1
|
Takeuchi M, Fujiwara-Nagata E, Kuroda K, Sakata K, Narihiro T, Kikuchi J. Fecal metagenomic and metabolomic analyses reveal non-invasive biomarkers of Flavobacterium psychrophilum infection in ayu ( Plecoglossus altivelis). mSphere 2024; 9:e0030124. [PMID: 38884486 PMCID: PMC11288038 DOI: 10.1128/msphere.00301-24] [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: 04/19/2024] [Accepted: 05/07/2024] [Indexed: 06/18/2024] Open
Abstract
With the rapid growth of inland aquaculture worldwide, side effects such as the discharge of nutrients and antibiotics pose a threat to the global environments. A sustainable future for aquaculture requires an effective management system, including the early detection of disease through the monitoring of specific biomarkers in aquaculture tanks. To this end, we investigated whether fish feces in aquaculture tanks could be used for non-invasive health monitoring using ayu (Plecoglossus altivelis) infected with Flavobacterium psychrophilum, which causes bacterial cold-water disease worldwide. Feces that were subsequently produced in the tanks were used for metagenomic and metabolomic analyses. The relative abundances of the genera Cypionkella (0.6% ± 1.0%, 0.1% ± 0.2%), Klebsiella (11.2% ± 10.0%, 6.2% ± 5.9%), and F. psychrophilum (0.5% ± 1.0%, 0.0% ± 0.0%) were significantly higher in the feces of the infection challenge test tanks than in those of the control tanks. The abundances of cortisol, glucose, and acetate in the feces of the infection challenge test tanks were 2.4, 2.4, and 1.3 times higher, respectively, than those of the control tanks. Metagenome analysis suggested that acetate was produced by microbes such as Cypionkella. The abundances of indicated microbes or metabolites increased after day 4 of infection at the earliest, and were thus considered possible biomarkers. Our results suggest that feces produced in aquaculture tanks can potentially be used for non-invasive and holistic monitoring of fish diseases in aquaculture systems. IMPORTANCE The aquaculture industry is rapidly growing, yet sustainability remains a challenge. One crucial task is to reduce losses due to diseases. Monitoring fish health and detecting diseases early are key to establishing sustainable aquaculture. Using metagenomic and metabolomic analyses, we found that feces of ayu infected with Flavobacterium psychrophilum contain various specific biomarkers that increased 4 days post-challenge, at the earliest. Our findings are the first step in establishing a novel, non-invasive, and holistic monitoring method for fish diseases in aquaculture systems, especially in ayu, which is an important freshwater fish species in Asia, promoting a sustainable future.
Collapse
Affiliation(s)
- Mio Takeuchi
- Biomedical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ikeda, Osaka, Japan
| | | | - Kyohei Kuroda
- Bioproduction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Sapporo, Hokkaido, Japan
| | - Kenji Sakata
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
| | - Takashi Narihiro
- Bioproduction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Sapporo, Hokkaido, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
| |
Collapse
|
2
|
Tomita S, Inaoka T, Endo A, Okada S. Raw material-dependent changes in bacterial and compositional profiles are involved in insufficient pH decrease in natural lactic fermentation of Brassica rapa leaves. Food Chem 2024; 437:137934. [PMID: 37956596 DOI: 10.1016/j.foodchem.2023.137934] [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: 06/21/2023] [Revised: 10/19/2023] [Accepted: 11/02/2023] [Indexed: 11/15/2023]
Abstract
Sunki is an unsalted lactic fermented pickle made from red turnip leaves in the Kiso district, Japan. Accidental insufficient decrease in pH during sunki fermentation seriously reduces the product quality. To obtain insights into how the insufficient decrease occurs, we comprehensively analyzed differences in the microbiological and chemical properties of sunki made from three different turnip harvests and found a significant difference in their final pH. Microbiota and metabolome analyses revealed that the insufficient pH decrease showed strong relationships with the chemical composition (low lactic acid and high ammonia levels) and bacterial community structure (low Lactobacillus and high Limosilactobacillus). In vitro sunki fermentation experiments demonstrated that accumulated ammonia was associated with a decrease in glutamine and an increase in glutamic acid. Limosilactobacillus reuteri, a species of lactic acid bacteria possessing heterolactic metabolism, was suggested to be mainly responsible for insufficient decrease in pH related to accumulated ammonia during sunki fermentation.
