such as satellites, drones, observation ships, and semiconductor censors has increased the data availability for various environmental factors. Based on these background, the role of computational science is to provide a mechanism for integrating and understanding these seemingly disparate data sets. This review describes machine learning and the need for structural equations and statistical causal inference of these data to solve these problems. In addition to introducing actual examples of how these technologies can be utilized, we will discuss how to use these technologies to implement environmentally friendly technologies in society.
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Affiliation(s)
- Hirokuni Miyamoto
- Graduate School of Horticulture, Chiba University, Matsudo, Chiba 271-8501, Japan
- RIKEN Center for Integrative Medical Science, Yokohama, Kanagawa 230-0045, Japan
- Sermas Co., Ltd., Ichikawa, Chiba 272-0033, Japan
- Japan Eco-science (Nikkan Kagaku) Co. Ltd., Chiba, Chiba 260-0034, Japan
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi, Yokohama 230-0045, Japan
| | - Jun Kikuchi
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi, Yokohama 230-0045, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045, Japan
- Graduate School of Bioagricultural Sciences, Nagoya University, Chikusa, Nagoya 464-8601, Japan
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Sanchez-Arcos C, Paris D, Mazzella V, Mutalipassi M, Costantini M, Buia MC, von Elert E, Cutignano A, Zupo V. Responses of the Macroalga Ulva prolifera Müller to Ocean Acidification Revealed by Complementary NMR- and MS-Based Omics Approaches. Mar Drugs 2022; 20:md20120743. [PMID: 36547890 PMCID: PMC9783899 DOI: 10.3390/md20120743] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022] Open
Abstract
Ocean acidification (OA) is a dramatic perturbation of seawater environments due to increasing anthropogenic emissions of CO2. Several studies indicated that OA frequently induces marine biota stress and a reduction of biodiversity. Here, we adopted the macroalga Ulva prolifera as a model and applied a complementary multi-omics approach to investigate the metabolic profiles under normal and acidified conditions. Our results show that U. prolifera grows at higher rates in acidified environments. Consistently, we observed lower sucrose and phosphocreatine concentrations in response to a higher demand of energy for growth and a higher availability of essential amino acids, likely related to increased protein biosynthesis. In addition, pathways leading to signaling and deterrent compounds appeared perturbed. Finally, a remarkable shift was observed here for the first time in the fatty acid composition of triglycerides, with a decrease in the relative abundance of PUFAs towards an appreciable increase of palmitic acid, thus suggesting a remodeling in lipid biosynthesis. Overall, our studies revealed modulation of several biosynthetic pathways under OA conditions in which, besides the possible effects on the marine ecosystem, the metabolic changes of the alga should be taken into account considering its potential nutraceutical applications.
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Affiliation(s)
- Carlos Sanchez-Arcos
- Institute for Zoology, Cologne Biocenter University of Cologne, 50674 Köln, Germany
| | - Debora Paris
- Consiglio Nazionale delle Ricerche (CNR), Istituto di Chimica Biomolecolare (ICB), 80078 Pozzuoli, Italy
| | - Valerio Mazzella
- Department of Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Ischia Marine Center, 80077 Ischia, Italy
| | - Mirko Mutalipassi
- Department of Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Calabria Marine Centre, 87071 Amendolara, Italy
| | - Maria Costantini
- Department of Ecosustainable Marine Biotechnology, Stazione Zoologica Anton Dohrn, 80121 Napoli, Italy
| | - Maria Cristina Buia
- Department of Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Ischia Marine Center, 80077 Ischia, Italy
| | - Eric von Elert
- Institute for Zoology, Cologne Biocenter University of Cologne, 50674 Köln, Germany
| | - Adele Cutignano
- Consiglio Nazionale delle Ricerche (CNR), Istituto di Chimica Biomolecolare (ICB), 80078 Pozzuoli, Italy
- Department of Ecosustainable Marine Biotechnology, Stazione Zoologica Anton Dohrn, 80121 Napoli, Italy
- Correspondence: (A.C.); (V.Z.); Tel.: +39-081-8675313 (A.C.); +39-081-5833503 (V.Z.)
| | - Valerio Zupo
- Department of Ecosustainable Marine Biotechnology, Stazione Zoologica Anton Dohrn, 80077 Ischia, Italy
- Correspondence: (A.C.); (V.Z.); Tel.: +39-081-8675313 (A.C.); +39-081-5833503 (V.Z.)