Collapse
Affiliation(s)
- Satoru Tomita
- Institute of Food Research, National Agriculture and Food Research Organization, 2-1-12 Kannondai, Tsukuba, Ibaraki 305-8642, Japan.
| | - Takashi Inaoka
- Institute of Food Research, National Agriculture and Food Research Organization, 2-1-12 Kannondai, Tsukuba, Ibaraki 305-8642, Japan
| | - Akihito Endo
- Department of Nutritional Science and Food Safety, Faculty of Applied Bio-Science, Tokyo University of Agriculture, 1-1-1 Sakuragaoka, Setagaya, Tokyo 156-8502, Japan
| | - Sanae Okada
- Department of Applied Biological Science, Faculty of Agriculture, Takasaki University of Health and Welfare, 54 Nakaorui, Takasaki, Gunma 370-0033, Japan; Kiso Town Resource Institute, 2326-6 Fukushima, Kisomachi, Kiso, Nagano 397-8588, Japan
| |
Collapse
|
3
|
Ghini V, Meoni G, Vignoli A, Di Cesare F, Tenori L, Turano P, Luchinat C. Fingerprinting and profiling in metabolomics of biosamples. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2023; 138-139:105-135. [PMID: 38065666 DOI: 10.1016/j.pnmrs.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 12/18/2023]
Abstract
This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.
Collapse
Affiliation(s)
- Veronica Ghini
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Gaia Meoni
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy.
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy; Giotto Biotech S.r.l., Sesto Fiorentino, Italy.
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring. Processes (Basel) 2022. [DOI: 10.3390/pr10071264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
Low-cost and user-friendly benchtop low-field nuclear magnetic resonance (NMR) spectrometers are typically used to monitor food processes in the food industry. Because of excessive spectral overlap, it is difficult to characterize food mixtures using low-field NMR spectroscopy. In addition, for standard compounds, low-field benchtop NMR data are typically unavailable compared to high-field NMR data, which have been accumulated and are reusable in public databases. This work focused on NMR parameter visualization of the chemical structure and mobility of mixtures and the use of high-field NMR data to analyze benchtop NMR data to characterize food process samples. We developed a tool to easily process benchtop NMR data and obtain chemical shifts and T2 relaxation times of peaks, as well as transform high-field NMR data into low-field NMR data. Line broadening and time–frequency analysis methods were adopted for data processing. This tool can visualize NMR parameters to characterize changes in the components and mobilities of food process samples using benchtop NMR data. In addition, assignment errors were smaller when the spectra of standard compounds were identified by transferring the high-field NMR data to low-field NMR data rather than directly using experimentally obtained low-field NMR spectra.
Collapse
|
6
|
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.
Collapse
|
7
|
Yamazaki K, Kato T, Tsuboi Y, Miyauchi E, Suda W, Sato K, Nakajima M, Yokoji-Takeuchi M, Yamada-Hara M, Tsuzuno T, Matsugishi A, Takahashi N, Tabeta K, Miura N, Okuda S, Kikuchi J, Ohno H, Yamazaki K. Oral Pathobiont-Induced Changes in Gut Microbiota Aggravate the Pathology of Nonalcoholic Fatty Liver Disease in Mice. Front Immunol 2021; 12:766170. [PMID: 34707622 PMCID: PMC8543001 DOI: 10.3389/fimmu.2021.766170] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 09/28/2021] [Indexed: 12/12/2022] Open
Abstract
Background & Aims Periodontitis increases the risk of nonalcoholic fatty liver disease (NAFLD); however, the underlying mechanisms are unclear. Here, we show that gut dysbiosis induced by oral administration of Porphyromonas gingivalis, a representative periodontopathic bacterium, is involved in the aggravation of NAFLD pathology. Methods C57BL/6N mice were administered either vehicle, P. gingivalis, or Prevotella intermedia, another periodontopathic bacterium with weaker periodontal pathogenicity, followed by feeding on a choline-deficient, l-amino acid-defined, high-fat diet with 60 kcal% fat and 0.1% methionine (CDAHFD60). The gut microbial communities were analyzed by pyrosequencing the 16S ribosomal RNA genes. Metagenomic analysis was used to determine the relative abundance of the Kyoto Encyclopedia of Genes and Genomes pathways encoded in the gut microbiota. Serum metabolites were analyzed using nuclear magnetic resonance-based metabolomics coupled with multivariate statistical analyses. Hepatic gene expression profiles were analyzed via DNA microarray and quantitative polymerase chain reaction. Results CDAHFD60 feeding induced hepatic steatosis, and in combination with bacterial administration, it further aggravated NAFLD pathology, thereby increasing fibrosis. Gene expression analysis of liver samples revealed that genes involved in NAFLD pathology were perturbed, and the two bacteria induced distinct expression profiles. This might be due to quantitative and qualitative differences in the influx of bacterial products in the gut because the serum endotoxin levels, compositions of the gut microbiota, and serum metabolite profiles induced by the ingested P. intermedia and P. gingivalis were different. Conclusions Swallowed periodontopathic bacteria aggravate NAFLD pathology, likely due to dysregulation of gene expression by inducing gut dysbiosis and subsequent influx of gut bacteria and/or bacterial products.
Collapse
Affiliation(s)
- Kyoko Yamazaki
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Tamotsu Kato
- Laboratory for Intestinal Ecosystem, RIKEN Centre for Integrative Medical Sciences (IMS), Yokohama, Japan
| | - Yuuri Tsuboi
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Eiji Miyauchi
- Laboratory for Intestinal Ecosystem, RIKEN Centre for Integrative Medical Sciences (IMS), Yokohama, Japan
| | - Wataru Suda
- Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Keisuke Sato
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Mayuka Nakajima
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Mai Yokoji-Takeuchi
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Miki Yamada-Hara
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takahiro Tsuzuno
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Aoi Matsugishi
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Naoki Takahashi
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Koichi Tabeta
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Nobuaki Miura
- Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shujiro Okuda
- Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Medical AI Center, Niigata University School of Medicine, Niigata, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Hiroshi Ohno
- Laboratory for Intestinal Ecosystem, RIKEN Centre for Integrative Medical Sciences (IMS), Yokohama, Japan
- Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan
| | - Kazuhisa Yamazaki
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Laboratory for Intestinal Ecosystem, RIKEN Centre for Integrative Medical Sciences (IMS), Yokohama, Japan
| |
Collapse
|
8
|
Gómez-Cebrián N, Vázquez Ferreiro P, Carrera Hueso FJ, Poveda Andrés JL, Puchades-Carrasco L, Pineda-Lucena A. Pharmacometabolomics by NMR in Oncology: A Systematic Review. Pharmaceuticals (Basel) 2021; 14:ph14101015. [PMID: 34681239 PMCID: PMC8539252 DOI: 10.3390/ph14101015] [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: 09/08/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 12/14/2022] Open
Abstract
Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.
Collapse
Affiliation(s)
- Nuria Gómez-Cebrián
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain;
| | | | | | | | - Leonor Puchades-Carrasco
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain;
- Correspondence: (L.P.-C.); (A.P.-L.); Tel.: +34-963246713 (L.P.-C.)
| | - Antonio Pineda-Lucena
- Molecular Therapeutics Program, Centro de Investigación Médica Aplicada, 31008 Navarra, Spain
- Correspondence: (L.P.-C.); (A.P.-L.); Tel.: +34-963246713 (L.P.-C.)