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6
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Materials informatics approach using domain modelling for exploring structure-property relationships of polymers. Sci Rep 2022; 12:10558. [PMID: 35732681 PMCID: PMC9217937 DOI: 10.1038/s41598-022-14394-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/06/2022] [Indexed: 11/23/2022] Open
Abstract
In the development of polymer materials, it is an important issue to explore the complex relationships between domain structure and physical properties. In the domain structure analysis of polymer materials, 1H-static solid-state NMR (ssNMR) spectra can provide information on mobile, rigid, and intermediate domains. But estimation of domain structure from its analysis is difficult due to the wide overlap of spectra from multiple domains. Therefore, we have developed a materials informatics approach that combines the domain modeling (http://dmar.riken.jp/matrigica/) and the integrated analysis of meta-information (the elements, functional groups, additives, and physical properties) in polymer materials. Firstly, the 1H-static ssNMR data of 120 polymer materials were subjected to a short-time Fourier transform to obtain frequency, intensity, and T2 relaxation time for domains with different mobility. The average T2 relaxation time of each domain is 0.96 ms for Mobile, 0.55 ms for Intermediate (Mobile), 0.32 ms for Intermediate (Rigid), and 0.11 ms for Rigid. Secondly, the estimated domain proportions were integrated with meta-information such as elements, functional group and thermophysical properties and was analyzed using a self-organization map and market basket analysis. This proposed method can contribute to explore structure–property relationships of polymer materials with multiple domains.
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7
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Yokoyama D, Suzuki S, Asakura T, Kikuchi J. Chemometric Analysis of NMR Spectra and Machine Learning to Investigate Membrane Fouling. ACS OMEGA 2022; 7:12654-12660. [PMID: 35474825 PMCID: PMC9025983 DOI: 10.1021/acsomega.1c06891] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/02/2022] [Indexed: 05/26/2023]
Abstract
Efficient membrane filtration requires the understanding of the membrane foulants and the functional properties of different membrane types in water purification. In this study, dead-end filtration of aquaculture system effluents was performed and the membrane foulants were investigated via nuclear magnetic resonance (NMR) spectroscopy. Several machine learning models (Random Forest; RF, Extreme Gradient Boosting; XGBoost, Support Vector Machine; SVM, and Neural Network; NN) were constructed, one to predict the maximum transmembrane pressure, for revealing the chemical compounds causing fouling, and the other to classify the membrane materials based on chemometric analysis of NMR spectra, for determining their effect on the properties of the different membrane types tested. Especially, RF models exhibited high accuracy; the important chemical shifts observed in both the regression and classification models suggested that the proportional patterns of sugars and proteins are key factors in the fouling progress and the classification of membrane types. Therefore, the proposed strategy of chemometric analysis of NMR spectra is suitable for membrane research, which aims at investigating comprehensively the fouling phenomenon and how the foulants and environmental conditions vary according to the filtration systems.
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Affiliation(s)
- Daiki Yokoyama
- 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
| | - Sosei Suzuki
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Taiga Asakura
- 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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Beale DJ, Jones OA, Bose U, Broadbent JA, Walsh TK, van de Kamp J, Bissett A. Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience. Emerg Top Life Sci 2022; 6:185-199. [PMID: 35403668 PMCID: PMC9023019 DOI: 10.1042/etls20210261] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/17/2022] [Accepted: 03/22/2022] [Indexed: 12/15/2022]
Abstract
Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of 'ecosurveillance', especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.
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Affiliation(s)
- David J. Beale
- Land and Water, Commonwealth Scientific and Industrial Research Organisation, Ecosciences Precinct, Dutton Park QLD 4102, Australia
| | - Oliver A.H. Jones
- Australian Centre for Research on Separation Science (ACROSS), School of Science, RMIT University, Bundoora West Campus, PO Box 71, Bundoora, VIC 3083, Australia
| | - Utpal Bose
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Queensland Bioscience Precinct, St Lucia, QLD 4067, Australia
| | - James A. Broadbent
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Queensland Bioscience Precinct, St Lucia, QLD 4067, Australia
| | - Thomas K. Walsh
- Land and Water, Commonwealth Scientific and Industrial Research Organisation, Acton, ACT 2601, Australia
| | - Jodie van de Kamp
- Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation, Battery Point, TAS 7004, Australia
| | - Andrew Bissett
- Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation, Battery Point, TAS 7004, Australia
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9
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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10
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Hashemi S, Hashemi SE, Lien KM, Lamb JJ. Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production. Microorganisms 2021; 9:microorganisms9061162. [PMID: 34071282 PMCID: PMC8226781 DOI: 10.3390/microorganisms9061162] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/17/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022] Open
Abstract
The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.