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Ito K, Xu X, Kikuchi J. Improved Prediction of Carbonless NMR Spectra by the Machine Learning of Theoretical and Fragment Descriptors for Environmental Mixture Analysis. Anal Chem 2021; 93:6901-6906. [PMID: 33929838 DOI: 10.1021/acs.analchem.1c00756] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
As the first multidimensional NMR approach, 2D J-resolved (2DJ) spectroscopy is distinguished by signal resolution and detection sensitivity with remarkable advantages for the exhaustive evaluation of complex mixtures and environmental samples due to its carbonless feature without the requirement of 13C connectivity. Generally, the 2DJ signal assignment of metabolic mixtures is problematic in spite of references to experimental NMR databases, owing to the existence of metabolic "dark matter." In this study, a new method to predict 2DJ spectra was developed with a combination of quantum mechanical (QM) computation and machine learning (ML). The predictive accuracy of J-coupling constants was evaluated using validated data. The root-mean-square deviation (RMSD) for QM computation was 3.52 Hz, while the RMSD for QM + ML was 1.21 Hz, indicating a substantial increase in predictive accuracy. The proposed model was applied to predict the 2DJ spectra of 60 standard substances and 55 components of seawater. Furthermore, two practical environmental samples were used to evaluate the robustness of the constructed predictive model. A J-coupling tree and J-split spectra produced from QM + ML of aliphatic moieties had good consistency with the experimental data, as compared with the theoretical data produced by QM computation. The predicted J-coupling tree for the J-coupling multiplet analysis of freely rotating bonds in the complex mixture, which is traditionally difficult, was interpretable. In addition, in silico identification of the J-split 1H NMR signals, which was independent of experimental databases, aided in the discovery of new components in a mixture.
Collapse
Affiliation(s)
- Kengo Ito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Xiangru Xu
- Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
| |
Collapse
|
11
|
Araneda JF, Hui P, Leskowitz GM, Riegel SD, Mercado R, Green C. Lithium-7 qNMR as a method to quantify lithium content in brines using benchtop NMR. Analyst 2021; 146:882-888. [PMID: 33236728 DOI: 10.1039/d0an02088e] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
A novel 7Li quantitative NMR (qNMR) method to analyze lithium was developed to determine the lithium content in real brine samples using benchtop NMR instruments. The method was validated, and limits of detection and quantification of 40 and 100 ppm, respectively, were determined. Linearity, precision, and bias were also experimentally determined, and the results are presented herein. The results were compared to those obtained using atomic absorption (AA) spectroscopy, currently one of the few validated methods for the quantification of lithium. The method provides both accurate and precise results, as well as excellent correlation with AA. The absence of matrix effects, combined with no need for sample preparation or deuterated solvents, shows potential applicability in the mining industry.
Collapse
Affiliation(s)
- Juan F Araneda
- Nanalysis Corp., 1-4600 5 St NE, Calgary, AB T2E 7C3, Canada.
| | | | | | | | | | | |
Collapse
|
12
|
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.
Collapse
|
13
|
Edison AS, Colonna M, Gouveia GJ, Holderman NR, Judge MT, Shen X, Zhang S. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem 2020; 93:478-499. [DOI: 10.1021/acs.analchem.0c04414] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
14
|
Crook AA, Powers R. Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications. Molecules 2020; 25:E5128. [PMID: 33158172 PMCID: PMC7662776 DOI: 10.3390/molecules25215128] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 12/19/2022] Open
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.
Collapse
Affiliation(s)
- Alexandra A. Crook
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional 1H-NMR Spectral Database. Molecules 2020; 25:molecules25081966. [PMID: 32340308 PMCID: PMC7221887 DOI: 10.3390/molecules25081966] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/18/2020] [Accepted: 04/21/2020] [Indexed: 01/03/2023] Open
Abstract
Conventional proton nuclear magnetic resonance (1H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional 1H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures. In this study, we introduced an integrated analytical strategy based on the combination of additional measurement using a diffusion filter, covariation peak separation, and matrix decomposition in a small-scale training dataset. This strategy is aimed to extract signal profiles of soluble macromolecular components from conventional 1H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional 1H-NMR spectra of water-soluble fish muscle extracts and investigated the distribution characteristics of fish diversity and muscle soluble macromolecular components, such as lipids and collagens. We identified a cluster of fish species with low content of lipids and high content of collagens in muscle, which showed great potential for the development of functional foods. Because this mechanical data processing method requires additional measurement of only a small-scale training dataset without special sample pretreatment, it should be immediately applicable to extract macromolecular signals from accumulated conventional 1H-NMR databases of other complex gelatinous mixtures in foods.
Collapse
|