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Affiliation(s)
- Seyedbehnam Hashemi
- Department of Energy and Process Engineering & Enersense, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway; (S.H.); (S.E.H.); (K.M.L.)
| | - Sayed Ebrahim Hashemi
- Department of Energy and Process Engineering & Enersense, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway; (S.H.); (S.E.H.); (K.M.L.)
| | - Kristian M. Lien
- Department of Energy and Process Engineering & Enersense, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway; (S.H.); (S.E.H.); (K.M.L.)
| | - Jacob J. Lamb
- Department of Energy and Process Engineering & Enersense, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway; (S.H.); (S.E.H.); (K.M.L.)
- Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
- Correspondence:
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11
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Date Y, Wei F, Tsuboi Y, Ito K, Sakata K, Kikuchi J. Relaxometric learning: a pattern recognition method for T 2 relaxation curves based on machine learning supported by an analytical framework. BMC Chem 2021; 15:13. [PMID: 33610164 PMCID: PMC7897374 DOI: 10.1186/s13065-020-00731-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 12/15/2020] [Indexed: 11/10/2022] Open
Abstract
Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T2 relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method. ![]()
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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
| | - Feifei Wei
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Yuuri Tsuboi
- 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
| | - Kenji Sakata
- 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, 464-8601, Japan.
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12
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Wei F, Ito K, Sakata K, Asakura T, Date Y, Kikuchi J. Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties. Sci Rep 2021; 11:3766. [PMID: 33580151 PMCID: PMC7881121 DOI: 10.1038/s41598-021-83194-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/27/2021] [Indexed: 01/13/2023] Open
Abstract
Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3-95.4%. Markov blanket-based feature selection method with an ecological-chemical-physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.
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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
| | - Kengo Ito
- 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
| | - Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 235-0045, 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 Suehirocho, 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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13
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Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials. Int J Mol Sci 2021; 22:ijms22031086. [PMID: 33499371 PMCID: PMC7865946 DOI: 10.3390/ijms22031086] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 01/15/2021] [Accepted: 01/17/2021] [Indexed: 01/19/2023] Open
Abstract
Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as Euglena gracilis. During cellulose degradation, 13C cross-polarization (CP)-magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO2. Using these methods, the 1H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design.
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14
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Lin Y, Yan M, Su J, Huang Y, Feng J, Chen Z. Improving efficiency of measuring individual 1H coupling networks by pure shift 2D J-resolved NMR spectroscopy. J Chem Phys 2020; 153:174114. [PMID: 33167634 DOI: 10.1063/5.0025962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The 1H coupling networks, including 1H-1H correlation and J coupling values, provide the important information for structure elucidation and conformation analysis. However, the presence of a large number of couplings and the phase-twist lineshapes often prevents revealing 1H coupling networks. Here, we provide a clean absorption-mode 2D NMR method, SIMAJ (SImple Methods for 2D Absorption mode J-resolved spectrum), for a straightforward assignment and measurement of the coupling network involving the chosen proton. Relying on the pure shift element, 1H-1H couplings and chemical shift evolution are totally separately demonstrating along the F1 and F2 dimensions, respectively. Processing with a single experiment dataset and free of 45° spectral shearing, an absorption-mode 2D J-resolved spectrum can be reconstructed. Two pulse sequences were proposed as examples. The SIMAJ signal processing method will be a general procedure for obtaining absorption-mode lineshapes when analyzing the experiment datasets with chemical shifts and J coupling multiplets in the orthogonal dimensions. With excellent sensitivity, high spectral purity, and ability of easily identifying 1H-1H correlations, significant improvements are beneficial for structural, conformational, or complex composition analyses.
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Affiliation(s)
- Yulan Lin
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Ming Yan
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Jianwei Su
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Yuqing Huang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Jianghua Feng
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China
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15
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An applied ecological approach for the assessment of anthropogenic disturbances in urban wetlands and the contributor river. ECOLOGICAL COMPLEXITY 2020. [DOI: 10.1016/j.ecocom.2020.100852] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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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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17
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Yamada S, Kurotani A, Chikayama E, Kikuchi J. Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix Factorization. Int J Mol Sci 2020; 21:ijms21082978. [PMID: 32340198 PMCID: PMC7215856 DOI: 10.3390/ijms21082978] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 04/15/2020] [Accepted: 04/19/2020] [Indexed: 01/08/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time-frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T2* relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.
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Affiliation(s)
- Shunji Yamada
- Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Nagoya 464-8601, Chikusa-ku, Japan;
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Yokohama 230-0045, Tsurumi-ku, Japan; (A.K.); (E.C.)
| | - Atsushi Kurotani
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Yokohama 230-0045, Tsurumi-ku, Japan; (A.K.); (E.C.)
| | - Eisuke Chikayama
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Yokohama 230-0045, Tsurumi-ku, Japan; (A.K.); (E.C.)
- Department of Information Systems, Niigata University of International and Information Studies, 3-1-1 Mizukino, Niigata 950-2292, Nishi-ku, Japan
| | - Jun Kikuchi
- Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Nagoya 464-8601, Chikusa-ku, Japan;
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Yokohama 230-0045, Tsurumi-ku, Japan; (A.K.); (E.C.)
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Yokohama 230-0045, Tsurumi-ku, Japan
- Correspondence: ; +81-45-508-9439
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18
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Kostopoulou S, Ntatsi G, Arapis G, Aliferis KA. Assessment of the effects of metribuzin, glyphosate, and their mixtures on the metabolism of the model plant Lemna minor L. applying metabolomics. CHEMOSPHERE 2020; 239:124582. [PMID: 31514011 DOI: 10.1016/j.chemosphere.2019.124582] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 08/10/2019] [Accepted: 08/12/2019] [Indexed: 05/14/2023]
Abstract
Chemical plant protection products (PPPs) is a major group of xenobiotics that are being released in the environment. Although the effects of individual active ingredients (a.i.) on organisms have been studied, information on those of mixtures, is fragmented. Aquatic environments are being polluted by PPPs, posing serious risks for the environment, human, and other organisms. Based on the potential of the model aquatic plant Lemna minor L. in the assessment of PPPs-caused stresses, we have undertaken the task of developing a metabolomics approach for the study of the effects of metribuzin and glyphosate, and their mixtures. Bioassays revealed that metribuzin exhibit higher toxicity than glyphosate and metabolomics highlighted corresponding changes in its metabolome. Treatments had a substantial impact on plants' amino acid pool, resulting in elevated levels of the majority of the identified amino acids. Results indicate that the increased proteolytic activity is a common effect of the a.i. and their mixtures. Additionally, the activation of salicylate-signaling pathways was recorded as a response to the toxicity caused by mixtures. Among the identified metabolites that were discovered as biomarkers were γ-aminobutyric acid (GABA), salicylate, caffeate, α,α-trehalose, and squalene, which play multiple roles in plants' metabolism such as, signaling, antioxidant, and structure protection. No reports exist on the combined effects of PPPs on Lemna and results confirm the applicability of Lemna metabolomics in the study of the combined effects of herbicides and its potential in the monitoring of the environmental health of aquatic environments based on fluctuations of the plant's metabolism.
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Affiliation(s)
- Sofia Kostopoulou
- Laboratory of Pesticide Science, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 118 55, Athens, Greece; Laboratory of Vegetable Production Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece
| | - Georgia Ntatsi
- Institute of Plant Breeding and Genetic Resources, Hellenic Agricultural Organization ELGO-DEMETER, Thermi, Thessaloniki, GR-57001, Greece; Laboratory of Vegetable Production Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece
| | - Gerasimos Arapis
- Laboratory of Ecology and Environmental Sciences, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 118 55, Athens, Greece.
| | - Konstantinos A Aliferis
- Laboratory of Pesticide Science, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 118 55, Athens, Greece; Department of Plant Science, McGill University, Macdonald Campus, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada.
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19
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Hoch JC. If machines can learn, who needs scientists? JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 306:162-166. [PMID: 31362904 PMCID: PMC6941139 DOI: 10.1016/j.jmr.2019.07.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 07/10/2019] [Accepted: 07/11/2019] [Indexed: 05/16/2023]
Abstract
Machine learning has been used in NMR in for decades, but recent developments signal explosive growth is on the horizon. An obstacle to the application of machine learning in NMR is the relative paucity of available training data, despite the existence of numerous public NMR data repositories. Other challenges include the problem of interpreting the results of a machine learning algorithm, and incorporating machine learning into hypothesis-driven research. This perspective imagines the potential of machine learning in NMR and speculates on possible approaches to the hurdles.
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20
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Emwas AH, Roy R, McKay RT, Tenori L, Saccenti E, Gowda GAN, Raftery D, Alahmari F, Jaremko L, Jaremko M, Wishart DS. NMR Spectroscopy for Metabolomics Research. Metabolites 2019; 9:E123. [PMID: 31252628 PMCID: PMC6680826 DOI: 10.3390/metabo9070123] [Citation(s) in RCA: 519] [Impact Index Per Article: 103.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 06/14/2019] [Accepted: 06/18/2019] [Indexed: 12/14/2022] Open
Abstract
Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.
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Affiliation(s)
- Abdul-Hamid Emwas
- Core Labs, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Raja Roy
- Centre of Biomedical Research, Formerly, Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Uttar Pradesh 226014, India
| | - Ryan T McKay
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2W2, Canada
| | - Leonardo Tenori
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - G A Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
- Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue, Seattle, WA 98109, USA
| | - Fatimah Alahmari
- Department of NanoMedicine Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia
| | - Lukasz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Mariusz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada
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Pinu FR, Beale DJ, Paten AM, Kouremenos K, Swarup S, Schirra HJ, Wishart D. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites 2019; 9:E76. [PMID: 31003499 PMCID: PMC6523452 DOI: 10.3390/metabo9040076] [Citation(s) in RCA: 314] [Impact Index Per Article: 62.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 04/15/2019] [Accepted: 04/16/2019] [Indexed: 02/07/2023] Open
Abstract
The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.
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Affiliation(s)
- Farhana R Pinu
- The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand.
| | - David J Beale
- Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosciences Precinct, Dutton Park, Dutton Park, QLD 4102, Australia.
| | - Amy M Paten
- Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Research and Innovation Park, Acton, ACT 2601, Australia.
| | - Konstantinos Kouremenos
- Trajan Scientific and Medical, Ringwood, VIC 3134, Australia.
- Bio21 Institute, The University of Melbourne, Parkville, VIC 3010, Australia.
| | - Sanjay Swarup
- Department of Biological Sciences, National University of Singapore, Singapore 117411, Singapore.
| | - Horst J Schirra
- Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD 4072, Australia.
| | - David Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.
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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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Oita A, Tsuboi Y, Date Y, Oshima T, Sakata K, Yokoyama A, Moriya S, Kikuchi J. Profiling physicochemical and planktonic features from discretely/continuously sampled surface water. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 636:12-19. [PMID: 29702398 DOI: 10.1016/j.scitotenv.2018.04.156] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 04/02/2018] [Accepted: 04/10/2018] [Indexed: 06/08/2023]
Abstract
There is an increasing need for assessing aquatic ecosystems that are globally endangered. Since aquatic ecosystems are complex, integrated consideration of multiple factors utilizing omics technologies can help us better understand aquatic ecosystems. An integrated strategy linking three analytical (machine learning, factor mapping, and forecast-error-variance decomposition) approaches for extracting the features of surface water from datasets comprising ions, metabolites, and microorganisms is proposed herein. The three developed approaches can be employed for diverse datasets of sample sizes and experimentally analyzed factors. The three approaches are applied to explore the features of bay water surrounding Odaiba, Tokyo, Japan, as a case study. Firstly, the machine learning approach separated 681 surface water samples within Japan into three clusters, categorizing Odaiba water into seawater with relatively low inorganic ions, including Mg, Ba, and B. Secondly, the factor mapping approach illustrated Odaiba water samples from the summer as rich in multiple amino acids and some other metabolites and poor in inorganic ions relative to other seasons based on their seasonal dynamics. Finally, forecast-error-variance decomposition using vector autoregressive models indicated that a type of microalgae (Raphidophyceae) grows in close correlation with alanine, succinic acid, and valine on filters and with isobutyric acid and 4-hydroxybenzoic acid in filtrate, Ba, and average wind speed. Our integrated strategy can be used to examine many biological, chemical, and environmental physical factors to analyze surface water.
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Affiliation(s)
- Azusa Oita
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yuuri Tsuboi
- RIKEN Center for Sustainable Resource Science, 1-7-22 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
| | - Takahiro Oshima
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kenji Sakata
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Akiko Yokoyama
- Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8572, Japan; Center for Regional Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Shigeharu Moriya
- 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, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan.
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Bingol K. Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods. High Throughput 2018; 7:E9. [PMID: 29670016 PMCID: PMC6023270 DOI: 10.3390/ht7020009] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 04/09/2018] [Accepted: 04/13/2018] [Indexed: 12/23/2022] Open
Abstract
Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.
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Affiliation(s)
- Kerem Bingol
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
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